Pub Date : 2010-07-18DOI: 10.1109/FUZZY.2010.5584896
Suraphon Chumklin, S. Auephanwiriyakul, N. Theera-Umpon
Breast cancer is an important deleterious disease. Mortality rate from this cancer is effectively high and rapidly increasing. The detection at the earlier state can help to reduce the mortality rate. In this paper, we develop a system that helps radiologists to detect microcalcification in mammograms. In particular, we apply the interval type-2 fuzzy logic system with four features, i.e., B-descriptor, D-descriptor, average intensity inside boundary, and intensity difference between inside and outside boundaries. We also compare the result with the result from a type-1 Mamdani fuzzy inference system with the same set of features. The result from the type-1 fuzzy logic system yields 87.95% correct classification with 11.33 false positives per image whereas interval type-2 fuzzy logic system provides 90.36% correct classification with only 4.73 false positives per image.
{"title":"Microcalcification Detection in Mammograms Using Interval Type-2 Fuzzy Logic System","authors":"Suraphon Chumklin, S. Auephanwiriyakul, N. Theera-Umpon","doi":"10.1109/FUZZY.2010.5584896","DOIUrl":"https://doi.org/10.1109/FUZZY.2010.5584896","url":null,"abstract":"Breast cancer is an important deleterious disease. Mortality rate from this cancer is effectively high and rapidly increasing. The detection at the earlier state can help to reduce the mortality rate. In this paper, we develop a system that helps radiologists to detect microcalcification in mammograms. In particular, we apply the interval type-2 fuzzy logic system with four features, i.e., B-descriptor, D-descriptor, average intensity inside boundary, and intensity difference between inside and outside boundaries. We also compare the result with the result from a type-1 Mamdani fuzzy inference system with the same set of features. The result from the type-1 fuzzy logic system yields 87.95% correct classification with 11.33 false positives per image whereas interval type-2 fuzzy logic system provides 90.36% correct classification with only 4.73 false positives per image.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124839143","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 : 2007-08-27DOI: 10.1109/FUZZY.2007.4295592
L. Nosková, I. Perfilieva
The problem of solvability of a system of fuzzy relation equations with sup-* composition is considered in semilinear vector spaces. Based on the fact that a complete set of solutions is determined by minimal solutions, we focused on characterization of them. At first, sets of all minimal solutions of a single equation have been described under different assumptions on an underlying algebra. Dependently on the ordering of the support set, either necessary or sufficient conditions, or criteria of being a minimal solution have been obtained. Then minimal solutions of a system are build from minimal solutions of single equations.
{"title":"System of fuzzy relation equations with sup-* composition in semi-linear spaces: minimal solutions","authors":"L. Nosková, I. Perfilieva","doi":"10.1109/FUZZY.2007.4295592","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295592","url":null,"abstract":"The problem of solvability of a system of fuzzy relation equations with sup-* composition is considered in semilinear vector spaces. Based on the fact that a complete set of solutions is determined by minimal solutions, we focused on characterization of them. At first, sets of all minimal solutions of a single equation have been described under different assumptions on an underlying algebra. Dependently on the ordering of the support set, either necessary or sufficient conditions, or criteria of being a minimal solution have been obtained. Then minimal solutions of a system are build from minimal solutions of single equations.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010653","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 : 2007-08-27DOI: 10.1109/FUZZY.2007.4295486
C. Lynch, H. Hagras, V. Callaghan
Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the encountered uncertainties. Type-2 fuzzy logic controllers (FLCs) have shown that they can handle such uncertainties and give a superior performance to the existing commercial controllers. However, there are a number of computational bottlenecks that pose as significant barriers to the widespread deployment of type-2 FLCs in commercial embedded control systems. This paper explores the use of parallel hardware implementations of interval type-2 FLC as a means to eradicate these barriers thus producing bespoke co-processors for a soft core implementation of a FPGA based 32 bit RISC micro-processor. These coprocessors will perform functions such as fuzzification and type reduction and are currently utilised as part of a larger embedded interval type-2 fuzzy engine management system (T2FEMS). Numerous timing comparisons were undertaken between the co-processors and their sequential counterparts where the type-2 co-processors reduced significantly the computational cycles required by the type-2 FLC. This reduction in computational cycles allowed the T2FEMS to produce faster control responses whilst offering a superior control performance to the commercial engine management systems. Thus the proposed co-processors enable us to fully explore the potential of interval and possibly general type-2 FLCs in commercial embedded applications.
{"title":"Parallel Type-2 Fuzzy Logic Co-Processors for Engine Management","authors":"C. Lynch, H. Hagras, V. Callaghan","doi":"10.1109/FUZZY.2007.4295486","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295486","url":null,"abstract":"Marine diesel engines operate in highly dynamic and uncertain environments, hence they require robust and accurate speed controllers that can handle the encountered uncertainties. Type-2 fuzzy logic controllers (FLCs) have shown that they can handle such uncertainties and give a superior performance to the existing commercial controllers. However, there are a number of computational bottlenecks that pose as significant barriers to the widespread deployment of type-2 FLCs in commercial embedded control systems. This paper explores the use of parallel hardware implementations of interval type-2 FLC as a means to eradicate these barriers thus producing bespoke co-processors for a soft core implementation of a FPGA based 32 bit RISC micro-processor. These coprocessors will perform functions such as fuzzification and type reduction and are currently utilised as part of a larger embedded interval type-2 fuzzy engine management system (T2FEMS). Numerous timing comparisons were undertaken between the co-processors and their sequential counterparts where the type-2 co-processors reduced significantly the computational cycles required by the type-2 FLC. This reduction in computational cycles allowed the T2FEMS to produce faster control responses whilst offering a superior control performance to the commercial engine management systems. Thus the proposed co-processors enable us to fully explore the potential of interval and possibly general type-2 FLCs in commercial embedded applications.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134334780","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295542
P. Codara, O. D'Antona, V. Marra
Godel propositional logic is the logic of the minimum triangular norm, and can be axiomatized as propositional intuitionistic logic augmented by the prelinearity axiom (alpha rarr beta) V (beta rarr alpha). Its algebraic counterpart is the subvariety of Heyting algebras satisfying prelinearity, known as Godel algebras. A Delannoy path is a lattice path in Z2 that only uses northward, eastward, and northeastward steps. We establish a representation theorem for free n-generated Godel algebras in terms of the Boolean n-cube {0,1}n, enriched by suitably generalized Delannoy paths.
哥德尔命题逻辑是最小三角范数的逻辑，可以公理化为由预线性公理(α rarr β) V (β rarr α)扩充的命题直觉逻辑。它的代数对应物是满足预线性的Heyting代数的子变种，称为哥德尔代数。Delannoy路径是Z2中的格子路径，只使用向北、向东和东北的台阶。我们建立了一个用布尔n立方{0,1}n表示的自由n生成哥德尔代数的表示定理，该定理由适当的广义Delannoy路径充实。
{"title":"Propositional Gödel Logic and Delannoy Paths","authors":"P. Codara, O. D'Antona, V. Marra","doi":"10.1109/FUZZY.2007.4295542","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295542","url":null,"abstract":"Godel propositional logic is the logic of the minimum triangular norm, and can be axiomatized as propositional intuitionistic logic augmented by the prelinearity axiom (alpha rarr beta) V (beta rarr alpha). Its algebraic counterpart is the subvariety of Heyting algebras satisfying prelinearity, known as Godel algebras. A Delannoy path is a lattice path in Z2 that only uses northward, eastward, and northeastward steps. We establish a representation theorem for free n-generated Godel algebras in terms of the Boolean n-cube {0,1}n, enriched by suitably generalized Delannoy paths.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124261080","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295582
A. Sala, J. Ramirez, B. Tormos, Manuel Yago
This paper discusses a knowledge-base encoding methodology for diagnostic tasks. It transform "expert"-provided rules into algebraic expressions so inference of the "possible" disorders is carried out via associated constrained optimisation problems. In this way, the need of conventional fuzzy inference systems or "uncertain"-logic schemes is no longer present in the particular setting in this paper. An oil-analysis diagnosis case study is presented as an application example, with actual experimental data. The problem is solved by efficient linear programming tools, in principle able to cope with large-scale problems. The only software used was Mathematica reg 5.2.
{"title":"An Optimization Approach to Fuzzy Diagnosis: Oil Analysis Application","authors":"A. Sala, J. Ramirez, B. Tormos, Manuel Yago","doi":"10.1109/FUZZY.2007.4295582","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295582","url":null,"abstract":"This paper discusses a knowledge-base encoding methodology for diagnostic tasks. It transform \"expert\"-provided rules into algebraic expressions so inference of the \"possible\" disorders is carried out via associated constrained optimisation problems. In this way, the need of conventional fuzzy inference systems or \"uncertain\"-logic schemes is no longer present in the particular setting in this paper. An oil-analysis diagnosis case study is presented as an application example, with actual experimental data. The problem is solved by efficient linear programming tools, in principle able to cope with large-scale problems. The only software used was Mathematica reg 5.2.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117155412","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295560
G. Vachkov
This paper presents a method for extraction of knowledge from a real time process by using the so called evolving neural model (ENM). The ENM learns from real time data streams by a specially proposed evolving unsupervised learning algorithm. This algorithm is further development of the off-line neural-gas learning with a different way of updating the neurons. It also uses a special logic to prevent the neurons from gradually becoming "idling" during the evolutions. Two characteristics of the ENM, namely the center-of-gravity COG and the weighted average size WAS of the model are further used to capture the general trends of operation changes in the process. Big changes serve as indication for acquisition of a new knowledge about the process that should be saved in the knowledge base. Normalized data taken from different operations of a diesel engine for hydraulic excavator are used to test and verify the merits of the proposed learning algorithm and the whole knowledge acquisition method.
{"title":"Real Time Knowledge Acquisition Based on Unsupervised Learning of Evolving Neural Models","authors":"G. Vachkov","doi":"10.1109/FUZZY.2007.4295560","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295560","url":null,"abstract":"This paper presents a method for extraction of knowledge from a real time process by using the so called evolving neural model (ENM). The ENM learns from real time data streams by a specially proposed evolving unsupervised learning algorithm. This algorithm is further development of the off-line neural-gas learning with a different way of updating the neurons. It also uses a special logic to prevent the neurons from gradually becoming \"idling\" during the evolutions. Two characteristics of the ENM, namely the center-of-gravity COG and the weighted average size WAS of the model are further used to capture the general trends of operation changes in the process. Big changes serve as indication for acquisition of a new knowledge about the process that should be saved in the knowledge base. Normalized data taken from different operations of a diesel engine for hydraulic excavator are used to test and verify the merits of the proposed learning algorithm and the whole knowledge acquisition method.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117172442","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295651
M. Hassine, Habib Ounelli, A. Touzi, José Galindo
Fuzzy databases (DB) have been introduced to deal with uncertain or incomplete information in many applications demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate the flexible querying, to handle imprecise data or to use fuzzy data mining tools, minimizing the transformation costs. The best solution is to offer a smoothly migration toward this technology. This paper presents a new approach for the migration from relational DB (RDB) towards fuzzy relational DB (FRDB). The goal of this migration is to permit an easy mapping of the existing data, schemas and programs, while integrating the different fuzzy concepts. This paper presents two migration strategies. The first one, named "partial migration", is useful to introduce fuzzy queries in RDB without changing existing data. The "total migration" is the second migration strategy which offers in addition to the flexible querying, the possibility to store imprecise data. This strategy requires a modification of schemas, data and eventually programs.
{"title":"A Migration Approach from Crisp Databases to Fuzzy Databases","authors":"M. Hassine, Habib Ounelli, A. Touzi, José Galindo","doi":"10.1109/FUZZY.2007.4295651","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295651","url":null,"abstract":"Fuzzy databases (DB) have been introduced to deal with uncertain or incomplete information in many applications demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate the flexible querying, to handle imprecise data or to use fuzzy data mining tools, minimizing the transformation costs. The best solution is to offer a smoothly migration toward this technology. This paper presents a new approach for the migration from relational DB (RDB) towards fuzzy relational DB (FRDB). The goal of this migration is to permit an easy mapping of the existing data, schemas and programs, while integrating the different fuzzy concepts. This paper presents two migration strategies. The first one, named \"partial migration\", is useful to introduce fuzzy queries in RDB without changing existing data. The \"total migration\" is the second migration strategy which offers in addition to the flexible querying, the possibility to store imprecise data. This strategy requires a modification of schemas, data and eventually programs.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124723836","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295667
M. Murakami, N. Honda
The ink drop spread (IDS) method is a modeling technique that is proposed as a new paradigm of soft computing. The structure of IDS models is similar to that of artificial neural networks (ANNs): they comprise distributed processing units. The beneficial property of fault tolerance is obtained when such parallel processing networks are implemented with dedicated hardware. Among the ANNs, radial basis function networks (RBFNs) are known to possess superior fault tolerance. This study evaluates the fault tolerances of the IDS models and RBFNs using the approximation of continuous functions. The experimental results demonstrate that the IDS models are highly fault tolerant in comparison with the RBFNs.
{"title":"Experimental Investigation of the Fault Tolerance of IDS Models","authors":"M. Murakami, N. Honda","doi":"10.1109/FUZZY.2007.4295667","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295667","url":null,"abstract":"The ink drop spread (IDS) method is a modeling technique that is proposed as a new paradigm of soft computing. The structure of IDS models is similar to that of artificial neural networks (ANNs): they comprise distributed processing units. The beneficial property of fault tolerance is obtained when such parallel processing networks are implemented with dedicated hardware. Among the ANNs, radial basis function networks (RBFNs) are known to possess superior fault tolerance. This study evaluates the fault tolerances of the IDS models and RBFNs using the approximation of continuous functions. The experimental results demonstrate that the IDS models are highly fault tolerant in comparison with the RBFNs.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125137820","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295610
S. Vieira, J. Sousa, F. Durão
The process under study has four manipulating variables: feed flow rate, washing water, air and rejected flow rates. Column flotation process is an example of a complex, nonlinear and multivariable system. Fuzzy modeling is a well-known modeling technique, which has been applied to complex and nonlinear processes. Fuzzy multivariable modeling with fuzzy model predictive control is used in this paper to control a laboratory setup of a flotation column. Moreover, the control strategy is tested in real-time. Results show that the applied methods led to a good control performance of all the controlled variables.
{"title":"Real-Time Fuzzy Predictive Control of a Column Flotation Process","authors":"S. Vieira, J. Sousa, F. Durão","doi":"10.1109/FUZZY.2007.4295610","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295610","url":null,"abstract":"The process under study has four manipulating variables: feed flow rate, washing water, air and rejected flow rates. Column flotation process is an example of a complex, nonlinear and multivariable system. Fuzzy modeling is a well-known modeling technique, which has been applied to complex and nonlinear processes. Fuzzy multivariable modeling with fuzzy model predictive control is used in this paper to control a laboratory setup of a flotation column. Moreover, the control strategy is tested in real-time. Results show that the applied methods led to a good control performance of all the controlled variables.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116061402","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295637
Estevam Hruschka, H. Camargo, M. E. Cintra, M. C. Nicoletti
Traditional algorithms for learning Bayesian classifiers (BCs) from data are known to induce accurate classification models. However, when using these algorithms, two main concerns should be considered: i) they require qualitative data and ii) generally the induced models are not easily comprehensible by human beings. This paper deals with the two above issues by proposing a hybrid method named BayesFuzzy that learns from quantitative data and induces a fuzzy rule based model that enhances comprehensibility. BayesFuzzy has been implemented as an automatic system that combines a fuzzy strategy, for transforming numerical data into qualitative information, with a Bayes-based approach for inducing rules. Promising empirical results of the use of the BayesFuzzy system in four knowledge domains are presented and discussed.
{"title":"BayesFuzzy: using a Bayesian Classifier to Induce a Fuzzy Rule Base","authors":"Estevam Hruschka, H. Camargo, M. E. Cintra, M. C. Nicoletti","doi":"10.1109/FUZZY.2007.4295637","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295637","url":null,"abstract":"Traditional algorithms for learning Bayesian classifiers (BCs) from data are known to induce accurate classification models. However, when using these algorithms, two main concerns should be considered: i) they require qualitative data and ii) generally the induced models are not easily comprehensible by human beings. This paper deals with the two above issues by proposing a hybrid method named BayesFuzzy that learns from quantitative data and induces a fuzzy rule based model that enhances comprehensibility. BayesFuzzy has been implemented as an automatic system that combines a fuzzy strategy, for transforming numerical data into qualitative information, with a Bayes-based approach for inducing rules. Promising empirical results of the use of the BayesFuzzy system in four knowledge domains are presented and discussed.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116527532","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：