In order to detect the sleep spindles simply and efficiently, a novel time-domain approach to detect sleep spindles based on the principles of visual organization is proposed. The code idea of the visual organization ...In order to detect the sleep spindles simply and efficiently, a novel time-domain approach to detect sleep spindles based on the principles of visual organization is proposed. The code idea of the visual organization is to organize the primary visual elements according to some rules of organization, and to form a more meaningful object of visual processing, as the input of next process. After the collected EEG is processed with the merging algorithm based on the principle of visual organization, it can extract the time-domain feature frequency and duration time better. Use these features with a simple algorithm to detect spindles achieving sensitivity of 92.5% and specificity of 98.1%, which verifies the validity of this method to detect the sleep spindles.展开更多
The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be mode...The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be modeled using the Maximum Ordinality Principle and its associated formal language, known as the “Incipient” Differential Calculus (IDC).展开更多
With the explosion of services in grid environment, it＇s necessary to develop a mechanism which has the ability of discovering suitable grid services efficiently. This paper attempts to establish a layered resource m...With the explosion of services in grid environment, it＇s necessary to develop a mechanism which has the ability of discovering suitable grid services efficiently. This paper attempts to establish a layered resource management model based on the locality principle which classifies services into different domains and virtual organizations （VOs） according to their shared purposes. We propose an ontologybased search method applying the ontology theory for characterizing semantic information. In addition, we extend the UD- D1 in querying, storing, and so on. Simulation experiments have shown that our mechanism achieves higher performance in precision, recall and query response time.展开更多
Based on the maximum flux principle(MFP),a water quality evaluation model for surface water ecosystem is presented by using self-organization map(SOM) neural network simulation algorithm from the aspect of systematic ...Based on the maximum flux principle(MFP),a water quality evaluation model for surface water ecosystem is presented by using self-organization map(SOM) neural network simulation algorithm from the aspect of systematic structural evolution.This evaluation model is applied to the case of surface water ecosystem in Xindu District of Chengdu City in China.The values reflecting the water quality of five cross-sections of the system at different developing stages are obtained,with stable values of 1.438,2.952,1.869,2.443 and 2.479,respectively.The simulation also indicates that the larger the value,the more serious the water pollution.Furthermore,a classification graph is given to reflect the evolution of structural pattern.The combination of MFP and SOM neural network reveals the formation of different structural patterns in the system during the interaction of internal components.It is shown that a dominant pattern is finally reserved,which starts from a variety of combination patterns for a time period.The results agree with those from traditional evaluation methods,which indicates that the proposed model has high accuracy.This model embodies the evolutionary dynamic mechanisms and characteristics of temporal and spatial changes,which helps to guide the prediction of water quality status of surface water ecosystem.展开更多
文摘In order to detect the sleep spindles simply and efficiently, a novel time-domain approach to detect sleep spindles based on the principles of visual organization is proposed. The code idea of the visual organization is to organize the primary visual elements according to some rules of organization, and to form a more meaningful object of visual processing, as the input of next process. After the collected EEG is processed with the merging algorithm based on the principle of visual organization, it can extract the time-domain feature frequency and duration time better. Use these features with a simple algorithm to detect spindles achieving sensitivity of 92.5% and specificity of 98.1%, which verifies the validity of this method to detect the sleep spindles.
文摘The main objective of this paper is to demonstrate that the internal processes of Self-Organizing Systems represent a unique and singular process, characterized by their specific generativity. This process can be modeled using the Maximum Ordinality Principle and its associated formal language, known as the “Incipient” Differential Calculus (IDC).
基金Supported by the High Technology Research andDevelopment Program of China (2003AA414210) and the NationalNatural Science Foundation of China (60173051)
文摘With the explosion of services in grid environment, it＇s necessary to develop a mechanism which has the ability of discovering suitable grid services efficiently. This paper attempts to establish a layered resource management model based on the locality principle which classifies services into different domains and virtual organizations （VOs） according to their shared purposes. We propose an ontologybased search method applying the ontology theory for characterizing semantic information. In addition, we extend the UD- D1 in querying, storing, and so on. Simulation experiments have shown that our mechanism achieves higher performance in precision, recall and query response time.
基金Supported by National Water Science and Technology Research Project(No.2008ZX07102-001)
文摘Based on the maximum flux principle(MFP),a water quality evaluation model for surface water ecosystem is presented by using self-organization map(SOM) neural network simulation algorithm from the aspect of systematic structural evolution.This evaluation model is applied to the case of surface water ecosystem in Xindu District of Chengdu City in China.The values reflecting the water quality of five cross-sections of the system at different developing stages are obtained,with stable values of 1.438,2.952,1.869,2.443 and 2.479,respectively.The simulation also indicates that the larger the value,the more serious the water pollution.Furthermore,a classification graph is given to reflect the evolution of structural pattern.The combination of MFP and SOM neural network reveals the formation of different structural patterns in the system during the interaction of internal components.It is shown that a dominant pattern is finally reserved,which starts from a variety of combination patterns for a time period.The results agree with those from traditional evaluation methods,which indicates that the proposed model has high accuracy.This model embodies the evolutionary dynamic mechanisms and characteristics of temporal and spatial changes,which helps to guide the prediction of water quality status of surface water ecosystem.
