• 한국응용과학기술학회지
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• 제40권1호
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• pp.146-159
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• 2023

동안 이미지를 위한 메이크업 기법으로서 일반적인 사항에 따라 동안 메이크업의 표현 기법에 대한 인식의 차이가 있을 것이다.' '전체적인 특성에 따라 동안 메이크업 표현 기법에 대한 인식의 차이가 있을 것'이라는 두 가지 가설이 뒷받침되었으며, 동안 이미지를 연출하기 위한 메이크업 기법은 남녀 모두에게 중요한 기능임은 물론, 외모로. 사회활동을 위한 '신체적 자원'으로 일상생활에서 심신의 능률향상과 정신능력의 현저한 향상이 있음을 확인하였다. 동안 이미지 메이크업 표현'에 대한 연구 결과를 통해 동안 이미지에 대한 인식과 관심은 높지만 동안 이미지 제작에 대한 연구가 필요하다. 동안 메이크업을 위한 표정 요소의 필요성은 동안 이미지 개발을 위한 기초자료로 활용될 것으로 예상되며, 본 연구는 동안 이미지 및 동안 메이크업을 위한 외적 얼굴 관리에 중점을 두었다.

• 국제학술발표논문집
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• The 3th International Conference on Construction Engineering and Project Management
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• pp.732-737
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• 2009

Due to various project delivery methods and the complexity of construction projects in the construction industry, developing the framework of construction management for critical, highly complex projects in the construction industry has become problematic. Currently, a set of construction manuals play a pivotal role in planning and managing construction projects as subcontractors try to complete their scope of work according to the instructions of a general contractor. It is challenging for general contractors to write a construction management procedure manual to cover various types of project delivery methods and construction projects. In construction, the construction procedure manuals describe specific actions to be taken through the project. In reality a few contactors own such manuals and their construction schedules include more construction operation activities. Thus, it is hard to estimate the workload and productivity of construction managers because the manual and the schedule do not present the amount of management efforts required to complete a project. This paper proposes a framework to present construction management tasks according to project delivery methods which can be applied to various construction projects. Actions for management tasks were mapped and were integrated with construction activities throughout the project life cycle. The framework can then be used to give specific instructions to project participants, collect management actions, and replicate management actions throughout the project life cycle. The framework can also be can used to visualize complete construction project to analyze and manage construction management activities in each phase of a project in order to enhance productivity and efficiency. The studies of existing construction manuals were carried out to identify construction managers' responsibilities. An artificial intelligence program, CLIPS (C-Language Integrated Production System) was used to search for appropriate actions for impending tasks from a set of predefined actions to be performed for a given situation. The framework would significantly help construction managers to understand interrelations among management tasks or actions within a project. Furthermore, the framework can be embedded into Building Information Modeling (BIM) or Facility Management Systems (FMS) so that designers and constructors would execute constructability review before construction begins.

• 한국지반공학회논문집
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• 제39권8호
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• pp.7-16
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• 2023

본 연구는 매립지 침출수 내 황산염 농도를 저감하기 위해 친환경 지반개량 공법인 Enzyme Induced Carbonate Precipitation(EICP) 공법을 활용하였다. 황산염의 화학적 침전을 유도하기 위해 충분한 탄산칼슘을 생성함과 동시에 여분의 칼슘 이온을 남길 수 있는 최적의 EICP 혼합비가 계산되었다. 최적 혼합비로 처리된 사질토 시편에서 황산염 침전이 전단 강성도에 미치는 영향을 확인하고자 전단파 속도를 측정하였고 전단파 속도 측정은 EICP 반응 및 황산염 반응 시간동안 수행되었다. 실험 결과, 생성된 침전물에 따른 전단 강성도의 발달을 확인하였고 주사전자현미경(SEM)으로 침전물의 유형 및 패턴을 시각적으로 관찰하였다. 고순도 우레아제의 대체제로서 백태가루를 효소로 사용한 EICP 용액의 경우 고순도 EICP 용액과 동일한 탄산칼슘 생성 효율에서 보다 낮은 황산염 제거 효율을 보였는데 이는 백태가루에 포함된 불순물이 석고의 침전을 방해하기 때문이다.

• 대한토목학회논문집
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• 제43권5호
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• pp.659-666
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• 2023

건설업은 경기에 민감하게 반응하며 완전 경쟁에 가까운 산업 구조를 갖기 때문에 재무 상태가 취약한 기업은 불황기에 손쉽게 도태될 수 있다. 건설 기업의 지속적 생존과 성장을 도모하기 위해서는 재무 상태와 경영 성과를 반복적으로 측정할 필요성이 있다. 이에 본 연구는 2000년부터 2019년까지 최소한 한 번 이상 외부감사를 받은 건설 기업 6,252개의 재무제표를 사용하여 합산 재무제표를 생성하였고, 이 재무제표를 기준으로 건설 기업의 평균적인 재무 상태와 경영 현황을 파악하였다. 이 과정을 통하여 건설업은 매출액 성장과 이익률 개선에 한계가 있으며 높은 레버리지비율은 재무적 안정성을 해할 수 있으므로 총자산회전율 개선과 같은 생산효율을 추구하는 경향을 발견하였다. 이 경향은 2008년 세계금융위기 이후에 더 선명하게 나타났다.

• 한국환경과학회지
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• 제31권12호
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• pp.1051-1059
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• 2022

This study investigated microbial communities and their diversity in a full-scale mesophilic anaerobic digester treating sewage sludge. Influent sewage sludge and anaerobic digester samples collected from a wastewater treatment plant in Busan were analyzed using high-throughput sequencing. It was found that the microbial community structure and diversity in the anaerobic digester could be affected by inoculation effect with influent sewage sludge. Nevertheless, distinct microbial communities were identified as the dominant microbial communities in the anaerobic digester. Twelve genera were identified as abundant bacterial communities, which included several groups of syntrophic bacteria communities, such as Candidatus Cloacimonas, Cloacimonadaceae W5, Smithella, which are (potential) syntrophic-propionate-oxidizing bacteria and Mesotoga and Thermovigra, which are (potential) syntrophic-acetate-oxidizing bacteria. Lentimicrobium, the most abundant genus in the anaerobic digester, may contribute to the decomposition of carbohydrates and the production of volatile fatty acids during the anaerobic digestion of sewage sludge. Of the methanogens identified, Methanollinea, Candidatus Methanofastidiosum, Methanospirillum, and Methanoculleus were the dominant hydrogenotrophic methanogens, and Methanosaeta was the dominant aceticlastic methanogens. The findings may be used as a reference for developing microbial indicators to evaluate the process stability and process efficiency of the anaerobic digestion of sewage sludge.

• 한국수소및신에너지학회논문집
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• 제33권6호
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• pp.707-714
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• 2022

Hydrogen production using solid oxide electrolysis cells (SOEC) is a promising technology because of its efficiency, cleanness, and scalability. Especially, high-power SOEC system has received a lot of attention from researchers. This study compared and analyzed the low-power and high-power SOEC system in term of economic. By using revenue requirement method, levelized cost of hydrogen (LCOH) was calculated for comparison. In addition, the sensitivity analysis was performed to determine the dependence of hydrogen cost on input variables. The results indicated that high-power SOEC system is superior to a low-power SOEC system. In the capital cost, the stack cost is dominant in both systems, but the electricity cost is the most contributed factor to the hydrogen cost. If the high-power SOEC system combines with a nuclear power plant, the hydrogen cost can reach 3.65 $/kg when the electricity cost is 3.28 ¢/kWh and the stack cost is assumed to be 574 $/kW.

• 품질경영학회지
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• 제51권1호
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• pp.55-65
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• 2023

Purpose: The purpose of this study is to propose an optimization process to improve product yield in the process using process data. Recently, research for low-cost and high-efficiency production in the manufacturing process using machine learning or deep learning has continued. Therefore, this study derives major variables that affect product defects in the manufacturing process using eXplainable Artificial Intelligence(XAI) method. After that, the optimal range of the variables is presented to propose a methodology for improving product yield. Methods: This study is conducted using the injection molding machine AI dataset released on the Korea AI Manufacturing Platform(KAMP) organized by KAIST. Using the XAI-based SHAP method, major variables affecting product defects are extracted from each process data. XGBoost and LightGBM were used as learning algorithms, 5-6 variables are extracted as the main process variables for the injection process. Subsequently, the optimal control range of each process variable is presented using the ICE method. Finally, the product yield improvement methodology of this study is proposed through a validation process using Test Data. Results: The results of this study are as follows. In the injection process data, it was confirmed that XGBoost had an improvement defect rate of 0.21% and LightGBM had an improvement defect rate of 0.29%, which were improved by 0.79%p and 0.71%p, respectively, compared to the existing defect rate of 1.00%. Conclusion: This study is a case study. A research methodology was proposed in the injection process, and it was confirmed that the product yield was improved through verification.

• Animal Bioscience
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• 제36권5호
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• pp.692-703
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• 2023

Objective: The main goals of this investigation were to i) assess the population structure and genetic diversity and ii) determine the efficiency of the ongoing breeding program in a closed flock of Angora rabbits through pedigree analysis. Methods: The pedigree records of 6,145 animals, born between 1996 to 2020 at NTRS, ICAR-CSWRI, Garsa were analyzed using ENDOG version 4.8 software package. The genealogical information, genetic conservation index and parameters based on gene origin probabilities were estimated. Results: Analysis revealed that, 99.09% of the kits had both parents recorded in the whole dataset. The completeness levels for the whole pedigree were 99.12%, 97.12%, 90.66%, 82.49%, and 74.11% for the 1st, 2nd, 3rd, 4th, and 5th generations, respectively, reflecting well-maintained pedigree records. The maximum inbreeding, average inbreeding and relatedness were 36.96%, 8.07%, and 15.82%, respectively. The mean maximum, mean equivalent and mean completed generations were 10.28, 7.91, and 5.51 with 0.85%, 1.19%, and 1.85% increase in inbreeding, respectively. The effective population size estimated from maximum, equivalent and complete generations were 58.50, 27.05, and 42.08, respectively. Only 1.51% of total mating was highly inbred. The effective population size computed via the individual increase in inbreeding was 42.83. The effective numbers of founders (f_e), ancestors (f_a), founder genomes (f_g) and non-founder genomes (f_ng) were 18, 16, 6.22, and 9.50, respectively. The fe/fa ratio was 1.12, indicating occasional bottlenecks had occurred in the population. The six most influential ancestors explained 50% of genes contributed to the gene pool. The average generation interval was 1.51 years and was longer for the sire-offspring pathway. The population lost 8% genetic diversity over time, however, considerable genetic variability still existed in the closed Angora population. Conclusion: This study provides important and practical insights to manage and maintain the genetic variability within the individual flock and the entire population.

• 한국수소및신에너지학회논문집
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• 제33권1호
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• pp.8-18
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• 2022

With rapid industrialization and population growth, fossil fuel use has increased, which has a significant impact on the environment. Hydrogen does not cause contamination in the energy production process, so it seems to be a solution, but it is essential to find an appropriate storage method due to its low efficiency. In this study, Mg-based alloys capable of ensuring safety and high volume and hydrogen storage density per weight was studied, and Mg₂NiH_x synthesized with Ni capable of improving hydrogenation kinetics. In addition, in order to improve thermal stability, a hydrogen storage composite material synthesized with CaO was synthesized to analyze the change in hydrogenation reaction. In order to analyze the changes in the metallurgical properties of the materials through the process, XRD, SEM, BET, etc. were conducted, and hydrogenation behavior was confirmed by TGA and hydrogenation kinetics analysis. In addition, in order to evaluate the impact of the process on the environment, the environmental impact was evaluated through "Material Life Cycle Assessments" based on CML 2001 and EI99' methodologies, and compared and analyzed with previous studies. As a result, the synthesis of CaO caused additional power consumption, which had a significant impact on global warming, and further research is required to improve this.

• 한국재료학회지
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• 제33권5호
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.