A study on a quantum-inspired evolutionary algorithm based on pair swap

A quantum-inspired evolutionary algorithm (QEA) is proposed as a stochastic algorithm to perform combinatorial optimization problems. The QEA is evolutionary computation that uses quantum bits and superposition states in quantum computation. Although the QEA is a coarse-grained parallel algorithm, it involves many parameters that must be adjusted manually. This paper proposes a new method, named pair swap, which exchanges each best solution information between two individuals instead of migration in the QEA. Experimental results show that our proposed method is a simpler algorithm and can find a high quality solution in the 0-1 knapsack problem. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Cooperation of categorical and behavioral learning in a practical solution to the abstraction problem

Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture is able to learn efficiently and adapt to unexpected changes of the environment autonomously. Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI). Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for knowledge base systems.     

Dielectric Dispersion of Polycrystalline Aluminum Nitride at Microwave Frequencies

Complex permittivity of polycrystalline AlN was measured over the frequency range 100 MHz to 13 GHz using reflection coefficient and cavity resonator methods. Dielectric relaxation peaks of loss tangent were observed in the frequency range 200 MHz to 3 GHz. The maximum value of loss tangent was ∼200 × 10⁻⁴. Relative permittivities decreased by ∼4% with increased frequency at the region of relaxation peaks. It was shown that these dispersion frequencies were inversely proportional to the grain sizes of AlN and independent of purities and processes. Model calculations for elastic vibration of a single-crystal grain of AlN were performed to investigate the origin of the dielectric dispersion due to piezoelectric effect. The calculated results of elastic sphere and 14-faced-polyhedron models showed a good quantitative agreement with the observed dispersion frequencies, which suggested that the dispersion was due to a piezoelectric vibration of a single grain of AlN ceramics at microwave frequencies.     

Strength Analysis of Yttria-Stabilized Tetragonal Zirconia Polycrystals

Tensile strength of Y₂O₃-stabilized ZrO₂ polycrystals (Y-TZP) was measured by a newly developed tensile testing method with a rectangular bar. The tensile strength of Y-TZP was lower than that of the three-point bend strength, and the shape of the tensile strength distribution was quite different from that of the three-point bend strength distribution. It was difficult to predict the distribution curve of the tensile strength using the data of the three-point bend strength by one-modal Weibull distribution. The distribution of the tensile strength was analyzed by two- or three-model Weibull distribution coupled with an analysis of fracture origins. The distribution curve of the three-point bend strength which was estimated by multimodal Weibull distribution agreed favorably with that of the measured three-point bend strength values. A two-modal Weibull distribution function was formulated approximately from the distributions of the tensile and three-point bend strengths, and the estimated two-modal Weibull distribution function for the four-point bend strength agreed well with the measured four-point bend strength.     

A high‐performance sorting algorithm for multicore single‐instruction multiple‐data processors

Many sorting algorithms have been studied in the past, but there are only a few algorithms that can effectively exploit both single‐instruction multiple‐data (SIMD) instructions and thread‐level parallelism. In this paper, we propose a new high‐performance sorting algorithm, called aligned‐access sort (AA‐sort), that exploits both the SIMD instructions and thread‐level parallelism available on today's multicore processors. Our algorithm consists of two phases, an in‐core sorting phase and an out‐of‐core merging phase. The in‐core sorting phase uses our new sorting algorithm that extends combsort to exploit SIMD instructions. The out‐of‐core algorithm is based on mergesort with our novel vectorized merging algorithm. Both phases can take advantage of SIMD instructions. The key to high performance is eliminating unaligned memory accesses that would reduce the effectiveness of SIMD instructions in both phases. We implemented and evaluated the AA‐sort on PowerPC 970MP and Cell Broadband Engine platforms. In summary, a sequential version of the AA‐sort using SIMD instructions outperformed IBM's optimized sequential sorting library by 1.8 times and bitonic mergesort using SIMD instructions by 3.3 times on PowerPC 970MP when sorting 32 million random 32‐bit integers. Also, a parallel version of AA‐sort demonstrated better scalability with increasing numbers of cores than a parallel version of bitonic mergesort on both platforms. Copyright © 2011 John Wiley & Sons, Ltd.     

粉末注射成形奥氏体不锈钢的疲劳和冲击强度

本文的目的是研究粉末性能对粉末注射成形奥氏体不锈钢的力学性能(特别是疲劳和冲击强度)的影响。试样用混有聚酰氨粘结剂系的水雾化(WA)和气雾化(GA)粉末制成。注射坯在空气中脱粘，而后在不同温度、不同保温时间下真空烧结。粉末性能对烧结体的密度、显微组织和力学性能有显著影响。孔和析出物显示出Ostward时效，而孔和析出物的长大满足Lifshitz-Wagner方程。WA和GA粉末试样的疲劳极限分别约为300MPa和310MPa。它们的疲劳强度略低于常规锻材。烧结体的冲击值随密度增加线性增大。     

Quantum Monte Carlo Device Simulation of Nano-Scaled SOI-MOSFETs

Quantum transport properties of nano-scaled SOI-MOSFETs are investigated based on a quantum Monte Carlo (MC) device simulation. The quantum mechanical effects are incorporated in terms of a quantum correction of potential in the well-developed particle MC computational techniques. The ellipsoidal multi-valleys of silicon conduction band are also considered in the simulation. First, the validity of the quantum MC technique is verified by comparing the simulated results with a self-consistent Schrödinger-Poisson solution at thermal equilibrium. Then, we apply the technique to non-equilibrium and quasi-ballistic quantum transport in nano-scaled SOI-MOSFETs.