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システム/制御/情報 Vol. 29 (2016), No. 3

ISIJ International
belloff
オンライン版ISSN: 2185-811X
冊子版ISSN: 1342-5668
発行機関: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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  30. Vol. 8 (1995)

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  32. Vol. 6 (1993)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  37. Vol. 1 (1988)

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16 May. (Last 30 Days)

システム/制御/情報 Vol. 29 (2016), No. 3

改良型応答曲面法を用いたサポートベクターマシンの最適ハイパーパラメータ決定法

関根 嵩之, 相吉 英太郎

pp. 105-113

DOI:

10.5687/iscie.29.105

抄録

Support Vector Machine (SVM) has been studied actively as one of discriminators for a large number of data. In order to obtain a high identification rate, it is necessary to decide the best parameters of the SVM, and the problem to decide the parameters are formulated as an optimization problem, where the objective function is not described by mathematical formula, and a function call to evaluate the objective function value requires calculation quantity. In this study, to break through the problems, it is considered to adopt the Response Surface Method (RSM) with meta-heuristic algorithm to solve the SVM’s parameter optimization problem.

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改良型応答曲面法を用いたサポートベクターマシンの最適ハイパーパラメータ決定法

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3次元距離画像センサを用いた進化論的多目的最適化に基づく上肢関節角度推定

大保 武慶, 日下 純也, 久保田 直行

pp. 114-121

DOI:

10.5687/iscie.29.114

抄録

In this research, we have developed a rehabilitation support system for analysis of upper body motion using 3D image sensor. Such a sensor has embedded systems to detect and track joint positions, but it is difficult to directly apply the data to calculation of inverse kinematics to estimate the joint angles because of errors in measurement. To solve the issue, some researchers have proposed methods of joint angle estimation using kinematic model and search technique. In these works, objective functions were defined to evaluate the similarity of posture between a person and kinematic model, and the problem was typically considered as a single-objective optimization problem. However, the data basically includes not only feature of personal behavior patterns but also noise. Therefore, we address a multi-objective optimization problem in order to scale back the influence of noise. This paper proposes a method of joint angle estimation of upper limb base on Evolutionary Multi-criterion Optimization (EMO). Furthermore, we apply a feedforward neural network to motion pattern modeling to realize a predictive search, combining with the EMO.

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3次元距離画像センサを用いた進化論的多目的最適化に基づく上肢関節角度推定

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Ris

プラント変動の推定に基づく近似ベイジアン強化学習とペグ・イン・ホール・タスクへの適用

泉田 啓, 菱沼 徹, 谷 百合夏

pp. 122-129

DOI:

10.5687/iscie.29.122

抄録

In a general reinforcement learning problem, a plant, i.e. state transition probabilities, is estimated, and a learning policy for the estimated plant is applied to a real plant. If there is a difference between the estimated plant and the real plant, the obtained policy may not work well for the real plant. In this study, the real plant variation is parameterized by an interpolation of several estimated plants. This study proposes a reinforcement learning method based on estimation of parameter variation, and applies this method to 2-dimensional Peg-in-Hole Task. The effectiveness of the proposed method is demonstrated by numerical and experimental results.

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プラント変動の推定に基づく近似ベイジアン強化学習とペグ・イン・ホール・タスクへの適用

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ファジィk-memberクラスタリングによる顔画像匿名化を伴うプライバシー保護群集行動分析

本多 克宏, 大森 正博, 生方 誠希, 野津 亮

pp. 130-135

DOI:

10.5687/iscie.29.130

抄録

Privacy preservation is an important issue in such personal information analysis as crowd movement analysis with face image recognition. This paper proposes a novel framework for estimating crowd movement characteristics without exactly distinguishing each person, in which personal authentication is performed in eigen-face spaces after fuzzy k-member clustering-based k-anonymization of feature vectors. An experimental result demonstrates that, supported by fuzzy partitioning, the novel framework can improve not only the noise sensitivity and anonymization quality of the conventional k-member clustering but also the reproducibility of crowd movement.

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ファジィk-memberクラスタリングによる顔画像匿名化を伴うプライバシー保護群集行動分析

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逆誤差補正とベイズ回帰を用いた高精度逐次比較型A/D変換器の誤差補正追加学習

巽 啓司, 松岡 俊匡, 谷 貞宏

pp. 136-142

DOI:

10.5687/iscie.29.136

抄録

Recently, a high-precision and low-power analog-to-digital converter (ADC) is required for a wearable biomedical measurement sensor which is driven by a battery. For achieving the purpose,a software-level calibration method was proposed for the successive approximation register ADC (SAR-ADC), which trains a calibration function with an incremental learning, and selects additional training data by using the Bayesian linear regression. Some numerical experiments showed that the desirable precision is obtained and the method needs a small amount of training data. However, since the additional training data specified by the selection method cannot necessarily be obtained in the practical viewpoint, its straightforward implementation is extremely difficult. Therefore, we propose a selection method of additional training data which can be obtained by using an inverse calibration,and moreover, we show the effectiveness of the proposed method through numerical experiments.

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逆誤差補正とベイズ回帰を用いた高精度逐次比較型A/D変換器の誤差補正追加学習

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点列として与えられる経路に対する追従制御とボール&プレートシステムへの適用

梅本 和希, 江上 正

pp. 143-151

DOI:

10.5687/iscie.29.143

抄録

In this research, a path-following control system design for a path consist of sequence of points is proposed. To interpolating the points, a smooth path is made from the points as a polynomial curve. Non-inertial coordinate system is constructed that the origin is a reference point on the smooth path and axes are tangential and normal direction on the reference point. Movement of the reference point relates to a reference speed and a tangential component of the non-inertial coordinate. In order to implementing path-following, tracking control is achieved of a moving object to the reference point. Effectiveness of the proposed design is validated by control experiments with a boll and plate experimental system.

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点列として与えられる経路に対する追従制御とボール&プレートシステムへの適用

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知識の転移と選別機能を備えた強化学習手法の複数タスク下における性能検証

小谷 直樹

pp. 152-154

DOI:

10.5687/iscie.29.152
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知識の転移と選別機能を備えた強化学習手法の複数タスク下における性能検証

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