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Transactions of the Institute of Systems, Control and Information Engineers Vol. 35 (2022), No. 6

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. 35 (2022), No. 6

Photorealistic Augmented Reality Image Generation with Generative Adversarial Network Focusing on Structural Edge

Shunya Iketani, Masataka Imura

pp. 133-144

Abstract

In order to represent virtual objects photorealistically in augmented reality (AR), the problem of optical consistency is important. There are several methods to achieve optical consistency using known real objects and special cameras, but they are difficult to use in AR applications. In this research, we propose an end-to-end method to convert an optically inconsistent AR image into an optically consistent AR image using a generative adversarial network (GAN). In addition, we propose a GAN that focuses on the structural edges of virtual objects in order to be able to handle different virtual object shapes. We confirmed that the GAN can generate photorealistic AR images consistent with the real world and that it is possible to generate images with versatility for virtual object shapes.

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Article Title

Photorealistic Augmented Reality Image Generation with Generative Adversarial Network Focusing on Structural Edge

Design of Database-Driven Model Predictive Control System for Digging of an Autonomous Excavator

Tomofumi Okada, Toru Yamamoto, Takayuki Doi, Kazushige Koiwai, Koji Yamashita

pp. 145-152

Abstract

The initiative of Digital Transformation (DX) for the purpose of productivity improvement and reforming work style has been more and more active in the construction industry. In particular, research and development of autonomous construction machinery has been carried out with the aim of improving productivity through autonomous construction. On the other hand, Internal Model Control (IMC) system based on Database-Driven Modeling for an autonomous excavator is developed by authors. However, the control performance of this control system may deteriorate by the sudden change of the control target property. In addition, the control system can't deal with constraints explicitly in the case of the limitation of the hardware such as actuators. This paper presents a method of Database-Driven Model Predictive Control (DD-MPC) system which has also good control performance during the change of the control target property and deals with constraints explicitly. The effectiveness of the proposed method is verified by the numerical simulations and the experiment using a radio-controlled (RC) excavator.

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Article Title

Design of Database-Driven Model Predictive Control System for Digging of an Autonomous Excavator

Nonlinear Control via the SOS Method for the Output Voltage of Buck Converters Based on Discretized Bilinear Models

Xi Yang, Yoshio Ebihara, Tomomichi Hagiwara

pp. 153-164

Abstract

In this paper, nonlinear controllers are designed for buck converters via the Sum of Squares (SOS) method based on their discretized bilinear model. We first consider a class of control laws in a rational funciton. The condition of Lyapunov stability theory is converted to an SOS condition, and by using the SOS method, a nonlinear stabilizing controller for the discrete-time system is derived to ensure that input constraints on buck converters can also be satisfied. However, with such a control law, the rate of convergence may be poor. To solve this problem, we next obtain through the SOS method a control law satisfying the input constraints and maximizing the Lyapunov function decrement in each sampling time. Finally, simulation and experimental results are shown to confirm the effectiveness of the second control law.

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Article Title

Nonlinear Control via the SOS Method for the Output Voltage of Buck Converters Based on Discretized Bilinear Models

The Colored Noise Gain Function of the Single Input and Single Output Discrete-time System

Nobuo Komatsu

pp. 165-174

Abstract

A method for obtaining the colored noise gain function is proposed. The colored noise gain function is the function which gives the auto-covariance of the output signal of SISO (single input and single output system) from the auto-covariance of the input singnal. The input signal is asumed to be white or colored Gausian noise. And ACF(auto-covariance function) for ARMA (autoregressive moving average) model is derived from the proposed function with white noise input in closed form. Some results of the calculation of ACF with white noise input and the simulation of the system with colored noise input show the effectiveness of the proposed method.

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Article Title

The Colored Noise Gain Function of the Single Input and Single Output Discrete-time System

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