Converting between “fuzzy concept” and “numerical value” in computer aided assessment is rather difficult in many applications. This paper presents a LVQ neural network paradigm for sensory evaluation. This intell...Converting between “fuzzy concept” and “numerical value” in computer aided assessment is rather difficult in many applications. This paper presents a LVQ neural network paradigm for sensory evaluation. This intelligent approach utilizes predefined class information for supervised learning in order to solve the converting problem and keep the fuzziness and imprecision of the whole sensory information. The method is validated by the experiment on stimulation evaluation of cigarette sensory.展开更多
Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is ...Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.展开更多
The Pathfinder paradigm has been used in generating and analyzing graph models that support clustering similar concepts and minimum-cost paths to provide an associative network structure within a domain. The co-occurr...The Pathfinder paradigm has been used in generating and analyzing graph models that support clustering similar concepts and minimum-cost paths to provide an associative network structure within a domain. The co-occurrence pathfinder network （ CPFN ） extends the traditional pathfinder paradigm so that co-occurring concepts can be calculated at each sampling time. Existing algorithms take O（n（s）） time to calculate the pathfinder network （PFN） at each sampling time for a non-completed input graph of a CPFN （r = ∞, q = n - 1）, where n is the number of nodes in the input graph, r is the Minkowski exponent and q is the maximum number of links considered in finding a minimum cost path between vertices. To reduce the complexity of calculating the CPFN, we propose a greedy based algorithm, MEC（G） algorithm, which takes shortcuts to avoid unnecessary steps in the existing algorithms, to correctly calculate a CPFN （r = ∞, q= n - 1） in O（klogk） time where k is the number of edges of the input graph. Our example demonstrates the efficiency and correctness of the proposed MEC（G） algorithm, confirming our mathematic analysis on this algorithm.展开更多
Reinforcement learning is an excellent approach which is used in artificial intelligence,automatic control, etc. However, ordinary reinforcement learning algorithm, such as Q-learning with lookup table cannot cope wit...Reinforcement learning is an excellent approach which is used in artificial intelligence,automatic control, etc. However, ordinary reinforcement learning algorithm, such as Q-learning with lookup table cannot cope with extremely complex and dynamic environment due to the huge state space. To reduce the state space, modular neural network Q-learning algorithm is proposed, which combines Q-learning algorithm with neural network and module method. Forward feedback neural network, Elman neural network and radius-basis neural network are separately employed to construct such algorithm. It is revealed that Elman neural network Q-learning algorithm has the best performance under the condition that the same neural network training method, i.e. gradient descent error back-propagation algorithm is applied.展开更多
As the product of the mutual infiltration of the various disciplines such as the control theory, information theory, system theory, computer science, physiology, psychology, mathematics, philosophy and so on, the rese...As the product of the mutual infiltration of the various disciplines such as the control theory, information theory, system theory, computer science, physiology, psychology, mathematics, philosophy and so on, the research field of the theory and application of artificial intelligence technology covers almost all the areas of human activity. In recent years, the rapid development of computer network technology produces and drives a batch of new scientific research fields. Among them, the application of artificial intelligence in the computer network technology is a hot topic which is academically and technically strong and can bring obvious economic benefit.展开更多
This highly interdisciplinary research paper discusses some new insights into the fundamentalproperties of information-rich social networks.It mainly focuses on:i)Postulating the generalproperties of an information-ba...This highly interdisciplinary research paper discusses some new insights into the fundamentalproperties of information-rich social networks.It mainly focuses on:i)Postulating the generalproperties of an information-based networking economy;ii)Modeling emergent and self-organizing featuresof social networks;iii)Discussing how to simulate complex social systems using a field-basedapproach and multi-agent platforms.Additionally,this paper gives some ideas of how to construct avirtual field-based communications network of intelligent agents using currently available computationalintelligence methods.A new simulation paradigm offers some useful concepts to transform multidimensionalfactor space(representing a multiplicity of phenomenal forms and interactions)into the mostuniversal spectral coding system.This paper gives some ideas of how not only the communicationmechanism but also the social agents can be simulated as oscillating processes.展开更多
文摘Converting between “fuzzy concept” and “numerical value” in computer aided assessment is rather difficult in many applications. This paper presents a LVQ neural network paradigm for sensory evaluation. This intelligent approach utilizes predefined class information for supervised learning in order to solve the converting problem and keep the fuzziness and imprecision of the whole sensory information. The method is validated by the experiment on stimulation evaluation of cigarette sensory.
文摘Traffic monitoring is of major importance for enforcing traffic management policies.To accomplish this task,the detection of vehicle can be achieved by exploiting image analysis techniques.In this paper,a solution is presented to obtain various traffic parameters through vehicular video detection system(VVDS).VVDS exploits the algorithm based on virtual loops to detect moving vehicle in real time.This algorithm uses the background differencing method,and vehicles can be detected through luminance difference of pixels between background image and current image.Furthermore a novel technology named as spatio-temporal image sequences analysis is applied to background differencing to improve detection accuracy.Then a hardware implementation of a digital signal processing (DSP) based board is described in detail and the board can simultaneously process four-channel video from different cameras. The benefit of usage of DSP is that images of a roadway can be processed at frame rate due to DSP′s high performance.In the end,VVDS is tested on real-world scenes and experiment results show that the system is both fast and robust to the surveillance of transportation.
文摘The Pathfinder paradigm has been used in generating and analyzing graph models that support clustering similar concepts and minimum-cost paths to provide an associative network structure within a domain. The co-occurrence pathfinder network （ CPFN ） extends the traditional pathfinder paradigm so that co-occurring concepts can be calculated at each sampling time. Existing algorithms take O（n（s）） time to calculate the pathfinder network （PFN） at each sampling time for a non-completed input graph of a CPFN （r = ∞, q = n - 1）, where n is the number of nodes in the input graph, r is the Minkowski exponent and q is the maximum number of links considered in finding a minimum cost path between vertices. To reduce the complexity of calculating the CPFN, we propose a greedy based algorithm, MEC（G） algorithm, which takes shortcuts to avoid unnecessary steps in the existing algorithms, to correctly calculate a CPFN （r = ∞, q= n - 1） in O（klogk） time where k is the number of edges of the input graph. Our example demonstrates the efficiency and correctness of the proposed MEC（G） algorithm, confirming our mathematic analysis on this algorithm.
文摘Reinforcement learning is an excellent approach which is used in artificial intelligence,automatic control, etc. However, ordinary reinforcement learning algorithm, such as Q-learning with lookup table cannot cope with extremely complex and dynamic environment due to the huge state space. To reduce the state space, modular neural network Q-learning algorithm is proposed, which combines Q-learning algorithm with neural network and module method. Forward feedback neural network, Elman neural network and radius-basis neural network are separately employed to construct such algorithm. It is revealed that Elman neural network Q-learning algorithm has the best performance under the condition that the same neural network training method, i.e. gradient descent error back-propagation algorithm is applied.
文摘As the product of the mutual infiltration of the various disciplines such as the control theory, information theory, system theory, computer science, physiology, psychology, mathematics, philosophy and so on, the research field of the theory and application of artificial intelligence technology covers almost all the areas of human activity. In recent years, the rapid development of computer network technology produces and drives a batch of new scientific research fields. Among them, the application of artificial intelligence in the computer network technology is a hot topic which is academically and technically strong and can bring obvious economic benefit.
基金supported by EU-Funded Research Project Reg. under Grant No.S-VP2-1.3-UM-01-K-01-065
文摘This highly interdisciplinary research paper discusses some new insights into the fundamentalproperties of information-rich social networks.It mainly focuses on:i)Postulating the generalproperties of an information-based networking economy;ii)Modeling emergent and self-organizing featuresof social networks;iii)Discussing how to simulate complex social systems using a field-basedapproach and multi-agent platforms.Additionally,this paper gives some ideas of how to construct avirtual field-based communications network of intelligent agents using currently available computationalintelligence methods.A new simulation paradigm offers some useful concepts to transform multidimensionalfactor space(representing a multiplicity of phenomenal forms and interactions)into the mostuniversal spectral coding system.This paper gives some ideas of how not only the communicationmechanism but also the social agents can be simulated as oscillating processes.
