Recently, much attention is paid on the laparoscopic surgery, that is a minimally invasive one and is performed through the incisions of about 10mm. Therefore it produces good effects on the patients in many aspects. But it has difficulty in performing surgery with less dexterity and perception. So far, some manipulators have been developed to assist the laparoscopic surgery. However the problem of measuring the force applied to organs still remains. Internal organ may be damaged without the sense of force. In order to prevent the accident, this paper proposes a method to detect the force more easily and exactly.

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This paper describes a solution scheme for inverse dynamics of link mechanisms, which has already been proposed and applied in several inplane motions. In this theory, FEM is used for the discretization of equations of motion. This scheme calculates nodal forces by evaluating equations of motion in a matrix form, and thus information from the entire system can be handled simply, and the torques input to each joint of link mechanisms to achieve required motion are calculated easily. This scheme is suitable to the feed-forward control of closed-loop or continuously link mechanisms, and it may provide good control law. The proposed scheme is implemented in the transverse walking problem of a robot. The results of simulation illustrate the potential that the proposed solution scheme has for the control of walking motion of robot.

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Adjustment of drawing dies is not easy because the dies are generally con figure-to-order products one by one. Getting the dies into production is a continuous process of trial and error by skilled worker. Therefore, the introduction of a technological technique is requested although the process is commonly based on worker's skill. In this study, we describe the system which has a multi-point support structure for dies and a method of optimizing the locations of support devices by using genetic algorithm. The devices are arranged so that the deflections of each device become equal. The results using this method and our previous method (hill-climbing method) are compared for verification of the suitability of this method. The locations of support devices are similar to the results of the previous method.

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This paper proposes a new method which is called as Adaptive Range Particle Swarm Optimization (ARPSO), based on Adaptive Range Genetic Algorithm. That is, the search domain is determined by using the mean and standard deviation of each design variable. At the initial search stage it is preferable to explore the design domain widely, and is also preferable to explore the small design domain as the search goes on. To achieve this objective, new parameter which determines the search domain is introduced. Through numerical examples, the effectiveness and validity of proposed method are examined.

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In this paper, Stochastic Schemata Exploiter (SSE) and its extended algorithm named as Extended SSE (ESSE) are compare with Bayesian Optimization Algorithm (BOA), which is one of the Estimation of Distribution Algorithm in order to com firm their features.

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SPH using Constramed-Moving-Least-Square Method (C-MLS) is proposed and is applied to large deformation elastic perfectly plastic analysis. The spatial approximation of SPH is less accurate due to the only 0-th order consistency. Various Kernel approximations have been enhanced for the improvement of accuracy, In this study, C-MLS interpolation that improves MLS is applied to SPH. C-MLS is an improvement over MLS in the case when the approximation function does not pass through the node. The Poisson's equation for pressure is solved to take account of incompressibility in plastic analysis. Numerical analysis of longitudinal vibrating bar and cylinder impact are demonstrated to validate the present method.

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