Meijuan Gao

Path Planning for Mobile Robot Based on Chaos Genetic Algorithm

Aiming at the existent problem of global path planning for mobile robot, a path planning for mobile robot based on chaos genetic algorithm is proposed in this paper. Reasonable coding way and fitness function are used in the chaos genetic algorithm, and the chaos operation is added to the genetic algorithm (GA), the convergence rate of genetic algorithm is improved, and the local optimization can be avoided by using the chaos genetic algorithm. So the solution which obtained by chaos genetic algorithm can not only satisfy the path shortest but also effective avoid the collision with obstacle. The simulation result shows that the method is correct and feasible.

Environmental Monitoring System with Wireless Mesh Network Based on Embedded System

A kind of environmental monitoring system based on wireless mesh network with the core of embedded system ARM9 S3C2410 microprocessor is presented in the paper. The flexible and self-organizing wireless mesh network is used to achieve the real time acquisition and multi-hop wireless communication of parameters of the monitoring atmospheric environment such as SO2, NO2, NO, temperature, humidity and air pressure, etc. The network structure of the system is established, the hardware architecture of the system is designed, and the system working procedures is given. The entire monitoring system can be quickly arranged and rapidly withdrew without support of base station and has a strong self-healing capability and network robustness and can be used for a variety of occasional atmospheric environmental monitoring.

Network Intrusion Detection Method Based on High Speed and Precise Genetic Algorithm Neural Network

Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of neural network, an intrusion detection method based on high speed and precise genetic algorithm neural network is presented in this paper. The high speed and precise genetic algorithm neural network is combined the adaptive and floating-point code genetic algorithm with BP network which has higher accuracy and faster convergence speed. We construct the network structure, and give the algorithm flow. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong self-learning and faster convergence of high speed and precise genetic algorithm neural network, the network intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.

Dynamic Collision Avoidance Path Planning for Mobile Robot Based on Multi-sensor Data Fusion by Support Vector Machine

Statistical learning theory is introduced to movement planning of intelligent robot, considering the issues that the dynamic collision avoidance planning of mobile robot is a complicated and nonlinear system, and combine the advantages of the support vector machine (SVM) possessed, a method of mobile robot dynamic collision avoidance planning based on multi-sensor data fusion by SVM is presented in this paper. We utilize 5 ultrasonic sensors and an image sensor get environmental information in this method, and the SVM is used to do multi-sensor data fusion to compute these information, in order to achieve the purpose that dynamic control the mobile robots next action. The method fully utilizes the potential of the SVM and the multi-sensor data fusion to solve dynamic path planning problem of mobile robot. The simulation result shows that this method is feasible and effective.

Network Intrusion Detection Method Based on Radial Basic Function Neural Network

Aimed at the network intrusion behaviors are characterized with uncertainty, complexity, diversity and dynamic tendency and the advantages of radial basic function neural network (RBFNN), an intrusion detection method based on radial basic function neural network is presented in this paper. We construct the structure of RBFNN that used for detection network intrusion behavior, and adopt the K-nearest neighbor algorithm and least square method to train the network. We discussed and analyzed the impact factor of intrusion behaviors. With the ability of strong function approach and fast convergence of radial basic function neural network, the network intrusion detection method based on radial basic function neural network can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic information. The experimental result shows that this intrusion detection method is feasible and effective.

Path Planning for Mobile Robot Based on Improved Simulated Annealing Artificial Neural Network

A mobile robot path planning method based on improved simulated annealing algorithm and artificial neural network is proposed. First the simulated annealing algorithm with the best reserve mechanism is introduced and it is combined with Powell algorithm to form improved simulated annealing mixed optimize algorithm which not only add the good solution protective measures but also improve the convergence rate of simulated annealing algorithm. Then we take the obstacle collision penalty function which expressed using neural network and the path length as the energy function of improved simulated annealing mixed optimize algorithm, thereby the solution which obtained by the improved simulated annealing mixed optimize algorithm can not only satisfy the path shortest but also effective avoid the collision with obstacle. The simulation result shows that the proposed method is feasible and valid.

Fault Detection of Oil Pump Based on Classify Support Vector Machine

Statistical learning theory is introduced to fault detection of oil pump. Considering the issues that the relationship between the fault of oil pump existent and fault information is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The support vector machine (SVM) has the ability of strong nonlinear function approach and the ability of strong generalization and also has the feature of global optimization. In this paper, a fault detection method of oil pump based on SVM is presented, moreover, the genetic algorithm(GA) was used to optimize SVM parameters. With the ability of strong self-learning and well generalization of SVM, the detection method can truly diagnosticate the fault of oil pump by learning the fault information of oil pump. The real detection results show that this method is feasible and effective.

Design and Implementation of Wireless Sensor Network Data Collection Terminal Based on ARM9

Traditional data collection terminals mainly utilize the method of wire, but on the occasion of scattered collection points and the scene complex environment, it is very difficult to wire. Based on wireless sensor network technology, a kind of data collection terminal with ARM9 as a core processor is proposed in this paper, which replaces the traditional actual wiring with wireless sensor network and can send the collected data to control centre or upper PC in wireless multi-hop way. We establish the construction of wireless sensor network for online collection terminal, design collection terminal overall structure, and give the data collection terminal software implementation flow. This data collection terminal can be used in a variety of multi-tasking multi-point data acquisition systems and control systems of many industrial settings.

Research of web classification mining based on classify support vector machine

With the development and widely used of Internet and information technology, the Web has become one of the most important means to obtain information for people. According to the web document classification and the theory of artificial neural network, a web classification mining method based on classify support vector machine (SVM) is presented in this paper. The SVM network structure that used for web text information classification is established, and we use the genetic algorithm (GA) to optimize SVM parameters, thereby enhancing the convergence rate and the classification accuracy. The structure of web classification mining system based on classify support vector machine is given. With the ability of strong pattern classification and self-learning and well generalization of SVM, the classification mining method can truly classify the web text information. The actual classification results show that this method is feasible and effective.

Community Intrusion Detection and Pre-warning System Based on Wireless Mesh Network

A community intrusion detection and pre-warning system based on wireless mesh network is presented in this paper. This system is composed of ARM (advanced RISC Machines) data acquisition nodes, wireless mesh network and control centre. The flame, smog and infrared signal of human can be detected by ARM nodes, and video and audio signal is collected and transmitted to control centre by wireless mesh network when any of those situation happened. The control centre stores information to special area and gives alarm signal to monitors. The ARM data acquisition nodes use embedded system use its centre control unit and transmit signal by wireless transmission module which can connected to ARM9 and send video and audio signal. The system can establish network automatically by using the advantage of multi-hop communication of mesh network. Redundancy node can be placed on important area to make sure reliability and resist destroy.