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Similarity Measure Construction with Fuzzy Entropy and Distance Measure

  • Lee Sang-Hyuk
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.5 no.4
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    • pp.367-371
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    • 2005
  • The similarity measure is derived using fuzzy entropy and distance measure. By the elations of fuzzy entropy, distance measure, and similarity measure, we first obtain the fuzzy entropy. And with both fuzzy entropy and distance measure, similarity measure is obtained., We verify that the proposed measure become the similarity measure.

Feature extraction with distance measures and fuzzy entropy

  • Lee, Sang-Hyuk;Kim, Sung-Shin;Hyeon Bae;Kim, Youn-Tae
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.543-546
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    • 2003
  • Representation and quantification of fuzziness are required for the uncertain system modelling and controller design. Conventional results show that entropy of fuzzy sets represent the fuzziness of fuzzy sets. In this literature, the relations of fuzzy enropy, distance measure and similarity measure are discussed, and distance measure is proposed. With the help of relations of fuzzy entropy, distance measure and similarity measure, fuzzy entropy is proposed by the distance measure. Finally, proposed entropy is applied to measure the fault signal of induction machine.

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Similarity Measure Construction of the Fuzzy Set for the Reliable Data Selection (신뢰성 있는 정보의 추출을 위한 퍼지집합의 유사측도 구성)

  • Lee Sang-Hyuk
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.9C
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    • pp.854-859
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    • 2005
  • We construct the fuzzy entropy for measuring of uncertainty with the help of relation between distance measure and similarity measure. Proposed fuzzy entropy is constructed through distance measure. In this study, the distance measure is used Hamming distance measure. Also for the measure of similarity between fuzzy sets or crisp sets, we construct similarity measure through distance measure, and the proposed 려zzy entropies and similarity measures are proved.

Construction of Fuzzy Entropy and Similarity Measure with Distance Measure (거리 측도를 이용한 퍼지 엔트로피와 유사측도의 구성)

  • Lee Sang-Hyuk;Kim Sung-Shin
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.5
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    • pp.521-526
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    • 2005
  • The fuzzy entropy is proposed for measuring of uncertainty with the help of relation between distance measure and similarity measure. The proposed fuzzy entropy is constructed through a distance measure. In this study, Hamming distance measure is employed for a distance measure. Also a similarity measure is constructed through a distance measure for the measure of similarity between fuzzy sets or crisp sets and the proposed fuzzy entropies and similarity measures are proved.

Quantification of Entire Change of Distributions Based on Normalized Metric Distance for Use in PSAs

  • Han, Seok-Jung;Chun, Moon-Hyun;Tak, Nam-Il
    • Nuclear Engineering and Technology
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    • v.33 no.3
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    • pp.270-282
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    • 2001
  • A simple measure of uncertainty importance based on normalized metric distance to quantify the entire change of cumulative distribution functions (CDFs) has been developed for use in probability safety assessments (PSAs). The metric distance measure developed in this study reflects the relative impact of distributional changes of inputs on the change of an output distribution, white most of the existing uncertainty importance measures reflect the magnitude of relative contribution of input uncertainties to the output uncertainty. Normalization is made to make the metric distance measure a dimensionless quantity. The present measure has been evaluated analytically for various analytical distributions to examine its characteristics. To illustrate the applicability and strength of the present measure, two examples are provided. The first example is an application of the present measure to a typical problem of a system fault tree analysis and the second one is for a hypothetical non-linear model. Comparisons of the present result with those obtained by existing uncertainty importance measures show that the metric distance measure is a useful tool to express the measure of uncertainty importance in terms of the relative impact of distributional changes of inputs on the change of an output distribution.

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The Performance Improvement of Speech Recognition System based on Stochastic Distance Measure

  • Jeon, B.S.;Lee, D.J.;Song, C.K.;Lee, S.H.;Ryu, J.W.
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.2
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    • pp.254-258
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    • 2004
  • In this paper, we propose a robust speech recognition system under noisy environments. Since the presence of noise severely degrades the performance of speech recognition system, it is important to design the robust speech recognition method against noise. The proposed method adopts a new distance measure technique based on stochastic probability instead of conventional method using minimum error. For evaluating the performance of the proposed method, we compared it with conventional distance measure for the 10-isolated Korean digits with car noise. Here, the proposed method showed better recognition rate than conventional distance measure for the various car noisy environments.

A note on distance measure and similarity measure defined by Choquet integral on interval-valued fuzzy sets (구간치 퍼지집합 상에서 쇼케이적분에 의해 정의된 거리측도와 유사측도에 관한 연구)

  • Jang, Lee-Chae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.4
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    • pp.455-459
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    • 2007
  • Interval-valued fuzzy sets were suggested for the first time by Gorzafczany(1983) and Turksen(1986). Based on this, Zeng and Li(2006) introduced concepts of similarity measure and entropy on interval-valued fuzzy sets which are different from Bustince and Burillo(1996). In this paper, by using Choquet integral with respect to a fuzzy measure, we introduce distance measure and similarity measure defined by Choquet integral on interval-valued fuzzy sets and discuss some properties of them. Choquet integral is a generalization concept of Lebesgue inetgral, because the two definitions of Choquet integral and Lebesgue integral are equal if a fuzzy measure is a classical measure.

Local Collision Avoidance of Multiple Robots Using Avoidability Measure and Relative Distance

  • Ko, Nak-Yong;Seo, Dong-Jin;Kim, Koung-Suk
    • Journal of Mechanical Science and Technology
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    • v.18 no.1
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    • pp.132-144
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    • 2004
  • This paper presents a new method driving multiple robots to their goal position without collision. To consider the movement of the robots in a work area, we adopt the concept of avoidability measure. The avoidability measure figures the degree of how easily a robot can avoid other robots considering the velocity of the robots. To implement the concept to avoid collision among multiple robots, relative distance between the robots is proposed. The relative distance is a virtual distance between robots indicating the threat of collision between the robots. Based on the relative distance, the method calculates repulsive force against a robot from the other robots. Also, attractive force toward the goal position is calculated in terms of the relative distance. These repulsive force and attractive force are added to form the driving force for robot motion. The proposed method is simulated for several cases. The results show that the proposed method steers robots to open space anticipating the approach of other robots. In contrast, since the usual potential field method initiates avoidance motion later than the proposed method, it sometimes fails preventing collision or causes hasty motion to avoid other robots. The proposed method works as a local collision-free motion coordination method in conjunction with higher level of task planning and path planning method for multiple robots to do a collaborative job.

Relation between Certainty and Uncertainty with Fuzzy Entropy and Similarity Measure

  • Lee, Sanghyuk;Zhai, Yujia
    • Journal of the Korea Convergence Society
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    • v.5 no.4
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    • pp.155-161
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    • 2014
  • We survey the relation of fuzzy entropy measure and similarity measure. Each measure represents features of data uncertainty and certainty between comparative data group. With the help of one-to-one correspondence characteristics, distance measure and similarity measure have been expressed by the complementary characteristics. We construct similarity measure using distance measure, and verification of usefulness is proved. Furthermore analysis of similarity measure from fuzzy entropy measure is also discussed.

On the Measurement of the Depth and Distance from the Defocused Imagesusing the Regularization Method (비초점화 영상에서 정칙화법을 이용한 깊이 및 거리 계측)

  • 차국찬;김종수
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.32B no.6
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    • pp.886-898
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    • 1995
  • One of the ways to measure the distance in the computer vision is to use the focus and defocus. There are two methods in this way. The first method is caculating the distance from the focused images in a point (MMDFP: the method measuring the distance to the focal plane). The second method is to measure the distance from the difference of the camera parameters, in other words, the apertures of the focal planes, of two images with having the different parameters (MMDCI: the method to measure the distance by comparing two images). The problem of the existing methods in MMDFP is to decide the thresholding vaue on detecting the most optimally focused object in the defocused image. In this case, it could be solved by comparing only the error energy in 3x3 window between two images. In MMDCI, the difficulty is the influence of the deflection effect. Therefor, to minimize its influence, we utilize two differently focused images instead of different aperture images in this paper. At the first, the amount of defocusing between two images is measured through the introduction of regularization and then the distance from the camera to the objects is caculated by the new equation measuring the distance. In the results of simulation, we see the fact to be able to measure the distance from two differently defocused images, and for our approach to be robuster than the method using the different aperture in the noisy image.

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