• Journal of the Korean Society for Precision Engineering
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• v.17 no.9
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• pp.174-180
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• 2000

A reliable analytic model that determines the cross sectional shape of a workpiece(material) in round-oval(or oval-round) pass sequence has been developed. the cross sectional shape of an outgoing workpiece is predicted by using the linear interpolation of the radius of curvature of an incoming workpiece and that of roll groove to the roll axis direction. The requirements we placed on the choice of the weighting function were to ensure boundary conditions specified. The validity of the analytic model has been examined by hot rod rolling experiment with the roll gap and specimen size changed. The cross sectional shape and area of a workpiece predicted by the proposed analytic model were good agreement with those obtained experimentally. It was found that the analytic model has not only simplicity and accuracy for practical usage but also save a large amount of computational time compared with finite element method.

• Phonetics and Speech Sciences
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• v.6 no.4
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• pp.127-132
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• 2014

This paper describes a robust speech recognition technique by reconstructing spectral components mismatched with a training environment. Although the cluster-based reconstruction method can compensate the unreliable components from reliable components in the same spectral vector by assuming an independent, identically distributed Gaussian-mixture process of training spectral vectors, the presented method exploits the temporal dependency of speech to reconstruct the components by introducing a hidden-Markov-model prior which incorporates an internal state transition plausible for an observed spectral vector sequence. The experimental results indicate that the described method can provide temporally consistent reconstruction and further improve recognition performance on average compared to the conventional method.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.533-549
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• 1996

This paper is concerned with a sequencing problem in mixed model assembly lines, which is important to efficient utilization of the lines. In the problem, we deal with the two objectives of minimizing the risk of stoppage and leveling part usage, and consider sequence-dependent setup time. In this paper, we present a genetic algorithm(GA) suitable for the multi-objective optimization problem. The aim of multi-objective optimization problems is to find all possible non-dominated solutions. The proposed algorithm is compared with existing multi-objective GAs such as vector evaluated GA, Pareto GA, and niched Pareto GA. The results show that our algorithm outperforms the compared algorithms in finding good solutions and diverse non-dominated solutions.

• MALSORI
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• v.68
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• pp.33-47
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• 2008

In this paper, we propose a hybrid acoustic and pronunciation model adaptation method based on context dependency for Korean-English speech recognition. The proposed method is performed as follows. First, in order to derive pronunciation variant rules, an n-best phoneme sequence is obtained by phone recognition. Second, we decompose each rule into a context independent (CI) or a context dependent (CD) one. To this end, it is assumed that a different phoneme structure between Korean and English makes CI pronunciation variabilities while coarticulation effects are related to CD pronunciation variabilities. Finally, we perform an acoustic model adaptation and a pronunciation model adaptation for CI and CD pronunciation variabilities, respectively. It is shown from the Korean-English speech recognition experiments that the average word error rate (WER) is decreased by 36.0% when compared to the baseline that does not include any adaptation. In addition, the proposed method has a lower average WER than either the acoustic model adaptation or the pronunciation model adaptation.

• Journal of KIISE:Information Networking
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• v.29 no.6
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• pp.674-684
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• 2002

Anomaly detection techniques have teen devised to address the limitations of misuse detection approach for intrusion detection. An HMM is a useful tool to model sequence information whose generation mechanism is not observable and is an optimal modeling technique to minimize false-positive error and to maximize detection rate, However, HMM has the short-coming of login training time. This paper proposes an effective HMM-based IDS that improves the modeling time and performance by only considering the events of privilege flows based on the domain knowledge of attacks. Experimental results show that training with the proposed method is significantly faster than the conventional method trained with all data, as well as no loss of recognition performance.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.1-8
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• 2020

In this paper, we propose a gene family based RNA-seq read distribution method in means to accelerate the overal transcriptome assembly computation time. To measure the performance of our transcriptome sequence data distribution method, we evaluated the performance by testing four types of data sets of the Arabidopsis thaliana genome (Whole Unclassified Reads, Family-Classified Reads, Model-Classified Reads, and Randomly Classified Reads). As a result of de novo transcript assembly in distributed nodes using model classification data, the generated gene contigs matched 95% compared to the contig generated by WUR, and the execution time was reduced by 4.2 times compared to a single node environment using the same resources.

• Journal of the Korean Society of Safety
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• v.17 no.3
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• pp.123-130
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• 2002

This paper presents a new dynamic human reliability analysis method and its application for quantifying the human error probabilities in implementing accident management actions. The action associated with implementation of the cavity flooding during a station blackout sequence is considered for its application. This method is based on the concept of the quantified correlation between the performance requirement and performance achievement. For comparisons of current HRA methods with the new method, the characteristics of THERP, HCR, and SLIM-MAUD, which m most frequency used method in PSAs, are discussed. The MAAP code and Latin Hypercube sampling technique are used to determine the uncertainty of the performance achievement parameter. Meanwhile, the value of the performance requirement parameter is obtained from interviews. Based on these stochastic obtained, human error probabilities are calculated with respect to the various means and variances of the things. It is shown that this method is very flexible in that it can be applied to any kind of the operator actions, including the actions associated with the implementation of accident management strategies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7A
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• pp.682-688
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• 2006

In this paper, a soft-decision method for initial PN code acquisition in pilot-channel aided Direct Sequence Code Division Multiple Access (DS-CDMA) systems is proposed in order to improve the acquisition performance. We apply this technique to the conventional hybrid search algorithm and analyze it in terms of mean code acquisition time. For the analysis, we present mathematical model of proposed algorithm and also perform the simulation under IMT-2000 channel models. Numerical results show that our proposed scheme outperforms the conventional one by 0.2 - 0.4 sec with respect to the mean code acquisition time because the soft decision technique can mitigate the possible decline in search performance caused by the use of a hard-decision technique.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3821-3828
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• 2000

Many software projects collect grouped failure data (failures in some failure interval or in variable time interval) rather than individual failure times or failure count data during the testing or operational phase. This paper presents the neural network (NN) modeling that is dble to predict cumulative failures in the variable future time for grouped failure data. ANN's predictive ability can be affected by what it learns and in its ledming sequence. Eleven training regimes that represents the input-output of NN are considered. The best training regimes dre selected rJdsed on the next' step dvemge reldtive prediction error (AE) and normalized AE (NAE). The suggested NN models are compared with other well-known KN models and statistical software reliability growth models (SHGlvls) in order to evaluate performance, Experimental results show that the NN model with variable time interval information is necessary in order to predict cumulative failures in the variable future time interval.

• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.111-122
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• 2003

Military wargame simulation models must support the HLA in order to facilitate interoperability with other simulations, and using parallel simulation engines offer efficiency in reducing system overhead generated by propelling interoperability. However, legacy military simulation model engines process events using sequential event-driven method. This is due to problems generated by parallel processing such as synchronous reference to global data domains. Additionally. using legacy simulation platforms result in insufficient utilization of multiple CPUs even if a multiple CPU system is under use. Therefore, in this paper, we propose conversing the simulation engine to an object model-based parallel simulation engine to ensure military wargame model's improved system processing capability, synchronous reference to global data domains, external simulation time processing, and the sequence of parallel-processed events during a crash recovery. The converted parallel simulation engine is designed and implemented to enable parallel execution on a multiple CPU system (SMP).