COMPUTATIONAL experiments method is an essential tool for analyzing,designing,managing,and integrating complex systems.However,a significant challenge arises in constructing agents with human-like characteristics to f...COMPUTATIONAL experiments method is an essential tool for analyzing,designing,managing,and integrating complex systems.However,a significant challenge arises in constructing agents with human-like characteristics to form an AI society.Agent modeling typically encompasses four levels:1)The autonomy features of agents,e.g.,perception,behavior,and decision-making;2)The evolutionary features of agents,e.g.,bounded rationality,heterogeneity,and learning evolution;3)The social features of agents,e.g.,interaction,cooperation,and competition;4)The emergent features of agents,e.g.,gaming with environments or regulatory strategies.Traditional modeling techniques primarily derive from ABMs(Agent-based Models)and incorporate various emerging technologies(e.g.,machine learning,big data,and social networks),which can enhance modeling capabilities,while amplifying the complexity[1].展开更多
An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variat...An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.展开更多
The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of exp...The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.展开更多
In this paper, the fuzzy-set-based structural possibility theory is investigated, and this theory can be used to deal with the subjective uncertainties in the design of engineering structures. Furthermore, a comprehen...In this paper, the fuzzy-set-based structural possibility theory is investigated, and this theory can be used to deal with the subjective uncertainties in the design of engineering structures. Furthermore, a comprehensive model of structural safety assessment, which can merge subjective uncertainties with objective uncertainties, is presented. In this model, the fuzziness of stress-strength inference model, safety margin functions of single or multiple limit-state, structural failure state and the final assessment result are taken into account. This continuous model can be transformed into an equivalent model of probability-based and solved by the present structural reliability analysis method and parallel algorithm. An example is given to show the main idea of the method presented in this paper.展开更多
Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is pre...Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.展开更多
Since plastic products are with the features as light, anticorrosive and low cost etc., that are generally used in several of tools or components. Consequently, the requirements on the quality and effectiveness in pro...Since plastic products are with the features as light, anticorrosive and low cost etc., that are generally used in several of tools or components. Consequently, the requirements on the quality and effectiveness in production are increasingly serious. However, there are many factors affecting the yield rate of injection products such as material characteristic, mold design, and manufacturing parameters etc. involved with injection machine and the whole manufacturing process. Traditionally, these factors can only be designed and adjusted by many times of trial-and-error tests. It is not only waste of time and resource, but also lack of methodology for referring. Although there are some methods as Taguchi method or neural network etc. proposed for serving and optimizing this problem, they are still insufficient for the needs. For the reasons, a method for determining the optimal parameters by the inverse model of manufacturing platform is proposed in this paper. Through the integration of inverse model basing on MANFIS and Taguchi method, inversely, the optimal manufacturing parameters can be found by using the product requirements. The effectiveness and feasibility of this proposal is confirmed through numerical studies on a real case example.展开更多
基金supported in part by National Key Research and Development Program of China(2021YFF0900800)National Natural Science Foundation of China(62472306,62441221,62206116)+2 种基金Tianjin University’s 2024 Special Project on Disciplinary Development(XKJS-2024-5-9)Tianjin University Talent Innovation Reward Program for Literature&Science Graduate Student(C1-2022-010)Shanxi Province Social Science Foundation(2020F002).
文摘COMPUTATIONAL experiments method is an essential tool for analyzing,designing,managing,and integrating complex systems.However,a significant challenge arises in constructing agents with human-like characteristics to form an AI society.Agent modeling typically encompasses four levels:1)The autonomy features of agents,e.g.,perception,behavior,and decision-making;2)The evolutionary features of agents,e.g.,bounded rationality,heterogeneity,and learning evolution;3)The social features of agents,e.g.,interaction,cooperation,and competition;4)The emergent features of agents,e.g.,gaming with environments or regulatory strategies.Traditional modeling techniques primarily derive from ABMs(Agent-based Models)and incorporate various emerging technologies(e.g.,machine learning,big data,and social networks),which can enhance modeling capabilities,while amplifying the complexity[1].
基金The National Natural Science Foundation of China under contract No.51379002the Fundamental Research Funds for the Central Universities of China under contract Nos 3132016322 and 3132016314the Applied Basic Research Project Fund of the Chinese Ministry of Transport of China under contract No.2014329225010
文摘An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.
文摘The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.
文摘In this paper, the fuzzy-set-based structural possibility theory is investigated, and this theory can be used to deal with the subjective uncertainties in the design of engineering structures. Furthermore, a comprehensive model of structural safety assessment, which can merge subjective uncertainties with objective uncertainties, is presented. In this model, the fuzziness of stress-strength inference model, safety margin functions of single or multiple limit-state, structural failure state and the final assessment result are taken into account. This continuous model can be transformed into an equivalent model of probability-based and solved by the present structural reliability analysis method and parallel algorithm. An example is given to show the main idea of the method presented in this paper.
基金supported by 2013 Comprehensive Reform Pilot of Marine Engineering Specialty(No.ZG0434)
文摘Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similaritybased inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.
文摘Since plastic products are with the features as light, anticorrosive and low cost etc., that are generally used in several of tools or components. Consequently, the requirements on the quality and effectiveness in production are increasingly serious. However, there are many factors affecting the yield rate of injection products such as material characteristic, mold design, and manufacturing parameters etc. involved with injection machine and the whole manufacturing process. Traditionally, these factors can only be designed and adjusted by many times of trial-and-error tests. It is not only waste of time and resource, but also lack of methodology for referring. Although there are some methods as Taguchi method or neural network etc. proposed for serving and optimizing this problem, they are still insufficient for the needs. For the reasons, a method for determining the optimal parameters by the inverse model of manufacturing platform is proposed in this paper. Through the integration of inverse model basing on MANFIS and Taguchi method, inversely, the optimal manufacturing parameters can be found by using the product requirements. The effectiveness and feasibility of this proposal is confirmed through numerical studies on a real case example.
