• Title/Summary/Keyword: weighted matching

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Optimal algorithm of part-matching process using neural network (신경 회로망을 이용한 부품 조립 공정의 최적화 알고리즘)

  • 오제휘;차영엽
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.143-146
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    • 1996
  • In this paper, we propose a hopfield model for solving the part-matching which is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and net total path of part-connection. Therefore, this kind of problem is referred to as a combinatorial optimization problem. First of all, we review the theoretical basis for hopfield model to optimization and present two method of part-matching; Traveling Salesman Problem (TSP) and Weighted Matching Problem (WMP). Finally, we show demonstration through computer simulation and analyzes the stability and feasibility of the generated solutions for the proposed connection methods.

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Massive MIMO Channel Estimation Algorithm Based on Weighted Compressed Sensing

  • Lv, Zhiguo;Wang, Weijing
    • Journal of Information Processing Systems
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    • v.17 no.6
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    • pp.1083-1096
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    • 2021
  • Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value uop by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and uop. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

A Study on teh Seal Indentification by using the Image Processing (영상처리에 의한 인장식별에 관한 연구)

  • 이기돈;전병민;김상운
    • Proceedings of the Korean Institute of Communication Sciences Conference
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    • 1984.10a
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    • pp.101-103
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    • 1984
  • The proposed seal identification procedure consists of the thresholding smoothing, rotation, thinning, and matching techniques. The weighted map is constructed by ditance weighted correlation CK is computed. The CK is compared with the dicision constant Cs or Cd for the purpose of seal identification.

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Context-Weighted Metrics for Example Matching (문맥가중치가 반영된 문장 유사 척도)

  • Kim, Dong-Joo;Kim, Han-Woo
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.43 no.6 s.312
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    • pp.43-51
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    • 2006
  • This paper proposes a metrics for example matching under the example-based machine translation for English-Korean machine translation. Our metrics served as similarity measure is based on edit-distance algorithm, and it is employed to retrieve the most similar example sentences to a given query. Basically it makes use of simple information such as lemma and part-of-speech information of typographically mismatched words. Edit-distance algorithm cannot fully reflect the context of matched word units. In other words, only if matched word units are ordered, it is considered that the contribution of full matching context to similarity is identical to that of partial matching context for the sequence of words in which mismatching word units are intervened. To overcome this drawback, we propose the context-weighting scheme that uses the contiguity information of matched word units to catch the full context. To change the edit-distance metrics representing dissimilarity to similarity metrics, to apply this context-weighted metrics to the example matching problem and also to rank by similarity, we normalize it. In addition, we generalize previous methods using some linguistic information to one representative system. In order to verify the correctness of the proposed context-weighted metrics, we carry out the experiment to compare it with generalized previous methods.

Real-time Phoneme Recognition System Using Max Flow Matching (최대 흐름 정합을 이용한 실시간 음소인식 시스템 구현)

  • Lee, Sang-Yeob;Park, Seong-Won
    • Journal of Korea Game Society
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    • v.12 no.1
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    • pp.123-132
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    • 2012
  • There are many of games using smart devices. Voice recognition is can be useful way for input. In the game, voice have to be quickly recognized, at the same time it have to be manipulated promptly as well. In this study, we developed the optimized real-time phoneme recognition using max flow matching that it can be efficiently used in the game field. Firstly, voice wavelength is transformed to FFT, secondly, transformed value is made by a graph in Z plane, thirdly, data is extracted in specific area, and then data is saved in database. After all the value is recognized using weighted bipartite max flow matching. This way would be useful method in game or robot field when researchers hope to recognize the fast voice recognition.

Histogram Matching Algorithm for Content-Based Dnage Retrieval (내용기반 영상검색을 위한 히스토그램 매칭 알고리즘)

  • You, Kang-Soo;Yoo, Gi-Hyoung;Kwak, Hoon-Sung
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.1C
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    • pp.45-52
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    • 2008
  • In this paper, we describe the Perceptually Weighted Histogram(PWH) and the Gaussian Weighted Histogram Intersection(GWHI) algorithms. These algorithms are able to provide positive results in image retrieval. But these histogram methods alter the histogram of an image by using particular lighting conditions. Even two pictures with little differences in lighting are not easily matched. Therefore, we propose that the Histogram Matching Algorithm(HMA) is able to overcome the problem of an image being changed by the intensity or color in the image retrieval. The proposed algorithm is insensitive to changes in the lighting. From the experiment results, the proposed algorithm can achieve up to 32% and up to 30% more recall than the PWH and GWHI algorithms, respectively. Also, it can achieve up to 38% and up to 34% more precision than PWH and GWHI, respectively Therefore, with our experiments, we are able to show that the proposed algorithm shows limited variation to changes in lighting.

Improving Weighted k Nearest Neighbor Classification Through The Analytic Hierarchy Process Aiding

  • Park, Cheol-Soo;Ingoo Han
    • Proceedings of the Korea Database Society Conference
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    • 1999.06a
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    • pp.187-194
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    • 1999
  • Case-Based Reasoning(CBR) systems support ill structured decision-making. The measure of the success of a CBR system depends on its ability to retrieve the most relevant previous cases in support of the solution of a new case. One of the methodologies widely used in existing CBR systems to retrieve previous cases is that of the Nearest Neighbor(NN) matching function. The NN matching function is based on assumptions of the independence of attributes in previous case and the availability of rules and procedures for matching.(omitted)

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Feature matching toy Omnidirectional Image based on Singular Value Decomposition

  • Kim, Do-Yoon;Lee, Young-Jin;Myung jin Chung
    • 제어로봇시스템학회:학술대회논문집
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    • 2002.10a
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    • pp.98.2-98
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    • 2002
  • $\textbullet$ Omnidirectional feature matching $\textbullet$ SVD-based matching algorithm $\textbullet$ Using SSD instead of the zero-mean correlation $\textbullet$ The similarity with the Gaussian weighted $\textbullet$ Low computational cost $\textbullet$ It describes the similarity of the matched pairs in omnidirectional images.

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Stereo Matching Using Independent Component Analysis

  • Jeon, S.H.;Lee, K.H.
    • Proceedings of the KSRS Conference
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    • 2003.11a
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    • pp.496-498
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    • 2003
  • Signal is composed of the independent components that can describe itself. These components can distinguish itself from any other signals and be extracted by analysis itself. This algorithm is called Independent Component Analysis (ICA) and image signal is considered as linear combination of independent components and features that is the weighted vector of independent component. This algorithm is already used in order to extract the good feature for image classification and very effective In this paper, we'll explain the method of stereo matching using independent component analysis and show the experimental result.

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Performance Analysis of Automatic Target Recognition Using Simulated SAR Image (표적 SAR 시뮬레이션 영상을 이용한 식별 성능 분석)

  • Lee, Sumi;Lee, Yun-Kyung;Kim, Sang-Wan
    • Korean Journal of Remote Sensing
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    • v.38 no.3
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    • pp.283-298
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    • 2022
  • As Synthetic Aperture Radar (SAR) image can be acquired regardless of the weather and day or night, it is highly recommended to be used for Automatic Target Recognition (ATR) in the fields of surveillance, reconnaissance, and national security. However, there are some limitations in terms of cost and operation to build various and vast amounts of target images for the SAR-ATR system. Recently, interest in the development of an ATR system based on simulated SAR images using a target model is increasing. Attributed Scattering Center (ASC) matching and template matching mainly used in SAR-ATR are applied to target classification. The method based on ASC matching was developed by World View Vector (WVV) feature reconstruction and Weighted Bipartite Graph Matching (WBGM). The template matching was carried out by calculating the correlation coefficient between two simulated images reconstructed with adjacent points to each other. For the performance analysis of the two proposed methods, the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset was used, which has been recently published by the U.S. Defense Advanced Research Projects Agency (DARPA). We conducted experiments under standard operating conditions, partial target occlusion, and random occlusion. The performance of the ASC matching is generally superior to that of the template matching. Under the standard operating condition, the average recognition rate of the ASC matching is 85.1%, and the rate of the template matching is 74.4%. Also, the ASC matching has less performance variation across 10 targets. The ASC matching performed about 10% higher than the template matching according to the amount of target partial occlusion, and even with 60% random occlusion, the recognition rate was 73.4%.