• Title/Summary/Keyword: markov models

검색결과 490건 처리시간 0.038초

ANALYSIS OF TWO COMMODITY MARKOVIAN INVENTORY SYSTEM WITH LEAD TIME

  • Anbazhagan, N.;Arivarignan, G.
    • Journal of applied mathematics & informatics
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    • 제8권2호
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    • pp.519-530
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    • 2001
  • A two commodity continuous review inventory system with independent Poisson processes for the demands is considered in this paper. The maximum inventory level for the i-th commodity fixed as $S_i$(i = 1,2). The net inventory level at time t for the i-th commodity is denoted by $I_i(t),\;i\;=\;1,2$. If the total net inventory level $I(t)\;=\;I_1(t)+I_2(t)$ drops to a prefixed level s $[{\leq}\;\frac{({S_1}-2}{2}\;or\;\frac{({S_2}-2}{2}]$, an order will be placed for $(S_{i}-s)$ units of i-th commodity(i=1,2). The probability distribution for inventory level and mean reorders and shortage rates in the steady state are computed. Numerical illustrations of the results are also provided.

Hand Gesture Recognition using Improved Hidden Markov Models

  • Xu, Wenkai;Lee, Eung-Joo
    • 한국멀티미디어학회논문지
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    • 제14권7호
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    • pp.866-871
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    • 2011
  • In this paper, an improved method of hand detecting and hand gesture recognition is proposed, it can be applied in different illumination condition and complex background. We use Adaptive Skin Threshold (AST) to detect the areas of hand. Then the result of hand detection is used to hand recognition through the improved HMM algorithm. At last, we design a simple program using the result of hand recognition for recognizing "stone, scissors, cloth" these three kinds of hand gesture. Experimental results had proved that the hand and gesture can be detected and recognized with high average recognition rate (92.41%) and better than some other methods such as syntactical analysis, neural based approach by using our approach.

무작위 초 보유 자원을 이용한 신뢰성 모델

  • Kim, Songkyoo
    • 한국경영과학회:학술대회논문집
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    • 한국경영과학회 2001년도 추계학술대회 논문집
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    • pp.199-202
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    • 2001
  • This article deals with stochastic reliability systems that include a repair facility and unreliable machines: the main facility of working and an auxiliary facility of "super-reserve" machines. The number of super-reserve machines are random number with a arbitrarily distribution and working machines break down exponentially. Defective machines line up for repair, whose durations are arbitrarily distributed. Refurbished machines return to the main facility. If the main facility is restored to its original quantity, the repair facility leaves on routine maintenance until all of super-reserve machines are exhausted. Then, the busy period is regenerated. The whole system also falls into the category of closed queues, with more options than those of basic models. The techniques include two-variate Markov and semi-regenerative processes, and a duality principle, to find the probability distribution of the number of intact machines. Explicit formulas obtained demonstrate a relatively effortless use of functionals of the main stochastic characteristics (such as expenses due to repair, maintenance, waiting, and rewards for higher reliability) and optimization of their objective function. Applications include computer networking, human resources, and manufacturing processes.

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부분 손상된 음성의 인식성능 향상을 위한 가중 필터뱅크 분석 및 모델 적응 (Weighted filter bank analysis and model adaptation for improving the recognition performance of partially corrupted speech)

  • 조훈영;오영환
    • 대한음성학회지:말소리
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    • 제44호
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    • pp.157-169
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    • 2002
  • We propose a weighted filter bank analysis and model adaptation (WFBA-MA) scheme to improve the utilization of uncorrupted or less severely corrupted frequency regions for robust speech recognition. A weighted met frequency cepstral coefficient is obtained by weighting log filter bank energies with reliability coefficients and hidden Markov models are also modified to reflect the local reliabilities. Experimental results on TIDIGITS database corrupted by band-limited noises and car noise indicated that the proposed WFBA-MA scheme utilizes the uncorrupted speech information well, significantly improving recognition performance in comparison to multi-band speech recognition systems.

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보호계전기의 오동작에 의한 배전계통 신뢰도에 대한 연구 (A Study of the Reliability of Distribution System for Malfunction of Protective Relay)

  • 신정진;이희태;김재철
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2009년도 제40회 하계학술대회
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    • pp.205_206
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    • 2009
  • Protection system is important part in distribution system. Protection system in power systems can fail either by not responding when they should(failure to operate) or by operating when they should not(false tripping). Customers has dissatisfied that due to the failure of protection system. The reliability of distribution system also has influenced. However, the failure of protection system can reduced by the maintenance. This paper has focused on malfunction of protection system. The approach is based on Markov models.

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복잡한 통신 시스템의 성능분석을 위한 유한소스 대기 모형 (Finite Source Queueing Models for Analysis of Complex Communication Systems)

  • Che-Soong Kim
    • 산업경영시스템학회지
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    • 제26권2호
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    • pp.62-67
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    • 2003
  • This paper deals with a First-Come, First-Served queueing model to analyze the behavior of heterogeneous finite source system with a single server Each sources and the processor are assumed to operate in independently Markovian environments, respectively. Each request is characterized by its own exponentially distributed source and service time with parameter depending on the state of the corresponding environment, that is, the arrival and service rates are subject to random fluctuations. Our aim is to get the usual stationary performance measures of the system, such as, utilizations, mean number of requests staying at the server, mean queue lengths, average waiting and sojourn times. In the case of fast arrivals or fast service asymptotic methods can be applied. In the intermediate situations stochastic simulation Is used. As applications of this model some problems in the field of telecommunications are treated.

무선 근거리 통신망에서의 BRAM(The Broadcast Recognizing Access Method) 프로토콜 성능 분석 (Performance Analysis of a BRAM (The Broadcast Recognizing Aceess Method) Protocol in a Wireless LAN)

  • 김재현;이종규
    • 전자공학회논문지A
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    • 제31A권1호
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    • pp.1-8
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    • 1994
  • In this paper, we have analyzed the performance of a BRAM (The Broadcasting Recognizing Access Method) protocol, as a CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) scheme, which is widely used in wireles LAN systems. We have selected a Fair BRAM protocol among CSMA/CA schemes, considering the fairness of channel usage and the simplicity of the protocol. We have compared the performance of BRAM protocol to that of CSMA/CD. to research the characteristics of BRAM in wireless LAN system. In order to analyze the performance of this system, we have set up an imbedded Markov chain and calculated state transition probabilities. Then we have calculated steady state probabilities and finally derived the throughput of a Fair BRAM moder. To verify our analysis, we have simulated practical models. Then, we have found that analytic results are very close to simulation ones. Our analysis of the BRAM protocol will be expected to be very helpful to design and evaluate a MAC (Media Access Control) protocol in wireless LAN systems.

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Discriminative Training of Stochastic Segment Model Based on HMM Segmentation for Continuous Speech Recognition

  • Chung, Yong-Joo;Un, Chong-Kwan
    • The Journal of the Acoustical Society of Korea
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    • 제15권4E호
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    • pp.21-27
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    • 1996
  • In this paper, we propose a discriminative training algorithm for the stochastic segment model (SSM) in continuous speech recognition. As the SSM is usually trained by maximum likelihood estimation (MLE), a discriminative training algorithm is required to improve the recognition performance. Since the SSM does not assume the conditional independence of observation sequence as is done in hidden Markov models (HMMs), the search space for decoding an unknown input utterance is increased considerably. To reduce the computational complexity and starch space amount in an iterative training algorithm for discriminative SSMs, a hybrid architecture of SSMs and HMMs is programming using HMMs. Given the segment boundaries, the parameters of the SSM are discriminatively trained by the minimum error classification criterion based on a generalized probabilistic descent (GPD) method. With the discriminative training of the SSM, the word error rate is reduced by 17% compared with the MLE-trained SSM in speaker-independent continuous speech recognition.

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Introduction to Gene Prediction Using HMM Algorithm

  • Kim, Keon-Kyun;Park, Eun-Sik
    • Journal of the Korean Data and Information Science Society
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    • 제18권2호
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    • pp.489-506
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    • 2007
  • Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated structures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. There are Ab Initio method, Similarity-based method, and Ensemble method for gene prediction method for eukaryotic genes. Each Method use various algorithms. This paper introduce how to predict genes using HMM(Hidden Markov Model) algorithm and present the process of gene prediction with well-known gene prediction programs.

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Bayesian curve-fitting with radial basis functions under functional measurement error model

  • Hwang, Jinseub;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • 제26권3호
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    • pp.749-754
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    • 2015
  • This article presents Bayesian approach to regression splines with knots on a grid of equally spaced sample quantiles of the independent variables under functional measurement error model.We consider small area model by using penalized splines of non-linear pattern. Specifically, in a basis functions of the regression spline, we use radial basis functions. To fit the model and estimate parameters we suggest a hierarchical Bayesian framework using Markov Chain Monte Carlo methodology. Furthermore, we illustrate the method in an application data. We check the convergence by a potential scale reduction factor and we use the posterior predictive p-value and the mean logarithmic conditional predictive ordinate to compar models.