• 한국분말야금학회:학술대회논문집
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• 한국분말야금학회 2006년도 Extended Abstracts of 2006 POWDER METALLURGY World Congress Part 1
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• pp.457-458
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• 2006

• 대한핵의학회:학술대회논문집
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• 대한핵의학회 1971년도 제10차 학술대회
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• pp.86.1-86.1
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• 1971

• 대한핵의학회:학술대회논문집
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• 대한핵의학회 1971년도 제10차 학술대회
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• pp.79.2-79.2
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• 1971

• 대한핵의학회:학술대회논문집
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• 대한핵의학회 1971년도 제10차 학술대회
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• pp.76.2-77
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• 1971

• 대한핵의학회:학술대회논문집
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• 대한핵의학회 1971년도 제10차 학술대회
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• pp.80.2-80.2
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• 1971

• 전기학회논문지
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• 제61권10호
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• pp.1393-1398
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• 2012

Due to environmental regulation and technical improvements, renewable energy sources (RES) are increasingly penetrated and operated in power systems. Clean energy technologies have become cost-competitive with conventional power systems, and in the near future, the generation cost of RES is expected to approach grid parity. In this situation, it should be considered an extraordinarily important issue to be maximized resulting in utilization of RES as well as to develop technologies for efficiency improvement of RES. Therefore, in this paper, the method for determining an optimal installed capacity and interconnected location of RES is proposed in order to minimize the cost of energy not supplied, which can contribute to improve distribution reliability.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 C
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• pp.1640-1642
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• 2000

We were fabricated of NiCr thin film resitors(TFR) on $Al_{2}O_3$ substrates by dc magnetic sputtering, system. The characteristics of electrical resistance by substrates & annealing condition on the resistors were studied by X-ray Diff. and SEM, ESCA.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2766-2768
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• 2000

We were made the automatic measurement system of sheet resistance and resistivity of semiconductor and metals with accuacy. The use of this system measured SRM(Standard Reference Materials) silicon wafers which calibrated by NIST. From this result. this system operated with the standard deviation within maximum ${\pm}$1% error.

• 한국생물환경조절학회:학술대회논문집
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• 한국생물환경조절학회 2003년도 추계학술대회 논문집
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• pp.196-199
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• 2003

• 한국멀티미디어학회논문지
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• 제22권6호
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• pp.665-675
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• 2019

In this paper, we compare and analyze the classification performance of deep learning algorithm Convolutional Neural Network(CNN) ac cording to ensemble generation and combining techniques. We used several CNN models(VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, GoogLeNet) to create 10 ensemble generation combinations and applied 6 combine techniques(average, weighted average, maximum, minimum, median, product) to the optimal combination. Experimental results, DenseNet169-VGG16-GoogLeNet combination in ensemble generation, and the product rule in ensemble combination showed the best performance. Based on this, it was concluded that ensemble in different models of high benchmarking scores is another way to get good results.