• 한국정보통신학회논문지
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• 제12권11호
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• pp.2083-2090
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• 2008

본 논문에서는 에너지 절약을 위한 방법으로 냉방기의 적정 온도 및 풍향을 제어하기 위하여 열 영상과 퍼지 추론 규칙을 적용한 온도 및 풍향 제어 기법을 제안한다. 온도 제어를 위한 시뮬레이션에서는 열 영상을 분석하기 위해서 영상을 $300{\times}400$의 크기를 가지는 색상 분포 영상으로 변환한다. 색상 분포 영상은 Red, Magenta, Yellow, Green, Cyan, Blue의 온도 값을 가지는 R,G, B 값으로 구성된다. 각 색상은 $24.0^{\circ}C$에서 $27.0^{\circ}C$의 분포의 온도 값을 가지며, 색상 분포 영상은 레벨 1에서 레벨 10의 높이 계층으로 분류한다. 분류된 각 계층은 고유의 색상 분포도를 가지며 색상이 가지는 온도 수치에 따라 계층별로 온도 값이 할당된다. 실내 공간의 전체적인 온도의 균형과 풍향을 제어하는 과정은 다음과 같다. 풍향의 방향 및 지속 시간, 그리고 풍향의 강도를 구하기 위한 색상 분포 영상의 온도 및 높이 값을 적용하여 퍼지 소속 함수를 설계한 후, 소속 함수의 소속도를 구하고 퍼지 추론 규칙을 적용하여 풍향의 강도를 구한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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• pp.423-426
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• 2005

In this paper, we proposed a PID tuning algorithm by the fuzzy set theory to improve the performance of the PID controller. The new tuning algorithm for the PID controller has the initial value of parameter Kp, $\tau_{I}$, $\tau_{D}$. by the Ziegler-Nichols formula that uses the ultimate gain and ultimate period from a relay tuning experiment. We will get the error and the error rate of plant output corresponding to the initial value of parameter and fnd the new proportion gain(Kp) and the integral time ($\tau_{I}$) from fuzzy tuner by the error and error rate of plant oueut as a membership function of fuzzy theory. This fuzzy auto tuning algorithm for PID controller considerably reduced the overshoot and rise time as compared to any other PID controller tuning algorithms. And in real parametric uncertainty systems, it constitutes an appreciable improvement of performance. The significant property of this algorithm is shown by simulation

• 전자공학회논문지S
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• 제36S권1호
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• pp.115-122
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• 1999

본 논문은 음성신호가 카오스 신호임을 확인하고 화자인식 파라미터로 사용하기 위해 상관차원을 분석하였다. 화자식별과 인식 향상을 위하여 개인의 성도특성을 매우 잘 나타내는 음성의 스트레인지 어트렉터를 구성하고 퍼지유사도를 상관차원에 적용하여 퍼지상관차원을 제안하였다. 퍼지상관차원은 어트렉터 구성점들의 상관관계글 퍼지상관적분으로 추정하고 공간차원에 따라 퍼지상관지수가 일정하게 수렴되는 차원값을 구하여 표준패턴 어트렉터와 시험패턴 어트렉터의 변동을 흡수하였다. 퍼지상관차원에 대해 화자와 표준패턴별로 식별오차의 평균값에 따른 거리를 추정함으로써 화자인식파라미터의 타당성을 검토하였다.

• 한국육종학회지
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• 제42권1호
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• pp.11-22
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• 2010

The population structure of a domesticated species is influenced by the natural history of the populations of its pre-domesticated ancestors, as well as by the breeding system and complexity of breeding practices implemented by humans. In the genetic and population structure analysis of 122 South Asia collections using 29 simple sequence repeat (SSR) markers, 362 alleles were detected, with an average of 12.5 per locus. The average expected heterozygosity and polymorphism information content (PIC) for each SSR locus were 0.74 and 0.72,respectively. The model-based structure analysis revealed the presence of three clusters with the 91.8% (shared > 75%) membership, with 8.2% showing admixture. The genetic distances of Clusters 1-3 were 0.55, 0.56, and 0.68, respectively. Polymorphic information content followed the same trend (Cluster 3 had the highest value and Cluster 1 had smallest value), with genetic distances for each cluster of 0.52, 0.52, and 0.65, respectively. This result could be used for supporting rice breeding programs in South Asia countries.

• International Journal of Internet, Broadcasting and Communication
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• 제12권3호
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• pp.149-155
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• 2020

Air pollution is a problem of environmental health risk in big cities. Recently, researchers have proposed using various artificial intelligence technologies to predict air pollution. The proposed model is Cooperative of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), to predict air pollution of Korean cities using Python. Data air pollutant variables were collected and the Air Korean Web site air quality index was downloaded. This paper's aim was to predict on the health risks and the very unhealthy values of air pollution. We have predicted the air pollution of the environment based on the air quality index. According to the results of the experiment, our model was able to predict a very unhealthy value.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제11권3호
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• pp.178-183
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• 2011

The deterministic annealing-based clustering algorithm is an EM-based algorithm which behaves like simulated annealing method, yet less sensitive to the initialization of parameters. Pairwise clustering is a kind of clustering technique to perform clustering with inter-entity distance information but not enforcing to have detailed attribute information. The pairwise deterministic annealing-based clustering algorithm repeatedly alternates the steps of estimation of mean-fields and the update of membership degrees of data objects to clusters until termination condition holds. Lacking of attribute value information, pairwise clustering algorithms do not explicitly determine the centroids or medoids of clusters in the course of clustering process or at the end of the process. This paper proposes a method to identify the medoids as the centers of formed clusters for the pairwise deterministic annealing-based clustering algorithm. Experimental results show that the proposed method locate meaningful medoids.

• 대한안전경영과학회지
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• 제6권2호
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• pp.187-197
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• 2004

The recycling cell formation problem means that disposal products are classified into recycling product families using group technology in their end-of-life phase. Disposal products have the uncertainties of product condition usage influences. Recycling cells are formed considering design, process and usage attributes. In this paper, a new approach for the design of cellular recycling system is proposed, which deals with the recycling cell formation and assignment of identical products concurrently. Fuzzy ART neural networks are applied to describe the condition of disposal product with the membership functions and to make recycling cell formation. The approach leads to cluster materials, components, and subassemblies for reuse or recycling and can evaluate the value at each cell of disposal products. Disposal refrigerators are shown as an example.

• 한국멀티미디어학회논문지
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• 제10권12호
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• pp.1726-1732
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• 2007

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. Anomaly detection is a pattern recognition task whose goal is to report the occurrence of abnormal or unknown behavior in a given system being monitored. This paper presents an efficient rough set based anomaly detection method that can effectively identify a group of especially harmful internal attackers - masqueraders in cellular mobile networks. Our scheme uses the trace data of wireless application layer by a user as feature value. Based on this, the used pattern of a mobile's user can be captured by rough sets, and the abnormal behavior of the mobile can be also detected effectively by applying a roughness membership function with the age of the user profile. The performance of the proposed scheme is evaluated by using a simulation. Simulation results demonstrate that the anomalies are well detected by the proposed scheme that considers the age of user profiles.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1586-1592
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• 2017

Voltage sourced converter (VSC) based direct-current (DC) grid has the ability to control power flow flexibly and securely, thus it has become one of the most valid approaches in aspect of large-scale renewable power generation, oceanic island power supply and new urban grid construction. To solve the optimal power flow (OPF) problem in DC grid, an adaptive particle swarm optimization (PSO) algorithm based on fuzzy control theory is proposed in this paper, and the optimal operation considering both power loss and voltage quality is realized. Firstly, the fuzzy membership curve is used to transform two objectives into one, the fitness value of latest step is introduced as input of fuzzy controller to adjust the controlling parameters of PSO dynamically. The proposed strategy was applied in solving the power flow issue in six terminals DC grid model, and corresponding results are presented to verify the effectiveness and feasibility of proposed algorithm.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2004년도 춘계학술대회 논문집
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• pp.1155-1162
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• 2004

This research proposes a new risk analysis method in order to guarantee successful performance of construction projects. The proposed risk analysis methods consists of four phases. First step, AHP model can help contractors decide whether or not they bid for a project by analysing risks involved in the project. Second step, the influence diagraming, decision tree and Monte Carlo simulation are used as tools to analyze and evaluate project risks quantitatively. Third step, Monte Carlo simulation is used to assess risk for groups of activities with probabilistic branching and calendars. Finally, Fuzzy theory suggests a risk management method for construction projects, which is using subjective knowledge of an expert and linguistic value, to analyze and quantify risk. The result of study is expected to improve the accuracy of risk analysis because three factors, such as probability, impact and exposure, for estimating membership function are introduced to quantify each risk factor. Consequently, it will help contractors identify risk elements in their projects and quantify the impact of risk on project time and cost.