• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.168-173
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• 1991

A neural optimization network is proposed to control the redundant robot manipulators in an environment with the obstacle. The weightings of the network are adjusted by considering both the joint dexterity and the capability of collision avoidance of joint differential motion. The fuzzy rules are proposed to determine the capability of collision avoidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planner type with three degrees of freedom.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.2
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• pp.91-97
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• 2014

Chaotic dynamics is an active research area in fields such as biology, physics, sociology, psychology, physiology, and engineering. Interest in chaos is also expanding to the social sciences, such as politics, economics, and societal events prediction. Most people pursue happiness, both spiritual and physical in many cases. However, happiness is not easy to define, because people differ in how they perceive it. Happiness can exist in mind and body. Therefore, we need to be happy in both simultaneously to achieve optimal happiness. To do this, we need to synchronize mind and body. In this paper, we propose a chaotic synchronization method in a mathematical model of happiness organized by a second-order ordinary differential equation with external force. This proposed mathematical happiness equation is similar to Duffing's equation, because it is derived from that equation. We introduce synchronization method from our mathematical happiness model by using the derived Duffing equation. To achieve chaotic synchronization between the human mind and body, we apply an idea of mind/body unity originating in Oriental philosophy. Of many chaotic synchronization methods, we use only coupled synchronization, because this method is closest to representing mind/body unity. Typically, coupled synchronization can be applied only to non-autonomous systems, such as a modified Duffing system. We represent the result of synchronization using a differential time series mind/body model.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.379-384
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• 1994

In this paper, a fish-drying control method is proposed, which utilizes prediction of proper change in- weight of material fish based on skilled worker's performance. The function of the proposed system is largely broken down into two procedures: The procedure before drying and the one during drying. The procedure before drying is the determination of necessary drying conditions and the required drying time. Required drying time and proper changes in weight for a specific product are obtained by using fuzzy inference and regression models. The procedure during drying is the prediction of the state of material at the end of drying, or the state of product and regulation of drying conditions to attain the prescribed goal before drying. The prediction of product is obtained by using a set of linear-differential equations obtained by the authors' previous work. Drying conditions are regulated by using fuzzy inference. A good agreement between the results of simulation and experiments is obtained, which implies the usefulness of the present control method.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.19-24
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• 2011

Edges represent significant boundary information between objects or classes. Various methods, which are based on differential operation, such as Sobel, Prewitt, Roberts, Canny, and etc. have been proposed and widely used. The methods are based on a linear convolution of mask with pre-assigned coefficients. In this paper, we propose an edge detection method based on Dempster-Shafer's evidence theory to evaluate edgeness of the given pixel. The effectiveness of the proposed method is shown through experimental results on several test images and compared with conventional methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.131-135
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• 2005

This paper proposes an efficient face recognition using independent component analysis(ICA) derived from the fixed point(FP) algorithm based on secant method. The secant method can exclude the complex computation of differential process from the FP based on Newton method. The proposed ICA has been applied to recognize the 20 Yale face images of $324\times324$ pixels. The experimental results show that the proposed ICA is superior to PCA not only in the restoration performance of basis images but also in the recognition performance of the trained images and the test images. Then negative angle as similarity measures has better recognition ratio than city-block and Euclidean.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.41-48
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• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.129-141
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• 2001

Kansei engineering or image technology is a tool to analyze relation between product design components and the impression or feeling of human for physical products. This paper attempts to construct the designer\`s aid tool for developing corporate identity(CI) symbol mark based on the hybrid Kansei engineering. It combines the forward Kansei engineering for translating consumer\`s feeling into design components of CI symbol mark and the backward Kansei engineering for evaluating consumer\`s feeling for CI symbol mark. The semantic differential(SD) evaluation experiment is carried out to find the relations between image and design. The backward Kansei engineering system is modelled by fuzzy neural network. This research is expected to contribute to the development of CI symbol mark that correspond to comsumer\`s image.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.132-137
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• 1993

In this paper, a fish drying process control system is proposed, which predicts the proper change with time in weight of the material fish and the drying conditions in advance, based on the performance of skilled worker. In order to implement a human expertise into an automated fish drying process control system, an experimental analysis is made and a model for the process is built. The proposed system divided into two procedures: The procedure before drying and the one during drying. The procedure before drying is for the prediction of necessary drying time. To estimate the necessary drying time, first, the proper change in weight for the product is obtained by using fuzzy reasoning. The condition part of the production rule consists of the factors of fish body and the expected degree of dryness. Kext, the necessary drying time is obtained by regression models. The variables employed in the models are the factors, inferred change in weight and drying conditions. The model for the procedure during drying is also proposed for more accurate estimation, which is described by a system of linear-differential equations.

• IEMEK Journal of Embedded Systems and Applications
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• v.4 no.4
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• pp.180-189
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• 2009

Currently, most automobile companies are interested in research on intelligent autonomous vehicle. They are mainly focused on driver's intelligent assistant and driver replacement. In order to develop an autonomous vehicle, lateral and longitudinal control is necessary. This paper presents a lateral and longitudinal control system for autonomous vehicle that has only mono-vision camera. For lane detection, we present a new lane detection algorithm using clothoid parabolic road model. The proposed algorithm in compared with three other methods such as virtual line method, gradient method and hough transform method, in terms of lane detection ratio. For adaptive control, we apply a vanishing point estimation to fuzzy control. In order to improve handling and stability of the vehicle, the modeling errors between steering angle and predicted vanishing point are controlled to be minimized. So, we established a fuzzy rule of membership functions of inputs (vanishing point and differential vanishing point) and output (steering angle). For simulation, we developed 1/8 size robot (equipped with mono-vision system) of the actual vehicle and tested it in the athletics track of 400 meter. Through the test, we prove that our proposed method outperforms 98 % in terms of detection rate in normal condition. Compared with virtual line method, gradient method and hough transform method, our method also has good performance in the case of clear, fog and rain weather.