• 반도체디스플레이기술학회지
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• 제10권3호
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• pp.75-81
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• 2011

This paper presents a novel method for image classification based on a hybrid genetic algorithm (GA) and support vector machine (SVM) approach which can significantly improve the classification performance for content-based image retrieval (CBIR). Though SVM has been widely applied to CBIR, it has some problems such as the kernel parameters setting and feature subset selection of SVM which impact the classification accuracy in the learning process. This study aims at simultaneously optimizing the parameters of SVM and feature subset without degrading the classification accuracy of SVM using GA for CBIR. Using the hybrid GA and SVM model, we can classify more images in the database effectively. Experiments were carried out on a large-size database of images and experiment results show that the classification accuracy of conventional SVM may be improved significantly by using the proposed model. We also found that the proposed model outperformed all the other models such as neural network and typical SVM models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 G
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• pp.2908-2910
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• 1999

In this paper, a fuzzy model is identified and optimized using the hybrid algorithm and HCM clustering method. Here, the hybrid algorithm is carried out as the structure combined with both a genetic algorithm and the improved complex method. The one is utilized for determining the initial parameters of membership function, the other for obtaining the fine parameters of membership function. HCM clustering algorithm is used to determine the confined region of initial parameters and also to avoid overflow phenomenon during auto-tuning of hybrid algorithm. And the standard least square method is used for the identification of optimum consequence parameters of fuzzy model. Two numerical examples are shown to evaluate the performance of the proposed model.

• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.159-166
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• 2023

The COI gene is a sequence of approximately 650 bp at the 5' terminal of the mitochondrial Cytochrome c Oxidase subunit I (COI) gene. As an effective DeoxyriboNucleic Acid (DNA) barcode, it is widely used for the taxonomic identification and evolutionary analysis of species. We created a CNN-LSTM hybrid model by combining the gene features partially extracted by the Long Short-Term Memory ( LSTM ) network with the feature maps obtained by the CNN. Compared to K-Means Clustering, Support Vector Machines (SVM), and a single CNN classification model, after training 278 samples in a training set that included 15 genera from two orders, the CNN-LSTM hybrid model achieved 94% accuracy in the test set, which contained 118 samples. We augmented the training set samples and four genera into four orders, and the classification accuracy of the test set reached 100%. This study also proposes calculating the cosine similarity between the training and test sets to initially assess the reliability of the predicted results and discover new species.

• 컴퓨터교육학회논문지
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• 제18권6호
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• pp.43-51
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• 2015

본 논문의 목적은 진로 상담 분야에서 직접 코치와 학습자가 만나서 확인하고 상담하는 면대면 코칭과 인터넷을 통해 코치와 학습자가 상담하는 E-코칭을 조합한 하이브리드 진로코칭 모형을 개발하고 대학생들의 진로 지도에 적용하고 그 효과를 분석하는데 있다. 하이브리드 진로코칭에서는 프로파일, 적성 검사 등 인터넷 상에서 원격 접속하여 수행 가능하고 데이터 저장이 필요한 것은 E-코칭으로 처리하고, 점검, 상담 및 성찰 등 학생의 상황을 파악하고 대화가 필요한 부분은 면대면으로 설계하였다. 제안한 하이브리드 진로코칭 모형은 진로코칭 안내, 자기이해, 진로의사결정, 진로실행계획 수립, 실행계획 및 실행의지 점검, 실행결과 점검 6단계로 나누어 진행하였다. 하이브리드 진로코칭 모형을 대학생에게 적용하여 진로개발을 위한 인지적, 행동적 행동변화를 촉진하고 그 효과를 분석하였다. 연구 결과 진로준비행동이 높아졌으며, 코칭을 통해 자신의 현재 상태 분석과 실행 전략이 중요성 및 학습법과 자기조절능력 등을 획득하였다고 분석되었다. 이러한 결과를 토대로 이 연구에서 개발된 하이브리드 진로코칭 모형은 진로지도가 강화되는 대학에서 교수의 진로지도를 지원할 수 있을 것이라 기대된다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2013년도 춘계학술대회 논문집
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• pp.500-501
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• 2013

• Smart Structures and Systems
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• 제14권6호
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• pp.1081-1103
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• 2014

Real-time hybrid simulation (RTHS) has emerged as an important tool for testing large and complex structures with a focus on rate-dependent specimen behavior. Due to the real-time constraints, accurate dynamic control of servo-hydraulic actuators is required. These actuators are necessary to realize the desired displacements of the specimen, however they introduce unwanted dynamics into the RTHS loop. Model-based actuator control strategies are based on linearized models of the servo-hydraulic system, where the controller is taken as the model inverse to effectively cancel out the servo-hydraulic dynamics (i.e., model-based feedforward control). An accurate model of a servo-hydraulic system generally contains more poles than zeros, leading to an improper inverse (i.e., more zeros than poles). Rather than introduce additional poles to create a proper inverse controller, the higher order derivatives necessary for implementing the improper inverse can be calculated from available information. The backward-difference method is proposed as an alternative to discretize an improper continuous time model for use as a feedforward controller in RTHS. This method is flexible in that derivatives of any order can be explicitly calculated such that controllers can be developed for models of any order. Using model-based feedforward control with the backward-difference method, accurate actuator control and stable RTHS are demonstrated using a nine-story steel building model implemented with an MR damper.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2001년도 추계학술대회 논문집
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• pp.97-102
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• 2001

This paper describes an investigation how to carry out model tests of deepwater moorings exceeding the basin depth range. A hybrid mooring model, a combination of mooring lines scaled model and a couple of linear springs, is taken into account as an equivalent substitute of a full depth mooring system. Such an idea is applied to the model test of an OTEC mooring system to be installed in 1000m deep ocean. A 1/25 scaled model test of surface vessel and the upper part of mooring system is performed at ocean engineering basin. Possibility and limitation of the hybrid mooring modeling is discussed.

• 기술혁신연구
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• 제15권2호
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• pp.83-102
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• 2007

This study extends the existing method of Technology Forecasting with Data Envelopment Analysis (TFDEA) by incorporating a ranking method into the model so that we can reduce the required number of DMUs (Decision Making Units). TFDEA estimates technological rate of change with the set of observations identified by DEA(Data Envelopment Analysis) model. It uses an excessive number of efficient DMUs(Decision Making Units), when the number of inputs and outputs is large compare to the number of observations. Hence, we investigated the possibility of incorporating CCCA(Constrained Canonical Correlation Analysis) into TFDEA so that the ranking of DMUs can be made. Using the ranks developed by CCCA(Constrained Canonical Correlation Analysis), we could limit the number of efficient DMUs that are to be used in the technology forecasting process. The proposed hybrid model could establish technology frontiers with the efficient DMUs for each generation of technology with the help of CCCA that uses the common weights. We applied our hybrid model to forecast the technological progress of main battle tank in order to demonstrate its forecasting capability with practical application. It was found that our hybrid model generated statistically more reliable forecasting results than both TFDEA and the regression model.

• 한국물환경학회지
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• 제31권5호
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• pp.491-506
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• 2015

Distributed models represent watersheds using a network of numerous, uniform calculation units to provide spatially detailed and consistent evaluations across the watershed. However, these models have a disadvantage in general requiring a high computing cost. Semi-distributed models, on the other hand, delineate watersheds using a simplified network of non-uniform calculation units requiring a much lower computing cost than distributed models. Employing a simplified network of non-uniform units, however, semi-distributed models cannot but have limitations in spatially-consistent simulations of hydrogeochemical processes and are often not favoured for such a task as identifying critical source areas within a watershed. Aiming to overcome these shortcomings of both groups of models, a hybrid watershed model STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model) was developed in this study. Like a distributed model, STREAM divides a watershed into square grid cells of a same size each of which may have a different set of hydrogeochemical parameters reflecting the spatial heterogeneity. Like many semi-distributed models, STREAM groups individual cells of similar hydrogeochemical properties into representative cells for which real computations of the model are carried out. With this hybrid structure, STREAM requires a relatively small computational cost although it still keeps the critical advantage of distributed models.

• 디지털콘텐츠학회 논문지
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• 제9권3호
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• pp.509-516
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• 2008

컴퓨터 그래픽의 다중 해상도를 지원하는 하이브리드 렌더링 방법을 제안한다. 기본적으로 단말기 환경의 성능과 사용자 요구조건에 따른 그래픽을 위한 다중해상도 방법은 메시를 이용하거나 렌더링 부분에서 텍스쳐의 밉매핑이나 옥트리를 이용한 레이 트레이싱들의 적응 방법이 사용되었다. 본 연구에서는 제안한 하이브리드 렌더링 방법은 지역조명 모델에서 기존의 고로 음영과 평면 음영 라이브러리를 개선한 방법으로 하나의 물체를 이루는 여러 개의 폴리곤에 각각 다른 음영법을 적절하게 적용한다. 실시간 렌더링 시간을 줄일 수 있는 효율적인 대안 방법이 될 수 있으며 이러한 장점이 유비쿼터스 환경에서 다양한 단말기 환경의 그래픽 콘텐츠의 실시간 적응 서비스에 매우 적절하게 사용될 수 있다.