• Title/Summary/Keyword: stochastic matching

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A Shape Matching Algorithm for Occluded Two-Dimensional Objects (일부가 가리워진 2차원 물체의 형상 정합 알고리즘)

  • 박충수;이상욱
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.27 no.12
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    • pp.1817-1824
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    • 1990
  • This paper describes a shape matching algorithm for occluded or distorted two-dimensional objects. In our approach, the shape matchin is viewed as a segment matching problem. A shape matching algorithm, based on both the stochastic labeling technique and the hypothesis generate-test paradigm, is proposed, and a simple technique which performs the stochastic labeling process in accordance with the definition of consisten labeling assignment without requiring an iterative updating process of probability valiues is also proposed. Several simulation results show that the proposed algorithm is very effective when occlusion, scaling or change of orientation has occurred in the object.

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Estimating State-Level Matching Efficiencies in the Indian Labor Market

  • Lee, Woong;Lee, Soon-Cheul
    • East Asian Economic Review
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    • v.24 no.3
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    • pp.275-301
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    • 2020
  • We analyze state-level matching efficiencies in the Indian labor market using stochastic frontier analysis. The key contribution of this research is the estimation of matching efficiencies at the state level because these can be used for a state-level measure of labor market conditions. Next, we explore the relationship between the estimated matching efficiencies and population density, labor market flexibility, and the Ease of Doing Business index, respectively. The results show that matching efficiency is heterogeneous across states with considerable variation in accordance with the regional diversity in India. However, we find that there is little relationship between the estimated matching efficiencies and the labor market conditions of interest, suggesting that other regional diversity affects matching efficiencies across states in India.

Plain Fingerprint Classification Based on a Core Stochastic Algorithm

  • Baek, Young-Hyun;Kim, Byunggeun
    • IEIE Transactions on Smart Processing and Computing
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    • v.5 no.1
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    • pp.43-48
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    • 2016
  • We propose plain fingerprint classification based on a core stochastic algorithm that effectively uses a core stochastic model, acquiring more fingerprint minutiae and direction, in order to increase matching performance. The proposed core stochastic algorithm uses core presence/absence and contains a ridge direction and distribution map. Simulations show that the fingerprint classification accuracy is improved by more than 14%, on average, compared to other algorithms.

Job Mismatch in Korean Regional labor Market: Matching Efficiency Analysis using Stochastic Production Frontier Approach (한국 지역노동시장에서의 일자리 미스매치에 관한 연구 - 확률변경생산함수를 이용한 결합 효율성 분석 -)

  • Yang, Jun Seok;Jeon, Yongil
    • Journal of the Economic Geographical Society of Korea
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    • v.15 no.4
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    • pp.752-765
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    • 2012
  • We analyze the relation empirically between regional labor market characteristics and job matching efficiency with the adaption of stochastic frontier production function. Our empirical results suggest that, as both the ratio of 50 years-old-job seekers and the level of spatial density increase, job matching efficiencies also rise. In contrast, an increase in the ratio of firms which offer permanent contracts lowers job matching efficiencies. And the job matching efficiency is relatively higher in metropolitan areas. Finally, We find a negative relation between total industrial production and job matching efficiency, which implies that job seekers tend to accept uncongenial jobs in the recessions.

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Gaussian Approximation of Stochastic Lanchester Model for Heterogeneous Forces (혼합 군에 대한 확률적 란체스터 모형의 정규근사)

  • Park, Donghyun;Kim, Donghyun;Moon, Hyungil;Shin, Hayong
    • Journal of Korean Institute of Industrial Engineers
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    • v.42 no.2
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    • pp.86-95
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    • 2016
  • We propose a new approach to the stochastic version of Lanchester model. Commonly used approach to stochastic Lanchester model is through the Markov-chain method. The Markov-chain approach, however, is not appropriate to high dimensional heterogeneous force case because of large computational cost. In this paper, we propose an approximation method of stochastic Lanchester model. By matching the first and the second moments, the distribution of each unit strength can be approximated with multivariate normal distribution. We evaluate an approximation of discrete Markov-chain model by measuring Kullback-Leibler divergence. We confirmed high accuracy of approximation method, and also the accuracy and low computational cost are maintained under high dimensional heterogeneous force case.

A New Statistical Linearization Technique of Nonlinear System (비선형시스템의 새로운 통계적 선형화방법)

  • Lee, Jang-Gyu;Lee, Yeon-Seok
    • Proceedings of the KIEE Conference
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    • 1990.07a
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    • pp.72-76
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    • 1990
  • A new statistical linearization technique for nonlinear system called covariance matching method is proposed in this paper. The covariance matching method makes the mean and variance of an approximated output be identical real functional output, and the distribution of the approximated output have identical shape with a given random input. Also, the covariance matching method can be easily implemented for statistical analysis of nonlinear systems with a combination of linear system covariance analysis.

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Voice Dialing system using Stochastic Matching (확률적 매칭을 사용한 음성 다이얼링 시스템)

  • 김원구
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.04a
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    • pp.515-518
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    • 2004
  • This paper presents a method that improves the performance of the personal voice dialling system in which speaker Independent phoneme HMM's are used. Since the speaker independent phoneme HMM based voice dialing system uses only the phone transcription of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the system which uses the speaker dependent models due to the phone recognition errors generated when the speaker Independent models are used. In order to solve this problem, a new method that jointly estimates transformation vectors for the speaker adaptation and transcriptions from training utterances is presented. The biases and transcriptions are estimated iteratively from the training data of each user with maximum likelihood approach to the stochastic matching using speaker-independent phone models. Experimental result shows that the proposed method is superior to the conventional method which used transcriptions only.

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Advanced Rake Receiver for Multiple Access M-ary Modulation UWB System in the IEEE Multipath Channel (IEEE 다중경로 채널에서 다중접속 M진 변조 초광대역 시스템을 위한 개선된 Rake 수신기)

  • An, Jinyoung
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.12
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    • pp.12-19
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    • 2014
  • In this paper, an advanced UWB (ultra wideband) Rake receiving technique based on the statistical distribution model is studied in the M-ary TH-PPM system with multiple access interference (MAI). In order to improve the performance of the Rake receiver, the stochastic model, which can flexibly express the behavior of MAI-plus-noise, is required and the Laplace distribution and the generalized normal Laplace (GNL) model applied by the curtosis matching method are considered. The performance of Rake receiver based on each probability distribution is evaluated in the IEEE multipath fading channel and compared to that of the conventional Rake receiver. The suggested approach shows a superior BER performance than that of conventional Rake receiver.

Speaker Adaptation for Voice Dialing (음성 다이얼링을 위한 화자적응)

  • ;Chin-Hui Lee
    • The Journal of the Acoustical Society of Korea
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    • v.21 no.5
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    • pp.455-461
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    • 2002
  • This paper presents a method that improves the performance of the personal voice dialling system in which speaker independent phoneme HMM's are used. Since the speaker independent phoneme HMM based voice dialing system uses only the phone transcription of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the system which uses the speaker dependent models due to the phone recognition errors generated when the speaker independent models are used. In order to solve this problem, a new method that jointly estimates transformation vectors for the speaker adaptation and transcriptions from training utterances is presented. The biases and transcriptions are estimated iteratively from the training data of each user with maximum likelihood approach to the stochastic matching using speaker-independent phone models. Experimental result shows that the proposed method is superior to the conventional method which used transcriptions only.

Phonetic Transcription based Speech Recognition using Stochastic Matching Method (확률적 매칭 방법을 사용한 음소열 기반 음성 인식)

  • Kim, Weon-Goo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.5
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    • pp.696-700
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    • 2007
  • A new method that improves the performance of the phonetic transcription based speech recognition system is presented with the speaker-independent phonetic recognizer. Since SI phoneme HMM based speech recognition system uses only the phoneme transcription of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the speaker dependent system due to the phoneme recognition errors generated from using SI models. A new training method that iteratively estimates the phonetic transcription and transformation vectors is presented to reduce the mismatch between the training utterances and a set of SI models using speaker adaptation techniques. For speaker adaptation the stochastic matching methods are used to estimate the transformation vectors. The experiments performed over actual telephone line shows that a reduction of about 45% in the error rates could be achieved as compared to the conventional method.