Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551656
Alexander Giehl, Michael P. Heinl, Maximilian Busch
Big data continues to grow in the manufacturing domain due to increasing interconnectivity on the shop floor in the course of the fourth industrial revolution. The optimization of machines based on either real-time or historical machine data provides benefits to both machine producers and operators. In order to be able to make use of these opportunities, it is necessary to access the machine data, which can include sensitive information such as intellectual property. Employing the use case of machine tools, this paper presents a solution enabling industrial data sharing and cloud collaboration while protecting sensitive information. It employs the edge computing paradigm to apply differential privacy to machine data in order to protect sensitive information and simultaneously allow machine producers to perform the necessary calculations and analyses using this data.
{"title":"Leveraging Edge Computing and Differential Privacy to Securely Enable Industrial Cloud Collaboration Along the Value Chain","authors":"Alexander Giehl, Michael P. Heinl, Maximilian Busch","doi":"10.1109/CASE49439.2021.9551656","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551656","url":null,"abstract":"Big data continues to grow in the manufacturing domain due to increasing interconnectivity on the shop floor in the course of the fourth industrial revolution. The optimization of machines based on either real-time or historical machine data provides benefits to both machine producers and operators. In order to be able to make use of these opportunities, it is necessary to access the machine data, which can include sensitive information such as intellectual property. Employing the use case of machine tools, this paper presents a solution enabling industrial data sharing and cloud collaboration while protecting sensitive information. It employs the edge computing paradigm to apply differential privacy to machine data in order to protect sensitive information and simultaneously allow machine producers to perform the necessary calculations and analyses using this data.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122312033","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551577
Animesh Sahu, Harikumar Kandath, K. Krishna
This paper presents a model predictive control (MPC) based algorithm for tracking multiple targets using a swarm of unmanned aerial vehicles (UAVs). All the UAVs belong to fixed-wing category with constraints on flight velocity, climb rate and turn rate. Each UAV carries a camera to detect and track the target. Two cases are considered where for the first case, the number of the UAVs is equal to the number of targets. For the second case, the number of UAVs is lesser than the number of targets leading to a conservative solution where the objective is to maximize the average time duration for which the targets are in the field-of-view (FOV) of any one of the UAV's camera. A data driven Gaussian process (GP) based model is developed to relate the hyperparameters used in MPC to the mission efficiency. Bayesian optimization is performed to obtain the hyperparameters of the MPC that maximize the mission efficiency. Numerical simulations are performed for both cases using algorithm based on distributed MPC formulation. A performance comparison is provided with the centralized MPC formulation.
{"title":"Model predictive control based algorithm for multi-target tracking using a swarm of fixed wing UAVs","authors":"Animesh Sahu, Harikumar Kandath, K. Krishna","doi":"10.1109/CASE49439.2021.9551577","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551577","url":null,"abstract":"This paper presents a model predictive control (MPC) based algorithm for tracking multiple targets using a swarm of unmanned aerial vehicles (UAVs). All the UAVs belong to fixed-wing category with constraints on flight velocity, climb rate and turn rate. Each UAV carries a camera to detect and track the target. Two cases are considered where for the first case, the number of the UAVs is equal to the number of targets. For the second case, the number of UAVs is lesser than the number of targets leading to a conservative solution where the objective is to maximize the average time duration for which the targets are in the field-of-view (FOV) of any one of the UAV's camera. A data driven Gaussian process (GP) based model is developed to relate the hyperparameters used in MPC to the mission efficiency. Bayesian optimization is performed to obtain the hyperparameters of the MPC that maximize the mission efficiency. Numerical simulations are performed for both cases using algorithm based on distributed MPC formulation. A performance comparison is provided with the centralized MPC formulation.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"170 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122786181","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551461
Xiaolong Ma, Linqi Ye, Houde Liu, Xueqian Wang, Bin Liang
In the research of quadruped robots, stability is a very important consideration for gait design. When the robots have symmetrical structure, stability can be easily guaranteed. However, when the robots are carrying some additional devices or payloads unevenly, the position of the center of gravity (COG) may deviate from the geometrical center, which makes it a challenging task to guarantee stability. To handle this, it is of great significance to improve the stability margin during gait design. To this end, a smooth static walking gait with the maximum stability margin is developed in this paper. An algorithm of COG trajectory optimization based on the lemniscate of Gerono is proposed. The advantage of this algorithm is that the COG trajectory is smooth and continuous at any order, which avoids abrupt changes in velocity or acceleration of the robot during walking. The two parameters in the lemniscate are the main tuning parameters. According to the size of the robot, the algorithm can automatically calculate the optimal parameters (adjust the shape of the Gerono lemniscate curve) and balance the relationship between the step size and the stability margin during the robot movement. Simulation results demonstrate the effectiveness of the proposed method, and we use a mass block experiment to prove the insensitivity of the gait algorithm to the position of the COG.
{"title":"Smooth Static Walking for Quadruped Robots based on the Lemniscate of Gerono","authors":"Xiaolong Ma, Linqi Ye, Houde Liu, Xueqian Wang, Bin Liang","doi":"10.1109/CASE49439.2021.9551461","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551461","url":null,"abstract":"In the research of quadruped robots, stability is a very important consideration for gait design. When the robots have symmetrical structure, stability can be easily guaranteed. However, when the robots are carrying some additional devices or payloads unevenly, the position of the center of gravity (COG) may deviate from the geometrical center, which makes it a challenging task to guarantee stability. To handle this, it is of great significance to improve the stability margin during gait design. To this end, a smooth static walking gait with the maximum stability margin is developed in this paper. An algorithm of COG trajectory optimization based on the lemniscate of Gerono is proposed. The advantage of this algorithm is that the COG trajectory is smooth and continuous at any order, which avoids abrupt changes in velocity or acceleration of the robot during walking. The two parameters in the lemniscate are the main tuning parameters. According to the size of the robot, the algorithm can automatically calculate the optimal parameters (adjust the shape of the Gerono lemniscate curve) and balance the relationship between the step size and the stability margin during the robot movement. Simulation results demonstrate the effectiveness of the proposed method, and we use a mass block experiment to prove the insensitivity of the gait algorithm to the position of the COG.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122821673","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551650
T. Nakano, Kajita Daiki, Heming Chen, Ilya Kovalenko, Efe C. Balta, Yassine Qamsane, K. Barton
This research aims to develop methods to quickly build new manufacturing lines in response to changes in product varieties and manufacturing fluctuations in a factory. We propose a meta-heuristic algorithm for solving large-scale optimizations of the line design process, which includes resource configuration, process design, control design, and line configuration. The proposed framework improves the automation and system-level interactions of the line design process as compared to conventional methods that manually solve each step in the process design problem individually using skilled line engineers with previous experience. This research introduces the concept of a resource group or module that consists of various manufacturing resources such as robots, tools, autonomous guided vehicles, and conveyors. The line design process is then reconfigured for module or group configuration. To demonstrate the proposed framework, a case study is conducted in which the proposed framework is applied to the line design of an assembly manufacturing facility with production costs and manufacturing lead times selected as the key performance indicators of interest. Results indicate improved line costs and manufacturing lead times concurrently.
{"title":"Manufacturing Line Design Configuration with Optimized Resource Groups","authors":"T. Nakano, Kajita Daiki, Heming Chen, Ilya Kovalenko, Efe C. Balta, Yassine Qamsane, K. Barton","doi":"10.1109/CASE49439.2021.9551650","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551650","url":null,"abstract":"This research aims to develop methods to quickly build new manufacturing lines in response to changes in product varieties and manufacturing fluctuations in a factory. We propose a meta-heuristic algorithm for solving large-scale optimizations of the line design process, which includes resource configuration, process design, control design, and line configuration. The proposed framework improves the automation and system-level interactions of the line design process as compared to conventional methods that manually solve each step in the process design problem individually using skilled line engineers with previous experience. This research introduces the concept of a resource group or module that consists of various manufacturing resources such as robots, tools, autonomous guided vehicles, and conveyors. The line design process is then reconfigured for module or group configuration. To demonstrate the proposed framework, a case study is conducted in which the proposed framework is applied to the line design of an assembly manufacturing facility with production costs and manufacturing lead times selected as the key performance indicators of interest. Results indicate improved line costs and manufacturing lead times concurrently.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114445759","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551647
Saeideh Khatiry Goharoodi, T. Ooijevaar, A. Bey-Temsamani, G. Crevecoeur
In today's fast growing vehicle industry, the number of functionalities (comfort features, monitoring features, safety features, etc.) is steadily increasing. Each of these functionalities are developed independently from each other, hence the sensors are not shared among them. Although this design approach results into robust monitoring of these different functionalities, it requires a large number of sensors in different locations resulting in a complex hardware and software architecture (e.g. complex wires). This paper describes our approach where a multi sensor design method is used to optimally select locations of sensors that are shared by different functionalities. This results into a reduced number of sensors that monitor the same amount of functionalities. We demonstrate in this paper, an optimization algorithm based on Multi-Objective Integer Programming (MOIP) for optimal sensor placement for monitoring Motion Sickness Dose Value (MSDV) estimation and Speed Bump Detection (SBD) as part of a driver assistant system. The algorithm is further validated on a numerical data-set captured from an IPG CarMaker vehicle model. The methodology can be further extended to more functionalities with large number of applications in vehicle industry.
{"title":"Sparse Multi-sensor Monitoring System Design for Vehicle Application","authors":"Saeideh Khatiry Goharoodi, T. Ooijevaar, A. Bey-Temsamani, G. Crevecoeur","doi":"10.1109/CASE49439.2021.9551647","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551647","url":null,"abstract":"In today's fast growing vehicle industry, the number of functionalities (comfort features, monitoring features, safety features, etc.) is steadily increasing. Each of these functionalities are developed independently from each other, hence the sensors are not shared among them. Although this design approach results into robust monitoring of these different functionalities, it requires a large number of sensors in different locations resulting in a complex hardware and software architecture (e.g. complex wires). This paper describes our approach where a multi sensor design method is used to optimally select locations of sensors that are shared by different functionalities. This results into a reduced number of sensors that monitor the same amount of functionalities. We demonstrate in this paper, an optimization algorithm based on Multi-Objective Integer Programming (MOIP) for optimal sensor placement for monitoring Motion Sickness Dose Value (MSDV) estimation and Speed Bump Detection (SBD) as part of a driver assistant system. The algorithm is further validated on a numerical data-set captured from an IPG CarMaker vehicle model. The methodology can be further extended to more functionalities with large number of applications in vehicle industry.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117038451","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551493
Asma Gasmi, V. Augusto, Paul-Antoine Beaudet, J. Faucheu, C. Morin, Xavier Serpaggi, F. Vassel
In the context of the Internet of Things (IoT) healthcare, biophysical features collected during sleep needs robust analysis methods to be efficiently used to detect sleep disorders. In this paper, analysis methods using a limited number of input variables (cardiac, respiratory, and body movement) have been used to perform the classification of sleep stages. The efficiency of each classification method has been compared to a reference method that combines a large number of biophysical features referred to as PolySomnoGraphy (PSG). Five classical machine learning methods were evaluated by testing their accuracy on the same collected data. Finally, using a neural network with a short memory method, the classification task fitted 91.34% of the PSG classification.
{"title":"Supervised Classification with Short-Term Memory of Sleep Stages using Cardio-respiratory and Body Movement Variables","authors":"Asma Gasmi, V. Augusto, Paul-Antoine Beaudet, J. Faucheu, C. Morin, Xavier Serpaggi, F. Vassel","doi":"10.1109/CASE49439.2021.9551493","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551493","url":null,"abstract":"In the context of the Internet of Things (IoT) healthcare, biophysical features collected during sleep needs robust analysis methods to be efficiently used to detect sleep disorders. In this paper, analysis methods using a limited number of input variables (cardiac, respiratory, and body movement) have been used to perform the classification of sleep stages. The efficiency of each classification method has been compared to a reference method that combines a large number of biophysical features referred to as PolySomnoGraphy (PSG). Five classical machine learning methods were evaluated by testing their accuracy on the same collected data. Finally, using a neural network with a short memory method, the classification task fitted 91.34% of the PSG classification.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"476 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129720818","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551573
Xiyue Ren, Xiuxian Wang, Na Geng, Zhibin Jiang
In the real manufacturing system, rescheduling is inevitable because of rush orders. To improve the rush order inserting problem of rescheduling, this paper focuses on the just-in-time job-shop rescheduling problem (JIT JSRP), in which each job has its own due date and any earliness /tardiness leads to the penalty. A mixed integer programming model is established to minimize the weighted penalty cost of earliness/tardiness and the starting time deviations. The paper develops a hybrid tabu-variable neighborhood search (HTVNS) algorithm to solve the problem. Moreover, the adaptive shake operator selection algorithm and two improved N5 neighborhood structures are introduced to improve the efficiency of the algorithm. In numerical experiments, the improved algorithm is testified using 36 cases with different scales and arrival times of rush orders, and compared with classical meta-heuristic algorithms. The computational results show the effectiveness of the proposed improved algorithm.
{"title":"The Just-In-Time Job-Shop Rescheduling with Rush Orders by Using a Meta-Heuristic Algorithm","authors":"Xiyue Ren, Xiuxian Wang, Na Geng, Zhibin Jiang","doi":"10.1109/CASE49439.2021.9551573","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551573","url":null,"abstract":"In the real manufacturing system, rescheduling is inevitable because of rush orders. To improve the rush order inserting problem of rescheduling, this paper focuses on the just-in-time job-shop rescheduling problem (JIT JSRP), in which each job has its own due date and any earliness /tardiness leads to the penalty. A mixed integer programming model is established to minimize the weighted penalty cost of earliness/tardiness and the starting time deviations. The paper develops a hybrid tabu-variable neighborhood search (HTVNS) algorithm to solve the problem. Moreover, the adaptive shake operator selection algorithm and two improved N5 neighborhood structures are introduced to improve the efficiency of the algorithm. In numerical experiments, the improved algorithm is testified using 36 cases with different scales and arrival times of rush orders, and compared with classical meta-heuristic algorithms. The computational results show the effectiveness of the proposed improved algorithm.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"13 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128540171","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551587
Hao Cheng, Houde Liu, Xueqian Wang, Bin Liang
In recent years, continuum robots have attracted more attention for they can work in more severe environments. However, at present, most of the research focuses on the mechanical structure innovation, and there are few pieces of research on the control of this kind of robot. Since continuum robots are deformable, their shape is a general curve in space. Therefore, they are not fully defined by actuator positions, which are different from the traditional rigid robots. To achieve more accurate control, a method of sensing robot configuration in real-time is necessary. However, the existing visual-based approaches all adopt external global cameras, which is difficult to adapt to the demand of unknown unstructured environments. This paper presents a system capable of estimating the configuration of continuum robots under piecewise constant curvature (PCC) assumption from cameras mounted on each constant curvature segment. Specifically, we first proposed the PCC 2R model, which is equivalent to each cc-segment of PCC continuum robots by two joints rigid bodies, thereby reducing the problem complexity and improving the numerical stability of the estimation. Then, based on the PCC 2R model, we proposed the PCC generalized epi-polar constraint to completely constrain the four degrees of freedom of each cc-segment in planar, it can be solved through one corresponds, to estimate the configuration of continuum robots under PCC. Finally, the above approach is verified by experiment.
{"title":"Configuration Estimation of Continuum Robots Using Piecewise Constant Curvature Generalized Epi-Polar Constraint Model","authors":"Hao Cheng, Houde Liu, Xueqian Wang, Bin Liang","doi":"10.1109/CASE49439.2021.9551587","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551587","url":null,"abstract":"In recent years, continuum robots have attracted more attention for they can work in more severe environments. However, at present, most of the research focuses on the mechanical structure innovation, and there are few pieces of research on the control of this kind of robot. Since continuum robots are deformable, their shape is a general curve in space. Therefore, they are not fully defined by actuator positions, which are different from the traditional rigid robots. To achieve more accurate control, a method of sensing robot configuration in real-time is necessary. However, the existing visual-based approaches all adopt external global cameras, which is difficult to adapt to the demand of unknown unstructured environments. This paper presents a system capable of estimating the configuration of continuum robots under piecewise constant curvature (PCC) assumption from cameras mounted on each constant curvature segment. Specifically, we first proposed the PCC 2R model, which is equivalent to each cc-segment of PCC continuum robots by two joints rigid bodies, thereby reducing the problem complexity and improving the numerical stability of the estimation. Then, based on the PCC 2R model, we proposed the PCC generalized epi-polar constraint to completely constrain the four degrees of freedom of each cc-segment in planar, it can be solved through one corresponds, to estimate the configuration of continuum robots under PCC. Finally, the above approach is verified by experiment.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127037840","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551512
Lun Hu, Xiangyu Pan, Xin Luo
Many complicated systems can be represented by complex networks. Their accurate clustering analysis plays a critical role in understanding their intrinsic organizations. An effective Fuzzy-based Clustering Algorithm for Networks (FCAN) has thus been developed. However, its major disadvantage is its slow convergence to optimal or near-optimal solutions. To overcome this problem, we make use of a generalized momentum method to accelerate it and accordingly propose a fast fuzzy clustering algorithm, namely F2 CAN. Experimental results on several practical datasets demonstrate that F2 CAN performed better than FCAN in terms of efficiency while maintaining the same-level accuracy. Hence, it is more promising to conduct an accurate and fast clustering analysis for complex networks.
{"title":"Incorporating Generalized Momentum Method to Accelerate Clustering Analysis of Complex Networks","authors":"Lun Hu, Xiangyu Pan, Xin Luo","doi":"10.1109/CASE49439.2021.9551512","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551512","url":null,"abstract":"Many complicated systems can be represented by complex networks. Their accurate clustering analysis plays a critical role in understanding their intrinsic organizations. An effective Fuzzy-based Clustering Algorithm for Networks (FCAN) has thus been developed. However, its major disadvantage is its slow convergence to optimal or near-optimal solutions. To overcome this problem, we make use of a generalized momentum method to accelerate it and accordingly propose a fast fuzzy clustering algorithm, namely F2 CAN. Experimental results on several practical datasets demonstrate that F2 CAN performed better than FCAN in terms of efficiency while maintaining the same-level accuracy. Hence, it is more promising to conduct an accurate and fast clustering analysis for complex networks.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123469724","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551531
Bo You, Liu Kang, Shuaishuai Wang, Xueen Li
The coal transportation system comprises several belt conveyors, which includes lots of electrical equipment. The devices are interconnected, and a problem with one of them will cause all devices to be paralyzed. So rapid and accurate multi-device collaborative control plays an important role in high safety performance and production efficiency. Traditional multi-device collaborative control algorithms depend on complex modeling with large-scale, complex constraints, uncertainties, and multi-objective conditions. Here, we propose a data-driven multi-device collaborative control method. In this paper, the neural network algorithm is selected to model the relationship between the equipment control operation and the environment, equipment status, and human activities, thus forming the multi-device collaborative control operation knowledge to guide the multi-device collaborative control in the actual operation process. Furthermore, experiments on real production datasets demonstrate the proposed approach can realize multi-device collaborative control in the coal transportation system, meeting the three goals of safety, energy-saving, and high efficiency simultaneously.
{"title":"A Data-Driven Multi-Device Collaborative Control Method in Coal Transportation System","authors":"Bo You, Liu Kang, Shuaishuai Wang, Xueen Li","doi":"10.1109/CASE49439.2021.9551531","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551531","url":null,"abstract":"The coal transportation system comprises several belt conveyors, which includes lots of electrical equipment. The devices are interconnected, and a problem with one of them will cause all devices to be paralyzed. So rapid and accurate multi-device collaborative control plays an important role in high safety performance and production efficiency. Traditional multi-device collaborative control algorithms depend on complex modeling with large-scale, complex constraints, uncertainties, and multi-objective conditions. Here, we propose a data-driven multi-device collaborative control method. In this paper, the neural network algorithm is selected to model the relationship between the equipment control operation and the environment, equipment status, and human activities, thus forming the multi-device collaborative control operation knowledge to guide the multi-device collaborative control in the actual operation process. Furthermore, experiments on real production datasets demonstrate the proposed approach can realize multi-device collaborative control in the coal transportation system, meeting the three goals of safety, energy-saving, and high efficiency simultaneously.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121168233","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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