Time Evolution of Demixing in Oxides under an Oxygen Potential Gradient

Time evolution of concentration profiles leading to steady-state demixing in oxides under oxygen partial pressure gradient is treated based on a microscopic model by applying the path probability method of irreversible statistical mechanics. The redistribution of vacancies and cations toward the steady-state distribution has essentially been reduced to the diffusion of vacancies and the interdiffusion of cations along the specimen. Atomistic features of the problem, such as a vast difference in the relaxation times for the redistribution of vacancies and cations, are emphasized. Time required for the system to reach the steady state is then estimated in terms of fundamental material properties.     

Protein-Protein Interaction of Fish Myosin Fragments

Protein-protein interaction of myosin fragments from flying fish and white marlin muscles was studied by means of absorbance changes resulting from aggregation at temperatures of 20°C to 70°C. Subfragment-1 (S-1) exhibited a high extent of interaction with the transition temperature of 35–36°C, while the interaction of heavy meromyosin (HMM) was very weak. Though light meromyosin (LMM) gave lower interaction values throughout the heating temperature, the addition of butanol promoted markedly the interactions at the temperature above 50°C. The degree of promotion was high for flying fish and low for white marlin.     

Preparation of High-Coercivity BaFe12O19

Particles of BaFe₁₂O₁₂ were prepared by chemical coprecipitation; their magnetic properties were studied. A coercive force of 6000 Oe, one of the highest reported for isotropic BaFe₁₂O₁₉, was obtained. X-ray and Moessbauer studies were conducted to examine the mechanism of formation. Superparamagnetic α-Fe₂O₃ was present during synthesis. Ferrites were sintered from these precipitated powders by both the usual method and a hot-press-forging method. The observed magnetic characteristics result from partial orientation of defect-free single-domain coprecipitated powders.     

Learning automaton as a model to simulate and analyse learning behaviour in rats

A technique is presented for using a learning automaton as a model to simulate and analyse learning behaviour in rats, and the usefulness of this model is illustrated. This automaton was proposed as a learning machine by Tsetlin (1961) and refined by Aso and Kimura (1976). Some learning properties of such an automaton are first examined and it is shown that the automaton can be used to simulate a variety of learning behaviour, composed of several kinds of action, by the proper selection of parameters. Secondly, a mathematical model of the learning experiment carried out in our laboratory with rats is formulated as an interactive system between a learning automaton and its environment. Finally, the effectiveness of the mathematical model is discussed and proved through the simulation and analysis of learning behaviour with multiple actions in a specific rat.     

微复制金刚石研磨片对玻璃加工质量的影响

利用固结式微复制金刚石研磨片(Trizact Diamond Tile,TDT)对不同玻璃进行减薄研磨,确定不同粒度金刚石TDT的磨削去除率;研究了研磨后的玻璃加工质量,测量了玻璃表面粗糙度及玻璃亚表面损伤层的状态。同时用9μm粒度碳化硅浆料做对比研磨试验。结果表明,同样粒度的金刚石TDT与传统的碳化硅浆料研磨相比可以得到更高的磨削去除率,减少玻璃亚表面损伤层,降低粗糙度。对于康宁玻璃,9μm粒度的TDT可以达到95μm/min的磨削去除率,是同粒度碳化硅浆料研磨的2倍多;Ra可以达到0.37μm,明显好于碳化硅浆料研磨;亚表面损伤也减轻很多。采用2μm粒度的TDT研磨后可获得Ra0.09μm、接近透明的表面。     

复合材料力学的弹性学、材料力学基础理论

本文系日本以石川编辑委员长（航空宇宙技术研究所）为主导,为了大力推进和提高日本复合材料专业领域中如力学等主要学科知识的普及,于１９９６～１９９７年间组织多位专家编辑出版的《复合材料力学入门》一书的第一部分。本刊征得作者同意,在《纤》刊以中译文发表,目的也是为促进我国玻璃钢、复合材料领域非力学专业各类技术人员理论知识和素质的提高。     

L2-bounded stability for a stochastic system†

Both types of linear and non-linear systems with random coefficients are treated in this paper. Four theorems are proved by using the stochastic Liapunov functional, which give the sufficient condition in matrix form for exponential asymptotic stability in the largo with probability one and L₂-bounded stability with probability one.     

An improvement of a back propagation algorithm by the extended Kalman filter and demand forecasting by layered neural networks

Recently, Watanabe et al. proposed a back propagation algorithm via the extended Kalman filter, in which the learning rate was time-varying. In their algorithm the weights and biases are treated as independent variables. It is, however, natural that the weights and biases are not always independent, and generally have mutual correlation. In this paper, we improve the back propagation algorithm by considering that there is mutual correlation among the weights and bias directly connected to the unit. Through some numerical examples, our improved learning algorithm is compared with Watanabe et al.'s algorithm in learning ability. Furthermore, we consider demand forecasting as a kind of pattern recognition, and propose a demand forecasting method using layered neural networks with the improved learning algorithm. The effectiveness of this demand forecasting method is also discussed through some simulations.     

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.