Fault diagnosis of rolling bearings is a key issue in the field of engineering. To solve the problem that the accuracy of the current fault diagnosis of rolling bearings is not high and the model construction time is long, This paper proposed a new fault diagnosis method for rolling bearings based on invariant moments of three-dimensional vibration spectrogram. The pseudo-Wigner-Ville distribution time-frequency analysis method was adopted to generate vibration spectrum images of the rolling bearings by means of signal processing. This method extracts the point cloud three-dimensional invariant moments of the vibration spectrogram as the characteristics of the failure mode, and realizes the bearing fault identification with the BP neural network. The experimental results show that the proposed method not only has better recognition rate than the feature extraction method of the two-dimensional Hu invariant moment, but also can effectively identify and classify faults such as inner ring and outer ring, which has strong application value in the fault diagnosis of bearings and other rotating machinery.
{"title":"Research on fault diagnosis of rolling bearing based on invariant moments of three-dimensional vibration spectrogram","authors":"Bingbing Shen, C. Zhang, Liang Hua, Ling Jiang, Juping Gu, Zhenkun Xu, Bingbing Shen, Liang Hua, Ling Jiang","doi":"10.1109/YAC.2018.8406495","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406495","url":null,"abstract":"Fault diagnosis of rolling bearings is a key issue in the field of engineering. To solve the problem that the accuracy of the current fault diagnosis of rolling bearings is not high and the model construction time is long, This paper proposed a new fault diagnosis method for rolling bearings based on invariant moments of three-dimensional vibration spectrogram. The pseudo-Wigner-Ville distribution time-frequency analysis method was adopted to generate vibration spectrum images of the rolling bearings by means of signal processing. This method extracts the point cloud three-dimensional invariant moments of the vibration spectrogram as the characteristics of the failure mode, and realizes the bearing fault identification with the BP neural network. The experimental results show that the proposed method not only has better recognition rate than the feature extraction method of the two-dimensional Hu invariant moment, but also can effectively identify and classify faults such as inner ring and outer ring, which has strong application value in the fault diagnosis of bearings and other rotating machinery.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131713543","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 : 2018-05-01DOI: 10.1109/YAC.2018.8406528
Bo Zhao, Guang Shi, Chao Li
This paper presents an online updated cost function based self-learning optimal control scheme for uncertain nonlinear systems. By establishing an online updated cost function with the help of disturbance observer, the Hamilton-Jacobi-Bellman equation is solved by constructing a critic neural network, whose weight vector is tuned by self-learning algorithm. And then, the optimal control scheme is derived indirectly. Based on Lyapunov stability analysis, the closed-loop system with the proposed scheme is guaranteed to be stable. The simulation results show the effectiveness of the developed self-learning optimal control scheme. The cost function reflects the system uncertainties in real time, which implies that this method relaxes the assumptions on available upper-bounds and matching condition for system dynamics in compared with many existing methods.
{"title":"Self-learning optimal control for uncertain nonlinear systems via online updated cost function","authors":"Bo Zhao, Guang Shi, Chao Li","doi":"10.1109/YAC.2018.8406528","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406528","url":null,"abstract":"This paper presents an online updated cost function based self-learning optimal control scheme for uncertain nonlinear systems. By establishing an online updated cost function with the help of disturbance observer, the Hamilton-Jacobi-Bellman equation is solved by constructing a critic neural network, whose weight vector is tuned by self-learning algorithm. And then, the optimal control scheme is derived indirectly. Based on Lyapunov stability analysis, the closed-loop system with the proposed scheme is guaranteed to be stable. The simulation results show the effectiveness of the developed self-learning optimal control scheme. The cost function reflects the system uncertainties in real time, which implies that this method relaxes the assumptions on available upper-bounds and matching condition for system dynamics in compared with many existing methods.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129201553","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 : 2018-05-01DOI: 10.1109/YAC.2018.8406430
Nan Gu, Bin Zhang, Shuai Ren, Dan Wang, Zhouhua Peng
This paper considers the guidance law design for distributed containment maneuvering of a group of fully-actuated autonomous surface vehicles subject to velocity constraints under a directed graph. A saturated guidance law is designed for each vehicle based on a constant bearing guidance method and a containment maneuvering approach. By using the presented saturated guidance law, the fully-actuated marine surface vehicles are able to track a convex hull spanned by multiple virtual leaders moving along multiple parameterized paths. A key feature of the proposed saturated guidance law is that velocity constraints are not violated and aggressive maneuvers during transient phase can be avoided. On the basis of Lyapunov theory and graph theory, the globally uniformly asymptotically stable and locally uniformly exponentially stable of the closed-loop system is analyzed. Finally, the effectiveness of the proposed saturated guidance law is illustrated by the simulation study.
{"title":"Saturated guidance law for distributed containment maneuvering of fully-actuated autonomous surface vehicles under a directed graph","authors":"Nan Gu, Bin Zhang, Shuai Ren, Dan Wang, Zhouhua Peng","doi":"10.1109/YAC.2018.8406430","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406430","url":null,"abstract":"This paper considers the guidance law design for distributed containment maneuvering of a group of fully-actuated autonomous surface vehicles subject to velocity constraints under a directed graph. A saturated guidance law is designed for each vehicle based on a constant bearing guidance method and a containment maneuvering approach. By using the presented saturated guidance law, the fully-actuated marine surface vehicles are able to track a convex hull spanned by multiple virtual leaders moving along multiple parameterized paths. A key feature of the proposed saturated guidance law is that velocity constraints are not violated and aggressive maneuvers during transient phase can be avoided. On the basis of Lyapunov theory and graph theory, the globally uniformly asymptotically stable and locally uniformly exponentially stable of the closed-loop system is analyzed. Finally, the effectiveness of the proposed saturated guidance law is illustrated by the simulation study.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121317881","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 : 2018-05-01DOI: 10.1109/YAC.2018.8406457
Changjun Gu, Gan Sun, Yun Feng, Dongying Tian, Yan Peng, Xiaomao Li, Yang Cong
Recently, multiple cameras can be calibrated with high precision calibration object. However, most existing methods are often difficult to design and further cause high computational cost. Therefore in this paper, we propose a new method to simultaneously calibrate multiple cameras into a network using free fall motion. Specifically, it first estimates the feature points based on synchronization or asynchronous free fall motion. The extracted feature points are then used as corresponding points for multi-camera calibration. After adding an uncalibrated node into a network of calibrated cameras, our method can fully automatic calibrate multiple cameras using several free fall motion. Finally, the proposed method is evaluated using synthetic data.
{"title":"Motion-based pose estimation via free falling","authors":"Changjun Gu, Gan Sun, Yun Feng, Dongying Tian, Yan Peng, Xiaomao Li, Yang Cong","doi":"10.1109/YAC.2018.8406457","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406457","url":null,"abstract":"Recently, multiple cameras can be calibrated with high precision calibration object. However, most existing methods are often difficult to design and further cause high computational cost. Therefore in this paper, we propose a new method to simultaneously calibrate multiple cameras into a network using free fall motion. Specifically, it first estimates the feature points based on synchronization or asynchronous free fall motion. The extracted feature points are then used as corresponding points for multi-camera calibration. After adding an uncalibrated node into a network of calibrated cameras, our method can fully automatic calibrate multiple cameras using several free fall motion. Finally, the proposed method is evaluated using synthetic data.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125144849","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 : 2018-05-01DOI: 10.1109/YAC.2018.8405802
Xiaoling Li, Linlin Hou, Haibin Sun
The issue of stabilization for sampled-data control system is studied in this paper. The sampling control system is modeled as a switching system based on whether the control input is missing or not. Then the mode-dependent average dwell time method composed of slow switching and fast switching is used to derive the relevant conclusion. Finally, an example is presented to show the effectiveness of the result.
{"title":"Stabilization of sampled-data control system via mode-dependent average dwell time","authors":"Xiaoling Li, Linlin Hou, Haibin Sun","doi":"10.1109/YAC.2018.8405802","DOIUrl":"https://doi.org/10.1109/YAC.2018.8405802","url":null,"abstract":"The issue of stabilization for sampled-data control system is studied in this paper. The sampling control system is modeled as a switching system based on whether the control input is missing or not. Then the mode-dependent average dwell time method composed of slow switching and fast switching is used to derive the relevant conclusion. Finally, an example is presented to show the effectiveness of the result.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994381","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 : 2018-05-01DOI: 10.1109/YAC.2018.8406391
Y. W. Chen, K. Guo, Y. Pan
The formalism of Tensor Network (TN) provides a compact way to approximate many-body quantum states with 1D chain of tensors. The 1D chain of tensors is found to be efficient in capturing the local correlations between neighboring subsystems, and machine learning approaches have been proposed using artificial neural networks (NN) of similar structure. However, a long chain of tensors is difficult to train due to exploding and vanishing gradients. In this paper, we propose methods to decompose the long-chain TN into short chains, which could improve the convergence property of the training algorithm by allowing stable stochastic gradient descent (SGD). In addition, the short-chain methods are robust to network initializations. Numerical experiments show that the short-chain TN achieves almost the same classification accuracy on MNIST dataset as LeNet-5 with less trainable network parameters and connections.
{"title":"Robust supervised learning based on tensor network method","authors":"Y. W. Chen, K. Guo, Y. Pan","doi":"10.1109/YAC.2018.8406391","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406391","url":null,"abstract":"The formalism of Tensor Network (TN) provides a compact way to approximate many-body quantum states with 1D chain of tensors. The 1D chain of tensors is found to be efficient in capturing the local correlations between neighboring subsystems, and machine learning approaches have been proposed using artificial neural networks (NN) of similar structure. However, a long chain of tensors is difficult to train due to exploding and vanishing gradients. In this paper, we propose methods to decompose the long-chain TN into short chains, which could improve the convergence property of the training algorithm by allowing stable stochastic gradient descent (SGD). In addition, the short-chain methods are robust to network initializations. Numerical experiments show that the short-chain TN achieves almost the same classification accuracy on MNIST dataset as LeNet-5 with less trainable network parameters and connections.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131513587","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 : 2018-05-01DOI: 10.1109/YAC.2018.8406376
Zhongxi Sun
ICA concerns high-order dependencies between variables. In this paper, a new feature extraction method is proposed by combining Inverse FDA with ICA. ICA is applied to sample images to provide the high-order statistical information and reduce dimension. Inverse FDA is used for discrimination. Experimental results on FKP database show that our proposed method is efficient.
{"title":"FKP recognition using ICA-based inverse FDA","authors":"Zhongxi Sun","doi":"10.1109/YAC.2018.8406376","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406376","url":null,"abstract":"ICA concerns high-order dependencies between variables. In this paper, a new feature extraction method is proposed by combining Inverse FDA with ICA. ICA is applied to sample images to provide the high-order statistical information and reduce dimension. Inverse FDA is used for discrimination. Experimental results on FKP database show that our proposed method is efficient.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116523045","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 : 2018-05-01DOI: 10.1109/YAC.2018.8406456
Shanhe Jiang, Chaolong Zhang, Wenjin Wu, Yanmei Li
In this paper, an improved hybrid particle swarm optimization (IHPSO) was proposed by using the learning strategies framework of the particle swarm optimization (PSO), and adapting the gravitational search algorithm (GSA) into the PSO. To be specific, the IHPSO adopts three learning strategies, namely dependent random coefficients, fixed iteration interval cycle, and adaptive evolution stagnation cycle. The particle first enters into the PSO stage and updates its velocity based on the first strategy to enhance the exploration ability. Particles that fail to improve their fitness then enter into the GSA operators in terms of the latter two strategies to decrease the computational cost in the hybridization. To evaluate the effectiveness and feasibility of the IHPSO, the simulations were performed on various test functions. Results reveal that the IHPSO exhibits superior performance in terms of accuracy, reliability and efficiency compared to PSO, GSA and other recently developed hybrid variants.
{"title":"An improved hybrid particle swarm optimization with dependent random coefficients for global optimization","authors":"Shanhe Jiang, Chaolong Zhang, Wenjin Wu, Yanmei Li","doi":"10.1109/YAC.2018.8406456","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406456","url":null,"abstract":"In this paper, an improved hybrid particle swarm optimization (IHPSO) was proposed by using the learning strategies framework of the particle swarm optimization (PSO), and adapting the gravitational search algorithm (GSA) into the PSO. To be specific, the IHPSO adopts three learning strategies, namely dependent random coefficients, fixed iteration interval cycle, and adaptive evolution stagnation cycle. The particle first enters into the PSO stage and updates its velocity based on the first strategy to enhance the exploration ability. Particles that fail to improve their fitness then enter into the GSA operators in terms of the latter two strategies to decrease the computational cost in the hybridization. To evaluate the effectiveness and feasibility of the IHPSO, the simulations were performed on various test functions. Results reveal that the IHPSO exhibits superior performance in terms of accuracy, reliability and efficiency compared to PSO, GSA and other recently developed hybrid variants.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133435633","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 : 2018-05-01DOI: 10.1109/YAC.2018.8406494
Zhaocong Sun, Ji-Sheng Xia, Chi Zhang, Wanqi Cui, Tianyi Shi
With the development of economy, energy using has attracted more and more attention. To help decision-makers and governors manage the energy utilization better, we write this paper to introduce Energy Evaluation and Prediction System (EEPS). Based on the data provided by SEDS, this paper proposed four models to select and aggregate important information. Firstly, Energy Profile Model (EPM) is established to cluster the data. The data is divided into for parts: production, consumption, unit price and total expenditure. Secondly, based on EPM, time is considered and we get the main energy percentage diagram of each state during 50 years. To help the governors understand the similarities and differences of the four states in using clean and renewable energy, we establish Energy Correlation Analysis Model (ECAM) to study the correlation between new energy using and the factors. Thirdly, to determine which of the four states appeared to use clean energy best in 2009, New Energy Profile Model (NEPM) is established. We suggest an objective function of different energy on production and consumption. After that we use TOPSIS to get the best solution, which shows AZS use clean energy best. Fourthly, Energy Profile Prediction Model (EPPM) is established to predict the energy profile of 2025 and 2050. We use BP and LSSVM algorithm in the model. From the prediction results, AZS will produce most of the petroleum products by 2025, and renewable energy will account for one quarter of the energy used. By 2050, the production of electricity and fossil fuels will be the main source of energy. Fifthly, EPPM and NEPM are used to predict and evaluate the use condition of energy in 2025 and 2050. From the prediction results, the clean energy used is increasing.
{"title":"Energy evaluation and prediction system based on data mining","authors":"Zhaocong Sun, Ji-Sheng Xia, Chi Zhang, Wanqi Cui, Tianyi Shi","doi":"10.1109/YAC.2018.8406494","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406494","url":null,"abstract":"With the development of economy, energy using has attracted more and more attention. To help decision-makers and governors manage the energy utilization better, we write this paper to introduce Energy Evaluation and Prediction System (EEPS). Based on the data provided by SEDS, this paper proposed four models to select and aggregate important information. Firstly, Energy Profile Model (EPM) is established to cluster the data. The data is divided into for parts: production, consumption, unit price and total expenditure. Secondly, based on EPM, time is considered and we get the main energy percentage diagram of each state during 50 years. To help the governors understand the similarities and differences of the four states in using clean and renewable energy, we establish Energy Correlation Analysis Model (ECAM) to study the correlation between new energy using and the factors. Thirdly, to determine which of the four states appeared to use clean energy best in 2009, New Energy Profile Model (NEPM) is established. We suggest an objective function of different energy on production and consumption. After that we use TOPSIS to get the best solution, which shows AZS use clean energy best. Fourthly, Energy Profile Prediction Model (EPPM) is established to predict the energy profile of 2025 and 2050. We use BP and LSSVM algorithm in the model. From the prediction results, AZS will produce most of the petroleum products by 2025, and renewable energy will account for one quarter of the energy used. By 2050, the production of electricity and fossil fuels will be the main source of energy. Fifthly, EPPM and NEPM are used to predict and evaluate the use condition of energy in 2025 and 2050. From the prediction results, the clean energy used is increasing.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134288315","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 : 2018-05-01DOI: 10.1109/YAC.2018.8406534
Zhenyu Chen, Yun Chen, A. Xue
This paper is concerned with the problem of H∞ performance analysis for delayed nonlinear Markov jump systems with piecewise-constant transition rates. The delays and nonlinearities are randomly occurring in a probabilistic way, described by Bernoulli sequences. The transition rates are time-varying and subject to the average dwell time switching. The sufficient stochastic stability condition is established based on average dwell time switching approach and Lyapunov functional method. The sufficient condition ensuring the system has a guaranteed H∞ noise-attenuation performance index is presented. A numerical example is presented to demonstrate the validity of the method.
{"title":"H∞ performance analysis of delayed nonlinear Markov jump systems with piecewise-constant transition rates","authors":"Zhenyu Chen, Yun Chen, A. Xue","doi":"10.1109/YAC.2018.8406534","DOIUrl":"https://doi.org/10.1109/YAC.2018.8406534","url":null,"abstract":"This paper is concerned with the problem of H∞ performance analysis for delayed nonlinear Markov jump systems with piecewise-constant transition rates. The delays and nonlinearities are randomly occurring in a probabilistic way, described by Bernoulli sequences. The transition rates are time-varying and subject to the average dwell time switching. The sufficient stochastic stability condition is established based on average dwell time switching approach and Lyapunov functional method. The sufficient condition ensuring the system has a guaranteed H∞ noise-attenuation performance index is presented. A numerical example is presented to demonstrate the validity of the method.","PeriodicalId":226586,"journal":{"name":"2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134474951","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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