• Title/Summary/Keyword: Minimum Distance Classification

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Fast Automatic Modulation Classification by MDC and kNNC (MDC와 kNNC를 이용한 고속 자동변조인식)

  • Park, Cheol-Sun;Yang, Jong-Won;Nah, Sun-Phil;Jang, Won
    • Journal of the Korea Institute of Military Science and Technology
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    • v.10 no.4
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    • pp.88-96
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    • 2007
  • This paper discusses the fast modulation classifiers capable of classifying both analog and digital modulation signals in wireless communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulated signals. In this paper, we investigate the performance of the two types of fast modulation classifiers (i.e. 2 nearest neighbor classifiers and 2 minimum distance classifiers) and compare the performance of these classifiers with that of the state of the art for the existing classification methods such as SVM Classifier. Computer simulations indicate good performance on an AWGN channel, even at low signal-to-noise ratios, in case of minimum distance classifiers (MDC for short) and k nearest neighbor classifiers (kNNC for short). Besides a good performance, these type classifiers are considered as ideal candidate to adapt real-time software radio because of their fast modulation classification capability.

Classification for landfast sea ice types in Greenland with texture analysis images (텍스쳐 이미지를 이용한 그린란드 정착빙의 분류)

  • Hwang, Do-Hyun;Hwang, Byong-Jun;Yoon, Hong-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.8 no.4
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    • pp.589-593
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    • 2013
  • Remote sensing of SAR images is suitable for sea ice observations to obtain the sea ice data if clouds or weather conditions change. There are various types of sea ice, classification results can be seen more easily to detect the change by types of sea ice. In this study, we classified the image by supervised classification method, which is minimum distance was used. Also, we compared the overall accuracy when compared to the results with classification result of SAR images and the result of texture images. When using Radarsat-2 texture images, the overall accuracy was the highest, generally, when using the SAR images had higher overall accuracy.

Classification of Epilepsy Using Distance-Based Feature Selection (거리 기반의 특징 선택을 이용한 간질 분류)

  • Lee, Sang-Hong
    • Journal of Digital Convergence
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    • v.12 no.8
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    • pp.321-327
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    • 2014
  • Feature selection is the technique to improve the classification performance by using a minimal set by removing features that are not related with each other and characterized by redundancy. This study proposed new feature selection using the distance between the center of gravity of the bounded sum of weighted fuzzy membership functions (BSWFMs) provided by the neural network with weighted fuzzy membership functions (NEWFM) in order to improve the classification performance. The distance-based feature selection selects the minimum features by removing the worst features with the shortest distance between the center of gravity of BSWFMs from the 24 initial features one by one, and then 22 minimum features are selected with the highest performance result. The proposed methodology shows that sensitivity, specificity, and accuracy are 97.7%, 99.7%, and 98.7% with 22 minimum features, respectively.

An Object Oriented Approach for Multi-Channel and Multi-Polarization NASA/JPL POLSAR Image Classification

  • Tsay, Jaan-Rong;Lin, Chia-Chu
    • Proceedings of the KSRS Conference
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    • pp.363-365
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    • 2003
  • This paper presents an object oriented approach(OOA) for classification of multi-channel and multi-polarization NASA/JPL POLSAR images. Some test results in Taiwan are also given and analyzed. It is concluded that this approach can utilize as more information of both low- and high-levels involved in all images as possible for image classification and thus provides a better classification accuracy. For instance, the OOA has a better overall classification accuracy(98.27%) than the nearest-neighbor classifier(91.31%) and minimum-distance classifier(80.52%).

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An Adaptive Reclosing Scheme Based on the Classification of Fault Patterns in Power distribution System (사고 패턴 분류에 기초한 배전계통의 적응 재폐로방식)

  • Oh, Jung-Hwan;Kim, Jae-Chul;Yun, Sang-Yun
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.50 no.3
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    • pp.112-119
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    • 2001
  • This paper proposes an adaptive reclosing scheme which is based on the classification of fault patterns. In case that the first reclosing is unsuccessful in distribution system employing with two-shot reclosing scheme, the proposed method can determine whether the second reclosing will be attempted of not. If the first reclosing is unsuccessful two fault currents can be measured before the second reclosing is attempted, where these two fault currents are utilized for an adaptive reclosing scheme. Total harmonic distortion and RMS are used for extracting the characteristics of two fault currents. And the pattern of two fault currents is respectively classified using a mountain clustering method a minimum-distance classifier. Mountain clustering method searches the cluster centers using the acquired past data. And minimum-distance classifier is used for classifying the measured two currents into one of the searched centers respectively. If two currents have the different pattern it is interpreted as temporary fault. But in case of the same pattern, the occurred fault is interpreted as permanent. The proposed method was tested for the fault data which had been measured in KEPCO's distribution system, and the test results can demonstrate the effectiveness of the adaptive reclosing scheme.

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The Classification of U.T Defects in the Pressure Vessel Weld using the Pattern Recognition Analysis (형상인식을 이용한 압력용기 용접부 결함 특성 분류)

  • Shim, C.M.;Joo, Y.S.;Hong, S.S.;Jang, K.O.
    • Journal of the Korean Society for Nondestructive Testing
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    • v.13 no.2
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    • pp.11-19
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    • 1993
  • It is very essential to get the accurate classification of defects in primary pressure vessel weld for the safety of nuclear power plant. The signal analysis using the digital signal processing and pattern recognition is performed to classify UT defects extracting feature vector from ultrasonic signals. The minimum distance classifier and the maximum likelihood classifier based on statistics were applied in this experiment to discriminate ultrasonics data obtained form both the training specimens (slit, hole) and the testing specimens(crack, slag). The classification rate was measured using pattern classifier. Results of this study show the promise in solving the many flaw classification problems that exist today.

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High-Reliable Classification of Multiple Induction Motor Faults Using Vibration Signatures based on an EM Algorithm (EM 알고리즘 기반 강인한 진동 특징을 이용한 고 신뢰성 유도 전동기 다중 결함 분류)

  • Jang, Won-Chul;Kang, Myeongsu;Choi, Byeong-Keun;Kim, Jong-Myon
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • pp.346-353
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    • 2013
  • Industrial processes need to be monitored in real-time based on the input-output data observed during their operation. Abnormalities in an induction motor should be detected early in order to avoid costly breakdowns. To early identify induction motor faults, this paper effectively estimates spectral envelopes of each induction motor fault by utilizing a linear prediction coding (LPC) analysis technique and an expectation maximization (EM) algorithm. Moreover, this paper classifies induction motor faults into their corresponding categories by calculating Mahalanobis distance using the estimated spectral envelopes and finding the minimum distance. Experimental results shows that the proposed approach yields higher classification accuracies than the state-of-the-art approach for both noiseless and noisy environments for identifying the induction motor faults.

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Performance Improvement Strategies on Minimum Distance Classification for Large-Set handwritten Character Recognition (대용량 필기 문자인식을 위한 최소거리 분류법의 성능 개선 전략)

  • Kim, Soo-Hyung
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.10
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    • pp.2600-2608
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    • 1998
  • This paper proposes an algorithm for off line recognition of handwritten characters, especially effective for large-set characters such as Korean and Chinese characters. The algorithm is based on a minimum distance dlassification method which is simple and easy to implement but suffers from low recognition performance. Two strategies have been developed to improve its performance; one is multi-stage pre-classification and the other is candicate reordering. Effectiveness of the algorithm has been proven by and experimet with the samples of 574 classes in a handwritten Korean character catabase named PE02, where 86.0% of recognition accuracy and 15 characters per second of processing speed have been obtained.

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Edit Distance Problem for the Korean Alphabet with Phoneme Classification System (음소의 분류 체계를 이용한 한글 편집 거리 알고리즘)

  • Roh, Kang-Ho;Park, Kun-Soo;Cho, Hwan-Gue;Chang, So-Won
    • Journal of KIISE:Computer Systems and Theory
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    • v.37 no.6
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    • pp.323-329
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    • 2010
  • The edit distance problem is finding the minimum number of edit operations to transform a string into another one. It is one of the important problems in algorithm research and there are some algorithms that compute an optimal edit distance for the one-dimensional languages such as the English alphabet. However, there are a few researches to find the edit distance for the more complicated language such as the Korean or Chinese alphabet. In this paper, we define the measure of the edit distance for the Korean alphabet with the phoneme classification system to improve the previous edit distance algorithm and present an algorithm for the edit distance problem for the Korean alphabet.

A Study on the Unsupervised Classification of Hyperion and ETM+ Data Using Spectral Angle and Unit Vector

  • Kim, Dae-Sung;Kim, Yong-Il;Yu, Ki-Yun
    • Korean Journal of Geomatics
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    • v.5 no.1
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    • pp.27-34
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    • 2005
  • Unsupervised classification is an important area of research in image processing because supervised classification has the disadvantages such as long task-training time and high cost and low objectivity in training information. This paper focuses on unsupervised classification, which can extract ground object information with the minimum 'Spectral Angle Distance' operation on be behalf of 'Spectral Euclidian Distance' in the clustering process. Unlike previous studies, our algorithm uses the unit vector, not the spectral distance, to compute the cluster mean, and the Single-Pass algorithm automatically determines the seed points. Atmospheric correction for more accurate results was adapted on the Hyperion data and the results were analyzed. We applied the algorithm to the Hyperion and ETM+ data and compared the results with K-Means and the former USAM algorithm. From the result, USAM classified the water and dark forest area well and gave more accurate results than K-Means, so we believe that the 'Spectral Angle' can be one of the most accurate classifiers of not only multispectral images but hyperspectral images. And also the unit vector can be an efficient technique for characterizing the Remote Sensing data.

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