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Transactions of the Institute of Systems, Control and Information Engineers Vol. 29 (2016), No. 2

ISIJ International
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ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 29 (2016), No. 2

Assigning Library Classification Numbers to People on the Web and Developing People-search Directory

Harumi Murakami, Yoshinobu Ura, Yusuke Kataoka

pp. 51-64

Abstract

To help users select and understand people while searching for them, we present a method of assigning Nippon Decimal Classification (NDC), which is a system of library classification numbers, to people on the web. By assigning NDC numbers to people, we can assign not only labels to people but also build a NDC-based people-search directory. We use a relative index in NDC, which lists the related index terms attached to NDC. We count the number of relative index terms contained in the titles of web pages (HTML files) and assign the top five NDC9 numbers to people. We developed a prototype of a people-search directory by assigning NDC9 numbers to HTML files that were manually classified from web people-search results. We evaluated the usefulness of our approach by comparing four methods and six documents and found that our method (extracting relative index terms) from the titles of web pages outperformed other methods and documents.

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Assigning Library Classification Numbers to People on the Web and Developing People-search Directory

The Active Search Algorithm to Automate the Adjustment of the Control Parameters and Its Applications

Akio Noda, Hikaru Nagano, Yukiyasu Domae, Tatsuya Nagatani, Ken-ichi Tanaka

pp. 65-75

Abstract

In the optimization task of the control parameters of some industrial processes, it is necessary to explore unknown response landscape of the system by performing plural sampling of the output for input parameter combination of the system. Skilled operators has been conducting such tasks based on their experience and knowledge. In this study, the authors had formulated this problem as a machine learning process, and had developed an algorithm that sequentially selects quasi-optimal next sampling. Experimental results discovered a case that the algorithm can reduce the control parameter optimization task which requires 3 months duration into about 1 week.

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The Active Search Algorithm to Automate the Adjustment of the Control Parameters and Its Applications

Incremental Learning for a Calibration of a High-precision SAR-ADC by Using the Bayesian Linear Regression

Toshifumi Kurata, Keiji Tatsumi, Tetsuzo Tanino, Yusaku Hirai, Toshimasa Matsuoka, Sadahiro Tani

pp. 76-85

Abstract

In this paper, we discuss a high-precision and low-power analog-to-digital converter (ADC) which is required for wearable biomedical measurement sensors driven by a battery. In particular, we focus on the successive approximation register ADC (SAR-ADC), and propose its calibration algorithm using the machine learning. We derive a calibration function for the outputs of the SAR-ADC by taking into account its characteristics, and show the least squares method of determining the parameter values of the function to minimize the residual errors. Furthermore, from the practical viewpoint, we propose an incremental learning for the calibration, where additional data sets are selected on the basis of the Bayesian predictive distributions which are obtained at each additional learning step. Through numerical experiments, we observed that the mean residual errors obtained by the proposed method are less than 1 LSB, and the method needs a small amount of training data.

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Incremental Learning for a Calibration of a High-precision SAR-ADC by Using the Bayesian Linear Regression

Agent-based Energy Market toward the Optimal Trading in a Power Network

Masayoshi Suzuki, Kazutoshi Sakakibara, Masataka Seo, Ikuko Nishikawa

pp. 86-92

Abstract

Power trading market among ordinary houses equipped with solar power generators is considered.Trading is mediated by several brokers who represent the buying or selling by each house. Objective is to investigate whether the optimal power usage is able to be realized by the optimal trading strategy of each broker. The optimal power exchange is given by a linear programming (LP) model,which purely considers the power flow without any price. Each broker has a pricing strategy to maximize the reward, and the trading amount is assigned according to the relative price. Agent based simulation results show that reinforcement learning is effective to obtain the pricing strategy with the effective usage of the battery under the environment of unsteady and unbalanced amount of the power generation and consumption. The obtained trading results in a similar power flow with the optimal power exchange derived from the LP.

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Agent-based Energy Market toward the Optimal Trading in a Power Network

Algorithm to Compensate the Time Delay for the Appearance Control System using Camera and Projector

Kunioki Kono, Shun Ushida, Toshiyuki Amano

pp. 93-100

Abstract

The appearance control system is one of the visual feedback control systems that can manipulate the appearance of an object by using a projector-camera system. This system processes the captured image and projects the image on the object overlappingly for manipulating the appearance in real time. However, the afterimage is caused by a part of the image which is not overlapped on the object and remains in capture range of the camera. Therefore, this afterimage makes the appearance control system unstable. In this paper, we propose a new projection algorithm that can compensate the time delay for the appearance control system.We verify the effectiveness of our algorithm which can suppress the influence of the afterimage against the time delay in the visual feedback loop.

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Algorithm to Compensate the Time Delay for the Appearance Control System using Camera and Projector

Two-Stage System Identification Method Incorporating with Pre-Identification Result

Ayaka Matsubayashi, Masaki Inoue, Shuichi Adachi

pp. 101-103

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Two-Stage System Identification Method Incorporating with Pre-Identification Result

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