• Journal of the Korean Solar Energy Society
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• v.23 no.4
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• pp.55-61
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• 2003

Heat transfer characteristics for an air jet vertically impinging on a flat plate with a set of hybrid rods was investigated numerically using the RNG k-$\varepsilon$turbulent model. A commercial finite-volume code FLUENT is used. The rods had cross sections of half circular and rectangular shapes. The heating surface was heated with a constant heat flux value of $1020W/m^2$. Parameters investigated were the jet Reynolds number, nozzle -to-plate spacing, the rod pitch and rod-to-plate clearance. The local and average Nusselt number were found to be dependent on the rod pitch and the clearance because installing rods disturbed the flow. Higher convective heat transfer rate occurred in the whole plate as well as in the wall jet region.

• Electronics and Telecommunications Trends
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• v.24 no.6
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• pp.52-60
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• 2009

인터넷의 보급과 더불어 폭발적으로 늘어나기 시작한 데이터 통신은 FTTH의 보급과 IPTV, VoD 등 본격적인 멀티미디어 수요의 증가에 따라 매 18개월마다 통신량이 2배씩 증가하는 "광 무어의 법칙"이 성립되고 있다. 지속적인 대역폭의 증가를 수용하기 위해서는 핵심 광부품의 소형화 및 저가화가 반드시 필요하며 이러한 요구사항 수용을 위한 핵심요소는 광집적화 기술 개발을 통한 광소자의 고집적화 및 고속화 달성이다. 광집적화 기술은 크게 두 가지 분류로 나눌 수 있는데 이종 물질간의 하이브리드결합에 의한 구현과 단일 혹은 유사 물질에 의한 모놀리식 집적에 의한 방법이다. 본 고에서는 하이브리드 집적 및 모놀리식 집적 기술의 핵심 요소 및 기술발전 현황에 대해 살펴본다.

• KIPE Magazine
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• v.16 no.2
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• pp.25-31
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• 2011

최근 10년동안 지구환경 문제는 인류가 하는 모든 활동에 있어 기본전제가 되고 있다. 지구온난화 문제로 인해 자동차의 $CO_2$ 배출량을 줄이는 문제가 최근 수송기계 분야의 초미 관심사가 되고 있으며 온실가스 문제와 석유자원 고갈에 따른 대체에너지 문제에 따라 자동차 산업은 큰 전환기를 맞고 있다. 세계 각국에서 이산화탄소 배출규제가 법제화되면서 내연기관 중심의 자동차 기술은 그린카(친환경자동차) 기술로의 패러다임의 변화를 보이고 있다. 그린카는 전기자동차, 클린 디젤차, 연료전지차 정도로 대분류되며 전기자동 전기자동차(EV), 하이브리드 전기자동차(HEV), 플러그인 하이브리드 전기자동차(PHEV) 등으로 분류된다. 이미 그린카의 양산이 수년전부터 활발하게 진행되어 왔으며, 최근 2~3년내로 각국의 major급 자동차 메이커에서 이에 대한 양산 계획을 속속 발표하고 있다. 이러한 전 세계의 그린카 정책동향과 국내 그린카 활성화 정책 및 현정부의 광역경제권 선도산업으로 추진중인 호남광역경제권 전기자동차 산업 육성 정책에 대해 소개하고자 한다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.481-482
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• 2023

양자 컴퓨팅 환경에서 빅데이터를 이용하는 Quantum Artificial Intelligence(QAI)는 빠른 계산 속도를 추구한다. 최근 금융, 물류, 교통 분야의 QAI 모델과 이미지 분류용 quantum convolutional neural network가 소개됐지만 아직 완벽한 성능은 달성하지 못했다. 본 논문은 성능 향상을 위한 모듈을 새로 제시하고, 이를 소형 양자 컴퓨터에 적용하며 하이브리드 모델 구성을 가능하게 한다. 실험 결과, 제안하는 방법은 기존 네트워크와 비교해 우수한 성능을 보였다.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1357-1367
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• 2013

Recently, as one of images based methods in facial expression recognition, the research which used ULBP block histogram feature and SVM classifier was performed. Due to the properties of LBP introduced by Ojala, such as highly distinction capability, durability to the illumination changes and simple operation, LBP is widely used in the field of image recognition. In this paper, we combined $LBP_{8,2}$ and $LBP_{8,1}$ to describe micro features in addition to shift, size change in calculating ULBP block histogram. From sub-windows of 660 of $LBP_{8,1}$ and 550 of $LBP_{8,2}$, ULBP histogram feature of 1210 were extracted and weak classifiers of 50 were generated using AdaBoost. By using the combined $LBP_{8,1}$ and $LBP_{8,2}$ hybrid type of ULBP histogram feature and SVM classifier, facial expression recognition rate could be improved and it was confirmed through various experiments. Facial expression recognition rate of 96.3% by hybrid boosted ULBP block histogram showed the superiority of the proposed method.

• Journal of KIISE
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• v.43 no.1
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• pp.80-86
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• 2016

A speech-act is a behavior intended by users in an utterance. Speech-act classification is important in a dialogue system. The machine learning and rule-based methods have mainly been used for speech-act classification. In this paper, we propose a speech-act classification method based on the combination of support vector machine (SVM) and transformation-based learning (TBL). The user's utterance is first classified by SVM that is preferentially applied to categories with a low utterance rate in training data. Next, when an utterance has negative scores throughout the whole of the categories, the utterance is applied to the correction phase by rules. The results from our method were higher performance over the baseline system long with error-reduction.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.8
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• pp.754-759
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• 2001

In this paper, we propose a TSK(Takagi-Sugeno-Kang)-type fuzzy classifier using PCA(Principal Component Analysis), FCM(Fuzzy c-Means) clustering, ANFIS(Adaptive Neuro-Fuzzy Inference System) and hybrid GA(Genetic Algorithm). First, input data is transformed to reduce correlation among the data components by PCA. FCM clustering is applied to obtain a initial TSK-type fuzzy classifier. Parameter identification is performed by AGA(Adaptive GA) and RLSE(Recursive Least Square Estimate). Finally, we applied the proposed method to Iris data classificationl problems and obtained a better performance than previous works.

• Composites Research
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• v.22 no.6
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• pp.23-31
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• 2009

This paper investigated acoustic emission (AE) characteristics in association with various fracture processes of glass fiber reinforced plastic skin/ aluminum honeycomb core (GF-AH) hybrid composites under compressive and bending loads. Various failure modes such as skin layer fracture, skin/core interfacial fracture, and local plastic yield buckling and cell wall adhesive fracture occurring in the honeycomb cell wall were classified through the fracture identification in association with the AE frequency and amplitude analysis. The distribution of the event-rate in which it has a high amplitude showed a procedure of cell wall adhesive fracture, skin/core interfacial debonding and fiber breakage, whereas distribution of different peak frequencies indicated the plastic deformation of aluminum cell wall and the friction between honeycomb walls. Consequently, the fracture behaviors of GF-AH hybrid composites could be characterized through a nondestructive evaluation employing the AE technique.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.45-54
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• 2014

In this paper, an improved two-step P2P traffic classification scheme is proposed to overcome the limitations of the existing methods. The first step is a signature-based classifier at the packet-level. The second step consists of pattern heuristic rules and a statistics-based classifier at the flow-level. With pattern heuristic rules, the accuracy can be improved and the amount of traffic to be classified by statistics-based classifier can be reduced. Based on the analysis of different decision tree algorithms, the statistics-based classifier is implemented with REPTree. In addition, the ensemble algorithm is used to improve the performance of statistics-based classifier Through the verification with the real datasets, it is shown that our hybrid scheme provides higher accuracy and lower overhead compared to other existing schemes.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.185-191
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• 2007

Remote sensing images are multispectral image data collected from several band divided by wavelength ranges. The classification of remote sensing images is the method of classifying what has similar spectral characteristics together among each pixel composing an image as the important algorithm in this field. This paper presents a pattern classification method of remote sensing images by applying a possibilistic fuzzy c-means (PFCM) algorithm. The PFCM algorithm is a hybridization of a FCM algorithm, which adopts membership degree depending on the distance between data and the center of a certain cluster, combined with a PCM algorithm, which considers class typicality of the pattern sets. In this proposed method, we select the training data for each class and perform supervised classification using the PFCM algorithm with spectral signatures of the training data. The application of the PFCM algorithm is tested and verified by using Landsat TM and IKONOS remote sensing satellite images. As a result, the overall accuracy showed a better results than the FCM, PCM algorithm or conventional maximum likelihood classification(MLC) algorithm.