Thermodynamics of oxygen,nitrogen and sulfur in liquid iron equilibrated with CaO-TiOx and CaO-Al2O3-TiOx melts

Equilibria between CaO-TiO_x or CaO-Al₂O₃-TiO_x melts and liquid iron with respect to O, N and S were studied at 1873 K as a function of Ti (or Al) content in metal, using a CaO or an Al₂O₃ crucible. The Al-O and Ti-O relations in liquid iron were studied, and nitride and sulfide capacities defined by and , respectively, were obtained from nitrogen and sulfur distribution ratios, coupled with the analyzed oxygen content in liquid iron or the P_O2 determined by Ti/TiO₂ equilibrium. Based on these results, activities of Al₂O₃ and activity coefficients of TiO₂ and TiO_1.5 were evaluated.     

Event-triggered control for discrete-time multi-agent average consensus

We consider the average consensus problem for the multi-agent system in the discrete-time domain. Three triggering based control protocols are developed, which dictate the broadcast and control update instants of individual agents to alleviate communication and computational burden. Lyapunov-based design methods prescribe when agents should communicate and update their control so that the network converges to the average of agents' initial states. We start with a static version of the distributed event-triggering law and then generalize it so that it involves an internal auxiliary variable to regulate the threshold dynamically for each agent. The third protocol uses a self-triggering algorithm to avoid continuous listening wherein each agent estimates its next triggering time and broadcasts it to its neighbors at the current triggering time. Numerical simulations are shown to validate the efficacy of the proposed algorithms.     

Measurement of aluminum or silicon activity in Fe-Ni (< 30 %) alloys using mullite and ZrO2-based solid electrolyte galvanic cell

Using mullite (3Al₂O₃·2SiO₂)-tube type and ZrO₂ (MgO mole fractions of 9 %)-plug type solid electrolyte, the activities of Al in the Fe-15, 29% Ni-0.002 to 0.84% Al (mass contents in%) alloys were measured at 1873 K in an Al₂O₃ crucible, and those of Si in the Fe-13, 28% Ni-0.13 to 2.3% Si alloys were measured at 1823 K in a mullite crucible with an excess of SiO₂. The interaction coefficients of and were determined as ?0.0173 ± 0.0052 and 0.00021 ± 0.00007, respectively, and those of and were determined as 0.0094 ± 0.0049 and ?0.00008 ± 0.00004, respectively. The oxygen activities obtained by the EMF measurements were compared with those obtained from the contents of analyzed oxygen. The oxygen activities in the Fe-Ni-Al alloys were found to be supersaturated. The interaction coefficients of and were determined as 0.0027 ± 0.0012 and 0.00012 ± 0.00004, respectively, from the contents of analyzed oxygen in Fe-Ni-Si alloys.     

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.     

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.     

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.     

Objective Oriented Multistage Ring Shear Test for Shear Strength of Landslide Soil

Ring shear tests were conducted on five samples of different nature with a modified Imperial College type ring shear machine. The three different testing methods used, (1) individual sample testing for each normal stress, (2) increasing load multistage ring shear test, and (3) reducing load multistage ring shear test, all showed similar effective residual internal friction angle for the samples, irrespective of testing method. However, effective residual shear intercept was different according to the testing methodology. The internal friction angle did not vary, particularly after the first minimum point in the stress displacement curve, although the residual shear intercept decreased with increase in the displacement. The thickness of the shearing zone increased along with the displacement. The remolded peak shear strength for saturated conditions at field dry density varied with the consolidation history. Measurement of remolded peak shear strength was possible in a single sample using the increasing load multistage ring shear test at normal consolidation. The equilibrium water content of the sample after the ring shear test was nearly equal to the plastic limit.     

Semantic Web: A road to the knowledge infrastructure on the internet

In this article, I describe the basic technologies for Semantic Web and relationship between Semantic Web and Knowledge Representation in Artificial Intelligence. Semantic Web is planned as an extension of the current web in order to help cooperation between computers and humans, i.e., computers and humans are expected to understand each other in the knowledge level. I first describe the vision of the Semantic Web, then introduce the current Semantic Web technologies, i.e., RDF, RDFS, and OWL. I describe relationship between the trend of Semantic Web and Knowledge Representation, and clarify challenges and difficulties of Semantic Web from the point of view of Knowledge Representation. Hideaki Takeda: He is a professor at National Institute of Informatics (NII) and a professor in Department of Informatics at the Graduate University of Advanced Studies (Sokendai). He received his Ph.D. from the University of Tokyo in 1991. His research interest in computer science includes ontology engineering, community informatics and knowledge sharing systems.