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To study the effect of blood-letting puncture at "Well-points" of the twelve meridians on hippocampal mitophagy of hypobaric hypoxia-induced brain injury (HHIBI) rats, so as to explore its biological mechanisms underlying improvement of high altitude hypoxia-induced brain injury.Male SD rats were randomly divided into normal control group （Compared with the normal control group, the levels of serum HIF-1α and VEGF at each time point, and the expressions of hippocampal Beclin-1 at 12 and 24 h, LC3-Ⅱat each time point were significantly increased in the model group (Blood-letting of "Well-points" can up-regulate serum HIF-1α and VEGF contents and hippocampal Beclin-1 and LC3-Ⅱ (mitophagy related proteins) expressions in HHIBI rats, which may contribute to its effect in reducing hypoxic brain injury.

When the dynamic environment, product process updates, machine failures, and unit processing functions or processing capabilities fail to meet the order requirements, you need to select the appropriate time node to reconfigure the cell. In this paper, the complexity of flexible manufacturing cell is represented by information entropy, and the cell complexity model is established. The model is combined with the cusp catastrophe theory to establish a flexible manufacturing cell reconfiguration decision model. The disaster point is the cell reconfiguration decision point. Finally, an example is given to analyze the validity and practicability of the choice of reconfiguration decision points.

Performing tasks of different complexity under different time pressures has different effects on the operator's operational performance. In this paper, the Lego model is used to simulate the actual light assembly production operation, and the light assembly experiment with different complexity and different time pressure is designed. The operator's operational performance indicators are recorded to analyze the impact of task complexity and time pressure on the operational performance of light assembly operators. The results show that the task complexity leads to significant differences in the operator's operational performance. Time pressure does not lead to significant differences in the operator's operational performance. There is an interaction between the two; there are significant differences between the four types of assembly task completion error rates.

In recent years, moving target tracking technology has been widely used in video surveillance and other fields. But there are many factors that are not conducive to the system. Among them, the large area occlusion are the main factors leading to tracking failure. In addition, the interference of illumination and some similar colors is also an important factor affecting the tracking accuracy. Aiming at the disadvantage of moving target tracking, this paper proposes a dual discriminant adaptive tracking algorithm based on mean shift algorithm and Kalman filter algorithm. When the target is tracked with illumination, large area occlusion, long time occlusion, background interference and similar color interference, the algorithm can track the target well, with good accuracy and stability.

Aiming to get better real time performance and effect of detection in dynamic scene of intelligent video surveillance system, non-parametric kernel density estimation (KDE) is used to model the background. And to solve the foreground detection is not precise enough, background subtraction method is fused to detect the foreground. And some modified work is done to suppress shadow and noise. Experiments show that the method proposed can get better real time performance and low noise detect result in intelligent video surveillance system.

In this paper, the global exponential stability is discussed for static neural networks with time varying delays. On the basis of the linear matrix inequalities (LMIs) technique, and Lyapunov functional method, we have obtained the main condition to ensure the global exponential stability of the equilibrium point for this system, which is dependent on the change rate of time varying delays. The proposed result is less restrictive than those given in the earlier literatures, easier to check in practice. Remarks are made with other previous works to show the superiority of the obtained results, and the simulation examples are used to demonstrate the effectiveness of our results.

Robust moving target tracking has become an important topic in the field of computer vision. The fusion of feature such as color, texture, edge strength and motion has proved to be a promising approach to robust visual tracking in situations where no single feature is suitable. Due to the poor tracking performance in complicated scenarios, a new visual target tracking scheme is proposed. This algorithm exploits the fusion feature of color feature and local binary pattern (LBP) texture feature under the framework of particle filter to improve the robustness of target tracking. In order to get the accurate color model of target, the multi-part color histogram with spatial information is introduced. Comparing with the particle filter tracking algorithm based on single feature, experimental results show that the proposed method is more robust in terms of pose changes and illumination changes, especially in scenarios where the target object contains cluttered background with similar color distributions.

Moving target tracking is the key part of intelligent visual surveillance system. Among the various tracking algorithms, the Beysian tracking algorithms and the kernel tracking algorithm are two algorithms that frequently used. The Beysian tracking algorithms mainly conclude Kalman filtering algorithm, extended Kalman filtering algorithm and particle filtering algorithm. Mean Shift is the most representative algorithm of the kernel target tracking. In this survey, the status and development of target tracking algorithms has been studied more extensively with providing a few examples of modified tracking algorithms. Then a comparison was presented based on the limitations and scope of applications. Finally, the paper showed further research prospects of moving target tracking are introduced.

Spectrum sensing performance of building indoor environment has been the focus of attention and research in low signal-to-noise ratio. In this paper, a primary users sensing approach to signal classification combining spectral correlation analysis and support vector machine (SVM) is introduced. Three spectral coherence characteristic parameters are chosen via spectral correlation analysis. By utilizing a nonlinear SVM, primary user signal has been detected. Simulations indicate that the overall success rate is above 90.2% when SNR is equal to-5dB and 80.1% in-15dB. Compared to the existing methods including the classifiers based on MME and ANN, the proposed approach is more effective in the case of low SNR and limited training numbers. The results show that the validity and superiority of the proposed algorithm in building indoor environment.

Energy self-supply is one of the most important problems, and there are many undesirable energy difficulties when carrier keeps working underwater, and. A method based on inertial pendulum has been presented, but the model of wave energy absorption efficiency which is a significant parameter is strongly nonlinear and difficult to be gained. A method based on radial basis function (RBF) neural network is adopted to model in time domain. To prevent the problem that structure and parameters of neural network are hard to be tuned, improved RBF network based on particle swarm optimization (PSO) is proposed. The results obtained by presented method and by simulation software ADAMS are compared, which validate the method used to be correct and make basis on future research.

In this paper, the global robust exponential stability is discussed for interval Cohen-Grossgerg neural network with time varying delays. On the basis of the linear matrix inequalities (LMIs) technique, and Lyapunov functional method combined with the Bellman inequality and Jensen inequality technique, we have obtained the main condition to ensure the global robust exponential stability of the equilibrium point for this system. The obtained stability criterion is dependent on the upper bound of time varying delays. The proposed result is less restrictive, and suitable of the cases of slow or fast time varying delays, easier to check in practice. Remarks are made with other previous works to show the superiority of the obtained result, and the simulation example is used to demonstrate the effectiveness of our result.

While the methods of gray image segmentation keeping innovation, researches on color image segmentation have a marked development, and the gray image segmentation provides research base for color image segmentation. At present, the problem of color image segmentation has become a fundamental one and had significant impact on both new pattern recognition algorithms and applications. In order to make the research in this field has a more comprehensive understanding, In this paper, several typical approaches are presented and discussed, and expected to seek an algorithm with better real-time and robustness. algorithm combining both SOFM and FCM clustering is selected to be better in robustness, and reduce the hassle of looking for a suitable feature space.

Because of high fire frequency and huge losses, the research of fire signal detection in the monitoring system is an important task in the fire-preventing field. The fire signal detection method based on vision can overcome the shortcomings that exist in some traditional methods i.e. it can surmount the large impact on environmental interference factors, such as temperature, photographic and smoke of environment. With many researcher’s results, it shows clearly that the error rate of flame recognition is low, and also the real-time ability and the anti-disturbance ability are very good.

In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations axe optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.

This paper presents a comprehensive review of the available literature on applications of the rough set theory. Concepts of the rough set theory are discussed for approximation, dependence and reduction of attributes, decision tables and decision rules. The applications of rough sets are discussed in pattern recognition, information processing, business and finance, industry, environment engineering, medical diagnosis and medical data analysis, system fault diagnosis and monitoring and intelligent control systems. Development trends and future efforts are outlined. An extensive list of references is also provided to encourage interested readers to pursue further investigations.

A shape-shifting robot with changeable configurations can accomplish search and rescue tasks which could not be achieved by manpower sometimes. The accessibility of this robot to uneven environment was efficiently enlarged by changing its configuration. In this paper, a path planning method is presented that integrates the reconfigurable ability of the robot with the potential field law. An adaptive genetic algorithm is applied to solve effectively the local minimum problem. The experiments show that the robot's configurations can be changed to perform the path planning with the environmental variation. Moreover, the path has been shortened effectively.

Variable precision rough sets (VPRS) is used to reduce the redundant features in terms of its ability of knowledge reducts. An improved network algorithm including additional momentum, self-adaptive learning rate and dynamic error segmenting is presented to solve the shortcomings of traditional BP neural network (NN). The reduced features after VPRS are fed into the improved neural network proposed to inspect the defects of surface for wood veneer, which results in short training time and a high classification accuracy with a typical application in defect inspection of wood veneer.

An automatic inspection method based on rough set theory, fuzzy set and an improved BP algorithm is presented. The rough set method is used to remove redundant features for its data analysis and procession ability. The reduced data is fuzzified to represent the feature data in a more suitable form as input data of a BP network classifier. By the experimental research, the hybrid method shows good classification accuracy and short running time, which are better than the results using BP network and neural network with fuzzy input.

Variable precision rough sets (VPRS), as a extension of rough sets (RS) is adopted to reduce the redundant features for its ability of more useful information adopted compared with RS. The reduced features after VPRS are fed into the improved BP network proposed to inspect the defects of surface quality, which results in short training time and a high classification accuracy with a typical application in defect inspection of wood veneer.

A vision-based inspection method based on rough set theory, fuzzy set and neural network algorithm is presented. The rough set method is proposed to remove redundant features for its data analysis and processing. The reduced data is fuzzified to represent the feature data in a more suitable form for input to a BP network classifier. The BP neural classifier is considered the most popular, effective and easy-to-learn model for complex, multi-layered network. By the experiment research, the hybrid method shows good classification accuracy and short running time, which are better than the results using BP network and neural network with fuzzy input

In this paper, hierarchical clustering method is introduced for attribute discretization. It can determine automatically the significant clusters. First, the best classes for discretization are picked from scatter plots of several statistics. Moreover, these classes keep consistent with extracted clusters from dendrograms. By comparison, hierarchical clustering discretization method is typically more effective and advisable among several cluster algorithms with the defect inspection of wood veneer

In this paper, hierarchical clustering is introduced. The method can determine automatically the significant clusters in a hierarchical cluster representation. It could choose best classes for discretization by scatter plots of several statistics primarily. Moreover we can extract the clusters from dendrograms that contain essentially the same information, which shows the two discretization results are consistent. By comparison among several cluster algorithms with the defect inspection of wood veneer, hierarchical clustering discretization method is typically more effective and advisable.

To solve the shortcomings of the traditional BP network, the improved algorithm is presented to accelerate the training and improve the accuracy, and reduce the possibility of getting into the local minimum. For optimal network structure, the UD method is introduced to optimise the parameters, and the 'best' level-combination is obtained so that the performance of the classifier is further improved.

A hybrid method of rough set and genetic algorithms is presented to raise the speed and accuracy of path planning of robot. Firstly, gain the decision rule by rough set theory. And then, come to a series of available paths by training the gained minimal decision rule. Finally, optimize the population of the paths above using genetic algorithms, and obtain the most excellent path. The results show that the hybrid method is good at raising the speed of path planning of robot.

In this paper, a novel visual inspection and classification technology based on rough sets and neural network algorithm is presented. The rough set algorithm of data classification is discussed. As a large quantity of ambiguous and redundant data can be removed effectively using rough set theory, training time of neural networks is further decreased and the classification accuracy is also improved. Combined with anti-disturbance of the neural network, the effectiveness of classification technology is performed for the defect inspection of wood veneer with its rapid classification capacity and high classification accuracy.