Fuzzy logic systems have been widely used for controlling nonlinear and complex dynamic systems by programming heuristic knowledge. But these systems are computationally complex and resource intensive. This paper presents a technique of development and porting of a fuzzy logic approximation PID controller (FLAC) in an automatic cruise control (ACC) system. ACC is a highly nonlinear process and its control is trivial due to the large change in parameters. Therefore, a suitable controller based on heuristic knowledge will be easy to develop and provide an effective solution. But the major problem with employing fuzzy logic controller (FLC) is its complexity. Moreover, the designing of Rulebase requires efficient heuristic knowledge about the system which is rarely found. Therefore, in this paper, a novel rule extraction process is used to derive a FLAC. This controller is then ported on a C6748 DSP hardware with timing and memory optimization. Later, it is seamlessly connected to a network to support remote reconfigurability. A performance analysis is drawn based on processor-in loop test with Simulink model of a cruise control system for vehicle.
{"title":"Design and Implementation of Fuzzy Approximation PI Controller for Automatic Cruise Control System","authors":"P. Maji, S. K. Patra, K. Mahapatra","doi":"10.1155/2015/624638","DOIUrl":"https://doi.org/10.1155/2015/624638","url":null,"abstract":"Fuzzy logic systems have been widely used for controlling nonlinear and complex dynamic systems by programming heuristic knowledge. But these systems are computationally complex and resource intensive. This paper presents a technique of development and porting of a fuzzy logic approximation PID controller (FLAC) in an automatic cruise control (ACC) system. ACC is a highly nonlinear process and its control is trivial due to the large change in parameters. Therefore, a suitable controller based on heuristic knowledge will be easy to develop and provide an effective solution. But the major problem with employing fuzzy logic controller (FLC) is its complexity. Moreover, the designing of Rulebase requires efficient heuristic knowledge about the system which is rarely found. Therefore, in this paper, a novel rule extraction process is used to derive a FLAC. This controller is then ported on a C6748 DSP hardware with timing and memory optimization. Later, it is seamlessly connected to a network to support remote reconfigurability. A performance analysis is drawn based on processor-in loop test with Simulink model of a cruise control system for vehicle.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79441301","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}
Human gaze is not directed to the same part of an image when lighting conditions change. Current saliency models do not consider light level analysis during their bottom-up processes. In this paper, we introduce a new saliency model which better mimics physiological and psychological processes of our visual attention in case of free-viewing task (bottom-up process). This model analyzes lighting conditions with the aim of giving different weights to color wavelengths. The resulting saliency measure performs better than a lot of popular cognitive approaches.
{"title":"Pop-Out: A New Cognitive Model of Visual Attention That Uses Light Level Analysis to Better Mimic the Free-Viewing Task of Static Images","authors":"Makiese Mibulumukini","doi":"10.1155/2015/471483","DOIUrl":"https://doi.org/10.1155/2015/471483","url":null,"abstract":"Human gaze is not directed to the same part of an image when lighting conditions change. Current saliency models do not consider light level analysis during their bottom-up processes. In this paper, we introduce a new saliency model which better mimics physiological and psychological processes of our visual attention in case of free-viewing task (bottom-up process). This model analyzes lighting conditions with the aim of giving different weights to color wavelengths. The resulting saliency measure performs better than a lot of popular cognitive approaches.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72636671","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}
In machine transliteration, it is common that the transliterated names in the target language come from multiple language origins. A conventional maximum likelihood based single model can not deal with this issue very well and often suffers from overfitting. In this paper, we exploit a coupled Dirichlet process mixture model (cDPMM) to address overfitting and names multiorigin cluster issues simultaneously in the transliteration sequence alignment step over the name pairs. After the alignment step, the cDPMM clusters name pairs into many groups according to their origin information automatically. In the decoding step, in order to use the learned origin information sufficiently, we use a cluster combination method (CCM) to build clustering-specific transliteration models by combining small clusters into large ones based on the perplexities of name language and transliteration model, which makes sure each origin cluster has enough data for training a transliteration model. On the three different Western-Chinese multiorigin names corpora, the cDPMM outperforms two state-of-the-art baseline models in terms of both the top-1 accuracy and mean F-score, and furthermore the CCM significantly improves the cDPMM.
{"title":"A Dirichlet Process Mixture Based Name Origin Clustering and Alignment Model for Transliteration","authors":"Chunyue Zhang, T. Zhao, Tingting Li","doi":"10.1155/2015/927063","DOIUrl":"https://doi.org/10.1155/2015/927063","url":null,"abstract":"In machine transliteration, it is common that the transliterated names in the target language come from multiple language origins. A conventional maximum likelihood based single model can not deal with this issue very well and often suffers from overfitting. In this paper, we exploit a coupled Dirichlet process mixture model (cDPMM) to address overfitting and names multiorigin cluster issues simultaneously in the transliteration sequence alignment step over the name pairs. After the alignment step, the cDPMM clusters name pairs into many groups according to their origin information automatically. In the decoding step, in order to use the learned origin information sufficiently, we use a cluster combination method (CCM) to build clustering-specific transliteration models by combining small clusters into large ones based on the perplexities of name language and transliteration model, which makes sure each origin cluster has enough data for training a transliteration model. On the three different Western-Chinese multiorigin names corpora, the cDPMM outperforms two state-of-the-art baseline models in terms of both the top-1 accuracy and mean F-score, and furthermore the CCM significantly improves the cDPMM.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90825113","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}
Majority of Indian languages have originated from two language families, namely, Indo-European and Dravidian. Therefore, certain kind of similarity among languages of a particular family can be expected to exist. Also, languages spoken in neighboring regions show certain similarity since there happens to be a lot of intermingling between population of neighboring regions. This paper develops a technique to measure similarity among Indian languages in a novel way, using language verification framework. Four verification systems are designed for each language. Acceptance of one language as another, which relates to false acceptance in language verification framework, is used as a measure of similarity. If language A shows false acceptance more than a predefined threshold with language B, in at least three out of the four systems, then languages A and B are considered to be similar in this work. It is expected that the languages belonging to the same family should manifest their similarity in experimental results. Also, similarity between neighboring languages should be detected through experiments. Any deviation from such fact should be due to specific linguistic or historical reasons. This work analyzes any such scenario.
{"title":"Study on Similarity among Indian Languages Using Language Verification Framework","authors":"D. Sengupta, G. Saha","doi":"10.1155/2015/325703","DOIUrl":"https://doi.org/10.1155/2015/325703","url":null,"abstract":"Majority of Indian languages have originated from two language families, namely, Indo-European and Dravidian. Therefore, certain kind of similarity among languages of a particular family can be expected to exist. Also, languages spoken in neighboring regions show certain similarity since there happens to be a lot of intermingling between population of neighboring regions. This paper develops a technique to measure similarity among Indian languages in a novel way, using language verification framework. Four verification systems are designed for each language. Acceptance of one language as another, which relates to false acceptance in language verification framework, is used as a measure of similarity. If language A shows false acceptance more than a predefined threshold with language B, in at least three out of the four systems, then languages A and B are considered to be similar in this work. It is expected that the languages belonging to the same family should manifest their similarity in experimental results. Also, similarity between neighboring languages should be detected through experiments. Any deviation from such fact should be due to specific linguistic or historical reasons. This work analyzes any such scenario.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82170324","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}
A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.
{"title":"Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification","authors":"D. Salih, S. Noor, M. Marhaban, R. Kamil","doi":"10.1155/2015/184318","DOIUrl":"https://doi.org/10.1155/2015/184318","url":null,"abstract":"A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79451159","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}
Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.
{"title":"Two Artificial Neural Networks for Modeling Discrete Survival Time of Censored Data","authors":"Taysseer Sharaf, C. Tsokos","doi":"10.1155/2015/270165","DOIUrl":"https://doi.org/10.1155/2015/270165","url":null,"abstract":"Artificial neural network (ANN) theory is emerging as an alternative to conventional statistical methods in modeling nonlinear functions. The popular Cox proportional hazard model falls short in modeling survival data with nonlinear behaviors. ANN is a good alternative to the Cox PH as the proportionality of the hazard assumption and model relaxations are not required. In addition, ANN possesses a powerful capability of handling complex nonlinear relations within the risk factors associated with survival time. In this study, we present a comprehensive comparison of two different approaches of utilizing ANN in modeling smooth conditional hazard probability function. We use real melanoma cancer data to illustrate the usefulness of the proposed ANN methods. We report some significant results in comparing the survival time of male and female melanoma patients.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80902386","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}
Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable amount of time, using the experience of prior actions. An environment consisting solely of the occurrence or nonoccurrence of specific events attributable to a human actor may appear to lack the necessary structure for the positioning of responding agents in time and space using reinforcement learning. Digital pheromones can be used to synthetically augment such an environment with event sequence information to create a more persistent and measurable imprint on the environment that supports reinforcement learning. We implemented this method and combined it with the ability of agents to learn from actions not taken, a concept known as fictive learning. This approach was tested against the historical sequence of Somali maritime pirate attacks from 2005 to mid-2012, enabling a set of autonomous agents representing naval vessels to successfully respond to an average of 333 of the 899 pirate attacks, outperforming the historical record of 139 successes.
{"title":"Reinforcement Learning in an Environment Synthetically Augmented with Digital Pheromones","authors":"Salvador E. Barbosa, Mikel D. Petty","doi":"10.1155/2014/932485","DOIUrl":"https://doi.org/10.1155/2014/932485","url":null,"abstract":"Reinforcement learning requires information about states, actions, and outcomes as the basis for learning. For many applications, it can be difficult to construct a representative model of the environment, either due to lack of required information or because of that the model's state space may become too large to allow a solution in a reasonable amount of time, using the experience of prior actions. An environment consisting solely of the occurrence or nonoccurrence of specific events attributable to a human actor may appear to lack the necessary structure for the positioning of responding agents in time and space using reinforcement learning. Digital pheromones can be used to synthetically augment such an environment with event sequence information to create a more persistent and measurable imprint on the environment that supports reinforcement learning. We implemented this method and combined it with the ability of agents to learn from actions not taken, a concept known as fictive learning. This approach was tested against the historical sequence of Somali maritime pirate attacks from 2005 to mid-2012, enabling a set of autonomous agents representing naval vessels to successfully respond to an average of 333 of the 899 pirate attacks, outperforming the historical record of 139 successes.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86496280","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}
This paper presents the design of modified radial basic function neural controller (MRBFNC) for the pitch control of an aircraft to obtain the desired pitch angel as required by the pilot while maneuvering an aircraft. In this design, the parameters of radial basis function neural controller (RBFNC) are optimized by implementing a feedback mechanism which is controlled by a tuning factor "α" (T factor). For a given input, the response of the RBFN controller is tuned by using T factor for better performance of the aircraft pitch control system. The proposed system is demonstrated under different condition (absence and presence of sensor noise). The simulation results show that MRBFNC performs better, in terms of settling time and rise time for both conditions, than the conventional RBFNC. It is also seen that, as the value of the T factor increases, the aircraft pitch control system performs better and settles quickly to its reference trajectory. A comparison between MRBFNC and conventional RBFNC is also established to discuss the superiority of the former techniques.
{"title":"Design of a T Factor Based RBFNC for a Flight Control System","authors":"C. Mohanty, P. S. Khuntia, D. Mitra","doi":"10.1155/2014/897691","DOIUrl":"https://doi.org/10.1155/2014/897691","url":null,"abstract":"This paper presents the design of modified radial basic function neural controller (MRBFNC) for the pitch control of an aircraft to obtain the desired pitch angel as required by the pilot while maneuvering an aircraft. In this design, the parameters of radial basis function neural controller (RBFNC) are optimized by implementing a feedback mechanism which is controlled by a tuning factor \"α\" (T factor). For a given input, the response of the RBFN controller is tuned by using T factor for better performance of the aircraft pitch control system. The proposed system is demonstrated under different condition (absence and presence of sensor noise). The simulation results show that MRBFNC performs better, in terms of settling time and rise time for both conditions, than the conventional RBFNC. It is also seen that, as the value of the T factor increases, the aircraft pitch control system performs better and settles quickly to its reference trajectory. A comparison between MRBFNC and conventional RBFNC is also established to discuss the superiority of the former techniques.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78672414","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}
Water resources and urban flood management require hydrologic and hydraulic modeling. However, incomplete precipitation data is often the issue during hydrological modeling exercise. In this study, gene expression programming (GEP) was utilised to correlate monthly precipitation data from a principal station with its neighbouring station located in Alor Setar, Kedah, Malaysia. GEP is an extension to genetic programming (GP), and can provide simple and efficient solution. The study illustrates the applications of GEP to determine the most suitable rainfall station to replace the principal rainfall station (station 6103047). This is to ensure that a reliable rainfall station can be made if the principal station malfunctioned. These were done by comparing principal station data with each individual neighbouring station. Result of the analysis reveals that the station 38 is the most compatible to the principal station where the value of R2 is 0.886.
{"title":"Estimation of Missing Rainfall Data Using GEP: Case Study of Raja River, Alor Setar, Kedah","authors":"N. Ghani, Z. A. Hasan, T. L. Lau","doi":"10.1155/2014/716398","DOIUrl":"https://doi.org/10.1155/2014/716398","url":null,"abstract":"Water resources and urban flood management require hydrologic and hydraulic modeling. However, incomplete precipitation data is often the issue during hydrological modeling exercise. In this study, gene expression programming (GEP) was utilised to correlate monthly precipitation data from a principal station with its neighbouring station located in Alor Setar, Kedah, Malaysia. GEP is an extension to genetic programming (GP), and can provide simple and efficient solution. The study illustrates the applications of GEP to determine the most suitable rainfall station to replace the principal rainfall station (station 6103047). This is to ensure that a reliable rainfall station can be made if the principal station malfunctioned. These were done by comparing principal station data with each individual neighbouring station. Result of the analysis reveals that the station 38 is the most compatible to the principal station where the value of R2 is 0.886.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81370193","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}
Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection.The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length.Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.
{"title":"A New Evolutionary-Incremental Framework for Feature Selection","authors":"M. Sigari, Muhammad Reza Pourshahabi, H. Pourreza","doi":"10.1155/2014/679847","DOIUrl":"https://doi.org/10.1155/2014/679847","url":null,"abstract":"Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection.The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length.Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80958322","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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