An Investigation of Heart Rate and Oxygen Saturation Level (SpO2) in Indicating Driving Fatigue

Malaysian Journal of Medicine and Health Sciences


Regression Analysis of Heart Rate for Driving Fatigue using Box-Behnken Design


Electroencephalogram Analysis Method to Detect Unspoken Answers to Questions Using Multistage Neural Networks

Braincomputer interfaces (BCI) facilitate communication between the human brain and computational systems, additionally offering mechanisms for environmental control to enhance human life. The current study focused on the application of BCI for communication support, especially in detecting unspoken answers to questions. Utilizing a multistage neural network (MSNN) replete with convolutional and pooling layers, the proposed method comprises a threefold approach: electroencephalogram (EEG) measurements, EEG feature extraction, and answer classification. The EEG signals of the participants are captured as they mentally respond with yes or no to the posed questions. Feature extraction was achieved through an MSNN composed of three distinct convolutional neural network models. The first model discriminates between the EEG signals with and without discernible noise artifacts, whereas the subsequent two models are designated for feature extraction from EEG signals with or without such noise artifacts. Furthermore, a support vector machine is employed to classify the answers to the questions. The proposed method was validated via experiments using authentic EEG data. The mean and standard deviation values for sensitivity and precision of the proposed method were 99.6% and 0.2%, respectively. These findings demonstrate the viability of attaining high accuracy in a BCI by preliminarily segregating the EEG signals based on the presence or absence of artifact noise and underscore the stability of such classification. Thus, the proposed method manifests prospective advantages of separating EEG signals characterized by noise artifacts for enhanced BCI performance.


REGRESSION ANALYSIS OF OXYGEN SATURATION LEVEL FOR CRITICAL DRIVING FATIGUE FACTORS USING BOX-BEHNKEN DESIGN

Journal of Advanced Manufacturing Technology


Mathematical Regression Analysis of Oxygen Saturation for Driving Fatigue using Box-Behnken Design

Malaysia have become an alarming concern. Mathematical modelling is the process of describing a real-world issue in mathematical terms to understand the original issue. Hence, this paper aims to develop a mathematical regression model to predict the relationship between five input variables namely (i) driving duration, (ii) driving speed, (iii) body mass index (BMI), (iv) types of roads and (v) gender and an output response (oxygen saturation level) as the causes of driving fatigue. The regression analysis utilized Box-Behnken design method by Design Expert (6.0.8) software. The results revealed that the Prob > F values for all input variables were less than 0.01%, implying that all the variables were significant in influencing the oxygen saturation level. The regression model was validated to determine its accuracy in predicting the output response. The analysis presented excellent prediction accuracy as the model was capable to predict the data within 95% predictive interval, which met the minimum quantitative condition of 90% predictive interval. Furthermore, the residual errors were less than 10%, indicating that the model has excellent accuracy in predicting the oxygen saturation. The model prediction is expected to be useful in guiding researchers and policy makers in road safety field to take measures in minimizing traffic accidents due to driving fatigue.

International Journal of Emerging Technology and Advanced Engineering


Designing a graphical user interface for the decision support system of driving fatigue

The paper presents the continuity study from the previous work, which designing the graphical user interface (GUI) for a decision support system (DSS) of driving fatigue. As driving fatigue has been recognized as one of the significant contributory factors to road accidents and fatalities in Malaysia, the author developed the DSS that providing analysis, and proving solutions and recommendations to the road users. In other words, the DSS acts as the advisory and decision-maker tool. In designing the GUI for a DSS, the Django based on Python programming language was used by the authors. There are five main GUI that has been designed in this study: admin GUI user profile and driving information GUI, regression model GUI, risk factor analysis GUI and superuser GUI. Further testing and validation of the graphical user interface for the DSS are suggested before it is used commercially.

International Journal of Advanced Mechatronic Systems


A Critical Review on Driver Fatigue Detection and Monitoring System

This paper reviews existing and future fatigue detection and monitoring systems. Over the past few years, there has been an increase of interest in technologies, systems, and procedures to detect and monitor driver fatigue to reduce the number of road accidents. The driving activity has become more important as this medium is morepractical, faster,and cheaper in connecting humans around the world. However, driving activity can cause disastersor deathsto human in daily life as they get fatigued while driving. Driver fatigue is a vital contributor to road accidents. Studies show that 80.6% of road accidents are caused by human error which includes fatigue or drowsiness. Statistics indicate the need for a reliable driver fatigue detection and monitoring system, which could alert or warn the driver before anymishapshappens. Several approaches and methods have been developed to reduce the risk of fatigueamongdrivers, which uses the following measures:(1) vehicle-based measures; (2) behavioural measures; (3) physiological measures; (4) psychophysical measures;and(5) biomechanical measures. In this paper, the authorsbriefly review the literature on fatigue detection and monitoring systems. The findings from this review are discussed in the light of directions for future studies and the development of fatigue countermeasures.


Development of Decision Support System via Ergonomics Approach for Driving Fatigue Detection

Driving operation has become more important nowadays as this method becomes practical, faster, and cheaper to move humans from one location to another. However, driving activity can cause a human being to suffer tragedy or death in everyday life as they get exhausted while driving. Driver fatigue is a major contributing factor in road crashes. This paper's primary aim was to develop a decision support system (DSS) for the monitoring of driving fatigue. The decision support system seeks to provide systematic analysis and approaches to minimize the risk associated with driving exhaustion and the number of accidents involved. Four major stages involved as the cornerstone in the development of decision support system; knowledge acquisition, knowledge integration, development of driving fatigue strain index using fuzzy logic membership function, development of the fatigue driving decision support system (DSSfDF) model using the graphical user interface. The decision support system is an essential program for evaluating the risk factors which would significantly contribute to driving fatigue associated with driving activity. Furthermore, the decision support system offers users solutions and recommendations to minimize the number of road accidents in Malaysia.

Journal of Social Sciences and Technical Education


Human-Wants Detection Based on Electroencephalogram Analysis During Exposure to Music

We propose a method to detect human wants by using an electroencephalogram (EEG) test and specifying brain activity sensing positions. EEG signals can be analyzed by using various techniques. Recently, convolutional neural networks (CNNs) have been employed to analyze EEG signals, and these analyses have produced excellent results. Therefore, this paper employs CNN to extract EEG features. Also, support vector machines (SVMs) have shown good results for EEG pattern classification. This paper employs SVMs to classify the human cognition into wants, not wants, and other feelings. In EEG measurements, the electrical activity of the brain is recorded using electrodes placed on the scalp. The sensing positions are related to the frontal cortex and/or temporal cortex activities although the mechanism to create wants is not clear. To specify the sensing positions and detect human wants, we conducted experiments using real EEG data. We confirmed that the mean and standard deviation values of the detection accuracy rate were 99.4% and 0.58%, respectively, when the target sensing positions were related to the frontal and temporal cortex activities. These results prove that both the frontal and temporal cortex activities are relevant for creating wants in the human brain, and that CNN and SVMare effective for the detection of human wants.


Individual Differences in Brain Activities When Human Wishes to Listen to Music Continuously Using Near-Infrared Spectroscopy

This paper introduces an individual difference in the activities of the prefrontal cortex when a person wants to listen to music using near-infrared spectroscopy. The individual differences are confirmed by visualizing the variation in oxygenated hemoglobin level. The sensing positions used to record the brain activities are around the prefrontal cortex. The existence of individual differences was verified by experiments. The experiment results show that active positions while feeling a wish to listen to music are different in each subject, and an oxygenated hemoglobin level is different in each subject compared to its value when a subject does not feel the wish to listen to music. The experiment results show that it is possible to detect a wish to listen to the music based on changes in the oxygenated hemoglobin level. Also, these results suggest that active positions are different in each subject because the sensitivities and how to feel on stimulus are different. Lastly, the results suggest that it is possible to express the individual differences as differences in active positions.

International Journal of Advanced Science and Technology


Japanese sign language classification based on gathered images and neural networks

This paper proposes a method to classify words in Japanese Sign Language (JSL). This approach employs a combined gathered image generation technique and a neural network with convolutional and pooling layers (CNNs). The gathered image generation generates images based on mean images. Herein, the maximum difference value is between blocks of mean and JSL motions images. The gathered images comprise blocks that having the calculated maximum difference value. CNNs extract the features of the gathered images, while a support vector machine for multi-class classification, and a multilayer perceptron are employed to classify 20 JSL words. The experimental results had 94.1% for the mean recognition accuracy of the proposed method. These results suggest that the proposed method can obtain information to classify the sample words.

International Journal of Advances in Intelligent Informatics


Development of driving fatigue strain index using fuzzy logic to analyze risk levels of driving activity

The objective of this study is to develop a driving fatigue strain index using fuzzy logic to analyze the risk levels of driving activity among road users. Driving fatigue is always related to the driving activity and has been identified as one of the vital contributors to the road accidents and fatalities in Malaysia. Therefore, the present article introduces the use of fuzzy logic for the development of strain index to provide the systematic analysis and propose an appropriate solution in minimizing the number of road accidents and fatalities. The development of strain index is based on the six risk factors associated with driving fatigue; muscle activity, heart rate, hand grip pressure force, seat pressure distribution, whole-body vibration, and driving duration. The data are collected for all the risk factors, and consequently the three conditions or risk levels are defined as safe, slightly unsafe, and unsafe. A membership function is defined for each fuzzy condition. IF-THEN rules were used to define the input and output variables, which correspond to physical measures. This index is a reliable advisory tool for providing analysis and solutions to driving fatigue problem, which constitutes the first effort toward the minimization of road accidents and fatalities.

この研究の目的は，ファジーロジックを使用して，道路利用者の運転活動のリスクレベルを分析する運転疲労ひずみ指数を開発することです．運転疲労は常に運転活動に関連しており，マレーシアでの交通事故と死亡の重大な原因の1つとして特定されています．したがって，本記事では，系統的分析を提供し，交通事故と死亡者の数を最小限に抑えるための適切なソリューションを提案するために，歪み指標の開発にファジーロジックの使用を紹介する．ひずみ指数の開発は，運転疲労に関連する6つのリスク要因に基づいています．筋肉活動，心拍数，ハンドグリップ圧力，シート圧力分布，全身振動，および運転時間．データはすべてのリスク要因について収集されるため，3つの条件またはリスクレベルは「安全」，「やや安全ではない」，「安全でない」と定義される．メンバーシップ関数は，ファジー条件ごとに定義される． IF-THENルールを使用して，物理的測定値に対応する入力変数と出力変数を定義した．このインデックスは，運転中の疲労問題の分析と解決策を提供するための信頼できる助言ツールであり，これは交通事故と死亡者の最小化に向けた最初の取り組みである．

IEEJ Transactions on Electrical and Electronic Engineering


Verification of the Usefulness of Personal Authentication with Aerial Input Numerals Using Leap Motion

With the progress of IoT, everything is going to be connected to the network. It will bring us a lot of benefits however some security risks will be occurred by connecting network. To avoid such problems, it is indispensable to strengthen security more than now. We focus on personal authentication as one of the security.As a security enhancement method, we proposed a method to carry out numeral identification and personal authentication using numerals written in the air with Leap motion sensor. In this paper, we also focus on proper handling of aerial input numerals to verify whether the numerals written in the air are helpful for authentication. We collect numerals 0 to 9 from five subjects, then apply three preprocessing to these data, learn and authenticate them by CNN (convolutional neural network) which is a method of machine learning. As a result of learning, an average authentication accuracy was 92.4%. This result suggests that numerals written in the air are possible to carry out personal authentication and it will be able to construct a better authentication system.

IoTの進歩により，すべてがネットワークに接続されるようになる．それは多くの利点をもたらすが，ネットワークを接続することによっていくつかのセキュリティリスクが発生する．このような問題を回避するには，現在よりもセキュリティを強化することが不可欠である．セキュリティの1つとして個人認証に焦点を当てており，セキュリティ強化の方法として，Leapモーションセンサーで空中に書かれた数字を使って数字の識別と個人認証を行う方法を提案した．本論文では，空中に入力された数字が認証に役立つかどうかを検証するために，空中入力数字の適切な処理にも焦点を当てている． 5人の被験者から0~9の数字を収集し，これらのデータに3つの前処理を適用して，機械学習の方法であるCNN(畳み込みニューラルネットワーク)で学習および認証している．学習の結果，平均認証精度は92.4%であった．この結果は，空中に書かれた数字が個人認証を実行することが可能であり，より良い認証システムを構築できることを示唆している．

Advances in Science, Technology and Engineering Systems


A Gathered Images Analysis Method to Evaluate Sound Sleep

This paper proposes a method to evaluate a sound sleep using an image gathering technique and itsanalysis techniques. The proposed method consists of three phases; gathered images generation, gathered images analysis and sound sleep evaluation. The gathered images designed to gather sleep postures and their changes are generated at 1 second, 10 seconds, 1 minute, 10 minutes, 1 hour intervals and all times, respectively. In the gathered image analysis, the gathered images are analyzed by calculating difference values among the gathered images of 10-minute and all times. Then, the sound sleep conditions are evaluated by visual inspection and analysis results. In order to show the effectiveness of the proposed method, we conduct experiments using real movies and their images. In experimental results, we confirm that there were sound sleep conditions, bad sleep conditions and borderline cases by checking subjective evaluation using questionnaire and generated gathered images visually. Moreover, we confirm that the calculated difference values among the gathered images of 10-minute and all times are different between sound sleep and other cases. Furthermore, the analyzed results show that the proposed method was successful in the sleep conditions classifications on four of five subjects. These results suggest that the gathered images analysis method is effective for evaluating whether sleep condition is sound sleep or not. In particular, it is important to calculate the difference values among the gathered images of 10-minute and all times to evaluate sleeping conditions.

Journal of the Institute of Industrial Applications Engineers


An Electroencephalogram Analysis Method to Detect Preference Patterns Using Gray Association Degrees and Support Vector Machines

This paper introduces an electroencephalogram (EEG) analysis method to detect preferences for particular sounds. Our study aims to create novel braincomputer interfaces (BMIs) to control human mental (NBMICM), which are used to detect human mental conditions i.e., preferences, thinking, and consciousness, choose stimuli to control these mental conditions, and evaluate these choices. It is important to detect the preferences on stimuli. If the stimuli related to the preference can be detected, the NBCIMC can provide stimuli to the user based on their emotions by detecting their favorite stimuli. The proposed method adopted EEG recording technique, extraction techniques of EEG features and detection methods of preferences. EEG recording employs a simple electroencephalograph, for which the measurement position is the left frontal lobe (Fp1) of the brain. We assume that the differences of the EEG activities on the patterns of preference are expressed in the association between the changes of the power spectra on each frequency band of the EEG. To calculate the association, we employ the gray theory model. The EEG feature is extracted by calculating the gray association degree, then, the preferences are detect using a support vector machine (SVM). Experiments are conducted to test the effectiveness of this method, which is validated by a mean accuracy rate >88% on the favorite sound detection. These results suggest that the detection of subjects favorite sounds becomes easy when the EEG signals are analyzed while the gray associate degrees are used as the EEG feature and the SVM is used as the classifier.

聴取音に対する嗜好を脳波による分類する手法を提案する．提案手法は，脳波計測部，特徴抽出部，嗜好検出部で構成される．脳波計測部では，乾式センサを有する簡易脳波計を用いて，音聴取時の脳波を計測する．計測箇所は左の額である．特徴抽出部では，周波数分析された脳波の周波数に対する時間変化の値を算出し，周波数の時間変化の関連性を灰色理論を用いて，灰色関連度を算出することで計算する．その計算結果を特徴量とみなす．嗜好検出部では，サポートベクターマシンを用いて，嗜好を検出する．提案手法の有効性を検証するために，実験を実施した結果，良好な結果を得るに至った．


舌骨上筋群の筋電信号に基づくCNNを用いた舌動作と寡声母音の推定

舌骨上筋群の筋電信号に基づくCNNを用いた舌動作と寡声母音の推定

In this paper, we propose a method to estimate the tongue motion direction and silent speech based on convolutional neural network (CNN) using the surface electromyogram (EMG) from the suprahyoid muscles. Conventional human machine interface (HMI) is difficult to use for users who are unable to freely move the muscles below the neck due to nerve damage or the like. Therefore, we have developed a method to estimate the tongue motion in 6 directions and 5 vowels of silent speeches from 4 channel EMG. As a result of verification experiment, we obtained averaged accuracy was about 81.2% in the estimation of the tongue directions and the silent speeches. Thus, it was suggested that simultaneous estimation is possible based on EMG measured from electrodes on the anterior neck region.

本論文では，舌動作の推定とサイレント音声の認識を畳み込みニューラルネットワーク(CNN)に基づいて行う方法を提案している．筋電は舌骨上筋から計測している．神経的ダメージにより，首周囲の筋肉を自由に動かせない人たちがいる．そのため，舌の6方向への動作と5母音のサイレント音声認識を4チャンネル筋電により推定する方法を開発した．この方法により，舌の向き推定と5母音のサイレント音声認識に対して81.2%の精度を得た．したがって，前頸部に設置した乾式センサにより，それら両者の同時推定が可能であることが示唆された．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Development of Driving Fatigue Strain Index for Reducing Accident Risk Among Drivers

Driving has become more important as this medium being practically, faster and cheaper in connecting human from one to other places. However, in some occurrences driving activity can cause disaster or death to human in daily life as they get fatigued while driving. Driving fatigue is one of the top contributor to the road crashes. Therefore this study is to develop a driving fatigue strain index (DFSI), collaborate with Decision Support System (DSS), to quantify the risk levels caused by driving activity, and to propose an appropriate solution in minimizing the number of road accidents caused by the driving fatigue. The decision support system provide fast and systematic analysis, and solutions to minimize the risk and the number of accidents associated with driving fatigue. The development of DFSI is based on risk factors associated with driving activity such as muscle activity, heart rate, hand grip force, seat pressure distribution, whole-body vibration, and driving duration. All risk factors are assigned with multipliers, and the DFSI is the output or result of those multipliers. The development of DFSI is essential to analyze the risk factors that would contribute significantly to discomfort and fatigue associated with driving. Besides, in the future this index will have a capability to recommend alternative solutions to minimize fatigue while driving.

人をある場所から他の場所に移動させるには，この媒体が実質的に，より速く安価であることから，運転がますます重要になってきている．しかし，いくつかの事例では，運転中の活動は，運転中の疲れのため，日常生活の中で人に災害や死を引き起こす可能性がある．運転疲労は，道路事故の一因となっている．そこで，運転疲労ひずみ指数(DFSI)を開発し，意思決定支援システム(DSS)と連携して，運転活動による危険度を定量化し，その事故による道路事故の発生を最小限にする適切な解決策を提案する．意思決定支援システムは，迅速かつ体系的な分析と，運転疲労に関連するリスクと事故の数を最小限に抑えるソリューションを提供する． DFSIの発達は，筋肉活動，心拍数，ハンドグリップ力，シート圧力分布，全身振動，および運転期間などの運転活動に関連する危険因子に基づく．すべてのリスク要因には乗数が割り当てられ，DFSIはそれらの乗数の出力または結果である． DFSIの開発は，運転に伴う不快感や疲労に大きく寄与するリスク要因を分析するために不可欠である．さらに，今後，この指標は，運転中に疲労を最小限に抑えるための代替ソリューションを推奨する機能を備えている．

International Journal of Electrical & Electronic Systems Research


A Specification Method of Character String Region in Augmented Reality

This paper proposes a method to enter characters and/or character string in an augmented reality usinga gesture motion. The proposed method detects the region of character string using the gesture motion. It consistsof five phases; template generation, skin color detection, hand region detection, gesture motion extraction anddesignation of character string region. The template image consists of two fingers because a gesture is to takehold the tips of the first and second fingers. In the skin color detection, we extract the skin color on the basis ofvalues in saturation by using threshold processing. The hand region is detected by calculating areas and detectingthe area with the maximum value as a hand. The gesture motion is extracted using template matching. In orderto show the effectiveness of the proposed method, we conduct experiments for character string specification.

拡張現実空間において，手指ジェスチャーを用いて，文字および文字列の領域を特定する手法を提案する．提案手法は，テンプレート操作，肌色検出，手領域検出，ジャスチャー認識，文字列領域特定，で構成される．テンプレートは親指と人差し指が閉じている状態を用いる．また，手指ジェスチャーでは，親指と人差し指を閉じるジェスチャーをスイッチの代替とし，文字および文字列を囲むスタート地点と終了地点を特定している．手指ジェスチャーを検出するために，肌色・手領域を特定した後に，テンプレートマッチングにより，閉じているかどうかの判断をする．提案手法の有効性を検証するために実験を実施した．


Japanese Janken Recognition by Support Vector Machine Based on Electromyogram of Wrist

In this paper, we propose a method which can discriminate hand motions. We measured an electromyogram of wrist by using 8 dry type sensors. We focused on four motions, such as ``Rock-Scissors-Paper'' and ``Neutral''. ``Neutral'' is a state that does not do anything. In the proposed method, we apply fast Fourier transformation (FFT) to measured EMG data, and then remove hum noise. Next, we combine values of sensors based on a gaussian function. In this gaussian function, variance and mean are 0.2 and 0, respectively. After that, we apply normalization by linear transformation to the values. Subsequently, we resize the values into range from -1 to 1. Finally, support vector machine (SVM) conducts learning and discrimination. We conducted experiments in three subjects. Discrimination accuracy of the proposed method for three subjects was 96.9%, 95.3%, 92.2%, respectively. It is clear that the gaussian function is robust to difference of sensor position because this function combines both adjacent channels of sensors.

本論文では，8個の乾式センサを手首周囲に設置して計測された筋電によるての 動作識別の方法を提案している．手の動作は日本のジャンケン(グー，チョキ，パー)とニュートラル動作である．提案方法では，筋電信号にFFTを適用し周波数成分に変換し，ハムノイズを除去している．その後，筋電センサの信号にチャネル間の平滑化を適用する．そして，信号値を-1∼+1に正規化し，サポートベクタマシンで学習識別を行った．識別率は，被験者3名に対し，96.9%，95,3%，92.2%であった．センサ間の平滑化により，センサ設置位置のずれに対して頑健であることが示された．

ECTI Transactions on Computer and Information Technology


A Consideration for Electroencephalogram Analysis using Self-Organizing Map Based on Learning Algorithm for Plural-Attribue Information

複属性データ対応型自己組織化マップを用いた脳波分析に関する一考察

This paper discusses a method to detect electroencephalogram (EEG) patterns using a self-organizing map(SOM) based on a learning algorithm for plural-attribute information (SOMPA). The input data for SOMPA has two attributes which are EEG feature and individual feature. We set the EEG feature to main feature and individual feature to sub-attribute information. The winning node in the learning algorithm of SOMPA is determined by using main feature and sub-attribute information. In the preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band which are , and , respectively. The individual feature is analyzed though the ego analysis using psychological testing. In order to prove the effectiveness of the proposed method, we conduct experiments using real EEG data. The experimental results show that the EEG pattern detection accuracy using SOMPA improves compared with the standard SOM.

サブ属性を考慮した改良型勝者ノード決定アルゴリズムを有する自己組織化マップを用いた脳はパターン検出法について考察する．改良型勝者ノー決定アルゴリズムでは，主属性のデータと副属性のデータを別々に使用し，それぞれ勝者ノード候補を決定する．それぞれの勝者ノード候補から任意の距離内にあるノード領域を選抜し，その領域が重なっている場合，重なった領域内において，最も主属性のデータと一致するノードを勝者ノードとする．重なりが無い場合，主属性の勝者ノード候補を勝者ノードと賭する．脳波を用いて嗜好を検出する問題に適用し，提案手法の有効性を検証した結果，良好な結果を得るに至った．

電気学会論文誌C (電子，情報，システム部門誌)


Method to Classify Matching Patterns between Music and Humans Mood Using EEG Analysis Technique Considering Personality

In this paper we introduce a method to classify matching patterns between music and human mood using an electroencephalogram (EEG) analysis technique and considering personality. We analyse the EEG of the left prefrontal cortex by single-point sensing. The EEG recording device uses dry-type sensors. The feature vector is created by connecting the personality quantification results and the EEG features. Egogramsthe Yatabe-Guilford personality inventory and a Kretschmer-type personality inventory are used to quantify personality. The EEG features are extracted using fast Fourier transform. Then, the matching patterns are classified using the k-nearest neighbour method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data.

ヒトのパーソナリティを考慮した脳波分析を利用して気分と一致した音楽を分類するための手法を紹介する．提案手法は左前頭極から得られる脳波を分析する．脳波計には乾式タイプのセンサを有する簡易脳波計を使用する．パーソナリティの分析には，エゴグラム，YG検定，KT検定を使用する．脳波分析は，周波数分析およびk近傍法を用いる．提案手法の有効性を検証するために，実験を実施した．

The Online Journal on Computer Science and Information Technology, OJCSIT


Development of Eye Mouse Using EOG signals and Learning Vector Quantization Method

Recognition of eye motions has attracted more and more attention of researchers all over the world in recent years. Compared with other body movements, eye motion is responsive and needs a low consumption of physical strength. In particular, for patients with severe physical disabilities, eye motion is the last spontaneous motion for them to make a respond. In order to provide an efficient means of communication for patients such as ALS (amyotrophic lateral sclerosis) who cannot move even their muscles except eye, in this paper we proposed a system that uses EOG signals and Learning Vector Quantization algorithm to recognize eye motions. According to recognition results, we use API (application programming interface) to control cursor movements. This system would be used as a means of communication to help ALS patients.

眼の動作識別は多くの興味を引きつけている．特に，身体の不自由な患者(ALS等の四肢麻痺など)にとって，眼の動作は最終段階まで利用・応答できる重要な要素である．それらの患者(眼以外を動かせない人)のために効率的な通信手段を提供するために，本論文では眼電に基づくシステムを提案している．本手法は，眼電位を学習ベクトル量子化により学習識別し，さらに筋電の識別ルールに基づいて高精度な認識を実現している．さらに，ウィンドウズ上で動作するシステムを構築し，実際に眼電位でマウスを操作できるシステムを実現した．このシステムはALS患者たちにとって，非常に重要な通信手段となる．

Journal of the Institute of Industrial Applications Engineers


Lost Property Detection by Template Matching using Genetic Algorithm and Random Search

In this paper, we propose an object search method which is adapted to transformation of an object to be searched to detect lost property. Object search is divided into two types; global and local searches. We used a template matching using Genetic Algorithm (GA) in the global search. Moreover we use a random search in the local search. According to experimental results, this system can detect rough position of the object to be searched. The search accuracy obtained using the present method is 83.6%, and that of a comparative experiment using only GA is 42.1%. We have verified that our proposed method is effective for lost property detection. In the future, we need to increase search accuracy to search objects more stably. In particular, we need to improve local search.

本論文では，監視カメラの画像を想定し自動で監視する方法を提案している．通常，監視カメラの映像での物体探索では様々なサイズと形状の変化がある．そのため，物体の変形に対応できる方法が必要である．本論文の方法では，遺伝的アルゴリズムを用いることにより，映像内に残された忘れ物を高速・高精度に検出する方法を提案している．特に，大域的探索を遺伝的アルゴリズムが担当し，局所的探索をランダム探索法で行う方法を提案している．さらに，複数の忘れ物を候補として検出する方法により，比較的高精度に忘れ物の検出が可能であることを示している．結果として本手法で，83.6%の探索精度を実現している．


Preference Classification Method Using EEG Analysis Based on Gray Theory and Personality Analysis

This paper introduces a method to classify the preference patterns of sounds on the basis of an electroencephalogram (EEG) analysis and a personality analysis. We analyze the EEG of the left prefrontal cortex by single-point sensing. For EEG recording, a dry-type sensor and few electrodes were used. The proposed feature extraction method employs gray relational grade detection on the frequency bands of EEG and egogram. The gray relational grade is used for extracting the EEG feature. The egogram is extracted for quantifying the subject's personality. The preference patterns generated when the subject is hearing a sound are classified using the nearest neighbor method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data. These results show that the accuracy rate of the preference classification using the proposed method is better than that using the method that does not to consider the subject's personality.

本論文では，音刺激に対する嗜好パターンを脳波分析技術を用いて検出する手法を提案する．脳波分析では，脳波の周波数帯域間の関連性を灰色理論に基づいて分析し，灰色関連度を算出し，脳波の特徴量とみなす．脳波に含まれる個人差を緩和するために，ヒトの性格をエゴグラムを用いて定量化する．脳波の特徴量とエゴグラムを特徴ベクトルとし，嗜好パターンを分類する．実験検証の結果，提案手法の有効性を示すことが出来た．


A Study of Safety Driving Support System focusing on Driver's Head Posture Categorization

In this paper, we analyze drivers head posture during safety verification and propose a method for classifying head posture using two types of unsupervised neural networks: Self-Organizing Maps (SOMs) and fuzzy Adaptive Resonance Theory (ART). The proposed method can generate the optimal number of cluster-generated labels for the target problem. We experimentally assess the effectiveness of the proposed method by adjusting the fuzzy ART network vigilance parameters. In addition, we indicate that drivers head posture during safety verification can be categorized according to their individual properties.

本論文では，ドライバの安全確認行動中の頭部姿勢を分析し，その結果に基づいて2つの教師なしニューラルネットワーク(Self-Organizing Maps(SOMs)とfuzzy Adaptive Resonance Theory(ART))により頭部姿勢をカテゴライズする手法を提案する．提案手法は対象問題に合わせたクラスタ数を決定可能である．実験では，fuzzy ARTのパラメータの変化による頭部姿勢の分類性能を評価した．最終的に，安全確認行動中の頭部姿勢は個人特性に合わせてカテゴライズできる可能性が示唆された．

International Journal of Engineering Research and Technology


Road Traffic Signs Recognition using Genetic Algorithms and Neural Networks

International Journal of Machine Learning and Computing


Rhythm Features Based on Linear Predictive Coding of Energy Variations for Musical Mood Classification

エネルギー変化の線形予測符号化に基づくリズム特徴量を用いた音楽印象識別

In this paper, we propose a novel rhythm feature, which we call Rhythm feature based on Linear Predictive Coding (RLPC), to improve mood classification performance. The proposed feature is extracted with Linear Predictive Coding (LPC) on energy variations of an audio signal and is able to represent periodicity of rhythm in musical audio signals. To evaluate the proposed feature in comparison with 5 conventional rhythm features, mood classification experiments were conducted for 7 moods. From these experimental results, average accuracy of the proposed feature was 83.7% and 1.1 point higher than that of the conventional features. In addition, in case of combining base features, which indicate intensity, timbre, and harmony features, with RLPC, average accuracy was 89.5%. The accuracy was 2.0 point higher than that of base features and 0.6 point higher than that of combining base features with the conventional features. From results of hypothesis test on each mood, accuracies of the proposed feature were significant against that of the 4 conventional features.

本論文では音楽の印象識別を高精度で行う特徴量として,線形予測符号化に基づくリズム特徴量(Rhythm feature based on Linear Predictive Coding:RLPC)を提案する.RLPCは,音響信号のエネルギー変化に対して線形予測符号化を適用することにより求められるケプストラムであり,音楽におけるリズムの周期性をとらえることが可能である.7つの印象に対する音楽印象識別実験により,ジャンル分類ならびに印象分類における5種類の従来のリズム特徴量との比較を行った.実験結果より,RLPCを用いた場合の平均識別率は83.7%であり,従来のリズム特徴量を用いた場合より1.3ポイント高い識別率が得られた.さらに,音量・音色・和音特徴量にRLPCを併用した場合の平均識別率は89.5%であり,音量・音色・和音特徴量のみを用いる場合と比較して2.0ポイント,従来のリズム特徴量を併用した場合よりも0.6ポイント高い識別率が得られた.また,各印象においてRLPCと従来のリズム特徴量で仮説検定を行った結果,4種類の従来のリズム特徴量に対して,RLPCを用いた場合の識別精度が有意であった.

Transactions of Information Processing Society of Japan


A Nonlinear Learning Algorithm for Large Scale Datasets

Nonlinear feature generation techniques are among the most important processing tools in pattern recognition. As linear feature generation techniques, Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (FLDA) have been developed and improved in various ways. Simple-FLDA (SFLDA) is an improved version of FLDA and has demonstrated good performance in recognition experiments. In SFLDA, eigenvectors spanning the eigenspace are obtained with simple iterative calculations, unlike the original FLDA which needs to solve eigenvalue problem of a covariance matrix created with all input samples. However, FLDA and SFLDA algorithms are both linear feature extraction methods. Therefore, the steps used for obtaining the eigenspace from an input space might not be suitable with respect to large, complex datasets. For adjustability to such datasets, the kernel trick is applied to many linear feature generation methods, turning them into nonlinear methods. In this paper, we propose Simple Kernel Discriminant Analysis (SKDA) for higher recognition performance by applying a kernel trick to the SFLDA algorithm. Similar to the SFLDA algorithm, SKDA algorithm is composed with simple calculations, but in recognition experiments using the UCI datasets as well as face image dataset; its features equal or surpass those of the SFLDA algorithm.

非線形な特徴生成手法はパターン認識の分野で最も重要なツールの一つである．線形の主成分分析(PCA)とフィッシャー判別分析(FLDA)が様々な方法で改良されており，高性能な特性が評価されている．また，判別分析の近似的手法であるSimple-FLDAも提案されているが，大規模な複雑データに対しては必ずしも妥当な方法ではない．本論文では，Simple-FLDAに対してカーネルトリックを適用して非線形高次元空間に拡張したSimple Kernel Discriminant Analysis (SKDA)を提案する．本手法をUCIデータセットと顔画像データセットに適用し，その非線形での有効性を評価している．

International Journal of Engineering and Innovative Technology


A new optimization method of the geometric distance in an automatic recognition system for bied vocalisations

We have been developing an automatic recognition system for bird vocalisations. Many biologists have been using the early 32 bit version of our system, and we have been working on a 64 bit version. The software segments a waveform of the bird vocalisation from a three-hour continuous recording and extracts the sound spectrum pattern from the waveform using the LPC spectrum analysis. Next, the software compares the sound spectrum pattern (the input pattern) with the standard pattern (that was extracted in advance) using a similarity scale. We use a new similarity scale called the ``Geometric Distance''. The Geometric Distance is more accurate than the conventional similarities in the noisy environment. In the 64 bit version, the software matches an input pattern with the 40,000 elements of the standard patterns per second and per processor, and it is 2.8 times faster than the conventional cosine similarity. In this paper, we introduce an automatic segmentation method of bird vocalisations and a new optimization method of the Geometric Distance. The new optimization method offers improvements of an order of magnitude over the conventional Geometric Distance.

本論文では取りの鳴き声を識別するための方法を開発している．開発したソフトウェアではIPCスペクトル解析によりスペクトルパターンを抽出し，提案する類似性尺度により標準パターンと比較する．この幾何学的尺度は従来の類似性尺度よりも，より正確な特徴を抽出でき，ノイズのある環境でも優れている．鳥の鳴き声分類に対するシミュレーションにより提案する類似性尺度が従来の方法よりも有効であることが示されている．

Acoustique & Techniques (TRIMESTRIEL D'INFORMATION DES PROFESSIONELS DE L'ACOUSTIQUE in French)


Novel Approximate Statistical Algorithm for Large Complex Datasets

In the field of pattern recognition, principal component analysis (PCA) is one of the most well-known feature extraction methods for reducing the dimensionality of high-dimensional datasets. Simple-PCA (SPCA), which is a faster version of PCA, performs effectively with iterative operated learning. However, SPCA might not be efficient when input data are distributed in a complex manner because it learns without using the class information in the dataset. Thus, SPCA cannot be said to be optimal from the perspective of feature extraction for classification. In this study, we propose a new learning algorithm that uses the class information in the dataset. Eigenvectors spanning the eigenspace of the dataset are produced by calculating the data variations within each class. We present our proposed algorithm and discuss the results of our experiments that used UCI datasets to compare SPCA and our proposed algorithm.

パターン認識の分野では，従来から主成分分析(PCA)が特徴抽出やデータ次元の圧縮に対して多く使われてきた．本論文では，主成分分析(PCA)の近似解法であるSimple-PCAに焦点を当て，その性能を改善する方法を提案している．特に，従来のSimple-PCAに対してクラス情報を付加したアルゴリズムを構成し，学習速度を維持した状態で，優れた特徴抽出機能を実現している．本アルゴリズムの有効性を定量的に評価するために，UCIデータベースを用いて多次元の大規模データに対する特徴抽出性能を評価している．


Facial Impression Recognition Based on Facial Texture Information

Facial consciousness and recognition have been studied for many years. However, these studies have only extracted the feature of facial impression. Some studies have treated the evaluation of facial impression. In those studies, liking, age, and gender were evaluated with respect to facial impression. Therefore, there are no details on whether we evaluate various general facial impressions in daily life. Moreover, the result of evaluation using those studied features depends on the facial-parts-extraction accuracy. Then, we use texture features for facial impression recognition in this study. The texture features can be extracted stably, because we need not specify their positions. We extract texture features in the mesh state. We can extract physical features by this technique without specifying the detailed location of features, unlike the conventional method. Finally, we show the effectiveness of our proposed method compared with previous studies.

顔の知覚と認識は長い間研究されているが，顔印象の特徴を抽出するだけであった．本論文では，顔印象の認識においてテクスチャ特徴を用いる．テクスチャ特徴は安定して簡単に抽出でき，それらの正確な位置を特定する必要がない利点がある．本論文では，従来法と異なり，顔をメッシュ状に分割しテクスチャ特徴を抽出する．最後に，本手法の有効性を示すために，従来の方法との比較を行っている．


Interindividual Difference Analysis in Prefrontal Cortex EEGs Based on the Relationship with Personality

In this study, we propose a method to analyse interindividual differences in electroencephalograms (EEGs) taken while a subject listened to music, which is based on the relationship of these EEGs to human personality. The frequencies of the EEGs that are analysed have components that contain both significant and immaterial information as well as different levels of importance. We express the different levels of importance through the weight values of frequencies using a real-coded genetic algorithm. Then, the EEG patterns, which are determined based on the evaluation of the impression on the music, are detected using the $k$-nearest neighbour method. We assume that differences between detection results for EEG patterns with the highest and lowest recognition accuracies show interindividual differences. Moreover, ego analysis based on psychological testing is used to analyse personality, and the ego score is determined using a questionnaire. Finally, we discuss the relationship between personality and interindividual differences observed in experimental EEGs. An interesting tendency of a person with a combined ego type is that he or she has a unique response to negative stimuli compared with that of positive stimuli.

聴取音楽に対する感受性の違いを分析する方法を提案する．提案手法は脳波分析部，パーソナリティ分析部，および聴取音楽の印象分類ぶで構成される．脳波分析部では，遺伝的アルゴリズムを用いて脳聴取音楽の印象分類に有用な周波数帯域を選定する．パーソナリティ分析部では，心理テストを用いてヒトの性格を定量化する．印象分類部では，k近傍法を用いて聴取音楽に対する印象を分類する．個体間差を分析するために，脳波による聴取音楽の印象分類結果と定量化された性格との関連性を分析した．その結果，ネガティブな印象の検出が容易な被験者は，内向的な性格である可能性が示唆された．


Detection of Abandoned Luggage and Owner Tracking at Sensitive Public Areas

Abandoned objects in sensitive congested public areas like airports or train stations pose a major security threat. Therefore, in this paper, to solve the problem, we propose a novel method for the detection of abandoned luggage, tracking its owner and extracting any other necessary information using multi-threshold pixel based dynamic background model and Earth Mover's Distance (EMD) signature matching. The public area selected for experiments is a train station. The background model is created by learning the color variance in all pixels by allowing multiple thresholds per pixel. After learning, pruning unnecessary thresholds improves the foreground extraction speed. Blob noise and outliers including shadows are deleted by a binarization method based on the Discriminant Analysis (DA) method. A color signature created using the HSV color space that is fast to process, is matched using the EMD metric to track blobs. Once an abandoned object candidate is found, a slower but more accurate SURF algorithm is used to extract feature points for further tracking. Stationary objects after this phase are considered to be abandoned luggage. To prove the effectiveness of the proposed method, experiments are conducted using the i-Lids dataset (2007 IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2007)) achieving a frame-based average accuracy of about 93%.

本論文では，動的背景モデルとEarth Mover's Distanceシグネチャマッチングを用いて，鉄道の駅などで放棄された荷物を検出し，所有者を追跡する手法を提案する．この手法では，影の影響を受けないようDiscriminant Analysis(DA)法による2値化法により対象を検出した上で，SURFアルゴリズムを用いることで放棄した所有者を追跡する．i-Lidsデータセットを用いた実験で，約93%の精度が達成できることが確認できた．

IEEJ Transactions on Electronics, Information and Systems


Automatic Background Updating for Abandoned Object Detection at Train Stations

In video surveillance using overhead cameras, it is very important to capture and regularly updated the background to enable accurate extraction of persons or objects of interest. However, in scenes with a lot of movement and stationary objects, for example a train station, it is not easy to update the background without including such objects. In this work, we investigate several objects features and combine them to maintain a stable background image. The features include the speed of the object, texture, shape, associations between persons and objects, etc. The data used is a subset of the i-Lids dataset that was captured for analyzing video systems. It is captured in a train station using one overhead camera. Each video segment is about three minutes long. Index TermsActive background, abandoned objects, shape matching.

本論文では，オーバーヘッドカメラによるビデオ監視のための背景画像の更新手法を提案した．本手法では，鉄道の駅において，放置物の検知のために，象物体の速度，テクスチャ，形状，人とオブジェクトの間の関連などを特徴量として抽出し，さらにそれらを組み合わせることで安定した背景画像更新を可能としている．ビデオシステムの分析のためにキャプチャされたi-Lidsデータセットを用いて実験を行なったところ，本手法の有効性を確認することができた．

International Journal of Machine Learning and Computing


A Simple Interface for Mobile Robot Equipped with Single Camera using Motion Stereo Vision


Monotonous Tasks and Alcohol Consumption effects on the Brain by EEG Analysis using Neural Networks

An analysis of the Electroencephalogram (EEG) signals while performing a monotonous task and drinking alcohol using principal component analysis (PCA), linear discriminant analysis (LDA) for feature extraction and Neural Networks (NNs) for classification is proposed. The EEG is captured while performing a monotonous task that can adversely affect the brain and possibly cause stress. Moreover, we investigate the effects of alcohol on the brain by capturing the data continuously after consumption of equal amounts of alcohol. We hope that our work will shed more light on the relationship between such actions and EEG, and investigate if there is any relation between the tasks and mental stress. EEG signals offers a rare look at brain activity, while, monotonous activities are well known to cause irritation which may contribute to mental stress. We apply PCA and LDA to characterize the change in each component, extract it and discriminate using a NN. After experiments, it was found that PCA and LDA are effective analysis methods in EEG signal analysis.

主成分分析および線形判別分析，ニューラルネットワークを用いた飲酒に関するEEGの解析手法を提案する．本手法では，EEG信号から，主成分分析と線形判別分析を用いることで特徴量を抽出し，ニューラルネットワークを用いて解析するものである．本手法の有効性を検証するための実験を行なったところ，主成分分析と線形判別分析はEEG信号の解析に有効であることが確認された．

International Journal of Computational Intelligence and Applications


Automatic system to remove unpleasant images detected by pupil-size changes.

International Journal of Computer Science Issues


Study on Model for Predicting the Intra-Individual Difference in Left Prefrontal Pole Electroencephalogram Variability and Its Evaluation

This paper introduces a novel statistical model to estimate an intra-individual difference in left prefrontal cortex electroencephalogram (EEG) activities, and a method for evaluating the proposed model. It is known that an EEG contains the individual characteristics. However, extraction of these individual characteristics has not been reported. The analyzed frequency components of an EEG can be classified as the components that contain significant number of features and the ones that do not contain any. From the viewpoint of these feature differences, we propose the model for extracting features of the EEG. The model assumes a latent structure and employs factor analysis by considering the model error as personal error. We consider the first factor loading that is calculated by eigenvalue decomposition as the EEG feature. Furthermore, we use k-nearest neighbor (kNN) algorithm for evaluating the proposed model and the extracted EEG features. In general, the distance metric used is Euclidean distance. It is possible that the distance metric used depends on the characteristic of the extracted EEG feature and on the subject. Therefore, depending on the subject, we use one of the three distance metrics: Euclidean distance, cosine distance and correlation coefficient. Finally, in order to show the effectiveness of the proposed model, we perform an experiment using real EEG data.

脳波の特徴抽出精度向上ための手法と抽出された脳波の特徴データを客観的に評価する方法を提案した．特徴抽出は，一般的な潜在構造モデルのモデル誤差を個人誤差と定義したモデルにより行なう．その際に，未知のパラメータである個人誤差をRGAにより推定している．客観的評価は，目視できる3次元空間へ射影する方法を提案している．そして音楽聴取時の脳波データを用いて，提案手法の有効性を検証した．


Face detection using RBF network in AIBO

AIBOにおけるRBFネットワークを用いた顔検出

We propose a face-tracking system for AIBO by using the skin color, in the first step of acquiring users characteristics. In this paper, we focus on the human-face, which has many kinds of characteristic parts in a human. We detect faces using the Neural Network (NN) for the purpose of estimating whether one pixel is skin color or not. However, the hierarchical NN may give this system false recognition for unknown color of background. In this paper, we propose high generality face recognition system for AIBO using the radial basis function (RBF) network. Also, in order to show the effectiveness of the proposed method, we perform computer simulations. First of all, we can see the skin color recognition results using the RBF network. In various light conditions, we have the relatively good results of the skin color recognition. Furthermore, we show output value distribution in color space. We can see the possibility of dealing with unknown color using the RBF network. Moreover, we have achieved the skin color recognition for AIBO in a real system. We will show that AIBO can track skin color in many kinds of light conditions.

電気学会論文誌C (電子，情報，システム部門誌)


Hand Posture Recognition Robust for Posture Changing in Complex Background

In this paper, a method for hand posture recognition, which is robust to changing hand posture in an actual environment, is proposed. Conventionally, a data glove device and a 3D scanner have been used for feature extraction from hand shape. However, the performance of each approach is adversely affected by changing hand posture. Therefore, we propose a posture fluctuation model for efficient hand posture recognition based on the 3D hand shape and color features obtained from a stereo camera. A large dictionary for posture recognition is built from various inclined hand images, which were auto-created from one scanned hand image, based on the proposed method. In order to show the effectiveness of the proposed method, performance and processing times for posture recognition and compared with those of conventional methods. In addition, we perform an evaluation experiment using Japanese sign language.

本論文では，実環境で変化する手の姿勢に頑健な手の姿勢認識法を提案する．従来は，データグローブや3次元スキャナーを用いているが，手の姿勢変動に影響されている．そこで，本論文ではステレオカメラからの手の3次元形状と色特徴により効率的に手の姿勢認識を行える姿勢変動モデルを提案する．姿勢認識のための膨大な辞書は，本手法により一つのスキャン画像から自動で生成される．本手法の有効性を評価するために，姿勢認識の精度と時間を従来方法と比較し，また，日本語のサイン語を用いた評価実験も行った．


A Local DCT-II Feature Extraction Approach for personal Identification Based on Palmprint

Biometric applications based on the palmprint have recently attracted increased attention from various researchers. In this paper, a method is presented that differs from the commonly used global statistical and structural techniques by extracting and using local features instead. The middle palm area is extracted after preprocessing for rotation, position and illumination normalization. The segmented region of interest is then divided into blocks of either 8×8 or 16×16 pixels in size. The type-II Discrete Cosine Transform (DCT) is applied to transform the blocks into DCT space. A subset of coefficients that encode the low to medium frequency components is selected using the JPEG-style zigzag scanning method. Features from each block are subsequently concatenated into a compact feature vector and used in palmprint verification experiments with palmprints from the PolyU Palmprint Database. Results indicate that this approach achieves better results than many conventional transform-based methods, with an excellent recognition accuracy above 99% and an Equal Error Rate (EER) of less than 1.2% in palmprint verification.

掌紋に基づくバイオメトリクスの応用は多くの研究者を引きつけてきた．本論文では，大域的な特徴ではなく，局所的な特徴を用いる方法を提案している．本手法の掌の画像に対して回転，位置，輝度の正規化を行い，掌紋の中心部分を抽出する．切り出した領域は，8x8，16x16ピクセルのサイズに変更される．そして，タイプIIの離散コサイン変換(DCT)を適用し，DCT空間に変換する．底中周波数成分を符号化する係数の部分集合が，JPEGスタイルのジグザグ法で選択される．各ブロックの特徴がコンパクトな特徴ベクトルに集約されて，掌紋認証実験が行われる．本手法の有効性を評価するために，PolyU掌紋データベースに対する計算機実験が行われ，99%の精度と1.2%のEqual Error Rate (EER)を達成した．

IEEJ Transactions on Electronics, Information and Systems


Invisible calibration pattern based on human visual perception

人の視知覚特性に基づく視認困難なキャリブレーションパターン

In this paper, we propose an arrangement and detection method of an invisible calibration pattern based on characteristics of human visual perception. A calibration pattern is arranged around contents where invisible data is embedded, as some feature points between an original image and the scanned image for normalization of the scanned image. However, it is clear that conventional methods interfere with page layout and artwork of contents. Moreover, conventional visible patterns show a third person the position of embedded data. Therefore, visible calibration patterns are not suitable for security service. The most important part of human visual perception in the proposed method is the spectral luminous efficiency characteristic and the chromatic spatial frequency characteristic. In addition, a back ground color in surrounding of contents is not restricted to uniform color by using the proposed calibration pattern. It is suggest that the proposed method protect page layout and artwork.

本論文では， 人の視知覚特性に基づく視認困難なキャリブレーションパターンを検出する方法を提案している．キャリブレーションパターンは，原画像とスキャンされた画像の間の特徴点として，視認困難なデータが埋め込まれているコンテンツに配置されている．しかし，従来法はページレイアウトや挿入図と干渉しており，また従来に視認パターンは埋め込みデータを第三者に示していた．いたがって，視認キャリブレーションパターンはセキュリティサービスに適用ではなかった．本手法において人間の視覚特性で最も重要なのは比視感度特性と色の時空間特性である．加えて，対象の周囲の背景色は，本手法により一意には制限されない．本手法はページレイアウトと挿入図を保護することが示唆された．

電気学会論文誌C (電子，情報，システム部門誌)


A New Geometric Distance Method to Remove Pseudo Difference in Shapes

In our previous paper, a new similarity scale called the Geometric Distance was proposed. With the conventional geometric distance algorithm, there are the following three shortcomings. 1. Since standard and input patterns are normalized to have the same area, a pseudo difference in shapes occurs between them. 2. Since ``shape variation'' is calculated in each combination of the standard and input patterns, the processing overhead increases when the number of standard patterns increases. 3. Since reference patterns are evaluated for each movement position of a normal distribution, the computational memory overhead increases when the number of components of standard and input patterns increases. To counter these shortcomings, a new geometric distance algorithm is proposed. 1. It is derived without normalization of the standard and input patterns, so that the pseudo difference in shapes is removed. 2. It reduces the processing overhead by separating the calculation of ``shape variation'' into registration process and recognition process. 3. It reduces the computational memory overhead by sharing a single reference pattern. Experiments in vowel recognition were carried out using the same voice data as the previous paper. At a mean of 5 dB SNR, the recognition accuracy improved from 78% to 82% over the conventional algorithm.

我々の以前の論文で，幾何学的尺度に基づく類似性尺度を提案した．しかし，幾つかの欠点があったため，本論文で新しい幾何学的距離アルゴリズムを提案する．これはパターンの標準化(正規化)を必要とせず，計算時間を短縮でき，使用するメモリも節約できる方法である．音声認識に対する計算機シミュレーションにより，従来の方法と同じデータを用いて，78%から82%に精度を改善できた．

International Journal of Advanced Intelligence (IJAI)


Vowel Recognition Using Akamatsu Integral and Differential Transforms


New Similarity Scale to Measure the Difference in Like Patterns with Noise

A new similarity scale called the Geometric Distance, that numerically evaluates the degree of likeness between two patterns is proposed. Traditionally, the similarity scales known as the Euclidean distance and cosine similarity have been widely used to measure likeness. Traditional methods do not perform well in the presence of noise or pattern distortions. In this paper, a new mathematical model for a similarity scale is proposed which overcomes these limitations of the earlier models, while improving the overall recognition accuracy. Experiments in speech vowel recognition were carried out under various SNR levels in a variety of noisy environments. In all cases a significant improvementin recognition accuracy is demonstrated, with the improvement most pronounced in the noisiest conditions. In fact, at a SNR of 5 dB in a subway, the recognition accuracyimproved from 65% to 75% and at 20 dB SNR from 98.4% to 99.6% over the MFCC method. Numerical modeling of simple patterns is used to demonstrate the principles behind the Geometric Distance.

類似性の度合いを数値的に評価するために幾何学的尺度と呼ぶ新しい類似性尺度を提案する．通常，類似性の尺度はユークリッド距離やコサイン類似度が使用されているが，ノイズや歪みの存在する場合には，上手く機能しない．本論文は，それらの欠点を克服する類似性尺度の新しい数学的モデルを提案し，認識精度が改善できた．様々なノイズが含まれる母音認識で実験を行い，全ての場合でかなりの改善効果が見られ，MFCC法よりも優れていることが判った．


Development of a Block based Palmprint Recognition Methodology Using the Discrete Cosine Transform

Hand-based biometric applications based on the palmprint have recently become a popular choice due to their high acceptance by the general public. In this paper, a method is presented which applies the type-II Discrete Cosine Transform (DCT) in a novel way by extracting selected coefficients from small overlapping blocks of scanned, medium-resolution palmprint images to form a feature vector for personal identification. The middle palm area is extracted after rotation, position and illumination normalization during the preprocessing stage before dividing it into 8×8 pixel blocks. The type-II DCT is then used to transform the blocks, and only a small set of the coefficients selected from each block after ranking based on magnitude only. The selected coefficients are concatenated to create a compact feature vector that represents each palmprint. Experiments using a subset of the PolyU Palmprint Database show that this block-based approach effectively discriminates between palmprints, with excellent palmprint recognition results and an Equal Error Rate (EER) of less than 2% in palmprint authentication when using a 64×64 pixel input image block.

掌紋に基づくバイオメトリクス応用は近年，一般的な使用に関して受け入れられつつある．本論文では，個人認証のための特徴ベクトルを構成する小領域から選択的に特徴を抽出するタイプIIの離散コサイン変換(DCT)を用いる方法を提案する．中庸な掌領域は画像正規化後に抽出される．その後，8x8ピクセル領域に分割され，DCTを適用し，少数の係数を取り出し，コンパクトな特徴ベクトルとする．本手法の有効性を評価するために，PolyU の掌紋データベースを用いて実験を行い，64x64ピクセルの入力画像ブロックに対して，2%以下のEqual Error Rate (EER)という優れた結果を得られた．

Australian Journal of Intelligent Information Processing Systems


Fast Approximate Incremental Learning Algorithm based on of Simple-FLDA

This paper presents an Incremental Simple-FLDA. The Incremental Simple-FLDA is a fast incremental learning algorithm based on Simple-FLDA. This algorithm need not hold all training samples because it enables update of an eigenvector according to incremental samples. Moreover, this algorithm has an advantage that it can calculate the eigenvector at high-speed because matrix calculation is not needed. We performed computer simulations using UCI datasets, EMG data and face data to verify the effectiveness of the proposed algorithm. As a result, its availability was confirmed from the standpoint of recognition accuracy and convergence performance of the eigenvector compared with the other methods.

逆行列計算を不要とすることによって計算高速性を実現するSimple-FLDA (Fisher Linear Discriminant analysis)において，漸近的な学習を実現するIncremental Simple-FLDAを開発している．この手法は，固有ベクトルを高速計算することが可能であり，学習データの増加に伴い，効率的な識別(パターン認識)を達成することが出来る．実際に，パターン認識で標準的に用いられるUCIデータ(Iris Data)の他，EMGデータ，顔データ などで，識別精度と計算時間の観点からの有用性を評価している．


Detection and Recognition of Vehicle License Plates Using Template Matching, Genetic Algorithms and Neural Networks

International Journal of Innovative Computing, Information and Control


Interactive Interface with Evolutionary Eye Sensing and Physiological Knowledge

The purpose of this study is to develop an interactive interface using eye movement to operate a welfare apparatus, such as a feeding device for an orthopedically-impaired person. A part of the purpose is to eliminate special calibration and re-calibration during the operation. Originalities of this proposed system are as follows: eye sensing with evolutionary processing and interactive operation screen based on some physiological knowledge. The proposed system uses a non-contact type interface by the eye movement. An iris is tracked and eye movement is measured by the evolutionary eye sensing (EES) method. An operation screen is divided into 9 areas, which has visual stimulation using the physiological knowledge. A user can select these areas by eye movement and decide by eye fixation. The effectiveness of the proposed system is evaluated by comparison experiments with 20 subjects. The results indicate that the proposed system is easy to use for first-timer, who can become proficient in operation just after a few exercises.

本論文の目的は，肢体不自由者のために，食事装置のような福祉機器を眼の動作で動かす対話的インタフェースを開発することである．特に，本手法は進化的手法を用いて眼(虹彩)の動作(9個のエリアへの分離)を追跡し，生理学的知識を用いてさらに改善している．提案手法は非接触であり，特別なキャリブレーションを必要としない利点がある．本手法の有効性を検証するために，20名の被験者に対して実験を行い，ほとんど訓練がない状態でも高精度に眼の動作を検出できることが判った．

IEEJ Transactions on Electronics, Information and Systems


A DFT-Based Method of Feature Extraction for Palmprint Recognition

Over the last quarter century, research in biometric systems has developed at a breathtaking pace and what started with the focus on the fingerprint has now expanded to include face, voice, iris, and behavioral characteristics such as gait. Palmprint is one of the most recent additions, and is currently the subject of great research interest due to its inherent uniqueness, stability, user-friendliness and ease of acquisition. This paper describes an effective and procedurally simple method of palmprint feature extraction specifically for palmprint recognition, although verification experiments are also conducted. This method takes advantage of the correspondences that exist between prominent palmprint features or objects in the spatial domain with those in the frequency or Fourier domain. Multi-dimensional feature vectors are formed by extracting a GA-optimized set of points from the 2-D Fourier spectrum of the palmprint images. The feature vectors are then used for palmprint recognition, before and after dimensionality reduction via the Karhunen-Loeve Transform (KLT). Experiments performed using palmprint images from the `PolyU Palmprint Database' indicate that using a compact set of DFT coefficients, combined with KLT and data preprocessing, produces a recognition accuracy of more than 98% and can provide a fast and effective technique for personal identification.

過去20∼30年の間，指紋に代表されるバイオメトリクスの研究が活発に行われてきた．掌紋は，最も最近になり研究されだしたが，その一意性，安定性，使いやすさ等で興味が高くなっている．本論文は，効率的で簡単な掌紋認識のための特徴抽出法を提案している．本手法は周波数特徴に変換してから遺伝的アルゴリズムで特徴を最適化する．その後，KL変換で次元圧縮している．本手法の有効性をPolyUデータセットで評価し，98%以上の個人認証の認識精度を得た．


Hour-glass Neural Network based Daily Money Flow Estimation for Automatic Teller Machines

Monetary transactions using Automated Teller Machines (ATMs) have become a normal part of our daily lives. At ATMs, one can withdraw, send or debit money and even update passbooks among other many possi- ble functions. ATMs are turning the banking sector into a ubiquitous service. However, while the advantages for the ATM users (Financial institution customers) are many, the Financial institution side faces an uphill task in management and maintaining the cash flow in the ATMs. On one hand, too much money in a rarely used ATM is wasteful, while on the other, insuficient amounts would adversely affect the customers and may result in a lost business opportunity for the financial institution. Therefore, in this paper, we propose a daily cash flow estimation system using neural networks that enables better daily forecasting of the money required at the ATMs. The neural network used in this work is a fiveve layered hour glass shaped structure that achieves fast learning, even for the time series data for which seasonality and trend feature extraction is difficult. Feature extraction is carried out using the Akamatsu Integral and Differential transforms. This work achieves an average estimation accuracy of 92.6%.


Image Morphing and Warping: Application to Speech Simulation using a Single Image

Journal of Advanced Computational Intelligence and Intelligent Informatics


Color feature extraction of the regions using the GA for the scenery image retreival

風景画像検索のための遺伝的アルゴリズムを用いた画像領域の色特徴量取得

Recently, the keyword image retrieval is widely studied. By using these technologies, we can obtain the images with the corresponding keywords easily. In case of conventional image search systems, we search according to the file names basically. However, filenames which is named are frequently incorrect. To resolve this problem, we propose the automatic keyword addition method for scene images. In this paper, there are two important points. One of them is the image segmentation method using the maximum distance algorithm (MDA). The other is the automatic keyword addition using the color feature of regions. The other is the color feature extraction of regions. In the image segmentation method, we propose the automatic decision method of parameters in the MDA. For this purpose, we investigate the relation between the optimal parameters and features of regions. In the color feature extraction of regions, we propose the genetic algorithm(GA). Moreover, in order to show the effectiveness of the proposed method, we show the simulation examples. According to the results of the simulations, we achieve the keyword addition for scene images.

近年，キーワードに基づく画像検索が広く用いられ研究されている．従来の画像検索では，ファイルネームや説明タグに基づく方法となるが，画像の内容とは関係のない場合が多い．そこで本論文では，風景画像に対して自動でキーワードを付加する方法を提案する．本手法は，画像を最大距離法で領域分割し，領域の色特徴情報でキーワードを付加する．領域からの色特徴抽出には遺伝的アルゴリズムを用いている．本手法の有効性を評価するために，風景画像に対する計算機実験を行っている．

電気学会論文誌C (電子，情報，システム部門誌)


A Design of Apparent Age Estimation System by the Empirical Mode Decomposition

Recently, the automation of the age estimation technique is hoped for in various fields. Therefore, we propose an apparent-age estimation system using empirical mode decomposition (EMD). Conventional study reported that the time-frequency features are important for age estimation. However, these cannot necessarily extract the time-frequency feature in detail, because the classical technique that have a relationship of trade-off between the time resolution and the frequency resolution are used. On the other hand, the EMD is the novel time-frequency analysis technique that do not have the relationship of trade-off between the time resolution and the frequency resolution. The EMD gives a time-frequency analysis decomposing a signal into several intrinsic mode functions (IMFs). The IMF together with their Hilbert transforms are called the Hilbert Huang spectrum, which leads to instantaneous frequency and amplitude. We use these features effectively for extracting human's age perception. We estimate the age by a neural network that learns pairs of face image and the Hilbert Huang spectrum. Furthermore, we compress the data for neural network by using the simple principal component analysis (SPCA). In order to show the effectiveness of the proposed method, computer simulations are done by the actual human data.

近年，様々な場面で自動的な年齢推定が期待されている．本論文では経験的モード分解(EMD)に基づく見た目年齢推定システムを提案する．従来の研究では，顔の年齢推定では時空間特徴が重要と報告されているが，時間と空間の分解能に関するトレードオフがある．EMDは信号を幾つかの固有のモード関数に分解し時空間解析の方法を与える．本論文では，EMDを用い，さらに主成分分析で次元圧縮してからニューラルネットワークを用いて年齢推定を行う．本手法の有効性を示すために，実際の顔画像に対する実験を行っている．

Journal of Circuits, Systems, and Computers


A model for extracting personal features of an electroencephalogram and its evaluation method

脳波の個人特性推定モデルとその評価方法に関する一考察

This paper introduces a model for extracting features of an electroencephalogram (EEG) and a method for evaluating the model. In general, it is known that an EEG contains personal features. However, extraction of these personal features has not been reported. The analyzed frequency components of an EEG can be classified as the components that contain significant number of features and the ones that do not contain any. From the viewpoint of these feature differences, we propose the model for extracting features of the EEG. The model assumes a latent structure and employs factor analysis by considering the model error as personal error. We consider the EEG feature as a first factor loading, which is calculated by eigenvalue decomposition. Furthermore, we use a k-nearest neighbor (kNN) algorithm for evaluating the proposed model and extracted EEG features. In general, the distance metric used is Euclidean distance. We believe that the distance metric used depends on the characteristic of the extracted EEG feature and on the subject. Therefore, depending on the subject, we use one of the three distance metrics: Euclidean distance, cosine distance, and correlation coefficient. Finally, in order to show the effectiveness of the proposed model, we perform a computer simulation using real EEG data.

脳波の特徴抽出精度向上ための手法と抽出された脳波の特徴データを客観的に評価する方法を提案した．特徴抽出は，一般的な潜在構造モデルのモデル誤差を個人誤差と定義したモデルにより行なう．その際に，未知のパラメータである個人誤差をRGAにより推定している．客観的評価は，目視できる3次元空間へ射影する方法を提案している．そして音楽聴取時の脳波データを用いて，提案手法の有効性を検証した．

電気学会論文誌C (電子，情報，システム部門誌)


Fast Incremental Algorithm of Simple Principal Component Analysis

This paper presents a new algorithm for incremental learning, which is named Incremental Simple-PCA. This algorithm adds an incremental learning function to the Simple-PCA that is an approximation algorithm of the principal component analysis where an eigenvector can be calculated by a simple repeated calculation. Using the proposed algorithm, it is possible to update the eigenvector faster by using incremental data. We carry out computer simulations on personal authentication that uses face images and wrist motion recognition that uses wrist EMG by incremental learning to verify the effectiveness of this algorithm. These results were compared with the results of Incremental PCA that introduced incremental learning function to the conventional PCA.

本論文では，Simple-PCA(主成分分析に近似法)を用いる追加学習法を提案している．Simple-PCAは主成分分析を行う最速の近似的方法であり，簡単な繰り返し演算で，固有ベクトルが計算できる．提案手法では，主成分分析の近似学習で，追加されたデータに対して，簡単な方法で固有ベクトルを修正して新たな固有ベクトルを獲得できる．計算機シミュレーションにより，従来の方法と比較して，その有効性を評価している．


Extraction of EEG Characteristics While Listening to Music and Its Evaluation Based on a Latency Structure Model with Individual Characteristics

EEG is characterized by the unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The real-coded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to a real EEG data. The result of our experiment shows the effectiveness of the proposed method.

本論文では，以下の3点に着目し，脳波の特徴精度向上のための手法を提案した．1点目は，脳波インタフェース応用を考慮し，一つのセンサから得られる情報のみを使用する．2点目は，脳波の問題点の一つである個人差による特徴抽出精度の低下軽減のため，一般的な統計学モデルの一つである潜在構造モデルのモデル誤差を，個人誤差と定義したモデルの考案である．なお，個人誤差は未知のパラメータであるため，実数値GAを用いて最適化している．3点目は，特徴抽出精度を客観的に評価するため，可視化空間へ写像することである．さらに，音楽聴取時の脳波データを用いて，聴取曲を分類する問題に適用し，有効性を検証した．


Liver tumor extraction from CT images using genetic algorithm

遺伝的アルゴリズムによるCT画像からの肝腫瘍抽出

本論文では，腹部X線CT画像における医師への診断補助システムの構築である．その第一歩として，1スライスのX線CT画像内の病変可能性が最も高い個所を自動的に抽出する領域分割手法を提案している．形状認識の点から，動的輪郭決定手法としてSnakesやLive-wireといった手法が提案されているが，これらの手法は輪郭が不明瞭でCT値も未知な腫瘍に対して効果的ではない．本論文では，遺伝的アルゴリズムにより腫瘍領域形状を最適化する方法を提案している．計算機実験により，その有効性を評価している．


Fingerprints Images Enhancement Using a Concavity and Convexity method

Biomedical Soft Computing and Human Sciences


Objectively Psychological Evaluation Using the EEG

The animal assisted therapy (AAT) has been known to have psychological and social effects on human being. However, scientific research is insufficient yet. Because it is difficult to have done in the medical clinic. Furthermore, the government cannot allow as a medical care because of nothing of clear proof. Therefore, in order to show a scientific basis of the AAT effects, we analyze the electroencephalogram (EEG) in the case of using the AAT. The EEG is generated by the brain activity, so that there is the correlation between the EEG and mentality. First of all, we define the healing by questionnaires. Secondly, we extract the correlation between the EEG features and mentality. Finally, we investigate the correlation between EEG features and the healing.

アニマルセラピーは人間に対する心理的および社会的影響を有することが知られている． しかし，科学的研究はまだ不十分である． それは内科診療所内での実施が難しいからです． さらに政府は明確な証拠がないため，医療として許可することはできない． したがって，AAT効果の科学的根拠を示すために，AATを脳波(EEG)を分析することで評価する． EEGは脳活動を頭皮上から観測した電気信号であるため，EEGと精神性との間に相関があると考えられる．実験的検証では，まず，アンケートによる治癒を定義します． 第2に，脳波の特徴と精神性との相関を抽出する． 最後に，EEGの特徴と癒しの相関を調べる．


Apparent Age Feature Extraction by Empirical Mode Decomposition


Enhancement of Palmprint Images Using an Optimized Hexagonal Multilayer Perceptron Neural Network


Evolutionary Video Processing for Lips Tracking

In this paper, real-time tracking and information acquisition of lip region of a person during speech in active video scenes are addressed. The lip is deformed by speech, and the size and orientation are changed by camera motion. The difficulty is mainly due to change of these appearances of the lip. We use template matching with a genetic algorithm to overcome these problems. A high speed and accurate tracking scheme using Evolutionary Video Processing is proposed. Usually, a genetic algorithm is unsuitable for a tracking accuracy of 91.6% and an average processing time of 26.0 milliseconds per frame are achieved.

本国際会議論文では，動作を伴うビデオシーンで会話中の人の口唇領域オンラインで追跡し，かつパラメータを獲得する方法を提案している．更新形状は発話により変形し，カメラ動作に伴い，サイズと方向も変化する．その困難さは主に更新形状の見た目の変化に依存する．そこで，これらの問題を解決するために，進化的ビデオ処理手法を用いて更新領域を追跡する方法を提案する．本手法では，遺伝的アルゴリズムに基づくテンプレートマッチングを使用し，高速性を達成する．本手法により，26ミリ秒で91.6%の検出精度を達成した．

International Journal of Intelligent Computing in Medical Sciences and Image Processing


Genetic Algorithms based Adaptive Search Area Control for Real Time Multiple Face Detection using Neural Networks


An adaptive graininess suppression method for restoration of color degraded images

カラー劣化画像復元のための適応的粒状抑制の一手法

Previous studies of image restoration for noise images were based on a mask processing. These conventional noise removal methods based on the mask processing have an issue of defining degradation to accompany a spacial processing. In this paper, we propose a graininess suppression method based on edge shape. In this method, we detect edges from an noise image and perform graininess suppression for this image based on edge information. On the edge detection, we execute an image transformation for an image that enables us to extract edge by making principal component images. Moreover, we use the canny edge detection operator with can detect a weak edge that relates to a real edge, and do not detect a lie edge. In the suppression process, we use Wiener filter that can restore an noise image without making a complete edge map and the original signal map. We demonstrated the effectiveness of the present method for the noise added images and confirmed it by means of computer simulation.

ノイズを含む画像の補正に関する従来の研究は，マスク処理に関するものが多かった．それらか空間処理に基づくため画像が劣化することがある．本論文では，画像形状(エッジ情報)に基づいて粒状を抑制する方法を提案している．特に，主成分分析とキャニーフィルタを用い，さらにウィーナーフィルタを用いてエッジ特徴を抽出している．本手法の有効性を化粧するために，ノイジィ画像に対する計算機シミュレーションを行い評価している．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Detection Method of Music to Match Users Mood in Prefrontal Cortex EEG Activity

前頭前野の脳波を用いた気分一致曲の検出法

In this article, we propose a method for detecting music to match a users mood in prefrontal cortex electroencephalogram (EEG) activity. The frequencies of the EEG analyzed are the components that contain significant and immaterial information. We focused on the significant frequency combinations. These frequency combinations are thought to express personal features of EEG activity. The proposed method calculates the percentage of the spectrum of these frequency combinations and evaluates whether the music matches the users mood through a simple threshold processing. A genetic algorithm (GA) is used to specify the frequency of personal features on the EEG.

ユーザの気分と一致する曲を脳波を用いて自動で検出する手法を提案する．脳波は複数の要因が複雑に絡み合う個人差の著しい時系列データである．脳波の周波数成分には重要な情報を多く含む成分とそうでない成分とが共存している．本論文では脳波の個人差を周波数成分の組み合わせにより表現することで，個人差による脳波ばらつきの抑制および気分一致曲の検出精度向上を試みる．また，気分一致曲は，遺伝的アルゴリズムによって選定された個人特性を表わす周波数成分群を用いて，パワースペクトル対比を演算し，単純な閾値処理により検出される．提案手法の有効性を検証した結果，気分不一致曲の正答率が70%∼90%以上と比較的良好な結果を得ることができた．


Downsized Evolutionary Video Processing for Lips Tracking and Data Acquisitiong

In this paper, high-speed lips tracking and data acquisition of a talking person in natural scenes are presented. Our approach is based on the Evolutionary Video Processing. This method has a trade-off between accuracy and a processing time. To solve this problem, in this paper, we proposed Evolutionary Video Processing with automatic SD-Control. In our simulations, the effectiveness of the proposed method is verified by a comparison experiment. The proposed method improves the performance, speed and accuracy, from 68.4% to 86.2%. Furthermore, it is evaluated that our proposed method can continue to chase the lips region even in such a case. It is demonstrated that the lips region detection and tracking at high speed and with high accuracy is possible, with acquisition of its numerical geometric change information.

本論文では，一般のシーンで，高速高精度に口唇領域を追跡し，口唇形状の有するパラメータを獲得できる方法を提案している．本手法は進化的ビデオ処理に基づいている．また，遺伝的アルゴリズムの探索領域を自動制御する方法も提案している．提案手法では，従来手法の精度を改善しており，86.2%の精度を獲得している．計算機シミュレーションにより定量的に評価し，その有効性と口唇パラメータの獲得が可能であることを示している．


A robust gender and age estimation under varying facial pose

姿勢変動に影響されない顔画像からの性別年齢推定

This paper presents a method for gender and age estimation which is robust for facial pose changing. We propose a feature point detection method which is the Adapted Retinal Sampling Method (ARSM), and a feature extraction method. A basic concept of the ARSM is to add knowledge about the facial structure into the Retinal Sampling Method. In this method, feature points are detected based on 7 points corresponding to facial organ from face image. The reason why we used 7 points to basis of feature point detection is that facial organ is conspicuous in facial region, and it is comparatively easy to extract. As features which is robust for facial pose changing, a skin texture, a hue and a gabor jet are used for the gender and age estimation. For classification of gender and estimation of seriate age, we use a multi-layered neural network. Moreover, we examine the left-right symmetric property of the face concerning gender and age estimation by the proposed method.

電気学会論文誌C (電子，情報，システム部門誌)


High Speed Genetic Lips Detection by Dynamic Search Domain Control

In this paper, high-speed size and orientation invariant lips detection of a talking person in an active scene using template matching and genetic algorithms is proposed. As part of the objectives, we also try to acquire numerical parameters to represent the lips. The information is very important for many applications, where high performance is required, such as audio-visual speech recognition, speaker identification systems, robot perception and personal mobile devices interfaces. The difficulty in lips detection is mainly due to deformations and geometric changes of the lips during speech and the active scene by free camera motion. In order to enhance the performance in speed and accuracy, initially, the performance is improved on a single still image, that is, the base of video processing. Our proposed system is based on template matching using genetic algorithms (GA). Only one template is prepared per experiment. The template is the closed mouth of a subject, because the application is for personal devices. In our previous study, the main problem was trade-off between search accuracy and search speed. To overcome this problem, we use two methods: scaling window and dynamic search domain control (SD-Control). We therefore focus on the population size of the GA, because it has a direct effect on search accuracy and speed. The effectiveness of the proposed system is demonstrated by performing computer simulations. We achieved a lips detection accuracy of 91.33% at an average processing time of 33.70 milliseconds per frame.

本論文では，高速で向きとサイズに依存しない口唇領域抽出方法を提案している．特に遺伝的アルゴリズムを用いるテンプレートマッチングによりシステムを構築している．また，口唇領域を構成する位置，サイズ，向きのパラメータは重要であり，これらも抽出できることを示している．また，発話中の口唇領域の変形に対しても対応でき，モバイルフォン等のインタフェースとしても活用できる．特に本手法ではただ一つのテンプレートだけ使用しており汎用性にも優れている．また，遺伝的アルゴリズムの性能を改善するために二つの方法を提案している．本手法の有効性を定量的に評価し，33.7ミリ秒で91.33%の精度を得ていることを示している．

IEEJ Transactions on Electronics, Information and Systems


EEG Characteristic Extraction Method of Listening Music and Objective Estimation Method Based on Latency Structure Model in Individual Characteristics

個人特性を考慮した潜在構造モデルによる音楽聴取時の脳波特徴抽出法と客観的評価方法

EEG is characterized by the unique and individual characteristics. Little research has been done to take into account the individual characteristics when analyzing EEG signals. Often the EEG has frequency components which can describe most of the significant characteristics. Then there is the difference of importance between the analyzed frequency components of the EEG. We think that the importance difference shows the individual characteristics. In this paper, we propose a new EEG extraction method of characteristic vector by a latency structure model in individual characteristics (LSMIC). The LSMIC is the latency structure model, which has personal error as the individual characteristics, based on normal distribution. The real-coded genetic algorithms (RGA) are used for specifying the personal error that is unknown parameter. Moreover we propose an objective estimation method that plots the EEG characteristic vector on a visualization space. Finally, the performance of the proposed method is evaluated using a realistic simulation and applied to a real EEG data. The result of our experiment shows the effectiveness of the proposed method.

本論文では，以下の3点に着目し，脳波の特徴精度向上のための手法を提案した．1点目は，脳波インタフェース応用を考慮し，一つのセンサから得られる情報のみを使用する．2点目は，脳波の問題点の一つである個人差による特徴抽出精度の低下軽減のため，一般的な統計学モデルの一つである潜在構造モデルのモデル誤差を，個人誤差と定義したモデルの考案である．なお，個人誤差は未知のパラメータであるため，実数値GAを用いて最適化している．3点目は，特徴抽出精度を客観的に評価するため，可視化空間へ写像することである．さらに，音楽聴取時の脳波データを用いて，聴取曲を分類する問題に適用し，有効性を検証した．

電気学会論文誌C (電子，情報，システム部門誌)


Feature Generation Method by Geometrical Interpretation of Fisher Linear Discriminant Analysis

This paper presents a new algorithm for feature generation, which is derived based on geometrical interpretation of the fisher linear discriminant analysis (FLDA). This algorithm (Simple-FLDA) is an approximation algorithm that calculates eigenvectors sequentially by an easy iterative calculation by expressing the maximization of variance between classes and minimization of variance in each class without the use of matrix calculation. We carry out computer simulations about recognition of wrist motion patterns by EMG measured from wrist and personal authentications that use face images to verify the effectiveness of this technique. The result was compared with the result of principal component analysis (Simple-PCA).

本論文では，判別分析の幾何学的解釈に基づいて，フィッシャー判別分析の近似的学習アルゴリズムを提案している．本提案方法では通常の判別分析と異なり，行列演算は不要であり，簡単な繰り返しアルゴリズムで判別分析の近似的固有ベクトルを計算することが可能である．本提案手法の有効性を示すために，手首で計測されたEMG(筋電)に対する認識実験と顔認識の実験を行い，Simple-PCA(主成分分析の近似法)との比較において，その有効性を定量的に評価している．


Using Genetic Algorithm for Eye Detection and Tracking in Video Sequence

We propose a high-speed size and orientation invariant eye tracking method, which can acquire numerical parameters to represent the size and orientation of the eye. In this paper, we discuss that high tolerance in human head movement and real-time processing that are needed for many applications, such as eye gaze tracking. The generality of the method is also important. We use template matching with genetic algorithm, in order to overcome these problems. A high speed and accuracy tracking scheme using Evolutionary Video Processing for eye detection and tracking is proposed. Usually, a genetic algorithm is unsuitable for a realtime processing, however, we achieved real-time processing. The generality of this proposed method is provided by the artificial iris template used. In our simulations, an eye tracking accuracy is 97.9% and, an average processing time of 28 milliseconds per frame.

本論文では，サイズと向きに不変な眼の追跡システムを提案し，さらにサイズと向きを表現しているパラメータを獲得できることを示している．本手法では，人間の頭部の動きに柔軟に対応でき，かつ実時間で処理が可能ある．本手法では遺伝的アルゴリズムを用いるテンプレートマッチングを使用している．進化的ビデオ処理により，高速高精度な眼の追跡システムを構築できている．本手法の有効性を定量的に評価し，28ミリ秒で97.9%の検出精度を得ている．

Journal of Systemics, Cybernetics and Informatics


Automatic Human Faces Morphing Using Genetic Algorithms Based Control Points Selection

International Journal of Innovative Computing, Information and Control


Hybrid EMG recognition system using linear discriminant analysis and principal component analysis

重判別分析と主成分分析によるハイブリッドEMG認識システム

本論文は，手首EMG(筋肉電位)を用いたポインティングデバイス構築のためのEMG認識方式の提案を行っている．特に，主成分分析と重判別分析を組み合わせて大分類と小分類を効率的に行う方法を提案している．本手法により，手首動作認識に有効な特徴を生成することができ，計算機シミュレーションにより，通常のアプローチよりもよりも高精度にEMG認識できることを定量的に示している．本手法は，DSP学習ボード上でも実装されている．

Transactions of the Institute of Systems, Control and Information Engineers

システム制御情報学会論文誌


Genetic Eye Detection Using Artificial Template


Optimizing Feature Extraction for the Camera Mouse using Genetic Algorithms


Genetic Algorithms Based On-line Size and Rotation Invariant Face Detection


Face Recognition in Color Images Using Neural Networks and Genetic Algorithms

International Journal of Computational Intelligence and Applications


EMG Signal Recognition System Using Feature Vectors by Genetic Function Identification

遺伝的関数同定による特徴ベクトルを用いたEMG信号認識システム

本論文は，手首EMG(筋肉電位)を用いたポインティングデバイス構築のためのEMG認識方式の提案を行っている．特に，遺伝的アルゴリズムにより筋電信号の必要成分を選択し，かつその組み合わせから認識関数を構成する方法を提案している．本手法により，手首動作認識に有効な特徴を生成することができ，計算機シミュレーションにより，通常のアプローチよりもよりも高精度にEMG認識できることを定量的に示している．


Using the Thresholds Function Method to Detect License Plates

閾値関数を用いたナンバープレート検出手法

本論文は，自動車ナンバープレート検出を行う方法を提案している．特に実数値型遺伝的アルゴリズムにより，画像の輝度情報からプレート領域を決定するための閾値を効率的に決定する方法を提案している．また，精度を改善するために，自動車全部におけるプレートの位置情報，プレートの形状情報，テンプレートマッチングを併用することにより効果が高いことを示している．実際の自動車画像に対する計算機シミュレーションにより，プレート検出が昼夜ともに，高速かつ高精度に行えることを検証した．

The Journal of the Institute of Image Information and Television Engineers


Morphing Human Faces: Automatic Control Points selection and Color Transition

Trans. on Engineering Computing and Technolog


Recognition of Smiling Faces Using Neural Networks and SPCA

In this paper, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos θ is calculated using an eigenvector by SPCA as well as a gray-scale image vector of each picture pattern. By using neural networks (NNs), the difference in the value of cos θ between the true and the false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smiles, computer simulations are done with real images. Furthermore, an experiment using the self-organisation map (SOM) is also conducted as a comparison.

本論文は，カラー顔画像を用いて顔表情分類を行う方法を提案している．特に，真の笑顔と作り笑い(苦笑い)を区別することを主目的とし，Simple PCAによる特徴抽出とニューラルネットワークによる学習識別をハイブリッド化する方法を提案している．本手法は主成分分析を高速に行うことが可能であり，オンライン化にも適している．16名の顔画像を用いた計算機シミュレーションにより，その有効性を定量的に検証した．


Proposal of the EEG Analysis Method Using the Individual Characteristic of the EEG

脳波の個人特性を考慮した脳波分析法の提案

本論文は，音楽を聴いている場合の脳波を解析することにより，脳の状態を推定する手法を提案している．特に，遺伝的アルゴリズムによる特徴選択と因子分析による特徴解析により脳波分類に適した個人特徴を抽出し，ニューラルネットワークにより学習識別する手法を提案している．音楽鑑賞時における実際の脳波信号に対する計算機シミュレーションにより，本手法の有効性を定量的に評価している．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Genetic lips extraction method for varying shape

形状に不変な遺伝的口唇領域抽出法

本論文は，モバイル器機での使用を前提にした音声認識のための口唇領域の抽出方法を提案している．高速高精度に口唇領域を抽出するために改良型の遺伝的アルゴリズム(GA)を提案している．本方法は通常のGAにペナルティ関数を導入することによりテンプレートマッチングの誤差に基づく適応度関数を改良し，計算機シミュレーションにより，改善効果を検証した．

電気学会論文誌C (電子，情報，システム部門誌)


Recognition of wrist EMG signal patterns using neural networks

we propose a recognition system based on wrist movements by focusing on ElectroMyoGram (EMG), using the body signals generated by voluntary movements of subject muscles, as the initial stage for construction of a total operation device. This paper tries to recognize EMG signals using neural networks (NNs). The electrodes under the dry state are attached to wrists and then EMG signals are measured. These EMG signals are classified using NNs into seven categories: neutral, up and down, right and left, inside twist, outside twist. The NN learns the FFT spectra of these signals in order to classify them. Moreover, we introduce a modular structure of the NN for improving the recognition accuracy. Computer simulations show that our approach is effective to classifying the EMG signals. This system is partly implemented on a DSP learning board.

本論文は，手首EMG(筋肉電位)を用いたポインティングデバイス構築のためのEMG認識の提案を行っている．特に，複数のニューラルネットワークを組み合わせて構造化し，精度改善する方法を提案している．本手法では，EMG信号解析を大分類と小分類で実現でき，精度改善に効果が大きい．計算機シミュレーションにより，1種類のニューラルネットワークよりも高精度にEMG信号を認識できることを示している．本システムは部分的にDSP学習ボード(組込システム)上で実装(実現)されている．

Journal of Intelligent and Fuzzy Systems


A feature extraction method for personal authentication using SPCA and RGA

SPCAとRGAを用いた個人認証のための特徴抽出法

本論文は，高精度な個人認証システムを実現するための特徴抽出法を提案している．まず実数値型遺伝的アルゴリズムにより，入力画像の重み付けでの入力特徴選択を行い．選択された特徴をSimple PCA(主成分分析)により学習·認識する．本方法を用いると認証に必要な顔部分データだけを用いて高精度な個人認証が可能であることを示している．計算機シミュレーションにより，100名の被験者に対する個人認証を従来法よりも高精度に達成できることを示した．

ヒューマンインタフェース学会論文誌

ヒューマンインタフェース学会論文誌


Neuro rainfall forecast with data mining by real-coded genetical processing

実数値GAを用いたデータマイニングによるニューロ降雨予測システムの設計

本論文は，地域的な高精度天気予報を実現するためのデータマイニング法を提案している．まず実数値型遺伝的アルゴリズムにより，降雨予測のための入力特徴選択を行い．選択された属性をニューラルネットワークにより学習·認識する．本方法を用いると地域的な気象データだけを用いて高精度な降雨予測が可能であることを示している．計算機シミュレーションにより大阪地方の気象データにより，大阪地方の降雨予測を通常の天気予報よりも高精度に達成できることを示した．

電気学会論文誌C (電子，情報，システム部門誌)


Skin Color Correction through Illuminant Estimation Using Neural Networks and Analytical Methods

本論文は，カラー画像中の顔(肌色部分)を対象として．過露光や露光不足などの光温度が極端に異なる場合の肌色の補正方法を提案している．顔領域を正確に抽出する場合には，顔の肌色範囲を正確に指定することが重要であり，その前処理として肌色部分の色補正を行う方法を提案している．特にニューラルネットワークの学習と円柱座標系での距離計算に基づく補正方法により，高精度に肌色情報を補正し，顔領域を検出できることを計算機シミュレーションにより定量的に検証した．


Recognition of true smile based on simple-PCA

Simple PCAに基づく笑顔の真偽分類

本論文は，笑顔の真偽分類を行うことを目的とし，主成分分析の高速化アルゴリズムとニューラルネットワークによる高精度真偽分類の方法を提案している．本論文の特徴抽出の方法はSimple PCAであり，繰り返し演算により主成分分析を行う方法である．この方法により90%の寄与率までの固有ベクトルを計算し，入力信号である顔画像との内積をニューラルネットワークにより学習し，真偽分類を行う．顔画像での計算シミュレーションにより，本手法の有効性を定量的に検証した．


Human Face Detection in Visual Scenes Using Neural Networks

本論文は，カラー画像中から人間の顔を検出するための高速·高精度な方法を提案している．特に，顔の特徴である肌色情報をニューラルネットワークにより学習検出し，後処理の顔判定を行う領域を低減させ高速化を達成している．後処理では顔の幾何学的特徴を別のニューラルネットワークで学習し，かつ構造化学習によりニューラルネットワークの構造を最小化し，高精度と高速を同時に失言している．カラー画像での計算シミュレーションにより，複数顔が含まれる場合でも高精度に顔検出できることを示した．

IEEJ Transactions on Electronics, Information and Systems


An Evolutionary Approach to Rule Generation from Trained Neural Pattern Recognition Systems

A method of extracting rules from neural pattern recognition systems formed using an evolutionary algorithm is presented. The evolutionary algorithm used here is a random optimization method (ROM). In particular, deterministic mutation (DM) is introduced in ROM. It is performed on the basis of the result of neural network strucutre learning. The DM procedure can evolve a candidate of a solution to increase a ROM fitness function in a deterministic manner. In the paper iris data are used as inputs. ROM are utilized to reduce the number of connection weights in the neural network. The network weights survived after the ROM training represent rules to perform pattern classification for the iris data. The rules are then extracted from the networks in which hidden units use signum and sigmoid functions to produce binary outputs. It enables us to extract simple logical functions from the network. Simulation results show this approach can generate a simple network structure and as a result simple rules for pattern classification.

本論文は，パターン認識問題を学習したニューラルネットワークからルール抽出する方法を提案している．特に，硬貨画像のような一般的な画像から如何にして認識ルールを生成するかに関して考察している．本方法では，まず入力属性数が特に多い場合の入力次元を削減し，かつ入力をクラス分類に結びつける方法を提案し，少ない入力属性のルール形式を生成している．計算シミュレーションの結果から，低コストで硬貨識別を実現しルール生成できることを示した．

Journal of SYSTEMS RESEARCH and INFORMATION SYSTEMS


Knowledge Incorporation and Rule Extraction in Neural Networks

ニューラルネットワークへの知識の埋込とルール抽出

本論文は，ブラックボックスであるニューラルネットワークでの知識処理の方法を提案している，具体的には人間の有するパターン分類のための知識をニューラルネットワークに初期知識として埋め込み，学習を通して洗練化を行い，学習後にルールを抽出する方法の提案である．パターン分類に対する計算シミュレーションの結果から，本方法がニューラルネットワークで人間の有する知識の洗練化に有効であることを示した．(アルゴリズムの提案，シミュレーション，論文執筆を担当，ただし，ページの抽出は困難)

高速信号処理応用技術学会誌(電子技術)


Fast Face Detection Method Using the GA-based Threshold Method considering lighting condition

光の状態を考慮した閾値の遺伝的決定法による高速な顔領域の検出法

本論文は，カラー画像中で人間の顔を検出するための高速·高精度な方法を提案している．本方法の特徴は，画像の撮影状態に依存せずに顔特徴である肌色領域を正確に検出できる方法を実現した点にある．まず，カラー画像の輝度情報に注目し，輝度と肌色範囲の関係を遺伝的アルゴリズムにより数式化する．検出時には画像の輝度から直接数式を経て肌色範囲の閾値を導出可能である．この方法により高速性と高精度性を実現できる．カラー画像での計算シミュレーションにより，本手法の有効性を定量的に検証した．

高速信号処理応用技術学会誌(電子技術)


教師あり学習によるベクトル空間情報検索モデルの精度改善

教師あり学習によるベクトル空間情報検索モデルの精度改善

本論文では，教師あり学習によるベクトル空間情報検索モデルの精度改善手法について述べた．提案手法を用いて，線形変換により検索質問集合に対する適合性情報および文書間距離情報を検索モデルに組み入れることにより，ユーザからのフィードバック情報を長期的に検索システムに反映させることが可能となった．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


A detection method of face region in color images by using the lip detection neural network and the skin distinction neural network

唇検出NNと肌色識別NNによるカラー画像中からの顔領域の検出法

本論文は，カラー画像中から人間の顔を検出することを目的とし，複数のニューラルネットワークシステムによる認識システムを提案している．特に，顔の肌色領域と唇色領域とを抽出するニューラルネットワークを学習的に構築し両方の結果を統合して顔と判断するシステムとした．カラー画像に対する計算機シミュレーションの結果から，本手法は多人数の含まれている画像であっても高精度に顔を検出することが可能であることが検証できた．

電気学会論文誌C (電子，情報，システム部門誌)


A method for universal rule generation by genetic algorithm with virus infection

ウィルス感染型遺伝的アルゴリズムによるユニバーサル·ルール生成の一方法

本論文は，学習済みのニューラルネットワークからルール抽出するための効果的な方法を提案している．本方法は生物界のウィルス感染をモデル化した改良型遺伝的アルゴリズムを構成し，様々な問題に対して統一的にルール生成できることを示している．計算シミュレーションの結果から，本方法がパターン分類とカオスの同定に同じ枠組みで学習し，高精度に同定できることを示した．

電気学会論文誌C (電子，情報，システム部門誌)


Rotation-Invariant Neural Pattern Recognition System with Application to Coin Recognition

Transactions of the Society of Instrument and Control Engineers


Fog occurrence forecast bynusing LVQ with a genetical preprocessing

遺伝的前処理を行なうLVQ を用いた霧発生の予測法

本論文は，北海道千歳空港での霧発生を予測する問題を対象とし，ニューラルネットワークの1種であるLVQに対して遺伝的前処理(特徴選択)を組み込むことにより，高精度予測と必要属性の選択によるデータ解析を行う方法を提案している．計算機シミュレーションの結果から，非常に高精度に霧発生を予測でき，かつ予測に重要な入力属性を決定することができることが検証できた．

電気学会論文誌C (電子，情報，システム部門誌)


Design and evaluation of neural networks for coin recognition by using GA and SA

GAとSAを併用したニューラルネットワークの設計と硬貨識別への応用

本論文では，硬貨の識別システム構築を目的とし，新しい画像取得センサからの画像を用いて最適設計の方法を提案している．特に遺伝的アルゴリズムとシミュレーテッド·アニーリングをハイブリッド化したアルゴリズムにより，効率的に硬貨識別システムを設計する方法が実現されている．計算機シミュレーションにより，本ハイブリッド型アルゴリズムは解探索で大域的探索と局所的探索の両方において従来法より効果的であることが検証できた．


A method to extract rules from neural networks formed using evolutionary algorithms

進化的アルゴリズムにより構造化されたニューラルネットワークからのルール抽出の一方法

本論文では，学習されたニューラルネットワークから効率的にルール抽出するための方法を提案している．特に，シグモイド型中間層ユニットを用いてニューラルネットワークの学習をするが，ルール抽出の段階では，シグナム(符号)型ユニットに置き換えてルール抽出を行う．この際，パターン分類問題での精度が落ちないように，中間層での再学習を行う．アヤメ問題と硬貨識別問題で本方法の有効性を定量的に検証した．

電気学会論文誌C (電子，情報，システム部門誌)


Rule extraction from neural networks formed using random optimization method with deterministic mutation

決定論的変異を含むランダム探索法により構造化されたニューラルネットワークからのルール抽出

本論文は，学習済みのニューラルネットワークからルール抽出するための新しい方法を提案している．本手法は，まずニューラルネットワークをルール抽出に適した構造まで最小化する．この際に認識精度を下げないように中間層の学習方法を工夫し，かつルール抽出し易い中間層の2値構造を実現する．本手法をアヤメの分類問題に適用し，従来方法と比べて格段に優れたルール抽出で，かつ簡単なルールを生成できることを計算機シミュレーションにより検証している．


Designing a Neural Network Using a Genetic Algorithm with Deterministic Mutation and Partial Fitness

In this paper a method of designing a neural pattern recognition system for a rotated coin recognition problem using a genetic algorithm (GA) with deterministic mutation (DM) and partial fitness (PF) is presented. In this method, chromosomes of individuals in the GA are divided into several parts and their PF functions are evaluated for GA operations. Furthermore, the DM, which is based on neural network learning, is introduced. The DM can evolve chromosomes of individuals to increase their fitness functions in a deterministic manner. In the pattern recognition system described in this paper, the Fourier transform is used as a preprocessor which produces rotation invariant features. These features are recognized by a multilayered neural network. The GA is utilized to reduce the number of signals, Fourier spectra, and input to the neural network. This approach using the GA is a type of feature selection problem. It is shown that the present method is better than conventional GAs with respect to convergence in learning, and results in the formation of a small neural network

本論文は，回転した硬貨画像の認識を行うためのニューラルネットワークを最適設計するための方法を提案している．特に，部分的適応度と決定論的変異を新たに導入した改良型遺伝的アルゴリズムを提唱している．硬貨識別システムの最適設計を行う計算機シミュレーションの結果から，本アルゴリズムが効果的に最適なシステム構造を獲得できることを定量的に示している．

Journal of Intelligent and Fuzzy Systems


Genetic algorithm with deterministic mutation based on neural network learning

ニューラルネットワークの学習に基づいた決定論的変異を有する遺伝的アルゴリズム

本論文は，遺伝的アルゴリズムとニューラルネットワークのハイブリッド型アルゴリズムにより，新しい原理の工学的進化手法を提案している．ニューラルネットワークでパターンを識別し，その際の構造学習により不要となった結合の情報を遺伝的アルゴリズムに逆転写し，進化を加速している．硬貨識別システムの最適化に関する計算機シミュレーションにより，本ハイブリッドシステムの有効性を定量的に検証している．

The Transactions of the Institute of Electronics, Information and Communication Engineers D-II

電子情報通信学会論文誌(D-II)


Rotation-Invariant Neural Pattern Recognition System Estimating a Rotation Angle

A rotation-invariant neural pattern recognition system, which can recognize a rotated pattern and estimate its rotation angle, is considered. It is well-known that humans sometimes recognize a rotated form by means of mental rotation. The occurrence of mental rotation can be explained in terms of the theory of information types. Therefore, we first examine the applicability of the theory to a rotation-invariant neural pattern recognition system. Next, we present a rotation-invariant neural network which can estimate a rotation angle. The neural network consists of a preprocessing network to detect the edge features of input patterns and a trainable multilayered network. Furthermore, a rotation-invariant neural pattern recognition system which includes the rotation-invariant neural network is proposed. This system is constructed on the basis of the above-mentioned theory. Finally, it is shown that, by means of computer simulations of a binary pattern and a coin recognition problem, the system is able to recognize rotated patterns and estimate their rotation angle

本論文では，心理学分野で研究されている心的回転(メンタルローテーション)を考慮に入れた回転不変システムを提案している．本システムは脳の構造と心理学的知見を考慮に入れて，回転不変性と回転依存性を同時に実現し，かつ認識精度を向上させるためのシステム構造を実現している．心理学的心的回転を考慮した世界初の工学システムである．計算機シミュレーションにより，本システムの有効性を定量的に検証した結果，回転パターンの認識に効果が大きいことが検証されている．


A method to design a neural network by the genetic algorithm with patial fitness

部分的適応度を考慮した遺伝的アルゴリズムによるニューラルネットワークの一設計法

本論文は，遺伝的アルゴリズムにより，硬貨識別システムの最適設計を行う方法を提案している．特に，硬貨の回転不変特徴であるフーリエスペクトルに対して特徴選択を行い，システムの小規模化による高速性と高精度な認識率を同時に実現している．また，遺伝的アルゴリズムに部分的適応度が導入されており，高速な進化学習が実現されている．計算機シミュレーションの結果から，本方法の有効性を定量的に検証している．(アルゴリズムの提案，シミュレーション，論文執筆を担当，ただし，ページの抽出は困難)

Transactions of the Institute of Systems, Control and Information Engineers

システム制御情報学会論文誌


Rotated pattern recognition and rotation angle estimation by neural networks

ニューラルネットワークによる回転パターンの認識と回転角度の推定

本論文は，回転パターンの認識と回転角度の推定を同時並列的に行えるシステムを提案している．前処理部は脳の視覚野をモデル化した特徴検出器が配置され，学習では回転不変性と回転依存性を同時に満足する構造となっている．学習部の出力層が二つに分かれており，他は全く共通である．出力層で，回転不変パターン認識と回転依存の角度推定が出力される．計算機シミュレーションの結果から，本システムの工学的有効性を定量的に検証している．(システムの提案，シミュレーション，論文執筆を担当，ただし，ページの抽出は困難)

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)