Pub Date : 2021-04-01DOI: 10.1080/18824889.2021.1894886
Kyohei Sagawa, Y. Shimakawa, H. Goto
ABSTRACT A common type of scheduling policy includes first-in-first-out (FIFO) and earliest-outset bases. Among many approaches to this, max-plus linear representation is beneficial for event-driven discrete event systems (DESs). The earliest event occurrence times can be represented by linear relations in max-plus algebra, the resultant of which is analogous with the state equation in modern control theory. Methodologies in control theory such as evolution prediction and just-in-time scheduling can thus be utilized. Although useful, the description capability remains confined to FIFO contexts, for which the method would not be capable of producing an efficient solution for systems with two-level priorities. An entity, e.g. a task or token in scheduling contexts, with higher qualification would be prioritized over one with a lower qualification. A framework for overtaking tasks is necessary for this. Motivated by this need, this article is concerned with constructing a two-level priority scheduling methodology in a max-plus linear context. A numerical experiment applied to a simple manufacturing system highlights the significance of the constructed method.
{"title":"Two-level priority scheduling framework in a max-plus linear representation","authors":"Kyohei Sagawa, Y. Shimakawa, H. Goto","doi":"10.1080/18824889.2021.1894886","DOIUrl":"https://doi.org/10.1080/18824889.2021.1894886","url":null,"abstract":"ABSTRACT A common type of scheduling policy includes first-in-first-out (FIFO) and earliest-outset bases. Among many approaches to this, max-plus linear representation is beneficial for event-driven discrete event systems (DESs). The earliest event occurrence times can be represented by linear relations in max-plus algebra, the resultant of which is analogous with the state equation in modern control theory. Methodologies in control theory such as evolution prediction and just-in-time scheduling can thus be utilized. Although useful, the description capability remains confined to FIFO contexts, for which the method would not be capable of producing an efficient solution for systems with two-level priorities. An entity, e.g. a task or token in scheduling contexts, with higher qualification would be prioritized over one with a lower qualification. A framework for overtaking tasks is necessary for this. Motivated by this need, this article is concerned with constructing a two-level priority scheduling methodology in a max-plus linear context. A numerical experiment applied to a simple manufacturing system highlights the significance of the constructed method.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"5 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133596114","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 : 2021-03-19DOI: 10.1080/18824889.2021.1894878
Anggraini Puspita Sari, Hiroshi Suzuki, T. Kitajima, T. Yasuno, Dwi Arman Prasetya, Abd. Rabi'
This paper proposed deep learning to create an accurate forecasting system that uses a deep convolutional long short-term memory (DCLSTM) for forecasting wind speed and direction. In order to use the DCLSTM system, wind speed and direction are represented as an image in 2D coordinates and make it to time sequence data. The wind speed and direction data were obtained from AMeDAS (Automated Meteorological Data Acquisition System), Japan. The target of the proposed forecasting system was to improve forecasting accuracy compared to the system in SICE 2020 (The Society of Instrument and Control Engineers Annual Conference 2020) in all seasons. For verifying the efficiency of the forecasting system by comparison with persistent system, deep fully connected-LSTM (DFC-LSTM) and encoding-forecasting network with convolutional long short-term memory (CLSTM) systems were investigated. Forecasting performance of the system was evaluated by RMSE (root mean square error) between forecasted and measured data.
{"title":"Deep convolutional long short-term memory for forecasting wind speed and direction","authors":"Anggraini Puspita Sari, Hiroshi Suzuki, T. Kitajima, T. Yasuno, Dwi Arman Prasetya, Abd. Rabi'","doi":"10.1080/18824889.2021.1894878","DOIUrl":"https://doi.org/10.1080/18824889.2021.1894878","url":null,"abstract":"This paper proposed deep learning to create an accurate forecasting system that uses a deep convolutional long short-term memory (DCLSTM) for forecasting wind speed and direction. In order to use the DCLSTM system, wind speed and direction are represented as an image in 2D coordinates and make it to time sequence data. The wind speed and direction data were obtained from AMeDAS (Automated Meteorological Data Acquisition System), Japan. The target of the proposed forecasting system was to improve forecasting accuracy compared to the system in SICE 2020 (The Society of Instrument and Control Engineers Annual Conference 2020) in all seasons. For verifying the efficiency of the forecasting system by comparison with persistent system, deep fully connected-LSTM (DFC-LSTM) and encoding-forecasting network with convolutional long short-term memory (CLSTM) systems were investigated. Forecasting performance of the system was evaluated by RMSE (root mean square error) between forecasted and measured data.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122018391","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 : 2021-03-19DOI: 10.1080/18824889.2021.1894899
Toshiki Homma, M. Inoue, J. Imura, Kengo Urata
In real-world social infrastructures such as power systems, proven controllers are already implemented and operated stably. To further improve the control performance reliably, bi-layered control with inheriting the existing controller is proposed: the existing controller generates the baseline control signal in the upper layer, while multiple sub-controllers individually coordinate the signal to actuate the infrastructure system in the lower layer. The sub-controllers are characterized by few parameters that represent the degree of coordination. Then, the update strategy of the parameter with guaranteeing the safety of the overall system is proposed. The effectiveness of the bi-layered control is shown via a numerical experiment with a power system model.
{"title":"Safe-update of bi-layered controller and its application to power systems","authors":"Toshiki Homma, M. Inoue, J. Imura, Kengo Urata","doi":"10.1080/18824889.2021.1894899","DOIUrl":"https://doi.org/10.1080/18824889.2021.1894899","url":null,"abstract":"In real-world social infrastructures such as power systems, proven controllers are already implemented and operated stably. To further improve the control performance reliably, bi-layered control with inheriting the existing controller is proposed: the existing controller generates the baseline control signal in the upper layer, while multiple sub-controllers individually coordinate the signal to actuate the infrastructure system in the lower layer. The sub-controllers are characterized by few parameters that represent the degree of coordination. Then, the update strategy of the parameter with guaranteeing the safety of the overall system is proposed. The effectiveness of the bi-layered control is shown via a numerical experiment with a power system model.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133899278","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 : 2021-03-19DOI: 10.1080/18824889.2021.1894040
Jiarui Li, T. Sawaragi, Y. Horiguchi
With the development of artificial intelligence technologies, the high accuracy of machine learning methods has become a non-unique standard. People are beginning to be more concerned about the understandability between humans and machines. The interference procedure of the machines is hoped to accord with human thinking as much as possible, which has spawned the recent and ongoing demands for developing explainable models. The present study proposes a new explainable and persuasive model for machine learning problems by introducing Structural Equation Modelling into the picture. Six parts make up the model, from data collection to model evaluation. The model can be used for data analysis, machine learning, and causal analysis. The proposed model is also transparent and can be interpreted from design to application. A practical experiment shows its effectiveness in a healthcare problem.
{"title":"Introduce structural equation modelling to machine learning problems for building an explainable and persuasive model","authors":"Jiarui Li, T. Sawaragi, Y. Horiguchi","doi":"10.1080/18824889.2021.1894040","DOIUrl":"https://doi.org/10.1080/18824889.2021.1894040","url":null,"abstract":"With the development of artificial intelligence technologies, the high accuracy of machine learning methods has become a non-unique standard. People are beginning to be more concerned about the understandability between humans and machines. The interference procedure of the machines is hoped to accord with human thinking as much as possible, which has spawned the recent and ongoing demands for developing explainable models. The present study proposes a new explainable and persuasive model for machine learning problems by introducing Structural Equation Modelling into the picture. Six parts make up the model, from data collection to model evaluation. The model can be used for data analysis, machine learning, and causal analysis. The proposed model is also transparent and can be interpreted from design to application. A practical experiment shows its effectiveness in a healthcare problem.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125184090","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 : 2021-03-19DOI: 10.1080/18824889.2021.1894900
Yuichi Saito, Fuma Kochi, M. Itoh, T. Fushima, Takashi Sugano, Yasunori Yamamoto
The pedestrian and cyclist-related accident fatality rate is higher than that of other traffic accidents. One of the pedestrian behaviours that leads to traffic accidents is the act of moving rapidly onto the road from a blind spot without warning. Expert drivers practice hazard-anticipatory driving and will naturally seek to reduce uncertainty by attempting to fit their current driving context into a pre-existing category. Risk management is the process of identifying hazards and assessing and controlling risks to attain safety. The purpose of this study was to evaluate the influence that driving context-altering road environmental elements exert on the road-crossing behaviour of pedestrians and cyclists. Thus, this study attempted to identify covert hazards (obscured pedestrians and cyclists). A logistic regression analysis was employed along with data from the near-miss incident database, in which approximately 140,000 near-crash-relevant events were registered in 2017. By using the logistic regression analysis along with the annotations recorded in the database, we constructed a predictive model to identify covert hazards. The study demonstrated the feasibility of using a set of environmental elements that shape the driving context to construct a predictive model that identifies covert hazards.
{"title":"Influence of road environmental elements on pedestrian and cyclist road crossing behaviour","authors":"Yuichi Saito, Fuma Kochi, M. Itoh, T. Fushima, Takashi Sugano, Yasunori Yamamoto","doi":"10.1080/18824889.2021.1894900","DOIUrl":"https://doi.org/10.1080/18824889.2021.1894900","url":null,"abstract":"The pedestrian and cyclist-related accident fatality rate is higher than that of other traffic accidents. One of the pedestrian behaviours that leads to traffic accidents is the act of moving rapidly onto the road from a blind spot without warning. Expert drivers practice hazard-anticipatory driving and will naturally seek to reduce uncertainty by attempting to fit their current driving context into a pre-existing category. Risk management is the process of identifying hazards and assessing and controlling risks to attain safety. The purpose of this study was to evaluate the influence that driving context-altering road environmental elements exert on the road-crossing behaviour of pedestrians and cyclists. Thus, this study attempted to identify covert hazards (obscured pedestrians and cyclists). A logistic regression analysis was employed along with data from the near-miss incident database, in which approximately 140,000 near-crash-relevant events were registered in 2017. By using the logistic regression analysis along with the annotations recorded in the database, we constructed a predictive model to identify covert hazards. The study demonstrated the feasibility of using a set of environmental elements that shape the driving context to construct a predictive model that identifies covert hazards.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131736660","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 : 2021-03-18DOI: 10.1080/18824889.2021.1893936
Kiyoshi Hamada, I. Maruta, K. Fujimoto, K. Hamamoto
ABSTRACT This paper proposes a new continuation method for solving optimal control problems. The proposed method is based on a shooting method. In the proposed method, a cost function of an optimal control problem is locally deformed to find the solution of the problem in a stable way. This paper also analyses a relationship between the variation of the continuation parameter and the proximity of the solutions before and after a deformation in the proposed method. The obtained relation provides guidance on how to deform the continuation parameter. The effectiveness of this method is confirmed through numerical examples.
{"title":"Locally deforming continuation method based on a shooting method for a class of optimal control problems","authors":"Kiyoshi Hamada, I. Maruta, K. Fujimoto, K. Hamamoto","doi":"10.1080/18824889.2021.1893936","DOIUrl":"https://doi.org/10.1080/18824889.2021.1893936","url":null,"abstract":"ABSTRACT This paper proposes a new continuation method for solving optimal control problems. The proposed method is based on a shooting method. In the proposed method, a cost function of an optimal control problem is locally deformed to find the solution of the problem in a stable way. This paper also analyses a relationship between the variation of the continuation parameter and the proximity of the solutions before and after a deformation in the proposed method. The obtained relation provides guidance on how to deform the continuation parameter. The effectiveness of this method is confirmed through numerical examples.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"70 S6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131623114","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 : 2021-03-18DOI: 10.1080/18824889.2021.1894001
A. Mahajan, Takayasu Kumano, Y. Yasui
This work focuses on decision making for automated driving vehicles in interaction rich scenarios like traffic merges in a flexibly assertive yet safe manner. We propose a Q-learning based approach, that takes in active intention inferences as additional inputs besides the directly observed state inputs. The outputs of Q-function are processed to select a decision by a modulation function, which can control how assertively or defensively the agent behaves.
{"title":"Intention estimation and controllable behaviour models for traffic merges","authors":"A. Mahajan, Takayasu Kumano, Y. Yasui","doi":"10.1080/18824889.2021.1894001","DOIUrl":"https://doi.org/10.1080/18824889.2021.1894001","url":null,"abstract":"This work focuses on decision making for automated driving vehicles in interaction rich scenarios like traffic merges in a flexibly assertive yet safe manner. We propose a Q-learning based approach, that takes in active intention inferences as additional inputs besides the directly observed state inputs. The outputs of Q-function are processed to select a decision by a modulation function, which can control how assertively or defensively the agent behaves.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126815243","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 : 2021-03-11DOI: 10.1080/18824889.2021.1894023
Weiya Chen, T. Sawaragi, T. Hiraoka
With the development of automated driving technologies, human factors involved in automated driving are gaining increasing attention for a balanced implementation of the convenience brought by the technology and safety risk in commercial vehicle models. One influential human factor is mental workload. In the take-over request (TOR) from autonomous to manual driving at level 3 of International Society of Automotive Engineers' (SAE) Levels of Driving Automation, the time window for the driver to have full comprehension of the driving environment is extremely short, which means the driver is under high mental workload. To support the driver during a TOR, we propose an adaptive multi-modal interface model concerning mental workload. In this study, we evaluated the reliability of only part of the proposed model in a driving-simulator experiment as well as using the experimental data from a previous study.
{"title":"Adaptive multi-modal interface model concerning mental workload in take-over request during semi-autonomous driving","authors":"Weiya Chen, T. Sawaragi, T. Hiraoka","doi":"10.1080/18824889.2021.1894023","DOIUrl":"https://doi.org/10.1080/18824889.2021.1894023","url":null,"abstract":"With the development of automated driving technologies, human factors involved in automated driving are gaining increasing attention for a balanced implementation of the convenience brought by the technology and safety risk in commercial vehicle models. One influential human factor is mental workload. In the take-over request (TOR) from autonomous to manual driving at level 3 of International Society of Automotive Engineers' (SAE) Levels of Driving Automation, the time window for the driver to have full comprehension of the driving environment is extremely short, which means the driver is under high mental workload. To support the driver during a TOR, we propose an adaptive multi-modal interface model concerning mental workload. In this study, we evaluated the reliability of only part of the proposed model in a driving-simulator experiment as well as using the experimental data from a previous study.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114826968","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 : 2021-03-11DOI: 10.1080/18824889.2021.1893971
Hiroaki Kata, S. Ueno
ABSTRACT Connectivity maintenance with application to target search considering the failure of unmanned vehicles is proposed. The unmanned vehicles form a network and exchange information with neighbours. Vehicle failures can cause network disconnection and disruption of information exchange. Therefore, the robust k-connected network, which the network is connected even if less than k unmanned vehicles fail, is configured in a decentralized system. Each vehicle determines the velocity input according to the partial vertex connectivity, which is an evaluation of connectivity for each vehicle, and triangulation input for collision avoidance. Target search simulation in the presence of obstacles shows that the proposed robust k-connected network control law is valid.
{"title":"Connectivity maintenance with application to target search","authors":"Hiroaki Kata, S. Ueno","doi":"10.1080/18824889.2021.1893971","DOIUrl":"https://doi.org/10.1080/18824889.2021.1893971","url":null,"abstract":"ABSTRACT Connectivity maintenance with application to target search considering the failure of unmanned vehicles is proposed. The unmanned vehicles form a network and exchange information with neighbours. Vehicle failures can cause network disconnection and disruption of information exchange. Therefore, the robust k-connected network, which the network is connected even if less than k unmanned vehicles fail, is configured in a decentralized system. Each vehicle determines the velocity input according to the partial vertex connectivity, which is an evaluation of connectivity for each vehicle, and triangulation input for collision avoidance. Target search simulation in the presence of obstacles shows that the proposed robust k-connected network control law is valid.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131024778","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 : 2021-02-22DOI: 10.1080/18824889.2021.1874679
H. Taku, Hamada Yuri, Kaburagi Takashi, Kurihara Yosuke
With the rapid aging of the population, urination management is one of the challenges experienced in nursing homes. Although constrained devices, such as ultrasonic sensors, have been used for urination management, and they can sequentially measure urinary volume in the bladder, unconstrained methods to obtain urinary volume are needed. To accomplish such goals, a mathematical model is required that considers the nature of the bladder, especially reabsorption of the primitive urine. In this paper, we propose a model based on the primary delay system with five parameters, which are determined based on the absorption spectrum of urine that is obtained immediately after urination, through regression analysis. In the regression analysis, the values of the five parameters and the absorption spectrum of urine are objective and explanatory variables, respectively, and the partial regression coefficients are determined through a genetic algorithm. When the values of the five parameters are estimated using the absorption spectrum of urine immediately after urination, we can predict the next time series of the urinary volume in the bladder based on the model. Finally, the predicted urinary volume is corrected using a multitask Gaussian process and the final predicted urinary volume is obtained. We performed a series of experiments to evaluate the proposed method and calculated the error rate between the actual urinary volume and the urinary volume predicted using the proposed method at the time of urination. The mean error rate of the proposed method is 13.32%.
{"title":"Predicting the bladder urinary volume with a reabsorbed primitive urine model","authors":"H. Taku, Hamada Yuri, Kaburagi Takashi, Kurihara Yosuke","doi":"10.1080/18824889.2021.1874679","DOIUrl":"https://doi.org/10.1080/18824889.2021.1874679","url":null,"abstract":"With the rapid aging of the population, urination management is one of the challenges experienced in nursing homes. Although constrained devices, such as ultrasonic sensors, have been used for urination management, and they can sequentially measure urinary volume in the bladder, unconstrained methods to obtain urinary volume are needed. To accomplish such goals, a mathematical model is required that considers the nature of the bladder, especially reabsorption of the primitive urine. In this paper, we propose a model based on the primary delay system with five parameters, which are determined based on the absorption spectrum of urine that is obtained immediately after urination, through regression analysis. In the regression analysis, the values of the five parameters and the absorption spectrum of urine are objective and explanatory variables, respectively, and the partial regression coefficients are determined through a genetic algorithm. When the values of the five parameters are estimated using the absorption spectrum of urine immediately after urination, we can predict the next time series of the urinary volume in the bladder based on the model. Finally, the predicted urinary volume is corrected using a multitask Gaussian process and the final predicted urinary volume is obtained. We performed a series of experiments to evaluate the proposed method and calculated the error rate between the actual urinary volume and the urinary volume predicted using the proposed method at the time of urination. The mean error rate of the proposed method is 13.32%.","PeriodicalId":413922,"journal":{"name":"SICE journal of control, measurement, and system integration","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125652380","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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