• 방사성폐기물학회지
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• 제20권2호
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• pp.209-229
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• 2022

This work provided a review of three techniques-(1) spectrochemical, (2) electrochemical, and (3) spectroelectrochemical-for molten salt medias. A spectroelectrochemical system was designed by utilizing this information. Here, we designed a spectroelectrochemical cell (SEC) and calibrated temperature controllers, and performed initial tests to explore the system's capability limit. There were several issues and a redesign of the cell was accomplished. The modification of the design allowed us to assemble, align the system with the light sources, and successfully transferred the setup inside a controlled environment. A preliminary run was executed to obtain transmission and absorption background of NaCl-CaCl₂ salt at 600℃. It shows that the quartz cuvette has high transmittance effects across all wavelengths and there were lower transmittance effects at the lower wavelength in the molten salt media. Despite a successful initial run, the quartz vessel was mated to the inner cavity of the SEC body. Moreover, there was shearing in the patch cord which resulted in damage to the fiber optic cable, deterioration of the SEC, corrosion in the connection of the cell body, and fiber optic damage. The next generation of the SEC should attach a high temperature fiber optic patch cords without introducing internal mechanical stress to the patch cord body. In addition, MACOR should be used as the cell body materials to prevent corrosion of the surface and avoid the mating issue and a use of an adapter from a manufacturer that combines the free beam to a fiber optic cable should be incorporated in the future design.

• 전기전자학회논문지
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• 제26권4호
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• pp.545-552
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• 2022

특정 분야의 특허출원수는 기술의 수명주기 및 산업의 활성화 정도와 밀접한 관계를 가지고 있다. 따라서 사전에 사업을 준비하는 기업들과 미래 유망 기술을 초기 단계에서 선발하여 투자하고자 하는 정부 기관들은 미래의 특허 출원수 예측에 대해 큰 관심을 가지고 있다. 본 논문에서는 시계열 데이터에 적합한 RNN의 기법 중 하나인 양방향 LSTM 기법을 이용하여 기존 예측 방법들보다 정확도를 높이는 방법을 제안한다. 5개 분야의 대한민국 특허 출원 데이터에 대해서 제안된 방법은 기존에 사용되던 확산 모델 중 하나인 Bass 모델과 비교하여 평균 절대 백분율 오차(MAPE)의 값이 약 16퍼센트 향상된 결과를 보여준다.

• 대한유전성대사질환학회지
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• 제22권1호
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• pp.15-20
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• 2022

The most common urea cycle disorder is ornithine transcarbamylase deficiency. More than 80 percent of patients with symptomatic ornithine transcarbamylase deficiency are late-onset, which can present various phenotypes from infancy to adulthood. With no regards to the severity of the disease, characteristic fluctuating courses due to hyperammonemia may develop unexpectedly, and can be precipitated by various metabolic stressors. Late-onset ornithine transcarbamylase deficiency is not merely related to a type of genetic variation, but also to the complex relationship between genetic and environmental factors that result in hyperammonemia; therefore, it is difficult to predict the prevalence of neurological symptoms in late-onset ornithine transcarbamylase deficiency. Most common acute neurological manifestations include psychological changes, seizures, cerebral edema, and death; subacute neurological manifestations include developmental delays, learning disabilities, intellectual disabilities, attention-deficit/hyperactivity disorder, executive function deficits, and emotional and behavioral problems. This review aims to increase awareness of late-onset ornithine transcarbamylase deficiency, allowing for an efficient use of biochemical and genetic tests available for diagnosis, ultimately leading to earlier treatment of patients.

• 방사성폐기물학회지
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• 제20권3호
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• pp.269-278
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• 2022

The buffer is a critical barrier component in an engineered barrier system, and its purpose is to prevent potential radionuclides from leaking out from a damaged canister by filling the void in the repository. No experimental parameters exist that can describe the buffer expansion phenomenon when Kyeongju bentonite, which is a buffer candidate material available in Korea, is exposed to groundwater. As conventional experiments to determine these parameters are time consuming and complicated, simple swelling pressure tests, numerical modeling, and machine learning are used in this study to obtain the parameters required to establish a numerical model that can simulate swelling. Swelling tests conducted using Kyeongju bentonite are emulated using the COMSOL Multiphysics numerical analysis tool. Relationships between the swelling phenomenon and mechanical parameters are determined via an artificial neural network. Subsequently, by inputting the swelling tests results into the network, the values for the mechanical parameters of Kyeongju bentonite are obtained. Sensitivity analysis is performed to identify the influential parameters. Results of the numerical analysis based on the identified mechanical parameters are consistent with the experimental values.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.686-693
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• 2022

Construction is a collaborative endeavor. The complexity in delivering construction projects successfully is impacted by the effective collaboration needs of a multitude of stakeholders throughout the project life-cycle. Technologies such as Building Information Modelling and relational project delivery approaches such as Alliancing and Integrated Project Delivery have developed to address this conundrum. However, with the onset of the pandemic, the digital economy has surged world-wide and advances in technology such as in the areas of machine learning (ML) and Artificial Intelligence (AI) have grown deep roots across specializations and domains to the point of matching its capabilities to the human mind. Several recent studies have both explored the role of AI in the construction process and highlighted its benefits. In contrast, literature in the organization studies field has highlighted the fear that tasks currently done by humans will be done by AI in future. Motivated by these insights and with the understanding that construction is a labour intensive sector where knowledge is both fragmented and predominantly tacit in nature, this paper explores the integration of AI in construction processes across project phases from planning, scheduling, execution and maintenance operations using literary evidence and experiential insights. The findings show that AI can complement human skills rather than provide a substitute for them. This preliminary study is expected to be a stepping stone for further research and implementation in practice.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.378-385
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• 2023

The importance of wearing respiratory protective equipment has been highlighted even more during the COVID-19 pandemic. Even if the suitability of respiratory protection has been confirmed through testing in a laboratory environment, there remains the potential for leakage points in the respirators due to improper application by the wearer, damage to the equipment, or sudden movements in real working conditions. In this paper, we propose a method to detect the occurrence of leak holes by measuring the pressure changes inside the mask according to the wearer's breathing activity by attaching an IoT sensor to a full-face respirator. We designed 9 experimental scenarios by adjusting the degree of leak holes of the respirator and the breathing cycle time, and acquired respiratory data for the wearer of the respirator accordingly. Additionally, we analyzed the respiratory data to identify the duration and pressure change range for each breath, utilizing this data to train a neural network model for detecting leak holes in the respirator. The experimental results applying the developed neural network model showed a sensitivity of 100%, specificity of 94.29%, and accuracy of 97.53%. We conclude that the effective detection of leak holes can be achieved by incorporating affordable, small-sized IoT sensors into respiratory protective equipment.

• Wind and Structures
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• 제38권4호
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• pp.231-244
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• 2024

As wind farms expand into low wind speed areas, an increasing number are being established in mountainous regions. To fully utilize wind energy resources, it is essential to understand the details of mountain flow fields. Reconstructing the wind speed field in complex terrain is crucial for planning, designing, operation of wind farms, which impacts the wind farm's profits throughout its life cycle. Currently, wind speed reconstruction is primarily achieved through physical and machine learning methods. However, physical methods often require significant computational costs. Therefore, we propose a Full Convolutional Neural Network (FCNN)-based reconstruction method for mountain wind velocity fields to evaluate wind resources more accurately and efficiently. This method establishes the mapping relation between terrain, wind angle, height, and corresponding velocity fields of three velocity components within a specific terrain range. Guided by this mapping relation, wind velocity fields of three components at different terrains, wind angles, and heights can be generated. The effectiveness of this method was demonstrated by reconstructing the wind speed field of complex terrain in Beijing.

• International Journal of Quality Innovation
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• 제8권3호
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• pp.156-172
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• 2007

The high-tech industry is the economic lifeline for Taiwan. Its characteristics are short product life cycle, rapid changes in the market, and a high obsolescence rate for new products. Under globalization, the high-tech industry has adopted Information Technology (IT) to shorten the manufacturing process, reduce costs and conduct product research and development (R&D) to increase the core competence of enterprises and achieve the goal of sustainable operations. Enterprises should actively strengthen their integration with internal and external resources and lead in R&D management to increase industrial operating performance. Effectively managing operations and R&D management evaluation in Taiwan's High-tech Industry has become a critical subject. This study adopted 4 major Balanced Scorecard (BSC) perspectives to establish the Total Performance Evaluation Indicators for the R&D management department in Taiwan's High-tech Industry. The Analytic Network Process (ANP) was applied to evaluate the overall performance of the R&D management department. The research framework is divided into 2 phases. The first phase is combined with the 4 major perspectives, Financial, Customer, Internal Business Process and Learning and Growth, as the related indicators for each measurement perspective. The Key Performance Indicators (KPI) were selected using Factor Analysis to identify the key factor from the complicated indicators. The relationship between the characteristics of each BSC's evaluation perspective is dependence and feedback. This study applied ANP to conduct the calculation and adjustment of correlation between each KPI, and determine on their relative weights for the objective KPI. The "Financial Perspective" for R&D management department in Taiwan's High-tech Industry focused on the budget achievement rate of R&D management. The weight indicator value is (0.05863). The "Customer Perspective" focused on problem-solving satisfaction. The weight value of this indicator is (0.17549). The "Internal Business Process Perspective" focused on the quantity and quality of R&D. The weight value of this indicator is (0.13506). The "Learning and Growth Perspective" focused on improving competence in the research personnel's professional techniques. The weight value of this indicator is (0.02789). From the total weighting indicators, the order of the Performance Indicators for the R&D management department in Taiwan's High-tech Industry is: (1) Customer Perspective; (2) Internal Business Process Perspective; (3) Financial Perspective; and (4) Learning and Growth Perspective.

• 컴퓨터교육학회논문지
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• 제13권5호
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• pp.51-70
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• 2010

본 연구는 사이버 일탈행위와 사이버 일탈행위의 유발요인에 대한 영향 관계를 실증적으로 규명하여 실천적 정보통신윤리 교육 방안을 논의하고자 하였다. 이를 위해 문헌연구를 통해 사이버 일탈행위에 영향을 미치는 요인으로 자기조절력, 사회적 정체성, 주관적 규범 요인을 고려하였으며, 이 영향 요인에 대한 선행요인으로 인터넷 중독성, 익명성, 질서의식 및 정보규범 학습경험에 대한 요인을 투입하여 사이버 일탈행위에 관한 모델을 제시하였다. 연구 결과를 요약하면 다음과 같다. 첫째, 사이버 일탈행위와 그 영향요인에 대한 분석 결과 주관적 규범, 사회적 정체성, 자기조절력 순으로 사이버 일탈행위에 영향을 미치는 것으로 나타났다. 둘째, 부정적 관점의 주관적 규범에 대해 익명성(+), 질서의식(-), 정보규범 학습경험(-) 및 사회적 정체성(+)이 영향을 미치는 것으로 밝혀졌으며, 자기조절력에 대해 인터넷 중독성(+), 익명성(+) 모두 유의미한 영향을 미쳤다. 연구 결과, 실천을 강화하기 위해서는 반성과 성찰의 기회를 많이 주고, 비판적 사고와 책임윤리를 키우며, 공감능력을 계발하여 실천을 유도하여야 한다. 이를 지원하기 위한 전략적 교수-학습 절차로 '반성적 실천지향 정보통신윤리 교육 절차'를 제안하였다. 절차의 프레임워크는 문제인식-위험분석-자기성찰-실천과 평가에 대한 4단계로 구성되며, 순환적으로 반복되는 나선형 구조를 갖는다.

• 한국터널지하공간학회 논문집
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• 제20권6호
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• pp.1161-1175
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• 2018

도로 터널의 주행은 시야의 제한으로 인해 유고상황이 발생한 후 2차 대형사고로 이어지기 쉽다. 따라서, 유고상황 발생 즉시, 상황을 자동 감지하여 신속히 초동대응이 이루어 져야 한다. 유고상황을 자동으로 감시할 수 있는 시스템은 기존에도 존재했지만, 폐합된 터널 내 열악 환경에서 촬영되는 CCTV 영상의 질적 한계로 인해 유고상황을 제대로 감지하지 못했다. 이러한 한계를 극복하기 위해 딥러닝을 기반으로 한 터널 영상유고 자동 감지 시스템을 개발하였으며, 지난 2017년 11월 딥러닝 객체 인식 네트워크에 대한 연구를 진행하여 우수한 객체인식 성능을 보인바 있다. 그러나 객체인식은 정지영상 기반으로 수행되므로 이동체의 이동방향과 속도를 알 수 없어, 정차 및 역주행 등 이동체의 이동특성에 따른 유고상황을 판단하기 힘들다. 본 논문에서는 객체인식으로 감지된 이동체의 객체정보를 기반으로 별도의 객체추적기법을 적용하여 이동체의 이동 특성을 자동으로 추적하는 프로세스를 제안하였다. 이를 통해 얻어진 이동체의 이동 방향과 속도 정보를 기반으로 정차 및 역주행을 판별하는 알고리즘을 개발하여 딥러닝 기반 터널 영상유고 자동감지 시스템을 완성하였다. 또한, 유고상황이 포함된 영상들에 대하여 유고상황 감지성능을 검증하였다. 검증 실험 결과, 화재, 정차와 역주행 상황에 대해서는 모두 100% 수준으로 완전한 유고상황 감지성능을 보였으나, 보행자 발생 상황에서는 78.5%로 상대적으로 낮은 성능을 보였다. 하지만, 향후 지속적인 영상유고 영상 빅데이터를 확장해 나가고 주기적인 재학습을 통해 유고상황에 대한 인지성능을 향상시켜 나갈 수 있을 것이다.