Pub Date : 2020-12-20DOI: 10.1109/STA50679.2020.9329354
Wafa Neji, S. Othman, H. Sakli
A Wireless Sensor Network (WSNs) consists of spatially distributed autonomous sensor nodes to monitor physical or environmental conditions. The major advantage of WSN is that it can be installed in harsh environment such as in volcanic eruption, seismic regions, battlefield and forest, etc. The sensor nodes are generally battery-powered devices, the key task in WSN is to reduce the energy consumption of nodes so that the lifetime of the network can be augmented. Energy efficiency and information gathering is a major concern in many applications of WSNs. Many techniques have been developed till now in order to achieve an energy efficient network. Hierarchical clustering is an effective method to save energy in WSNs. Some of the most common energy-efficiency sensor networks protocols is Low Energy Adaptive Clustering Hierarchy (LEACH) as source. In this paper, we propose Threshold sensitive Low Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks (T-LEACH). This last considers the node's heterogeneity of nodes and residual energy for choosing the optimal cluster head (CH). The simulation results have clearly shown that T-LEACH reduces the node's energy consumption, improves the network lifetime and packet transfer ratio.
{"title":"T-LEACH: Threshold sensitive Low Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks","authors":"Wafa Neji, S. Othman, H. Sakli","doi":"10.1109/STA50679.2020.9329354","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329354","url":null,"abstract":"A Wireless Sensor Network (WSNs) consists of spatially distributed autonomous sensor nodes to monitor physical or environmental conditions. The major advantage of WSN is that it can be installed in harsh environment such as in volcanic eruption, seismic regions, battlefield and forest, etc. The sensor nodes are generally battery-powered devices, the key task in WSN is to reduce the energy consumption of nodes so that the lifetime of the network can be augmented. Energy efficiency and information gathering is a major concern in many applications of WSNs. Many techniques have been developed till now in order to achieve an energy efficient network. Hierarchical clustering is an effective method to save energy in WSNs. Some of the most common energy-efficiency sensor networks protocols is Low Energy Adaptive Clustering Hierarchy (LEACH) as source. In this paper, we propose Threshold sensitive Low Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks (T-LEACH). This last considers the node's heterogeneity of nodes and residual energy for choosing the optimal cluster head (CH). The simulation results have clearly shown that T-LEACH reduces the node's energy consumption, improves the network lifetime and packet transfer ratio.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116628113","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329329
A. Toumi, R. Boucetta, Saloua Bel Hadj Ali
This article deals with modeling and control of an autonomous device for harvesting potable water,leading to performance improvement. The technique of agricultural greenhouse phenomenon is essentially used to determine a thermodynamic model of the harvesting water system, that is simple and realistic to design the behavior of the whole water-climatic harvest. The obtained model will be applied to develop an intelligent controller based on fuzzy logic concepts, presenting a power ful mean to optimise and facilate the climatic mangement of the device. To simulate the proposed harvest water system, climatic data of a humid and arid region us Gabes city are opted for the optimisation of state variablles favorable to condensation.
{"title":"Thermodynamics and Intelligent Control of a Harvesting Water System","authors":"A. Toumi, R. Boucetta, Saloua Bel Hadj Ali","doi":"10.1109/STA50679.2020.9329329","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329329","url":null,"abstract":"This article deals with modeling and control of an autonomous device for harvesting potable water,leading to performance improvement. The technique of agricultural greenhouse phenomenon is essentially used to determine a thermodynamic model of the harvesting water system, that is simple and realistic to design the behavior of the whole water-climatic harvest. The obtained model will be applied to develop an intelligent controller based on fuzzy logic concepts, presenting a power ful mean to optimise and facilate the climatic mangement of the device. To simulate the proposed harvest water system, climatic data of a humid and arid region us Gabes city are opted for the optimisation of state variablles favorable to condensation.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115016634","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329314
Imen Jammoussi, Mounir Ben Nasr
This work aims to introduce a new learning algorithm for classification issue. The suggested approach combine the self-organizing map (SOM) with the Extreme learning machine (ELM). In this algorithm, the load between input and hidden layers is made using the information retrieved from SOM on the training dataset. The weights of the output layer is adjusted applying an analytical method. Based on four classification benchmark, simulation results clarify that the new approach outperforms other learning algorithms and return sufficient performance in terms of learning speed and generalization. A comparative study with a number of other methods proves the efficiency of the proposed approach.
{"title":"A New Hybrid Approach For Classification Problem","authors":"Imen Jammoussi, Mounir Ben Nasr","doi":"10.1109/STA50679.2020.9329314","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329314","url":null,"abstract":"This work aims to introduce a new learning algorithm for classification issue. The suggested approach combine the self-organizing map (SOM) with the Extreme learning machine (ELM). In this algorithm, the load between input and hidden layers is made using the information retrieved from SOM on the training dataset. The weights of the output layer is adjusted applying an analytical method. Based on four classification benchmark, simulation results clarify that the new approach outperforms other learning algorithms and return sufficient performance in terms of learning speed and generalization. A comparative study with a number of other methods proves the efficiency of the proposed approach.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115091069","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329298
Sondess Mejdi, Anis Messaoud, Mouhib Allaoui, R. Abdennour
In this paper, robust Unknown Input MultiOb-servers (UIMOs) are designed, in the presence of disturbances, for nonlinear systems based on a discrete uncoupled state mul-timodel. The diagnosis is conducted in the presence of actuator and sensor faults. Firstly, a robust detection against disturbances is investigated. Then, a new bank of UIMOs is designed to accomplish the isolation task using generated structured residuals. Sufficient conditions are developed and given in terms of linear matrix inequalities with equality constraints to compute the uncoupled state multiobserver gains. The simulation results prove the efficiency of the proposed scheme of fault detection and isolation and show that the proposed fault detection and isolation technique is suitable for the disturbed Multiple-Input Multiple-Output (MIMO) nonlinear systems.
{"title":"Fault diagnosis for disturbed nonlinear systems based on robust discrete uncoupled state multiobservers","authors":"Sondess Mejdi, Anis Messaoud, Mouhib Allaoui, R. Abdennour","doi":"10.1109/STA50679.2020.9329298","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329298","url":null,"abstract":"In this paper, robust Unknown Input MultiOb-servers (UIMOs) are designed, in the presence of disturbances, for nonlinear systems based on a discrete uncoupled state mul-timodel. The diagnosis is conducted in the presence of actuator and sensor faults. Firstly, a robust detection against disturbances is investigated. Then, a new bank of UIMOs is designed to accomplish the isolation task using generated structured residuals. Sufficient conditions are developed and given in terms of linear matrix inequalities with equality constraints to compute the uncoupled state multiobserver gains. The simulation results prove the efficiency of the proposed scheme of fault detection and isolation and show that the proposed fault detection and isolation technique is suitable for the disturbed Multiple-Input Multiple-Output (MIMO) nonlinear systems.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130881029","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329317
Aicha Akrout, Amira Echtioui, R. Khemakhem, M. Ghorbel
Electroencephalography (EEG) signal recorded during motor imagery (MI) has been frequently used in noninvasive Brain-Computer Interface (BCI) is a new type of device that allows direct communication between user's brain and machine. This paper proposes a novel solution for extraction and classification of left/right hand, both feet, and tongue movement by exploiting two approaches of deep learning such as artificial neural network ANN and convolutional neural network CNN. A wide range of spatial and frequency domain features are extracted from the EEG signals and to train an ANN and CNN networks to perform the classification tasks. The EEG signals of mental tasks are extracted and classified by these architectures. In addition, the proposed methods are validated by the EEG dataset of the BCI competition IV-2a and we compared them with each other. The results show that the CNN model surpasses the ANN model by an accuracy value of 60.55%.
{"title":"Artificial and Convolutional Neural Network of EEG-Based Motor imagery classification: A Comparative Study","authors":"Aicha Akrout, Amira Echtioui, R. Khemakhem, M. Ghorbel","doi":"10.1109/STA50679.2020.9329317","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329317","url":null,"abstract":"Electroencephalography (EEG) signal recorded during motor imagery (MI) has been frequently used in noninvasive Brain-Computer Interface (BCI) is a new type of device that allows direct communication between user's brain and machine. This paper proposes a novel solution for extraction and classification of left/right hand, both feet, and tongue movement by exploiting two approaches of deep learning such as artificial neural network ANN and convolutional neural network CNN. A wide range of spatial and frequency domain features are extracted from the EEG signals and to train an ANN and CNN networks to perform the classification tasks. The EEG signals of mental tasks are extracted and classified by these architectures. In addition, the proposed methods are validated by the EEG dataset of the BCI competition IV-2a and we compared them with each other. The results show that the CNN model surpasses the ANN model by an accuracy value of 60.55%.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134258061","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329322
Dhouha Miri, Atef Khedher, K. BenOthman
This paper deals with the state and fault estimation for non linear systems modeled using the Takagi Sugeno approach. An artificial neural network with unknown inputs is used in the objective of estimate state and faults affecting the system. Firstly, the problem of state estimation is considered. In second step, the proposed approach is extended to the actuator fault estimations. The proposed method is applied to an academic example to show its efficiency.
{"title":"State and faults estimation via Artificial Neural Networks","authors":"Dhouha Miri, Atef Khedher, K. BenOthman","doi":"10.1109/STA50679.2020.9329322","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329322","url":null,"abstract":"This paper deals with the state and fault estimation for non linear systems modeled using the Takagi Sugeno approach. An artificial neural network with unknown inputs is used in the objective of estimate state and faults affecting the system. Firstly, the problem of state estimation is considered. In second step, the proposed approach is extended to the actuator fault estimations. The proposed method is applied to an academic example to show its efficiency.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"6 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132679418","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329325
Imen Azzouz, B. Boussaid, A. Zouinkhi, M. Abdelkrim
Wireless Sensor Networks are deployed in harsh environments. Their key advantage is there flexibility and low cost. But they can face many failures which created the need to improve data accuracy. Many artificial intelligence techniques has demonstrated impressive results in fault detection and faults diagnosis. Lately, machine learning emerged as a powerfull artificial intelligence based technique to solve the problem of failures in WSN. In this paper, a multi-fault classification is evaluated using deep learning technique based on LSTM classifier and then compared with different machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP)and Probabilistic Neural Network (PNN). The performance of this mentioned techniques used for fault detection in WSNs were compared based on four metrics: Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC)and False Alarm (FA).
{"title":"Multi-faults classification in WSN: A deep learning approach","authors":"Imen Azzouz, B. Boussaid, A. Zouinkhi, M. Abdelkrim","doi":"10.1109/STA50679.2020.9329325","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329325","url":null,"abstract":"Wireless Sensor Networks are deployed in harsh environments. Their key advantage is there flexibility and low cost. But they can face many failures which created the need to improve data accuracy. Many artificial intelligence techniques has demonstrated impressive results in fault detection and faults diagnosis. Lately, machine learning emerged as a powerfull artificial intelligence based technique to solve the problem of failures in WSN. In this paper, a multi-fault classification is evaluated using deep learning technique based on LSTM classifier and then compared with different machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP)and Probabilistic Neural Network (PNN). The performance of this mentioned techniques used for fault detection in WSNs were compared based on four metrics: Detection Accuracy (DA), True Positive Rate (TPR), Matthews Correlation Coefficients (MCC)and False Alarm (FA).","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132775935","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329341
Mounira Ben Yamna, H. Sakli
In this communication, a Ultra-Wide-Band (UWB) Multiple-Input Multiple-Output (MIMO) antenna with high isolation is exposed. Each antenna element consisting of simple microstrip fed square radiation patch with partial ground plane. Two UWB MIMO antennas with entire dimension of 80 x 100 mm2are simulated. Simulations results denote that the suggested antenna system operating at a frequency range 1.4-10.6 GHz, and the coefficient of decoupling is always inferior to -16.5 dB. A partial ground is employed to design an UWB antenna and the isolation has been enhanced by inserting a stub.
{"title":"UWB-MIMO Antenna with High Isolation Using Stub Dedicated to Connected Objects","authors":"Mounira Ben Yamna, H. Sakli","doi":"10.1109/STA50679.2020.9329341","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329341","url":null,"abstract":"In this communication, a Ultra-Wide-Band (UWB) Multiple-Input Multiple-Output (MIMO) antenna with high isolation is exposed. Each antenna element consisting of simple microstrip fed square radiation patch with partial ground plane. Two UWB MIMO antennas with entire dimension of 80 x 100 mm2are simulated. Simulations results denote that the suggested antenna system operating at a frequency range 1.4-10.6 GHz, and the coefficient of decoupling is always inferior to -16.5 dB. A partial ground is employed to design an UWB antenna and the isolation has been enhanced by inserting a stub.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132958872","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329327
Abir Hezzi, M. Abdelkrim, S. B. Elghali
A sensorless Backstepping drive was investigated in this work for the Five phase Permanent Magnet Synchronous Motor (PMSM), using an Unknown Input Observer for speed and load torque estimation. This control technique can solve the problem of the speed sensor fault, where the feedback speed information was deduced directly from the virtual sensor. Moreover, this method is able to ameliorate the performance of PMSM by the load torque estimation used for the real time compensation. The gains of Backstepping control and the Unknown Input Observer are carefully selected in aim to enhance estimation precision and stability dynamic performance. Simulation results prove both of efficiency and capability of the proposed approach to maintain a great operation and a continuity of the system operation.
{"title":"Sensorless Backstepping Drive for a Five-Phase PMSM based on Unknown Input Observer","authors":"Abir Hezzi, M. Abdelkrim, S. B. Elghali","doi":"10.1109/STA50679.2020.9329327","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329327","url":null,"abstract":"A sensorless Backstepping drive was investigated in this work for the Five phase Permanent Magnet Synchronous Motor (PMSM), using an Unknown Input Observer for speed and load torque estimation. This control technique can solve the problem of the speed sensor fault, where the feedback speed information was deduced directly from the virtual sensor. Moreover, this method is able to ameliorate the performance of PMSM by the load torque estimation used for the real time compensation. The gains of Backstepping control and the Unknown Input Observer are carefully selected in aim to enhance estimation precision and stability dynamic performance. Simulation results prove both of efficiency and capability of the proposed approach to maintain a great operation and a continuity of the system operation.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132897480","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 : 2020-12-20DOI: 10.1109/STA50679.2020.9329319
Wafa Znegui, H. Gritli, S. Belghith
This paper illustrates a stabilization approach of the passive bipedal locomotion of the compass-gait biped model based on an exclusively developed enhanced design of the closed form of the Controlled Poincaré Map (CPM). The followed technique relies on transforming the impulsive hybrid nonlinear dynamics of the passive motion into a linear form around a period-1 limit cycle. Forward, we simplify the complicated resulted expression using the second order of the Taylor Series. This technique, enables us to design a closed form of the CPM. The control of the passive bipedal locomotion starts with the identification of the period-1 fixed point of the non-CPM and continues with the determination of the linearized PM around such fixed point. Next, a feedback controller is adopted to stabilize this identified fixed point. Some simulation results are provided at the end to illustrate the efficiency of the control process of the passive walking motion of the compass-gait robot model.
{"title":"Walking Stabilization of the Passive Bipedal Compass robot using a Second Explicit Expression of the Controlled Poincaré Map","authors":"Wafa Znegui, H. Gritli, S. Belghith","doi":"10.1109/STA50679.2020.9329319","DOIUrl":"https://doi.org/10.1109/STA50679.2020.9329319","url":null,"abstract":"This paper illustrates a stabilization approach of the passive bipedal locomotion of the compass-gait biped model based on an exclusively developed enhanced design of the closed form of the Controlled Poincaré Map (CPM). The followed technique relies on transforming the impulsive hybrid nonlinear dynamics of the passive motion into a linear form around a period-1 limit cycle. Forward, we simplify the complicated resulted expression using the second order of the Taylor Series. This technique, enables us to design a closed form of the CPM. The control of the passive bipedal locomotion starts with the identification of the period-1 fixed point of the non-CPM and continues with the determination of the linearized PM around such fixed point. Next, a feedback controller is adopted to stabilize this identified fixed point. Some simulation results are provided at the end to illustrate the efficiency of the control process of the passive walking motion of the compass-gait robot model.","PeriodicalId":158545,"journal":{"name":"2020 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133532202","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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