• ETRI Journal
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• 제25권2호
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• pp.140-143
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• 2003

In this paper, we present a probabilistic approach to determining whether extracted facial features from a video sequence are appropriate for creating a 3D face model. In our approach, the distance between two feature points selected from the MPEG-4 facial object is defined as a random variable for each node of a probability network. To avoid generating an unnatural or non-realistic 3D face model, automatically extracted 2D facial features from a video sequence are fed into the proposed probabilistic network before a corresponding 3D face model is built. Simulation results show that the proposed probabilistic network can be used as a quality control agent to verify the correctness of extracted facial features.

• ETRI Journal
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• 제32권4호
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• pp.622-625
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• 2010

In this letter, we assess the scalability of a path computation flooding (PCF) approach to compute optimal end-to-end inter-domain paths in a path computation element-based multi-domain network. PCF yields a drastically reduced network blocking probability compared to a blind per-domain path computation but introduces significant network control overhead and path computation complexity. In view of this, we introduce and compare an alternative low overhead PCF (LoPCF) solution. From the obtained results, LoPCF leads to similar blocking probabilities to PCF while exhibiting around 50% path computation complexity and network control overhead reduction.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.382-382
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• 2020

과거 많은 연구에서 다수의 모형의 결과를 이용한 앙상블 방법론은 인공지능 모형 (artificial neural network)의 예측 능력에 향상을 갖고 온다 논하였다. 본 연구에서는 미계측유역의 저수량(low flow)의 예측을 위하여 Jittering을 기반으로 한 인공지능 모형을 제시하고자 한다. 기본적인 방법론은 설명변수들에게 백색 잡음(white noise)를 삽입하여 훈련되는 자료를 증가시키는 것이다. Jittering을 기반으로 한 인공지능 모형에 대한 효과를 검증하기 위하여 본 연구에서는 Multi-output neural network model을 기반으로 모형을 구축하였다. 다음으로 Jittering을 기반으로 한 앙상블 모형을 variable importance measuring algorithm과 결합시켜서 유역특성치와 예측되는 저수량의 특성치들의 관계를 추론하였다. 본 연구에서 사용되는 방법론들의 효용성을 평가하기 위해서 미동북부에 위치하고 있는 총 207개의 유역을 사용하였다. 결과적으로 본 연구에서 제시한 Jittering을 기반으로 한 인공지능 앙상블 모형은 단일예측모형 (single modeling approach)을 정확도 측면에서 우수한 것으로 확인되었다. 또한, 적은 숫자의 앙상블 모형에서도 그 정확성이 단일예측모형보다 우수한 것을 확인하였다. 마지막으로 본 연구에서는 유역특성치들의 효과가 살펴보고자 하는 저수량의 특성치들에 따라서 일관적으로 영향을 미치거나 그 중요도가 변화하는 것을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.490-495
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• 1992

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming cooperates with neural identification network is used to generate the optimum control law for the complicate continuous/batch chemical reactor systems that have inherent nonlinear dynamics. Based on our approach, we developed a neural model predictive controller(NMPC) which shows excellent performances on nonlinear, model-plant mismatch cases of chemical reactor systems.

• Journal of information and communication convergence engineering
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• 제10권2호
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• pp.141-148
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• 2012

This paper presents a simple but efficient scheme incorporating a reputation-based approach and a cross-layer approach, called the SIM scheme, for maintaining reliable connectivity and high performance in ad-hoc wireless networks. The SIM scheme incorporates the following two things: an ad-hoc routing scheme with a reputation-based approach exploiting the game theory concept based on an evolutionarily stable strategy, and a cross-layer approach between the network layer and the transport layer employing a reputation-based approach.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1999년도 춘계공동학술대회: 지식경영과 지식공학
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• pp.253-260
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• 1999

The recycling cell formation problem means that disposal products me classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences during product use phase and recycling cells are formed design, process and usage attributes. In order to treat the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem far disposal products. In this paper, a heuristic approach fuzzy ART neural network is suggested. The modified fuzzy ART neural network is shown that it has a great efficiency and give an extension for systematically generating alternative solutions in the recycling cell formation problem. We present the results of this approach applied to disposal refrigerators and the comparison of performances between other algorithms. This paper introduced a procedure which integrates economic and environmental factors into the disassembly of disposal products for recycling in recycling cells. A qualitative method of disassembly analysis is developed and its ai is to improve the efficiency of the disassembly and to generated an optimal disassembly which maximize profits and minimize environmental impact. Three criteria established to reduce the search space and facilitate recycling opportunities.

• 대한원격탐사학회지
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• 제20권1호
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• pp.57-64
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• 2004

In GIS-based network analysis, topological measure of network structure can be considered as one of important factors in the urban transportation analysis. Related to this measure, it is known that the connectivity indices such as alpha index and gamma index, which mean degree of network connectivity and complexity on a graph or a circuit, provide fundamental information. On the other hand, shimbel index is one of GIS-based spatial metrics to characterize degree of network concentration. However, the approach using these quantitative indices has not been widely used in practical level yet. In this study, an application program, in complied as extension, running on ArcView- GIS is implemented and demonstrated case examples using basic layers such as road centerline and administrative boundary. In this approach, geo-spatial imagery can be effectively used to actual applications to determine the analysis zone, which is composed of networks to extract these indices. As the results of the implementation and the case examples, it is notified that alpha and gamma indices as well as shimbel index can be used as referential data or auxiliary information for urban planning and transportation planning.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 춘계공동학술대회-지식경영과 지식공학
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• pp.253-260
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• 1999

The recycling cell formation problem means that disposal products are classified into recycling part families using group technology in their end of life phase. Disposal products have the uncertainties of product status by usage influences during product use phase and recycling cells are formed design, process and usage attributes. In order to treat the uncertainties, fuzzy set theory and fuzzy logic-based neural network model are applied to recycling cell formation problem for disposal products. In this paper, a heuristic approach for fuzzy ART neural network is suggested. The modified Fuzzy ART neural network is shown that it has a great efficiency and give an extension for systematically generating alternative solutions in the recycling cell formation problem. We present the results of this approach applied to disposal refrigerators and the comparison of performances between other algorithms. This paper introduced a procedure which integrates economic and environmental factors into the disassembly of disposal products for recycling in recycling cells. A qualitative method of disassembly analysis is developed and its aim is to improve the efficiency of the disassembly and to generated an optimal disassembly which maximize profits and minimize environmental impact. Three criteria established to reduce the search space and facilitate recycling opportunities.

• 스마트미디어저널
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• 제9권1호
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• pp.23-29
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• 2020

This paper presents an approach for dynamic hand gesture recognition by using algorithm based on 3D Convolutional Neural Network (3D_CNN), which is later extended to 3D Residual Networks (3D_ResNet), and the neural network based key frame selection. Typically, 3D deep neural network is used to classify gestures from the input of image frames, randomly sampled from a video data. In this work, to improve the classification performance, we employ key frames which represent the overall video, as the input of the classification network. The key frames are extracted by SegNet instead of conventional clustering algorithms for video summarization (VSUMM) which require heavy computation. By using a deep neural network, key frame selection can be performed in a real-time system. Experiments are conducted using 3D convolutional kernels such as 3D_CNN, Inflated 3D_CNN (I3D) and 3D_ResNet for gesture classification. Our algorithm achieved up to 97.8% of classification accuracy on the Cambridge gesture dataset. The experimental results show that the proposed approach is efficient and outperforms existing methods.

• 한국정보통신학회논문지
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• 제11권9호
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• pp.1679-1686
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• 2007

클러스터링은 ad hoc네트워크에서 확장성 개선과 네트워크 수명 연장뿐만 아니라 클러스터 내 멤버 관리기능과 링크용량을 개선해주는 토폴로지 제어 기법이다. 본 논문에서는 단말의 이동성과 네트워크의 이종성을 고려하여 multi-radio무선 메쉬 네트워크를 위한 클러스터링 알고리즘을 설계하였다. 수학적 분석을 통하여 제안 알고리즘은 시간 t내에 종료되며, 메시지 제어 처리를 위한 복잡도는 각 노드 당 O(1)임을 알 수 있었다. 이는 본 제안 알고리즘을 적용함으로써 무선 메쉬 네트워크의 안정성, 연결성, 확장성과 에너지 효율성이 향상됨을 알 수 있었다.