• Title/Summary/Keyword: sustainable performance

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Estimating Development Density Constrained by Traffic Congestion in the Downtown, Seoul (교통혼잡을 고려한 서울 도심부 개발가능밀도 추정)

  • Hwang, Kee Yeon;Shin, Sang Young;Kang, Jun Mo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.1D
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    • pp.49-58
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    • 2006
  • The purpose of this study is to develop transportation-sensitive land use densities in a metropolitan context. It analyses traffic impacts according to 20 different development density scenarios in the downtown Seoul, and estimates the density ceiling. The results identify that the transportation-wise sustainable density in the downtown can be extended up to the FAR level of 460% with an option of 2,000 won congestion charge levied on the downtown area. It also finds that the region-wide sustainability measured by congestion level can be improving as the level of congestion charge increases. Without the congestion charge, however, the density ceiling slides down to 430%. It is recommended that, in order to bring in higher density developments in the region, transportation demand management (TDM) measures are indispensible.

Performance Evaluation of Smart Accelerometers for Structural Health Monitoring (구조 건전성 감시를 위한 스마트 가속도계의 성능 평가)

  • Yi, Jin-Hak;O, Hye-Sun;Yun, Chung-Bang
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.4A
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    • pp.605-609
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    • 2006
  • In this study, two kinds of smart accelerometers are investigated for the application of smart sensors to the structural health monitoring of infrastructures. Smart optical Fiber Bragg Grating (FBG) type and Micro-Electo-Mechanical System (MEMS) type accelerometers are selected for this study and the high sensitive ICP type accelerometer is used for the reference sensor. Small size shaking table tests were performed with 3-story shear building model using random input ground motions. The output only modal identification was carried out using stochastic subspace identification and the performances of sensors are compared in modal domain indirectly. The modal sensitivity method was applied to update the story stiffness of numerical model and the updated results were verified using the additional experiments for the same structure with additional mass.

Current Status and Policy Issues of Senior Clubs: With Focus on Senior Employment Programs (시니어클럽의 현황 및 정책과제: 노인일자리사업을 중심으로)

  • Won, Young-Hee
    • 한국노년학
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    • v.32 no.2
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    • pp.525-540
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    • 2012
  • The study aimed to investigate the current status and policy issues of senior clubs, focusing on the senior employment programs in South Korea. The current status of senior clubs was reviewed based on their legal basis, organization, project type, finance, project performance, and operational difficulties(low revenue yields, high labor intensity and low wages, lack of funding, weakness in provision of a legal basis, etc.). The policy directions of such clubs were also defined as follows: (1) establishment of the role of senior clubs(a local senior center of job creation projects, an execution entity of business senior employment programs matching the regional characteristics, and a center for community change through the promotion of awareness of the problems of and related to the elderly and through empowerment of the elderly); (2) improvement of the quality of jobs(need-based and sustainable job creation, quality improvement in education, improvement of the elderly practitioners' working conditions, and collaboration and partnerships among the public-private sectors); and (3) legislation and amendment of senior employment programs.

Energy harvesting by Tesla Turbine

  • Duong Phan Anh;Ryu Bo Rim;Lee Jin Uk;Kang Ho Keun
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2021.11a
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    • pp.132-133
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    • 2021
  • In recent years, energy harvesting from natural sources and waste heat has been attracting more attention from researchers in response to ever-growing energy demands, high energy prices, and climate-change-mitigation purposes. It is also an important step towards future sustainable energy usages. In thermal dynamic cycles, expanders are playing as the most important equipment for waste heat recovery and energy harvesting as well. As a kind of expander, the bladeless turbine has a promising future and more widely using owning its advantages on relatively long life, good off-design performance, easy operation cleaning and maintenance, a simple structure, no blade corrosion, and low manufacturing costs. There are numerous studies about using the Tesla Turbine as a key technology for energy harvesting in a wide range of applications and conditions. They are presented to help identify technologies that have sufficient potential for applicating to our life and marine industrial engineering. This review paper, initially, presents an overview of current studies both theoretical and experimental of Tesla Turbine usage for waste heat recovery alongside its challenges and investigation on the effect of its configuration, working fluid selection as well. To conclude, future perspectives besides possible ways of transforming waste heat energy to electricity or work, which leads to circular energy, are discussed. The ambition of this paper is to act as a first-hand reference, through the well-defined possible directions, to the young researchers and senior scientists.

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Analyzing Soybean Growth Patterns in Open-Field Smart Agriculture under Different Irrigation and Cultivation Methods Using Drone-Based Vegetation Indices

  • Kyeong-Soo Jeong;Seung-Hwan Go;Kyeong-Kyu Lee;Jong-Hwa Park
    • Korean Journal of Remote Sensing
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    • v.40 no.1
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    • pp.45-56
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    • 2024
  • Faced with aging populations, declining resources, and limited agricultural productivity, rural areas in South Korea require innovative solutions. This study investigated the potential of drone-based vegetation indices (VIs) to analyze soybean growth patterns in open-field smart agriculture in Goesan-gun, Chungbuk Province, South Korea. We monitored multi-seasonal normalized difference vegetation index (NDVI) and the normalized difference red edge (NDRE) data for three soybean lots with different irrigation methods (subsurface drainage, conventional, subsurface drip irrigation) using drone remote sensing. Combining NDVI (photosynthetically active biomass, PAB) and NDRE (chlorophyll) offered a comprehensive analysis of soybean growth, capturing both overall health and stress responses. Our analysis revealed distinct growth patterns for each lot. LotA(subsurface drainage) displayed early vigor and efficient resource utilization (peaking at NDVI 0.971 and NDRE 0.686), likely due to the drainage system. Lot B (conventional cultivation) showed slower growth and potential limitations (peaking at NDVI 0.963 and NDRE 0.681), suggesting resource constraints or stress. Lot C (subsurface drip irrigation) exhibited rapid initial growth but faced later resource limitations(peaking at NDVI 0.970 and NDRE 0.695). By monitoring NDVI and NDRE variations, farmers can gain valuable insights to optimize resource allocation (reducing costs and environmental impact), improve crop yield and quality (maximizing yield potential), and address rural challenges in South Korea. This study demonstrates the promise of drone-based VIs for revitalizing open-field agriculture, boosting farm income, and attracting young talent, ultimately contributing to a more sustainable and prosperous future for rural communities. Further research integrating additional data and investigating physiological mechanisms can lead to even more effective management strategies and a deeper understanding of VI variations for optimized crop performance.

Detection of Individual Trees in Human Settlement Using Airborne LiDAR Data and Deep Learning-Based Urban Green Space Map (항공 라이다와 딥러닝 기반 도시 수목 면적 지도를 이용한 개별 도시 수목 탐지)

  • Yeonsu Lee ;Bokyung Son ;Jungho Im
    • Korean Journal of Remote Sensing
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    • v.39 no.5_4
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    • pp.1145-1153
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    • 2023
  • Urban trees play an important role in absorbing carbon dioxide from the atmosphere, improving air quality, mitigating the urban heat island effect, and providing ecosystem services. To effectively manage and conserve urban trees, accurate spatial information on their location, condition, species, and population is needed. In this study, we propose an algorithm that uses a high-resolution urban tree cover map constructed from deep learning approach to separate trees from the urban land surface and accurately detect tree locations through local maximum filtering. Instead of using a uniform filter size, we improved the tree detection performance by selecting the appropriate filter size according to the tree height in consideration of various urban growth environments. The research output, the location and height of individual trees in human settlement over Suwon, will serve as a basis for sustainable management of urban ecosystems and carbon reduction measures.

Intelligent prediction of engineered cementitious composites with limestone calcined clay cement (LC3-ECC) compressive strength based on novel machine learning techniques

  • Enming Li;Ning Zhang;Bin Xi;Vivian WY Tam;Jiajia Wang;Jian Zhou
    • Computers and Concrete
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    • v.32 no.6
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    • pp.577-594
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    • 2023
  • Engineered cementitious composites with calcined clay limestone cement (LC3-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC3-ECC compressive strength makes the prediction of LC3-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC3-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC3-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC3-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R2) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC3-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

Analyzing the Effects of Consumer Value Perception, Environmental Motives, and Perceived Barriers on the Purchase Intention of Vegan Cosmetics (비건 화장품의 구매의도에 영향을 미치는 소비자 가치 인식, 환경적 동기 및 지각된 장벽의 영향 분석)

  • Eun-Hee Lee;Seunghee Bae
    • Journal of the Korean Applied Science and Technology
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    • v.40 no.5
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    • pp.1043-1054
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    • 2023
  • Amidst the rapid growth of the vegan cosmetics market, consumer orientation towards environmental and ethical values has been intensifying. However, research on this subject remains limited. This study delves into the relationship between consumer value perception, environmental motivations, and perceived barriers influencing the purchase intentions of vegan cosmetics. Conducting a PLS-SEM analysis on a sample of 300 women with experience using vegan cosmetics, it was discerned that monetary value, social value, brand value, emotional value, quality value, and environmental knowledge play significant roles in influencing purchase intentions. The moderating effect analysis highlighted image barriers and value barriers as crucial factors. Through Importance-Performance Map Analysis, emotional value emerged as a pivotal element in strategizing to strengthen the purchasing intentions for vegan cosmetics. This research contributes both theoretically and practically to enhancing the competitive edge of the vegan cosmetics market and promoting sustainable consumption behavior.

Mechanical properties of sustainable green self-compacting concrete incorporating recycled waste PET: A state-of-the-art review

  • Shireen T. Saadullah;James H. Haido;Yaman S.S. Al-Kamaki
    • Advances in concrete construction
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    • v.16 no.1
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    • pp.35-57
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    • 2023
  • Majority of the plastic produced each year is being disposed in land after single-use, which becomes waste and takes up a lot of storage space. Therefore, there is an urgent need to find alternative solutions instead of disposal. Recycling and reusing the PET plastic waste as aggregate replacement and fiber in concrete production can be one of the eco- friendly methods as there is a great demand for concrete around the world, especially in developing countries by raising human awareness of the environment, the economy, and Carbon dioxide (CO2) emissions. Self-compacting concrete (SCC) is a key development in concrete technology that offers a number of attractive features over traditional concrete applications. Recently, in order to improve its durability and prevent such plastics from directly contacting the environment, various kinds of plastics have been added. This review article summarizes the latest evident on the performance of SCC containing recycled PET as eco-friendly aggregates and fiber. Moreover, it highlights the influence of substitution content, shape, length, and size on the fresh and properties of SCC incorporating PET plastic. Based on the findings of the articles that were reviewed for this study, it is observed that SCC made of PET plastic (PETSCC) can be employed in construction era owing to its acceptable mechanical and fresh properties. On the other hand, it is concluded that owing to the lightweight nature of plastic aggregate, Reusing PET waste in the construction application is an effective approach to reduces the earthquake risk of a building.

Research on the Financial Data Fraud Detection of Chinese Listed Enterprises by Integrating Audit Opinions

  • Leiruo Zhou;Yunlong Duan;Wei Wei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.12
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    • pp.3218-3241
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    • 2023
  • Financial fraud undermines the sustainable development of financial markets. Financial statements can be regarded as the key source of information to obtain the operating conditions of listed companies. Current research focuses more on mining financial digital data instead of looking into text data. However, text data can reveal emotional information, which is an important basis for detecting financial fraud. The audit opinion of the financial statement is especially the fair opinion of a certified public accountant on the quality of enterprise financial reports. Therefore, this research was carried out by using the data features of 4,153 listed companies' financial annual reports and audits of text opinions in the past six years, and the paper puts forward a financial fraud detection model integrating audit opinions. First, the financial data index database and audit opinion text database were built. Second, digitized audit opinions with deep learning Bert model was employed. Finally, both the extracted audit numerical characteristics and the financial numerical indicators were used as the training data of the LightGBM model. What is worth paying attention to is that the imbalanced distribution of sample labels is also one of the focuses of financial fraud research. To solve this problem, data enhancement and Focal Loss feature learning functions were used in data processing and model training respectively. The experimental results show that compared with the conventional financial fraud detection model, the performance of the proposed model is improved greatly, with Area Under the Curve (AUC) and Accuracy reaching 81.42% and 78.15%, respectively.