• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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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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• v.11 no.2
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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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• v.7 no.3
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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.

• Journal of Environmental Science International
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• v.13 no.7
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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.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.285-290
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• 2009

This paper implemented a Speech Recognition System in order to recognize Commands of Ticketing Machine (314 station-names) at real-time using Continuous Hidden Markov Model. Used 39 MFCC at feature vectors and For the improvement of recognition rate composed 895 tied-state triphone models. System performance valuation result of the multi-speaker-dependent recognition rate and the multi-speaker-independent recognition rate is 99.24% and 98.02% respectively. In the noisy environment the recognition rate is 93.91%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1861-1870
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• 2013

A climate change-driven increased hydrological variability has been widely acknowledged over the past decades. In this regards, rainfall simulation techniques are being applied in many countries to consider the increased variability. This study proposed a Homogeneous Hidden Markov Chain(HMM) designed to recognize rather complex patterns of rainfall with discrete hidden states and underlying distribution characteristics via mixture probability density function. The proposed approach was applied to Seoul and Jeonju station to verify model's performance. Statistical moments(e.g. mean, variance, skewness and kurtosis) derived by daily and seasonal rainfall were compared with observation. It was found that the proposed HMM showed better performance in terms of reproducing underlying distribution characteristics. Especially, the HMM was much better than the existing Markov Chain model in reproducing extremes. In this regard, the proposed HMM could be used to evaluate a long-term runoff and design flood as inputs.

• Advances in environmental research
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• v.5 no.3
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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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• v.36 no.5
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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.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.10 s.352
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• pp.79-89
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• 2006

TCP is the most widely used transport protocol in Internet applications that guarantees a reliable data transfer. But, in the wireless multi-hop networks, TCP performance is degraded because it is designed for wired networks. The main reasons of TCP performance degradation are contention for wireless medium at the MAC layer, hidden terminal problem, exposed terminal problem, packet losses in the link layer, unfairness problem, reordering problem caused by path disconnection, bandwidth waste caused by exponential backoff of retransmission timer due to node's mobility and so on. Specially, in the mobile ad-hoc networks, discrepancy between a station's transmission range and interference range produces hidden terminal problem that decreases TCP performance greatly by limiting simultaneous transmission at a time. In this paper, we propose a new MAC algorithm for mobile ad-hoc networks to solve the problem that a node can not transmit and just increase CW by hidden terminal. In the IEEE 802.11 MAC DCF, a node increases CW exponentially when it fails to transmit, but the proposed algorithm, changes CW adaptively according to the reason of failure so we get a TCP performance enhancement. We show by ns-2 simulation that the proposed algorithm enhances the TCP performance by fairly distributing the transmission opportunity to the failed nodes by hidden terminal problems.

• Journal of Korea Water Resources Association
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• v.34 no.6
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• pp.701-711
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• 2001

Forecasting of water quality variation is not an easy process due to the complicated nature of various water quality factors and their interrelationships. The objective of this study is to test the applicability of neural network models to the forecasting of the water quality at Gongju station in Geum River. This is done by forecasting monthly water qualities such as DO, BOD, and TN, and comparing with those obtained by ARIMA model. The neural network models of this study use BP(Back Propagation) algorithm for training. In order to improve the performance of the training, the models are tested in three different styles ; MANN model which uses the Moment-Adaptive learning rate method, LMNN model which uses the Levenberg-Marquardt method, and MNN model which separates the hidden layers for judgement factors from the hidden layers for water quality data. the results show that the forecasted water qualities are reasonably close to the observed data. And the MNN model shows the best results among the three models tested