• 대한산업공학회지
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• 제8권2호
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• pp.15-21
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• 1982

This paper offers an alternative to the common probability generating function approach to the solution of steady state equations when a Markovian queue has a multivariate state space. Identifying states and substates and grouping them into vectors appropriately, we formulate a two - dimensional Markovian queue as a Markov chain. Solving the resulting matrix equations the transition point steady state probabilities (SSPs) are obtained. These are then converted into arbitrary time SSPs. The procedure uses only probabilistic arguments and thus avoids a large and cumbersome state space which often poses difficulties in the solution of steady state equations. For the purpose of numerical illustration of the approach we solve a Markovian queue with one server and two classes of customers.

• 한국물환경학회지
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• 제36권5호
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• pp.353-363
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• 2020

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.

• 전자공학회논문지CI
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• 제45권5호
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• pp.125-133
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• 2008

본 논문은 다양한 서비스 클래스가 제공되는 BcN (Broadband convergence Network) 환경에서 상위 우선순위 서비스의 품질을 보장하기 위한 능동 큐 관리 메커니즘 (Active-WRED)을 제안한다. 네트워크 혼잡 상황에서 제안된 메커니즘은 상대적으로 낮은 우선순위를 갖는 서비스의 drop 확률을 증가시키고 이에 따라 상위 우선순위 서비스의 drop 확률을 감소시킴으로써 상위 우선순위 서비스에 대해 패킷 손실로부터 품질을 보장할 수 있다. 제안된 능동 큐 관리 메커니즘의 성능을 분석하기 위해 이산 시간 큐잉 시스템의 통계적 분석 방법을 도입하였다. 두 서비스 클래스 (GS: Guaranteed Service, BS: Best Effort Service)만을 고려하여 제안 방안의 성능 분석을 위해 MMBP-2 (two-state Markov-Modulated Bernoulli arrival Process) 트래픽 소스 모델과 2차 이산시간 Markov 체인을 도입하였다.

• 대한전자공학회논문지TC
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• 제42권2호
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• pp.23-38
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• 2005

무선 네트워크에서는 여러 가지의 환경적 요인으로 인해 발생하는 페이딩 현상 및 노이즈로 인하여 무선 단말기 간의 채널의 상태가 자주 변화한다. 따라서 시변 채널 특성을 지니는 무선 네트워크에서 신뢰성 있는 데이터 전송을 위해서는 시변 채널 특성을 파악하는 것이 중요하다. 본 논문에서는 무선 네트워크 시변(Time-varying) 채널 상에서 패킷 전송 시간 및 대기큐에 대해 분석한다. 무선 네트워크 시변 채널 상태를 반영하기 위해 채널의 상태를 2-상태, 3-상태 각각의 경우로 구분하고 SFG(Signal Flow Graph) 모델을 이용하여 채널 상태를 해석한다. 각각의 상태에 대한 SFG 모델로부터 하나의 패킷에 대해 평균 전송시간과 분산을 구하고 이에 대한 확률 분포를 가우시안(Gaussian) 분포로 생각한다. 패킷의 도착분포가 프아송(Poisson) 프로세스를 따르는 전송 시스템을 M/G/1으로 모델링하고 에러 정정 기법으로 SW ARQ 기법을 적용하여 패킷의 PER의 변화 및 패킷 도착 비율의 변화에 따른 평균 패킷 전송시간과 평균 대기큐의 길이에 대해 해석하고 시뮬레이션을 통하여 검증한다.

• Asian Journal of Atmospheric Environment
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• 제10권2호
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• pp.67-79
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• 2016

The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the greatest concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of $PM_{2.5}$ was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, $NO_2$, $NO_x$, CO, $SO_2$ and $PM_{10}$ were used as inputs to the artificial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting $PM_{2.5}$ concentrations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coefficient of determination ($R^2$), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neural network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural network, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused $R^2$ to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an acceptable accuracy and precision. We concluded the probability of occurrence state duration and transition of $PM_{2.5}$ pollution is predictable using a Markov chain method.

• Geomechanics and Engineering
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• 제28권4호
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• pp.421-435
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• 2022

The necessity of estimating the time and cost required for tunnel construction has led to extensive research in this regard. Since geological conditions are significant factors in terms of time and cost of road tunnels, considering these conditions is crucial. Uncertainties about the geological conditions of a tunnel alignment cause difficulties in planning ahead of the required construction time and costs. In this paper, the continuous-space, discrete-state Markov process has been used to predict geological conditions. The Monte-Carlo (MC) simulation (MCS) method is employed to estimate the construction time and costs of a road tunnel project using the input data obtained from six tunneling expert questionnaires. In the first case, the input data obtained from each expert are individually considered and in the second case, they are simultaneously considered. Finally, a comparison of these two modes based on the technique presented in this article suggests considering views of several experts simultaneously to reduce uncertainties and ensure the results obtained for geological conditions and the construction time and costs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1234-1239
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• 1990

In this paper, we propose a new type of decentralized learning automata for the control finite state Markov chains with unknown transition probabilities and rewards. In our scheme a .betha.-type learning automaton is associated with each state in which two or more actions(desisions) are available. In this decentralized learning automata system, each learning automaton operates, requiring only local information, to improve its performance under local environment. From simulation results, it is shown that the decentralized learning automata will converge to the optimal policy that produces the most highly total expected reward with discounting in all initiall states.

• 산업기술연구
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• 제23권A호
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• pp.41-46
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• 2003

Because the quality of a radio link in real environment is generally varied with time, there is a difference between the delay in the real environment and one obtained from the analytic model where a time-varying link model is not used as a link model for a Bluetooth. This paper analyzes the transmission delay of a message in the time-varying radio link model for the Bluetooth. The time-varying radio link is modeled with a two-state Markov model. The mean transmission delay of the message is analytically obtained in terms of the arrival rate of the message, the state transition probability in the Markov model, and the packet error rate.

• Journal of Electrical Engineering and Technology
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• 제12권4호
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• pp.1657-1662
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• 2017

Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods.

• 대한산업공학회지
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• 제15권1호
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• pp.89-103
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• 1989

In this paper the approximate average outgoing quality and properties of a class of continuous sampling plans in a short production run are investigated when the quality of successive units follows a two-state time-homogeneous Markov chain. The results of previous studies can be obtained as special cases. It is observed that the long-run average outgoing quality limit values under the statistical control differ significantly as compared to the case of short production runs in a Markov-dependent production process.