14185433
Affective Computing Model Based on Rough Fuzzy Sets
In human affective research, the fuzziness and roughness of the affective computing is the chief difficult. This paper builds a novel affective model based on rough fuzzy sets for the first time and presents a serial of static description results, which effectively solve the fuzzy and rough problems in affective computing. The model can realize the precision control of affective computing conveniently. Finally, the simulation shows that the results of the model for the affective static description are reasonable. This paper extends the results in (Yong Chen and Tong He, 2005) and lays the foundation of the affective computing in depth
20327796
An effective algorithm of motif finding problem
Identification of the short DNA sequence motif, which serves as binding targets for transcription factors, is a fundamental problem in both computer science and molecular biology. Especially, finding the subtle motifs with variable gaps is more challenging. In this paper, a new algorithm is presented, which explores some new strategies. Based on a neighbourhood set concept, a new probability matrix is defined, which can capture the target motifs effectively. An iterative restart strategy is used, by which we can use several similar motifs' information to detect the real motif to demonstrate the effectiveness of our algorithm. We test it on several kinds of data and compare it with some other current representation algorithms. Simulation shows that the algorithm can effectively detect the subtle motifs.
16724396
Weighted Rough Graph and Its Application
Rough graph is the graph that can be used to research uncertainty problems, and it is a new research tool to analyze the rough characteristic of the system according to Tong He and Kaiquan Shi. This paper makes further research on rough graph, and gives the definitions of class connection of rough graph and some subgraphs of rough graph. Furthermore, this paper presents the definition of weighted rough graph by enduing the edges of rough graph with weight attribute, and gives the algorithm of exploring the class optimal tree in weighted rough graph, which generalizes the classical Kruskal algorithm of exploring the optimal tree. Finally, the application in relationship analysis shows the effectiveness of the algorithm
38801994
Generalized Q-S (lag, anticipated and complete) synchronization in modified Chua's circuit and Hindmarsh-Rose systems
Abstract Based on symbolic computation system Maple and Lyapunov stability theory, a systematic, powerful and concrete scheme is extended to study the generalized Q – S (lag, anticipated and complete) synchronization between two identical modified Chua’s circuit with different initial values and between two different chaotic systems: Hindmarsh–Rose system and modified Chua’s circuit. Numerical simulations are used to verify the effectiveness of the obtained controller.
21338568
Research on Risk Probability Estimating Using Fuzzy Clustering for Dynamic Security Assessment
Effective network security management requires assessment of inherently uncertain events and circumstances dynamically. This paper addresses the problems of risk probability assessment and presents an alternative approach for estimating probability of security risk associated with some interaction in ubiquitous computing. A risk probability assessment formula is proposed, and an estimating model adopting the Fuzzy C-Means clustering algorithm is presented. An experiment based on DARPA intrusion detection evaluation data is given to support the suggested approach and demonstrate the feasibility and suitability for use. The practices indicate that fuzzy clustering technique provides concepts and theoretical results that are valuable in formulating and solving problems in dynamic security assessment.
40119866
A fault tolerant topology control in wireless sensor networks
Summary form only given. Topology control of wireless sensor networks (WSNs) is a key design challenge in terms of extending the lifetime of the network. The paper presents a fault tolerant topology control by adding necessary redundant nodes to the network's simple communication backbone, which results a higher vertex connectivity degree. It provides not only fault tolerance for unreliable node failure, but also support for upper level protocols. The paper also identifies several factors and synchronization methods which may affect the redundant node selection. A simulation study shows the improvement of network lifetime with a desired vertex connectivity degree.
38601346
A model and algorithm of two-dimensional optimum layout in blanking
In this paper, a model and algorithm of optimum layout is proposed for blank layout of single pattern on single rectangular sheet. The approach arranges the cutting patterns on rectangular sheet in the typical style of Double Opposite Layout (DOL). The remains of the sheet, which has been left in one direction after arranging patterns, called as "Step Leavings" (SL) is considered while the optimum layout is discussed We make full use of material of the sheet, including the A in x-direction and y-direction, and set up a reasonable mathematical model for the two-dimensional optimum layout. A corresponding algorithm is also provided to work out the optimum layout quickly and automatically. In this way, we can get the best layout scheme to arrange the maximum shapes on the sheet.
15834568
An Artificial Neural Network Based on CIEA
Chaotic Immune evolutionary algorithm (CIEA) is proposed on the shortage of current algorithms training artificial neural network, biological immune mechanism and the characteristics of evolution. A novel artificial neural network based on chaotic immune evolutionary algorithm (CIEANN) is presented in this paper. The algorithm has the merits of chaos, immunity and evolutionary algorithm. It can ensure the ability of global search and local search and enhance the performances of the algorithm. At last, we analyzed the performance of CIEANN with a typical XOR problem and compared it with several common ANNs. The analysis results show that CIEANN converges quickly and avoids immature convergence and is an effective way to solve optimization problem.