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Transactions of the Institute of Systems, Control and Information Engineers Vol. 31 (2018), No. 5

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. 31 (2018), No. 5

Co-cluster Structure Visualization by Spectral Ordering and Its Characteristics

Takuya Sako, Katsuhiro Honda, Seiki Ubukata, Akira Notsu

pp. 177-183

Abstract

Cluster structure analysis is often utilized as the basic step in unsupervised classification. In this paper, a spectral ordering-based visual cluster assessment method for relational data analysis is modified so that it can be applied to co-cluster structure analysis in cooccurrence information data. Object-by-item cooccurrence information is first enlarged into an (object + item)-by-(object + item) relational data matrix, and then, co-cluster structure is visually assessed through simultaneous ordering of objects and items in the enlarged matrix. Additionally, due to the sparse nature of the enlarged matrix, the computational cost can be decreased with the eigen problem of a reduced matrix. The characteristic features of the proposed approach are demonstrated through several numerical experiments including social analysis of Japanese prefectural statistics.

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Co-cluster Structure Visualization by Spectral Ordering and Its Characteristics

Statistical-Mechanical Analysis of Multi-channel Active Noise Control

Tomoki Murata, Yoshinobu Kajikawa, Seiji Miyoshi

pp. 184-190

Abstract

We analyze the behaviors of the Multiple-Error Filtered-X LMS (MEFXLMS) algorithm for multiple-channel active noise control(ANC). Correlations between the impulse response of a primary path and an adaptive filter are treated as macroscopic variables. We analytically solve the equations to obtain the correlations and finally compute the MSE. The derived theory can predict the behaviors including the phenomenon that the MSE is affected by the secondary path that is not directly connected.

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Statistical-Mechanical Analysis of Multi-channel Active Noise Control

On-Vehicle Danger Forecast System based on Knowledge-based Artificial Intelligence

Kouhei Hashimoto, Yutaro Ishida, Ryutaro Ichise, Hiroaki Wagatsuma, Hakaru Tamukoh

pp. 191-201

Abstract

Danger forecast and its avoidance are highly important for the design of automated driving system and the function is expected to be solved by artificial intelligences (AIs). However the reasoning process is unclear in the conventional scheme such as machine learning and as deep neural network models. In this paper, we focus on the ability of the logical reasoning based on the Semantic Web techniques, which is called knowledge-based AI, and demonstrate successfully its implementation into a module of Robot Operating System (ROS) to control a real vehicle. The processing speed of proposed system is evaluated using the real vehicle in the situation of the pedestrian avoidance in the crossroad.

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On-Vehicle Danger Forecast System based on Knowledge-based Artificial Intelligence

Control System for a Quadrotor Using the Harmonic Potential Field Information around the Robot

Kimiko Motonaka, Keigo Watanabe, Shoichi Maeyama

pp. 202-208

Abstract

The autonomous control of the quadrotor has received attention. In the previous research, we proposed the kinodynamic control using a harmonic potential field (HPF), which is a method to design a control input by considering kinematic constraints and dynamic constraints simultaneously. However, it was assumed that the environment was already known and the HPF of the environment was prepared in advance. Therefore, in this paper, assume that a final target position is given to the quadrotor and it moves only by relying on the mounted sensors, without using the HPF information calculated in advance. It is confirmed, by a simulation using the Matlab, that the proposed method can guide the quadrotor to the target position.

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Control System for a Quadrotor Using the Harmonic Potential Field Information around the Robot

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