High-temperature oxidation of Al-deposited stainless-steel foils

The oxidation resistance of Al-deposited Fe–Cr–Al foils containing small amounts of La and Ce was assessed by a cyclic oxidation test with temperature varying between room temperature and 1323 K to 1423 K in static air. (1) The Al content of Fe–Cr–Al–La, Ce foils can be increased by depositing an Al layer from the vapor phase. The deposition of a 1-m-thick Al layer on both sides of the 50-m-thick foil is equivalent to a 1.5 mass% increase in the Al content. The deposited Al diffuses into the foil during heat treatment. The uniform distribution of Al is obtained by heating at 1273 K for 18 ks. (2) After the initial transition stage the oxidation follows the parabolic law until breakaway sets in. The scale consists mainly of -Al₂O₃ during the parabolic period. (3) The increase in the Al content by more than 5 mass% by the Al-deposition remarkably improves high-temperature oxidation resistance (smaller parabolic rate constant and longer protection time). (4) The Al-deposited foils have better oxidation resistance than the conventional foils with the same contents of Al and rare-earth elements. This is attributable to the different nature of the initially formed oxide on the Al-deposited foil. (5) The so-called rare-earth element effect was also observed for the Al-deposited foils. Predominant diffusion of oxygen through the Al₂O₃ scale and vacancy-sink mechanism are applicable to the present results.     

Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity

Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.     

Design of an adaptive fuzzy sliding mode control for uncertain discrete-time nonlinear systems based on noisy measurements

This paper presents the design of an adaptive fuzzy sliding mode control (AFSMC) for uncertain discrete-time nonlinear dynamic systems. The dynamic systems are described by a discrete-time state equation with nonlinear uncertainties, and the uncertainties include the modelling errors and the external disturbances to be unknown but nonlinear with the bounded properties. The states are measured by the restriction of measurement sensors and the contamination with independent measurement noises. The nonlinear uncertainties are approximated by using the fuzzy IF-THEN rules based on the universal approximation theorem, and the approximation error is compensated by adding an adaptive complementary term to the proposed AFSMC. The fuzzy inference approach based on the extended single input rule modules is proposed to reduce the number of the fuzzy IF-THEN rules. The estimates for the un-measurable states and the adjustable parameters are obtained by using the weighted least squares estimator and its simplified one. It is proved that under some conditions the estimation errors will remain in the vicinity of zero as time increases, and the states are ultimately bounded subject to the proposed AFSMC. The effectiveness of the proposed method is indicated through the simulation experiment of a simple numerical system.     

Bottom-up pyramid cellular acceptors with three-dimensional layers

In 1997, C.R. Dyer and A. Rosenfeld introduced an acceptor on a two-dimensional pattern (or tape), called the pyramid cellular acceptor, and demonstrated that many useful recognition tasks are executed by pyramid cellular acceptors in time proportional to the logarithm of the diameter of the input. They also introduced a bottom-up pyramid cellular acceptor which is a restricted version of the pyramid cellular acceptor, and proposed some interesting open problems of the bottom-up pyramid cellular acceptors. On the other hand, we think that the study of threedimensional automata has been meaningful as the computational model of three-dimensional information processing such as computer vision, robotics, and so forth. In this paper, we investigate about bottom-up pyramid cellular acceptors with three-dimensional layers, and show their some accepting powers. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Self-organizing maps with asymmetric neighborhood function

The self-organizing map (SOM) is an unsupervised learning method as well as a type of nonlinear principal component analysis that forms a topologically ordered mapping from the high-dimensional data space to a low-dimensional representation space. It has recently found wide applications in such areas as visualization, classification, and mining of various data. However, when the data sets to be processed are very large, a copious amount of time is often required to train the map, which seems to restrict the range of putative applications. One of the major culprits for this slow ordering time is that a kind of topological defect (e.g., a kink in one dimension or a twist in two dimensions) gets created in the map during training. Once such a defect appears in the map during training, the ordered map cannot be obtained until the defect is eliminated, for which the number of iterations required is typically several times larger than in the absence of the defect. In order to overcome this weakness, we propose that an asymmetric neighborhood function be used for the SOM algorithm. Compared with the commonly used symmetric neighborhood function, we found that an asymmetric neighborhood function accelerates the ordering process of the SOM algorithm, though this asymmetry tends to distort the generated ordered map. We demonstrate that the distortion of the map can be suppressed by improving the asymmetric neighborhood function SOM algorithm. The number of learning steps required for perfect ordering in the case of the one-dimensional SOM is numerically shown to be reduced from O(N(3)) to O(N(2)) with an asymmetric neighborhood function, even when the improved algorithm is used to get the final map without distortion.     

Basic constructs of the SIM operating system

The basic constructs of SIMPOS (Sequential Inference Machine Programming and Operating System) are explained. SIMPOS is an operating system for a super-personal computer (SIM), based on logic programming language (a modified Prolog, called KL0). Our design principle is simplicity both in concept and structure. The entire system will be constructed using these basic constructs.     

Theory of ion mobility in liquid3He

An ion interacting with quasiparticles in liquid³He is treated theoretically by summing most divergent terms in perturbation series of the self-energy of the ion and the vertex part. The ion Green's function is renormalized by a factorZ(T), and the vertex part byZ(T) ^?1, where \({\text{Z(T)}} = {\text{(T/T}}_F {\text{)}}^{{\text{2V}}_{{\text{0}}^{\rho ^{\text{2}} } }^{\text{2}} } \) , forT ₀?T?T _F. Here,T ₀=(m/M)T _F, withm the³He mass andM the ion mass, and \({\text{V}}_{0^\rho } \) is the strength of the interaction. The factor explains the weak temperature dependence of the mobility around the minimum atT ₀; we also discuss its effect on the behavior of the mobility in³He-B nearT _c.     

Theoretical and evolutionary parameter tuning of neural oscillators with a double-chain structure for generating rhythmic signals

A neural oscillator with a double-chain structure is one of the central pattern generator models used to simulate and understand rhythmic movements in living organisms. However, it is difficult to reproduce desired rhythmic signals by tuning an enormous number of parameters of neural oscillators. In this study, we propose an automatic tuning method consisting of two parts. The first involves tuning rules for both the time constants and the amplitude of the oscillatory outputs based on theoretical analyses of the relationship between parameters and outputs of the neural oscillators. The second involves an evolutionary tuning method with a two-step genetic algorithm (GA), consisting of a global GA and a local GA, for tuning parameters such as neural connection weights that have no exact tuning rule. Using numerical experiments, we confirmed that the proposed tuning method could successfully tune all parameters and generate sinusoidal waves. The tuning performance of the proposed method was less affected by factors such as the number of excitatory oscillators or the desired outputs. Furthermore, the proposed method was applied to the parameter-tuning problem of some types of artificial and biological wave reproduction and yielded optimal parameter values that generated complex rhythmic signals in Caenorhabditis elegans without trial and error.     

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.