• 한국수자원학회논문집
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• 제55권1호
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• pp.11-21
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• 2022

지난 2019년 말 발생한 COVID-19는 2020년을 기점으로 국내에 본격적으로 확산되기 시작하여, 사회 전반에 커다란 영향을 미치고 있다. COVID-19 확산을 억제하기 위한 방역수칙들은 인간 생활에 많은 변화를 가져왔으며, 사회적 거리두기 등 사회활동 제한에 따른 다양한 영향이 사회 전반에 걸쳐 나타나고 있다. 본 연구에서는 물 분야 COVID-19 위기 대응의 일환으로, COVID-19 확산에 따른 국내 상수도 사용량 변화를 분석하고, 상수도 사용량의 변화가 공급 서비스에 미치는 위협을 알아보고자 하였다. 국내 중소규모 도시인 S시 주거지역을 대상으로 COVID-19 확산 전후 일정기간 동안의 1시간 단위 용수 사용량 자료를 수집하였으며, 먼저 수집 데이터를 분석 목적에 따라 정제하고 전체 용수 사용량의 변화 및 사용 비중 변화, 그리고 시간별 용수 사용 패턴 변화 등을 분석하였다. 분석 결과, 가정용수 및 영업용수 사용량 및 이용패턴이 COVID-19 확산 이후 뚜렷한 변화를 보였으며, 일부 사용량 변화는 상수도 운영관리 차원에서의 검토가 필요한 것으로 나타났다.

• Journal of the Korean Wood Science and Technology
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• 제50권6호
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• pp.458-474
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• 2022

Many methods have been explored to increase the palatability of pine (Pinus merkusii), the most common wood used for termite baiting. However, because of the undersupply of pine in Indonesia, it is crucial to vary the wood species for termite baiting and look for potential alternatives. Furthermore, various studies have shown that baiting time influences the intensity and pattern of termite attacks. Therefore, the present research aimed to study the preferences of subterranean termites and find the ideal baiting time among community wood species from Bogor, West Java, as a baiting alternative to pine. The woods tested were Acacia mangium (acacia), Falcataria moluccana (sengon), Anthocephalus cadamba (jabon), Maesopsis eminii (manii), Swietenia mahagoni (mahogany), Hevea brasiliensis (rubberwood), and P. merkusii (pine). Field tests were carried out based on the American Society for Testing and Materials D 1758-06 at the Arboretum, Faculty of Forestry and Environment, IPB University, with a baiting time of one to six months. The results led to the identification of four species of termites, namely Microtermes sp., Macrotermes sp., Shedorhinotermes sp., and Capritermes sp.. The frequency of termite attacks on the test site reached 93.1%. Rubberwood was the most potential wood bait for subterranean termites, indicated by the highest average weight loss value (65.8%) with a shorter optimal baiting time (up to one month) than that of other tested woods.

• Advances in materials Research
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• 제12권3호
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• pp.179-192
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• 2023

We have studied the structural and optical properties of the non-doped and Co 0.08 at.%, Co 0.02 at.%, and Co 0.11 at.% doped ZnO nanorods (NRs) synthesized using the simple low-temperature chemical bath deposition (CBD) method at 95℃ for 2 hours. The scanning electron microscope (SEM) images confirmed the morphology of the ZnO NRs are affected by Co incorporation. As observed, the Co 0.08 at.% doped ZnO NRs have a larger dimension with an average diameter of 153.4 nm. According to the International Centre for Diffraction Data (ICDD) number #00-036-1451, the x-ray diffraction (XRD) pattern of non-doped and Co-doped ZnO NRs with the preferred orientation of ZnO NRs in the (002) plane possess polycrystalline hexagonal wurtzite structure with the space group P63mc. Optical absorbance indicates the Co 0.08 at.% doped ZnO NRs have stronger and blueshift bandgap energy (3.104 ev). The room temperature photoluminescence (PL) spectra of ZnO NRs exhibited excitonicrelates ultraviolet (UV) and defect-related green band (GB) emissions. By calculating the UV/GB intensity, the Co 0.08 at.% is the proper atomic percentage to have fewer intrinsic defects. We predict that Co-doped ZnO NRs induce a blueshift of near band edge (NBE) emission due to the Burstein-Moss effect. Meanwhile, the redshift of NBE emission is attributed to the modification of the lattice dimensions and exchange energy.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1123-1146
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• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.

• Smart Structures and Systems
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• 제31권5호
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• Journal of Korea Trade
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• 제26권3호
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• pp.79-101
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• 2022

Purpose - The purpose of this study is to analyze and compare the international competitiveness of digital service trade between Korea and China and to help enhance the competitive advantage of digital service trade between the two countries. Design/methodology - This paper designs and establishes a comprehensive evaluation system for the international competitiveness of the Korea-China digital service trade. By using the analytical methods of combining theory and demonstration through qualitative and quantitative analysis, this paper makes a concrete and complete theoretical deconstruction and empirical measurement of its international competitiveness from the two levels of overall competitiveness and departmental competitiveness. At the same time, the study also analyzes the competitive advantages and comparative disadvantages of the two countries. Findings - It is found that South Korea has a strong competitive advantage in the sector competitiveness of digital service trade, and the export structure is reasonable and balanced, but the deficit pattern affects the overall competitiveness. China has a strong competitive advantage in the overall competitiveness of the digital service trade. However, the structural imbalance in the export sector weakens the competitiveness of the sector. Both Korea and China have the space advantage and competitive potential to enhance international competitiveness in terms of development trends. Originality/value - This paper takes the lead in solving the pain point of the relative lack of similar research topics. It demonstrates the evolution process, development trends, and structural characteristics of the digital service trade. A new combination of competitive power research methods is innovated, and a comprehensive evaluation system is established. The above innovation points show the academic theoretical value and practical application value of this study.

• 한국전자통신학회논문지
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• 제17권2호
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• pp.245-254
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• 2022

본 논문에서는 항 재밍 GPS 안테나의 설계 최적화에 관하여 연구하였다. 이를 위해 항 재밍 성능 분석 기준 및 방법을 제시하였다. 제시된 방법을 적용하기 위한 안테나 시스템의 구조는 7개의 방사소자가 배열된 구조이다. 여기서 6개 방사소자는 원형 등각 배열되었으며, 나머지 1개 방사소자는 원형 중앙에 배치하였다. 제시된 기준 및 방법을 기반으로 상기 안테나를 최적화 하였으며, 최적화된 안테나의 설계 요소(원형 배열의 반지름)의 전기적 길이는 0.48 λ이다. 모의실험 결과, 주 빔의 조향각도(θ, ϕ)가 (0°, 0°)인 경우, 항 재밍을 위한 패턴 널 형성 범위(θ 기준)가 57°부터 90°가 됨을 확인하였다.

• 산업진흥연구
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• 제8권4호
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• pp.257-266
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• 2023

이 연구는 중국 서부지역의 대학을 중심으로 진행되었으며, 2017년부터 2021년까지 12개 대학의 연구수준을 정적 효율성과 동적 효율성을 모두 고려하여 조사하였다. 정적 효율성은 데이터 포락 분석(DEA)을 사용하여 검사하였고, 동적 효율은 Malmquist 모델을 사용하여 분석하였다. 분석결과, 서부12개 대학의 과학연구 효율성은 일반적으로 높지 않았으며 '쌍일류' 건설의 맥락에서 대학의 과학연구 효율성은 증가하는 추세를 보이고 있으며, 과학연구 효율성의 원인으로 최근 몇 년 동안 효율성이 크게 증가하였다. 연구 활동의 TFP (Total Factor Productivity)는 기술진보지수의 영향을 받아 초기에 증가하다가 감소하다가 다시 증가하는 패턴을 보였다. 연구 결론은 서부 대학들의 과학연구 활동을 위해 자원을 합리적으로 배분하여 효과적인 과학연구 메커니즘을 갖추어서, 관리기준을 개선하여야 한다. 이에 과학혁신과 이에 상응하는 성과를 촉진하여, 궁극적으로 중국 서부의 과학기술 수준을 높일 수 있어야 하겠다.

• 경영정보학연구
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• 제22권1호
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• pp.167-182
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• 2020

조직의 양면성은 일반적으로 경쟁적 시장에서 생존하기 위해 중요한 조직적 혁신을 가능하게 하는 유연성으로 해석된다. 정보시스템 사용자의 탐색적 혹은 활용적 사용의 양면성이 역동적 혹은 운영적 작업 간에 유연한 전환을 가능하게 하여 결과적으로 작업성과를 높이는 데 기여하게 된다. 본 연구는 개인 수준에서의 정보시스템 사용 양면성의 보완적 적합성이 업무 성과를 향상시키는지 검증하고자 하였다. 나아가 이 둘이 양면적 사용에서 차지하는 비중에 따라 업무의 유형에 따른 성과에 기여하는지도 알아보았다. 다항적 회귀분석과 표면분석을 통해 정보시스템 사용 패턴의 부조화적 적합성의 효과를 확인하였다. 이를 확산적 양면성과 수렴적 양면성으로 분류하고 각 패턴의 효과는 작업의 역동적 혹은 운영적 유형에 따라 다르게 나타남을 확인하였다.