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A recently modern stochastic optimization algorithm has been developed by observing the life of slime mold physarum polycephalum in nature. The algorithm is called the slime mold algorithm (SMA) with an excellent exploratory capacity and exploitation inclination. Still, slipping into optimal local is easy to happen and slowly converges speed while dealing with complicated problems. This article proposes a new process of improving SMA (namely ISMA) by adapting the weight coefficient and cooperating the reverse learning strategy in the expression of agents updating locations to enhance the algorithm’s optimization performance. Many selected benchmark functions and the optimal operation of cascade reservoirs are applied to evaluate the performance of the proposed algorithm. Comparisons of the proposed approach’s results with the various algorithms under the case situations show that the recommended solution produces better performance than the different competing algorithms.

In this study, we used functional Magnetic Resonance Imaging (fMRI) to measure human brain activation in tactile recognition using tactile length recognition task performs without visual guidance by normal volunteer 12 persons. The result suggested that the cognitive processing includes initial processing from stimulus, a joint and a line into feeling information, and integration of feeling information and movement information. This result is an important meaning to the elucidation of the mechanism of object recognition by tactile sense.

Case clustering is the core of case-based reasoning system. This paper partitions clustering to case base and designs a case retrieval strategy based on heterogeneous case base. Then, the paper analyzes the partitional principles of case base and the clustering rules of cases, and focuses on the case retrieval strategy based on partitional clustering. The experimental results show that this method can effectively improve the efficiency of case clustering and the maintainability of case base.

Due to the advantages of the intrinsically safe circuit, people are increasingly willing to make use of it in comprehensive automatization system and communication equipment. If practical non-explosive testing methods can be constructed base on discharge theory of intrinsically safe circuit, the designers will find it easier to grasp the performance of the circuit, thus resulting in the achieve- ment of shorter researching time and lower costs. This thesis has established the non-explosive assessment system of the intrinsically safe circuit with ANFIS as its core. ANFIS input Eigenvector has been affirmed according to the circuit quality, experimental gas types, discharge power, maximum value of the instantaneous power, the energy comparison of the average power value and the discharge time. The system uses the explosive experimental data from the spark test system to extract the Eigenvector to perform ANFIS training. After succeed training, the established circuit discharge models are used to extract the Eigenvec- tor to make the assessment of the non-explosive intrinsi- cally safe performance of the intrinsically safe circuit. Ac- cording the explosive experiment, this system boasts high accuracy and practical value. Keywords-intrinsically safe; non-explosive assessment methods; Adaptive Network-based Fuzzy Inference System

The main goal of RoboCup (namely robot world cup football game) is to challenge researchers, in this paper, we will introduce the key technology of RoboCup, and show the signification of the research in Robot.

to segment CT (computed tomography) images of liver cancer effectively, a new method based on Snake model was proposed. Image segmentation based on threshold was combined with image segmentation based on Snake model, and a contour correction process was added. This method obtains a better result on liver cancer CT images segmentation, overcomes the defect that traditional Snake model can not segment the images with serious depressions. The results of the experiment demonstrate that this algorithm is an effective method for segmenting the liver cancer image.

Research profiling is a large-scale analysis method based on the literature information to depict the state-of-the-art scientific researches wisely. Much research profiling work has been carried out based on formal, structured scientific publications (such as Chemical Abstracts) while there is much information contained in abundant, unstructured scientific web resources. In order to profiling research based on those unstructured scientific web resources, the authors bring forth a solution that tries to extract useful knowledge objects from them. Three kinds of knowledge objects have been extracted: (1) research related objects (research objects), such as institutes, scientists, projects, conferences etc.; (2) the relationships between the research objects which reflected in scientific web resources, such as one scientist worked in one institute; (3) the terms which indicate the topic of the research areas. The authors implemented the knowledge objects extraction system and did some experiments to test and evaluate the effect of this system.