• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.501-511
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• 2022

In 2021, ransomware attacks became popular, and the number is rapidly increasing every year. Since PowerShell is used as the primary ransomware technique, the need for PowerShell-based malware detection is ever increasing. However, the existing detection techniques have limits in that they cannot detect obfuscated scripts or require a long processing time for deobfuscation. This paper proposes a simple and fast deobfuscation method and a deep learning-based classification model that can detect PowerShell-based malware. Our technique is composed of Word2Vec and a convolutional neural network to learn the meaning of a script extracting important features. We tested the proposed model using 1400 malicious codes and 8600 normal scripts provided by the AI-based PowerShell malicious script detection track of the 2021 Cybersecurity AI/Big Data Utilization Contest. Our method achieved 5.04 times faster deobfuscation than the existing methods with a perfect success rate and high detection performance with FPR of 0.01 and TPR of 0.965.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.4
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• pp.691-708
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• 2022

As the technology of machine learning and deep learning became common, it began to be applied to research on anomaly(abnormal) detection of industrial control systems. In Korea, the HAI dataset was developed and published to activate artificial intelligence research for abnormal detection of industrial control systems, and an AI contest for detecting industrial control system security threats is being conducted. Most of the anomaly detection studies have been to create a learning model with improved performance through the ensemble model method, which is applied either by modifying the existing deep learning algorithm or by applying it together with other algorithms. In this study, a study was conducted to improve the performance of anomaly detection with a post-processing method that detects abnormal data and corrects the labeling results, rather than the learning algorithm and data pre-processing process. Results It was confirmed that the results were improved by about 10% or more compared to the anomaly detection performance of the existing model.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2001.11a
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• pp.7-7
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• 2001

The increasing interest in light weight materials coupled to the need for cost -effective processing have combined to create a significant opportunity for aluminum P/M. particularly in the automotive industry in order to reduce fuel emissions and improve fuel economy at affordable prices. Additional potential markets for Al PIM parts include hand tools. Where moving parts against gravity represents a challenge; and office machinery, where reciprocating forces are important. Aluminum PIM adds light weight, high compressibility. low sintering temperatures. easy machinability and good corrosion resistance to all advantages of conventional iron bm;ed P/rv1. Current commercial alloys are pre-mixed of either the AI-Si-Mg or AL-Cu-Mg-Si type and contain 1.5% ethylene bis-stearamide as an internal lubricant. The powder is compacted in closed dies at pressure of 200-500Mpa and sintered in nitrogen at temperatures between $580~630^{\circ}C$ in continuous muffle furnace. For some applications no further processing is required. although most applications require one or more secondary operations such as sizing and finishing. These sccondary operations improve the dimension. properties or appearance of the finished part. Aluminum is often considered difficult to sinter because of the presence of a stable surface oxide film. Removal of the oxide in iron and copper based is usually achieved through the use of reducing atmospheres. such as hydrogen or dissociated ammonia. In aluminum. this occurs in the solid st,lte through the partial reduction of the aluminum by magncsium to form spinel. This exposcs the underlying metal and facilitates sintering. It has recently been shown that < 0.2% Mg is all that is required. It is noteworthy that most aluminum pre-mixes contain at least 0.5% Mg. The sintering of aluminum alloys can be further enhanced by selective microalloying. Just 100ppm pf tin chnnges the liquid phase sintering kinetics of the 2xxx alloys to produce a tensile strength of 375Mpa. an increilse of nearly 20% over the unmodified alloy. The ductility is unnffected. A similar but different effect occurs by the addition of 100 ppm of Pb to 7xxx alloys. The lend changes the wetting characteristics of the sintering liquid which serves to increase the tensile strength to 440 Mpa. a 40% increase over unmodified aIloys. Current research is predominantly aimed at the development of metal matrix composites. which have a high specific modulus. good wear resistance and a tailorable coefficient of thermal expnnsion. By controlling particle clustering and by engineering the ceramic/matrix interface in order to enhance sintering. very attractive properties can be achicved in the ns-sintered state. I\t an ils-sintered density ilpproaching 99%. these new experimental alloys hnve a modulus of 130 Gpa and an ultimate tensile strength of 212 Mpa in the T4 temper. In contest. unreinforcecl aluminum has a modulus of just 70 Gpa.