• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.74-81
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• 2023

Based on the hypothesized model and the data obtained from the questionnaire, this paper analyzed the data using the statistical analysis software SPSS 25.0 and AMOS 20.0 to empirically prove the relationship between the variables of transformational leadership, organizational support, organizational justice, and employee engagement. It was found that: First, transformational leadership has a significant positive effect on employee commitment; Second, transformational leadership has a significant positive effect on perceived organizational support. Third, perceived organizational support has a significant effect on employee commitment; Fourth, in the relationship between transformational leadership and employee commitment, the mediating effect of perceived organizational support on them holds; At last, organizational justice plays a moderating role in the relationship between transformational leadership and the perceived organizational support. This study enriches and integrates the theoretical systems and research categories of employee engagement, organizational justice, transformational leadership, and perceived organizational support.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.255-256
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• 2021

The performance measurement of construction projects has traditionally been evaluated as a prerequisite for successful project completion. Considering this importance, the UK and the US are operating quantitative performance measurement systems for construction projects. However, in the case of Korea, there is a limit to the use of existing methods due to the limitation of data collection. Recently, in consideration of the domestic situation, research is being conducted to measure the quantitative performance of a project by using big data including progress and project attribute information. Therefore, this study aims to present the design direction of a performance monitoring system using Quality Function Deployment.

• Electronics and Telecommunications Trends
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• v.38 no.4
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• pp.36-46
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• 2023

This paper presents concepts, policies, industry trends, and related issues in urban air mobility (UAM). UAM will contribute to transportation by mitigating traffic congestion and environmental problems in the future. Accordingly, governments of major countries are promoting UAM policies and demonstration projects as well as preparing laws and certification standards. In UAM, overseas startups lead airframe developments, and major companies from the aircraft, automotive, and information technology industries are also participating. In addition, startups and major companies are building the corresponding infrastructure. For the development of UAM, issues related to technology, regulation systems, and infrastructure still need to be resolved.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.710-711
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• 2015

A nuclear power plant construction is a complex form of construction which comprises various stakeholders and contractors. Therefore, contract disputes will occur due to conflicting interests of contracting parties and unpredictable factors which arise during construction work. Even if the contract is well prepared, it cannot fully prepare for future situations in actuality. Claims management is very important in carrying out construction management. This study intends to define claim, and delve into development of claims management processes from the viewpoint of owners and contractor through consideration on international contract terms on claims management and the details of the claims management of the Construction Extension to the PMBOK. In addition, it is needed to accumulate and manage data on claims that have occurred so that they can be referenced in the future. As information should be accumulated so that type classification can be carried out and that lessons can be learned on claims that have occurred in each business site, study on establishing data-based systems relating to claims processes will be needed in the future.

• ETRI Journal
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• v.45 no.6
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• pp.1007-1021
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• 2023

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.107-120
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• 2023

In this study, we investigate the changes and realities of the content production process focusing on Image generation AI revolutions such as Stable Diffusion, Midjourney, and DELL-E, and examine the current status of related department operations at universities and Find out the status of the current curriculum. Through this, we suggest the need to produce AI-adaptive content talent through re-establishing the capabilities of content-related departments in art universities and quickly introducing curriculum. This is because it can be input into the efficient AI content development system currently being applied in industrial fields, and it is necessary to cultivate talent who can perform managerial and technical roles using various AI systems in the future. In conclusion, we will prepare cornerstone research to establish the university's status as a source of talent that can lead the content industry beyond the AI content production era, and focus on convergence capabilities and experience with the goal of producing convergence talent to cultivate AI adaptive content talent, suggests the direction of curriculum application for value creation.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.1
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• pp.25-34
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• 2024

The Satellite Based Augmentation System (SBAS) improves the accuracy and reliability of user positioning by transmitting the error correction and integrity information of the global navigation satellite system signal from geostationary satellites in real time. For this reason, SBAS was designed for aircraft operations and approach procedures and is now in operational or development stages in many countries. Time has passed since the construction of SBAS and many changes have occurred in the composition of the monitoring stations and the geostationary satellites. These changes have been investigated and the current operation and development status of SBAS globally are surveyed. The development and test schedules for the transition to dual frequency multi-constellation, an important topic in SBAS, are discussed.

• Annual Conference of KIPS
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• 2024.10a
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• pp.744-747
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• 2024

고령화로 인해 실명을 유발하는 안질환의 발병률이 지속적으로 증가하고 있다. 이에 본 연구는 딥러닝 기반의 안저사진 분석을 통해 다중 안질환 분류 모델의 적용성을 향상시키고자 한다. Ocular Disease Intelligent Recognition (ODIR) dataset과 같은 다양한 공용 데이터셋에 순환학습과 regularization 기법을 적용하여 녹내장, 백내장, 황반변성 등의 질환을 효과적으로 분류를 돕는 기법을 제안한다. 이를 통해 안질환 진단의 정확성을 높이고, 임상에서 활용가능한 신뢰성 있는 안질환 진단 모델을 구축하고자 한다.

• Annual Conference of KIPS
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• 2024.10a
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• pp.44-45
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• 2024

최근 이기종 메모리 및 캐시 구조가 컴퓨터 시스템에서 자주 활용되고 있다. 그러나 이기종 환경에서 기존의 균일한 캐시 관리 방식을 사용하여 성능을 향상하는 데에는 한계가 있으며, 이기종 시스템의 캐시 구조를 고려한 최적화 방법이 필요하다. 본 논문에서는 이기종 시스템에서의 캐시 최적화 방법 동향에 대해 살펴본다. 특히, 데이터 배치 최적화, 동적 캐시 할당, 그리고 에너지 효율적인 메모리 아키텍처에 초점을 맞춘 최신 연구들을 분석한다.

• Annual Conference of KIPS
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• 2024.10a
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• pp.461-462
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• 2024

In pediatric healthcare, early detection of cardiovascular diseases in newborns is crucial. Analyzing heart sounds using stethoscopes can be subjective and reliant on physician expertise, potentially leading to delayed diagnosis. There is a need for a simple method that can help even inexperienced doctors detect heart abnormalities without an electrocardiogram or ultrasound. Automated heart sound diagnosis systems can aid clinicians by providing accurate and early detection of abnormal heartbeats. To address this, we developed an intelligent deep-learning model incorporating CNN and LSTM to detect heart abnormalities based on artificial intelligence using heart sound data from stethoscope recordings. Our research achieved a high accuracy rate of 97.8%. Using audio data to introduce advanced models for cardiac abnormalities in children is essential for enhancing early detection and intervention in pediatric cardiovascular healthcare.