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37 results on '"Cao, Qiping"'

28. The Brainwave Response of Optical Illusion Stimulus

Author

Tang, Hongsuo, Cao, Qiping, Tang, Zheng, Todo, Yuki, Tang, Hongsuo, Cao, Qiping, Tang, Zheng, and Todo, Yuki

Abstract

Several centuries ago the overarching obstacle in the evolution of vision was documented when George Berkeley pointed out that information in retinal images cannot be mapped unambiguously back onto their real-world sources. In this experiment, we used the EEG to analyze the brainwave dates of 2 stimuli, one can cause human optical illusion, but the other cannot. The purpose of this work was to find out the locations and mechanisms of brainwave when the human beings watch the 2D images under the optical illusion stimulus. The results showed that the responds of the optical illusion have different response time and voltage with the different coarseness at the visual area. Recognition potential (RP.) occurs in about 250ms.

Published
2017

29. Using the N1 Potential for the Analysis of the 2D Optical Illusion Stimulus

Author

Cheng, Yu, Tang, Hongsuo, Cao, Qiping, Tang, Zheng, and Todo, Yuki

Subjects
optical, N1, ERP, visual illusion
Abstract

Recently, the brain science has become one of most attractive and important fields in modern science and technology. Several centuries ago the overarching obstacle in the evolution of vision, especially the visual illusion was documented when George Berkeley pointed out that information in retinal images cannot be mapped unambiguously back onto their real-world sources. In this paper, we construct an experiment that uses the abutting line grating illusion contour stimulus with different number of lines, so that some perceive illusion contours and some do not. Event-related potentials (ERP) involving N1 and power spectra are measured and compared to examine the difference of them when visual illusion stimuli and control stimuli are applied.

Published
2012

30. The Brainwave Response of Optical Illusion Stimulus

Author

Tang, Hongsuo, Cao, Qiping, Tang, Zheng, and Todo, Yuki

Subjects
RP, EEG, 2D optical illusion
Abstract

Several centuries ago the overarching obstacle in the evolution of vision was documented when George Berkeley pointed out that information in retinal images cannot be mapped unambiguously back onto their real-world sources. In this experiment, we used the EEG to analyze the brainwave dates of 2 stimuli, one can cause human optical illusion, but the other cannot. The purpose of this work was to find out the locations and mechanisms of brainwave when the human beings watch the 2D images under the optical illusion stimulus. The results showed that the responds of the optical illusion have different response time and voltage with the different coarseness at the visual area. Recognition potential (RP.) occurs in about 250ms.

Published
2011

31. A Chaotic Clonal Selection Algorithm and its Application to Synthesize Multiple-Valued Logic Functions

Author

Gao, Shangce, Zhang, Zhiqiang, Cao, Qiping, Tang, Zheng, and Todo, Yuki

Subjects
Multiple-valued logic, Computational complexity theory, Truth table, Chaotic, Information processing, Clonal selection algorithm, Minimization, Affinity function, Population diversity, Minification, Electrical and Electronic Engineering, Algorithm, Mathematics
Abstract

In this paper, a chaotic clonal selection algorithm (CCSA) is proposed to synthesize multiple-valued logic (MVL) functions. The MVL function is realized in a multiple-valued sum-of-products expression where product is indicated by MIN and sum by TSUM. The proposed CCSA, in which chaos is incorporated into the clonal selection algorithm to initialize antibodies and maintain the population diversity, is utilized to learn a given target MVL truth table. Furthermore, an adaptive length strategy of antibodies is also introduced to reduce the computational complexity, whereas an improved affinity function enables the algorithm to find less product terms for an MVL function. Simulation results based on a large number of MVL functions demonstrate the efficiency of the proposed method when compared with other traditional methodologies. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Published
2010
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36. A Chaotic Dynamic Local Search Method for Learning Multiple-Valued Logic Networks.

Author

Gao Shangce, Zhang Jianchen, Tang Zheng, and Cao Qiping

Subjects
ARTIFICIAL neural networks, DISTRIBUTION (Probability theory), ALGORITHMS, SIMULATION methods & models, APPROXIMATION theory
Abstract

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of the local search method (LS), which is based on the generalized reduced gradient algorithm, hybrid local search method is proposed by incorporating chaos. Thus, LS and chaos are hybridized to form a chaotic dynamic local search method (CDLS), which reasonably combines the searching ability of LS and chaotic searching behavior. In this paper, a CDLS method based on the logistic equation is presented to learn Multiple-Valued Logic (MVL) Networks. Simulation results and comparisons with the traditional back propagation algorithm (BP) and the standard LS method show that the CDLS can effectively enhance the searching efficiency and greatly improve the searching quality within reasonable number of iterations. [ABSTRACT FROM AUTHOR]

Published
2009

37. OPTIMAL COMPETITIVE HOPFIELD NETWORK WITH STOCHASTIC DYNAMICS FOR MAXIMUM CUT PROBLEM.

Author

Wang, Jiahai, Tanot, Zheng, Cao, Qiping, and Wang, Ronglong

Subjects
ARTIFICIAL neural networks, ARTIFICIAL intelligence, ALGORITHMS, STOCHASTIC analysis, COMPUTATIONAL complexity
Abstract

In this paper, introducing stochastic dynamics into an optimal competitive Hopfield network model (OCHOM), we propose a new algorithm that permits temporary energy increases which helps the OCHOM escape from local minima. The goal of the maximum cut problem, which is an NP-complete problem, is to partition the node set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The problem has many important applications including the design of VLSI circuits and design of communication networks. Recently, Galán-Marín et al. proposed the OCHOM, which can guarantee convergence to a global/local minimum of the energy function, and performs better than the other competitive neural approaches. However, the OCHOM has no mechanism to escape from local minima. The proposed algorithm introduces stochastic dynamics which helps the OCHOM escape from local minima, and it is applied to the maximum cut problem. A number of instances have been simulated to verify the proposed algorithm. [ABSTRACT FROM AUTHOR]

Published
2004
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