• Steel and Composite Structures
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• v.30 no.4
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• pp.393-403
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• 2019

Cold-formed steel (CFS) has a great potential to meet the global challenge of fast-track and durable construction. CFS members undergo large buckling instabilities due to their small wall thickness. CFS beams with corrugated webs have shown great resistance towards web buckling under flexure, when compared to the conventional I-sections. However, the magnitude of global imperfections significantly affects the performance of CFS members. This paper presents the first attempt made to experimentally study the effect of global imperfections on the structural efficiency of various strengthening schemes implemented in CFS beams with corrugated webs. Different strengthening schemes were adopted for two types of beams, one with large global imperfections and the other with small imperfections. Strength and stiffness characteristics of the beams were used to evaluate the structural efficiency of the various strengthening schemes adopted. Six tests were performed with simply supported end conditions, under four-point loading conditions. The load vs. mid-span displacement response, failure loads and modes of failure of the test specimens were investigated. The test results would compensate the lack of experimental data in this area of research and would help in developing numerical models for extensive studies for the development of necessary guidelines on the same. Strengthening schemes assisted in enhancing the member performance significantly, both in terms of strength and stiffness. Hence, providing an economic and time saving solution to such practical structural engineering problems.

• Journal of IKEEE
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• v.22 no.4
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• pp.922-932
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• 2018

Radial distance sensors are widely used for surveying and autonomous navigation. It is necessary to train the operation principle of these sensors and how to apply them. Although commercialization of radial distance sensor continues to be cost-effective through lower performance, but it is still expensive for educational purposes. In this paper, we propose a distance sensor module with object tracking using radial array of low cost phototransistor which can be used for educational robot. The proposed method is able to detect the position of a fast moving object immediately by arranging the phototransistor in the range of 180 degrees and improve the sensing angle range and track the object by the sensor rotation using the servo motor. The scan speed of the proposed sensor is 50~200 times faster than the commercial distance sensor, thus it can be applied to a high performance educational mobile robot with 1ms control loop.

• 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.

• Korean Journal of Construction Engineering and Management
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• v.23 no.6
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• pp.54-64
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• 2022

The continuous aging of the domestic construction industry and the shortage of human resource are no longer a problem for the future, but must be solved for the survival of the domestic construction industry, given the characteristics of the labor-intensive construction industry and the continuing negative image of young people toward the construction industry. It is undoubtedly a prerequisite. This study is aimed to tackle the fundamental problem of the construction labor shortage faced by the domestic construction industry by comparing and analyzing the case of MMC technology development in the UK, which has been preparing fast-track response measures for the past 5 years to solve the labor shortage problem, and the case of Buildability technology development in Singapore, which is seeking mid- to long-term countermeasures for about 20 years. Also, This research provides the initial research & development roadmap for Korean Countermeasures Strategy.

• 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.18 no.2
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• pp.77-89
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• 2009

Visual tracking technique is one of the essential things which are very important in the major fields of modern society. While contour tracking is especially necessary technique in the aspect of its fast performance with target's external contour information, it sometimes fails to track target motion because it is affected by the surrounding edges around target and weak egdes on the target boundary. To overcome these weak points, in this research it is suggested that PDMs can be obtained by generating the virtual 6-DOF motions of the mobile robot with a CCD camera and the image tracking system which is robust to the local minima around the target can be configured by constructing Active Shape Model in modular base. To show the effectiveness of the proposed method, the experiment is performed on the image stream obtained by a real mobile robot and the better performance is confirmed by comparing the experimental results with the ones of other major tracking techniques.

• 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.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.149-158
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• 2023

HMD, which is applied with the latest technology, minimizes motion sickness with high-resolution displays and fast motion recognition, and can accurately track location and motion. This can provide an environment where you can immerse yourself in a virtual three-dimensional space, and virtual reality contents such as disaster simulators and high-risk equipment learning spaces are developing using these characteristics. These advantages are also applicable in the field of basic science education. In particular, expanding the concepts of electric and magnetic fields in physics described by existing two-dimensional data into three-dimensional spaces and visualizing them in real time can greatly help improve learning understanding. In this paper, realistic physical education environments and contents based on three-dimensional virtual reality are developed and the developed learning contents are experienced by actual learning subjects to prove their effectiveness. A total of 46 middle school and college students were taught and experienced in real time the electric and magnetic fields expressed in three dimensions in a virtual reality environment. As a result of the survey, more than 85% of positive responses were obtained, and positive results were obtained that three-dimensional virtual space-based physical learning could be effectively applied.

• Journal of Information Technology Services
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• v.22 no.5
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• pp.109-124
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• 2023

This study aims to apply the concept of regulatory stringency to the regulatory sandbox with a fresh perspective. The regulatory sandbox is a system that gives opportunities under certain conditions to new technologies or businesses that have not been launched due to inadequacy or insufficiency in legal systems. Previous research on regulatory sandboxes has mainly focused on discussions about their impact on specific technologies or business domains. This study attention to the results according to the evaluations. Among them, whether special cases for demonstration can evolve into official permission has garnered significant attention. For this study, among the cases that passed the regulatory sandbox evaluation from February, 2019, to December, 2022, 162 cases in the field of ICT convergence were selected. The evaluation results were classified into three groups 'positive interpretation (Fast Track)', 'temporary permission', and 'special case for demonstration.' Each case was assigned to one of the three groups. Through the comparative analysis, the common characteristics and differences were summarized. Then, this study explored improvement measures to pass a less restrictive regulatory sandbox. The analysis of the cases revealed that the differences in each evaluation result were attributed to variations in the technological characteristics and user protection features. Considering these differences, as well as the higher weight and importance of the preparation stage for sandbox application, this study suggested a three-step approach to prepare for temporary permission and positive interpretation rather than special case for demonstration. In addition, this thesis discussed the policy limitations of the regulatory sandbox mechanism in South Korea and the limitations of the current study. Hopefully, the results of this study would be beneficial to individuals and companies, particularly venture companies and startups seeking to develop new technologies or businesses and utilize regulatory sandboxes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.177-184
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• 2024

In the situation of increasing data at fast speed, we use all kinds of complex ensemble and deep learning algorithms to get the highest accuracy. It's sometimes questionable how these models predict, classify, recognize, and track unknown data. Accomplishing this technique and more has been and would be the goal of intensive research and development in the data science community. A variety of reasons, such as lack of data, imbalanced data, biased data can impact the decision rendered by the learning models. Many models are gaining traction for such interpretations. Now, LIME and SHAP are commonly used, in which are two state of the art open source explainable techniques. However, their outputs represent some different results. In this context, this study introduces a coupling technique of LIME and Shap, and demonstrates analysis possibilities on the decisions made by LightGBM and Keras models in classifying a transaction for fraudulence on the IEEE CIS dataset.