• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.31-40
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• 2008

We generally use lower-dimensional transformations to convert high-dimensional sequences into low-dimensional points in similar sequence matching. These traditional transformations, however, show different characteristics in indexing performance by the type of time-series data. It means that the selection of lower-dimensional transformations makes a significant influence on the indexing performance in similar sequence matching. To solve this problem, in this paper we propose a hybrid approach that integrates multiple transformations and uses them in a single multidimensional index. We first propose a new notion of hybrid lower-dimensional transformation that exploits different lower-dimensional transformations for a sequence. We next define the hybrid distance to compute the distance between the transformed sequences. We then formally prove that the hybrid approach performs the similar sequence matching correctly. We also present the index building and the similar sequence matching algorithms that use the hybrid approach. Experimental results for various time-series data sets show that our hybrid approach outperforms the single transformation-based approach. These results indicate that the hybrid approach can be widely used for various time-series data with different characteristics.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.9
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• pp.89-96
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• 2006

In this study, fluid flow and thermal characteristics in a catalyst bed for nitrous oxide catalytic decomposition which is introduced as a hybrid rocket ignition system for small satellites were theoretically considered. To analyze the thermal phenomena of the catalyst bed, a so-called porous medium approach has been opted for modeling the honeycomb geometry of the catalyst bed. Using a Brinkman-extended Darcy model for fluid flow and the one-equation model for heat transfer, the analytical solutions for both velocity and temperature distributions in the catalyst bed are obtained and compared with experimental data to validate the porous medium approach. Based on the analytical solutions, parameters of engineering importance are identified to be the porosity of the catalyst bed, effective volumetric ratio, the ratio of the radius of the catalyst bed to the radius of a pore, heat flux generated by a heater, and pumping power. Their effects on thermal phenomena of the catalyst bed are studied.

• Composites Research
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• v.17 no.1
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• pp.18-28
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• 2004

In order to evaluate the strength of carbody structures of railway rolling stock made of laminated fiber-reinforced composite materials, total laminate approach was introduced. Structural analyses were conducted to check the basic design of hybrid composite carbody structures of the Korean Tilting Train eXpress(TTX) with the service speed of 180km/h. The mechanical tests were also conducted to obtain strengths of composite laminates. The results show that all stress components of composite carbody structures are inside of failure envelopes and total laminate approach is recommended to predict the failure of hybrid composite carbody structures at the stage of the basic design.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.70-70
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• 2023

강우 발생 중 용담댐 상류로부터 용담댐으로 유입되는 유입량을 정확하게 예측하는 것은 하류 지역의 홍수 피해를 최소화하기 위한 댐의 적절한 운영에 필수적이다. 물리 기반 강우-유출 시뮬레이션 모형은 물리적 과정의 이해를 바탕으로 홍수 예측 분야에 광범위하게 사용되고 있다. 그러나 복잡한 물리 과정을 완벽히 이해하는 것은 거의 불가능하므로 다양한 가정 조건들을 이용해 복잡한 과정을 단순화하여 계산해야 하는 한계가 존재한다. 최근에는 방대한 데이터의 축적과 컴퓨터 능력의 향상으로 인해 데이터 기반 모형이 다양한 실무 문제를 해결하는 데 강력한 도구로 활용되고 있을 뿐 아니라 시뮬레이션 및 예측 등에도 다양하게 이용되고 있다. 그러나 예측 시간이 늘어날수록 입력자료로 이용되는 과거 자료와 출력자료로 이용되는 미래자료와의 상관관계가 줄어들어 모형의 성능이 저하된다. 따라서 본 연구에서는 용담댐의 시간당 유입량을 예측하기 위해 물리 기반 강우-유출 모형과 오차 보정 모형을 결합한 하이브리드 접근 방식을 제안한다. 물리 기반 강우-유출 모형으로는 HEC-HMS 모형을 사용하였으며, 오차 보정 모형에는 기계학습 모형인 인공신경망(Artificial Neural Network, ANN) 모형을 사용하였다. HEC-HMS 모형, ANN 및 하이브리드 모형(HEC-HMS + ANN)의 성능을 비교하기 위해 20 개의 홍수 사상을 모형 구축 및 검증에 사용하였다. 그 결과 하이브리드 모형은 예측 시간이 늘어날수록 HEC-HMS 및 ANN 모형보다 우수한 성능을 나타냈다. 물리모형에 기계학습을 이용한 오차 보정 절차를 통합한 경우 홍수 유출 예측의 정확성이 향상되었다. 다양한 모형의 비교 결과 본 연구에서 적용한 하이브리드 모형이 물리기반 강우-유출 모형 및 순수 기계학습 모형보다 우수한 성능을 보여줌으로써, 하이브리드 모형은 물리모형과 순수 기계학습 모형의 단점들을 보완하는데 이용할 수 있음을 나타낸다. 이 연구의 주요 목적은 강우-유출 시물레이션 모형의 오차 보정 기술에 대한 더 깊은 이해를 제공하는데 있다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.3C
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• pp.241-248
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• 2007

In predictive image coding, a LS (Least Squares)-based adaptive predictor is an efficient method to improve image edge predictions. This paper proposes a hybrid interpolation with weighted edge detector. A hybrid approach of switching between bilinear interpolation and EDI (Edge-Directed Interpolation) is proposed in order to reduce the overall computational complexity The objective and subjective quality is also similar to the bilinear interpolation and EDI. Experimental results demonstrate that this hybrid interpolation method that utilizes a weighted edge detector can achieve reduction in complexity with minimal degradation in the interpolation results.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.800-801
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• 2007

본 연구는 지역별 전력수급을 고려한 발전기 보수 계획 수립에 관한 Hybrid Particle Swarm Optimization알고리즘(HPSO) 접근법을 제시하였다. 전체 계통의 예비력 확보에 초점이 맞춰진 기존의 연구에 지역별 예비력을 고려한 제약조건을 추가하였다. 본 연구의 목적함수로는 결정적 신뢰도 지수인 공급 예비율 분산값의 최소화(공급예비율 평활화)를 사용하였으며, IEEE RTS(1996) 계통에서의 사례연구를 수행하여 기존의 PSO알고리즘의 경우와의 비교분석을 통해 제안된 방법의 우수성을 보였다.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.159-169
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• 2020

A cold chain logistics (CCL) model for chilled food (-1℃ to 8℃) distributed in China was developed in this study. The CCL model consists of a distribution center (DC) and distribution target points (DT). The objective function of the CCL model is to minimize the total distribution routes of all distributors. To find the optimal result of the objective function, the hybrid genetic algorithm (HGA) approach is proposed. The HGA approach was constructed by combining the improved K-means and genetic algorithm (GA) approaches. In the case study, three scenarios were considered for the CCL model based on the distribution routes and the available distance, and they were solved using the proposed HGA approach. Analysis results showed that the distribution costs and mileage were reduced by approximately 19%, 20% and 16% when the proposed HGA approach was used.

• Journal of Chest Surgery
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• v.41 no.6
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• pp.747-750
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• 2008

Although surgical closure is the standard approach for a muscular ventricular septal defect, the procedure may be complicated by poor visualization and the need for incision on the ventricle. Another approach is, catheter-based intervention. However, it also has limitations. A hybrid procedure, the intraoperative combined use of an interventional device may reduce the procedure's invasiveness. We successfully managed two cases of muscular ventricular septal defect with a hybrid procedure. We report here on these 2 cases along with a review of the literature.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.12
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• pp.61-70
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• 2005

In this paper, we propose an efficient global placement algorithm which is an enhanced version of Hybrid Placer$^{[25]}$, a standard cell placement tool, which uses a middle-down approach. Combining techniques used in the well-known partitioner hMETIS and the RBLS(Relaxation Based Local Search) in Hybrid Placer improves the quality of global placements. Partitioning techniques of hMETIS is applied in a top-down manner and RBLS is used in each level of the top-down hierarchy to improve the global placement. The proposed new approach resolves the problem that Hybrid Placer seriously depends on initial placements and it speeds up without deteriorating the placement quality. Experimental results prove that solutions generated by the proposed method on the MCNC benchmarks are comparable to those by FengShui which is a well known placement tool. Compared to the results of the original Hybrid Placer, new method is 5 times faster on average and shows improvement on bigger circuits.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.