• Korea-Australia Rheology Journal
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• 제19권3호
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• pp.125-131
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• 2007

Biodegradable polymeric blends are expected to be widely used by industry due to their environmental friendliness and comparable mechanical and thermal properties. Poly (lactic acid) (PLA) and poly (butylene succinate) (PBS) are such biodegradable polymers which aim to replace commodity polymers in future applications. Since cost and brittleness of PLA is quite high, it is not economically feasible to use it alone for day to day use as a packaging material without blending. In this study, blends of PLA and PBS with various compositions were prepared by using a laboratory-scale twin-screw extruder at $180^{\circ}C$. Morphological, thermal, rheological and mechanical properties were investigated on the samples obtained by compression molding to explore suitability of these compositions for packaging applications. Morphology of the blends was investigated by scanning electron microscopy (SEM). Morphology showed a clear phase difference trend depending on blend composition. Modulated differential scanning calorimetry (MDSC) thermograms of the blends indicated that the glass transition temperature ($T_g$) of PLA did not change much with the addition of PBS, but analysis showed that for PLA/PBS blend of up to 80/20 composition there is partial miscibility between the two polymers. The tensile strength and modulus were measured by the Instron Universal Testing Machine. Tensile strength, modulus and percentage (%) elongation at break of the blends decreased with PBS content. However, tensile strength and modulus values of PLA/PBS blend for up to 80/20 composition nearly follow the mixing rule. Rheological results also show miscibility between the two polymers for PBS composition less than 20% by weight. PBS reduced the brittleness of PLA, thus making it a contender to replace plastics for packaging applications. This work found a partial miscibility between PBS and PLA by investigating thermal, mechanical and morphological properties.

• 대한원격탐사학회지
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• 제38권6_2호
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• pp.1581-1587
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• 2022

이제는 딥러닝 없는 원격탐사 데이터 처리는 상상하기도 어려운 시대가 되었다. 원격탐사의 활용기술 개발을 위해서는 먼저 인공지능(artificial intelligence, AI)을 위한 데이터를 설계 및 구축하고, AI모델을 학습시키는 과정을 거친다. AI모델은 빠르게 발전하여 모델 정확도가 나날이 높아지고 있지만, 모델을 훈련시키는 사람에 따라 정확도의 편차가 발생하고 있다. 결국 AI모델을 훈련시킬 수 있는 숙련도 높은 전문가가 더욱 더 필요한 시대가 되어가고 있다. 특히, 딥러닝기술은 원격탐사활용에 있어 자동화라는 키워드를 제공하고 있다. 예전에는 60% 이하의 정확도만 있었던 기술도 이제는 90%를 넘어 100%의 시대로 가고 있다. 이 특별호에서는 딥러닝기술이 원격탐사에 어떻게 활용되고 있는지에 관한 13편의 논문을 소개한다.

• 한국컴퓨터정보학회논문지
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• 제14권5호
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• pp.93-102
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• 2009

환경 관찰 응용은 광범위한 지역의 온도, 습도, 오염도 등의 물리 세계 정보를 실시간 및 주기적으로 측정하는 응용으로 유비쿼터스 사회에 필수적이다. 본 논문은 환경 관찰 응용에 적합한 센서 네트워크 MAC프로토콜을 제안한다. 제안하는 프로토콜은 SMAC 프로토콜처럼 센서 노드들을 다수의 그룹으로 형성함으로써 확장성을 갖도록 한다. 그러나 SMAC 프로토콜과 달리 인접한 그룹간의 계층 구조를 갖는다. 계층 구조의 하위 그룹은 상위 그룹에 동기화 되어 있기 때문에 데이터 전송 스케줄이 효율적이다. 따라서 종단간 전송지연을 줄일 수 있고, 순차적인 전송 스케줄을 통하여 에너지 소모를 줄인다. 그러나 동일 그룹 내의 노드들은 자신의 그룹에 속한 노드들에게만 동기화 되므로 DMAC과 같은 기존 계층 구조보다 확장성이 좋고 적응력을 갖는다. 시뮬레이션을 통해서 환경 관찰 응용에 적용할 때 제안하는 프로토콜이 SMAC보다 전송 지연과 에너지 측면에서 뛰어남을 보인다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.378-380
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• 2005

Remote sensing of atmospheric aerosol using MODIS satellite data has been proven to be very useful in global/regional scale aerosol monitoring. Due to their large spatial resolution of $10km^2$ MODIS aerosol optical thickness (AOT) data have limitations for local/urban scale aerosol monitoring applications. Modified Bremen Aerosol Retrieval (BAER) algorithm developed by von Hoyningen-Huene et al. (2003) and Lee et al. (2005) has been applied in this study to retrieve AOT in fe resolutions of $500m^2$ over Korea. Look up tables (LUTs) were constructed from the aerosol properties based on sun-photometer observation and radiation transfer model calculations. It was found that relative error between the satellite products and the ground observations was within about $15\%$. Resulting AOT products were correlated with surface PMIO concentration data. There was good correlation between MODIS AOT and surface PM concentration under certain atmospheric conditions, which supports the feasibility of using the high-resolution MODIS AOT for local and urban scale air quality monitoring

• Coupled systems mechanics
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• 제8권6호
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• pp.523-535
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• 2019

Artificial Intelligence (AI) is anticipated to be the future of technology. Hence, AI has been applied in various fields over the years and its applications are expected to grow in number with the passage of time. There has been a growing need for accurate, direct, and quick prediction of geotechnical and foundation engineering models especially since the success of each project relies on numerous amounts of data. In this study, two applications of AI in the field of geotechnical and foundation engineering are presented - spatial interpolation of standard penetration test (SPT) data and prediction of consolidation of clay. SPT and soil profile data may be predicted and estimated at any location and depth at a site that has no available borehole test data using artificial intelligence techniques such as artificial neural networks (ANN) based on available geospatial information from nearby boreholes. ANN can also be used to accelerate the calculation of various theoretical methods such as the one-dimensional consolidation theory of clay with high efficiency by using lesser computation resources. The results of the study showed that ANN can be a valuable, powerful, and practical tool in providing various information that is needed in geotechnical and foundation design.

• Environmental Engineering Research
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• 제14권2호
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• 환경영향평가
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• 제2권2호
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• pp.13-25
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• 1993

Environmental Impact Assessment (EIA) serves to identify a project's possible effects on the environment early enough sufficiently allowing for them to be taken into account in the decision-making process. The EIA in Germany is an instrument of environmental Policy which considers nature as an integrated system (ecosystem). In the german process of developing environmental policy, the EIA has been one of the instruments which introduced by law rather late. This means that the German Law of EIA has been made in 1900, 20 years after the beginning of the environmental policy in Germany. In the following paper I intend to ${\cdot}$ explain the basic concept of the EIA in Germany ${\cdot}$ describe the process of law building for EIA ${\cdot}$ give a short overview of applications and experiences with EIA ${\cdot}$ show the links between EIA and general environmental policy in Germany.

• Journal of Biosystems Engineering
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• 제41권4호
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• pp.396-407
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• 2016

Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

• 한국환경과학회지
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• 제33권1호
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• pp.43-57
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• 2024

The possible experimental time for cloud seeding was analyzed in South Korea. Rain gauge and radar precipitation data collected from September 2017 to August 2022 in from the three main target stations of cloud seeding experimentation (Daegwallyeong, Seoul, and Boryeong) were analyzed. In this study, the assumption that rainfall and cloud enhancement originating from the atmospheric updraft is a necessary condition for the cloud seeding experiment was applied. First, monthly and seasonal means of the precipitation duration and frequency were analyzed and cloud seeding experiments performed in the past were also reanalyzed. Results of analysis indicated that the experiments were possible during a monthly average of 7,025 minutes (117 times) in Daegwallyeong, 4,849 minutes (81 times) in Seoul, and 5,558 minutes (93 times) in Boryeong, if experimental limitations such as the insufficient availability of aircraft is not considered. The seasonal average results showed that the possible experimental time is the highest in summer at all three stations, which seems to be owing to the highest precipitable water in this period. Using the radar-converted precipitation data, the cloud seeding experiments were shown to be possible for 970-1,406 hours (11-16%) per year in these three regions in South Korea. This long possible experimental time suggests that longer duration, more than the previous period of 1 hour, cloud seeding experiments are available, and can contribute to achieving a large accumulated amount of enhanced rainfall.

• 한국환경보건학회:학술대회논문집
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• 한국환경보건학회 2005년도 Proceedings of KSEH.Minamata Forum
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• pp.115-117
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• 2005

In this study, the methods were developed to measure polycyclic aromatic hydrocarbons(PAHs) in the air, metabolites of pyrene and benzo(a)pyrene via human urine, genetic polymorphisms in human buccal cell for evaluation of the health effects about environmental pollution. We have also performed a preliminary molecular epidemiology study on residents in the metropolitan area and workers in workplace for these method applications.