High-precision positioning of PCB Mark plays an important role in the production of PCB. This paper proposes a high-accuracy method to recognize and locate the defective circular PCB Mark. Firstly, the template matching is used to extract the interested Mark region. And then Canny operator which has good noise resistance is used to detect the edges of the Mark. On the basis of angle features analysis of each small edge, the regular and similar edges are retained and the deformed or noisy edges are removed, which is the key process to reduce the influence of the defective edges. After that, the retained edges are used to fit an ellipse by least square method. Finally, the sub-pixel edge points near the ellipse are used to fit an ellipse again more accurately. The experimental results indicate that the positioning error of our method is small. And when processing the deformed or noisy Mark images, our method is robust and can achieve better results than Hough transform and the least square fitting based on sub-pixel edge points.
{"title":"Accurate Localization of Defective Circular PCB Mark Based on Sub-Pixel Edge Detection and Least Square Fitting","authors":"Zhen Wu, Fan Chen, Guoyuan Liang, Yimin Zhou, Xinyu Wu, Wei Feng","doi":"10.1109/DDCLS.2019.8909052","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909052","url":null,"abstract":"High-precision positioning of PCB Mark plays an important role in the production of PCB. This paper proposes a high-accuracy method to recognize and locate the defective circular PCB Mark. Firstly, the template matching is used to extract the interested Mark region. And then Canny operator which has good noise resistance is used to detect the edges of the Mark. On the basis of angle features analysis of each small edge, the regular and similar edges are retained and the deformed or noisy edges are removed, which is the key process to reduce the influence of the defective edges. After that, the retained edges are used to fit an ellipse by least square method. Finally, the sub-pixel edge points near the ellipse are used to fit an ellipse again more accurately. The experimental results indicate that the positioning error of our method is small. And when processing the deformed or noisy Mark images, our method is robust and can achieve better results than Hough transform and the least square fitting based on sub-pixel edge points.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"47 1","pages":"465-470"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78768780","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908955
Yufeng Tian, Zhanshan Wang
This paper focuses on the $H_{infty}$ control problem of singular Markovian jump delay systems with mode-dependent derivative-term coefficient through an extended decomposition system. By computing a proper Lyapunov functional, a generally stochastic stability condition of singular Markovian jump systems is achieved. On this basis, a delay-dependent stabilization condition of considered system is derived in terms of tractable linear matrix inequalities (LMIs), and the $H_{infty}$ controller gains are directly designed. Two numerical examples are introduced to illustrate the effectiveness of the proposed results.
{"title":"$H_{infty}$ Control for Singular Markovian Jump Delay Systems with Mode-Dependent Derivative-Term Coefficient","authors":"Yufeng Tian, Zhanshan Wang","doi":"10.1109/DDCLS.2019.8908955","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908955","url":null,"abstract":"This paper focuses on the $H_{infty}$ control problem of singular Markovian jump delay systems with mode-dependent derivative-term coefficient through an extended decomposition system. By computing a proper Lyapunov functional, a generally stochastic stability condition of singular Markovian jump systems is achieved. On this basis, a delay-dependent stabilization condition of considered system is derived in terms of tractable linear matrix inequalities (LMIs), and the $H_{infty}$ controller gains are directly designed. Two numerical examples are introduced to illustrate the effectiveness of the proposed results.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"11 1","pages":"426-431"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90049867","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909028
Xiaojing Song, Sen Chen, Wenchao Xue
This paper studies the control problem for a class of nonaffine uncertain systems with unknown saturation. The system model is built to describe many physical plants satisfying generalized saturation model of control input. The inflection point of the saturation model, which is critical for conventional controller design, is assumed to be unknown in this paper. The extended state observer is constructed to not only online estimate the “total disturbance” but also identify the inflection point. The simulation results on the typical aircraft flight control module show the effectiveness of the proposed controller.
{"title":"On Extended State Observer Based Control for A class of Nonaffine Systems with Unknown Saturation","authors":"Xiaojing Song, Sen Chen, Wenchao Xue","doi":"10.1109/DDCLS.2019.8909028","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909028","url":null,"abstract":"This paper studies the control problem for a class of nonaffine uncertain systems with unknown saturation. The system model is built to describe many physical plants satisfying generalized saturation model of control input. The inflection point of the saturation model, which is critical for conventional controller design, is assumed to be unknown in this paper. The extended state observer is constructed to not only online estimate the “total disturbance” but also identify the inflection point. The simulation results on the typical aircraft flight control module show the effectiveness of the proposed controller.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"17 1","pages":"1171-1176"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90466647","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908983
Dan Ye, Jiyan Wang
In this paper, a security issue for Cyber-Physical Systems (CPSs) is considered. We analyse a multi-sensor system equipped with a remote state estimation and a set of detectors. From the perspective of a malicious attacker, one intends to modify the innovation sequence by injecting a Gaussian noise and further destroys the system performance. The state estimation error covariance recursion are derived to quantify the effect of an attack. Furthermore, we study the worst-case false data injection (FDI) attack scenario, where the maximal attack probability is limited by the threshold of Kullback-Leibler divergence detector. Finally, a numerical example is shown to demonstrate the effectiveness of the worst-case FDI attack.
{"title":"False Data Injection Attack Design in Multi-sensor Systems Based on KL Divergence Theory","authors":"Dan Ye, Jiyan Wang","doi":"10.1109/DDCLS.2019.8908983","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908983","url":null,"abstract":"In this paper, a security issue for Cyber-Physical Systems (CPSs) is considered. We analyse a multi-sensor system equipped with a remote state estimation and a set of detectors. From the perspective of a malicious attacker, one intends to modify the innovation sequence by injecting a Gaussian noise and further destroys the system performance. The state estimation error covariance recursion are derived to quantify the effect of an attack. Furthermore, we study the worst-case false data injection (FDI) attack scenario, where the maximal attack probability is limited by the threshold of Kullback-Leibler divergence detector. Finally, a numerical example is shown to demonstrate the effectiveness of the worst-case FDI attack.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"10 1","pages":"333-337"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86543132","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908989
Jun Bao, Bo Ye, Weiquan Deng, Jiande Wu, Xiaodong Wang
Due to the complicated industrial environment and the poor surface conditions of detected materials, scanning images inevitably contain various noise in actual eddy current imaging detection, which seriously affects the detection result. Aiming at the above problem, we propose an eddy current scanning image denoising method based on principal component analysis (PCA) and locally linear embedding (LLE) in this paper. First, the method uses PCA to preliminarily remove noise from the scanning image. Then, the method uses the reconstruction algorithm of LLE to reconstruct the PCA-processed image by its neighborhoods, which further denoise the eddy current scanning image and optimize their details and edges while retaining their local geometric constructions. The experimental results have shown that, compared with other methods, the proposed method not only removes noise more effectively but also retains the details of the scanning image.
{"title":"Eddy Current Scanning Image Denoising Method Based on Principal Component Analysis and Manifold Learning","authors":"Jun Bao, Bo Ye, Weiquan Deng, Jiande Wu, Xiaodong Wang","doi":"10.1109/DDCLS.2019.8908989","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908989","url":null,"abstract":"Due to the complicated industrial environment and the poor surface conditions of detected materials, scanning images inevitably contain various noise in actual eddy current imaging detection, which seriously affects the detection result. Aiming at the above problem, we propose an eddy current scanning image denoising method based on principal component analysis (PCA) and locally linear embedding (LLE) in this paper. First, the method uses PCA to preliminarily remove noise from the scanning image. Then, the method uses the reconstruction algorithm of LLE to reconstruct the PCA-processed image by its neighborhoods, which further denoise the eddy current scanning image and optimize their details and edges while retaining their local geometric constructions. The experimental results have shown that, compared with other methods, the proposed method not only removes noise more effectively but also retains the details of the scanning image.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"13 1","pages":"563-567"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85928030","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908997
Jiaqing Yan, Zhanying Li, Peng Shao, Qi Chen
In recent years, the queuing equilibrium research based on traditional detector data is limited by the influence of data loss, resulting in inaccurate arrival rate and dissipation rate of the obtained vehicles. Based on this problem, this paper proposes a method of using the vehicle trajectory characteristics in the floating car data without calculating the arrival rate. Firstly, the genetic algorithm is used to find the optimal queuing intensity sequence. Then, by the relationship between the queue length and the green time, the green time of each phase is adjusted in real time, and the queue length equalization is realized, which effectively reduces the green light loss time. Through VISSIM simulation verification, the queuing delay and queuing intensity of each phase are reduced.
{"title":"Queuing Equilibrium Control of Urban Intersection Based on Floating Car Data","authors":"Jiaqing Yan, Zhanying Li, Peng Shao, Qi Chen","doi":"10.1109/DDCLS.2019.8908997","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908997","url":null,"abstract":"In recent years, the queuing equilibrium research based on traditional detector data is limited by the influence of data loss, resulting in inaccurate arrival rate and dissipation rate of the obtained vehicles. Based on this problem, this paper proposes a method of using the vehicle trajectory characteristics in the floating car data without calculating the arrival rate. Firstly, the genetic algorithm is used to find the optimal queuing intensity sequence. Then, by the relationship between the queue length and the green time, the green time of each phase is adjusted in real time, and the queue length equalization is realized, which effectively reduces the green light loss time. Through VISSIM simulation verification, the queuing delay and queuing intensity of each phase are reduced.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"1400-1404"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87454637","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908839
Liuqing Yang, Jiwei Liu
Emotion recognition based on physiological signal can be used in many applications such as, intelligent human-computer interface design, emotional disorder diagnoses. For traditional approaches, the prior knowledge is required to design and extract a range of features from physiological signal. The generalization ability of traditional methods is poor because of the lack of high-level features. Using deep-learning methodologies to analyze physiological signal, i.e. eeg, becomes increasingly attractive for recognizing emotions. In this paper, we design a sequence model based on deep-learning that uses Temporal Convolutional Network(TCN) to extract high-level features in consideration of the time dependence of physiological signals for EEG emotion recognition. Specifically, we extract the differential entropy feature in seconds and construct a time series with fixed-length time window data as the input to TCN, and then using softmax to classify. Furthermore, in order to get reliable results, we divide the samples according to the trials, avoiding the testing set samples and training set samples from the same trial. Specifically, we first divide the samples according to the trials as the testing set and the training set, and then segment the trials in the testing set and training set with fixed time window length to obtain more samples respectively. To evaluate the performance of the proposed model, we conduct the emotion classification experiments on DEAP database. The experimental results show the effectiveness of our proposed model for EEG emotion recognition.
{"title":"EEG-Based Emotion Recognition Using Temporal Convolutional Network","authors":"Liuqing Yang, Jiwei Liu","doi":"10.1109/DDCLS.2019.8908839","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908839","url":null,"abstract":"Emotion recognition based on physiological signal can be used in many applications such as, intelligent human-computer interface design, emotional disorder diagnoses. For traditional approaches, the prior knowledge is required to design and extract a range of features from physiological signal. The generalization ability of traditional methods is poor because of the lack of high-level features. Using deep-learning methodologies to analyze physiological signal, i.e. eeg, becomes increasingly attractive for recognizing emotions. In this paper, we design a sequence model based on deep-learning that uses Temporal Convolutional Network(TCN) to extract high-level features in consideration of the time dependence of physiological signals for EEG emotion recognition. Specifically, we extract the differential entropy feature in seconds and construct a time series with fixed-length time window data as the input to TCN, and then using softmax to classify. Furthermore, in order to get reliable results, we divide the samples according to the trials, avoiding the testing set samples and training set samples from the same trial. Specifically, we first divide the samples according to the trials as the testing set and the training set, and then segment the trials in the testing set and training set with fixed time window length to obtain more samples respectively. To evaluate the performance of the proposed model, we conduct the emotion classification experiments on DEAP database. The experimental results show the effectiveness of our proposed model for EEG emotion recognition.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"4 2 1","pages":"437-442"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80034084","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908856
Yunyun Jin, Yang Song, Taicheng Yang, Weiyan Hou, M. Schüller
This paper considers the global exponential stabilizability (GES) of a switched linear system under language constraints, which can be described by a nondeterministic finite state automaton. Firstly, the automaton is represented as a labeled diagraph to reduce the problem to the GES analysis in strongly connected components. Secondly, we analysis the properties of the lifted labeled diagraph, which can express the dwell time constraints intuitively. Based on the lifted labeled diagraph, we generalize the Lyapunov-Metzler condition to an M-step version, and propose a less conservative condition based on S-procedure. Finally, a numerical example is provided to demonstrate the S-procedure condition.
{"title":"Exponential Stabilizability Analysis for Constrained Switched System","authors":"Yunyun Jin, Yang Song, Taicheng Yang, Weiyan Hou, M. Schüller","doi":"10.1109/DDCLS.2019.8908856","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908856","url":null,"abstract":"This paper considers the global exponential stabilizability (GES) of a switched linear system under language constraints, which can be described by a nondeterministic finite state automaton. Firstly, the automaton is represented as a labeled diagraph to reduce the problem to the GES analysis in strongly connected components. Secondly, we analysis the properties of the lifted labeled diagraph, which can express the dwell time constraints intuitively. Based on the lifted labeled diagraph, we generalize the Lyapunov-Metzler condition to an M-step version, and propose a less conservative condition based on S-procedure. Finally, a numerical example is provided to demonstrate the S-procedure condition.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"88 1","pages":"934-938"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89050100","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908891
Minfeng Zhu, Lingjian Ye, Xiushui Ma
In this paper, an improved quadratic-criterion-based iterative learning control approach (Q-ILC) is proposed to obtain better trajectory tracking performance for the robotic arms. Besides of the position error information, which has been used in existing Q-ILC methods for robotic control, the velocity error information is also taken into consideration such that a new norm-optimal objective function is constructed. Convergence and error sensitivity properties for the proposed method are also analyzed. Furthermore, the Extended Kalman Filter (EKF) is utilized to estimate error states, which restrain the effects of model errors and measurement noise. Simulations on a 2DOF Robot manipulator demonstrate that our method achieves faster convergence and better transient performance, compared to the original Q-ILC.
{"title":"An Improved Quadratic-Criterion-Based Iterative Learning Control for Trajectory Tracking of Robotic Arms","authors":"Minfeng Zhu, Lingjian Ye, Xiushui Ma","doi":"10.1109/DDCLS.2019.8908891","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908891","url":null,"abstract":"In this paper, an improved quadratic-criterion-based iterative learning control approach (Q-ILC) is proposed to obtain better trajectory tracking performance for the robotic arms. Besides of the position error information, which has been used in existing Q-ILC methods for robotic control, the velocity error information is also taken into consideration such that a new norm-optimal objective function is constructed. Convergence and error sensitivity properties for the proposed method are also analyzed. Furthermore, the Extended Kalman Filter (EKF) is utilized to estimate error states, which restrain the effects of model errors and measurement noise. Simulations on a 2DOF Robot manipulator demonstrate that our method achieves faster convergence and better transient performance, compared to the original Q-ILC.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"93 1","pages":"243-248"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89084501","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908927
Mingxuan Sun, Xing Li
This paper is concerned with the convergence rate improvement of consensus of multi-agent systems, for which we introduce a generic attracting law (GAL), involving three terms which specify a generic action for improvement of convergence performance. The conventional double power-rate attracting law is modified for forming GAL, by adding a proportional term, and the convergence rate of the system can be dramatically improved. Through the two-phase analysis, an estimate for the upper bound of the settling time function is given, by which the obtained upper bound depends upon the initial state, and is finite without regard to the value of the initial state. The GAL is adopted for the purpose of consensus of multi-agent systems. A nonlinear protocol is designed to make the system undertaken achieve finite-duration consensus, and numerical results are presented to validate its effectiveness.
{"title":"Finite-Duration Consensus of Multi-Agent Systems Using a Generic Attracting Law","authors":"Mingxuan Sun, Xing Li","doi":"10.1109/DDCLS.2019.8908927","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908927","url":null,"abstract":"This paper is concerned with the convergence rate improvement of consensus of multi-agent systems, for which we introduce a generic attracting law (GAL), involving three terms which specify a generic action for improvement of convergence performance. The conventional double power-rate attracting law is modified for forming GAL, by adding a proportional term, and the convergence rate of the system can be dramatically improved. Through the two-phase analysis, an estimate for the upper bound of the settling time function is given, by which the obtained upper bound depends upon the initial state, and is finite without regard to the value of the initial state. The GAL is adopted for the purpose of consensus of multi-agent systems. A nonlinear protocol is designed to make the system undertaken achieve finite-duration consensus, and numerical results are presented to validate its effectiveness.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"37 1","pages":"691-696"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85932134","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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