The Experts below are selected from a list of 4143 Experts worldwide ranked by ideXlab platform
Ralf Steinmetz - One of the best experts on this subject based on the ideXlab platform.
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Electric Appliance classification based on distributed high resolution current sensing
Local Computer Networks, 2012Co-Authors: Andreas Reinhardt, Dominic Burkhardt, Manzil Zaheer, Ralf SteinmetzAbstract:Today's solutions to inform residents about their Electricity consumption are mostly confined to displaying aggregate readings collected at meter level. A reliable identification of Appliances that require disproportionate amounts of energy for their operation is generally unsupported by these systems, or at least requires significant manual configuration efforts. We address this challenge by placing low-cost measurement and actuation units into the mains connection of Appliances. The distributed sensors capture the current flow of individual Appliances at a sampling rate of 1.6kHz and apply local signal processing to the readings in order to extract characteristic fingerprints. These fingerprints are communicated wirelessly to the evaluation server, thus keeping the required airtime and energy demand of the transmission low. The evaluation server employs machine learning techniques and caters for the actual classification of attached Electric Appliances based on their fingerprints, enabling the correlation of consumption data and the Appliance identity. Our evaluation is based on more than 3,000 current consumption fingerprints, which we have captured for a range of household Appliances. The results indicate that a high accuracy is achieved when locally extracted
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Electric Appliance classification based on distributed high resolution current sensing
37th Annual IEEE Conference on Local Computer Networks - Workshops, 2012Co-Authors: Andreas Reinhardt, Dominic Burkhardt, Manzil Zaheer, Ralf SteinmetzAbstract:Today's solutions to inform residents about their Electricity consumption are mostly confined to displaying aggregate readings collected at meter level. A reliable identification of Appliances that require disproportionate amounts of energy for their operation is generally unsupported by these systems, or at least requires significant manual configuration efforts. We address this challenge by placing low-cost measurement and actuation units into the mains connection of Appliances. The distributed sensors capture the current flow of individual Appliances at a sampling rate of 1.6kHz and apply local signal processing to the readings in order to extract characteristic fingerprints. These fingerprints are communicated wirelessly to the evaluation server, thus keeping the required airtime and energy demand of the transmission low. The evaluation server employs machine learning techniques and caters for the actual classification of attached Electric Appliances based on their fingerprints, enabling the correlation of consumption data and the Appliance identity. Our evaluation is based on more than 3,000 current consumption fingerprints, which we have captured for a range of household Appliances. The results indicate that a high accuracy is achieved when locally extracted current consumption fingerprints are used to classify Appliances.
Masayuki Inaba - One of the best experts on this subject based on the ideXlab platform.
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MVA - Comparing Investigation of Images and 3D Mesh Model For Categorizing Electric Appliance Components
Journal of Machine Vision and Applications, 2020Co-Authors: Kimitoshi Yamazaki, Ryo Hanai, Hiroaki Yaguchi, Kotaro Nagahama, Katsuyoshi Yamagami, Masayuki InabaAbstract:Knowing about the type of objects that exist in real world is important thing for automation of conveyance or classification task. This paper reports our challenges the purpose of which is to categorize objects that are incorporated into home Electric Appliances. Our target includes small sized objects, flexible objects, and objects that have various appearances by the difference in view-points. Sensing methods we used are two types of measurement data; (1) Mesh model measured by a 3D digitizer, and (2) multi-viewpoint images captured by a high-resolution camera. Using these sensor data, we studied about feature descriptions. These descriptions are designed by regarding the characteristics of target objects and the property of each measurement data. Figure 1 shows our target objects that are composed of 31 series of Electric Appliance components. They include small sized objects (e.g. screw and clasp), relatively large objects (e.g. Electrical circuit sized hundreds millimeters), shiny and transparent objects (e.g. glass), and flexible objects (e.g. cable and wire). Meanwhile, some of components have a big difference on their appearances or shapes even though they are grouped as a same category. The goal of this research is to design high-precision classifier for these 31 categorized objects.
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Electric Appliance Parts Classification Using a Measure Combining the Whole Shape and Local Shape Distribution Similarities
2011 International Conference on 3D Imaging Modeling Processing Visualization and Transmission, 2011Co-Authors: Ryo Hanai, Kimitoshi Yamazaki, Hiroaki Yaguchi, Kei Okada, Masayuki InabaAbstract:Classification of Electric Appliance parts is one of the interesting and practically valuable applications for 3D object recognition. Based on existing works, in this paper we try classifying Electric Appliance parts data obtained in an automatable process, which becomes a basis for automated recycling system. The dataset includes deformable objects such as cables as well as various rigid objects, some of which lacking a large part of the surface because of self-occlusions and materials of the parts. To realize high accuracy in classification, after the comparison of several similarity measures, we combine a measure which describes well the whole shape similarity with a measure that expresses the ratio of local surface patterns that appears in each model. The latter measure is suitable to describe the similarity of deformable objects that the whole shapes are heavily dependent on their configurations. We also investigate how the scale of computing local feature affects the classification result.
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3DIMPVT - Electric Appliance Parts Classification Using a Measure Combining the Whole Shape and Local Shape Distribution Similarities
2011 International Conference on 3D Imaging Modeling Processing Visualization and Transmission, 2011Co-Authors: Ryo Hanai, Kimitoshi Yamazaki, Hiroaki Yaguchi, Kei Okada, Masayuki InabaAbstract:Classification of Electric Appliance parts is one of the interesting and practically valuable applications for 3D object recognition. Based on existing works, in this paper we try classifying Electric Appliance parts data obtained in an automatable process, which becomes a basis for automated recycling system. The dataset includes deformable objects such as cables as well as various rigid objects, some of which lacking a large part of the surface because of self-occlusions and materials of the parts. To realize high accuracy in classification, after the comparison of several similarity measures, we combine a measure which describes well the whole shape similarity with a measure that expresses the ratio of local surface patterns that appears in each model. The latter measure is suitable to describe the similarity of deformable objects that the whole shapes are heavily dependent on their configurations. We also investigate how the scale of computing local feature affects the classification result.
Andreas Reinhardt - One of the best experts on this subject based on the ideXlab platform.
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Electric Appliance classification based on distributed high resolution current sensing
Local Computer Networks, 2012Co-Authors: Andreas Reinhardt, Dominic Burkhardt, Manzil Zaheer, Ralf SteinmetzAbstract:Today's solutions to inform residents about their Electricity consumption are mostly confined to displaying aggregate readings collected at meter level. A reliable identification of Appliances that require disproportionate amounts of energy for their operation is generally unsupported by these systems, or at least requires significant manual configuration efforts. We address this challenge by placing low-cost measurement and actuation units into the mains connection of Appliances. The distributed sensors capture the current flow of individual Appliances at a sampling rate of 1.6kHz and apply local signal processing to the readings in order to extract characteristic fingerprints. These fingerprints are communicated wirelessly to the evaluation server, thus keeping the required airtime and energy demand of the transmission low. The evaluation server employs machine learning techniques and caters for the actual classification of attached Electric Appliances based on their fingerprints, enabling the correlation of consumption data and the Appliance identity. Our evaluation is based on more than 3,000 current consumption fingerprints, which we have captured for a range of household Appliances. The results indicate that a high accuracy is achieved when locally extracted
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Electric Appliance classification based on distributed high resolution current sensing
37th Annual IEEE Conference on Local Computer Networks - Workshops, 2012Co-Authors: Andreas Reinhardt, Dominic Burkhardt, Manzil Zaheer, Ralf SteinmetzAbstract:Today's solutions to inform residents about their Electricity consumption are mostly confined to displaying aggregate readings collected at meter level. A reliable identification of Appliances that require disproportionate amounts of energy for their operation is generally unsupported by these systems, or at least requires significant manual configuration efforts. We address this challenge by placing low-cost measurement and actuation units into the mains connection of Appliances. The distributed sensors capture the current flow of individual Appliances at a sampling rate of 1.6kHz and apply local signal processing to the readings in order to extract characteristic fingerprints. These fingerprints are communicated wirelessly to the evaluation server, thus keeping the required airtime and energy demand of the transmission low. The evaluation server employs machine learning techniques and caters for the actual classification of attached Electric Appliances based on their fingerprints, enabling the correlation of consumption data and the Appliance identity. Our evaluation is based on more than 3,000 current consumption fingerprints, which we have captured for a range of household Appliances. The results indicate that a high accuracy is achieved when locally extracted current consumption fingerprints are used to classify Appliances.
Shutao Zhao - One of the best experts on this subject based on the ideXlab platform.
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Modern Distributed Human Living State Auto Evaluation Based on Electric Signal and RBFNN
2006 6th World Congress on Intelligent Control and Automation, 2006Co-Authors: Shutao Zhao, Baoshu Li, Chengzong Pang, Jinsha YuanAbstract:Varied Electric signals are contained in the Electric space, no people can live out of the space in modern society. In the paper, the internal relation between the living action regulars and its household Appliances operation is discussed, and a novel method to study the life activity regulars of the selected human group is proposed. After acquiring the current signal of household Appliances running, the features extraction of current signal and radial basis function neural network (RBFNN) algorithm are utilized to identify the running state of Electric Appliance. Finally, based on Electric signal analysis and artificial neural network recognition, the household Appliances operating regular can be obtained, and the distributed people living state can be evaluated. Some necessary countermeasure can be done automatically with the special group's living state
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The Research of Electric Appliance Running Status Detecting Based on DSP
2005 IEEE PES Transmission & Distribution Conference & Exposition: Asia and Pacific, 2005Co-Authors: Baoshu Li, Shutao Zhao, Chao Quan, Weiguo TongAbstract:A new load detection method was presented based on its running status of power system. The loads are sorted according to their index of capacitive, inductive, and none-linear. The static character of each load was got according to the time-distribution. The concept of load detection is completed. The data-sample and wave recording equipment is designed and assembled based on the DSP for power loads status detecting. The superior software was designed for data management and analyzing. The recognizing status data was analyzed in both time-domain and frequency-domain; the identifying method for Electric power loads status is deduced based on the correlation coefficient; the mathematical model for the status identifying is built; the trueness and validity of the methods is verified based on the testing and analyzing
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Study on aptitude status appraise and countermeasure of distribute people in Electric space
Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), 2004Co-Authors: Shutao Zhao, Baoshu Li, Jinsha Yuan, Chao QuanAbstract:No people can live out of the Electric space in modern society. In the paper, a novel thinking of study on the living action regulars of the selected human group based on Electric Appliance is proposed. The technology of modern measurement, wireless communication, and computer-advanced algorithm are combined to monitor and identify the state of Electric Appliance by its current waveforms, and then system can judge the distributed people living status. The quality of life can be evaluated and the control solution can be done by the supply of its living status. Firstly, in this paper the internal relation of the living action regulars and the domestic Electric Appliances is introduced. Secondly, the whole structure of the monitoring, the function of system, and the status criterion are expounded. Finally, this method has been proved to be correct and effective through experiments.
Baoshu Li - One of the best experts on this subject based on the ideXlab platform.
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Modern Distributed Human Living State Auto Evaluation Based on Electric Signal and RBFNN
2006 6th World Congress on Intelligent Control and Automation, 2006Co-Authors: Shutao Zhao, Baoshu Li, Chengzong Pang, Jinsha YuanAbstract:Varied Electric signals are contained in the Electric space, no people can live out of the space in modern society. In the paper, the internal relation between the living action regulars and its household Appliances operation is discussed, and a novel method to study the life activity regulars of the selected human group is proposed. After acquiring the current signal of household Appliances running, the features extraction of current signal and radial basis function neural network (RBFNN) algorithm are utilized to identify the running state of Electric Appliance. Finally, based on Electric signal analysis and artificial neural network recognition, the household Appliances operating regular can be obtained, and the distributed people living state can be evaluated. Some necessary countermeasure can be done automatically with the special group's living state
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The Research of Electric Appliance Running Status Detecting Based on DSP
2005 IEEE PES Transmission & Distribution Conference & Exposition: Asia and Pacific, 2005Co-Authors: Baoshu Li, Shutao Zhao, Chao Quan, Weiguo TongAbstract:A new load detection method was presented based on its running status of power system. The loads are sorted according to their index of capacitive, inductive, and none-linear. The static character of each load was got according to the time-distribution. The concept of load detection is completed. The data-sample and wave recording equipment is designed and assembled based on the DSP for power loads status detecting. The superior software was designed for data management and analyzing. The recognizing status data was analyzed in both time-domain and frequency-domain; the identifying method for Electric power loads status is deduced based on the correlation coefficient; the mathematical model for the status identifying is built; the trueness and validity of the methods is verified based on the testing and analyzing
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Study on aptitude status appraise and countermeasure of distribute people in Electric space
Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), 2004Co-Authors: Shutao Zhao, Baoshu Li, Jinsha Yuan, Chao QuanAbstract:No people can live out of the Electric space in modern society. In the paper, a novel thinking of study on the living action regulars of the selected human group based on Electric Appliance is proposed. The technology of modern measurement, wireless communication, and computer-advanced algorithm are combined to monitor and identify the state of Electric Appliance by its current waveforms, and then system can judge the distributed people living status. The quality of life can be evaluated and the control solution can be done by the supply of its living status. Firstly, in this paper the internal relation of the living action regulars and the domestic Electric Appliances is introduced. Secondly, the whole structure of the monitoring, the function of system, and the status criterion are expounded. Finally, this method has been proved to be correct and effective through experiments.