Generic Guiding Law Between Irreversibility Field and Anisotropy—Chemical Control of Critical Current of High Temperature Superconductors

Systematic studies on the irreversibility field H _irr, and anisotropy factor ² of high temperature superconductors (HTSC) were performed using single crystals with high quality. The generic scaling law have been found to hold for all the HTSC systems examined, i.e., H _irr[Oe]=4×10^7–2 (1–T/T _c)1.5 at T0.7T _c. In addition, of each HTSC material is roughly expressed as 2=2 exp(0.78d[Å]) at the carrier optimally-doped state. Based on the generic scaling law, the behaviors of the variously doped superconductors, such as Bi(Pb)2212 and Hg(Re)1223, are discussed in terms of the critical current.     

Using interactive evolutionary computation to generate creative building designs

In this paper we describe how we have exploited Evolutionary Computation (EC) to generate creative building designs. The algorithm has been implemented to create a design tool for architects called Design Inspiration system (DIS). The interactive design inspiration tool is able to generate initial design concepts of architectural plans. Our approach illustrates what we regard as an ideal strategy towards an organic architecture that is inspired from living organisms and not concerned with architectural style. The current capabilities of the system are demonstrated by the example of designing a library building. The paper ends by describing a comprehensive experiment with a total of 28 volunteer architects as a cognitive performance measurement tool, capable of accurately positioning participants’ performance using the design system. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Polyhedral Surface Modeling with a Diffusion System

This paper presents a method of generating polyhedral surfaces by using a diffusion system that calculates the positional and normal vectors on their vertices. The system generates smooth shapes that satisfy the minimum norm property, and can be extended to imitate the shape controls of curvature continuous surfaces with bias and tension parameters. The shape of a surface is determined by the stable state of nonlinear and local calculations between vertices, and is easily controlled by adding constraints on arbitrary vertices. Such bottom-up calculation of surfaces enhances flexibility in the interactive design of complicated free-form shapes.     

Discriminative training via minimization of risk estimates based on Parzen smoothing

We describe a new approach to estimating classification risk in the domain of a suitably defined transformation that can be used as the basis for optimization of generic pattern recognition systems, including hidden Markov models and Multi-Layer Perceptrons. The two formulations of risk estimate described here are closely tied to the Minimum Classification Error/Generalized Probabilistic Descent (MCE/GPD) framework for discriminative training that is well-known to the speech recognition community. In the new approach, high-dimensional and possibly variable-length training tokens are mapped to the centers of Parzen kernels which are then easily integrated to find the risk estimate. The utility of such risk estimates lies in the fact that they are explicit functions of the system parameters and hence suitable for use in practical optimization methods. The use of Parzen estimation makes it possible to establish convergence of the risk estimate to the true theoretical classification risk, a result that formally expresses the benefit of linking the degree of smoothing to the training set size. Convergence of the minimized risk estimate is also analyzed. The new approach establishes a more general theoretical foundation for discriminative training than existed before, supporting previous work and suggesting new variations for future work.     

Discrete variable stochastic approximation procedures and recursive autoregressive model identification

Two stochastic approximation procedures are proposed for finding a point attaining the maximum of a regression function defined and observable only at points on a set of discrete variables. The asymptotic convergence property of the procedures is discussed using the theorem of almost supermartingales. The procedures are applied to the recursive identification of autoregressive time series models. The identification procedure consists of a recursive order estimation stage and a recursive autoregressive parameter updating stage, and gives the true autoregressive model or the best autoregressive approximation model.     

Efficient algorithms for NMR quantum computers with small qubits

The security of the RSA cryptosystems is based on the difficulty of factoring a large composite integer. In 1994, Shor showed that factoring a large composite is executable in polynomial time if we use a quantum Turing machine. Since this algorithm is complicated, straightforward implementations seem impractical judging from current technologies. In this paper, we propose simple and efficient algorithms for factoring and discrete logarithm problem based on NMR quantum computers. Our algorithms are easier to implement if we consider NMR quantum computers with small qubits. A part of this work was done while both authors were with NTT Communication Science Laboratories. Noboru Kunihiro, Ph.D.: He is Assistant Professor of the University of Electro-Communications. He received his B.E., M.E. and Ph.D. in mathematical engineering and information physics from the University of Tokyo in 1994, 1996 and 2001, respectively. He had been engaged in the research on cryptography and information security at NTT Communication Science Laboratories from 1996 to 2002. Since 2002, he has been working for Department of Information and Communication Engineering of the University of Elector-Communications. His research interest includes cryptography, information security and quantum computations. He was awarded the SCIS’97 paper prize. Shigeru Yamashita, Ph.D.: Associate Professor of Graduate School of Information Science, Nara Institute of Science and Technology, Nara 630-0192, Japan. He received his B.E., M.E. and Ph.D. degrees in information science from Kyoto University, Kyoto, Japan, in 1993, 1995 and 2001, respectively. His research interests include new type of computer architectures and quantum computation. He received the 2000 IEEE Circuits and Systems Society Transactions on Computer-Aided Design of Integrated Circuits and Systems Best Paper Award.     

Agent-Based Design Model of Adaptive Distributed Systems

A next generation distributed system is expected to adapt to various changes of both the users' requirements and the operational conditions of environment where the distributed system operates. The aim of our research is to establish a new design model of an adaptive distributed system (ADS) to deal with various changes occurred in the system environment. In this paper, we propose an agent-based architecture of ADS, based on the agent-based computing paradigm. Then, we implement a prototype of the ADS with respect to videoconferencing applications and also evaluate the adaptive functions of the ADS realized on the basis of the proposed architecture.     

Optimized bending sequences of sheet metal bending by robot

In the present study, a method is proposed which generates bending sequences of a sheet metal part handled by a robot. If parts are handled by a robot, the best grasping positions for each bending and the number of repositions must be indicated in advance. Using the proposed method, feasible bending sequences with grasping positions are obtained and the sequences are sorted in the order of the number of repositions. In generating the sequences, several important features for the sheet metal bending are considered by dividing them into channels, which is one of the base features. The error accumulated during bending operation is calculated for each sequence, and set-up positions can be selected so as to satisfy the preferential tolerance. The proposed method assists the sheet metal process planner by allowing him/her to plan in advance the best bending sequences and to confirm if the robot can perform the handling operation. A computer simulation was written based on the proposed method, and illustrative examples and results of various simulation models are also given.     

Forced temperature cycling of a catalyst layer and its application to propylene oxidation

In this study, a new reaction device suited for forced temperature cycling was developed. This device has a heating element in the reaction tube, and forced temperature cycling was realized by operating this element intermittently. The energy needed for the operation was about 20 W, which is much less than that required to operate reactors used in previous studies of temperature cycling. This reactor was used to examine the effect of periodic operation on the oxidation of propylene. It was found that the conversion under periodic conditions was higher than that observed under steady state. In addition, the reaction system approached a relaxed steady state as the cycle time was reduced to 1 s. The effect of forced temperature cycling on propylene oxidation was successfully demonstrated.