Fabrication of particulate reinforced magnesium composites by applying a spontaneous infiltration phenomenon

A new fabrication method of particulate reinforced magnesium matrix composites using a spontaneous infiltration phenomenon was developed. By mixing infiltration agent powder with reinforcement powder, the magnesium melt spontaneously infiltrated into the powder mixture. In this study, pure magnesium, SiO2 and SiC particle (SiCp, 1.2,2,3,4 and 8 m) powders were used as the matrix metal, infiltration agent and reinforcements, respectively. Experiments to clarify the conditions in which the spontaneous infiltration occurred were carried out under pure argon atmosphere at 973 K. Furthermore, the infiltration velocity was measured. Spontaneous infiltration occurred in the 2,3,4 and 8 m SiCp systems. A minimum SiO2 content necessary for spontaneous infiltration existed and it depended on SiCp diameter. Microstructural observation of the composites revealed that SiCp dispersed homogeneously, even though the diameter of SiCp was fine, and MgO and Mg2Si reaction products were observed. During infiltration, a temperature rise was observed, caused by Mg–SiO2 thermit reaction. Consequently, spontaneous infiltration was caused by improvement of wettability between the magnesium melt and SiCp, which resulted from Mg–SiO2 thermit reaction at the infiltration front.     

A subsystem design approach to continuous-time performance of decentralized multirate sampled-data systems

A decentralized controller design is presented for multirate sampled-data systems, where stability and input-output performance are considered in terms of continuous-time signals. The design procedure consists of analysis for the overall system and controller synthesis for individual subsystems. For the subsystem designs, a unique technique of removing lifting operation which is used in the analysis is proposed. Robust performance and robust stability in terms of continuous-time signals can be achieved by using the proposed design. A numerical example shows that the design method results in satisfactory controllers which guarantee closed-loop stability and performance in continuous time.     

Application of Monte Carlo method to optimal control for linear systems under measurement noise with Markov dependent statistical property

This paper presents an optimal control algorithm for linear systems with measurement noise which has a Markov dependent statistical property. Ordinarily, the optimal control for this problem involves a very large number of sequences, and the usual calculation method becomes impractical. In the algorithm proposed here, the optimal control is calculated with a relatively small number of sequences, sampled at random from the set of all the sequences.

Evidently, the algorithm for a control problem should be obtained directly from the performance criterion. Unlike the state estimation problem, the problem considered here has a difficulty that there exists an interaction between the algorithm and the state of the system. Because of this, a special consideration is required for the design of the algorithm. In this paper control-free measurement data are introduced to establish the convergence of the algorithm and to find a desirable way of sampling the sequences. Then, certain approximations are made to design a practical and efficient algorithm. A few digital simulation results appear to show the effectiveness of the proposed method.     

Failure diagnosis of a servovalve by neural networks with new learning algorithm and structure analysis

This paper considers a failure diagnosis of a pneumatic servovalve used in automated production systems. The valve is monitored by an accelerometer. Six parameters characterizing the vibration data are extracted, and fed into neural networks to solve four types of diagnosis problems. A conjugate gradient followed by a variable metric method is demonstrated as an effective learning algorithm. Neural network structures are analysed through Boolean expressions summarizing network simulation results for given learning patterns. The neural networks are found to utilize majority voting mechanisms. Irrelevant neurons can be identified and removed without degrading the diagnosis performance.     

Detecting Unstable Periodic Orbits in Hyperchaotic Systems Using Subspace Fixed-Point Iteration

<正>We present a numerical method for efficiently detecting unstable periodic orbits(UPO's)embedded in chaotic attractors of high-dimensional systems.This method,which we refer to as subspace fixed-point iteration, locates fixed points of Poincare maps using a form of fixed-point iteration that splits the phase space into appropriate subspaces.In this paper,among a number of possible implementations,we primarily focus on a subspace method based on the Schmelcher-Diakonos(SD)method that selectively locates UPO's by varying a stabilizing matrix,and present some applications of the resulting subspace SD method to hyperchaotic attractors where the UPO's have more than one unstable direction.     

Relaxation optimizing operations in extended probabilistic space

The classical probabilistic relaxation method has been widely used to solve optimization problems in various fields, including image processing and pattern recognition. The authors have also been developing handwritten character recognition system using the probabilistic relaxation method. However, we realize that there exist cases in which a probability theoretic model is inadequate, especially where there exists incompleteness in available information by noise such as patchy segments and ink spots. In that case, we must introduce ad hoc rules for one-to-two correspondence and no correspondence in the probabilistic algorithm. As the Dempster-Shafer (DS) theory is a kind of natural extension of the probabilistic theory by reducing additivity on probability measure and it can cope with incomplete data. This paper proposes a relaxation matching method based on the Dempster-Shafer theory. Then the update process in probabilistic relaxation method is derived as a special case of Dempster's combination rule in DS theory.