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Transactions of the Institute of Systems, Control and Information Engineers Vol. 25 (2012), No. 11

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

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  13. Vol. 25 (2012)

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  25. Vol. 13 (2000)

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  26. Vol. 12 (1999)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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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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  35. Vol. 3 (1990)

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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

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Keyword Ranking

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 25 (2012), No. 11

Optimal Vector Smoothing Splines with Coupled Constraints

Hiroyuki Fujioka, Hiroyuki Kano

pp. 299-307

DOI:

10.5687/iscie.25.299

Abstract

This paper considers the problem of designing optimal vector smoothing spline curves with equality and/or inequality constraints. The constraints are assumed to be cross-coupled among the element curves imposed at some time instant as well as over some time interval. The vector splines are constituted employing normalized uniform B-splines as the basis functions. Then various types of constraints are formulated as linear function of the so-called control points, and the problem is reduced to convex quadratic programming problem. The performance is examined by some numerical examples.

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Optimal Vector Smoothing Splines with Coupled Constraints

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Ris

Detection of Mobile Objects by Mixture PDF Model for Mobile Robots

Masahiro Tanaka, Masahiro Wada, Tomohiro Umetani, Minoru Ito

pp. 308-315

DOI:

10.5687/iscie.25.308

Abstract

The authors have been developing a mobile robot with sensors for various services in the university campus. A prominent feature of university campus is a substantial amount of pedestrians in the outdoor environment. This feature is also typical in the shopping streets where cars are shutout. This paper proposes an application of a stochastic model for the observation and state transition for detecting mobile objects while the localization process. This model can be treated in the framework of nonlinear Kalman filter. In this paper, we implemented the detection algorithm in the offline mode. We demonstrate the experimental detection results, which validate the usefulness of the proposed algorithm.

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Detection of Mobile Objects by Mixture PDF Model for Mobile Robots

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Location Estimation of Mobile Wireless LAN Client in Multistory Buildings using Strength of Received Signals in State Space Framework

Tomohiro Umetani, Susumu Yamane, Tomoya Yamashita, Yuichi Tamura

pp. 316-322

DOI:

10.5687/iscie.25.316

Abstract

This paper describes a method for location estimation of mobile wireless local area network (LAN) clients in multistory buildings using the strength of the received signals in a state space framework. Data pertaining to the physical positions of personal electronic devices or mobile robots are important for information services and robotic applications. We focus on integrating the estimation results with other sensor data based on a state space framework. The estimation model for location provides a variance of a mobile client’s location. We integrate the estimation results and the motion results of the mobile client using a Kalman filter. The estimation model is re-initialized when the mobile client moves to another floor in the building by detecting the change in the floor number where the mobile client has moved. This is done by using the Bayesian inference. Experimental results show the feasibility of this method.

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Location Estimation of Mobile Wireless LAN Client in Multistory Buildings using Strength of Received Signals in State Space Framework

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Target-Tracking based on Fusion of Unsynchronized Sensor Data from Vision System and Thermal Imaging Sensor

Tomohiro Umetani, Naomichi Kuga, Masahiro Tanaka, Masahiro Wada, Minoru Ito

pp. 323-327

DOI:

10.5687/iscie.25.323

Abstract

This paper proposes a method for the integration of sensor data from a thermal imaging sensor and a vision system such as a camera to enable autonomous robots to perform probabilistic target tracking. Person tracking is essential to enable robots to interact with people and to track a target person. It is necessary to integrate multiple types of sensor data to improve the recognition performance because that of a vision system alone is not very good. However, the sensor data from an imaging device are captured asynchronously. Moreover, the interval of the acquisition of the sensor data is different. We use a stochastic model based on the asynchronous updating of the model of the tracking target by using measurement data from the camera and the thermal imaging sensor. The tracking model is implemented using RT-Middleware, which is used as a platform for the construction of distributed networked robots. The experimental results indicate the feasibility of the proposed method.

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Target-Tracking based on Fusion of Unsynchronized Sensor Data from Vision System and Thermal Imaging Sensor

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Sensitivity Analysis of Expectation with respect to Stochastic Differential Equations with Long Memory through Malliavin Calculus

Kazuhiro Yasuda

pp. 328-335

DOI:

10.5687/iscie.25.328

Abstract

Sensitivity formulas of the expectation of stochastic systems, which are written by linear stochastic differential equations with long memory, are given through the Malliavin calculus. The fractional Brownian motions are used as noises with long memory. Through the Malliavin method, we do not need to introduce difference approximation parameters like the finite difference method, and numerical results do not depend on the parameter values. Numerical results with localization as variance reduction are also given in order to obtain more stable results.

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Sensitivity Analysis of Expectation with respect to Stochastic Differential Equations with Long Memory through Malliavin Calculus

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Collision Detection and Control of Single-link Flexible Arm Using Optimal Disturbance Decoupling and Kalman Filters

Yuichi Sawada, Atsushi Moritani, Tatsuya Fujimoto, Akio Tanikawa

pp. 336-341

DOI:

10.5687/iscie.25.336

Abstract

This paper describes a method using coupled state estimators for collision detection and control of a single-link flexible arm. To improve the control performance of the flexible arm with an innovation-based collision detection system when unobserved obstacles collide with the arm’s side, we introduce an Optimal Disturbance Decoupling Filter that decouples undesired collision force effects from the estimation error. The controller generates a control torque based on the state estimate by using the Optimal Disturbance Decoupling Filter. Collision detection is then realized through the innovation of a Kalman filter. The proposed system prevents deterioration of the control performance when collision occurs.

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Collision Detection and Control of Single-link Flexible Arm Using Optimal Disturbance Decoupling and Kalman Filters

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