• Title/Summary/Keyword: 퍼지 평균

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Tear Extraction from Ultrasonic Images of Shoulder using Fuzzy Stretching and SOM Based Quantization (퍼지 스트레칭과 SOM 기반 양자화를 이용한 어깨 초음파 영상에서의 인대 손상 영역 추출)

  • Kim, Yoon-Ho;Kim, Min-Ha;Song, Yu-Seon;Kim, Kwang-Beak
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2017.01a
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    • pp.9-12
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    • 2017
  • 본 논문에서는 어깨 초음파 영상을 분석하여 인대 손상(Tear) 영역을 추출하는 방법을 제안한다. 제안된 방법은 초음파 영상에서 ROI(Region of Interest) 영역을 추출하고 추출된 ROI 영역에서 사다리꼴 형태의 소속 함수를 적용한 퍼지 스트레칭 기법을 이용하여 명암 대비를 높인다. 명암 대비가 조정된 ROI 영역에서 밝기 평균 이진화 기법을 적용하여 ROI 영역을 이진화한다. 이진화가 적용된 ROI 영역에서 워터쉐드 기법을 적용하여 연골과 힘줄의 후보 영역들을 추출한다. 추출된 연골과 힘줄의 후보 영역들 중에서 위에서 아래로 스캔하여 수평 너비가 가장 큰 영역에 해당하는 힘줄 영역의 상단 경계선을 추출한다. 그리고 아래에서 위로 스캔하여 수평 너비가 가장 큰 영역의 상단 경계에 스플라인 곡선을 적용하여 연골 영역의 상단 경계선을 추출한다. 힘줄 영역의 상단 경계선과 연골 영역의 상단 경계선 양 끝에 2차 함수 곡선을 적용하여 곡선 사이의 양자화할 영역을 추출한 후, SOM 기법을 적용하여 인대 손상 후보 영역을 양자화한다. 양자화된 인대 손상 후보 영역을 분석하여 어깨 힘줄의 손상 영역과 비손상 영역을 구분하고 인대 손상(Tear) 영역을 추출한다. 제안된 방법을 어깨 힘줄이 있는 초음파 영상을 대상으로 실험한 결과, 인대 손상(Tear) 영역이 비교적 정확히 추출되었다.

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Development of ECG Identification System Using the Fuzzy Processor (퍼지 프로세서를 이용한 심전도 판별 시스템 개발)

  • 장원석;이응혁
    • Journal of Biomedical Engineering Research
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    • v.16 no.4
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    • pp.403-414
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    • 1995
  • It is very difficult to quantize the ECG analysis because the decision criterion for ECG is different with each other depending on the medical specialists of the heart and there are measured detecting errors for each ECG measurement system. Therefore, we developed the real-time ECG identification system using digital fuzzy processor for STD-BUS, in order to reduce ambiguity generated in the process of ECG identification and to analyze the irregular ECG stastically to ECG's repetition interval. The variables such as AGE (months), width of QRS, average RRI, and RRI were used to classify the ECG, and were applied to ECG signal indentification system which is developed for the purpose of research. It was found that the automatic diagnosis of ECG signal was possible in the real time process which was impossible in general process of algorithm.

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Moving Vehicle Tracking using Fuzzy Clustering (퍼지 클러스터링을 이용한 이동 차량 추적)

  • 양상규;이정재;소영성
    • Journal of the Korean Institute of Intelligent Systems
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    • v.6 no.4
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    • pp.92-101
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    • 1996
  • Due to I:he rapid increase of vehicles and poor availability of roads, traffic congestion problem is about to explode. To solve this problem, we need real time information about traffic flow to control traffic signals dynamically. Until now loop coil is the most prevalent sensor used for obtaining traffic flow information. However, it is not able to track individual vehicles which is essential in estimating the average vehicle speed. As a result, image sensors started to find their role in this problem domain. Several systems based on image sensors were proposed which assumes either gray level or color image sequence. In this paper, we propose moving vehicle tracking method based on fizzy clustering assuming a wlor image sequenc.

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A Study of Optimal Ratio of Data Partition for Neuro-Fuzzy-Based Software Reliability Prediction (뉴로-퍼지 소프트웨어 신뢰성 예측에 대한 최적의 데이터 분할비율에 관한 연구)

  • Lee, Sang-Un
    • The KIPS Transactions:PartD
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    • v.8D no.2
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    • pp.175-180
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    • 2001
  • This paper presents the optimal fraction of validation set to obtain a prediction accuracy of software failure count or failure time in the future by a neuro-fuzzy system. Given a fixed amount of training data, the most popular effective approach to avoiding underfitting and overfitting is early stopping, and hence getting optimal generalization. But there is unresolved practical issues : How many data do you assign to the training and validation set\ulcorner Rules of thumb abound, the solution is acquired by trial-and-error and we spend long time in this method. For the sake of optimal fraction of validation set, the variant specific fraction for the validation set be provided. It shows that minimal fraction of the validation data set is sufficient to achieve good next-step prediction. This result can be considered as a practical guideline in a prediction of software reliability by neuro-fuzzy system.

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Motion Analysis Using Competitive Learning Neural Network and Fuzzy Reasoning (경쟁학습 신경망과 퍼지추론법을 이용한 움직임 분석)

  • 이주한;오경환
    • Journal of the Korean Institute of Intelligent Systems
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    • v.5 no.3
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    • pp.117-127
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    • 1995
  • In this paper, we suggest a motion analysis method using ART-I1 competitive learning neural network and fuzzy reasoning by matching the same objects through the consecutive image sequence. we use the size and mean intensity of the region obtained from image segmentation for the region matching by the region and use a ART-I1 competitive learning neural network wh~ch has a learning ability to reflect the topology of the input patterns in order to select characteristic points to describe the shape of a region. Motion vectors for each regions are obtained by matching selected characteristic points. However, the two dimensional image, the projection of the the three dimensional real world, produces fuzziness in motion analysis due to its incompleteness by nature and the error from image segmentation used for extracting information about objects. Therefore, the belief degrees for each regions are calculated using fuzzy reasoning to l-nanipulate uncertainty in motion estimation.

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A Cost-Effective and Accurate COA Defuzzifier Without Multipliers and Dividers (승산기 및 제산기 없는 저비용 고정밀 COA 비퍼지화기)

  • 김대진;이한별;강대성
    • Journal of the Korean Institute of Intelligent Systems
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    • v.8 no.2
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    • pp.70-81
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    • 1998
  • This paper proposes an accurate and cost-effective COA defuzzifier of fuzzy logic controller (FLC). The accuracy of the proposed COA defuzzifier is obtained by involving both membership values and spans of membership functions in calculating a crisp value. The cost-effectiveness of the proposed COA defuzzifier is obtained by replacing the division in the COA defuzzifier by finding an equilibrium point of both the left and right moments. The proposed COA defuzzifier has two disadvantages that it ncreases the hardware complexity due to the additional multipliers and it takes a lot of computation time to find the moment equilibrium point. The first disadvantage is overcome by replacing the multipliers with the stochastic AND operations. The second disadvantage is alleviated by using a coarse-to-fine searching algorithm that accelerates the finding of moment equilibrium point. Application of the proposed COA defuzzifier to the truck backer-upper control problem is performed in the VHDL simulation and the control accuracy of the proposed COA defuzzifier is compared with that of the conventional COA defuzzifier in terms of average tracing distance.

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Cell Selection Method using Multi-Criteria Decision Making in Heterogeneous Networks (이종 망에서 퍼지 다기준 의사 결정을 이용한 셀 선정 방법)

  • Lee, Jong-Chan;Park, Sang-Joon
    • Journal of the Korea Society for Simulation
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    • v.17 no.3
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    • pp.1-8
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    • 2008
  • It is essential to maintain constant QoS despite of cell overload or erroneous wireless link during transmission because QoS of a multimedia service may be severely degraded by small delay or packet loss. This paper deals with a cell selection method for supporting the QoS of multimedia services over heterogeneous networks. The proposed scheme is based on Fuzzy Multi-Criteria Decision Making (FMCDM), in which uncertain parameters such as user system preference, the communication cost and cell load, and the transmission delay are used in the decision process using the aggregation function in fuzzy set theory. In this scheme, errors in the evaluation parameters impose milder changes on the total evaluation value than in binary logics. Simulation is focused on the average delay and packet loss rate, and the simulation results show that our proposed method provides mobile terminals the optimal performance.

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Call Admission Control in ATM by Neural Networks and Fuzzy Pattern Estimator (신경망과 퍼지 패턴 추정기를 이용한 ATM의 호 수락 제어)

  • Lee, Jin-Lee
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.8
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    • pp.2188-2195
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    • 1999
  • This paper proposes a new call admission control scheme utilizing an inverse fuzzy vector quantizer(IFVQ) and neuralnet, which combines benefits of IFVQ and flexibilities of FCM(Fuzzy-C-Means) arithmetics, to decide whether a requested call not to be trained in learning phase to be connected or not. The system generates the estimated traffic pattern for the cell stream of a new call, using feasible/infeasible patterns in codebook, fuzzy membership values that represent the degree to which each pattern of codebook matches input pattern, and FCM arithmetics. The input to the NN is the vector consisted of traffic parameters which are the means and variances of the number of cells arriving in decision as to whether to accept or reject a new call depends on whether the NN is used for decision threshold(+0.5). This method is a new technique for call admission control using the membership values as traffic parameter which declared to CAC at the call set up stage, and this is valid for a very general traffic model in which the calls of a stream can belong to an unlimited number of traffic classes. Through the simulations, it is founded the performance of the suggested method outperforms compared to the conventional NN method.

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A Study on the Improvement of Fault Detection Capability for Fault Indicator using Fuzzy Clustering and Neural Network (퍼지클러스터링 기법과 신경회로망을 이용한 고장표시기의 고장검출 능력 개선에 관한 연구)

  • Hong, Dae-Seung;Yim, Hwa-Young
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.3
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    • pp.374-379
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    • 2007
  • This paper focuses on the improvement of fault detection algorithm in FRTU(feeder remote terminal unit) on the feeder of distribution power system. FRTU is applied to fault detection schemes for phase fault and ground fault. Especially, cold load pickup and inrush restraint functions distinguish the fault current from the normal load current. FRTU shows FI(Fault Indicator) when the fault current is over pickup value or inrush current. STFT(Short Time Fourier Transform) analysis provides the frequency and time Information. FCM(Fuzzy C-Mean clustering) algorithm extracts characteristics of harmonics. The neural network system as a fault detector was trained to distinguish the inruih current from the fault status by a gradient descent method. In this paper, fault detection is improved by using FCM and neural network. The result data were measured in actual 22.9kV distribution power system.

Proxy Caching Grouping by Partition and Mapping for Distributed Multimedia Streaming Service (분산 멀티미디어 스트리밍 서비스를 위한 분할과 사상에 의한 프록시 캐싱 그룹화)

  • Lee, Chong-Deuk
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.1
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    • pp.40-47
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    • 2009
  • Recently, dynamic proxy caching has been proposed on the distributed environment so that media objects by user's requests can be served directly from the proxy without contacting the server. However, it makes caching challenging due to multimedia large sizes, low latency and continuous streaming demands of media objects. To solve the problems caused by streaming demands of media objects, this paper has been proposed the grouping scheme with fuzzy filtering based on partition and mapping. For partition and mapping, this paper divides media block segments into fixed partition reference block(R$_f$P) and variable partition reference block(R$_v$P). For semantic relationship, it makes fuzzy relationship to performs according to the fixed partition temporal synchronization(T$_f$) and variable partition temporal synchronization(T$_v$). Simulation results show that the proposed scheme makes streaming service efficiently with a high average request response time rate and cache hit rate and with a low delayed startup ratio compared with other schemes.