• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.224-227
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• 1989

In this paper, The implementation of modular neural net for fault diagnosis in power system is presented. Until now, there have been many researches on expert system for fault diagnosis. On expert system, a lot of time for searching goal is needed. But, neural net processes with high speed, as it has parallel distributed processing structure. So neural net has good performance in on-line fault diagnosis. For fault diagnosis in large power system, the constitution of modular neural net with partition of large power system is presented.

• 한국초등수학교육학회지
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• 제5권1호
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• pp.1-18
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• 2001

반복단위와 패턴의 부분 합성을 인식할 때 반복 패턴에 대한 수학적 발전의 잠재성이 충분히 발휘되고, 두 개의 같은 집합으로 분할 활동을 통해서 짝수 홀수의 인식 지도가 필요하며, 수 패턴을 통한 유도전략을 배워서 덧셈계산에서 가속적일 발전을 위한 교수 순서를 제시한다.

• IEIE Transactions on Smart Processing and Computing
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• 제5권4호
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• pp.235-242
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• 2016

The emerging High-Efficiency Video Coding (HEVC) standard attempts to improve coding efficiency by a factor of two over H.264/Advanced Video Coding (AVC) at the expense of an increase in computational complexity. Mode decision with motion estimation (ME) is still one of the most time-consuming computations in HEVC, as it is with H.264/AVC. Thus, fast mode decisions are not only an important issue to be researched, but also an urgent one. Several schemes for fast mode decisions have been presented in reference software and in other studies. However, the conventional hierarchical mode decision can be useless when block-level parallelism is exploited. This paper proposes operation-level exploration that offers more chances for early termination. An early termination condition is checked between integer and fractional MEs and between the parts of one partition type. The fast decision points of the proposed algorithm do not overlap those in previous works. Thus, the proposed algorithms are easily used with other fast algorithms, and consequently, independent speed-up is possible.

• 대한임베디드공학회논문지
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• 제11권4호
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• pp.235-241
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• 2016

Compared to the former H.264 standard, the number of the prediction modes has highly increased in HEVC intra prediction. Compression efficiency and accurate prediction are significantly improved. However, the computational complexity increases as well. To solve this problem, this paper proposes the new scheme where not only prediction modes but also block partition candidate are early chosen. Compared to the original intra prediction in HEVC, the proposed scheme achieves about 38% reduction in processing cycles with a marginal loss in compression efficiency.

• 한국통신학회논문지
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• 제14권6호
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• pp.675-685
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• 1989

상호접송망은 병렬처리 시스템에서 매우 중요한 부분이다. 최근 다단상호 접속망에 대한 연구가 많이 행해지고 있다. 본 논문에서는 회선스위칭 다단상호접속망의 시뮬레이션 방법을 확장하여 분할된 ADM/IADM 접속망의 성능 평가를 하였다. 시뮬레이션 데이터에 의하여, 적용된 분할 방법에 의한 접속망 성능과 접속망에서 사용된 저지처리방식에 의한 접속망 성능 간의 관계를 보였다. 분석 결과, hold 방식을 사용하는 IADM 접속망이 RST 면에서 가장 나은 접속망 동작을 보이는 것으로 나타났다.

• 한국환경독성학회:학술대회논문집
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• 한국환경독성학회 2003년도 추계국제학술대회
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• pp.152-152
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• 2003

The majority of the SKYBIO will be used in the treatment of water in food processing plants, swimming pools and cooling towers, in the manufacture fabric softeners in Australia. Most will eventually be released into domestic sewage system as a consequence of product use. The SKYBIO is not readily biodegradable (0% over 28 days), and is expected to have a low partition coefficient and high water solubility (285 g/L), all indicating that the material would be mobile in both aquatic and terrestrial compartment. The PEC/PNEC ratio for the aquatic environment is 56. This value is significantly greater than 1, indicating an immediate concern to the aquatic compartment. However as a consequence of it's cationic character, the SKYBIO will be expected to associate to negatively charged organic matter in soil and sediment.

• 대한전기학회논문지
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• 제39권9호
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• pp.985-996
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• 1990

An inverse kinematic solution of a robot manipulator using multilayer perceptrons is proposed. Neural networks allow the solution of some complex nonlinear equations such as the inverse kinematics of a robot manipulator without the need for its model. However, the back-propagation (BP) learning rule for multilayer perceptrons has the major limitation of being too slow in learning to be practical. In this paper, a new algorithm named Dynamically Reconfiguring BP is proposed to improve its learning speed. It uses a modified version of Kohonen's Self-Organizing Feature Map (SOFM) to partition the input space and for each input point, select a subset of the hidden processing elements or neurons. A subset of the original network results from these selected neuron which learns the desired mapping for this small input region. It is this selective property that accelerates convergence as well as enhances resolution. This network was used to learn the parity function and further, to solve the inverse kinematic problem of a robot manipulator. The results demonstrate faster learning than the BP network.

• 산업경영시스템학회지
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• 제22권50호
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• pp.183-193
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• 1999

The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In this paper, we propose an efficient algorithm for mining quantitative association rules with item constraints. For categorical attributes, we map the values of the attribute to a set of consecutive integers. For quantitative attributes, we can partition the attribute into values or ranges. While such constraints can be applied as a post-processing step, integrating them into the mining algorithm can reduce the execution time. We consider the problem of integrating constraints that are boolean expressions over the presence or absence of items containing quantitative attributes into the association discovery algorithm using Apriori concept.

• ETRI Journal
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• 제31권6호
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• pp.709-716
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• 2009

In this paper, we propose a novel reconfigurable processor using dynamically partitioned single-instruction multiple-data (DP-SIMD) which is able to process multimedia data. The SIMD processor and parallel SIMD (P-SIMD) processor, which is composed of a number of SIMD processors, are usually used these days. But these processors are inefficient because all processing units (PUs) should process the same operations all the time. Moreover, the PUs can process different operations only when every SIMD group operation is predefined. We propose a processor control method which can partition parallel processors into multiple SIMD-based processors dynamically to enhance efficiency. For performance evaluation of the proposed method, we carried out the inverse transform, inverse quantization, and motion compensation operations of H.264 using processors based on SIMD, P-SIMD, and DP-SIMD. Experimental results show that the DP-SIMD control method is more efficient than SIMD and P-SIMD control methods by about 15% and 14%, respectively.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2009년도 9th International Meeting on Information Display
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• pp.972-974
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• 2009

This paper presents smart regional contrast enhancement technique of partitioned image for local dimming backlight on small-sized mobile display to reach two goals. One is to save the power consumption, and the other to improve contrast ratio of display image. Recently new advanced method is proposed, named local dimming method, that backlight LED is positioned on backside of the display panel. So it is important to partition an image by sub blocks and then post-processing independantly. This means regional contrast enhancement. After partitioning, we compare the mean luminance(Y) value of each sub-block image with the one of original whole image. If some blocks have the mean value lower than the one of whole image, they are processed with the proposed method and others are bypassed. Simultaneously the information of the processed blocks are transferred to BLC(Backlight LED Controller). And then the supply current of each backlight LED is reduced to realize the contrast ratio enhancement and at the same time to power consumption reduction. In addition, we verify this proposed method is free from blocking artifacts.