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The aim ojtransmrssron e.xpansion planning is to determine which right-of-way to construct new 1ine.r in order to meet the,jiiture load in the most economical M'ay. This problem has been solved by the mathematical programming techniques, which require considerable computa tional efforts, or by .successive planning based on .sen.riti\iit), analysis, which find a single non-optimal solution. Althoiigh another method that has gfficiency,for combinatorial problems is the neuro-computing, this approach obtains poor solutions ~, h i l e it .saws computational efforts. The inost desirable approach, for this planning prohleni can ,find many good .rolution.~ in reasonabIe time, because experts of planning ~~1 1 1 easily plan the econotnical and wliable expansion according to these solutions by compare w i t h each other. This paper presents an approach f o r sohjing transmission expansion planning based on neuro-computing hybridized with genetic algorithm. This approach generates suitable initral ,states, M'liicli include past infomation, of neural networks utilizing gene t ic algorithm. Mingling 1irurc/-con7putirig and genetic algoritlm, the proposed approach can ,find many good solutions in rea.sonahle tiine making ,fill1 use of their merits. C'oinputational examples show the ef~ectivenr.s.s of flie proposed approach by cotnparison with conventional approaches

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This paper discusses a shaping strategy of a rheological object. We first introduce a seven-nodes viscoelastic model for approximating the outline and the dynamic characteristics of the object. Then, we show a shaping method of the object's outline, where the proportion of the input axis to another one, which is perpendicular to the input one, is controlled by using a parallel jaw gripper. Based on the plastic deformation distribution for the integrated input stress, the proposed method can actively manage the object's final outline. In addition to the contribution on simplifying the gripper's degree of freedom, this method has an advantage that the handling time is drastically reduced, compared with the position based passive method. We finally show the experimental results for confirming the validity of the proposed method.

The aim of transmission expansion planning is to determine which right‐of‐way to use when constructing new lines in order to meet a forecasted load in the most economical way. This problem has been solved previously by mathematical sensitivity analysis (which finds a single nonoptimal solution). It is difficult to plan for economical and reliable expansion due to its discrete and combinatorial nature. Although another method that has efficiency for combinatorial problems is neurocomputing, this approach saves computational efforts but obtains poor solutions. The most desirable approach for this planning problem is one in which many good solutions are found in reasonable time, because planning experts will then be able to plan economical and reliable expansion according to these solutions. This paper presents an approach for solving transmission expansion planning based on neurocomputing hybridized with a genetic algorithm. This approach generates suitable initial states, which include past information, of neural networks utilizing genetic algorithm. Mingling neurocomputing and a genetic algorithm, the proposed approach can find many good solutions in reasonable time making full use of their merits. Computational examples show the effectiveness of the proposed approach by comparison with conventional approaches.

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We have previously proposed the see-through retinal projection type super multi-view head-mounted display (HMD). The smooth motion parallax provided by the super multi-view technique enables a precise superposition of virtual 3D images on real scene. Moreover, if a viewer focuses one's eyes on the displayed 3D image, the stimulus for the accommodation of human eye is reproduced naturally. To realize the super multi-view condition, multiple parallax images must be projected onto the retina. However, in the previous proposed HMD, since the respective parallax images were spatially divided and were projected onto the retina, the image resolution was low and the optical system was complicated. In order to overcome these problems, we propose the improved see-through retinal projection type super multi-view HMD by using the time division projection optical system. The proposed HMD consists of a multiple exposure holographic lens with multi-convergence points, a high frame rate display device, and a high-speed optical shutter. Multiple parallax images are displayed by time division and are converged on respective points by the holographic lens. The optical shutter which synchronized to the display device passes only one convergence light corresponding to the right parallax image. Therefore, proposed HMD realizes the pseudo super multi-view condition and displays the virtual image at the distance within ability for focusing on the human eye. To verify the effectiveness of the proposed HMD, we confirmed the depth range of the 3D image by the prototype of the proposed HMD was more than 250 mm in front of the pupil.