• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.617-635
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• 2020

The purpose of this study is to discuss how to improve the maneuverability of lifting and diving for underwater vehicle's vertical motion. Therefore, to solve these problems, applied the 3-D numerical simulation, Taguchi's Design of Experiment (DOE), and intelligent parameter design methods, etc. We planned four steps as follows: firstly, we applied the 2-D flow simulation with NACA series, and then through the Taguchi's dynamic method to analyze the sensitivity (β). Secondly, take the data of pitching torque and total resistance from the Taguchi orthogonal array (L9), the ignal-to-noise ratio (SNR), and analysis each factorial contribution by ANOVA. Thirdly, used Radial Basis Function Network (RBFN) method to train the non-linear meta-modeling and found out the best factorial combination by Particle Swarm Optimization (PSO) and Weighted Percentage Reduction of Quality Loss (WPRQL). Finally, the application of the above methods gives the global optimum for multi-quality characteristics and the robust design configuration, including L/D is 9.4:1, the foreplane on the hull (Bow-2), and position of the sail is 0.25 Ls from the bow. The result shows that the total quality is improved by 86.03% in comparison with the original design.

• Annual Conference of KIPS
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• 2020.11a
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• pp.80-82
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• 2020

전통적으로 기초 과학 분야의 대규모 워크로드 작업들은 슈퍼컴퓨터와 같은 대용량 클러스터 시스템을 이용하여 수행해왔다. 그러나 최근 빅데이터 및 머신 러닝과 같은 새로운 분야에서의 컴퓨팅 자원 요구가 증가하고 기존 사용자의 요구 사항도 다양해짐에 따라 기존의 클러스터 시스템 운영 환경에서는 많은 어려움이 나타나고 있다. 이러한 문제를 해결하기 위해 한국과학기술정보연구원(KISTI)에서는 지난 3 월부터 KI (KISTI Intelligent) Cloud 서비스를 개발하여 서비스를 제공하고 있다. KI Cloud 서비스는 다음과 같은 특징이 있다. 첫째, Jupyter 과 RStudio 와 같은 대화형 개발 환경을 웹을 통해 제공함으로써 사용자는 언제, 어디서나 손쉽게 서비스를 활용할 수 있다. 둘째, 컨테이너 기술을 활용하여 사용자가 요구하는 개발 및 실행 환경을 실시간으로 구성하여 제공한다. 셋째, 사용자의 서비스 환경을 동적으로 구성하여 제공함으로써 컴퓨팅 자원의 효율성을 높일 수 있다.

• Annual Conference of KIPS
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• 2020.11a
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• pp.20-23
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• 2020

This research is to design an effective prefetching method required for DRAM-PCM hybrid main memory systems especially used for big data applications and massive-scale computing environment. Conventional prefetchers perform well with regular memory access patterns. However, workloads such as graph processing show extremely irregular memory access characteristics and thus could not be prefetched accurately. Therefore, this research proposes an efficient dynamical prefetching algorithm based on the regression method. We have designed an intelligent prefetch engine that can identify the characteristics of the memory access sequences. It can perform regular, linear regression or polynomial regression predictive analysis based on the memory access sequences' characteristics, and dynamically determine the number of pages required for prefetching. Besides, we also present a DRAM-PCM hybrid memory structure, which can reduce the energy cost and solve the conventional DRAM memory system's thermal problem. Experiment result shows that the performance has increased by 40%, compared with the conventional DRAM memory structure.

• Structural Engineering and Mechanics
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• v.90 no.4
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• pp.417-428
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• 2024

Nanocomposite-reinforced concrete systems have gained increasing attention in bridge construction due to their enhanced mechanical properties and durability. Understanding the transient dynamics of these advanced materials is crucial for ensuring the structural integrity and performance of bridge infrastructure under dynamic loading conditions. This paper presents a comprehensive study of the measurement techniques employed for assessing the transient dynamics of nanocompositereinforced concrete systems in bridge construction applications. A numerical method, including modal analysis are discussed in detail, highlighting their advantages, limitations, and applications. Additionally, recent advancements in sensor technologies, data acquisition systems, and signal processing techniques for capturing and analyzing transient responses are explored. The paper also addresses challenges and opportunities in the measurement of transient dynamics, such as the characterization of nanocomposite-reinforced concrete materials, the development of accurate numerical models, and the integration of advanced sensing technologies into bridge monitoring systems. Through a critical review of existing literature and case studies, this paper aims to provide insights into best practices and future directions for the measurement of transient dynamics in nanocompositereinforced concrete systems, ultimately contributing to the design, construction, and maintenance of resilient and sustainable bridge infrastructure.

• Advances in nano research
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• v.17 no.3
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• pp.249-255
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• 2024

The paper discusses the potential of nanomaterials in revolutionizing basketball equipment by applying them to advance padding and shock absorption technologies in order to bring more control and comfort to the players. Nanotechnology devised new solutions for the challenges that the players are exposed to by dealing with issues such as better control reducing shock to the hands and wrists during those most decisive periods of every game: dribbling, passing, and catching. This work embeds nanomaterials in basketballs to understand their efficacy in reducing the amount of force transmitted to players, thereby reducing the risk of injuries and fatigue. The research gives an in-depth look into the structural properties and performance benefits of nanomaterial-enhanced padding in balls for optimized comfort and control to players and improvement in the dynamics of gameplay. The future of nanotechnology in the design of basketball equipment finds further bases in an in-depth analysis and is experimentally validated with respect to the prospects of a ball that is safer, long-lasting, and with improved performance.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.3298-3306
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• 2024

Our research seeks to assess the influence of nuclear energy technology on carbon emissions in the top 10 European economies comprising the topmost nuclear energy R&D budget (France, Germany, Russia, the Netherlands, the UK, Finland, Spain, Sweden, Italy, and Switzerland). Unlike prior investigations predominantly relying on panel data methodologies without considering the distinctive characteristics of each economy, our study employs the advanced 'Quantile-on-Quantile' approach. This novel methodology enables us to investigate the interactions between variables within each unique nation, thereby improving the precision of our analysis. As a result, the study provides a thorough global perspective, revealing nuanced findings pertinent to each economy's specific attributes. Our outcomes demonSstrate a positive interconnection between nuclear energy technology and carbon emissions across various quantiles in our analyzed nations. Additionally, the study highlights diverse patterns in these associations within individual economies. These findings emphasize the significance of policymakers performing comprehensive measurements and devising effective strategies to monitor fluctuations in nuclear energy technology and carbon emissions.

• Convergence Security Journal
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• v.21 no.4
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• pp.23-30
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• 2021

As the 4^th Industrial revolution technology develops, it is expected that future technologies will be used in the military. The Army is developing the Army TIGER 4.0 system, which means innovative changes in mobile, networked, and intelligent ground forces. In order to utilize future technologies, it is necessary to be able to transmit and receive large amounts of data between weapon systems, but there are limitations to supporting this through TICN and ANASIS. Therefore, in this paper, the information exchange requirements generated by the Army TIGER 4.0 battalion and the amount of traffic by communication layer are analyzed based on the battalion defense operation scenario to suggest information distribution capability of the future tactical communication system.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.59-77
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• 2012

This study focuses on predicting which firms will increase capital by issuing new stocks in the near future. Many stakeholders, including banks, credit rating agencies and investors, performs a variety of analyses for firms' growth, profitability, stability, activity, productivity, etc., and regularly report the firms' financial analysis indices. In the paper, we develop predictive models for rights issues using these financial analysis indices and data mining techniques. This study approaches to building the predictive models from the perspective of two different analyses. The first is the analysis period. We divide the analysis period into before and after the IMF financial crisis, and examine whether there is the difference between the two periods. The second is the prediction time. In order to predict when firms increase capital by issuing new stocks, the prediction time is categorized as one year, two years and three years later. Therefore Total six prediction models are developed and analyzed. In this paper, we employ the decision tree technique to build the prediction models for rights issues. The decision tree is the most widely used prediction method which builds decision trees to label or categorize cases into a set of known classes. In contrast to neural networks, logistic regression and SVM, decision tree techniques are well suited for high-dimensional applications and have strong explanation capabilities. There are well-known decision tree induction algorithms such as CHAID, CART, QUEST, C5.0, etc. Among them, we use C5.0 algorithm which is the most recently developed algorithm and yields performance better than other algorithms. We obtained data for the rights issue and financial analysis from TS2000 of Korea Listed Companies Association. A record of financial analysis data is consisted of 89 variables which include 9 growth indices, 30 profitability indices, 23 stability indices, 6 activity indices and 8 productivity indices. For the model building and test, we used 10,925 financial analysis data of total 658 listed firms. PASW Modeler 13 was used to build C5.0 decision trees for the six prediction models. Total 84 variables among financial analysis data are selected as the input variables of each model, and the rights issue status (issued or not issued) is defined as the output variable. To develop prediction models using C5.0 node (Node Options: Output type = Rule set, Use boosting = false, Cross-validate = false, Mode = Simple, Favor = Generality), we used 60% of data for model building and 40% of data for model test. The results of experimental analysis show that the prediction accuracies of data after the IMF financial crisis (59.04% to 60.43%) are about 10 percent higher than ones before IMF financial crisis (68.78% to 71.41%). These results indicate that since the IMF financial crisis, the reliability of financial analysis indices has increased and the firm intention of rights issue has been more obvious. The experiment results also show that the stability-related indices have a major impact on conducting rights issue in the case of short-term prediction. On the other hand, the long-term prediction of conducting rights issue is affected by financial analysis indices on profitability, stability, activity and productivity. All the prediction models include the industry code as one of significant variables. This means that companies in different types of industries show their different types of patterns for rights issue. We conclude that it is desirable for stakeholders to take into account stability-related indices and more various financial analysis indices for short-term prediction and long-term prediction, respectively. The current study has several limitations. First, we need to compare the differences in accuracy by using different data mining techniques such as neural networks, logistic regression and SVM. Second, we are required to develop and to evaluate new prediction models including variables which research in the theory of capital structure has mentioned about the relevance to rights issue.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.125-140
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• 2018

As one of the processes in the manufacturing industry, quality inspection inspects the intermediate products or final products to separate the good-quality goods that meet the quality management standard and the defective goods that do not. The manual inspection of quality in a mass production system may result in low consistency and efficiency. Therefore, the quality inspection of mass-produced products involves automatic checking and classifying by the machines in many processes. Although there are many preceding studies on improving or optimizing the process using the data generated in the production process, there have been many constraints with regard to actual implementation due to the technical limitations of processing a large volume of data in real time. The recent research studies on big data have improved the data processing technology and enabled collecting, processing, and analyzing process data in real time. This paper aims to propose the process and details of applying big data for quality inspection and examine the applicability of the proposed method to the dairy industry. We review the previous studies and propose a big data analysis procedure that is applicable to the manufacturing sector. To assess the feasibility of the proposed method, we applied two methods to one of the quality inspection processes in the dairy industry: convolutional neural network and random forest. We collected, processed, and analyzed the images of caps and straws in real time, and then determined whether the products were defective or not. The result confirmed that there was a drastic increase in classification accuracy compared to the quality inspection performed in the past.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.203-212
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• 2019

Along with the recent fourth industrial revolution, the world's manufacturing powerhouses are pushing for national strategies to revive the sluggish manufacturing industry. Moon Jae-in, the government is in accordance with the trend, called 'advancement of science and technology is leading the fourth round of the Industrial Revolution' strategy. Intelligent information technology such as IoT, Cloud, Big Data, Mobile, and AI, which are key technologies that lead the fourth industrial revolution, is promoting the emergence of new industries such as robots and 3D printing and the smarting of existing major manufacturing industries. Advances in technologies such as smart factories have enabled IoT-based sensing technology to measure various data that could not be collected before, and data generated by each process has also exploded. Thus, this paper uses data generators to generate virtual data that can occur in smart factories, and uses them to analyze the cause of the defect in real time and to predict the replacement cycle of the facility.