I have developed and evaluated a data reduction system for point data by using rendering the target 3D object represented with points from multi directions. So far, although this method has been applied to surface data, it was extended to point data in this study. For point data, this method works as a filter that extracts points with sufficient density from the original points. In this case, it is possible to solve the duplication extraction problem which occurs with surface data, by using a point ID buffer that works similar to the Z buffer. I have implemented the prototype system with the function and evaluated the performance. In addition, the characteristics of the two parameters required for the proposal method was investigated. I confirmed that the system works effectively and learned that the total number of rendered pixels determined by the two parameters has a positive correlation with the quality of the extracted point. Occlusion at rendering is a problem for complicated shapes. In such cases, it was suggested that, if the total rendering pixel numbers are same, increasing the number of the rendering direction can improve the quality more than increasing the resolution per a rendering image.

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The programming education is very important in technical colleges, universities and enterprises. We have developed a problem solving environment (PSE) for the education and learning support: TSUNA-TASTE ⁽¹⁾. The TSUNA-TASTE system has a framework to make various programming systems. In this paper we propose and develop a new subsystem of the KOSEN Procon (KOSEN Proguraming Contest) Competition Section in Japan. In this paper we present the PSE system applied to the competition section of the KOSEN Procon. Depending the Procon game rule, the KOSEN procon PSE was rebuilt. The participants must search an appropriate solution among many combinations to win this game. Therefore, it is necessary to find out the most suitable algorithm. We also introduce the purpose of the programming contest and rule of the competition section in Section 2. We prepared an exercise system on Web to perform trials beforehand, and we also present the exercise system in Section 3.

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In order to recognize conditions of a plant growth in its seedling period effectively, we propose a problem solving environment to acquire both the growth information using sensors and the present conditions using image processing. To accelerate the user understanding of the conditions, we adopt distributed computing with Software Defined Networking. In the result, user obtains dataset related in the growth results effectively and the result indicates that it is possible to estimate provisioning on-demand computer resources for scalability.

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We study unique functions of an Autonomous Asynchronous Cooperation (AAC) useful to a Problem-Solving Environment (PSE) for a target class having local minima. In our paper, assume that an asynchronous cooperation system consists of network-linked computers and software packages. Several software packages deploy to network-linked computers to autonomously work toward common goals to solve a problem. The behavior of an AAC is complex and hardly predictable, even if any software does not have randomness. Our study clarified that our mathematical model for an AAC shows stochastic behavior which dynamically changes depending on the status of itself. An AAC is the execution environment itself as the distributed Monte Carlo method. Furthermore, the observed behavior of an AAC applied to solve a typical problem demonstrated a complexity shown in our mathematical model. An AAC effectively provides the unique functions in developing a PSE for a target class having local minima. Then, PSE application developers can concentrate on problem-solving without being bothered by programming technique.

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