• Journal of Korean Society of Industrial and Systems Engineering
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• v.15 no.25
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• pp.75-82
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• 1992

Standard acceptance sampling plans models the production pricess as a sequence of independent identically distributed Beruoulli random variables. However, the quality of items sampled sequentially from an ongoing production process of ten exhibits statistical dependency that is not accounted for in standard acceptance sampling plans. In this paper, a dependent production process is modelled as an ARMA process and as a two-state Markov chain. A simulation study of each is performed. A comparison of the probability of acceptance is done for the simulation method and for the approximation method.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.202-211
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• 2003

Abstract Formal definitions of syntax and semantics for the static and dynamic models in Object Oriented methods are already defined. But the behavior of interacting objects is not formalized. In this paper, we defined the common behavior of interacting objects in terms of process algebra using sequence diagram in UML and regularized properties of interacting objects. Based on the results, we can develop a formal specification by. using of the object interaction instead of the existence dependency suggested by M. Snoeck and G. Dedene[9].

• The Journal of Bigdata
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• v.5 no.1
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• pp.1-9
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• 2020

The study was conducted with funding from the government (Ministry of Agriculture, Food and Rural Affairs) in 2018 with support from the Agricultural, Food, and Rural Affairs Agency, 318069-03-HD040, and in based on artificial intelligence-based HPAI spread analysis and patterning. The model that is actively used in time series and text mining recently is LSTM (Long Short-Term Memory Models) model utilizing deep learning model structure. The LSTM model is a model that emerged to resolve the Long-Term Dependency Problem that occurs during the Backpropagation Through Time (BPTT) process of RNN. LSTM models have resolved the problem of forecasting very well using variable sequence data, and are still widely used.In this paper study, we used the data of the Call Detailed Record (CDR) provided by KT to identify the migration path of people who are expected to be closely related to the virus. Introduce the results of predicting the path of movement by learning the LSTM model using the path of the person concerned. The results of this study could be used to predict the route of HPAI propagation and to select routes or areas to focus on quarantine and to reduce HPAI spread.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.6
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• pp.642-649
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• 2015

Resistance Spot Welding (RSW) is the method for joining two overlapped base materials when high pressure and current is applied from electrodes. Due to the safety problem such high pressure and voltage, automation should be early adopted. In this paper, the spot welding is developed as a computational model of wheel house from GM Korea and the welding condition such as weld sequence is considered. The computational analysis is preceded as a static and elasto-plastic procedure and used thermal expansion coefficient represents a dependency of spot volume between two panels. In case of welding sequence, the efficiency which depends on the distance between current spot point and the other is calculated in several cases.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.79-86
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• 2021

In order to increase the probability of success in big data projects, quantified techniques are required to analyze the root cause of risks from complex causes and establish optimal countermeasures. To this end, this study measures risk factors and relationships through SNA analysis and presents a way to respond to risks based on them. In other words, it derives a dependency network matrix by utilizing the results of correlation analysis between risk groups in the big data projects presented in the preliminary study and performs SNA analysis. In order to derive the dependency network matrix, partial correlation is obtained from the correlation between the risk nodes, and activity dependencies are derived by node by calculating the correlation influence and correlation dependency, thereby producing the causal relationship between the risk nodes and the degree of influence between all nodes in correlation. Recognizing the root cause of risks from networks between risk factors derived through SNA between risk factors enables more optimized and efficient risk management. This study is the first to apply SNA analysis techniques in relation to risk management response, and the results of this study are significant in that it not only optimizes the sequence of risk management for major risks in relation to risk management in IT projects but also presents a new risk analysis technique for risk control.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.8
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• pp.642-653
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• 2008

In this paper, we propose a 3-D finite element (FE) analysis model with combined physical behavior and kinematical impact factors for evaluation of residual stress in multi-impact shot peening. The FE model considers both physical behavior of material and characteristics of kinematical impact. The physical parameters include elastic-plastic FE modeling of shot ball, material damping coefficient, dynamic friction coefficient. The kinematical parameters include impact velocity and diameter of shot ball. Multi-impact FE model consists of 3-D symmetry-cell. We can describe a certain repeated area of peened specimen under equibiaxial residual stress by the cell. With the cell model, we investigate the FE peening coverage, dependency on the impact sequence, effect of repeated cycle. The proposed FE model provides converged and unique solution of surface stress, maximum compressive residual stress and deformation depth at four impact positions. Further, in contrast to the rigid and elastic shots, plastically deformable shot produces residual stresses closer to experimental solutions by X-ray diffraction. Consequently, it is confirmed that the FE model with peening factors and plastic shot is valid for multi-shot peening analyses.

• ETRI Journal
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• v.43 no.2
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• pp.299-312
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• 2021

It is necessary to achieve high performance in the task of zero anaphora resolution (ZAR) for completely understanding the texts in Korean, Japanese, Chinese, and various other languages. Deep-learning-based models are being employed for building ZAR systems, owing to the success of deep learning in the recent years. However, the objective of building a high-quality ZAR system is far from being achieved even using these models. To enhance the current ZAR techniques, we fine-tuned a pretrained bidirectional encoder representations from transformers (BERT). Notably, BERT is a general language representation model that enables systems to utilize deep bidirectional contextual information in a natural language text. It extensively exploits the attention mechanism based upon the sequence-transduction model Transformer. In our model, classification is simultaneously performed for all the words in the input word sequence to decide whether each word can be an antecedent. We seek end-to-end learning by disallowing any use of hand-crafted or dependency-parsing features. Experimental results show that compared with other models, our approach can significantly improve the performance of ZAR.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.577-588
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• 2014

The collection and storage of a large volumes of data are becoming easier; however, the number of variables is sometimes more than the number of samples(objects). We now face the problem of dependency among variables(such as multicollinearity) due to the increased number of variables. We cannot apply various statistical methods without satisfying independency assumption. In order to overcome such a drawback we consider a categorizing (or discretizing) observations. We have a data set of nancial indices from banks in Korea that contain 78 variables from 16 banks. Genetic sequence data is also a good example of a large data and there have been numerous statistical methods to handle it. We discover lots of useful bank information after we transform bank data into categorical data that resembles genetic sequence data and apply bioinformatic techniques.

• Animal cells and systems
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• v.11 no.2
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• pp.175-182
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• 2007

The lysozyme II gene of cabbage butterfly Artogeia rapae was cloned from fat body of the larvae injected with E. coli and its nucleotide sequence was determined by the RACE-PCR. It has an open reading frame of 414 bp nucleotides corresponding to 138 amino acids including a signal sequence of 18 amino acids. The estimated molecular weight and the isoelectric point of the lysozyme II without the signal peptide were 13,649.38 Da and 9.11, respectively. The A. rapae lysozyme II (ARL II) showed the highest identity (81%) in the amino acid sequence to Manduca sexta lysozyme among other lepidopteran species. The two catalytic residues ($Glu^{32}$ and $Asp^{50}$) and the eight Cys residue motifs, which are highly conserved among other c-type lysozymes in invertebrates and vertebrates, are also completely conserved. A phylogenetic analysis based on amino acid sequences indicated that the ARL II was more closely related to M. sexta, Hyphantria cunea, Heliothis virescens, and Trichoplusia ni lysozymes. The ARL II gene was expressed in Spodoptera frugiperda 21 insect cells and the recombinant ARL II (rARL II) was purified from cell-conditioned media by cation exchange column chromatography and reverse phase FPLC. The purified rARL II was able to form a clear zone in lysoplate assay against Micrococcus luteus. The lytic activity was estimated to be 511.41 U/mg, 1.53 times higher than that of the chicken lysozyme. The optimum temperature for the lytic activity of the rARL II was $50^{\circ}C$, the temperature dependency of the absolute lytic activity of rARL II was higher than that of the chicken lysozyme at low temperatures under $65^{\circ}C$.