Pub Date : 2010-07-07DOI: 10.1109/COGINF.2010.5599769
Liu Hong, Zeng Qing-heng, Yang Min, Fan Mujiao
Aiming to the features of the load variation of gas pipeline, it is suggested Fuzzy Logic system and RBF Nerve Network Based on Artificial Immune Algorithm is used to predict the load of gas pipeline. The fuzzy logic system is applied to predict the load error and the error variation rate. Then, the RBF Nerve Network Based on Artificial Immune Algorithm is used to predict the load of gas pipeline. The results showed that the relative errors are all less than 8%, which proved that the novel RBF neural network model based on Artificial Immune Algorithm has less calculation, high precision and good generalization ability.
{"title":"Application of RBF network based on Artificial Immune Algorithm to predict gas pipeline load","authors":"Liu Hong, Zeng Qing-heng, Yang Min, Fan Mujiao","doi":"10.1109/COGINF.2010.5599769","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599769","url":null,"abstract":"Aiming to the features of the load variation of gas pipeline, it is suggested Fuzzy Logic system and RBF Nerve Network Based on Artificial Immune Algorithm is used to predict the load of gas pipeline. The fuzzy logic system is applied to predict the load error and the error variation rate. Then, the RBF Nerve Network Based on Artificial Immune Algorithm is used to predict the load of gas pipeline. The results showed that the relative errors are all less than 8%, which proved that the novel RBF neural network model based on Artificial Immune Algorithm has less calculation, high precision and good generalization ability.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121424597","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599709
G. Qian, Xuezhen Shen, S. Luo, Jesse S. Jin, W. Nowinski
Current Alzheimer's disease diagnosis and cognitive assessment are based on medical history assessment and evaluation of cognitive score systems. They are time-consuming and subjective. A rapid and automated method is developed by processing positron emission tomography neuroimages and performing statistical analysis. The brain areas are firstly extracted from the neuroimages by an atlas-assisted approach, and then transformed piecewise into a common atlas space by dividing the brain into 18 cubic regions based on the landmarks identified automatically. The statistical models of stepwise regressions and discriminant classification are applied to predict the cognitive scores and make a diagnosis on Alzheimer's disease or mild cognitive impairment. The proposed method is fully automatic and has been tested on 400 cases. The preliminary testing results are promising. For a group of 250 cases which are the samples of the regressions and discriminant classification, the success rates of disease diagnosis are 73.7%, 54.9%, and 79.7% for the patients with Alzheimer's disease, mild cognitive impairment, and normal subjects, respectively. The average success rate for another group of 150 cases is 61.3%.
{"title":"Rapid and automatic atlas-based approach of Alzheimer's disease assessment by positron emission tomography neuroimages","authors":"G. Qian, Xuezhen Shen, S. Luo, Jesse S. Jin, W. Nowinski","doi":"10.1109/COGINF.2010.5599709","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599709","url":null,"abstract":"Current Alzheimer's disease diagnosis and cognitive assessment are based on medical history assessment and evaluation of cognitive score systems. They are time-consuming and subjective. A rapid and automated method is developed by processing positron emission tomography neuroimages and performing statistical analysis. The brain areas are firstly extracted from the neuroimages by an atlas-assisted approach, and then transformed piecewise into a common atlas space by dividing the brain into 18 cubic regions based on the landmarks identified automatically. The statistical models of stepwise regressions and discriminant classification are applied to predict the cognitive scores and make a diagnosis on Alzheimer's disease or mild cognitive impairment. The proposed method is fully automatic and has been tested on 400 cases. The preliminary testing results are promising. For a group of 250 cases which are the samples of the regressions and discriminant classification, the success rates of disease diagnosis are 73.7%, 54.9%, and 79.7% for the patients with Alzheimer's disease, mild cognitive impairment, and normal subjects, respectively. The average success rate for another group of 150 cases is 61.3%.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132066302","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599797
Qi Wang, Yizhen Cao, Yongbin Wang
The computing resource of animation-based rendering based on WAN are distributed, heterogeneous, dynamical, so resource management and scheduling are challenging task, the performance evaluation, performance analysis and performance prediction have become increasingly important. In this paper, rendering pretreatment mechanism is introduced at first, then based on a detailed analysis of computing NRD (Node Reliability Degree) and multi-granularity segmentation for task, the resource scheduling strategy based on prediction is proposed, which provides a distributed solution for resource scheduling in distributed rendering environment.
{"title":"Research of scheduling strategy based on rendering pretreatment mechanism","authors":"Qi Wang, Yizhen Cao, Yongbin Wang","doi":"10.1109/COGINF.2010.5599797","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599797","url":null,"abstract":"The computing resource of animation-based rendering based on WAN are distributed, heterogeneous, dynamical, so resource management and scheduling are challenging task, the performance evaluation, performance analysis and performance prediction have become increasingly important. In this paper, rendering pretreatment mechanism is introduced at first, then based on a detailed analysis of computing NRD (Node Reliability Degree) and multi-granularity segmentation for task, the resource scheduling strategy based on prediction is proposed, which provides a distributed solution for resource scheduling in distributed rendering environment.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131191834","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599754
H. Zhen, Liu Hong, Mujiao Fan, Chunbi Xu
Support Vector Machines (SVM) represents a new and very promising approach to pattern recognition based on small dataset. The approach is systematic and properly motivated by Statistical Learning Theory (SLT). Training involves separating the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of Structural Risk Minimization (SRM) induction principle, therefore, SVM is more generalized performance and accurate as compared to artificial neural network which embodies the Embodies Risk Minimization (ERM) principle. In this paper, according to corrosion rate complicated reflection relation with influence factors, we studied the theory and method of Support Vector Machines based the statistical learning theory and proposed a pattern recognition method based Support Vector Machine to predict corrosion rate of injecting water pipeline. The outline of the method is as follows: First, we researched the injecting water quality corrosion influence factors in given experimental zones with Gray correlation method; then we used the LibSVM software based Support Vector Machine to study the relationship of those injecting water quality corrosion influence factors, and set up the mode to predict corrosion rate of injecting water pipeline. Application and analysis of the experimental results in Shengli oilfield proved that SVM could achieve greater accuracy than the BP neural network do, which also proved that application of SVM to predict corrosion rate of injecting water pipeline, even to the other theme in petroleum engineering, is reliable, adaptable, precise and easy to operate.
{"title":"Application of statistical learning theory to predict corrosion rate of injecting water pipeline","authors":"H. Zhen, Liu Hong, Mujiao Fan, Chunbi Xu","doi":"10.1109/COGINF.2010.5599754","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599754","url":null,"abstract":"Support Vector Machines (SVM) represents a new and very promising approach to pattern recognition based on small dataset. The approach is systematic and properly motivated by Statistical Learning Theory (SLT). Training involves separating the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of Structural Risk Minimization (SRM) induction principle, therefore, SVM is more generalized performance and accurate as compared to artificial neural network which embodies the Embodies Risk Minimization (ERM) principle. In this paper, according to corrosion rate complicated reflection relation with influence factors, we studied the theory and method of Support Vector Machines based the statistical learning theory and proposed a pattern recognition method based Support Vector Machine to predict corrosion rate of injecting water pipeline. The outline of the method is as follows: First, we researched the injecting water quality corrosion influence factors in given experimental zones with Gray correlation method; then we used the LibSVM software based Support Vector Machine to study the relationship of those injecting water quality corrosion influence factors, and set up the mode to predict corrosion rate of injecting water pipeline. Application and analysis of the experimental results in Shengli oilfield proved that SVM could achieve greater accuracy than the BP neural network do, which also proved that application of SVM to predict corrosion rate of injecting water pipeline, even to the other theme in petroleum engineering, is reliable, adaptable, precise and easy to operate.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132726742","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599733
Shijia Xi, F. Sun, Jianmin Wang
In this paper, we design a cognitive crawler to dramatically reduce the website crawling cost and extract nsefnl content from web pages in an unsupervised procedure. The main idea of reducing the crawling cost is to retrieving those lately modified pages and newly added pages only. However, in reality, it is impossible for traditional crawler to judge whether a page has been modified or newly added without doing a whole crawling. We propose a method to predict those lately modified pages and newly added pages without do any actual crawling; we also find a feasible and stable feature "structure pattern" to better indicates the modified probability of certain page. In the meanwhile, we develop a hybrid clustering method combined with K-means and agglomerative hierarchical clustering to automatically find all the structure patterns in certain website. Using structure pattern, we developed an unsupervised algorithm to generate website's templates; using templates, crawler can extract useful information of web pages much more easily and precisely. We also introduce feasible formulas to predict pages' modified probabilities and crawling time intervals. To evaluate the performance of an incremental crawling algorithm, we proposed three new indicators. Using the algorithm proposed, we could extract content of pages with high performance. The experimental results illustrate that structure pattern is very useful and the performance of this cognitive crawler is quite promising and it can save huge amount of bandwidth and is qualified for different websites of various scales.
{"title":"A cognitive crawler using structure pattern for incremental crawling and content extraction","authors":"Shijia Xi, F. Sun, Jianmin Wang","doi":"10.1109/COGINF.2010.5599733","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599733","url":null,"abstract":"In this paper, we design a cognitive crawler to dramatically reduce the website crawling cost and extract nsefnl content from web pages in an unsupervised procedure. The main idea of reducing the crawling cost is to retrieving those lately modified pages and newly added pages only. However, in reality, it is impossible for traditional crawler to judge whether a page has been modified or newly added without doing a whole crawling. We propose a method to predict those lately modified pages and newly added pages without do any actual crawling; we also find a feasible and stable feature \"structure pattern\" to better indicates the modified probability of certain page. In the meanwhile, we develop a hybrid clustering method combined with K-means and agglomerative hierarchical clustering to automatically find all the structure patterns in certain website. Using structure pattern, we developed an unsupervised algorithm to generate website's templates; using templates, crawler can extract useful information of web pages much more easily and precisely. We also introduce feasible formulas to predict pages' modified probabilities and crawling time intervals. To evaluate the performance of an incremental crawling algorithm, we proposed three new indicators. Using the algorithm proposed, we could extract content of pages with high performance. The experimental results illustrate that structure pattern is very useful and the performance of this cognitive crawler is quite promising and it can save huge amount of bandwidth and is qualified for different websites of various scales.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131845712","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599814
Taiyang Liu, Shicheng Wang, Zhiguo Liu, Haibo Min, R. Li
This paper presents a combination of intelligent learning algorithm, the Support Vector Machine, and the recognition of star pattern in Celestial Navigation. Considering the star pattern recognition's character, noticing the advantages of SVM in learning competence, the paper proposes a solution to star pattern recognition with multi-kernel SVM. A multi-kernel algorithm bases on Genetic Programming is designed. Topics of multi-kernel function generation are cited in detail, and a star pattern recognition routine including an “Indexing + recognition” scheme, feature vector definition and generation, SVM training realization are designed and realized.
{"title":"Multi-kernel SVM based star pattern recognition for Celestial Navigation","authors":"Taiyang Liu, Shicheng Wang, Zhiguo Liu, Haibo Min, R. Li","doi":"10.1109/COGINF.2010.5599814","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599814","url":null,"abstract":"This paper presents a combination of intelligent learning algorithm, the Support Vector Machine, and the recognition of star pattern in Celestial Navigation. Considering the star pattern recognition's character, noticing the advantages of SVM in learning competence, the paper proposes a solution to star pattern recognition with multi-kernel SVM. A multi-kernel algorithm bases on Genetic Programming is designed. Topics of multi-kernel function generation are cited in detail, and a star pattern recognition routine including an “Indexing + recognition” scheme, feature vector definition and generation, SVM training realization are designed and realized.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115796926","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599806
Yanhong Luo, Zhenwei Liu, Dongsheng Yang
The near-optimal control problem for nonlinear descriptor systems is solved by greedy iterative dual heuristic dynamic programming (DHP) algorithm. A nonquadratic performance index is developed in order to deal with the actuator saturation problem. In this way, the greedy iterative DHP algorithm can be properly introduced to solve the optimal control problem of the original descriptor system. For facilitating the implementation of the iterative algorithm, two neural networks are utilized to approximate the costate vector and compute the optimal control policy respectively. An example is given to show the effectiveness of the proposed optimal control scheme.
{"title":"Greedy iterative DHP algorithm-based near-optimal control for a class of nonlinear descriptor systems with actuator saturating","authors":"Yanhong Luo, Zhenwei Liu, Dongsheng Yang","doi":"10.1109/COGINF.2010.5599806","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599806","url":null,"abstract":"The near-optimal control problem for nonlinear descriptor systems is solved by greedy iterative dual heuristic dynamic programming (DHP) algorithm. A nonquadratic performance index is developed in order to deal with the actuator saturation problem. In this way, the greedy iterative DHP algorithm can be properly introduced to solve the optimal control problem of the original descriptor system. For facilitating the implementation of the iterative algorithm, two neural networks are utilized to approximate the costate vector and compute the optimal control policy respectively. An example is given to show the effectiveness of the proposed optimal control scheme.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115059851","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599781
R. Jennings, Yuesen Chen
We present a class of models (h-models) in which the sentences of a propositional language are represented as simple hypergraphs on the power set of a universe, and in which entailments are modeled by relations between hypergraphs. An h-model theoretic semantic analysis of the system FDE of first-degree entailment is proposed and a non-constructive completeness proof given in the familiar idiom of Henkin models. Finally we introduce a hitherto unstudied system of analytic entailment and provide its natural h-model representation.
{"title":"Articular models for first-degree paraconsistent systems","authors":"R. Jennings, Yuesen Chen","doi":"10.1109/COGINF.2010.5599781","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599781","url":null,"abstract":"We present a class of models (h-models) in which the sentences of a propositional language are represented as simple hypergraphs on the power set of a universe, and in which entailments are modeled by relations between hypergraphs. An h-model theoretic semantic analysis of the system FDE of first-degree entailment is proposed and a non-constructive completeness proof given in the familiar idiom of Henkin models. Finally we introduce a hitherto unstudied system of analytic entailment and provide its natural h-model representation.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116272775","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599812
Zhou Yong, Gou Hong Yun
The core idea of LLE is that high-dimensional data can be seen near-linear dependency from the local perspective. That is, at a local scale, a data point can be linear representation by its nearest neighbor interpolation, but for noisy data, its local linear representation will produce deviations, as result, the error of dimension reduction is greater. To address the problem, this article determines system noise points according to the density of data set. The low density points judged to be noise points and the noise points are not used in process of dimension reduction. This method reduces the impact of noise on LLE effectively, which is proved by simulation experiment on Swissrol dataset.
{"title":"A noise-insensitive LLE algorithm","authors":"Zhou Yong, Gou Hong Yun","doi":"10.1109/COGINF.2010.5599812","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599812","url":null,"abstract":"The core idea of LLE is that high-dimensional data can be seen near-linear dependency from the local perspective. That is, at a local scale, a data point can be linear representation by its nearest neighbor interpolation, but for noisy data, its local linear representation will produce deviations, as result, the error of dimension reduction is greater. To address the problem, this article determines system noise points according to the density of data set. The low density points judged to be noise points and the noise points are not used in process of dimension reduction. This method reduces the impact of noise on LLE effectively, which is proved by simulation experiment on Swissrol dataset.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129904483","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 : 2010-07-07DOI: 10.1109/COGINF.2010.5599818
Yong Yang, Guoyin Wang, Zhiyu Zhang, Kan Tian
Emotion recognition is very important for applications of human-computer intelligent interaction. It is always performed on facial or audio information with such method as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning is a hot topic in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method which is based on selective ensemble feature selection and rough set theory is proposed, and it meets the tradeoff between the accuracy and diversity of base classifiers. Moreover, the proposed method is taken as an emotion recognition method and proved to be effective according to the simulation experiments.
{"title":"A novel emotion recognition approach based on ensemble learning and rough set theory","authors":"Yong Yang, Guoyin Wang, Zhiyu Zhang, Kan Tian","doi":"10.1109/COGINF.2010.5599818","DOIUrl":"https://doi.org/10.1109/COGINF.2010.5599818","url":null,"abstract":"Emotion recognition is very important for applications of human-computer intelligent interaction. It is always performed on facial or audio information with such method as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning is a hot topic in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method which is based on selective ensemble feature selection and rough set theory is proposed, and it meets the tradeoff between the accuracy and diversity of base classifiers. Moreover, the proposed method is taken as an emotion recognition method and proved to be effective according to the simulation experiments.","PeriodicalId":365083,"journal":{"name":"9th IEEE International Conference on Cognitive Informatics (ICCI'10)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129658422","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：