• Proceedings of the Korean Information Science Society Conference
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• 2011.06d
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• pp.115-117
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• 2011

Intrusion detection systems (IDS) are supposed to be an efficient safety measure against inside attacks. In purely distributed IDS approach, IDS agent is installed in every node. It checks abnormal behavior of neighboring nodes locally. It collects the data that it receives from nodes in its radio range. Sensor nodes audit that data and generate alerts for abnormal activity. Here, there are two ways of taking decision. First, it can take decision individually and second, it can communicate with its neighbor to find the status of the claimed compromised nodes. In this paper, we propose a collaborative decision making scheme for purely distributed detection system. The proposed scheme is light weight compared to consensus based validation methodology. It provides a better scheme to find intrusions by interacting with other nodes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.440-443
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• 2005

The wide spread of internet business recently necessitates recommendation systems which can recommend the most suitable product fur customer demands. Currently the recommendation systems use content-based filtering and/or collaborative filtering methods, which are unable both to explain the reason for the recommendation and to reflect constantly changing requirements of the users. These methods guarantee good efficiency only if there is a lot of information about users. This paper proposes an algorithm called 'demand articulate & integration' which can perceive user's continuously varying intents and recommend proper contents. A method of knowledge classification which can be applicable to this algorithm is also developed in order to disassemble knowledge into basic units and articulate indices. The algorithm provides recommendation outputs that are close to expert's opinion through the tracing of articulate index. As a case study, a knowledge base for heritage information is constructed with the expert guide's knowledge. An intelligent recommendation system that can guide heritage tour as good as the expert guider is developed.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.219-220
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• 2008

• Journal of Engineering Education Research
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• v.23 no.3
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• pp.49-58
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• 2020

In the era of the fourth industrial revolution, the world is undergoing rapid social change. The purpose of this study is to predict the expected changes and necessary competencies and desired curriculum and teaching methods in the field of mechanical engineering in the near future. The research method was a Delphi study. It was conducted three times with 20 mechanical engineering experts. The results of the study are as follows: In the field of mechanical engineering, it will be increased the situational awareness by the use of measurement sensors, development of computer applications, flexibility and optimization by user's needs and mechanical equipment, and demand for robots equipped with AI. The mechanical engineer's career perspectives will be positive, but if it is stable, it will be a crisis. Therefore active response is needed. The competencies required in the field of mechanical engineering include collaborative skills, complex problem solving skills, self-directed learning skills, problem finding skills, creativity, communication skills, convergent thinking skills, and system engineering skills. The undergraduate curriculum to achieve above competencies includes four major dynamics, basic science, programming coding education, convergence education, data processing education, and cyber physical system education. Preferred mechanical engineering teaching methods include project-based learning, hands-on education, problem-based learning, team-based collaborative learning, experiment-based education, and software-assisted education. The mechanical engineering community and the government should be concerned about the education for mechanical engineers with the necessary competencies in the era of the 4th Industrial Revolution, which will make global competitiveness in the mechanical engineering fields.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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• pp.699-722
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• 2019

It is difficult to observe the potential risks of lifting or turn-over operations in the early stages before a real operation. Therefore, many dynamic simulations have been designed to predict the risks and to reduce the possibility of accidents. These simulations, however, have usually been performed for predetermined and fixed scenarios, so they do not reflect the real-time control of an operator that is one of the most important influential factors in an operation; additionally, lifting or turn-over operations should be a collaboration involving more than two operators. Therefore, this study presents an integrated method for a collaborative simulation that allows multiple workers to operate together in the virtual world. The proposed method is composed of four components. The first component is a dynamic analysis that is based on multibody-system dynamics. The second component is VR (virtual reality) for the generation of realistic views for the operators. The third component comprises the control devices and the scenario generator to handle the crane in the virtual environment. Lastly, the fourth component is the HLA (high-level architecture)-based integrated simulation interface for the convenient and efficient exchange of the data through the middleware. To show the applicability of the proposed method, it has been applied to a block turn-over simulation for which one floating crane and two crawler cranes were used, and an offshore module installation for which a DCR (dual-crane rig) was used. In conclusion, the execution of the proposed method of this study is successful regarding the above two applications for which multiple workers were involved.

• The Journal of the Convergence on Culture Technology
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• v.4 no.1
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• pp.187-192
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• 2018

Mind map is a very useful technique for brainstorming. Therefore, it can be effectively used for collaboration as a creative group conception. However, existing mind map software is not possible to collaborate because it is difficult to share mind map or discuss among members. In this study, we developed a mind map system that can support collaborative work as well as individual authoring and editing. This can incorporate individual mind maps of members into mind map of team, share and discuss them, and modify the mind map. This is convenient because it does not require a separate program installation, and verifies usability to support the process of collaborative process through usability evaluation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.56
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• pp.19-34
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• 2000

Impact of business process re-engineering requires the fundamental rethinking of how information systems are analyzed and designed. It is no longer sufficient to establish a monolithic system for fixed business environments. Information systems must be adaptive in nature. This demand is also applied in production domain. Enabling concept for the adaptive information system is reusability. This paper presents a new object-oriented analysis process for creating such reusable software components in production domain, especially for production planning and scheduling. Our process called MeCOMA is based on three meta-models: physical object meta-model, data object meta-model, and activity object meta-model. After the three meta-models are extended independently for a given production system, they are collaboratively integrated on the basis of integration pattern. The main advantages of MeCOMA are (1) to reduce software development time and (2) to consistently build reusable production software components.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.61-67
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• 2024

In recent years, artificial intelligence and deep learning technologies have made significant advances, and the BERT model has been recognized for its excellent contextual understanding in natural language processing based on the transformer architecture. This performance has the potential to take traditional recommendation systems to the next level. In this study, we adopt an approach that combines a collaborative filtering approach with a deep learning model to improve the performance of recommendation systems. Specifically, we implemented a system that uses BERT to analyze the sentiment of user reviews and embed users based on these review sentiments to find and recommend users with similar tastes. In the process, we also utilized Elasticsearch, an open-source search engine, for quick search and retrieval of recommended results. The approach of analyzing users' textual data to increase the accuracy and personalization of recommendations will play an important role in improving the user experience on various online services in the future.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.501-509
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• 2019

In this paper a body-centered cubic(BCC) crystal plasticity model based on microscopic dislocation mechanism is introduced and numerically implemented. The model is coupled with irradiation effect via tracking dislocation loop evolution on each slip system. On the basis of the model, uniaxial tensile tests of unirradiated and irradiated RPV steel(take Chinese A508-3 as an example) at different temperatures are simulated, and the simulation results agree well with the experimental results. Furthermore, crystal plasticity damage is introduced into the model. Then the damage behavior before and after irradiation is studied using the model. The results indicate that the model is an effective tool to study the effect of irradiation and temperature on the mechanical properties and damage behavior.