Pub Date : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226808
D. Gayme, S. Menon, C. Ball, D. Mukavetz, E. Nwadiogbu
In this paper, we present a fuzzy logic based method of fault detection and diagnosis in gas turbine engines. The fuzzy logic system rule base is derived using heuristics extracted from designed experiments and flight data representing component performance changes due to field service degradation. The fuzzy logic rule based method incorporates both sensed engine parameters that represent non-deteriorated engine operation and fault conditions related to engine performance such as high pressure turbine, high pressure compressor and combustor deterioration. The fuzzy logic system is evaluated using residuals calculated based on both empirical models as inputs. The efficacy of the fuzzy logic system in detecting and diagnosing engine faults is demonstrated using field test data. We also examine performance robustness in the presence of varying levels of sensor noise and measurement errors.
{"title":"Fault detection and diagnosis in turbine engines using fuzzy logic","authors":"D. Gayme, S. Menon, C. Ball, D. Mukavetz, E. Nwadiogbu","doi":"10.1109/NAFIPS.2003.1226808","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226808","url":null,"abstract":"In this paper, we present a fuzzy logic based method of fault detection and diagnosis in gas turbine engines. The fuzzy logic system rule base is derived using heuristics extracted from designed experiments and flight data representing component performance changes due to field service degradation. The fuzzy logic rule based method incorporates both sensed engine parameters that represent non-deteriorated engine operation and fault conditions related to engine performance such as high pressure turbine, high pressure compressor and combustor deterioration. The fuzzy logic system is evaluated using residuals calculated based on both empirical models as inputs. The efficacy of the fuzzy logic system in detecting and diagnosing engine faults is demonstrated using field test data. We also examine performance robustness in the presence of varying levels of sensor noise and measurement errors.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115193234","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226836
Haiyun Bian, Lawrence J. Mazlack
This paper proposes a new fuzzy-rough nearest-neighbor (NN) approach based on the fuzzy-rough sets theory. This approach is more suitable to be used under partially exposed and unbalanced data set compared with crisp NN and fuzzy NN approach. Then the new method is applied to China listed company financial distress prediction, a typical classification task under partially exposed and unbalanced learning space. Results suggest that the compared with crisp and fuzzy nearest neighbor classification methods, this method provides more accurate prediction result under this research design.
{"title":"Fuzzy-rough nearest-neighbor classification approach","authors":"Haiyun Bian, Lawrence J. Mazlack","doi":"10.1109/NAFIPS.2003.1226836","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226836","url":null,"abstract":"This paper proposes a new fuzzy-rough nearest-neighbor (NN) approach based on the fuzzy-rough sets theory. This approach is more suitable to be used under partially exposed and unbalanced data set compared with crisp NN and fuzzy NN approach. Then the new method is applied to China listed company financial distress prediction, a typical classification task under partially exposed and unbalanced learning space. Results suggest that the compared with crisp and fuzzy nearest neighbor classification methods, this method provides more accurate prediction result under this research design.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114069531","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226816
A. Nazemi, M. Akbarzadeh-T., S. Hosseini
By increasing the velocity of flow in coarse grain materials, local turbulences are often imposed to the flow. As a result, the flow regime through rockfill structures deviates from linear Darcy law; and nonlinear or non-Darcy flow equations will be applicable. Even though the structures of these nonlinear equations have some physical justifications, they still need empirical studies to estimate parameters of these equations. Hence there is a great deal of uncertainty as an inherent part of the estimation process. In this paper we investigate fuzzy systems paradigm to combine three of the most commonly validated and utilized empirical solutions in the current literature. In this way, the results of the three empirical equations serve as inputs, and the combination framework serve as fusion algorithm. The results show that when learning injected to fuzzy logic based models, the system provides a powerful solution with a strong ability to track reality. Specifically, this paper concludes that ANFIS provide accurate combination framework with greatest performance among the considered conventional alternatives as well as Mamdani structures.
{"title":"Fuzzy systems as a fusion framework for describing nonlinear flow in porous media","authors":"A. Nazemi, M. Akbarzadeh-T., S. Hosseini","doi":"10.1109/NAFIPS.2003.1226816","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226816","url":null,"abstract":"By increasing the velocity of flow in coarse grain materials, local turbulences are often imposed to the flow. As a result, the flow regime through rockfill structures deviates from linear Darcy law; and nonlinear or non-Darcy flow equations will be applicable. Even though the structures of these nonlinear equations have some physical justifications, they still need empirical studies to estimate parameters of these equations. Hence there is a great deal of uncertainty as an inherent part of the estimation process. In this paper we investigate fuzzy systems paradigm to combine three of the most commonly validated and utilized empirical solutions in the current literature. In this way, the results of the three empirical equations serve as inputs, and the combination framework serve as fusion algorithm. The results show that when learning injected to fuzzy logic based models, the system provides a powerful solution with a strong ability to track reality. Specifically, this paper concludes that ANFIS provide accurate combination framework with greatest performance among the considered conventional alternatives as well as Mamdani structures.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116316332","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226755
O. Ciftcioglu
Functional equivalence of radial basis function (RBF) networks and a class of fuzzy inference systems is considered. The class of fuzzy systems based on the Takagi-Sugeno model is referred to as TS-model of fuzzy inference. From the abstract mathematical viewpoint the functional equivalence between radial basis function networks and fuzzy inference systems is already shown. However, from the viewpoint of realisation of the models with data, the difference between the model performances is observed. The research makes comparisons between an RBF model and its fuzzy model counterpart and qualifications on the equivalence being observed are reported.
{"title":"On the equivalence of a RBF-like network to TS fuzzy systems: a GA approach for TS-network","authors":"O. Ciftcioglu","doi":"10.1109/NAFIPS.2003.1226755","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226755","url":null,"abstract":"Functional equivalence of radial basis function (RBF) networks and a class of fuzzy inference systems is considered. The class of fuzzy systems based on the Takagi-Sugeno model is referred to as TS-model of fuzzy inference. From the abstract mathematical viewpoint the functional equivalence between radial basis function networks and fuzzy inference systems is already shown. However, from the viewpoint of realisation of the models with data, the difference between the model performances is observed. The research makes comparisons between an RBF model and its fuzzy model counterpart and qualifications on the equivalence being observed are reported.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126510856","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226841
M. Sicilia
Personalized hypermedia and Web systems are confronted with the challenge of inferring complex user traits like knowledge or preferences from very basic data like the 'clickstream' or ordinal-scale ratings. In consequence, the resulting user models are only approximations that must be subject to continuous revision. Nonetheless, knowledge revision procedures are rarely made explicit in existing adaptive systems and models. In this paper, we sketch a frame-work for user modeling structured around revision and refutation of provisional conjectures drawn from basic data. This model can be used as a reference framework for the evaluation of the adequacy of the inferences carried out by existing adaptive hypermedia systems. Additionally, a number of existing adaptive systems is reviewed according to the core concepts of this model. It is also argued that Possibility Theory can be used to generalize different forms of uncertainty that are not precisely justified in existing applications.
{"title":"Observing web users: conjecturing and refutation on partial evidence","authors":"M. Sicilia","doi":"10.1109/NAFIPS.2003.1226841","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226841","url":null,"abstract":"Personalized hypermedia and Web systems are confronted with the challenge of inferring complex user traits like knowledge or preferences from very basic data like the 'clickstream' or ordinal-scale ratings. In consequence, the resulting user models are only approximations that must be subject to continuous revision. Nonetheless, knowledge revision procedures are rarely made explicit in existing adaptive systems and models. In this paper, we sketch a frame-work for user modeling structured around revision and refutation of provisional conjectures drawn from basic data. This model can be used as a reference framework for the evaluation of the adequacy of the inferences carried out by existing adaptive hypermedia systems. Additionally, a number of existing adaptive systems is reviewed according to the core concepts of this model. It is also argued that Possibility Theory can be used to generalize different forms of uncertainty that are not precisely justified in existing applications.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126389181","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226842
A. Khosla, S. Kumar, K. K. Aggarwal
This paper presents the identification of fuzzy controller for rapid Nickel-Cadmium (Ni-Cd) batteries charger by applying fuzzy c-means (FCM) clustering algorithm on the input-output training data. The identification of fuzzy model using input-output data consists of two parts: structure identification and parameter estimation. Structure identification involves the determination of antecedent and consequent variables and in parameter estimation step, antecedents' membership functions and rule consequents are determined. Fuzzy clustering is used to partition the training data into regions that leads to creation of local linear models expressed by fuzzy rules. The data for the batteries charger has been obtained through experimentation with an objective to charge the batteries as fast as possible. For the premise part identification, the input space is partitioned by FCM clustering and the consequent parameters for each rule are calculated as least-square estimate. The Takagi-Sugeno-Kang (TSK) model obtained through FCM clustering algorithm is further fine tuned through hybrid learning.
{"title":"Identification of fuzzy controller for rapid Nickel-Cadmium batteries charger through fuzzy c-means clustering algorithm","authors":"A. Khosla, S. Kumar, K. K. Aggarwal","doi":"10.1109/NAFIPS.2003.1226842","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226842","url":null,"abstract":"This paper presents the identification of fuzzy controller for rapid Nickel-Cadmium (Ni-Cd) batteries charger by applying fuzzy c-means (FCM) clustering algorithm on the input-output training data. The identification of fuzzy model using input-output data consists of two parts: structure identification and parameter estimation. Structure identification involves the determination of antecedent and consequent variables and in parameter estimation step, antecedents' membership functions and rule consequents are determined. Fuzzy clustering is used to partition the training data into regions that leads to creation of local linear models expressed by fuzzy rules. The data for the batteries charger has been obtained through experimentation with an objective to charge the batteries as fast as possible. For the premise part identification, the input space is partitioned by FCM clustering and the consequent parameters for each rule are calculated as least-square estimate. The Takagi-Sugeno-Kang (TSK) model obtained through FCM clustering algorithm is further fine tuned through hybrid learning.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126178734","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226803
W. W. Melek, A. Goldenberg
For pt.A, see ibid., p.2-7 (2003). In part A of this paper, we developed an intelligent neurofuzzy architecture that can be easily used in the presence of dynamic parameter uncertainty and unmodeled disturbances to control modular and reconfigurable manipulators. The proposed architecture has several levels of hierarchy built on top of a conventional PID controller. The present part B of the paper discussed systematic guidelines to design the skill module of the neurofuzzy control. Such module is used to update the adaptive control parameters of the neurofuzzy architecture when the robotic arm is reconfigured. Furthermore, in this part B of the paper, we present experiments that where conducted on a modular and reconfigurable robot. Some of the most notably significant experimental results are reported.
{"title":"Hierarchical intelligent control of modular manipulators Part B: Reconfigurability and experimental validation","authors":"W. W. Melek, A. Goldenberg","doi":"10.1109/NAFIPS.2003.1226803","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226803","url":null,"abstract":"For pt.A, see ibid., p.2-7 (2003). In part A of this paper, we developed an intelligent neurofuzzy architecture that can be easily used in the presence of dynamic parameter uncertainty and unmodeled disturbances to control modular and reconfigurable manipulators. The proposed architecture has several levels of hierarchy built on top of a conventional PID controller. The present part B of the paper discussed systematic guidelines to design the skill module of the neurofuzzy control. Such module is used to update the adaptive control parameters of the neurofuzzy architecture when the robotic arm is reconfigured. Furthermore, in this part B of the paper, we present experiments that where conducted on a modular and reconfigurable robot. Some of the most notably significant experimental results are reported.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125529789","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226749
O. Castillo, P. Melin
We describe in this paper a proposed new approach for fuzzy inference in intuitionistic fuzzy systems. The new approach combines the outputs of two traditional fuzzy systems to obtain the final conclusion of the intuitionistic fuzzy system. The new method provides an efficient way of calculating the output of an intuitionistic fuzzy system, and as consequence can be applied to real-world problems in many areas of application. We illustrate the new approach with a simple example to motivate the ideas behind this work. We also illustrate the new approach for fuzzy inference with a more complicated example of monitoring a non-linear dynamic plant.
{"title":"A new method for fuzzy inference in intuitionistic fuzzy systems","authors":"O. Castillo, P. Melin","doi":"10.1109/NAFIPS.2003.1226749","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226749","url":null,"abstract":"We describe in this paper a proposed new approach for fuzzy inference in intuitionistic fuzzy systems. The new approach combines the outputs of two traditional fuzzy systems to obtain the final conclusion of the intuitionistic fuzzy system. The new method provides an efficient way of calculating the output of an intuitionistic fuzzy system, and as consequence can be applied to real-world problems in many areas of application. We illustrate the new approach with a simple example to motivate the ideas behind this work. We also illustrate the new approach for fuzzy inference with a more complicated example of monitoring a non-linear dynamic plant.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125559283","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226785
C. Helgason, T. Jobe
As a natural sequel to our investigations in the application of the fuzzy model to clinical stroke diagnosis and treatment, we have developed direct measures of causality sensitive to initial conditions of the individual patient with stroke and are based on the fuzzy measure M of cardinality and the fuzzy subsethood theorem defined Kosko. In this paper we show and measure the effect of a previously un-represented element (dimension) on our causal clinical efficiency measure K sensitive to unique initial and final conditions. We show this by adding the new element to the patient as fuzzy set. Again, our causal measures are based on the same measure of fuzzy cardinality M and the fuzzy subsethood theorem. The definition of causal measures for Formal Causal Ground (FCG), Clinical Causal Effect (CCE) and K can be found. Two separate measures for K are calculated. The clinical efficiency of Foltx when the genetic mutation is included as information in the patient fuzzy set, and when it is not. The effect of the addition of elemental information as variable in the patient's fuzzy set is discussed.
{"title":"How the number of measured dimensions affects fuzzy causal measures of vitamin therapy for hyperhomocysteinemia in stroke patients","authors":"C. Helgason, T. Jobe","doi":"10.1109/NAFIPS.2003.1226785","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226785","url":null,"abstract":"As a natural sequel to our investigations in the application of the fuzzy model to clinical stroke diagnosis and treatment, we have developed direct measures of causality sensitive to initial conditions of the individual patient with stroke and are based on the fuzzy measure M of cardinality and the fuzzy subsethood theorem defined Kosko. In this paper we show and measure the effect of a previously un-represented element (dimension) on our causal clinical efficiency measure K sensitive to unique initial and final conditions. We show this by adding the new element to the patient as fuzzy set. Again, our causal measures are based on the same measure of fuzzy cardinality M and the fuzzy subsethood theorem. The definition of causal measures for Formal Causal Ground (FCG), Clinical Causal Effect (CCE) and K can be found. Two separate measures for K are calculated. The clinical efficiency of Foltx when the genetic mutation is included as information in the patient fuzzy set, and when it is not. The effect of the addition of elemental information as variable in the patient's fuzzy set is discussed.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115262218","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 : 2003-07-24DOI: 10.1109/NAFIPS.2003.1226760
V. Cross, T. Sudkamp
Several techniques have been proposed for making inferences using the information contained in an incomplete rule base. These fall into three major categories; interpolative reasoning, analogical inference, and rule base completion. Interpolation uses the relative locations and shapes of the fuzzy sets in a pair of bounding rules to construct an output when an input occurs between the antecedents of the bounding rules. Analogical inference employs similarity to a single proximate example to produce the output. Completion generates a set of rules whose antecedents link the antecedents of the bounding rules. In this paper we compare the underlying principles of interpolation, analogical inference, and rule base completion. In addition, we propose a completion technique that partitions the domain between the antecedents of the bounding rules. The size of the partition is determined by the variation between fuzzy regions specified by the bounding rules.
{"title":"Sparse data and rule base completion","authors":"V. Cross, T. Sudkamp","doi":"10.1109/NAFIPS.2003.1226760","DOIUrl":"https://doi.org/10.1109/NAFIPS.2003.1226760","url":null,"abstract":"Several techniques have been proposed for making inferences using the information contained in an incomplete rule base. These fall into three major categories; interpolative reasoning, analogical inference, and rule base completion. Interpolation uses the relative locations and shapes of the fuzzy sets in a pair of bounding rules to construct an output when an input occurs between the antecedents of the bounding rules. Analogical inference employs similarity to a single proximate example to produce the output. Completion generates a set of rules whose antecedents link the antecedents of the bounding rules. In this paper we compare the underlying principles of interpolation, analogical inference, and rule base completion. In addition, we propose a completion technique that partitions the domain between the antecedents of the bounding rules. The size of the partition is determined by the variation between fuzzy regions specified by the bounding rules.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125262670","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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