• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.135-142
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• 2022

In this paper, we propose a graph-based user route control for rapidly conducting virus inspections in emergency situations (eg, COVID-19) and a framework that can simulate this on a city map. A^* and navigation mesh data structures, which are widely used pathfinding algorithms in virtual environments, are effective when applied to CS(Computer science) problems that control Agents in virtual environments because they guide only a fixed static movement path. However, it is not enough to solve the problem by applying it to the real COVID-19 environment. In particular, there are many situations to consider, such as the actual road traffic situation, the size of the hospital, the number of patients moved, and the patient processing time, rather than using only a short distance to receive a fast virus inspection.

• Journal of Broadcast Engineering
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• v.28 no.1
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• pp.109-123
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• 2023

Deep learning has made rapid progress in recent years and is affecting various fields and industries. The art field cannot be an exception, and in this paper, we would like to explore and experiment and analyze research fields that creatively generate 2D images in 3D from a visual arts and engineering perspective. To this end, the original image of the domestic artist is learned through GAN or Diffusion Models, and then converted into 3D using 3D conversion software and deep learning. And we compare the results with prior algorithms. After that, we will analyze the problems and improvements of 2D to 3D creative generation.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.45-54
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• 2010

The inherent complexity of semiconductor fabrication processes makes it hard to solve well-known job scheduling problems in analytical ways, which leads us to rely practically on discrete event modeling simulations to learn the effects of changing the system's parameters. Meanwhile, unpredictable disturbances such as machine failures and maintenance diminish the productivity of semiconductor manufacturing processes with fixed scheduling policies; thus, it is necessary to adapt job scheduling policy in a timely manner in reaction to critical environmental changes (disturbances) in order for the fabrication process to perform optimally. This paper proposes an adaptive job control framework for a wafer fabrication process in a control system theoretical approach and implements it based on a DEVS modeling simulation environment. The proposed framework has the advantages in view of the whole systems understanding and flexibility of applying new rules compared to most ad-hoc software approaches in this field. Furthermore, it is flexible enough to incorporate new job scheduling rules into the existing rule set. Experimental results show that this control framework with adaptive rescheduling outperforms fixed job scheduling algorithms.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.309-318
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• 2010

Simulation-based acquisition(SBA) is a new acquisition paradigm to deliver combat systems cheaper, faster, and better. ROK MND adopts the vision of SBA and is pushing ahead with dramatic reform. However, ROK MND does not develop the SBA architecture framework which facilitates the reuse of tools and techniques and data software code and algorithms among participants of collaborative environments. In this paper, we propose a conceptual Model of architecture framework for SBA. To do so, we analyse acquisition process of MND and propose the to-be operational view that describes fundamental concept for how Government, Industry, and Academia can collaborate and share information more effectively throughout the acquisition process. Furthermore, we identify the tools and techniques that supports the operational nodes, and propose technical view and all view, too. technical view compose of set of standards that can ensure interoperability among tools, techniques and data, and all view provide an overarching description of the architecture.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2124-2139
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• 2023

In this article, the authors focus on the use of smart CCTV, a combnation of biometric recognition technology and AI algorithms. In fact, the advancements in relevant technologies brought a significant increase in the use of biometric information - fingerprint, retina, iris or facial recognition - across diverse sectors. Both the public and private sectors, with the developments of biometric technology, widely adopt and use an individual's biometric information for different reasons. For instance, smartphone users highly count on biometric technolgies for the purpose of security. Public and private orgazanitions control an access to confidential information-controlling facilities with biometric technology. Biometric infomration is known to be unique and immutable in the course of one's life. Given the uniquness and immutability, it turned out to be as reliable means for the purpose of authentication and verification. However, the use of biometric information comes with cost, posing a privacy issue. Once it is leaked, there is little chance to recover damages resulting from unauthorized uses. The governments across the country fully understand the threat to privacy rights with the use of biometric information and AI. The EU and the United States amended their data protection laws to regulate it. South Korea aligned with them. Yet, the authors point out that Korean data aprotection law still requires more improvements to minimize a concern over privacy rights arising from the wide use of biometric information. In particular, the authors stress that it is necessary to amend Section (2) of Article 23 of PIPA to reflect the concern by changing the basis for permitting the processing of sensitive information from 'the Statutes' to 'the Acts'.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.8
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• pp.333-340
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• 2023

Efficient prediction of corporate bankruptcy is an important part of making appropriate lending decisions for financial institutions and reducing loan default rates. In many studies, classification models using artificial intelligence technology have been used. In the financial industry, even if the performance of the new predictive models is excellent, it should be accompanied by an intuitive explanation of the basis on which the result was determined. Recently, the US, EU, and South Korea have commonly presented the right to request explanations of algorithms, so transparency in the use of AI in the financial sector must be secured. In this paper, an artificial intelligence-based interpretable classification prediction model was proposed using corporate bankruptcy data that was open to the outside world. First, data preprocessing, 5-fold cross-validation, etc. were performed, and classification performance was compared through optimization of 10 supervised learning classification models such as logistic regression, SVM, XGBoost, and LightGBM. As a result, LightGBM was confirmed as the best performance model, and SHAP, an explainable artificial intelligence technique, was applied to provide a post-explanation of the bankruptcy prediction process.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.62-69
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• 2022

An electrocardiogram (ECG) is one of the most important biosignals, and in particular, continuous ECG monitoring is very important in patients with arrhythmia. There are many different types of arrhythmia (sinus node, sinus tachycardia, atrial premature beat (APB), and ventricular fibrillation) depending on the cause, and continuous ECG monitoring during daily life is very important for early diagnosis of arrhythmias and setting treatment directions. The ECG signal of arrhythmia patients is very unstable, and it is difficult to detect the R-peak point, which is a key feature for automatic arrhythmias detection. In this study, we develped a continuous measuring Holter ECG monitoring device and software for analysis and confirmed the utility of R-peak of the ECG signal with MIT-BIH arrhythmia database. In future studies, it needs the validation of algorithms and clinical data for morphological classification and prediction of arrhythmias due to various etiologies.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.525-530
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• 2022

Although the iterative closest point (ICP) algorithm has been widely used to align two point clouds, ICP tends to fail when the initial orientation of the two point clouds are significantly different. In this paper, when two triangular meshes A and B have significantly different initial orientations, we present an algorithm to align them. After obtaining weighted centroids for meshes A and B, respectively, vertices that are likely to correspond to each other between meshes are set as feature points using the distance from the centroid to the vertices. After rotating mesh B so that the feature points of A and B to be close each other, RMSD (root mean square deviation) is measured for the vertices of A and B. Aligned meshes are obtained by repeating the same process while changing the feature points until the RMSD is less than the reference value. Through experiments, we show that the proposed algorithm aligns the mesh even when the ICP and Go-ICP algorithms fail.