Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334071
R. Tripathi, T. Hanamoto
In this paper the grid connected PV system is modelled and simulated by using the Fryze conductance based current minimization algorithm. The control algorithm for grid connected PV system is based upon the generation of average active component of current by the calculation of instantaneous average conductance for the load. The dedicated PI controller is used to maintain the dc link voltage. In this paper the control algorithm is implemented to force the source current and supply the average active component of load with the loss compensation current generated by PI controller to maintain the dc link voltage. The power quality improvements have been shown using the simulation results for non-linear load and linear load. The concept behind the current minimization is to supply the average active power of the loads from the source with minimum r.m.s. value of current.
{"title":"Improvement in power quality using Fryze conductance algorithm controlled grid connected solar PV system","authors":"R. Tripathi, T. Hanamoto","doi":"10.1109/ICIEV.2015.7334071","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334071","url":null,"abstract":"In this paper the grid connected PV system is modelled and simulated by using the Fryze conductance based current minimization algorithm. The control algorithm for grid connected PV system is based upon the generation of average active component of current by the calculation of instantaneous average conductance for the load. The dedicated PI controller is used to maintain the dc link voltage. In this paper the control algorithm is implemented to force the source current and supply the average active component of load with the loss compensation current generated by PI controller to maintain the dc link voltage. The power quality improvements have been shown using the simulation results for non-linear load and linear load. The concept behind the current minimization is to supply the average active power of the loads from the source with minimum r.m.s. value of current.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131382490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334056
Katsuhiro Honda, Masahiro Omori, S. Ubukata, A. Notsu
Crowd movement analysis is an important issue in social design. This paper studies an machine learning approach to crowd movement estimation through face image recognition. Although high performance face recognition is a powerful tool in individual authentication with surveillance camera images in public spaces, utilization of personal information is often hesitated under fear of privacy violation. In this paper, a privacy preserving framework for crowd movement analysis is proposed considering k-anonymization of face image features. k-anonymity is a quantitative measure of secureness in data mining and is expected to enhance the utility of personal information. An experimental result demonstrates the applicability of the secure framework in capturing crowd movement characteristics even if individual features are k-aonymized so that each individual is not distinguishable from others k - 1 ones.
{"title":"A privacy-preserving crowd movement analysis by k-member clustering of face images","authors":"Katsuhiro Honda, Masahiro Omori, S. Ubukata, A. Notsu","doi":"10.1109/ICIEV.2015.7334056","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334056","url":null,"abstract":"Crowd movement analysis is an important issue in social design. This paper studies an machine learning approach to crowd movement estimation through face image recognition. Although high performance face recognition is a powerful tool in individual authentication with surveillance camera images in public spaces, utilization of personal information is often hesitated under fear of privacy violation. In this paper, a privacy preserving framework for crowd movement analysis is proposed considering k-anonymization of face image features. k-anonymity is a quantitative measure of secureness in data mining and is expected to enhance the utility of personal information. An experimental result demonstrates the applicability of the secure framework in capturing crowd movement characteristics even if individual features are k-aonymized so that each individual is not distinguishable from others k - 1 ones.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131191095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334061
M. S. Parvin, S. M. Lutful Kabir
In developing countries, the utilities are facing difficulties in collecting electric bill in full scale. Therefore, prepaid metering system is becoming popular for ensuring the collection of bill in advance. But it does not account for the unauthorized energy usage because those cannot be metered. In this paper, a framework of a smart system for prepaid electricity metering scheme has been developed. This is based on collection of electric energy data hierarchically from the consumer ends to the source end. Similarly commands for the meters are communicated from the source end to the consumer end. The objectives of this development are to control and monitor the prepaid meters and at the same time to localize the energy pilferage and thereby bring the unauthorized energy usages under accounting.
{"title":"A framework of a smart system for prepaid electric metering scheme","authors":"M. S. Parvin, S. M. Lutful Kabir","doi":"10.1109/ICIEV.2015.7334061","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334061","url":null,"abstract":"In developing countries, the utilities are facing difficulties in collecting electric bill in full scale. Therefore, prepaid metering system is becoming popular for ensuring the collection of bill in advance. But it does not account for the unauthorized energy usage because those cannot be metered. In this paper, a framework of a smart system for prepaid electricity metering scheme has been developed. This is based on collection of electric energy data hierarchically from the consumer ends to the source end. Similarly commands for the meters are communicated from the source end to the consumer end. The objectives of this development are to control and monitor the prepaid meters and at the same time to localize the energy pilferage and thereby bring the unauthorized energy usages under accounting.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126841796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334066
J. Yoneyama, K. Hoshino
The paper is concerned with control design via static output feedback controller for a fuzzy descriptor system. A fuzzy descriptor system describes a natural representation for physical systems, which are usually modeled as a nonlinear system. In the actual situation, the state of the system is rarely measured and the output feedback control design is desired. In this paper, static output feedback control for fuzzy descriptor systems is considered and a control design of admissible controllers is proposed. The stability analysis of the closed-loop system and controller design are given in terms of Linear Matrix Inequality(LMI) conditions.
{"title":"Static output feedback control design for Takagi-Sugeno descriptor fuzzy systems","authors":"J. Yoneyama, K. Hoshino","doi":"10.1109/ICIEV.2015.7334066","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334066","url":null,"abstract":"The paper is concerned with control design via static output feedback controller for a fuzzy descriptor system. A fuzzy descriptor system describes a natural representation for physical systems, which are usually modeled as a nonlinear system. In the actual situation, the state of the system is rarely measured and the output feedback control design is desired. In this paper, static output feedback control for fuzzy descriptor systems is considered and a control design of admissible controllers is proposed. The stability analysis of the closed-loop system and controller design are given in terms of Linear Matrix Inequality(LMI) conditions.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"43 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114030617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7333986
Shaghayegh Gharghabi, Faraz Shamshirdar, Taher Abbas Shangari, Farhad Maroofkhani
People re-identification is a fundamental task for automated video-surveillance applications and has attracted attention of many researchers in past few years. Most of the studies in this field are based on 2D images and color information. In these methods it is assumed that the individuals do not change their clothes, thus these methods cannot be used for long term re-identification. To overcome this problem, we proposed a novel approach for people re-identification based on 3D information. In this paper we used a combination of 3D descriptors of body shape and skeleton data which is independent of the clothes color and illumination changes and it can be used for long term re-identification. We evaluated our work on the state-of-the-art RGB-D dataset BIWI. The results of this evaluation show that the proposed method achieved high performance in comparison to some recent methods.
{"title":"People re-identification using 3D descriptor with skeleton information","authors":"Shaghayegh Gharghabi, Faraz Shamshirdar, Taher Abbas Shangari, Farhad Maroofkhani","doi":"10.1109/ICIEV.2015.7333986","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7333986","url":null,"abstract":"People re-identification is a fundamental task for automated video-surveillance applications and has attracted attention of many researchers in past few years. Most of the studies in this field are based on 2D images and color information. In these methods it is assumed that the individuals do not change their clothes, thus these methods cannot be used for long term re-identification. To overcome this problem, we proposed a novel approach for people re-identification based on 3D information. In this paper we used a combination of 3D descriptors of body shape and skeleton data which is independent of the clothes color and illumination changes and it can be used for long term re-identification. We evaluated our work on the state-of-the-art RGB-D dataset BIWI. The results of this evaluation show that the proposed method achieved high performance in comparison to some recent methods.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114485496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334018
Daiki Kitayama, Yasufumi Touma, H. Hagiwara, K. Asami, M. Komori
In this paper 3D map construction using stereovision for autonomous mobile robot is presented. Parallax images by the triangulation method is obtained so as to calculate the 3D coordinates at the feature points. In addition, the Structure from Motion method is used so as to estimate the self-position of the camera to integrate the 3D map. In order to generate more accurate 3D map, the stereo vision system must deal with problems such as aperture problem and noise. The construction of the stereo measurement system and the experiment with mitigating the visual problems are conducted. The practical 3D map for the typical corridor environment was generated from the high-speed stereo image processing within 900 ms per scene.
{"title":"3D map construction based on structure from motion using stereo vision","authors":"Daiki Kitayama, Yasufumi Touma, H. Hagiwara, K. Asami, M. Komori","doi":"10.1109/ICIEV.2015.7334018","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334018","url":null,"abstract":"In this paper 3D map construction using stereovision for autonomous mobile robot is presented. Parallax images by the triangulation method is obtained so as to calculate the 3D coordinates at the feature points. In addition, the Structure from Motion method is used so as to estimate the self-position of the camera to integrate the 3D map. In order to generate more accurate 3D map, the stereo vision system must deal with problems such as aperture problem and noise. The construction of the stereo measurement system and the experiment with mitigating the visual problems are conducted. The practical 3D map for the typical corridor environment was generated from the high-speed stereo image processing within 900 ms per scene.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115127448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334004
Hiroaki Komori, S. Kobashi, N. Kamiura, Y. Hata, Ken-ichi Sorachi
In this paper, a method of analyzing relationships between items in specific health examination data is presented to cope with lifestyle-related diseases. The proposed method uses self-organizing maps, and focuses on twelve items such as hemoglobin A1c (HbA1c), glutamic-oxaloacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT), gamma-glutamyl transpeptidase (γ-GPT), and triglyceride (TG). The proposed method picks up the data from the examination dataset according to the standard specified by some item values. The training data are then generated by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. The proposed method labels neurons in the map by using item values of training data as parameters, and examine the relationships between items in the examination data by observing clusters formed in the map. Experimental results reveal the relationships among HbA1c, GOT, GPT, γ-GTP and TG both in the unfavorable case of HbA1c deteriorating and in the favorable case of HbA1c being improved.
{"title":"On relationship analysis of health examination items using self-organizing maps","authors":"Hiroaki Komori, S. Kobashi, N. Kamiura, Y. Hata, Ken-ichi Sorachi","doi":"10.1109/ICIEV.2015.7334004","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334004","url":null,"abstract":"In this paper, a method of analyzing relationships between items in specific health examination data is presented to cope with lifestyle-related diseases. The proposed method uses self-organizing maps, and focuses on twelve items such as hemoglobin A1c (HbA1c), glutamic-oxaloacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT), gamma-glutamyl transpeptidase (γ-GPT), and triglyceride (TG). The proposed method picks up the data from the examination dataset according to the standard specified by some item values. The training data are then generated by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. The proposed method labels neurons in the map by using item values of training data as parameters, and examine the relationships between items in the examination data by observing clusters formed in the map. Experimental results reveal the relationships among HbA1c, GOT, GPT, γ-GTP and TG both in the unfavorable case of HbA1c deteriorating and in the favorable case of HbA1c being improved.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115408930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334020
M. Nakano, M. Tamagawa, H. Matsuura, T. Yukimasa, M. Yamanaka
Accurate evaluation of fall accidents is very important. We have done the experiment in JARI using human-type robot which simulates the human accidental injury. The precise data of acceleration has been taken from the robot. Using the acceleration data, the motion of the head center is derived. The speed and the position of the head center are determined under a realistic condition. The deformation of head surface is also calculated and was found that it is 1.0~1.2 mm in a natural falling. The solutions of a long interval of 324 ms are shown.
{"title":"Precise analysis of head deformation at falling shock","authors":"M. Nakano, M. Tamagawa, H. Matsuura, T. Yukimasa, M. Yamanaka","doi":"10.1109/ICIEV.2015.7334020","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334020","url":null,"abstract":"Accurate evaluation of fall accidents is very important. We have done the experiment in JARI using human-type robot which simulates the human accidental injury. The precise data of acceleration has been taken from the robot. Using the acceleration data, the motion of the head center is derived. The speed and the position of the head center are determined under a realistic condition. The deformation of head surface is also calculated and was found that it is 1.0~1.2 mm in a natural falling. The solutions of a long interval of 324 ms are shown.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123063068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334030
A. Jalal, Yeonho Kim, S. Kamal, Adnan Farooq, Daijin Kim
Human activity recognition has been studied actively from decades using a sequence of 2D images/video. With the development of depth sensors, new opportunities arise to improve and advance this field. This study presents a depth imaging activity recognition system to monitor and recognize daily activities of the human without attaching optical markers or motion sensors. In this paper, we proposed a new feature representation and extraction method using a sequence of depth silhouettes. Particularly, we first extract the depth silhouette by removing background from noisy effects and then extract the joints plus body features as skin color detection from joint information and multi-view body shape from depth silhouettes (i.e., front and side views). We combine the joints plus body shape features to make feature vector. These features have two nice properties including invariant with respect to body shape or size and insensitive to small noise. Self-Organized Map (SOM) is then used to train and test the feature vectors. Experimental results regarding our proposed human activity dataset and publically available dataset demonstrate that our feature extraction method is more promising and outperforms the state-of-the-art feature extraction methods.
{"title":"Human daily activity recognition with joints plus body features representation using Kinect sensor","authors":"A. Jalal, Yeonho Kim, S. Kamal, Adnan Farooq, Daijin Kim","doi":"10.1109/ICIEV.2015.7334030","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334030","url":null,"abstract":"Human activity recognition has been studied actively from decades using a sequence of 2D images/video. With the development of depth sensors, new opportunities arise to improve and advance this field. This study presents a depth imaging activity recognition system to monitor and recognize daily activities of the human without attaching optical markers or motion sensors. In this paper, we proposed a new feature representation and extraction method using a sequence of depth silhouettes. Particularly, we first extract the depth silhouette by removing background from noisy effects and then extract the joints plus body features as skin color detection from joint information and multi-view body shape from depth silhouettes (i.e., front and side views). We combine the joints plus body shape features to make feature vector. These features have two nice properties including invariant with respect to body shape or size and insensitive to small noise. Self-Organized Map (SOM) is then used to train and test the feature vectors. Experimental results regarding our proposed human activity dataset and publically available dataset demonstrate that our feature extraction method is more promising and outperforms the state-of-the-art feature extraction methods.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127687300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-15DOI: 10.1109/ICIEV.2015.7334007
Mosaddik Hasan, Biswajit Bala, A. Yoshitaka
Principal Component analysis (PCA) is a powerful nonparametric tool in modern data analysis which is widely used in diverse fields from neuroscience to image processing. PCA can be calculated in two different ways: decomposition of eigenvectors and singular value decomposition (SVD). In this paper, we proposed a new method of PCA calculation using both SVD and decomposition of eigenvectors. We presented how the proposed method of calculation of PCA improve the performance of PCA in image denoising. We also showed that the proposed method produced better results than the state-of-the-art image denoising algorithms in terms of PSNR, SSIM and visual quality.
{"title":"SVD aided eigenvector decomposition to compute PCA and it's application in image denoising","authors":"Mosaddik Hasan, Biswajit Bala, A. Yoshitaka","doi":"10.1109/ICIEV.2015.7334007","DOIUrl":"https://doi.org/10.1109/ICIEV.2015.7334007","url":null,"abstract":"Principal Component analysis (PCA) is a powerful nonparametric tool in modern data analysis which is widely used in diverse fields from neuroscience to image processing. PCA can be calculated in two different ways: decomposition of eigenvectors and singular value decomposition (SVD). In this paper, we proposed a new method of PCA calculation using both SVD and decomposition of eigenvectors. We presented how the proposed method of calculation of PCA improve the performance of PCA in image denoising. We also showed that the proposed method produced better results than the state-of-the-art image denoising algorithms in terms of PSNR, SSIM and visual quality.","PeriodicalId":367355,"journal":{"name":"2015 International Conference on Informatics, Electronics & Vision (ICIEV)","volume":"276 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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