Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognitio...Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.展开更多
Considering the importance of the anticipated intelligence issue on the current scenario, this article aims at understanding the scientific production about the subject, contributing to future researchers with greater...Considering the importance of the anticipated intelligence issue on the current scenario, this article aims at understanding the scientific production about the subject, contributing to future researchers with greater academic knowledge about the productivity in the area. Therefore, a bibliometric research was carried out on weak signals and correlated matters between 1980 and 2010 and was supported by the literature review about the subject. The suggested analysis contributes to greater academic knowledge on productivity in the field, providing a broad perspective for future researchers who catch sight of this subject as a potential for further studies.展开更多
This contribution deals with the process of creation of symbol libraries for emergency situations cartography. The libraries were created as a TrueType font files. Two symbol libraries were created. The first one is a...This contribution deals with the process of creation of symbol libraries for emergency situations cartography. The libraries were created as a TrueType font files. Two symbol libraries were created. The first one is a set of symbols for description of map orientation （compass roses and north arrows） with correct national （in this case Czech） description. The second one is a set of symbols for description of CBRN （chemical, biological, radiological, and nuclear） threats and another CBRN issues. The TrueType font files are used as symbol libraries in various ESRI products. The font libraries created in this form are easily transferable between various operating systems and GIS programs and also can be easily converted into various different vector graphics formats. The whole process of font creation has taken place under Linux environment. Fortunately main programs used in this process are multiplatform and could be used under Linux, MS Windows and other various computer operating was part of Ubuntu repository and it was obtained free of charge. systems. All software used during the process of font creation展开更多
Abs Root-MUSIC （MUltiple Signal Classification） is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding o...Abs Root-MUSIC （MUltiple Signal Classification） is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding of the polynomial is the critical problem for Root-MUSIC and its improvements By analyzing the Root-MUSIC algorithm thoughly, the finding method of the polynomial coefficient is deduced and the concrete calculation formula is given, so that the speed of polynomial finding roots will get the bigger exaltation. The particular simulations are given and attest correctness of the theory analysis and also indicate that the proposed algorithm has preferable estimating performance.展开更多
This research estimates emotions of university students from their BVP （blood volume pulse）. Negative emotion of university students causes school dropout, which is becoming a serious problem in Japan. It is indisp...This research estimates emotions of university students from their BVP （blood volume pulse）. Negative emotion of university students causes school dropout, which is becoming a serious problem in Japan. It is indispensable for school staffs and counselors to know when and where students have negative emotion in the campus. Since BVP signals along with emotion changes vary with personality types, we build a model dependent on personality type, to estimate student emotion from characteristics of blood volume signals. Experimental results show that the model for each personality type improves the accuracy of emotion estimation for new students. Positive or negative emotion estimated from BVP signals contributes to enhancement of campus environment by school counselors.展开更多
So far,a lot of scientific studies have been carried out on nonverbal signals,which are considered as extrinsic expression of human’s intrapsychic state.Among them,emotion detection aims to automatically determine a ...So far,a lot of scientific studies have been carried out on nonverbal signals,which are considered as extrinsic expression of human’s intrapsychic state.Among them,emotion detection aims to automatically determine a person’s affective state,with immense potentials in many areas from health care,psychological detection to human-computer interaction.Traditional emotion detection is based on expressions,or linguistic and acoustic features in speech.However,展开更多
Music can trigger human emotion.This is a psychophysiological process.Therefore,using psychophysiological characteristics could be a way to understand individual music emotional experience.In this study,we explore a n...Music can trigger human emotion.This is a psychophysiological process.Therefore,using psychophysiological characteristics could be a way to understand individual music emotional experience.In this study,we explore a new method of personal music emotion recognition based on human physiological characteristics.First,we build up a database of features based on emotions related to music and a database based on physiological signals derived from music listening including EDA,PPG,SKT,RSP,and PD variation information.Then linear regression,ridge regression,support vector machines with three different kernels,decision trees,k-nearest neighbors,multi-layer perceptron,and Nu support vector regression(NuSVR)are used to recognize music emotions via a data synthesis of music features and human physiological features.NuSVR outperforms the other methods.The correlation coefficient values are 0.7347 for arousal and 0.7902 for valence,while the mean squared errors are 0.023 23 for arousal and0.014 85 for valence.Finally,we compare the different data sets and find that the data set with all the features(music features and all physiological features)has the best performance in modeling.The correlation coefficient values are 0.6499 for arousal and 0.7735 for valence,while the mean squared errors are 0.029 32 for arousal and0.015 76 for valence.We provide an effective way to recognize personal music emotional experience,and the study can be applied to personalized music recommendation.展开更多
基金Supported by the National Natural Science Foundation of China (No.60873143)the National Key Subject Foundation for Basic Psychology (No.NKSF07003)
文摘Recently,as recognizing emotion has been one of the hallmarks of affective computing,more attention has been paid to physiological signals for emotion recognition.This paper presented an approach to emotion recognition using ElectroCardioGraphy(ECG) signals from multiple subjects.To collect reliable affective ECG data,we applied an arousal method by movie clips to make subjects experience specific emotions without external interference.Through precise location of P-QRS-T wave by continuous wavelet transform,an amount of ECG features was extracted sufficiently.Since feature selection is a combination optimization problem,Improved Binary Particle Swarm Optimization(IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor(KNN) classifier.In the experiment,it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.
文摘Considering the importance of the anticipated intelligence issue on the current scenario, this article aims at understanding the scientific production about the subject, contributing to future researchers with greater academic knowledge about the productivity in the area. Therefore, a bibliometric research was carried out on weak signals and correlated matters between 1980 and 2010 and was supported by the literature review about the subject. The suggested analysis contributes to greater academic knowledge on productivity in the field, providing a broad perspective for future researchers who catch sight of this subject as a potential for further studies.
文摘This contribution deals with the process of creation of symbol libraries for emergency situations cartography. The libraries were created as a TrueType font files. Two symbol libraries were created. The first one is a set of symbols for description of map orientation （compass roses and north arrows） with correct national （in this case Czech） description. The second one is a set of symbols for description of CBRN （chemical, biological, radiological, and nuclear） threats and another CBRN issues. The TrueType font files are used as symbol libraries in various ESRI products. The font libraries created in this form are easily transferable between various operating systems and GIS programs and also can be easily converted into various different vector graphics formats. The whole process of font creation has taken place under Linux environment. Fortunately main programs used in this process are multiplatform and could be used under Linux, MS Windows and other various computer operating was part of Ubuntu repository and it was obtained free of charge. systems. All software used during the process of font creation
基金Supported by the National Outstanding Young Foundation (No.60825104)the National Natural Science Foundation of China (No.60736009)
文摘Abs Root-MUSIC （MUltiple Signal Classification） is the polynomial rooting form of MUSIC, namely, the spectrum peak searching is resplaced by the polynomial rooting in MUSIC implementation. The coefficients finding of the polynomial is the critical problem for Root-MUSIC and its improvements By analyzing the Root-MUSIC algorithm thoughly, the finding method of the polynomial coefficient is deduced and the concrete calculation formula is given, so that the speed of polynomial finding roots will get the bigger exaltation. The particular simulations are given and attest correctness of the theory analysis and also indicate that the proposed algorithm has preferable estimating performance.
文摘This research estimates emotions of university students from their BVP （blood volume pulse）. Negative emotion of university students causes school dropout, which is becoming a serious problem in Japan. It is indispensable for school staffs and counselors to know when and where students have negative emotion in the campus. Since BVP signals along with emotion changes vary with personality types, we build a model dependent on personality type, to estimate student emotion from characteristics of blood volume signals. Experimental results show that the model for each personality type improves the accuracy of emotion estimation for new students. Positive or negative emotion estimated from BVP signals contributes to enhancement of campus environment by school counselors.
文摘So far,a lot of scientific studies have been carried out on nonverbal signals,which are considered as extrinsic expression of human’s intrapsychic state.Among them,emotion detection aims to automatically determine a person’s affective state,with immense potentials in many areas from health care,psychological detection to human-computer interaction.Traditional emotion detection is based on expressions,or linguistic and acoustic features in speech.However,
基金Project supported by the Philosophy and Social Science Planning Fund Project of Zhejiang Province,China(No.20NDQN297YB)the National Natural Science Foundation of China(No.61702454)
文摘Music can trigger human emotion.This is a psychophysiological process.Therefore,using psychophysiological characteristics could be a way to understand individual music emotional experience.In this study,we explore a new method of personal music emotion recognition based on human physiological characteristics.First,we build up a database of features based on emotions related to music and a database based on physiological signals derived from music listening including EDA,PPG,SKT,RSP,and PD variation information.Then linear regression,ridge regression,support vector machines with three different kernels,decision trees,k-nearest neighbors,multi-layer perceptron,and Nu support vector regression(NuSVR)are used to recognize music emotions via a data synthesis of music features and human physiological features.NuSVR outperforms the other methods.The correlation coefficient values are 0.7347 for arousal and 0.7902 for valence,while the mean squared errors are 0.023 23 for arousal and0.014 85 for valence.Finally,we compare the different data sets and find that the data set with all the features(music features and all physiological features)has the best performance in modeling.The correlation coefficient values are 0.6499 for arousal and 0.7735 for valence,while the mean squared errors are 0.029 32 for arousal and0.015 76 for valence.We provide an effective way to recognize personal music emotional experience,and the study can be applied to personalized music recommendation.
