Existing commercial finite element analysis (FEA) codes do not exhibit the performance necessary for large scale analysis on parallel computer systems. In this paper, we demonstrate the performance characteristics of a commercial parallel structural analysis code, ADVC, on Blue Gene/L (BG/L). The numerical algorithm of ADVC is described, tuned, and optimized on BG/L, and then a large scale drop impact analysis of a mobile phone is performed. The model of the mobile phone is a nearly-full assembly that includes inner structures. The size of the model we have analyzed has 47 million nodal points and 142 million DOFs. This does not seem exceptionally large, but the dynamic impact analysis of a product model, with the contact condition on the entire surface of the outer case under this size, cannot be handled by other CAE systems. Our analysis is an unprecedented attempt in the electronics industry. It took only half a day, 12.1 hours, for the analysis of about 2.4 milliseconds. The floating point operation performance obtained has been 538 GFLOPS on 4096 node of BG/L.

This paper proposes a new approach to realize a reinforcement learning scheme for autonomous multiple agents system. In our approach, we treat the cooperative agents systems in which there are multiple autonomous mobile robots, and the seesaw balancing task is given. This problem is an example of corresponding tasks to find the appropriate locations for multiple mobile robots. Each robot agent on a seesaw keeps being balanced state. As a most useful algorithm, the Q-learning method is well known. However, feasible action values of robot agents must be categorized into some discrete action values. Therefore, in this study, the actor-critic method is applied to treat continuous values of agents' actions. Each robot agent has a set of normal distribution, that determines a distance of the robot movement for a corresponding state of the seesaw system. Based on a result of movement in this system, the normal distribution is modified by actor-critic learning method. The simulation result shows the effectiveness of our approaching method.

In observing behavior of multiple autonomous robots, a microscopic observation expressed by dynamic equations is usually used. However, it is very difficult to estimate behavior of robots or mutual interactions among them in real time. Furthermore, it is hard to realize the observed system by taking consideration in all the factors of the system. On the other hand, a macroscopic observation defined by state equations is efficient for recognizing behavior of multiple robots. In this study, a quantitative observation approach is proposed on behavior of multiple robots. This approach introduces macroscopic state quantities in thermodynamics into expression of the multiple robots system. The advantage of this approach is that observation on the behavior of autonomous robots in real world can be mapped to characteristic quantities in another conceptual state space. At first, the state quantities of multiple robots system are defined and their physical meaning is discussed regarding it as quantitative observation of multiple robots. The macroscopic state quantities in thermodynamics, such as temperature, pressure and entropy, are introduced into mobile robots system. Each mobile robot is regarded as a particle in term of thermodynamic systems. Experiments show that the states of robots system can be classified from a viewpoint of the macroscopic state quantities in thermodynamics. This verifies that the macroscopic quantitative observation is efficient and applicable to controlling multiple robots system.

A framework for effective energy saving control measures for Shinkansen ("Bullet Train") lines is presented. The future will see increased transportation capacity. For example, train frequency of the fastest train "Nozomi" is expected to be rise from one to four trains per hour. The dynamic power supply strategy is the best solution to avoid the needs to install extra electric facilities. Decreasing the top speed and matching the train operation phase are ways to cut the fluctuating load peak at power substations. So as to realize the desired power control characteristics, a system is proposed which consists of WTU (wayside telecommunication unit), zone controller at each power substation, Train Traffic Control Center and BIF (Broad Information Field Network). The BIF connects all the facilities [1]. The WTU communicates with trains using radio waves and with the zone controller using the Wayside Optical Network. Beforehand, a train time table suitable for energy saving is scheduled using the railway power simulation. The Train Traffic Control Center broadcasts three-dimensional "macro targets" for all trains in both directions. Using local conditions, the zone controller composes a "macro target" into a "zone run-curve". Firstly, trains are able to execute the operation of "target shooting" using the zone run-curve. Secondary, regenerated braking power is effectively transferred to another accelerating train. Thirdly even if the train schedule has been disrupted severely trains do not restart simultaneously, so the peak load at substations goes down successfully. Thus, the proposed dynamic power supply strategy for future shinkansen trains realizes not only energy saving, but also conserves the world resources.

A future framework for the traffic control system of an ultra high speed and huge transportation capacity Maglev (magnetically levitated) linear motor line is presented. This Maglev system must have the ability to recover from disrupted traffic scheduling situations and to provide electrical energy savings. The proposed system consists of the CSS (Central Commander Support System), RPCCs (Regional Power Control Centers), TPCSs (Traffic and Power Control Substations) on the wayside, TSS (Train Supervisor Support System) on board, and IFN (Information Field Network) joining all the facilities. The delay recovery control function has three layers. The top is a strategic rescheduling layer. The CSS helps to plan recovering strategy. The middle layer is a headway minimizing layer including the TPCSs which direct the Maglev train toward a 3D target so as to minimize the train headway. The bottom layer is a temporal speed control layer. The energy saving function has a two layered structure. The top is a total load control layer coordinating the powering/braking schedule of each train and the bottom is the same as in the traffic control function. The proposed architecture is expected to present quite controllable circumstances to the central commander, and also to realize the desirable overall power dissipation characteristic.

Attempts to realize an autonomous solving mechanism for a task planning problem by a machine learning system. As a robot task planning problem, the block stacking problem is treated. It is well known as one of the most difficult problems. In particular the difficulties of this problem increase in a multiagent environment where multiple robots exist in a problem domain and they cooperate or negotiate to achieve given tasks effectively. To realize an autonomous planning mechanism, a classifier system is applied to this problem. In this approach, a classifier corresponds to a production rule that instructs the next operation when its conditional part is fully matched for a current state. Each robot has an individual classifier system as the robot task planner.

The Tokaido and Sanyo Shinkansen (bullet train) has been maintaining remarkable levels of punctuality and safety. Furthermore, as the speed and density of its traffic have been increasing, the Shinkansen must be able to promptly restore scheduled services when they are interrupted. The paper proposes a system architecture based on the FADS concept, for a flexible Shinkansen system that will be inherited by the next generation. The new system architecture has two main functions: utilizing information and executing processes to cope with the change of environment.>

The friendly autonomous decentralized system (FADS) is proposed as a next generation railway system. It uses a concept based on a biological analogy to perform actions flexibly and speedily. The FADS is defined by three characteristics: the structure of the autonomous unit, the function of each unit, and the cooperation between units. The production (train control) department, the sales department, the management department, and the functions of each department are the autonomous units. These units always cooperate closely with each other. The cooperation among the units is effected through the information field.>

A new approach of how to solve the 3D packing problem automatically is proposed. This problem is well known as a complete combinatorial problem. The authors attempt to realize a mechanism in which the 3D packing rule is automatically tuned, and an optimum packing solution is obtained by applying genetic algorithms which mimic the process of a natural evolution system. The 3D packing strategy is controlled by two evaluation functions which dominate the selection of a next allocation position and a box. To find the near-optimal strategy, the weighted coefficients of the evaluation functions are tuned by applying the genetic operators such as reproduction, crossover and mutation. To use the obtained tuned results as accumulated successful strategies, a 3D packing rule-base is constructed. The rules in this rule-base are composed of a 'conditional part', which expresses the features of the given problem, and a 'procedural part', which gives the packing strategy.>