• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.984-984
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• 2012

지구온난화와 기후변화의 영향으로 전 지구적으로 이상홍수, 이상가뭄, 한파와 같은 이상기상 현상이 빈번하게 발생하고 있다. 국내에서는 2010년 추석 광화문 침수사태와 2011년 우면산 산사태와 같은 국지성 집중호우로 인한 인적 물적 피해가 속출하고 있다. 전통적으로 시기나 양적인 측면에서 대부분 장마기간에 국한되었던 강우집중현상이 과거와 달리 특정기간에 상관없이 발생하고 단기성, 국지성을 지닌 호우의 발생빈도가 높아지는 등 국내 강우의 특성이 변하고 있다. 이러한 변화에 대응하기 위해서 강우예측과 유출량예측의 정확도를 높이기 위한 시도가 다양하게 이루어지고 있다. 강우예측의 정확성을 높이기 위해 기상청에서는 단기예보를 목적으로 전지구 통합모델과 지역 통합모델을 연계한 동네예보를 수행하고 있으며, 초단기 예보를 위한 목적으로 VSRF, SCAN, VDRAS, MAPLE 등의 예보를 수행하고 있다. 홍수량 예측에서는 일반적으로 사용하고 있는 물리적 기반의 모형에 레이더강우와 같은 격자형 강우자료를 사용하여 정확성을 높이거나, 기존의 집중형 모형을 분포형 모형으로 대체하기 위한 연구 등이 이루어지고 있으며, 모형 구축이 간편하고 예측 정확도가 우수하다는 장점으로 인해 신경회로망이나 퍼지추론기법 등을 사용한 연구도 지속적으로 이루어지고 있다. 본 연구에서는 수자원분야에 산재한 불확실성을 적극적으로 인정하고 수학적으로 해석하기 위한 이론인 퍼지이론에 신경망 이론을 도입한 neuro-fuzzy 기법을 사용하여 홍수량을 예측하였다. 모형의 입력자료로는 관측된 강우자료와 유출량자료 및 기상청에서 제공하는 MAPLE(McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) 강우예측자료를 사용하여 적용성을 평가해보았다. 모형의 적용성을 평가하기 위해 시험유역을 충주댐 상류 유역으로 선정하였으며, 2010년 2011년 홍수기의 충주댐 유입량을 예측하였다. 모형의 입력자료를 변경하여 입력자료의 변화에 따른 결과를 비교하였고, clustering 반경의 변화에 따른 정확도를 비교하였다. 모형의 정확도는 평균제곱근오차와 첨두수위오차를 통해 비교하였으며, 비교결과 전반적으로 lead time이 길어질수록 MAPLE 사용 시 예측 정확도가 우수하였고, clustering 반경은 0.5일 때 가장 우수한 결과를 보였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.699-705
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• 2009

This paper is represented to research of driving control for the forklift AGV. The related works that were studied about AGV as heavy equipment used two methods which are magnet-gyro and wire guidance for localization. However, they have weaknesses that are high cost, difficult maintenance according to change of environment. In this paper, we develop localization system through sensor fusion with laser navigation system and encoder, gyro for robustness. Also we design driving controller using fuzzy and proportional control. It considers distance and angle difference between forklift AGV and pallet for engaging work. To analyze performance of the proposed control system, we experiment in same working condition over 10 times. In the results, the average error was presented with 54.16mm between simulation of control navigation and real control navigation. Consequently, experimental result shows that the performance of proposed control system is effective.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.29-39
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• 2015

This paper describes a design and implementation of fuzzy-based algorithm for hand-shake state detection and error compensation in the mobile optical image stabilization(OIS) motion detector. Since the gyro sensor output of the OIS motion detector includes inherent error signals, accurate error correction is required for prompt hand-shake error compensation and stable hand-shake state detection. In this research with a little computation overhead of fuzzy-based algorithm, the hand-shake error compensation could be improved by quickly reducing the angle and phase error for the hand-shake frequencies. Further, stability of the OIS system could be enhanced by the hand-shake states of {Halt, Little vibrate, Big vibrate, Pan/Tilt}, classified by subdividing the hand-shake angle. The performance and stability of the proposed algorithm in OIS motion detector is quantitatively and qualitatively evaluated with the emulated hand-shaking of ${\pm}0.5^{\circ}$, ${\pm}0.8^{\circ}$ vibration and 2~12Hz frequency. In experiments, the average error compensation gain of 3.71dB is achieved with respect to the conventional BACF/DCF algorithm; and the four hand-shake states are detected in a stable manner.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.165-172
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• 2010

In clinical data minig, choosing the optimal subset of features is such important, not only to reduce the computational complexity but also to improve the usefulness of the model constructed from the given data. Moreover the threshold values (i.e., cut-off points) of selected features are used in a clinical decision criteria of experts for differential diagnosis of diseases. In this paper, we propose a fuzzy discretization approach, which is evaluated by measuring the degree of separation of redundant attribute values in overlapping region, based on spatial distribution of data with continuous attributes. The weighted average of the redundant attribute values is then used to determine the threshold value for each feature and rough set theory is utilized to select a subset of relevant features from the overall features. To verify the validity of the proposed method, we compared experimental results, which applied to classification problem using 668 patients with a chief complaint of dyspnea, based on three discretization methods (i.e., equal-width, equal-frequency, and entropy-based) and proposed discretization method. From the experimental results, we confirm that the discretization methods with fuzzy partition give better results in two evaluation measures, average classification accuracy and G-mean, than those with hard partition.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.233-242
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• 2018

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting feature point based on only R peak through optimal R wave. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.

• The Journal of the Korea Contents Association
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• v.6 no.7
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• pp.58-68
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• 2006

There are two traditional scheduling methods known as PQ and WRR in the DiffServ network, however, these two scheduling methods have some drawbacks. In this paper, we propose an algorithm that can be adopted in WRR scheduler with making up for weak points of PQ and WRR. The proposed algorithm produces the control discipline by the fuzzy theory to dynamically assign the weight of WRR scheduler with checking the Queue status of each class. To evaluate the performance of the proposed algorithm, We accomplished a computer simulation using NS-2. From simulation results, the proposed algorithm improves the packet loss rate of the EF class traffic to 6.5% by comparison with WRR scheduling method and that of the AF4 class traffic to 45% by comparison with PQ scheduling method.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2005.04a
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• pp.72-75
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• 2005

본 연구에서는 야외규모 TCE (trichloroethylene) 질량전이역학을 평가하기 위해 TCE의 대수층 내 유입 및 용해상 거동을 고해상도 수치 모사를 실시하였다. 공간적으로 불균질한 대수층 $10{\times}10{\times}10m$ 도메인 내부의 유입된 TCE 분포를 모사하기 위해 본 연구를 통해 개발된 정성적 침투모델(percolation model)이 이용되었다. 이를 기초로 하여 연계되어진 (coupled) 지하수 유동 및 용해상 거동 장기 (long-term) 모사를 실시하였다. 지엽적으로 일어나는 질량전이는 기존 연구를 통하여 발표되어진 실험실규모 연구에 기초하였다. 지하수가 도메인을 지나 흘러나가는 경계면에서 측정되어진 용해상 (aqueous phase) TCE의 질량선속 (mass flux)을 통해 실질 야외규모 질량전이 상수가 계산되었다. 관찰된 바 야외규모 질량전이 상수는 실험실 연구를 통해 측정된 값에 비하여 휠씬 작은 값을 보였으며 이는 지하수 유속 및 TCE의 불균질한 분포에 기인한다. 야외규모 질량전이 상수는 평균 지하수 유속에 직접 비례하는 것으로 관찰되었고 이는 기존 실험실 연구를 통해 알려진 평균 지하수 유속의 0.7승이라는 결과와 대조되는 것이다. 또한 모사를 통해 관찰된 야외규모 질량전이 상수는 상대 TCE 질량의 고갈상수 승에 비례함을 보였다. 이러한 고갈상수는 TCE가 측방으로 퍼지는 현상이 강한 대수층, 즉 저투수성 층의 발달이 양호한 대수층, 에서는 1보다 작은 값을 갖고 그렇지 않은 대수층, 즉 저투수성 층의 발달이 미약한 대수층, 에서는 대체적으로 1보다 높은 값을 갖는 것으로 관측되었다. 이는 DNAPL의 측방 퍼짐이 강한 대수층에서는 용해로 인한 시간에 따른 오염원 부근에서의 농도 감소가 미약하기 때문인 것이며, 그와 반대로 DNAPL의 측방 퍼짐이 약한 대수층에서는 시간이 지남에 따라 용해에 의해 지속적으로 오염원 부근에서의 농도가 감소 또는 소멸함으로 인하여 측정되는 용해상 DNAPL의 질량 선속 역시 계속적으로 감소되는 것으로 밝혀졌다.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.93-100
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• 2011

In this paper, we propose a FDR(False Data Reduction) strategy for P2P environment that reduces false data. The key idea of our strategy is that we use FDR algorithm to stop transmitting of false data and to delete that. If a user recognizes false data in downloaded-data and the user's peer requests the others to stop the transmission of the false data immediately. Also, the FDR algorithm notifies the other peers to prohibit spreading of the false data in the environment. All this procedure is possible to be executed in each peer without any lookup server. The FDR algorithm needs only a little data exchange among peers. Through simulation, we show that it is more effective to reduce the network traffic than the previous P2P strategy. We also show that the proposed strategy improves the performance of network compared to previous P2P strategy. As a result, The FDR strategy is decreased 9.78 ~ 16.84% of mean true data transmission time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2769-2774
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• 2013

The key of USN(Ubiquitous Sensor Network) technology is low power wireless communication technology and proper resource allocation technology for efficient routing. The distinguished resource allocation method is needed for efficient routing in sensor network. To solve this problems, we propose an algorithm that can be adopted in USN with making up for weak points of PQ and WRR in this paper. The proposed algorithm produces the control discipline by the fuzzy theory to dynamically assign the weight of WRR scheduler with checking the Queue status of each class in sensor network. From simulation results, the proposed algorithm improves the packet loss rate of the EF class traffic to 6.5% by comparison with WRR scheduling method and that of the AF4 class traffic to 45% by comparison with PQ scheduling method.