• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1727_1728
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• 2009

Nowadays DC-DC converter has been used widely in electronic production. It has a high requirement in wide input voltage, load variations, stability, providing a fast transient response and lower overshoot. However, it is not easy to be controlled because of its nonlinearity. In this paper, the nonlinear model of DC-DC converter is approximatedby four linear models and sub-controllers are designed by using the LMI guaranteeing the stability of the sub-systems at the same time. For the robust of the control system, an integral sliding mode control (ISMC) is applied together with LMI fuzzy controller. The proposed controller supports a fast and almost no overshooting transient response for the DC-DC converter control.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1160-1168
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• 2000

Due to the increase in importance of factory-automation (FA) in the field of production, the importance of he autonomous guided vehicle's (AGV) role has also increased. This paper is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an behavior-based system evolving by itself is also being considered. In this paper, constructed an active and effective AGV fuzzy controller to be able to carry out self-organization. To construct it, we tuned suboptimally membership function using a genetic algorithm (GA) and improved the control efficiency by self-correction and the generation of control rules.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• Journal of Internet Computing and Services
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• v.2 no.2
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• pp.105-116
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• 2001

According to increase of the factory-automation's(FA) in the field of production, the autonomous guided vehicle's(AGV) role is also increased, The study about an active and effective controller which can flexibly prepare for the changeable circumstance is in progressed. For this study. the research about ac1ion base system to evolve by itself is also being actively considered In this paper. we composed an ac1ive and effective AGV fuzzy controller to be able to do self-organization, For composing it. we tuned suboptimally membership function using genetic algorithm(GA) and improved the control efficiency by the self-correction and generating the control rules. self-organizing controlled(SOC) fuzzy controller proposed in this paper is capable of Self-organizing by using the characteristics of fuzzy controller and genetic algorithm. It intuitionally controls AGV and easily adapts to the circumstance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.800-807
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• 2003

Decomposition of tasks in the ordinary manufacturing systems is usually based on the predefined goal of the system. To achieve the high-level-goals (e.g., factory goal or company goal), several sub-goals should be achieved in advance. However, goals can change along with the current status of the system and the external environmental situations. Thus, a manufacturing system should support the goal-formations which can be bearable these changes for efficient and effective operations. Therefore, it IS necessary to develop a systematic methodology for the goal-formations in a manufacturing system. Especially, the formation and/or change of goals in real-time should be possible for distributed and dynamic systems including the fractal manufacturing system (FrMS). In this paper, a threefold methodology is proposed for the goal-formation process (GFP) in the FrMS; 1) a goal­generating process (GGP) to make and propagate fuzzy goals, 2) a goal-harmonizing process (GHP) to eliminate or reduce conflicts and interferences of goals by using a mobile agent- based negotiation scheme, and 3) a goal-balancing process (GBP) to make a compromise between goals by using quantifiable indicators of the manufacturing system.

• Journal of Microbiology and Biotechnology
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• v.12 no.4
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• pp.687-691
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• 2002

To enhance the production of flavonoids [baicalin, wogonin-7-Ο-glucuronic acid (GA)], which are secondary metabolites of Scutellaria baicalensis Georgi(G.) plant cells, a multilayer perceptron control system was applied to regulate the substrate feeding in a fed-batch cultivation. The optimal profile for the substrate feeding rate in a fed-batch culture of S. baicalensis G. was determined by simulating a kinetic model using a genetic algorithm. Process variable profiles were then prepared for the construction of a multilayer perceptron controller that included massive parallelism, trainability, and fault tolerance. An error back-propagation algorithm was applied to train the multiplayer perceptron. The experimental results showed that neurocontrol incorporated with a genetic algorithm improved the flavonoid production compared with a simple fuzzy logic control system. Furthermore, the specific production yield and flavonoid productivity also increased.

• Advances in nano research
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• v.17 no.2
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• pp.109-124
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• 2024

In the era of smart manufacturing, precise prediction of springback-a common issue in ultra-thin sheet metal forming- and forming limits are critical for ensuring high-quality production and minimizing waste. This paper presents a novel approach that leverages the Internet of Things (IoT) and Artificial Neural Networks (ANN) to enhance springback and forming limits prediction accuracy. By integrating IoT-enabled sensors and devices, real-time data on material properties, forming conditions, and environmental factors are collected and transmitted to a central processing unit. This data serves as the input for an ANN model, which is trained with crystal plasticity simulations and experimental data to predict springback with high precision. Our proposed system not only provides continuous monitoring and adaptive learning capabilities but also facilitates real-time decision-making in manufacturing processes. Experimental results demonstrate significant improvements in prediction accuracy compared to traditional methods, highlighting the potential of IoT and ANN integration in advancing smart manufacturing. This approach promises to revolutionize quality control and operational efficiency in the industry, paving the way for more intelligent and responsive manufacturing systems.

• 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.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2001.11a
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• pp.139-144
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• 2001

Insulation aging diagnosis system provides early warning regarding electrical equipment defect. Early warning is very important in that it can avoid great losses resulting from unexpected shutdown of the production line. Since relations of insulation aging and partial discharge dynamics are non-linear, it is very difficult to provide early warning in an electrical equipment. In this paper, we propose the design method of insulation aging diagnosis system that use a magnetic wave and acoustic signal to diagnoses an electrical equipment. Proposed system measures the partial discharge on-line from DAS(Data Acquisition System) and acquires 2D patterns from analyzing it. For filtering the noise contained in sensor signals we used ICA algorithms. Using this data, design of the neuro-fuzzy model that diagnoses an electrical equipment is investigated. Validity of the new method is asserted by numerical simulation.

• Journal of the Korea Institute of Military Science and Technology
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• v.4 no.1
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• pp.41-51
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• 2001

In this paper, the mathematical modeling and the design of controllers were performed for the dynamic performance simulation of the diesel engine for underwater vehicle. Nonlinear equations are acquired through the mathematical modeling using mean torque production model technique. Three kinds of controllers were designed for the perform simulation of the engine model. As the result of simulation, it was confirmed that each controller can be applied with regard to system characteristics and desired conditions etc.