Applied Mechanics and Materials Vols. 143-144

Abstract: To solve the inverse kinematics problem of a robot manipulator without closed form solutions, one-dimensional iterative method is very useful. However, for a 5-DOF robot manipulator, because of the uncontrolable and uncertain orientation vectors, it's difficult to analytically express all joint variables by one of them, therefore one-dimensional iterative method can not be directedly used. By adding an appropriate virtual joint to it, a 5-DOF manipulator can be changed into a 6-DOF one so that the uncertain orientation vectors can be pre-given, and the difficulty is solved. To illustrate this virtual joint method a 5-DOF serial robot manipulator with prismatic arm joint and offset wrist is discussed in this paper as an example.

Abstract: To solve the motion control of mobile robot, a PID control optimized by improved immune clonal algorithm is presented. On the basis of immune clonal algorithm, a new virus evolutionary clonal algorithm (VECA) is provided firstly. The VECA focuses on the virus infection of population after immune mutation, which improves the population diversity and strengths the local search ability of immune clonal algorithm. Then the proposed VECA is used to optimize the algorithm parameters of PID control strategy for the robot motion. The simulation results show that VECA can realize the optimization of PID parameters and improve the control precision of path track.

Abstract: Robot Soccer provides a good experimental platform for the automatic control, artificial intelligence, robotics and other researches. A soccer robot system is also a typical multi-agent systems(MAS). Every robot in a soccer team is an agent,so the collaboration between agents, the rational apportionment of the robots' roles and the capacity for actions of the robots is the key to winning game. We present a strategy which is based on dynamic programming algorithm and genetic algorithm. Every robot has fitness to each role through which to carry out the dynamic role assignment, meanwhile, the parameters of the fitness functions could be optimized by genetic algorithm.

Abstract: This paper introduces the implementation of a new heuristic recursive algorithm for bin packing solution used in automated packaging application. The theoretical method proposed in this paper is successfully implemented on a real ABB robot arm with some important improvements such as added rotation flexibility and removing an added product from the structure. The computational results on a class of benchmark problems have shown that this algorithm not only finds shorter height than the known meta-heuristic ones, but also runs in shorter time. The average running time is very suitable for such kind of automated packaging application.

Abstract: This paper presented a genetic algorithm (GA) to optimize element placements of microphone array with the purpose of maximizing beamforming SNR for given possible distribution of sound sources. A function based on relationship between statistical geometry descriptors and array performance matrices was applied as the objective function of GA. Optimization experiments of 1D linear array and 2D planar array were performed to demonstrate that this algorithm can effectively sort out superior arrays with significant SNR improvements over randomly generated arrays and regular arrays. High successful rate, rapid convergence speed, and fast processing time observed in all the experiments demonstrate the feasibility of this algorithm as a practical tool for microphone array design.

Abstract: In recent years, with the development of intelligent control theory, fuzzy control theory has been applied to automatic control of the crane. This paper designs fuzzy anti-swing controller based on the nonlinear mathematical model of bridge crane, and MATLAB/Simulink simulation was carried out. The Simulink experiment proves that location fuzzy controller and angle fuzzy controller show the good anti-swing effect compared with conventional PID controller. The two controllers not only improve response speed times but also improve control accuracy.

Abstract: Retracted paper: The significant unification of interrupts and randomized algorithms is a confusing problem. Given the current status of lossless information, cryptographers urgently desire the emulation of Scheme. Our focus in this paper is not on whether multicast frameworks and virtual machines [2] are never incompatible, but rather on introducing a game-theoretic tool for refining 802.11b (HeyTidbit).

Abstract: In order to overcome shortcomings of existing range-free wireless sensor network (WSN) node localization methods such as huge computation volume and great effect of node density on localization precision, a WSN localization algorithm based on adaptive particle swarm optimization (APSO) was put forward in combination with particle swarm theory and DV-Hop algorithm. This algorithm improved localization precision by more than 20%, and the effect of node density on localization precision was significantly less than DV-Hop algorithm without any addition of hardware facilities and communication load.

Abstract: In view of the nonlinear, time-variable, long delay, large inertia character of main steam temperature system, the difficult point of control is summarized. The status quo of application study on main steam temperature by fuzzy control, neural network and fuzzy neural network control, genetic algorithms is introduced. And taking "the 600 MW concurrent boiler load in 100%" as an example, carry on the main steam temperature control simulation by means of Matlab/Simulink software. In this simulation, four control strategies are taking for the simulation experiment, and comparing with the traditional PID control algorithm. The simulation results prove that fuzzy neural network control strategies have good robustness, fast response, short setting time, and great potential for the control of main steam temperature.

Abstract: Based on the complexity of the evaluation index for supplier selection in green supply chain, this paper puts forward the evaluation index system in green supply chain and presents BP neural network model to select supplier with evaluation indexes as BP neural network's input and the outcome of DEA/AHP model as BP neural network's expected output. It can be proved that the model can effectively select suppliers in green supply chain by simulation experiment with LIINGO 9.0 and MATLAB7.0.