• Journal of the KSME
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• v.32 no.6
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• pp.496-503
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• 1992

상사실험법(analogous experimental method)이라 함은 물리적현상을 다른 물리적현상으로 변환 하여, 후자를 실험적으로 측정하여 전자의 제반 물리량을 얻는 과정을 말한다. 이 때 두 물리량 사이에는 수학적 상사관계, 특히 미분방정식 상의 유사관계가 성립함을 전제로 한다. 일반적으로 임의형상의 내부응력을 실험적으로 해석하는 데는 탄소성 변위를 직접 전기적저항으로 바꾸어 측정하는 방법(strain gauge method)이나 광파의 간섭무늬(fringe)로 가시화하는 광탄성 법(photoelastic method), 또는 전자계산기를 이용하여 분할요소해석의 연계집적으로서 얻는 유 한요소법(F.E.M) 등이 널리 사용되고 있으나, 이들은 다같이 그 나름대로의 장단점을 지니고 있다. 전기저항식은 변형을 직접 측정할 수 있어 측정의 오차를 줄일 수 있고, 특히 실물측정과 동하중 해석에는 큰 강점이 있으나, 점해석(point by analysis)이기 때문에 전시야적인 분포를 파악하기 어렵다. 또한 광탄성법은 명료한 전시야적 분포를 얻을 수 있지만 모형해석(model analysis)이기 때문에 정밀한 모형제작의 어려움이 수반되며, F.E.M.(B.E.M.도 포함)은 복잡한 형상에서의 요소분할이 매우 어렵고, 경계조건의 정확한 설정에 문제가 있다. 따라서 여러 실험적 방법은 실측대상에 따라 그 장단점을 감안하여 선택되어야 하며, 이 글에서 논술하고자 하는 상사실험법에 의한 응력해석도 이러한 관점에서 지금까지의 일련의 연구결과를 종합하여 그 효 용적인 용도, 응용 및 그 전망과 더불어 장차 해결하여야 할 2,3의 문제를 제시하고자 한다.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.17 no.1
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• pp.74-82
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• 1988

A numerical analysis of the mathematical model for fluidized bed coal combustion has been performed. Based on the physical nature of the specific surface variation due to the decreasing of coal particle diameter according to the combustion process, the modified model which has been added the specific surface variation to the S.ENDRENYI and B.PALANCZ's mathematical model was established in this study. From the numerical analysis of these two models, it was found that the perfect combustion time is increasing largely at least 5 seconds in the modified model in comparison with that of the S.ENDRENYI and B.PALANCZ's model, and the bed temperature and the coal particle surface temperature during the main combustion period represent constant with time in the S.ENDRENYI and B.PALANCZ's model, on the other hand, these properties are decreasing linearly with time in the modified model.

• Smart Media Journal
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• v.12 no.9
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• pp.9-18
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• 2023

Black box cryptography is a cryptographic scheme based on a hardware encryption device, operating under the assumption that the device and the user can be trusted. However, with the increasing use of cryptographic algorithms on unreliable open platforms, the threats to black box cryptography systems have become even more significant. As a consequence, white box cryptography have been proposed to securely operate cryptographic algorithms on open platforms by hiding encryption keys during the encryption process, making it difficult for attackers to extract the keys. However, unlike traditional cryptography, white box-based encryption lacks established specifications, making challenging verify its structural security. To promote the safer utilization of white box cryptography, CHES organizes The WhibOx Contest periodically, which conducts safety analyses of various white box cryptographic techniques. Among these, the Differential Computation Analysis (DCA) attack proposed by Bos in 2016 is widely utilized in safety analyses and represents a powerful attack technique against robust white box block ciphers. Therefore, this paper analyzes the research trends in white box block ciphers and provides a summary of DCA attacks and relevant countermeasures. adhering to the format of a research paper.

• Smart Media Journal
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• v.8 no.3
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• pp.95-103
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• 2019

The global manufacturing industry has reached the limit to growth due to a long-term recession, the rise of labor cost and raw material. As a solution to these difficulties, we promote the 4th Industry Revolution based on ICT and sensor technology. Following this trend, this paper proposes the design of a model using manufacturing data in the protection film process for smart manufacturing innovation. In the protective film process, the manufacturing data of temperature, pressure, humidity, and motion and thermal image are acquired by various sensors for the raw material blending, stirring, extrusion, and inspection processes. While the acquired manufacturing data is stored in mass storage, A.I. platform provides time-series image analysis and its visualization.

• Smart Media Journal
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• v.10 no.2
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• pp.110-118
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• 2021

The Internet of Things (IoT) technology, a key growth engine of the 4th industrial revolution, has grown to a stage where it can autonomously communicate with each other and process data according to space and circumstances. Accordingly, the IT infrastructure becomes increasingly complex and the importance of the monitoring field for maintaining the system stably is increasing. Monitoring technology has been used in the past, but there is a need to find a flexible monitoring system that can respond to the rapidly changing ICT technology. This paper conducts research on designing and testing an open source-based Prometheus monitoring system. We builds a simple infrastructure based on IoT devices and collects data about devices through the Exporter. Prometheus collects data based on pull and then integrates into one dashboard using Grafana and visualizes data to monitor device information.

• Smart Media Journal
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• v.10 no.1
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• pp.55-62
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• 2021

We applied machine learning of semi-supervised learning, transfer learning, and federated learning as examples of AI use cases that can be applied to the three major industries(Automobile industry, Energy industry, and AI/Healthcare industry) of Gwangju Metro-city, and established an ML strategy for AI services for the major industries. Based on the ML strategy of AI service, practical approaches are suggested, the semi-supervised learning approach is used for automobile image recognition technology, and the transfer learning approach is used for diabetic retinopathy detection in the healthcare field. Finally, the case of the federated learning approach is to be used to predict electricity demand. These approaches were tested based on hardware such as single board computer Raspberry Pi, Jaetson Nano, and Intel i-7, and the validity of practical approaches was verified.

• Smart Media Journal
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• v.10 no.1
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• pp.99-107
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• 2021

In addition to the 4th Industrial Revolution and Industry 4.0, the recent megatrends in the ICT field are Big-data, IoT, Cloud Computing, and Artificial Intelligence. Therefore, rapid digital transformation according to the convergence of various industrial areas and ICT fields is an ongoing trend that is due to the development of technology of AI services suitable for the era of the 4th industrial revolution and the development of subdivided technologies such as (Business Intelligence), IA (Intelligent Analytics, BI + AI), AIoT (Artificial Intelligence of Things), AIOPS (Artificial Intelligence for IT Operations), and RPA 2.0 (Robotic Process Automation + AI). This study aims to integrate and advance various machine learning services of infrastructure-side GPU, CDA (Connected Data Architecture) framework, and AI based on mass distributed Abyss storage in accordance with these technical situations. Also, we want to utilize AI business revenue model in various industries.

• Smart Media Journal
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• v.11 no.7
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• pp.61-69
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• 2022

In the learned machine learning, the performance of machine learning degrades at the same time as drift occurs in terms of learning models and learning data over time. As a solution to this problem, I would like to propose the concept and evaluation method of ML drift to determine the re-learning period of machine learning. An XAI test and an XAI test of an apple image were performed according to strawberry and clarity. In the case of strawberries, the change in the XAI analysis of ML models according to the clarity value was insignificant, and in the case of XAI of apple image, apples normally classified objects and heat map areas, but in the case of apple flowers and buds, the results were insignificant compared to strawberries and apples. This is expected to be caused by the lack of learning images of apple flowers and buds, and more apple flowers and buds will be studied and tested in the future.

• Journal of the Korean earth science society
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• v.39 no.6
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• pp.600-616
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• 2018

The purpose of this study was to explore how primary school students develop their interest in science. A survey questionnaire was used to investigate students' interest, change of their interest, and engagement in science related activities three times a year. 201 students of two primary schools in Seoul Metropolitan City initially participated in this study. A follow-up case study was conducted with students who showed an increased interest in science. Finally, seven students were chosen in the case study. They were asked to keep a photo journal for 12 weeks, and were interviewed in every other week by one of the researchers. Among these seven participants, two (TK and QQ) were chosen for analyzing their data in this case study because they showed positive changes in developing science interest throughout the study. The results of two participants' survey, photo-journal and interview were analyzed qualitatively. First, TK, whose science interest developed from situational interest II to individual interest I, engaged in doing experiments at home, doing mathematics activities, raising pets or plants, observing phenomena, and visiting informal educational centers. He tended to participate in hands-on activities by himself in out-of-school settings. Second, QQ who developed from situational interest I to situational interest II, engaged in taking pictures as a representative activity at home and school. He tended to participate in activities with either his father or one of the researchers. Both students showed personal characteristics such as doing place-based activities, interaction with others and activity subjectivity. The goal of TK's interactions with others on the various places was to develop in cognitive domain. On the contrary, QQ's goal of interactions with others was to develop in emotional communication. This study reported the cases of characteristics of students who developed their interests in science including activities in- and out-of-school settings and their accompanying people.

• Smart Media Journal
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• v.9 no.3
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• pp.31-40
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• 2020

Currently, black-box-based machine learning algorithms are used to analyze big data in manufacturing. This algorithm has the advantage of having high analytical consistency, but has the disadvantage that it is difficult to interpret the analysis results. However, in the manufacturing industry, it is important to verify the basis of the results and the validity of deriving the analysis algorithms through analysis based on the manufacturing process principle. To overcome the limitation of explanatory power as a result of this machine learning algorithm, we propose a manufacturing big data analysis method using genetic programming. This algorithm is one of well-known evolutionary algorithms, which repeats evolutionary operators such as selection, crossover, mutation that mimic biological evolution to find the optimal solution. Then, the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. Through this, input and output variable relations are derived to formulate the results, so it is possible to interpret the intuitive manufacturing mechanism, and it is also possible to derive manufacturing principles that cannot be interpreted based on the relationship between variables represented by formulas. The proposed technique showed equal or superior performance as a result of comparing and analyzing performance with a typical machine learning algorithm. In the future, the possibility of using various manufacturing fields was verified through the technique.