• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.876-877
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• 2023

3D 기술과 인공지능을 활용한 가상 옷장 앱 BBoM은 패스트패션의 환경 오염을 줄이고 지속 가능한 소비문화를 조성한다. 깊이 추정(depth estimation) 기술을 활용하여 2D 이미지를 3D 모델로 변환하였다. 이러한 기능은 소비자들의 소비 패턴에 도움을 줄 뿐만 아니라 1년 후에 연간탄소 배출량을 37% 감소시킬 수 있다.

• Smart Media Journal
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• v.13 no.2
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• pp.136-144
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• 2024

In recent years, with the rapid development of IT technology and the aging of the population, various solutions to the labor shortage have emerged. In the livestock industry, there are an increasing number of management systems that utilize artificial intelligence technology. The Metaverse Smart Farm is a system that combines the digital virtual world with advanced agricultural technology. With this system, farmers can monitor the health of their animals in real time without having to visit the barns, and analyze the data collected through sensors and cameras for more efficient agricultural management. In addition, the barn environment can be adjusted through a remote control function, which is expected to reduce labor and revitalize the livestock industry.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.991-998
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• 2003

There are many methods to enhance the fault tolerance of the CORBA based real time systems by viewpoints. Among them, this paper provides a method to enable seamless services where the systems based on the CORBA have object's faults originated processing real time information. Namely, this paper observes a method to deal efficiently with object's faults happening in 3 tier architecture environments. It is possible to replicate objects as a way to enhance the fault tolerance considering object's faults. Along with it, this paper shows a method to enhance the fault tolerance ultimately and then keep the service continuity by prividing a way to allow to continue to run the systems until the FT-CORBA based one's faults are recovered.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.154-154
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• 2023

수질모니터링은 수자원 보존과 공중 보건에 있어 매우 중요하다. 기후변화로 인한 이상강우와 산업화 등의 이유로 비점오염물질 및 오염원 배출량이 증가하여 하천과 호소에 영양염류가 증가하게 된다. 영얌염류의 증가로 하천에 부영양화 상태가 지속된다면 녹조발생 등으로 인해 생태계에 부정적 영향을 초래하게 된다. 또한 부영양화는 원수의 유기물량 증가로 인해 처리비용 증가, 이취미 문제 등 인간에게도 직접적인 문제를 유발한다. 특히 우리나라의 경우 하천 취수율이 높은 국가이며, 낙동강 중상류 지역에는 산업시설이 과도하게 밀집되어 있어 하천에 오염물질 유입이 되어 부영양화가 된다면 심각한 문제를 유발하게 된다. TN은 부영양화의 중요한 지표다. 우리나라의 TN 측정은 시료 채수 후 실험실에서 수질오염공정 시험기준에 따라 진행이 된다. 실험실 분석은 TN 농도를 분석하는 일반적인 방법이며, 정확한 검출 및 정량화를 목표로 한다. 하지만 이러한 방식은 정교한 장비를 갖춘 전문 실험실 및 전문 인력을 필요로 한다. 환경부에서 주요 하천에 수질측정망을 설치하여 수질현황에 대한 종합적인 조사를 통해 수질변화 추세를 파악하는 것이 가능하지만, 실시간 TN 농도를 감지하는데 매우 제한적이다. 현재 조사방식은 TN 농도 증가로 인한 문제에 대해 초기대응을 하기에는 한계가 있다. 최근 센서의 발전으로 다양한 항목을 신속하고 지속적으로 모니터링 할 수 있게 되었다. TN에 대한 직접적인 센서 모니터링은 불가능 하지만 여러 측정 항목이 TN과 상관관계가 있는 것이 여러 연구에서 입증되었다. 이러한 결과를 바탕으로 본 연구에서는 오염도가 높은 낙동강을 대상으로 TN 예측에 대한 기초 연구를 진행하였다. 과거 측정된 자료를 활용하여 센서로 측정 가능한 항목을 통해 TN 예측을 진행하며, 실제 활용을 위해 회귀식을 도출하고자 한다. 최근 환경부에서 실시간 수질 현황 및 오염도를 파악하기 위해 자동측정망 지점을 늘리는 추세인데, 본 연구의 결과를 활용한다면 실시간 TN 예측에 대한 기초자료 활용될 수 있을 것으로 판단된다.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.27-38
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• 2023

How the port, shipping, shipbuilding, and vender industries in Korea can have a seamless value chain through their digitally smooth cooperation by applying the latest information and communications technology through the "4th industrial revolution" was examined. Also considered was the proposition that the value chain should be a smart, seamless value chain among industries with successful hyperconnection. Their cooperative relationships were defined, and the crucial elements for the sustainable development of these industries were considered. As a result, the direction for achieving environmental, social, and governance management by realizing decarbonization through today's digitalization could be studied. In particular, the importance of digitization as a way to respond to the future market from the perspective of small and medium-sized enterprises and the role of digitization realized by small and medium-sized equipment companies in the overall industry were examined. The results simultaneously show the state of linkage between industries and the reason why the value chain must maintain a smooth relationship. In addition, using the lessons learned from recent failure cases from the Korean shipbuilding industry as a cornerstone, the direction for creating a strategic pathway for intelligent connection was investigated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.373-378
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• 2017

While on-line shopping markets are growing, consumer's procurement processes are being confused regardless of on or off line market and, smart consumers who want intelligent tailored services have emerged. Depending on the changeable pattern of consumer, most of related companies provide various Omni channel and O2O service. However, reactions of the fashion companies are tend to be late. Recently, the IoT environment has changed to standards-based open platform and it requires a variety of intelligent services depending on the type of environment and objects. This thesis proposes fashion O2O system using smart fitting display that is adaptable to fashion companies. This proposed system provides fitting information which is performed on off-line by users after constructing the database, it also support the works as on-line status, thus, it makes users' procurements to maintain continuously. For the more, customer oriented intelligent fitting service would be expected by the information connection with the shop and delivery systems.

• KIISE Transactions on Computing Practices
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• v.21 no.2
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• pp.154-159
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• 2015

Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users' POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the user's activity label extracted from the log data is then used to learn the patterns of the user's daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the user's time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.403-409
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• 2024

In this paper, the guidelines for the design of an Artificial Intelligence(AI) based Integrated Process Safety Management(PSM) system to enhance workplace safety using data from process safety reports submitted by hazardous and risky facility operators in accordance with the Occupational Safety and Health Act is proposed. The system composed of the proposed guidelines is to be implemented separately by individual facility operators and specialized process safety management agencies for single or multiple workplaces. It is structured with key components and stages, including data collection and preprocessing, expansion and segmentation, labeling, and the construction of training datasets. It enables the collection of process operation data and change approval data from various processes, allowing potential fault prediction and maintenance planning through the analysis of all data generated in workplace operations, thereby supporting decision-making during process operation. Moreover, it offers utility and effectiveness in time and cost savings, detection and prediction of various risk factors, including human errors, and continuous model improvement through the use of accurate and reliable training data and specialized datasets. Through this approach, it becomes possible to enhance workplace safety and prevent accidents.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.209-223
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• 2012

The purpose of this study is to examine the moderating effects of emotional intelligence in the relationship between transformational leadership and organizational commitment. To achieve the this research purpose, theoretical and empirical studies related to transformational leadership, organizational commitment, and emotional intelligence were carried out simultaneously. The established hypotheses related to transformational leadership, organizational commitment, and emotional intelligence were verified by the hierarchical regression analysis using SPSS. The result of this research are as followers : First, the hierarchical regression analysis revealed that moderation term was significant(${\beta}$=0.146, p<.01). The interaction term for charisma and emotional intelligence had a significant and positive relationship with organizational commitment. Second, the hierarchical regression analysis revealed that moderation term was insignificant(${\beta}$=2.295, p<.05) The interaction term for inspirational motivation and emotional intelligence had a significant and positive relationship with organizational commitment. Third, the hierarchical regression analysis revealed that moderation term was significant(${\beta}$=0.200, p<.001). The interaction term for intellectual stimulation and emotional intelligence had a significant and positive relationship with organizational commitment. Fourth, the hierarchical regression analysis revealed that moderation term was significant(${\beta}$=2.213, p<.01).