This paper is concerned with a finite element method that uses hybrid meshes of multiresolution structured and unstructured meshes to simulate earthquake ground motion in large basins. While standard octree-based mesh methods use cubic elements and rely on mesh refinement to model topography, the present hybrid meshes use tetrahedral elements to model complicated layer interfaces and above-ground surfaces and to provide a transition between cubic elements of different sizes. The hybrid meshes avoid the step-like character inherent to the cubic meshes and the resulting concomitant loss of accuracy. Several numerical experiments of increasing complexity are carried out to verify and illustrate the proposed technique.
Our main aim is to extract multiple rules from log files in the computer systems, to detect various levels of errors, and to inform these errors or configuration mistakes to the system administrators automatically, in order to manage them without expert knowledge. To satisfy this aim, we performed an extraction experiment from the log files of a system using Automatically Defined Groups (ADG), which is based on Genetic Programming. Moreover, we focused on "System State Pattern" related to the difference between normal daily state and abnormal state that some errors occur in the system. In this experiment, then, we tried to extract rules without any manually managed and supervised information, by using simple translation technique: regular expressions. As a result, 50 agents in the best individual were divided into 16 groups from 322 log files. This means that 16 rules were acquired. We confirmed these rules could detect some errors such as DNS configuration error. We could also find the importance of the rules because the rule with more agents tended to have a higher adopted frequency by evolutionary computation. Therefore, we consider that our method using ADG is useful for the diagnosis of computer systems, and helps administrators manage their systems without expert knowledge about their systems.
An evacuation simulation code based on Multi Agent Systems (MAS), with moderately complex agents in 2D grid envi- ronments, is developed. The main objective of this code is to estimate the effectiveness of the measures taken to smoothen and speedup the evacuation process of a large urban area, in time critical events like tsunami. A vision based autonomous navigation algorithm, which enables the agents to move through an urban environment and reach a far visible destination, is implemented. This simple algorithm enables a visitor agent to navigate through urban area and reach a destination which is several kilometers away. The navigation algorithm is verified comparing the simulated evacuation time and the paths taken by individual agents with those of theoretical. Further, a parallel computing extension is developed for studying mass evacuation of large areas; vision based autonomous navigation is computationally intensive. Several strategies like communication hiding, dynamic load balancing, etc. are implemented to attain high parallel scalability. Preliminary tests on the K-computer attained strong scalability above 94% at least up to 2048 CPU cores, with 2 million agents.
In this paper, we develop a FCM using a "concept template" instead of a concrete concept description. The concept template consists of a predicate and the case elements of the predicate, and they are used for both input concept and output concept in relation rules. The input concept templates accept the variation of similar input concepts and the output concept templates are used to generate the content of the output concept based on the content of input concepts. The inference process will be repeated until the concept state reaches convergence. We created 207 concept templates and a weighted matrix to show the relationship between concept templates. When 23 concepts were inputted to our system, 71 concepts were totally activated and the content of all output concepts were generated correctly.
