• Journal of Digital Convergence
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• v.17 no.3
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• pp.221-226
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• 2019

The characteristic of the beta wave among the EEG waves corresponds to the stress area of human perception. The over-bandwidth of the stress is extracted by analyzing the beta-wave correlation between the low-bandwidth and high-bandwidth. We present a KMeans clustering analysis model for unsupervised machine learning to construct an analytical model for analyzing and extracting the beta-wave correlation. The proposed model classifies the beta wave region into clusters of similar regions and identifies anomalous waveforms in the corresponding clustering category. The abnormal group of waveform clusters and the normal category leaving region are discriminated from the stress risk group. Using this model, it is possible to discriminate the degree of stress of the cognitive state through the EEG waveform, and it is possible to manage and apply the cognitive state of the individual.

• Journal of the Korean Geotechnical Society
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• v.39 no.4
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• pp.5-17
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• 2023

The advancement in large-scale underground excavation in urban areas necessitates monitoring and predicting technologies that can pre-emptively mitigate risk factors at construction sites. Traditionally, two methods predict the deformation of retaining walls induced by excavation: empirical and numerical analysis. Recent progress in artificial intelligence technology has led to the development of a predictive model using machine learning techniques. This study developed a model for predicting the deformation of a retaining wall under construction using a boosting-based algorithm and an ensemble model with outstanding predictive power and efficiency. A database was established using the data from the design-construction-maintenance process of the underground retaining wall project in a manifold manner. Based on these data, a learning model was created, and the performance was evaluated. The boosting and ensemble models demonstrated that wall deformation could be accurately predicted. In addition, it was confirmed that prediction results with the characteristics of the actual construction process can be presented using data collected from ground measurements. The predictive model developed in this study is expected to be used to evaluate and monitor the stability of retaining walls under construction.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.48-60
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• 2019

After the Paris Agreement in 2015, the government has been promoting various policies such as 'Hydrogen-Economy Roadmap(2019)' to supply hydrogen. As part of this, the government announced the goal of building 310 hydrogen refueling stations(HRS) until 2022. To this end, special case standard for the introduction of complex, packaged, and mobile hydrogen refueling stations(MHRS) have been enacted and promulgated. The MHRS has the advantage of being able to supply hydrogen to multiple regions. However, due to the movement and close distance between facilities, it is necessary to secure proper installation standards and operational safety through safety analysis. In this study, the possibility of introduction was investigated by designing a standard model and quantitative risk assessment(QRA). As a result of QRA, personal and social risk were acceptable, and the empirical test direction and implications were derived.

• Proceedings of the Safety Management and Science Conference
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• 2013.11a
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• pp.615-623
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• 2013

오늘날 기술의 발전으로 시스템들은 점차 대형화 복잡화 되어가고 있다. 이처럼 점차 대형화 복잡화 되어가고 있는 시스템들은 더욱 커진 사고 및 고장에 대한 위험을 내재하게 된다. 또한 대형 복합 시스템에서 발생하는 사고 및 고장은 바로 큰 재산피해나 인명피해와 직결 될 수 있다. 따라서 체계적인 안전관리의 필요성이 점차 커지고 있다. 이에 대응하여 철도, 항공, 해양 등의 산업에서는 각 산업에 적합한 안전관리체계를 수립하려 노력하고 있으며, 표준 및 매뉴얼을 제정하여 보급에 앞장서고 있다. 또한 시스템에서 전장품 및 소프트웨어가 차지하는 비중이 커지면서 기능안전이 안전분야의 이슈가 되고 있다. 이에 따라 IEC 61508, ISO 26262, IEC 61511 등 기능안전관련 표준들이 제정되어 기능안전을 달성하기 위한 기반을 제공하고 있다. 한편 국내 철도산업에서도 철도안전법의 재정을 기점을 철도 산업전반에 걸쳐 많은 환경변화가 이뤄지고 있고 이에 대응하기 위해 철도 안전시스템을 바탕으로 한 안전관리체계를 구축하였다. 한편 다양한 운영체계를 갖고 있는 철도시설 및 운영기관이 존재하는 국가 철도 안전관리체계의 안전규제를 체계화하기 위해서는 체계적인 요구사항의 분석에 따른 시스템 아키텍처의 설정이 요구되고 있다. 이러한 아키텍처의 설정은 현재에 대한 분석과 미래의 철도안전시스템의 특성을 구조화하여 향후 비전을 프레임워크로 표현함으로서 구현이 가능해진다. 본 논문에서는 현재의 안전관리체계의 도입 배경 및 도입 현황에 대해서 분석하고, 최근 기존의 안전관리체계와 더불어 최근에 안전분야에서 이슈가 되고 있는 기능안전 표준을 반영한 안전관리체계의 구축을 위해 안전관리체계에 대한 아키텍처를 구현하고자 하며 이때, 모델링을 바탕으로 한 접근을 제시한다.

• Proceedings of the Safety Management and Science Conference
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• 2013.04a
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• pp.285-293
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• 2013

오늘날 기술의 발전으로 시스템들은 점차 대형화 복잡화 되어가고 있다. 이처럼 점차 대형화 복잡화 되어가고 있는 시스템들은 더욱 커진 사고 및 고장에 대한 위험을 내재하게 된다. 또한 대형 복합 시스템에서 발생하는 사고 및 고장은 바로 큰 재산피해나 인명피해와 직결 될 수 있다. 따라서 체계적인 안전관리의 필요성이 점차 커지고 있다. 이에 대응하여 철도, 항공, 해양 등의 산업에서는 각 산업에 적합한 안전관리체계를 수립하려 노력하고 있으며, 표준 및 매뉴얼을 제정하여 보급에 앞장서고 있다. 국내 철도산업에서도 철도안전법의 재정을 기점을 철도 산업전반에 걸쳐 많은 환경변화가 이뤄지고 있고 이에 대응하기 위해 철도 안전시스템을 바탕으로 한 안전관리체계를 구축하였다. 한편 다양한 운영체계를 갖고 있는 철도시설 및 운영기관이 존재하는 국가 철도 안전관리체계의 안전규제를 체계화하기 위해서는 체계적인 요구사항의 분석에 따른 시스템 아키텍처의 설정이 요구되고 있다. 이러한 아키텍처의 설정은 현재에 대한 분석과 미래의 철도안전시스템의 특성을 구조화하여 향후 비전을 프레임워크로 표현함으로서 구현이 가능해진다. 본 논문에서는 현재의 안전관리체계의 도입 배경 및 도입 현황에 대해서 분석하고, 안전관리의 방식의 변화에 적절히 대응하는 안전관리체계의 구축을 위해 안전관리체계에 대한 아키텍처를 구현하고자 하며 이때, 모델링을 바탕으로 한 접근을 제시한다.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.411-422
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• 2022

The purpose of this study was to identify factors that affect the characteristics and intentions of the elderly to use digital wealth management services. The subjects of this study were 312 elderly people over 50 years old. Based on the Value-based Adoption Model(VAM), the research model added price value, social influence, and perceived risk as research variables. As a result of empirical analysis, it was found that usefulness, enjoyment, price value, and social influence all had a significant positive (+) effect on perceived value. It was found that technicality had a significant negative (-) effect. On the other hand, no significant effect relationship was tested on perceived risk. The perceived value had a significant positive (+) effect on the intention to use. This study was meaningful in the academic research that it applied a research model that reflected the characteristics of the elderly who were not treated as mainstream in the technology acceptance model for digital wealth management services. In addition, it provided practical implications for providers' marketing strategies and government/public institution policy establishment to increase the use of digital wealth management services for the elderly.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.12-20
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• 2011

Recently, construction site scale has been growing larger along with the growth of national economy. As construction market requires time reduction, cost saving, and improving quality, a cutting-edge technology applied research has been gradually studied for more efficient project management. In particular, the Real Time Location System (RTLS) technology, a real-time location tracking system of construction resources, can be effectively used in safety management. This technology has been studied and applied in various industries including architectural, marine, urban, and other industries. However, although tunnel construction in civil engineering has a narrow space and many safety risks, there are not researched about this content. Therefore, this study proposes an advanced safety management model for tunnel construction using the RTLS technology and a measurement method of the feasibility of this model in the construction site.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.131-138
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• 2022

Git's commit message is closely related to the project life cycle, and by this characteristic, it can greatly contribute to cost reduction and improvement of work efficiency by identifying risk factors and project status of project operation activities. Among these related fields, there are many studies that classify commit messages as types of software maintenance, and the maximum accuracy among the studies is 87%. In this paper, the purpose of using a solution using the commit classification model is to design and implement a complex classification model that combines several models to increase the accuracy of the previously published models and increase the reliability of the model. In this paper, a dataset was constructed by extracting automated labeling and source changes and trained using the DistillBERT model. As a result of verification, reliability was secured by obtaining an F1 score of 95%, which is 8% higher than the maximum of 87% reported in previous studies. Using the results of this study, it is expected that the reliability of the model will be increased and it will be possible to apply it to solutions such as software and project management.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.257-262
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• 2021

Accidents caused by black ice occur frequently every winter, and the fatality rate is very high compared to other traffic accidents. Therefore, a systematic method is needed to predict the black ice formation before accidents. In this paper, we proposed a black ice prediction model based on heterogenous and multi-type data. To this end, 12,574,630 cases of 46 types of land, infrastructure, transport public data and meteorological public data were collected. Subsequently, the data cleansing process including missing value detection and normalization was followed by the establishment of approximately 600,000 refined datasets. We analyzed the correlation of 42 factors collected to predict the occurrence of black ice by selecting only 21 factors that have a valid effect on black ice prediction. The prediction model developed through this will eventually be used to derive the route-specific black ice risk index, which will be utilized as a preliminary study for black ice warning alart services.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.319-326
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• 2013

There are many software reliability models that are based on the times of occurrences of errors in the debugging of software. Software error detection techniques known in advance, but influencing factors for considering the errors found automatically and learning factors, by prior experience, to find precisely the error factor setting up the testing manager are presented comparing the problem. It is shown that it is possible to do asymptotic likelihood inference for software reliability models based on infinite failure model and non-homogeneous Poisson Processes (NHPP). Statistical process control (SPC) can monitor the forecasting of software failure and thereby contribute significantly to the improvement of software reliability. Control charts are widely used for software process control in the software industry. In this paper, we proposed a control mechanism based on NHPP using mean value function of logarithmic hazard learning effects property.