• Journal of Welding and Joining
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• v.32 no.3
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• pp.27-33
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• 2014

This paper presents a brief summary on a relatively new plasma aided electrolytic surface treatment process for light metals. A brief discussion regarding the advantages, principle, process parameters and applications of this process is discussed. The process owes its origin to Sluginov who discovered an arc discharge phenomenon in electrolysis in 1880. A similar process was studied and developed by Markov and coworkers in 1970s who successfully deposited an oxide film on aluminium. Several investigation thereafter lead to the establishment of suitable process parameters for deposition of a crystalline oxide film of more than $100{\mu}m$ thickness on the surface of light metals such as aluminium, titanium and magnesium. This process nowadays goes by several names such as plasma electrolytic oxidation (PEO), micro-arc oxidation (MOA), anodic spark deposition (ASD) etc. Several startups and surface treatment companies have taken up the process and deployed it successfully in a range of products, from military grade rifles to common off road sprockets. However, there are certain limitations to this technology such as the formation of an outer porous oxide layer, especially in case of magnesium which displays a Piling Bedworth ratio of less than one and thus an inherent non protective oxide. This can be treated further but adds to the cost of the process. Overall, it can be said the PEO process offers a better solution than the conventional coating processes. It offers advantages considering the fact that he electrolyte used in PEO process is environmental friendly and the temperature control is not as strict as in case of other surface treatment processes.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.1
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• pp.83-95
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• 2010

This paper considers a firm that operates make-to-stock and make-to-order facilities in successive stages. The make-to-stock facility produces components which are consumed by the external market demand as well as the internal make-to-order operation. The make-to-order facility processes customer orders with the option of acceptance or rejection. In this paper, we address the problem of coordinating how to allocate the capacity of the make-to-stock facility to internal and external demands and how to control incoming customer orders at the make-to-order facility so as to maximize the firm's profit subject to the system costs. To deal with this issue, we formulate the problem as a Markov decision process and characterize the structure of the optimal inventory allocation and customer order control. In a numerical experiment, we compare the performance of the optimal policy to the heuristic with static inventory allocation and admission control under different operating conditions of the system.

• International Journal of Contents
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• v.12 no.4
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• pp.31-44
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• 2016

Knowledge creation and management are regarded as critical success factors for an organization's survival in the knowledge era. As a process of knowledge acquisition and sharing, organizational learning mechanisms (OLMs) guide the learning function of organizations represented by its different learning activities. We examined a variety of learning processes that constitute OLMs. In this study, we aimed to capture the process and framework of OLMs and knowledge sharing and acquisition. Factors facilitating OLMs were investigated at three levels: individual, group, and organizational. The concept of an OLM has received some attention in the field of organizational learning, however, the relationship among the factors generating OLMs has not been empirically tested. As part of the ongoing discussion, we attempted a systemic approach for OLMs. OLMs can be represented by factors that are inherent to the organization's system; therefore, prior to empirically testing the OLM generating factor(s), evaluation of its organizational integration is required to determine effective treatment of each factor. Thus, we developed a framework to manage knowledge and proposed a method to numerically evaluate factors influencing the OLMs. Specifically, composite importance (CI) of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was applied to explore the interaction effect of these factors based on systemic approach. The augmented matrix thus generated is expected to serve as a stochastic matrix of an absorbing Markov chain.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.211-221
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• 1998

It is becoming apparent that stereo matching algorithms need much information from high level cognitive processes. Otherwise, conventional algorithms based on bottom-up control alone are susceptible to local minima. We introduce a system that consists of two levels. A lower level, using a usual matching method, is based upon the local neighborhood and a second level, that can integrate the partial information, is aimed at contextual matching. Conceptually, the introduction of bottom-up and top-down feedback loop to the usual matching algorithm improves the overall performance. For this purpose, we model the image attributes using a Markov random field (MRF) and thereupon derive a maximum a posteriori (MAP) estimate. The energy equation, corresponding to the estimate, efficiently represents the natural constraints such as occlusion and the partial informations from the other levels. In addition to recognition, we derive a training method that can determine the system informations from the other levels. In addition to recognition, we derive a training method that can determine the system parameters automatically. As an experiment, we test the algorithms using random dot stereograms (RDS) as well as natural scenes. It is proven that the overall recognition error is drastically reduced by the introduction of contextual matching.

• Journal of Communications and Networks
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• v.4 no.2
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• pp.128-135
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• 2002

We propose a new space priority mechanism, and analyze its performance in a single Constant Bit Rate (CBR) server. The arrival process is derived from the superposition of two types of traffics, each in turn results from the superposition of homogeneous ON-OFF sources that can be approximated by means of a two-state Markov Modulated Poisson Process (MMPP). The buffer mechanism enables the Asynchronous Transfer Mode (ATM) layer to adapt the quality of the cell transfer to the Quality of Service (QoS) requirements and to improve the utilization of network resources. This is achieved by "Selective-Delaying and Pushing-ln"(SDPI) cells according to the class they belong to. The scheme is applicable to schedule delay-tolerant non-real time traffic and delay-sensitive real time traffic. Analytical expressions for various performance parameters and numerical results are obtained. Simulation results in term of cell loss probability conform with our numerical analysis.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.13-16
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• 2004

This paper deals with a one-searcher multi-target search problem where targets with different detection priorities move in Markov processes in each discrete time over a given space search area, and the total number of search time intervals is fixed. A limited search resource is available in each search time interval and an exponential detection function is assumed. The searcher can obtain a target detection award, if detected, which represents the detection priority of target and is non-increasing with time. The objective is to establish the optimal search plan which allocates the search resource effort over the search areas in each time interval in order to maximize the total detection award. In the analysis, the given problem is decomposed into intervalwise individual search problems each being treated as a single stationary target problem for each time interval. An associated iterative procedure is derived to solve a sequence of stationary target problems. The computational results show that the proposed algorithm guarantees optimality.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.139-139
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• 2021

Flooding events often result from extreme precipitations driven by various climate mechanisms, which are often disregarded in flood risk assessments. To bridge this gap, we propose a climate-mechanism-based flood frequency analysis that accommodates the direct linkage between the dominant climate processes and risk management decisions. Several statistical methods have been utilized in this approach including the Markov Chain analysis, K-nearest neighbor (KNN) resampling approach, and Z-score-based jittering method. After that, the impacts of climate change are associated with the modification of the transition matrix (TM) and the application of the quantile mapping approach. For this study, we have selected the Nam River Basin, South Korea, to consider the heterogeneous impacts of the two climate mechanisms, including the Tropical Cyclone (TC) and non-TCs. Based on our results, while both climate mechanisms have significant impacts on future flood extremes, TCs have been observed to bring more significant and immediate impacts on the flood extremes. The results in this study have proven that the proposed approach can lead to a new insights into future flooding management.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12A
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• pp.1922-1931
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• 1999

A multicast traffic’s feature is the function of providing a point to multipoints cell transmission, which is emerging from the main function of ATM switch. However, when a conventional point-to-point switch executes a multicast function, the excess load is occurred because unicast cell as well as multicast cell passed the copy network. Additionally, due to the excess load, multicast cells collide with other cells in a switch. Thus a deadlock that losses cells raises, extremely diminishes the performance of switch. An input queued switch also has a defect of the HOL (Head of Line) blocking that less lessens the performance of the switch. In the proposed multicast switch, we use shared memory switch to reduce HOL blocking and deadlock. In order to decrease switch’s complexity and cell's processing time, to improve a throughput, we utilize the method that routes a cell on a separated paths by traffic pattern and the scheduling algorithm that processes a maximum 2N cell at once in the control part. Besides, when cells is congested at an output port, a cell loss probability increases. Thus we use the Output Memory (OM) to reduce the cell loss probability. And we make use of the method that stores the assigned memory (UM, MM) with a cell by a traffic pattern and clears the cell of the Output memory after a fixed saving time to improve the memory utilization rate. The performance of the proposed switch is executed and compared with the conventional policy under the burst traffic condition through both the analysis based on Markov chain and simulation.

• Korean Journal of Microbiology
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• v.39 no.4
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• pp.207-215
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• 2003

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated stuructures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. We have developed EGSP, a eukaryotic gene structure program, using duration hidden markov model. The program consists of two major processes, one of which is a training process to produce parameter values from training data sets and the other of which is to predict protein coding regions based on the parameter values. The program predicts multiple genes rather than a single gene from a DNA sequence. A few computational models were implemented to detect signal pattern and their scanning efficiency was tested. Prediction performance was calculated and was compared with those of a few commonly used programs, GenScan, GeneID and Morgan based on a few criteria. The results show that the program can be practically used as a stand-alone program and a module in a system. For gene prediction of eukaryotic microbial genomes, training and prediction analysis was done with Saccharomyces chromosomes and the result shows the program is currently practically applicable to real eukaryotic microbial genomes.

• Phonetics and Speech Sciences
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• v.12 no.2
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• pp.29-37
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• 2020

Recently, the neural network-based deep learning algorithm has dramatically improved performance compared to the classical Gaussian mixture model based hidden Markov model (GMM-HMM) automatic speech recognition (ASR) system. In addition, researches on end-to-end (E2E) speech recognition systems integrating language modeling and decoding processes have been actively conducted to better utilize the advantages of deep learning techniques. In general, E2E ASR systems consist of multiple layers of encoder-decoder structure with attention. Therefore, E2E ASR systems require data with a large amount of speech-text paired data in order to achieve good performance. Obtaining speech-text paired data requires a lot of human labor and time, and is a high barrier to building E2E ASR system. Therefore, there are previous studies that improve the performance of E2E ASR system using relatively small amount of speech-text paired data, but most studies have been conducted by using only speech-only data or text-only data. In this study, we proposed a semi-supervised training method that enables E2E ASR system to perform well in corpus in different domains by using both speech or text only data. The proposed method works effectively by adapting to different domains, showing good performance in the target domain and not degrading much in the source domain.