• Journal of Microbiology and Biotechnology
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• 제11권5호
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• pp.739-748
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• 2001

A self-organizing fuzzy logic controller using a genetic algorithm is described, which controlled the glucose concentration for the enhancement of flavonoid production in a fed-batch cultivation of Scutellaria baicalensis G. plant cells. The substrate feeding strategy in a fed-batch culture was to increase the flavonoid production by using the proposed kinetic model. For the two-stage culture, the substrate feeding strategy consisted of a first period with 28 g/I of glucose to promote cell growth, followed by a second period with 5 g/I of glucose to promote flavonoid production. A simple fuzzy logic controller and the self-organizing fuzzy logic controller using a genetic algorithm was constructed to control the glucose concentration in a fed-batch culture. The designed fuzzy logic controllers were applied to maintain the glucose concentration at given set-points of the two-stage culture in fed-batch cultivation. The experimental results showed that the self-organizing fuzzy logic controller improved the controller\`s performance, compared with that of the simple fuzzy logic controller. The specific production yield and productivity of flavonoids in the two-stage culture were higher than those in the batch culture.

• 한국인터넷방송통신학회논문지
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• 제17권5호
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• pp.199-208
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• 2017

리튬 폴리머 배터리는 높은 안전성, 빠른 충전 및 긴 라이프 사이클 등으로 인해 에너지 저장치(ESS: Energy Storage System), 전기자동차(EVs: Electric Vehicles)등에 채택이 되어 사용되고 있으며, 그리고 현재는 농업용 드론에서 까지 사용이 되고 있다. 그러나 리튬 폴리머 배터리는 과충 방전에는 리튬-이온 배터리 내의 격차구조가 파괴되어 배터리 수명이 줄어들게 되며, 과충 방전을 방지하기 위해 불균등한 셀 전압을 균등 제어 할 수 있는 셀 밸런싱 시스템이 필수적이다. 본 논문은 각 셀의 충 방전할때의 전압차이를 검출하여 불균형된 셀을 확인하여 비선형 시스템에 적합한 퍼지 제어기를 개발하여 적용한 셀별 밸런싱 알고리즘을 제안한다. 본 논문은 농업용 드론의 배터리팩의 셀 밸런싱을 퍼지제어를 하여 셀 간 균등 제어를 위해 설계하였으며, 최종 결과로 셀 간 밸런싱이 잘 되는지 확인하고 자 셀이 2개 있을 때와 6개 그리고 최종적으로 12개의 각 셀 밸런싱이 되는지를 확인하였다. 이는 다른 제품에도 사용할 수 있는지를 실험하고자 하였으며, 확인결과 사용된 셀의 개수와는 관계없이 셀별 밸런싱이 잘 되고 있음을 확인하였다.

• 전기학회논문지P
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• 제67권3호
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• 한국지능시스템학회논문지
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• 제7권3호
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• pp.81-88
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• 1997

This paper dealt with the position control of a flexible miniature finger driven by piezoelectric bimorph cells, cemented on both side of the finger. Bending moments generated by cells drives the finger, and end-point of the finger is controlled, so as to move in synchrony with fluctation of target and maintain a constant distance between target surface and inger's tip. The voltage applied for the cell is controlled by tip displacement error and error rate. We proposed a PD-Fuzzy controller under conception of PD control strategy. It brought and advantage which reduce number of rules than that of same type conventional fuzzy system and more correct redponse than PID control results.

• Journal of Electrical Engineering and Technology
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• 제3권2호
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• pp.143-151
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• 2008

This paper presents the operation of Fuel Cell Distributed Generation(FCDG) systems in distribution systems. Hence, modeling, controller design, and simulation study of a Solid Oxide Fuel Cell(SOFC) distributed generation(DG) system are investigated. The physical model of the fuel cell stack and dynamic models of power conditioning units are described. Then, suitable control architecture based on fuzzy logic and the neural network for the overall system is presented in order to activate power control and power quality improvement. A MATLAB/Simulink simulation model is developed for the SOFC DG system by combining the individual component models and the controllers designed for the power conditioning units. Simulation results are given to show the overall system performance including active power control and voltage regulation capability of the distribution system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2420-2422
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• 2003

In this paper, two Fuzzy Logics for path planning of an autonomous mobile robot are proposed. If a target point is given, such problems regarding the velocity and object recognition are closely related with path to which the mobile robot navigates. Therefore, to ensure safety navigation of the mobile robot for two fuzzy logic parts, path planning considering the surrounding environment was performed in this paper. First, feature points for local and global path are determined by utilizing Cell Decomposition off-line computation. Second, the on-line robot using two Fuzzy Logics navigates around path when it tracks the feature points. We demonstrated optimized path planning only for local path using object recognition fuzzy logic corresponds to domestic situation. Furthermore, when navigating, the robot uses fuzzy logic for velocity and target angle. The proposed algorithms for path planning has been implemented and tested with pioneer-dxe mobile robot.

• 조명전기설비학회논문지
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• 제28권11호
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• pp.17-25
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• 2014

This paper proposes a novel maximum power point tracking (MPPT) system using a fuzzy logic control (FLC) algorithm for robust in-environment changing. The power available at the output of a photovoltaic (PV) cell continues to change with radiation and temperature because a solar cell exhibits nonlinear current-voltage characteristics. Therefore, the maximum power point (MPP) of PV cells varies with radiation and temperature. The MPPT methods are used in PV systems to make full utilization of the PV array output power, which depends on radiation and temperature. The conventional MPPT control methods such as constant voltage (CV), perturbation and observation (PO) and incremental conductance (IC) have been studied but these methods are problematic in that they fail to take into account the changing environment. The proposed FLC controller is based on the fuzzy control algorithm and facilitates robust control with the environmental changes. Also, the PV systems applied FLC controller is modeled by PSIM and the response characteristics of the FLC method according to environmental variations are analyzed through comparison with the performance of conventional methods. The validity of this controller is shown through response results.

• 한국지능시스템학회논문지
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• 제1권1호
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• ETRI Journal
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• 제40권3호
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• pp.318-329
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• 2018

Femtocell (FC) technology envisaged as a cost-effective approach to attain better indoor coverage of mobile voice and data service. Deployment of FCs over macrocell forms a heterogeneous network. In urban areas, the key factor limits the successful deployment of FCs is inter-cell interference (ICI), which severely affects the performance of victim users. Autonomous FC transmission power setting is one straightforward way for coordinating ICI in the downlink. Application of intelligent control using soft computing techniques has not yet explored well for wireless networks. In this work, autonomous FC transmission power setting strategy using Adaptive Neuro Fuzzy Inference System is proposed. The main advantage of the proposed method is zero signaling overhead, reduced computational complexity and bare minimum delay in performing power setting of FC base station because only the periodic channel measurement reports fed back by the user equipment are needed. System level simulation results validate the effectiveness of the proposed method by providing much better throughput, even under high interference activation scenario and cell edge users can be prevented from going outage.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.173-179
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• 1998

This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neural net, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Menas) arithmatics, to decide whether a requested call that is not trained in learning phase to be connected or not. The system generates the estimated traffic pattern of the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmatics. The input to the NN is the vector consisted of traffic parameters which is the means and variances of the number of cells arriving inthe interval. After training(using error back propagation algorithm), when the NN is used for decision making, the decision as to whether to accept or reject a new call depends on whether the output is greater or less then decision threshold(+0.5). This method is a new technique for call admi sion control using the membership values as traffic parameter which declared to CAC at the call set up stage, and is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simmulation. it is founded the performance of the suggested method outforms compared to the conventional NN method.