• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.256-258
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• 2009

현재 상용화되어 있는 결정질 태양전지의 경우 높은 실리콘 가격으로 인해 저가격화에 어려움을 격고 있다. 따라서 태양전지 저가화의 한 방법으로 박막태양전지가 주목을 받고 있다. P-I-N 구조의 박막태양전지에서 각 층의 thickness, activation energy, energy bandgap은 고효율 달성을 위한 중요한 요소이다. 본 논문에서는 박막태양전지 p-layer의 가변을 통하여 고효율을 달성하기 위한 simulation을 수행하였다. 가변 조건으로는 thickness $5\sim25nm$, activation energy $0.3\sim0.6$ eV 그리고 energy bandgap $1.6\sim1.8$ eV까지 단계별로 변화시켰다. 최종 simulation 결과 p-layer의 thickness 5nm, activation energy 0.3 eV 그리고 energy bandgap 1.8 eV에서 최고 효율 11.08%를 달성하였다.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2007년도 하계학술대회 논문집 Vol.8
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• pp.421-422
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• 2007

The fringe electric field driven transflective liquid crystal display with dual orientation has a problem that the voltage-dependent transmittance and reflectance curves do not match each other, requiring a dual driving circuit to achieve a high electro-optic performance. Optimizations of the electrode structure in the array substrate and rubbing direction solve this problem so that the transflective display with a single gap and a single gamma curve for reflective and transmissive region is possible.

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2005년도 하계학술대회 논문집 Vol.6
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• pp.471-472
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• 2005

본 연구는 TFT-LCD의 배경광원인 Backlight Unit(BLU)의 구조를 광학 시뮬레이션을 통하여 분석함으로써 BLU에서의 광효율을 극대화하는데 초점을 두었다. 일반적으로 LCD Monitor BLU는 형광램프, 반사시트, 램프 리플렉터, 도광판, 광학시트로 구성된다 여기에서는 20.1 인치 6램프로 구성된 Monitor용 Side Type BLU에 대하여 램프 리플렉터의 형상, 램프 리플렉터의 내부 공간 변화와 그에 따른 램프의 위치, 램프사이의 배열에 따른 램프에서 도광판으로의 입사광량을 광학 시뮬레이션을 통하여 분석하였다. 위 시뮬레이션의 결과, 램프리플렉터가 'ㄷ' 형상일 때, 램프리플렉터 내부공간의 약 1:2 되는 지점에 램프가 위치하고 Center Lamp가 도광판에 최대한 가깝게 위치할 때 입사광량이 최대가 되어 BLU에서의 광효율이 향상됨을 알 수 있었다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제11권4호
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• pp.1-8
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• 2007

In some wireless sensor networks, the sensor nodes are required to be located sparsely at designated positions over a wide area, introducing the problem of adding minimum number of relay nodes to interconnect the sensor nodes. The problem finds its a bstract form in literature: the Minimum number of Steiner Points. Since it is known to be NP-hard, this paper proposes an approximation scheme to estimate the minimum number of relay nodes through the properties of the abstract form. Note that by reducing the numb er of nodes in a sensor network, the amount of data exchange over the net will be far decreased.

• Advances in aircraft and spacecraft science
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• 제5권3호
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• pp.401-410
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• 2018

The aim of this work is to optimize the geometrical parameters as the adhesive thickness and the beveled angle to reduce the edge effect of the scarf and V bounded joint. A finite element analysis is done to define the generated stresses in the bounded joint. The geometrical optimum is obtained using the Experimental Design Method. Results show that the double scarf (V) joint is better than the simple scarf bounded joint.

• Bulletin of the Korean Chemical Society
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• 제18권11호
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• pp.1162-1166
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• 1997

Nature of palladium(Ⅱ) complexes with 7-membered chelates was studied by experimental and theoretical methods on a Pd(L)Cl2 system, where L is Ph2PNHCH2CH2NHPPh2(L1), Ph2PNHC6H4NHPPh2(L2). The palladium(Ⅱ) complexes were prepared and characterized by elemental analysis, IR, UV, 1H, and 31P NMR spectroscopy. Ab initio calculations with geometry optimizations were also performed for related model systems, Pd(L)Cl2; L=R2PNH(CH2)2NHPR2(L3), R2PNHC6H4NHPR2(L4), R2P(CH2)4PR2(L5), R2PCH2(C6H4)CH2PR2(L6); R=H, CH3.

• 통합자연과학논문집
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• 제11권4호
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• pp.204-208
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• 2018

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

• Journal of Information Processing Systems
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• 제15권1호
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• pp.116-126
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• 2019

In this paper, an improved one-against-one support vector machine algorithm is used to classify multiple power quality disturbances. To solve the problem of parameter selection, an improved particle swarm optimization algorithm is proposed to optimize the parameters of the support vector machine. By proposing a new inertia weight expression, the particle swarm optimization algorithm can effectively conduct a global search at the outset and effectively search locally later in a study, which improves the overall classification accuracy. The experimental results show that the improved particle swarm optimization method is more accurate than a grid search algorithm optimization and other improved particle swarm optimizations with regard to its classification of multiple power quality disturbances. Furthermore, the number of support vectors is reduced.

• ETRI Journal
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• 제43권3호
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• pp.549-560
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• 2021

Cyberattacks are often difficult to identify with traditional signature-based detection, because attackers continually find ways to bypass the detection methods. Therefore, researchers have introduced artificial intelligence (AI) technology for cybersecurity analysis to detect malicious PowerShell scripts. In this paper, we propose a feature optimization technique for AI-based approaches to enhance the accuracy of malicious PowerShell script detection. We statically analyze the PowerShell script and preprocess it with a method based on the tokens and abstract syntax tree (AST) for feature selection. Here, tokens and AST represent the vocabulary and structure of the PowerShell script, respectively. Performance evaluations with optimized features yield detection rates of 98% in both machine learning (ML) and deep learning (DL) experiments. Among them, the ML model with the 3-gram of selected five tokens and the DL model with experiments based on the AST 3-gram deliver the best performance.

• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1230-1244
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• 2022

This paper proposes an algorithm for signal validation using unsupervised methods in emergency situations at nuclear power plants (NPPs) when signals are rapidly changing. The algorithm aims to determine the stuck failures of signals in real time based on a variational auto-encoder (VAE), which employs unsupervised learning, and long short-term memory (LSTM). The application of unsupervised learning enables the algorithm to detect a wide range of stuck failures, even those that are not trained. First, this paper discusses the potential failure modes of signals in NPPs and reviews previous studies conducted on signal validation. Then, an algorithm for detecting signal failures is proposed by applying LSTM and VAE. To overcome the typical problems of unsupervised learning processes, such as trainability and performance issues, several optimizations are carried out to select the inputs, determine the hyper-parameters of the network, and establish the thresholds to identify signal failures. Finally, the proposed algorithm is validated and demonstrated using a compact nuclear simulator.