• 산업경영시스템학회지
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• 제45권3호
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• pp.57-65
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• 2022

DEA(data envelopment analysis) is a technique for evaluation of relative efficiency of decision making units (DMUs) that have multiple input and output. A DEA model measures the efficiency of a DMU by the relative position of the DMU's input and output in the production possibility set defined by the input and output of the DMUs being compared. In this paper, we proposed several DEA models measuring the multi-period efficiency of a DMU. First, we defined the input and output data that make a production possibility set as the spanning set. We proposed several spanning sets containing input and output of entire periods for measuring the multi-period efficiency of a DMU. We defined the production possibility sets with the proposed spanning sets and gave DEA models under the production possibility sets. Some models measure the efficiency score of each period of a DMU and others measure the integrated efficiency score of the DMU over the entire period. For the test, we applied the models to the sample data set from a long term university student training project. The results show that the suggested models may have the better discrimination power than CCR based results while the ranking of DMUs is not different.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.246-246
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• 2023

Multi-purpose dams are operated accounting for both physical and socioeconomic factors. This study aims to evaluate the utility of a deep learning algorithm-based model for three multi-purpose dam operation (Seomjin River dam, Juam dam, and Juam Control dam) in Seomjin River. In this study, the Gated Recurrent Unit (GRU) algorithm is applied to predict hourly water level of the dam reservoirs over 2002-2021. The hyper-parameters are optimized by the Bayesian optimization algorithm to enhance the prediction skill of the GRU model. The GRU models are set by the following cases: single dam input - single dam output (S-S), multi-dam input - single dam output (M-S), and multi-dam input - multi-dam output (M-M). Results show that the S-S cases with the local dam information have the highest accuracy above 0.8 of NSE. Results from the M-S and M-M model cases confirm that upstream dam information can bring important information for downstream dam operation prediction. The S-S models are simulated with altered outflows (-40% to +40%) to generate the simulated water level of the dam reservoir as alternative dam operational scenarios. The alternative S-S model simulations show physically inconsistent results, indicating that our deep learning algorithm-based model is not explainable for multi-purpose dam operation patterns. To better understand this limitation, we further analyze the relationship between observed water level and outflow of each dam. Results show that complexity in outflow-water level relationship causes the limited predictability of the GRU algorithm-based model. This study highlights the importance of socioeconomic factors from hidden multi-purpose dam operation processes on not only physical processes-based modeling but also aritificial intelligence modeling.

• 전력전자학회논문지
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• 제22권1호
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• pp.36-43
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• 2017

This paper describes a hybrid multi-output three-level DC/DC converter suitable for a wide, high-input voltage range of an auxiliary power supply for a high-power photovoltaic generating system. In a high-power photovoltaic generating system, the solar panel output voltage depends on solar radiation quantity and varies from 450Vdc to 1100Vdc. The proposed hybrid multi-output three-level DC/DC converter, which is an auxiliary power supply, would be used as power source for control printed circuit boards and relay and cooling fans in a high-power photovoltaic generating system. The proposed multi-output ($24V_{DC}/30A$, $230V_{DC}/5A$) hybrid three-level boost converter, which uses an energy recovery snubber, is controlled by variable-frequency and phase-shifted modulations and can achieve zero-voltage switching with all operating conditions of input voltage and load range. Experimental results of a 2kW prototype are evaluated and implemented to verify the performance of the proposed converter.

• 조명전기설비학회논문지
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• 제25권4호
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• pp.45-56
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• 2011

This paper is proposed artificial neural network(ANN) rotor resistance estimation of induction motor drive controlled by multi-adaptive fuzzy learning controller(AFLC). A simple double layer feedforward ANN trained by the back-propagation technique is employed in the rotor resistance identification. In this estimator, double models of the state variable estimations are used; one provides the actual induction motor output states and the other gives the ANN model output states. The total error between the desired and actual state variables is then back propagated to adjust the weights of the ANN model, so that the output of this model tracks the actual output. When the training is completed, the weights of the ANN correspond to the parameters in the actual motor. The estimation and control performance of ANN and multi-AFLC is evaluated by analysis for various operating conditions. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• 전기학회논문지
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• 제62권5호
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• pp.705-711
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• 2013

In this paper, we introduce an design of multi-output fuzzy neural networks based on Interval Type-2 fuzzy set. The proposed Interval Type-2 fuzzy set-based fuzzy neural networks with multi-output (IT2FS-based FNNm) comprise the network structure generated by dividing the input space individually. The premise part of the fuzzy rules of the network reflects the individuality of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions with interval sets such as constant, linear, and modified quadratic inference for pattern recognition. The learning of fuzzy neural networks is realized by adjusting connections of the neurons in the consequent part of the fuzzy rules, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, uncertainty factor, learning rate and momentum coefficient were automatically optimized by using real-coded genetic algorithm. The proposed model is evaluated with the use of numerical experimentation.

• 한국소음진동공학회논문집
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• 제17권11호
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• pp.1056-1063
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• 2007

Recently, there has been a growing consumer interest in the amount of noise produced by household electrical appliances. The designer of the product must identify the source of the noise, in order to reduce the noise. In the case of a household electric appliance such as the washing machine, there is consumer's complaint about the noise that is generated during the dehydrating condition. Because of several noise sources combined each other. It is difficult to identify the noise sources that contribute to the noise output. Multi-Dimensional Spectral Analysis (MDSA) is a method that can remove the correlation between different noise sources, and it expresses the key contributing factor as a unique output. This study utilized MDSA to analyze the contribution of each input in the noise output during the dehydrating condition.

• Journal of Communications and Networks
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• 제8권3호
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• pp.290-296
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• 2006

In this paper, a generalized blind adaptive algorithm is introduced for multi-user detection of direct sequence code division multiple access (OS-COMA) wireless communication systems. The main property of the proposed algorithm is its ability to resolve the multipath fading channel resulting in inter symbol interference (ISI) as well as multiple access interference (MAI). Other remarkable properties are its low complexity and mitigation to the near-far problem as well as its insensitivity to asynchronous transmission. The proposed system is based on the minimization of the output energy and convergence to the minimum mean square error (MMSE) detector. It is blind in the sense that it needs no knowledge of the other users' signatures, only the intended user signature and timing are required. Furthermore, the convergence of the minimum output energy (MOE) detector to the MMSE detector is analytically proven in case of M-ary PSK. Depicted results show that the performance of the generalized system dominates those previously considered. Further improvements are obtained when multiple input multiple output (MIMO) technique is employed.

• 전력전자학회논문지
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• 제13권6호
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• pp.439-446
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• 2008

다양한 기능을 동시에 구현하는 최근의 전자제품을 위한 전력 시스템은 다양한 종류의 전원을 구비해야 하며, 고효율 저가격 특성이 필수적이다. 이를 위해 본 논문은 단일 변압기를 이용한 중용량급의 고효율 저가격형 다중출력 LLC 공진형 컨버터를 제안한다. 제안된 컨버터는 단일 변압기를 이용하고, 요구되는 출력 당 고가의 DC/DC 컨버터의 추가 없이 1 개의 보조 스위치만으로 구현되므로 구조가 간단하고 저가격화 및 효율 개선에 유리하다. 또한 제안된 회로의 모든 전력 스위치들은 ZVS 또는 ZCS가 가능하므로 EMI 특성이 우수하며 스위칭 손실을 최소화 할 수 있다. 최종적으로 제안된 컨버터 및 전원시스템의 우수성과 이론적 분석의 타당성 검증을 위해 42" FHD급 PDP용 전원회로를 위한 시작품을 제작하여 고찰된 실험결과를 제시한다.

• Current Photovoltaic Research
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• 제8권4호
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• pp.124-128
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• 2020

Output power of photovoltaic (PV) modules installed outdoors decreases every year due to environmental conditions such as temperature, humidity, and ultraviolet irradiations. In order to promote the installation of PV modules, the reliability must be guaranteed. One of the important factors affecting reliability is intermetallic compounds (IMC) layer formed in ribbon solder joint. For this reason, various studies on soldering properties between the ribbon and cell have been performed to solve the reliability deterioration caused by excessive growth of the IMC layer. However, the IMC layer of the PV module interconnected by multi-wires has been studied less than using the ribbon. It is necessary to study soldering characteristics of the multi-wire module for improvement of its reliability. In this study, we analyzed the growth of IMC layer of the PV module with multi-wire and the degradation of output power through damp-heat test. The fabricated modules were exposed to damp-heat conditions (85 ºC and 85 % relative humidity) for 1000 hours and the output powers of the modules before and after the damp-heat test were measured. Then, the process of dissolving ethylene vinyl acetate (EVA) as an encapsulant of the modules was performed to observe the IMC layer. The growth of IMC layer was evaluated using OM and FE-SEM for cross-sectional analysis and EDS for elemental mapping. Based on these results, we investigated the correlation between the IMC layer and output power of modules.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.157-157
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• 2000

In the fuzzy control for the multi-variable system, it is difficult to obtain the fuzzy rule. Therefore, the parallel structure of the independent single input-single output fuzzy controller using a pairing between the input and output variable is applied to the multi-variable system. The concept of relative gain matrix is used to obtain the input-output pairs. However, among the input/output variables which are not paired the interactive effects should be taken into account. these mutual coupling of variables affect the control performance. Therefore, for the control system with a strong coupling property, the control performance is sometimes lowered. In this paper, the effect of mutual coupling of variables is considered by tile introduction of a simple compensator. This compensator adjusts the degree of coupling between variables using a neural network. In this proposed neuro-fuzzy controller, the Neural network which is realized by back-propagation algorithm, adjusts the mutual coupling weight between variables.