• Journal of Sensor Science and Technology
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• v.7 no.5
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• pp.334-341
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• 1998

In this paper, we present a new interpolation scheme for image enhancement using nonlinear operator. In general, interpolation techniques are based on linear operators which are essentially lowpass filters, hence, they tend to blur fine details in the original image. In our approach, the operator itself balances the strength of its sharpening and noise suppressing components according to the properties of the input image data.

• Journal of Microbiology and Biotechnology
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• v.16 no.6
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• pp.863-869
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• 2006

The behavior of submerged membrane bioreactor (SMBR) filtration systems utilizing rapid air backpulsing as a cleaning technique to remove reversible foulants was investigated using a genetic algorithm (GA). A customized genetic algorithm with suitable genetic operators was used to generate optimal time profiles. From experiments utilizing short and long periods of forward and reverse filtration, various experimental process parameters were determined. The GA indicated that the optimal values for the net flux fell between 263-270 LMH when the forward filtration time ($t_f$) was 30-37 s and the backward filtration time ($t_b$) was 0.19-0.27 s. The experimental data confirmed the optimal backpulse duration and frequency that maximized the net flux, which represented a four-fold improvement in 24-h backpulsing experiments compared with the absence of backpulsing. Consequently, the identification of a region of feasible parameters and nonlinear flux optimization were both successfully performed by the genetic algorithm, meaning the genetic algorithm-based optimization proved to be useful for solving SMBR flux optimization problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.7-12
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• 2005

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper. The expert system which introduces fuzzy logic in order to process uncertainties is called fuzzy expert system. The fuzzy expert system, however, has a potential problem which may lead to inappropriate results due to the ignorance of some information by applying fuzzy logic in reasoning process in addition to the knowledge acquisition problem. In order to overcome these problems, We construct fuzzy inference network by extending the concept of reasoning network in this paper. In the fuzzy inference network, the propositions which form fuzzy rules are represented by nodes. And these nodes have the truth values representing the belief values of each proposition. The logical operators between propositions of rules are represented by links. And the traditional propagation rule is modified.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.124-129
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• 1992

The goal of the proposed Intelligent Assisting System - IAS is to assist human operators in an intelligent way, while leaving decision and goal planning instances for the human. To realize the IAS the very important issue of manipulation skill identification and analysis has to be solved, which then is stored in a Skill Data Base. Using this data base the IAS is able to perform complex manipulations on the motion control level and to assist the human operator flexibly. We propose a model for manipulation skill based on the dynamics of the grip transformation matrix, which describes the dynamic transformation between object space and finger joint space. Interaction with a virtual world simulator allows the calculation and feedback of appropriate forces through controlled actuators of the sensor glove with 10 degrees-of-freedom. To solve the sensor glove calibration problem, we learn the nonlinear calibration mapping by an artificial neural network(ANN). In this paper we also describe the experimental system setup of the skill acquisition and transfer system as a first approach to the IAS. Some simple manipulation examples and simulation results show the feasibility of the proposed manipulation skill model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1286-1302
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• 2012

In this paper, the resource allocation problem with proportional fairness rate in cognitive OFDM-based wireless network is studied. It aims to maximize the total system throughput subject to constraints that include total transmit power for secondary users, maximum tolerable interferences of primary users, bit error rate, and proportional fairness rate among secondary users. It is a nonlinear optimization problem, for which obtaining the optimal solution is known to be NP-hard. An efficient bio-inspired suboptimal algorithm called immune clonal optimization is proposed to solve the resource allocation problem in two steps. That is, subcarriers are firstly allocated to secondary users assuming equal power assignment and then the power allocation is performed with an improved immune clonal algorithm. Suitable immune operators such as matrix encoding and adaptive mutation are designed for resource allocation problem. Simulation results show that the proposed algorithm achieves near-optimal throughput and more satisfying proportional fairness rate among secondary users with lower computational complexity.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.6
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• pp.722-728
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• 2003

In this paper, we propose a fuzzy traffic controller of Sugeno`s fuzzy model so as to model the nonlinear characteristics of controlling the traffic light. It use a degree of the traffic congestion of the preceding roads as an input so that it can cope with traffic congestion appropriately, which causes the loss of fuel and our discomfort. First, in order to construct fuzzy traffic controller of Sugeno`s fuzzy model, we model the control process of the traffic light by using Mamdani`s fuzzy model, which has the uniform membership functions of the same size and shape. Second, we make Mamdani`s fuzzy model with the non-uniform membership functions so that it can exactly reflect the knowledge of experts and operators. Last, we construct the fuzzy traffic controller of Sugeno`s fuzzy model by learning from the input/output data, which is retrieved from Mamdani`s fuzzy model with the non-uniform membership functions. We compared and analyzed the fixed traffic light controller, the fuzzy traffic controller of Mamdani`s fuzzy model and the fuzzy traffic controller of Sugeno`s fuzzy model by using the delay time and the proportion of the entered vehicles to the occurred vehicles. As a result of comparison, the fuzzy traffic controller of Sugeno`s fuzzy model showed the best performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.407-415
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• 2011

In this study, we propose the design of optimized fuzzy controller for the rotary inverted pendulum system by using differential evolution algorithm. The structure of the differential evolution algorithm has a simple structure and its convergence to optimal values is superb in comparison to other optimization algorithms. Also the differential evolution algorithm is easier to use because it have simpler mathematical operators and have much less computational time when compared with other optimization algorithms. The rotary inverted pendulum system is nonlinear and has a unstable motion. The objective is to control the position of the rotating arm and to make the pendulum to maintain the unstable equilibrium point at vertical position. The output performance of the proposed fuzzy controller is considered from the viewpoint of performance criteria such as overshoot, steady-state error, and settling time through simulation and practical experiment. From the result of both simulation and practical experiment, we evaluate and analyze the performance of the proposed optimal fuzzy controller from the comparison between PGAs and differential evolution algorithms. Also we show the superiority of the output performance as well as the characteristic of differential evolution algorithm.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.191-196
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• 2010

Power system restoration following a massive or complete blackout starts with energizing the primary restorative transmission system. During this primary restoration process, unexpected overvoltage may happen due to nonlinear interaction between the unloaded transformer and the transmission system. In the case of the Myanmar electric power system, there are so many wide outage experiences, including complete blackout cases, caused by 230kV line faults and so on. Consequently, Myanmar's system operators have been well trained to deal with wide blackouts. Howver, system blackout restoration has been conducted by relying on the experience of only a few specialists. So, more scientific analysis is required to meet the requirements necessary to ensure fast and reliable system restoration. This paper presents analytical results on the primary restorative transmission system of Myanmar, focusing on the problems during the early restoration process. Methodologies are presented that handle load pick-up, terminal voltage and the reactive capability limitation of black-start generators to compensate the Ferranti effect. Static and dynamic simulation with the PSSolution and EMTDC programs respectively for the six cases are performed in order to select the primary restorative transmission lines.

• Nonlinear Functional Analysis and Applications
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• v.29 no.1
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• pp.131-164
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• 2024

In this research, we study a modified relaxed Tseng method with a single projection approach for solving common solution to a fixed point problem involving finite family of τ-demimetric operators and a quasi-monotone variational inequalities in real Hilbert spaces with alternating inertial extrapolation steps and adaptive non-monotonic step sizes. Under some appropriate conditions that are imposed on the parameters, the weak and linear convergence results of the proposed iterative scheme are established. Furthermore, we present some numerical examples and application of our proposed methods in comparison with other existing iterative methods. In order to show the practical applicability of our method to real word problems, we show that our algorithm has better restoration efficiency than many well known methods in image restoration problem. Our proposed iterative method generalizes and extends many existing methods in the literature.

• The Journal of Korea Robotics Society
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• v.1 no.1
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• pp.17-24
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• 2006

Though the final goal of mobile robot navigation is to be autonomous, operators' intelligent and skillful decisions are necessary when there are many scattered obstacles. There are several limitations even in the camera-based tele-operation of a mobile robot, which is very popular for the mobile robot navigation. For examples, shadowed and curved areas cannot be viewed using a narrow view-angle camera, especially in bad weather such as on snowy or rainy days. Therefore, it is necessary to have other sensory information for reliable tele-operations. In this paper, sixteen ultrasonic sensors are attached around a mobile robot in a ring pattern to measure the distances to obstacles. A collision vector is introduced in this paper as a new tool for obstacle avoidance, which is defined as a normal vector from an obstacle to the mobile robot. Based on this collision vector, a virtual reflection force is generated to avoid the obstacles and then the reflection force is transferred to an operator who is holding a joystick to control the mobile robot. Relying on the reflection force, the operator can control the mobile robot more smoothly and safely. For this bi-directional tele-operation, a master joystick system using a hall sensor was designed to resolve the existence of nonlinear sections, which are usual for a general joystick with two motors and potentiometers. Finally, the efficiency of a force reflection joystick is verified through the comparison of two vision-based tele-operation experiments, with and without force reflection.