Pub Date : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949197
Sher Singh, Jr-Rou Chiu, Kuei-Ling Sun, E. C. Su
Despite extensive studies in allergen prediction, current approaches still have room for performance improvement and suffer from the problem of lack of interpretable biological features. Thus, developments of allergen prediction method from sequences have become highly important to facilitate in silico vaccine design. In this study, we propose a systematic approach to predict allergenic proteins by incorporating sequence and physicochemical properties in machine learning algorithms. In addition, predictive performance of previous studies could be overestimated due to high redundancy in the data sets. Therefore, we reduce sequence redundancy in the data set and experiment results show that we achieve better predictive performance when compared with other approaches. This study can help discover new prophylactic and therapeutic vaccines for diseases. Moreover, we analyze immunological features that can provide valuable insights into immunotherapies of allergy and autoimmune diseases in translational bioinformatics.
{"title":"Improving Allergenic Protein Prediction Using Physicochemical Features on Non-Redundant Sequences","authors":"Sher Singh, Jr-Rou Chiu, Kuei-Ling Sun, E. C. Su","doi":"10.1109/ICMLC48188.2019.8949197","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949197","url":null,"abstract":"Despite extensive studies in allergen prediction, current approaches still have room for performance improvement and suffer from the problem of lack of interpretable biological features. Thus, developments of allergen prediction method from sequences have become highly important to facilitate in silico vaccine design. In this study, we propose a systematic approach to predict allergenic proteins by incorporating sequence and physicochemical properties in machine learning algorithms. In addition, predictive performance of previous studies could be overestimated due to high redundancy in the data sets. Therefore, we reduce sequence redundancy in the data set and experiment results show that we achieve better predictive performance when compared with other approaches. This study can help discover new prophylactic and therapeutic vaccines for diseases. Moreover, we analyze immunological features that can provide valuable insights into immunotherapies of allergy and autoimmune diseases in translational bioinformatics.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131720569","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949295
Kentaro Mori, H. Nakajima, Yasuyo Kotake, Danni Wang, Y. Hata
In this paper, we described an automated analyzing method for the elemental processes. This method predicted the elemental processes from the sensor data by using labeled latent Dirichlet allocation (L-LDA) that is supervised topic model. The L-LDA studies automatically characteristic motion. We do not need to define characteristic motion by applying the L-LDA to motion analysis. The sensor data are motion sensors of both hands and a pressure sensor of working space. Numerical data obtained from the sensors were converted into word data by the threshold process using statistically determined thresholds. The automated analysis by the L-LDA was conducted by using the word data. We confirmed that recall by the method was over 86.9% by the evaluation experiment.
{"title":"Automated Analyzing System for Recognizing the Elemental Processes Based on the Labeled LDA","authors":"Kentaro Mori, H. Nakajima, Yasuyo Kotake, Danni Wang, Y. Hata","doi":"10.1109/ICMLC48188.2019.8949295","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949295","url":null,"abstract":"In this paper, we described an automated analyzing method for the elemental processes. This method predicted the elemental processes from the sensor data by using labeled latent Dirichlet allocation (L-LDA) that is supervised topic model. The L-LDA studies automatically characteristic motion. We do not need to define characteristic motion by applying the L-LDA to motion analysis. The sensor data are motion sensors of both hands and a pressure sensor of working space. Numerical data obtained from the sensors were converted into word data by the threshold process using statistically determined thresholds. The automated analysis by the L-LDA was conducted by using the word data. We confirmed that recall by the method was over 86.9% by the evaluation experiment.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124095733","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949227
Chin-Pan Huang, C. Hsieh, Chu-Cheng Huang
Memory deterioration is a common problem, and the locations of household objects such as remote controls, medicine bottles, and teacups are sometimes forgotten despite being in frequent use. To enhance the quality of life and reduce the amount of time wasted locating these objects, this study employs depth cameras for object tracking, segmentation, and recognition using color and depth data captured in the images and positions of objects during interaction with the skeleton of a hand. This process establishes an index of features relative to object positions that can be used to assist the user in recalling the location of household objects. Preliminary experiments have demonstrated promising performance of the proposed method.
{"title":"An Object Recall System Using RGBD Images","authors":"Chin-Pan Huang, C. Hsieh, Chu-Cheng Huang","doi":"10.1109/ICMLC48188.2019.8949227","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949227","url":null,"abstract":"Memory deterioration is a common problem, and the locations of household objects such as remote controls, medicine bottles, and teacups are sometimes forgotten despite being in frequent use. To enhance the quality of life and reduce the amount of time wasted locating these objects, this study employs depth cameras for object tracking, segmentation, and recognition using color and depth data captured in the images and positions of objects during interaction with the skeleton of a hand. This process establishes an index of features relative to object positions that can be used to assist the user in recalling the location of household objects. Preliminary experiments have demonstrated promising performance of the proposed method.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125333672","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949207
Wenjing Chen, Jianfeng Sun, Chunhui Gao
Accurate prediction of residue-residue contacts is of crucial importance for protein structure predictions and function studies. The advantages of coevolution-based methods to predict residue-residue contacts have been made manifest in the past decade. However, the prediction of residue-residue contacts remains a challenging task since these methods need abundant homologous protein sequences to obtain higher precision. Benefiting from the rapid development and the ever-widening use of deep learning methods, we attempted to use an intelligent method to predict residue-residue contacts at an intra-protein level. The backbone of the deep learning method is a recurrent neural network (RNN) with 5-layer long short-term memory (LSTM) cells. We describe this computational model for predicting residue-residue contacts, evaluate the method on three datasets of protein chain, and report the predictive performance in obtaining 45.72%, 40.35%, 39.06% prediction precisions on long range at cut-off value L, respectively, which shows a small improvement. In addition, we also display the effects of amino acid features involved in predicting residue-residue contacts by using three unsupervised machine learning methods. The performance of our method trained on a small dataset of protein sequences sheds light on the potential usefulness of applying recurrent neural network into residue-residue contact prediction.
{"title":"Improving Residue-Residue Contacts Prediction from Protein Sequences Using RNN-Based LSTM Network","authors":"Wenjing Chen, Jianfeng Sun, Chunhui Gao","doi":"10.1109/ICMLC48188.2019.8949207","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949207","url":null,"abstract":"Accurate prediction of residue-residue contacts is of crucial importance for protein structure predictions and function studies. The advantages of coevolution-based methods to predict residue-residue contacts have been made manifest in the past decade. However, the prediction of residue-residue contacts remains a challenging task since these methods need abundant homologous protein sequences to obtain higher precision. Benefiting from the rapid development and the ever-widening use of deep learning methods, we attempted to use an intelligent method to predict residue-residue contacts at an intra-protein level. The backbone of the deep learning method is a recurrent neural network (RNN) with 5-layer long short-term memory (LSTM) cells. We describe this computational model for predicting residue-residue contacts, evaluate the method on three datasets of protein chain, and report the predictive performance in obtaining 45.72%, 40.35%, 39.06% prediction precisions on long range at cut-off value L, respectively, which shows a small improvement. In addition, we also display the effects of amino acid features involved in predicting residue-residue contacts by using three unsupervised machine learning methods. The performance of our method trained on a small dataset of protein sequences sheds light on the potential usefulness of applying recurrent neural network into residue-residue contact prediction.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126355618","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949292
Han Liu, Shyi-Ming Chen
Image classification is a special type of applied machine learning tasks, where each image can be treated as an instance if there is only one target object that belongs to a specific class and needs to be recognized from an image. In the case of recognizing multiple target objects from an image, the image classification task can be formulated as image segmentation, leading to multiple instances being extracted from an image. In the setting of machine learning, each instance newly extracted from an image belongs to a specific class (a special type of target objects to be recognized) and presents specific features. In this context, in order to achieve effective recognition of each target object, it is crucial to undertake effective selection of features relevant to each specific class and appropriate setting of the training of classifiers on the selected features. In this paper, a multi-task approach of ensemble creation is proposed. The proposed approach is designed to first adopt multiple methods of multi-task feature selection for obtaining multiple groups of feature subsets (i.e., multiple subsets of features selected for each class), then to employ the C4.5 algorithm or the KNN algorithm to create an ensemble of classifiers using each group of feature subsets resulting from a specific one of the multi-task feature selection methods, and finally all the ensembles are fused to classify each instance. We compare the performance obtained using our proposed way of ensemble creation with the one obtained using classifiers trained on different feature sets prepared through various ways. The experimental results show some advances achieved in the overall classification performance through using our proposed ensemble creation approach, in comparison with the use of existing feature selection methods and learning algorithms.
{"title":"Multi-Task Ensemble Creation for Advancing Performance of Image Segmentation","authors":"Han Liu, Shyi-Ming Chen","doi":"10.1109/ICMLC48188.2019.8949292","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949292","url":null,"abstract":"Image classification is a special type of applied machine learning tasks, where each image can be treated as an instance if there is only one target object that belongs to a specific class and needs to be recognized from an image. In the case of recognizing multiple target objects from an image, the image classification task can be formulated as image segmentation, leading to multiple instances being extracted from an image. In the setting of machine learning, each instance newly extracted from an image belongs to a specific class (a special type of target objects to be recognized) and presents specific features. In this context, in order to achieve effective recognition of each target object, it is crucial to undertake effective selection of features relevant to each specific class and appropriate setting of the training of classifiers on the selected features. In this paper, a multi-task approach of ensemble creation is proposed. The proposed approach is designed to first adopt multiple methods of multi-task feature selection for obtaining multiple groups of feature subsets (i.e., multiple subsets of features selected for each class), then to employ the C4.5 algorithm or the KNN algorithm to create an ensemble of classifiers using each group of feature subsets resulting from a specific one of the multi-task feature selection methods, and finally all the ensembles are fused to classify each instance. We compare the performance obtained using our proposed way of ensemble creation with the one obtained using classifiers trained on different feature sets prepared through various ways. The experimental results show some advances achieved in the overall classification performance through using our proposed ensemble creation approach, in comparison with the use of existing feature selection methods and learning algorithms.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126368198","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949288
Chin-Sheng Yang, Kun Chen
Social media has become an important online social venue where people can connect and communicate with each other. However, despite the increasing value of social media, researchers have noticed that the participants are not necessarily as active as it has been believed. It is also not uncommon that some online communities have not attracted enough participants and turned into “cyber ghost towns.” In this paper, we concentrate on investigating the effect of participants' interactions on the sustainability of online communities. Social network analysis is adopted as the underlying analytical method and used to estimate diverse social network measures as indicators of participants' interactions for sustainability analysis. Three types of social network indicators are examined. Moreover, Reddit, a leading social news and media aggregation website, is adopted as our data source for empirical evaluation. Some interesting and promising results are identified and discussed.
{"title":"The effect of Participants' Interactions on the Sustainability of Online Communities","authors":"Chin-Sheng Yang, Kun Chen","doi":"10.1109/ICMLC48188.2019.8949288","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949288","url":null,"abstract":"Social media has become an important online social venue where people can connect and communicate with each other. However, despite the increasing value of social media, researchers have noticed that the participants are not necessarily as active as it has been believed. It is also not uncommon that some online communities have not attracted enough participants and turned into “cyber ghost towns.” In this paper, we concentrate on investigating the effect of participants' interactions on the sustainability of online communities. Social network analysis is adopted as the underlying analytical method and used to estimate diverse social network measures as indicators of participants' interactions for sustainability analysis. Three types of social network indicators are examined. Moreover, Reddit, a leading social news and media aggregation website, is adopted as our data source for empirical evaluation. Some interesting and promising results are identified and discussed.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124860538","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949298
Yuxin Ding, Jieke Hu, Wenting Xu, Xiao Zhang
In recent years, there is a rapid increase in the number of Android based malware. To protect users from malware attacks, different malware detection methods are proposed. In this paper, a novel static method is proposed to detect malware. We use the static analysis technique to analyze the Android applications and obtain their static behaviors. Two kinds of behaviors are extracted to represent malware. One kind of behaviors is the function call graph and the other kind is opcode sequences. To automatically learn behavioral features, we convert the function call graphs and opcode sequences into two dimensional data, and use deep learning method to build malware classifier. To further improve the performance of the malware classifier, a deep feature fusion model is proposed, which can combine different behavioral features for malware classification. The experimental results show the deep learning method is effective to detect malware and the proposed fusion model outperforms the single behavioral model.
{"title":"A Deep Feature Fusion Method for Android Malware Detection","authors":"Yuxin Ding, Jieke Hu, Wenting Xu, Xiao Zhang","doi":"10.1109/ICMLC48188.2019.8949298","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949298","url":null,"abstract":"In recent years, there is a rapid increase in the number of Android based malware. To protect users from malware attacks, different malware detection methods are proposed. In this paper, a novel static method is proposed to detect malware. We use the static analysis technique to analyze the Android applications and obtain their static behaviors. Two kinds of behaviors are extracted to represent malware. One kind of behaviors is the function call graph and the other kind is opcode sequences. To automatically learn behavioral features, we convert the function call graphs and opcode sequences into two dimensional data, and use deep learning method to build malware classifier. To further improve the performance of the malware classifier, a deep feature fusion model is proposed, which can combine different behavioral features for malware classification. The experimental results show the deep learning method is effective to detect malware and the proposed fusion model outperforms the single behavioral model.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123790365","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949189
Han Liu, Shyi-Ming Chen
Image classification is a special type of classification tasks in the setting of supervised machine learning. In general, in order to achieve good performance of image classification, it is important to select high quality features for training classifiers. However, different instances of images would usually present very diverse features even if the instances belong to the same class. In other words, one types of features may better describe some instances, whereas other instances present more other types of features. The above description can indicate that the same learning algorithm may be capable of learning from some parts of a data set but show weaker ability to learn from other parts of a data set, given that different algorithms usually show different suitability for learning from instances that show various characteristics. On the other hand, image features are typically in the form of continuous attributes which can be handled by decision tree learning algorithms in various ways, leading to diverse classifiers being trained. In this paper, we investigate diversified adoption of the C4.5 and KNN algorithms from different perspectives, such as diversified use of instances and various ways of handling continuous attributes. In particular, we propose a multi-perspective approach of diversity creation for image classification in the setting of ensemble learning. We compare the proposed approach with those popular algorithms that are used to train classifiers on either a full set of original features or a subset of selected features for image classification. The experimental results show that the performance of image classification is encouraging through the adoption of our proposed approach of ensemble creation.
{"title":"Multi-Perspective Creation of Diversity for Image Classification In Ensemble Learning Context","authors":"Han Liu, Shyi-Ming Chen","doi":"10.1109/ICMLC48188.2019.8949189","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949189","url":null,"abstract":"Image classification is a special type of classification tasks in the setting of supervised machine learning. In general, in order to achieve good performance of image classification, it is important to select high quality features for training classifiers. However, different instances of images would usually present very diverse features even if the instances belong to the same class. In other words, one types of features may better describe some instances, whereas other instances present more other types of features. The above description can indicate that the same learning algorithm may be capable of learning from some parts of a data set but show weaker ability to learn from other parts of a data set, given that different algorithms usually show different suitability for learning from instances that show various characteristics. On the other hand, image features are typically in the form of continuous attributes which can be handled by decision tree learning algorithms in various ways, leading to diverse classifiers being trained. In this paper, we investigate diversified adoption of the C4.5 and KNN algorithms from different perspectives, such as diversified use of instances and various ways of handling continuous attributes. In particular, we propose a multi-perspective approach of diversity creation for image classification in the setting of ensemble learning. We compare the proposed approach with those popular algorithms that are used to train classifiers on either a full set of original features or a subset of selected features for image classification. The experimental results show that the performance of image classification is encouraging through the adoption of our proposed approach of ensemble creation.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131382855","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949188
Guangcheng Li, Qinglin Zhao, Mengfei Song, Daidong Du, Jianwen Yuan, Xuanhui Chen, Hong Liang
Blockchain is a disruptive technology that enables disparate users to share their information in blocks trustworthily without a centralized entity. One fundamental problem is how to stable the block interval. To address this problem, our method is: 1. predict the computing power (i.e., hashrate) of a blockchain system by the cryptocurrency price; 2. stable the interval according to the predicted power. This paper focuses on the prediction of the global computing power. In our prediction, we adopt a LSTM-based regression algorithm to handle the hysteresis of computing power changes in response to the price changes. Taking the Bitcoin system as an example, we run extensive experiments that verify that our prediction algorithm is very accurate.
{"title":"Predicting Global Computing Power of Blockchain Using Cryptocurrency Prices","authors":"Guangcheng Li, Qinglin Zhao, Mengfei Song, Daidong Du, Jianwen Yuan, Xuanhui Chen, Hong Liang","doi":"10.1109/ICMLC48188.2019.8949188","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949188","url":null,"abstract":"Blockchain is a disruptive technology that enables disparate users to share their information in blocks trustworthily without a centralized entity. One fundamental problem is how to stable the block interval. To address this problem, our method is: 1. predict the computing power (i.e., hashrate) of a blockchain system by the cryptocurrency price; 2. stable the interval according to the predicted power. This paper focuses on the prediction of the global computing power. In our prediction, we adopt a LSTM-based regression algorithm to handle the hysteresis of computing power changes in response to the price changes. Taking the Bitcoin system as an example, we run extensive experiments that verify that our prediction algorithm is very accurate.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133182528","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 : 2019-07-01DOI: 10.1109/ICMLC48188.2019.8949304
Wen-Shyong Yu, Yufeng Lin
This paper mainly studies the realization of the wireless optimal charging gantry robot system using type-2 fuzzy adaptive control for mobile rechargeable devices. The wireless charging system is based on the energy management systems using the adaptive control algorithm to achieve the maximum charging power control. The type-2 fuzzy dynamic model is used to approximate the charging system in accordance with current standards without constructing sector dead-zone inverse, where the parameters of the fuzzy model are obtained both from the fuzzy inference and online update laws. The tracking trajectory tore chargeable devices including forward/inverse kinematics written by C# in Visual Studio is used for obtaining the joint angles of the xyz table corresponding to the desired trajectory. By feedback the charging current from the coil to detect position of the mobile devices, the optimal charging device tracking algorithm is given for obtaining the shortest distance and maximum power transmission between the induction coil and the rechargable device. Based on the Lyapunov criterion and Riccati-inequality, the control scheme is derived to stabilize the closed-loop system such that all states of the system are guaranteed to be bounded due to uncertainties, dead-zone nonlinearities, and external disturbances. The advantage of the proposed control scheme is that it can better handle the vagueness or uncertainties inherent in linguistic words using fuzzy set membership functions with adaptation capability by linear analytical results instead of estimating non-linear system functions as the system parameters are unknown. Finally, both simulation and experimental results are provided to verify the validity of the wireless optimal charging system.
{"title":"Fuzzy Adaptive Control for Wireless Optimal Charging Gantry Robot System","authors":"Wen-Shyong Yu, Yufeng Lin","doi":"10.1109/ICMLC48188.2019.8949304","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949304","url":null,"abstract":"This paper mainly studies the realization of the wireless optimal charging gantry robot system using type-2 fuzzy adaptive control for mobile rechargeable devices. The wireless charging system is based on the energy management systems using the adaptive control algorithm to achieve the maximum charging power control. The type-2 fuzzy dynamic model is used to approximate the charging system in accordance with current standards without constructing sector dead-zone inverse, where the parameters of the fuzzy model are obtained both from the fuzzy inference and online update laws. The tracking trajectory tore chargeable devices including forward/inverse kinematics written by C# in Visual Studio is used for obtaining the joint angles of the xyz table corresponding to the desired trajectory. By feedback the charging current from the coil to detect position of the mobile devices, the optimal charging device tracking algorithm is given for obtaining the shortest distance and maximum power transmission between the induction coil and the rechargable device. Based on the Lyapunov criterion and Riccati-inequality, the control scheme is derived to stabilize the closed-loop system such that all states of the system are guaranteed to be bounded due to uncertainties, dead-zone nonlinearities, and external disturbances. The advantage of the proposed control scheme is that it can better handle the vagueness or uncertainties inherent in linguistic words using fuzzy set membership functions with adaptation capability by linear analytical results instead of estimating non-linear system functions as the system parameters are unknown. Finally, both simulation and experimental results are provided to verify the validity of the wireless optimal charging system.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133705985","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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