• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.390-390
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• 2019

Application of Hidden Markov Model (HMM) to the hydrological time series would be an innovative way to identify extreme rainfall events in a series. Even though the optimum number of hidden states can be identify based on maximizing the log-likelihood or minimizing Bayesian information criterion. However, occasionally value for the log-likelihood keep increasing with the state which gives false identification of the optimum hidden state. Therefore, this study attempts to identify optimum number of hidden states for Colombo station, Sri Lanka as fundamental approach to identify frequency and percentage of extreme rainfall events for the station. Colombo station consisted of daily rainfall values between 1961 and 2015. The representative station is located at the wet zone of Sri Lanka where the major rainfall season falls on May to September. Therefore, HMM was ran for the season of May to September between 1961 and 2015. Results showed more or less similar log-likelihood which could be identified as maximum for states between 4 to 7. Therefore, measure of central tendency (i.e. mean, median, mode, standard deviation, variance and auto-correlation) for observed and simulated daily rainfall series was carried to each state to identify optimum state which could give statistically compatible results. Further, the method was applied for the second major rainfall season (i.e. October to February) for the same station as a comparison.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.480-489
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• 2016

Hidden station problem can occur in power-line communication (PLC) networks. A simple solution to the problem has been proposed to use request-to-send (RTS)/clear-to-send (CTS) exchange, but this approach cannot solve the hidden station problem perfectly. This paper revisits the problem for PLC networks and designs a protocol to solve it. We first analyze the throughput performance degradation when the hidden station problem occurs in PLC networks. Then, we propose an interferer aware multiple access (IAMA) protocol to enhance throughput and fairness performances, which uses unique characteristics of PLC networks. Using the RTS/CTS exchange adaptively, the IAMA protocol protects receiving stations from being interfered with neighboring networks. Through extensive simulations, we show that our proposed protocol outperforms conventional random access protocols in terms of throughput and fairness.

• Journal of Communications and Networks
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• 제7권3호
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• pp.313-324
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• 2005

The success of the IEEE 802.11 standard has prompted research into efficiency of the different medium access methods and their support for different traffic types. A modified version of the point coordination function (PCF) called modified PCF has been introduced as a way to improve the efficiency over the standard method. It has been shown through a simulation study and a mathematical analysis that channel utilization can be much improved compared to the standard, in case there is no so-called hidden station problem. However, under the hidden station problem, the efficiency of the modified PCF would obviously decrease. In this paper, some enhancements of the modified PCF are introduced. Firstly, we propose a retransmission process to allow frames involved in collisions to be retransmitted. Then, we propose a collision resolution mechanism to reduce the frame collision probability due to the hidden station problem. In addition, we propose a priority scheme to support prioritization for different traffic types such as interactive voice and video, and real-time data traffic in the modified PCF. To prevent the starvation of one low priority traffic, minimum transmission period is also guaranteed to each traffic type via an admission control algorithm. We study the performance of the modified PCF under the hidden station problem and the performance of the modified PCF with priority scheme through simulations. To illustrate the efficiency of the priority scheme, we therefore compare its simulation results with those of some standardized protocols: The distributed coordination function (DCF), the enhanced distributed channel access (EDCA), the PCF, and our previously proposed protocol: The modified PCF without priority scheme. The simulation results show that the increment of delay in the network due to the hidden station problem can be reduced using the proposed collision resolution mechanism. In addition, in a given scenario the modified PCF with priority scheme can provide better quality of service (QoS) support to different traffic types and also support a higher number of data stations than the previous proposals.

• 한국환경과학회지
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• 제13권7호
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• pp.611-618
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• 2004

The Neural Network Models which mathematically interpret human thought processes were applied to resolve the uncertainty of model parameters and to increase the model's output for the streamflow forecast model. In order to test and verify the flood discharge forecast model eight flood events observed at Kumho station located on the midstream of Kumho river were chosen. Six events of them were used as test data and two events for verification. In order to make an analysis the Levengerg-Marquart method was used to estimate the best parameter for the Neural Network model. The structure of the model was composed of five types of models by varying the number of hidden layers and the number of nodes of hidden layers. Moreover, a logarithmic-sigmoid varying function was used in first and second hidden layers, and a linear function was used for the output. As a result of applying Neural Networks models for the five models, the N10-6model was considered suitable when there is one hidden layer, and the Nl0-9-5model when there are two hidden layers. In addition, when all the Neural Network models were reviewed, the Nl0-9-5model, which has two hidden layers, gave the most preferable results in an actual hydro-event.

• 한국철도학회논문집
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• 제12권2호
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• pp.285-290
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• 2009

논문에서는 연속HMM(Continuos Hidden Markov Model)을 이용하여 실시간으로 발매기 명령어(314개 역명)를 인식 할 수 있도록 음성인식 시스템을 구현하였다. 특징 벡터로 39 MFCC를 사용하였으며, 인식률 향상을 위하여 895개의 tied-state 트라이폰 음소 모델을 구성하였다. 시스템 성능 평가 결과 다중 화자 종속 인식률은 99.24%, 다중화자 독립 인식률은 98.02%의 인식률을 나타내었으며, 실제 노이즈가 있는 환경에서 다중 화자 독립 실험의 경우 93.91%의 인식률을 나타내었다.

• 대한토목학회논문집
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• 제33권5호
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• pp.1861-1870
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• 2013

최근 기후변화 영향으로 인해 수문변동성이 크게 증가되고 있으며 이러한 변동성을 고려하기 위한 방안으로서 강수량 모의발생 기법에 대한 중요성이 대두되고 있다. 본 연구에서는 복잡한 강수발생 패턴을 인지하고 강수량의 다양한 분포특성을 고려할 수 있는 혼합분포를 이용한 동질성 Hidden Markov Chain(HMM) 모형을 제안하였다. HMM 모형의 개선효과를 검증하기 위해서 기존 Markov Chain 모형과 비교 하였으며 서울관측소 및 전주관측소를 대상으로 연구를 진행하였다. 계절강수량 및 일강수량 등 다양한 시간규모에서 모형의 적합성을 평가하기 위해서 천이확률, 평균, 분산, 왜곡도 및 첨예도 등을 비교하였으며 HMM 모형이 기존 Markov Chain 모형에 비해서 개선된 모의능력을 확인할 수 있었다. 특히, HMM 모형은 극치강수량을 재현하는데 있어서 기존 Markov Chain 모형에 비해서 월등한 모의능력을 보여주었다. 이러한 점에서 장기유출량 및 확률홍수량 등을 산정하기 위한 입력자료로 활용이 충분히 가능할 것으로 판단된다.

• Advances in environmental research
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• 제5권3호
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• ETRI Journal
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• 제36권5호
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• pp.696-703
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• 2014

In this paper, we present a walking guidance system for the visually impaired for use at subway stations. This system, which is based on environmental knowledge, automatically detects and recognizes both exit numbers and arrow signs from natural outdoor scenes. The visually impaired can, therefore, utilize the system to find their own way (for example, using exit numbers and the directions provided) through a subway station. The proposed walking guidance system consists mainly of three stages: (a) sign detection using the MCT-based AdaBoost technique, (b) sign recognition using support vector machines and hidden Markov models, and (c) three verification techniques to discriminate between signs and non-signs. The experimental results indicate that our sign recognition system has a high performance with a detection rate of 98%, a recognition rate of 99.5%, and a false-positive error rate of 0.152.

• 대한전자공학회논문지TC
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• 제43권10호
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• pp.79-89
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• 2006

TCP는 신뢰성을 보장하는 전송 프로토콜로서 인터넷 등에서 가장 널리 사용되고 있는 전송 방식이다. 하지만 TCP는 유선망에 적합하도록 설계되었기 때문에 무선망에서 TCP를 사용할 경우 성능 저하가 발생된다. TCP의 성능 저하 원인으로는 MAC 계층에서의 무선 매체 경쟁, hidden-terminal 문제와 exposed terminal 문제, 링크 계층에서의 패킷 손실, 불공정성의 문제들과 노드의 이동에 의한 경로 단절시 발생되는 패킷 순서 바뀜 문제와 경로의 단절로 인한 재전송 타이머의 exponential backoff에 의한 대역폭의 낭비 등이 있다. 특히 이동 ad-hoc 망에서는 전송 범위(transmission range)와 간섭범위(interference range)의 불일치로 인해 발생되는 hidden terminal 문제로 인해 동시에 전송할 수 있는 노드의 수가 제한되며 이로 인해 성능저하가 크게 발생된다. 본 논문에서는 IEEE 802.11 기반 이동 ad-hoc 망에서 발생되는 hidden terminal 문제로 인해 노드가 전송을 하지 못하고 CW(contention window)만 크게 증가되는 문제를 해결하기 위한 MAC 알고리즘을 제안한다. 기존의 802.11 MAC의 DCF(distributed coordination function)에서는 전송에 실패할 경우 CW를 지수적으로 증가시키지만 본 논문에서 제안하는 기법은 노드가 전송 실패를 하였을 경우 그 원인에 따라 CW를 적절하게 변화시킴으로 성능 향상을 얻을 수 있다. 이 기법을 사용하면 hidden terminal에 의해 전송을 실패하는 노드에게 공정한 전송 기회를 부여함으로써 TCP 성능 향상을 얻을 수 있음을 시뮬레이션을 통해 보였다.

• 한국수자원학회논문집
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• 제34권6호
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• pp.701-711
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• 2001

수질 인자들은 다양하고 관계가 복잡하여 수질 변화를 예측하는데 많은 어려움이 있다. 따라서 입력과 출력이 비교적 용이하고 비선형 예측에 적합한 신경망 모형을 이용하여 금강유역 공주지점의 DO, BOD, TN에 대한 월수질 예측을 수행하고 ARIMA 모형과 비교하여 적용 가능성을 검토하였다. 사용된 신경망 모형은 학습을 위해 BP(Back Propagation) 알고리즘을 적용하였으며 학습을 향상시키기 위한 모멘트-적응학습율(Moment-Adaptive learming rate) 방법을 이용한 MANN 모형, 레번버그-마쿼트(Levenberg-Marquardt) 방법을 이 용한 LMNN 모형, 그리고 정성적인 판단인자를 첨가하여 정량적인 월 수질 자료와 분별, 학습하 도록 은닉층을 분리한 MNN 모형으로 구분하였다. 대체로 신경망 모형의 예측치가 실측치에 근사한 결과를 보였으며, 은닉층을 분리한 MNN 모형이 가장 우수한 결과를 보였다.