• The Journal of the Acoustical Society of Korea
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• v.20 no.8
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• pp.12-18
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• 2001

In this paper, we propose the Recurrent Neural Predictive HMM (RNPHMM). The RNPHMM is the hybrid network of the recurrent neural network and HMM. The predictive recurrent neural network trained to predict the future vector based on several last feature vectors, and defined every state of HMM. This method uses the prediction value from the predictive recurrent neural network, which is dynamically changing due to the effects of the previous feature vectors instead of the stable average vectors. The models of the RNPHMM are Elman network prediction HMM and Jordan network prediction HMM. In the experiment, we compared the recognition abilities of the RNPHMM as we increased the state number, prediction order, and number of hidden nodes for the isolated digits. As a result of the experiments, Elman network prediction HMM and Jordan network prediction HMM have good recognition ability as 98.5% for test data, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.2
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• pp.63-81
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• 2008

WiMAX has been introduced as a competitive alternative for metropolitan broadband wireless access technologies. It is connection oriented and it can provide very high data rates, large service coverage, and flexible quality of services (QoS). Due to the large number of connections and flexible QoS supported by WiMAX, the uplink access in WiMAX networks is very challenging since the medium access control (MAC) protocol must efficiently manage the bandwidth and related channel allocations. In this paper, we propose and investigate a cost-effective WiMAX bandwidth management scheme, named the WiMAX partial sharing scheme (WPSS), in order to provide good QoS while achieving better bandwidth utilization and network throughput. The proposed bandwidth management scheme is compared with a simple but inefficient scheme, named the WiMAX complete sharing scheme (WCPS). A maximum entropy (ME) based analytical model (MEAM) is proposed for the performance evaluation of the two bandwidth management schemes. The reason for using MEAM for the performance evaluation is that MEAM can efficiently model a large-scale system in which the number of stations or connections is generally very high, while the traditional simulation and analytical (e.g., Markov models) approaches cannot perform well due to the high computation complexity. We model the bandwidth management scheme as a queuing network model (QNM) that consists of interacting multiclass queues for different service classes. Closed form expressions for the state and blocking probability distributions are derived for those schemes. Simulation results verify the MEAM numerical results and show that WPSS can significantly improve the network’s performance compared to WCPS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5343-5364
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• 2016

Considering the tradeoff between energy consumption and outage behavior in buffer-aided relay selection, a novel energy-efficient buffer-aided optimal relay selection scheme with power adaptation and Inter-Relay Interference (IRI) cancellation is proposed. In the proposed scheme, energy consumption minimization is the objective with the consideration of relay buffer state, outage probability and relay power control, in order to eliminate IRI. The proposed scheme selects a pair of optimal relays from multiple candidate relays, denoted as optimal receive relay and optimal transmit relay respectively. Source-relay and relay-destination communications can be performed within a time-slot, which performs as Full-Duplex (FD) relaying. Markov chain model is applied to analyze the evolution of relay buffer states. System steady state outage probability and achievable diversity order are derived respectively. In addition, packet transmission delay and power reduction performance are investigated with a specific analysis. Numerical results show that the proposed scheme outperforms other relay selection schemes in terms of outage behavior with power adaptation and IRI cancellation in the same relay number and buffer size scenario. Compared with Buffer State relay selection method, the proposed scheme reduces transmission delay significantly with the same amount of relays. Average transmit power reduction can be implemented to relays with the increasing of relay number and buffer size, which realizes the tradeoff between energy-efficiency, outage behavior and delay performance in green cooperative communications.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.10
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• pp.747-752
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• 2019

By using the failure information and the cumulative test execution time obtained by performing the reliability growth test, it is possible to estimate the parameter of the reliability growth model, and the Mean Time Between Failure (MTBF) of the product can be predicted through the parameter estimation. However the failure information could be acquired periodically or the number of sample data of the obtained failure information could be small. Because there are various constraints such as the cost and time of test or the characteristics of the product. This may cause the error of the parameter estimation of the reliability growth model to increase. In this study, the Bayesian method is applied to estimating the parameters of the reliability growth model when the number of sample data for the fault information is small. Simulation results show that the estimation accuracy of Bayesian method is more accurate than that of Maximum Likelihood Estimation (MLE) respectively in estimation the parameters of the reliability growth model.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.697-708
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• 1996

In this paper, we study reactive congestion control with priority in ATM network. The priority schemes for buffer access, partial buffer sharing have been investigated in order to improve the utilization of ATM network resources the network and to satisfy the most demanding traffic class. We consider in this paper a discrete-time queueing model for partial buffer sharing with two Markov modulated Poisson inputs. This model can be used to analyze the the effects of the partial buffer sharing priority scheme on system performance for realistic cases of bursty services. Explicit formulae are derived for the number of cells in the system and the loss probabilities for the traffic. Congestion may still occur because of unpredictable statistical fluctuation of traffic sources even when preventive control is performed in the network. In this Paper, we study reactive congestion control, in which each source changes its cell emitting rate a daptively to the traffic load at the switching node. Our intention is that,by incorporating such a congcstion control method in ATM network,more efficient congsestion control is established. We develope an analytical model,and carry out an approximateanalysis of reactive congestion con-trol with priority.Numerical results show that several orders of magnitude improvement in the loss probability can be achieved for the high priority class with little impact on the low priority class performance.And the results show that the reactive congestion control with priority are very effective in avoiding congestion and in achieving the statistical gain.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.727-739
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• 2003

In this paper, we propose a generation method of regression classes for adaptation in the HM-Net (Hidden Markov Network) system. The MLLR (Maximum Likelihood Linear Regression) adaptation approach is applied to the HM-Net speech recognition system for expressing the characteristics of speaker effectively and the use of HM-Net in various tasks. For the state level sharing, the context domain state splitting of PDT-SSS (Phonetic Decision Tree-based Successive State Splitting) algorithm, which has the contextual and time domain clustering, is adopted. In each state of contextual domain, the desired phoneme classes are determined by splitting the context information (classes) including target speaker's speech data. The number of adaptation parameters, such as means and variances, is autonomously controlled by contextual domain state splitting of PDT-SSS, depending on the context information and the amount of adaptation utterances from a new speaker. The experiments are performed to verify the effectiveness of the proposed method on the KLE (The center for Korean Language Engineering) 452 data and YNU (Yeungnam Dniv) 200 data. The experimental results show that the accuracies of phone, word, and sentence recognition system increased by 34∼37%, 9%, and 20%, respectively, Compared with performance according to the length of adaptation utterances, the performance are also significantly improved even in short adaptation utterances. Therefore, we can argue that the proposed regression class method is well applied to HM-Net speech recognition system employing MLLR speaker adaptation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.2
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• pp.134-141
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• 2008

This paper presents an innovative load management technique that can effectively lower the summer peak load by adjusting the aircondition loads through smoothe coordinations between utility companies and large customers. An intelligent hierarchical load management system composed of a Central Intelligent Load Management System(CIMS) and multiple Local Intelligent Management Systems(LIMS) is also proposed to implement the reposed technique. Upon receiving a load curtailment request from the utilities, CIMS issues tokens, which can be used by each LIMS as a right to turn on the airconditioner. CIMS creates and maintains a queue for fair allocation of the tokens among the LIMS demanding tokens. By adjusting the number tokens and queue management Policies, desired load factors can be achieved conveniently. The Markov Birth and Death Process and the Balance Equations are employed in estimating various queue performances. The proposed technique is tested using a summer load data of a large apartment complex and proved to be quite effective in load management while minimizing the customer inconveniences.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.297-306
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• 2017

Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.

• The Transactions of the Korea Information Processing Society
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• v.7 no.8
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• pp.2484-2496
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• 2000

High speed switches have been developing to interconnect a large number of nodes. It is important to analyze the switch performance under various conditions to satisfy the requirements. Queueing analysis, in general, has the intrinsic problem of large state space dimension and complex computation. In fact, The petri net is a graphical and mathematical model. It is suitable for various applications, in particular, manufacturing systems. It can deal with parallelism, concurrence, deadlock avoidance, and asynchronism. Currently it has been applied to the performance of computer networks and protocol verifications. This paper presents a framework for modeling and analyzing ATM switch using stochastic activity networks (SANs). In this paper, we provide the ATM switch model using SANs to extend easily and an approximate analysis method to apply A TM switch models, which significantly reduce the complexity of the model solution. Cell arrival process in output-buffered Queueing A TM switch with finite buffer is modeled as Markov Modulated Poisson Process (MMPP), which is able to accurately represent real traffic and capture the characteristics of bursty traffic. We analyze the performance of the switch in terms of cell-loss ratio (CLR), mean Queue length and mean delay time. We show that the SAN model is very useful in A TM switch model in that the gates have the capability of implementing of scheduling algorithm.

• Journal of KIISE:Software and Applications
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• v.35 no.5
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• pp.313-324
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• 2008

A sentence reduction is the information compression process which removes extraneous words and phrases and retains basic meaning of the original sentence. Most researches in the sentence reduction have required a large number of lexical and syntactic resources and focused on extracting or removing extraneous constituents such as words, phrases and clauses of the sentence via the complicated parsing process. However, these researches have some problems. First, the lexical resource which can be obtained in loaming data is very limited. Second, it is difficult to reduce the sentence to languages that have no method for reliable syntactic parsing because of an ambiguity and exceptional expression of the sentence. In order to solve these problems, we propose the sentence reduction method which uses templates and POS(part of speech) information without a parsing process. In our proposed method, we create a new sentence using both Sentence Reduction Templates that decide the reduction sentence form and Grammatical POS-based Reduction Rules that compose the grammatical sentence structure. In addition, We use Viterbi algorithms at HMM(Hidden Markov Models) to avoid the exponential calculation problem which occurs under applying to Sentence Reduction Templates. Finally, our experiments show that the proposed method achieves acceptable results in comparison to the previous sentence reduction methods.