• 인터넷정보학회논문지
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• 제12권4호
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• pp.27-33
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• 2011

다중경로 페이딩과 충격성 잡음에 의한 채널 왜곡을 보상하기 위하여 오차 엔트로피 최소화 (MEE)에 근거한 결정 궤환 등화 (DFE) 알고리듬을 제안하였다. MEE 성능기준이 아직 결정 궤환 구조나 충격성 잡음환경에 대해 연구된 바가 없다. 결정 궤환 구조의 등화기의 가중치에 대해 오차 엔트로피를 최소화함으로써 제안된 알고리듬은 심각한 다중경로와 충격성 잡음 환경에서 탁월한 잔여 심볼간 간섭제거능력을 보였다.

• 대한산업공학회지
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• 제27권4호
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• pp.394-405
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• 2001

Since most real-world application data involve continuous-valued attributes, properly addressing the discretization process for constructing a decision tree is an important problem. A continuous-valued attribute is typically discretized during decision tree generation by partitioning its range into two intervals recursively. In this paper, by removing the restriction to the binary discretization, we present a hybrid multi-interval discretization algorithm for discretizing the range of continuous-valued attribute into multiple intervals. On the basis of experiment using semiconductor etching machine, it has been verified that our discretization algorithm constructs a more efficient incremental decision tree compared to previously proposed discretization algorithms.

• 디지털산업정보학회논문지
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• 제16권1호
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• pp.55-65
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• 2020

With the development of display hardware, image interpolation techniques have been used in various fields such as image zooming and medical imaging. Traditional image interpolation methods, such as bi-linear interpolation, bi-cubic interpolation and edge direction-based interpolation, perform interpolation in the spatial domain. Recently, interpolation techniques in the discrete cosine transform or wavelet domain are also proposed. Using these various existing interpolation methods and machine learning, we propose decision tree classification-based image interpolation methods. In other words, this paper is about the method of adaptively applying various existing interpolation methods, not the interpolation method itself. To obtain the decision model, we used Weka's J48 library with the C4.5 decision tree algorithm. The proposed method first constructs attribute set and select classes that means interpolation methods for classification model. And after training, interpolation is performed using different interpolation methods according to attributes characteristics. Simulation results show that the proposed method yields reasonable performance.

• Journal of Communications and Networks
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• 제11권4호
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• pp.350-358
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• 2009

In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

• International Journal of Contents
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• 제17권4호
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• pp.79-90
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• 2021

With the deepening of population aging, pension has become an urgent problem in most countries. Community smart pension can effectively resolve the problem of traditional pension, as well as meet the personalized and multi-level needs of the elderly. To predict the pension intention of the elderly in the community more accurately, this paper uses the decision tree classification method to classify the pension data. After missing value processing, normalization, discretization and data specification, the discretized sample data set is obtained. Then, by comparing the information gain and information gain rate of sample data features, the feature ranking is determined, and the C4.5 decision tree model is established. The model performs well in accuracy, precision, recall, AUC and other indicators under the condition of 10-fold cross-validation, and the precision was 89.5%, which can provide the certain basis for government decision-making.

• 한국통신학회논문지
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• 제30권1C호
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• pp.73-81
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• 2005

본 논문에서는 미리 정의된 키 피쳐(key feature)를 수신된 변조 신호로부터 추출하여 동등 이득 조합(equal gain combining) 기법을 적용하는 자동 변조 인식 알고리즘을 제안하곡 의사 결정 이론(decision-theoretic) 알고리즘과 제안된 알고리즘의 성능을 비교, 분석하였다. 제안된 변조 인식 알고리즘은 키 피쳐 추출 단위인 세그먼트별로 미리 정의된 5 가지 종류의 키 피쳐를 추출하고, 전체 프레임에 걸쳐 평균화된 각 키 피쳐값을 결정-순서도(decision flowchart)에 적용하여 수신 신호의 변조 형식을 구분한다. 제안된 알고리즘의 성능을 검증하기 위하여 아날로그 변조 신호인 AM, FM, SSB 신호와 디지털 변조 신호인 FSK2, FSK4, PSK2, PSK4 신호를 대상으로 SNR의 변화 및 신호 수집 시간의 변화에 따른 변조 인식 성공률을 측정하였다. 그 결과 제안된 알고리즘이 기존의 의사 결정 이론 알고리즘에 거의 근접하는 성능을 나타내면서 낮은 복잡도를 나타내었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1161-1164
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• 1996

In this paper, a parameter optimization technique for a mobile robot navigation is discussed. Authors already have proposed a navigation algorithm for mobile robots with sonar sensors using fuzzy decision making theory. Fuzzy decision making selects the optimal via-point utilizing membership values of each via-point candidate for fuzzy navigation goals. However, to make a robot successfully navigate through an unknown and cluttered environment, one needs to adjust parameters of membership function, thus changing shape of MF, for each fuzzy goal. Furthermore, the change in robot configuration, like change in sensor arrangement or sensing range, invokes another adjusting of MFs. To accomplish an intelligent way to adjust these parameters, we adopted a genetic algorithm, which does not require any formulation of the problem, thus more appropriate for robot navigation. Genetic algorithm generates the fittest parameter set through crossover and mutation operation of its string representation. The fitness of a parameter set is assigned after a simulation run according to its time of travel, accumulated heading angle change and collision. A series of simulations for several different environments is carried out to verify the proposed method. The results show the optimal parameters can be acquired with this method.

• 전자공학회논문지
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• 제51권8호
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• pp.104-113
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• 2014

HEVC(high efficiency video coding) 표준에서 사용되는 기존의 rough mode decision(RMD) 알고리즘은 transform skip mode(TSM)와는 연관성이 낮은 DCT를 기반으로 하는 모드 선택 방법을 이용하고 있다. 따라서 기존 RMD의 결과로 구한 후보 모드가 TSM에서 사용될 때, 압축 효율이 손실되고 인코딩 시간을 낭비하게 된다. 본 논문은 HEVC에서의 TSM을 위한 새로운 RMD를 제안한다. 우리가 제안한 RMD 알고리즘은 TSM에서 최선의 모드를 선택할 확률을 높이는 새로운 비용 함수를 제안하여 코딩 효율을 향상시킨다. 또한, 제안하는 알고리즘은 새롭게 제안한 임계값을 기준으로 선택 가능성이 거의 없는 TSM의 인코딩 과정을 생략하여 인코딩 시간을 줄인다. 실험 결과 제안하는 방식은 HEVC 표준에 비해서 10%의 인코딩 시간을 줄이며 스크린 콘텐츠에 대해서 0.3%의 압축률을 향상시킨다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2156-2170
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• 2020

This paper presents a fire detection algorithm with a minimal false detection rate, intended for a thermal imaging surveillance environment, whose properties vary depending on temporal conditions of day or night and environmental changes. This algorithm was designed to minimize the false detection alarm rate while ensuring a high detection rate, as required in fire detection applications. It was necessary to reduce false fire detections due to non-flame elements occurring when existing fixed threshold-based fire detection methods were applied. To this end, adaptive flame thresholds that varied depending on the characteristics of input images, as well as the center of gravity of the heat-source and hot-source regions, were analyzed in an attempt to minimize such non-flame elements in the phase of selecting flame candidate blocks. Also, to remove any false detection elements caused by camera shaking, one of the most frequently raised issues at outdoor sites, preliminary decision thresholds were adaptively set to the motion pixel ratio of input images to maximize the accuracy of the preliminary decision. Finally, in addition to the preliminary decision results, the texture correlation and intensity of the flame candidate blocks were averaged for a specific period of time and tested for their conformity with the fire decision conditions before making the final decision. To verify the fire detection performance of the proposed algorithm, a total of ten test videos were subjected to computer simulation. As a result, the fire detection accuracy of the proposed algorithm was determined to be 94.24%, with minimum false detection, demonstrating its improved performance and practicality compared to previous fixed threshold-based algorithms.

• 한국정보통신학회논문지
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• 제19권8호
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• pp.1765-1772
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• 2015

수중음향통신 채널은 다중 경로 전달이 주요 장애 요인이 되며, 일반적으로 이러한 문제점을 극복하기 위해 등화기가 적용된다. 본 논문에서는 결정 궤환 등화기의 탭 길이를 유전자 알고리즘을 통해 최적화하는 방법을 제안하였다. 유전자 알고리즘의 유전 정보를 전방향 필터와 후방향 필터의 길이로 입력받은 후, 목적함수에 따라 훈련 신호 구간에서의 BER(bit error rate)을 계산하여 필터 길이를 최적화한다. 목적함수는 결정 궤환 등화기, BER 계산으로 설정되었다. 실험 결과, 수심 25 m에 배치된 수신기에 수신된 신호에 훈련 신호만을 이용하였을 때, BER이 0.0355로 나타났다. 모든 데이터를 목적함수 내의 BER계산에 이용하였을 때, BER이 0.0215로 나타났다.