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A Study on the Systematic Integration of WASP5 Water Quality Model with a GIS (GIS와 WASP5 수질모델의 유기적 통합에 관한 연구)

  • 최성규;김계현
    • Spatial Information Research
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    • v.9 no.2
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    • pp.291-307
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    • 2001
  • In today's environmental engineering practice, many technologies such as GIS have been adopted to analyze chemical and biological process in water bodies and pollutants movements on the land surface. However, the linkage between spatially represented land surface pollutants and the in-stream processes has been relatively weak. This lack of continuity needs to develop a method in order to link the spatially-based pollutant source characterization with the water quality modeling. The objective of this thesis was to develop a two-way(forward and backward) link between ArcView GIS software and the USEPA water quality model, WASP5. This thesis includes a literature review, the determination of the point source and non-point source loadings from WASP5 modeling, and the linkage of a GIS with WASP5 model. The GIS and model linkage includes pre-processing of the input data within a GIS to provide necessary information for running a model in the forms of external input files. The model results has been post-processed and stored in the GIS database to be reviewed in a user defined form such as a chart, or a table. The interface developed from this study would provide efficient environment to support the easier decision making form water quality management.

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Inhibitory Effect of Rumex Crispus L. Fraction on Adipocyte Differentiation in 3T3-L1 Cells (소리쟁이 분획물의 지방세포 분화 억제 효과)

  • Park, Sung-Jin;Choi, Jun-Hyeok;Jung, Yeon-Seop;Yu, Mi Hee
    • Korean Journal of Food Science and Technology
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    • v.45 no.1
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    • pp.90-96
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    • 2013
  • The anti-obesity effect of ethanol xtract and their fractions from Rumex Crispus L. on the differentiation of 3T3-L1 pre-adipocytes to adipocytes was investigated by suppressing adipocyte differentiation and lipid accumulation with Oil red O assay, western blot and real-time PCR analysis. Ethyl acetate fraction of Rumex crispus L. significantly inhibited adipocyte differentiation when treated during the adipocyte differentiation process, as assessed by measuring fat accumulation using Oil red O staining. In inducing differentiation of 3T3-L1 preadipocytes in the presence of an adipogenic cocktail, isobutylmethylxanthine (IBMX), dexamethasone- and insulin-along with ethyl acetate fraction residue processing treatment significantly decreased protein expression of obesity-related proteins, such as peroxisome-proliferators-activated-receptor-${\gamma}$ ($PPAR{\gamma}$) and CCAAT enhancer-binding-proteins ${\alpha}$ ($C/EBP{\alpha}$). These results indicate that ethyl acetate fraction of Rumex crispus L. is the most effective candidate for preventing obesity. However further studies will be needed to identify the active compounds that confer the anti-obesity activity of ethyl acetate fraction from Rumex crispus L.

Development of an AIDA(Automatic Incident Detection Algorithm) for Uninterrupted Flow Based on the Concept of Short-term Displaced Flow (연속류도로 단기 적체 교통량 개념 기반 돌발상황 자동감지 알고리즘 개발)

  • Lee, Kyu-Soon;Shin, Chi-Hyun
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.15 no.2
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    • pp.13-23
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    • 2016
  • Many traffic centers are highly hesitant in employing existing Automatic Incident Detection Algorithms due to high false alarm rate, low detection rate, and enormous effort taken in maintaining algorithm parameters, together with complex algorithm structure and filtering/smoothing process. Concerns grow over the situation particularly in Freeway Incident Management Area This study proposes a new algorithm and introduces a novel concept, the Displaced Flow Index (DiFI) which is similar to a product of relative speed and relative occupancy for every execution period. The algorithm structure is very simple, also easy to understand with minimum parameters, and could use raw data without any additional pre-processing. To evaluate the performance of the DiFI algorithm, validation test on the algorithm has been conducted using detector data taken from Naebu Expressway in Seoul and following transferability tests with Gyeongbu Expressway detector data. Performance test has utilized many indices such as DR, FAR, MTTD (Mean Time To Detect), CR (Classification Rate), CI (Composite Index) and PI (Performance Index). It was found that the DR is up to 100%, the MTTD is a little over 1.0 minutes, and the FAR is as low as 2.99%. This newly designed algorithm seems promising and outperformed SAO and most popular AIDAs such as APID and DELOS, and showed the best performance in every category.

A Proposal of Remaining Useful Life Prediction Model for Turbofan Engine based on k-Nearest Neighbor (k-NN을 활용한 터보팬 엔진의 잔여 유효 수명 예측 모델 제안)

  • Kim, Jung-Tae;Seo, Yang-Woo;Lee, Seung-Sang;Kim, So-Jung;Kim, Yong-Geun
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.22 no.4
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    • pp.611-620
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    • 2021
  • The maintenance industry is mainly progressing based on condition-based maintenance after corrective maintenance and preventive maintenance. In condition-based maintenance, maintenance is performed at the optimum time based on the condition of equipment. In order to find the optimal maintenance point, it is important to accurately understand the condition of the equipment, especially the remaining useful life. Thus, using simulation data (C-MAPSS), a prediction model is proposed to predict the remaining useful life of a turbofan engine. For the modeling process, a C-MAPSS dataset was preprocessed, transformed, and predicted. Data pre-processing was performed through piecewise RUL, moving average filters, and standardization. The remaining useful life was predicted using principal component analysis and the k-NN method. In order to derive the optimal performance, the number of principal components and the number of neighbor data for the k-NN method were determined through 5-fold cross validation. The validity of the prediction results was analyzed through a scoring function while considering the usefulness of prior prediction and the incompatibility of post prediction. In addition, the usefulness of the RUL prediction model was proven through comparison with the prediction performance of other neural network-based algorithms.

Descent Dataset Generation and Landmark Extraction for Terrain Relative Navigation on Mars (화성 지형상대항법을 위한 하강 데이터셋 생성과 랜드마크 추출 방법)

  • Kim, Jae-In
    • Korean Journal of Remote Sensing
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    • v.38 no.6_1
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    • pp.1015-1023
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    • 2022
  • The Entry-Descent-Landing process of a lander involves many environmental and technical challenges. To solve these problems, recently, terrestrial relative navigation (TRN) technology has been essential for landers. TRN is a technology for estimating the position and attitude of a lander by comparing Inertial Measurement Unit (IMU) data and image data collected from a descending lander with pre-built reference data. In this paper, we present a method for generating descent dataset and extracting landmarks, which are key elements for developing TRN technologies to be used on Mars. The proposed method generates IMU data of a descending lander using a simulated Mars landing trajectory and generates descent images from high-resolution ortho-map and digital elevation map through a ray tracing technique. Landmark extraction is performed by an area-based extraction method due to the low-textured surfaces on Mars. In addition, search area reduction is carried out to improve matching accuracy and speed. The performance evaluation result for the descent dataset generation method showed that the proposed method can generate images that satisfy the imaging geometry. The performance evaluation result for the landmark extraction method showed that the proposed method ensures several meters of positioning accuracy while ensuring processing speed as fast as the feature-based methods.

Automatic Generation of Bibliographic Metadata with Reference Information for Academic Journals (학술논문 내에서 참고문헌 정보가 포함된 서지 메타데이터 자동 생성 연구)

  • Jeong, Seonki;Shin, Hyeonho;Ji, Seon-Yeong;Choi, Sungphil
    • Journal of the Korean Society for Library and Information Science
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    • v.56 no.3
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    • pp.241-264
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    • 2022
  • Bibliographic metadata can help researchers effectively utilize essential publications that they need and grasp academic trends of their own fields. With the manual creation of the metadata costly and time-consuming. it is nontrivial to effectively automatize the metadata construction using rule-based methods due to the immoderate variety of the article forms and styles according to publishers and academic societies. Therefore, this study proposes a two-step extraction process based on rules and deep neural networks for generating bibliographic metadata of scientific articlles to overcome the difficulties above. The extraction target areas in articles were identified by using a deep neural network-based model, and then the details in the areas were analyzed and sub-divided into relevant metadata elements. IThe proposed model also includes a model for generating reference summary information, which is able to separate the end of the text and the starting point of a reference, and to extract individual references by essential rule set, and to identify all the bibliographic items in each reference by a deep neural network. In addition, in order to confirm the possibility of a model that generates the bibliographic information of academic papers without pre- and post-processing, we conducted an in-depth comparative experiment with various settings and configurations. As a result of the experiment, the method proposed in this paper showed higher performance.

Relationship between Alcohol Use Disorders Identification Test Fractional Anisotropy Value of Diffusion Tensor Image in Brain White Matter Region (알코올 선별 검사법(Alcohol Use Disorders Identification Test)과 뇌 백질 영역의 확산텐서 비등방도 계측 값의 관련성)

  • Lee, Chi Hyung;Kim, Gyeong Rip;Kwak, Jong Hyeok
    • Journal of the Korean Society of Radiology
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    • v.16 no.5
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    • pp.575-583
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    • 2022
  • Magnetic resonance diffusion tensor imaging (DTI) has revealed the disruption of brain white matter microstructure in normal aging and alcoholism undetectable with conventional structural MR imaging. we plan to analyze the FA measurements of the ROI of dangerous drinkers selected from Alcohol Use Disorders Identification Test (AUDIT) and Tract-Based Spatial Statics (TBSS) tool was used to extract FA values in the ROI from the image acquired through the pre-processing process. TBSS has a higher sensitivity of the FA value and MD value in the white matter than the brain gray matter, and has the advantage of quantitatively deriving the unlimited degree of brain nerve fibers, and more specialized in the brain white matter. We plan to analyze the fractional anisotropy (FA) measurement value for damage by selecting the center of the anatomical structure of the white matter region of the brain with high anisotropy among the brain neural networks that are particularly vulnerable to alcohol as the region of interest (ROI). In this study, we expected that alcohol causes damage to the brain white matter microstructure from FA value in various areas including both Choroid plexus. Especially, In the case of the moderate drunker, the mean value of FA in Lt, Rt. Choroid plexus was 0.2831 and 0.2872, whereas, in the case of the severe drunker, the mean value of FA was 0.1972 and 0.1936. We found that the higher the score on the AUDIT scale, the lower the FA value in ROI region of the brain white matter. Using the AUDIT scale, the guideline for the FA value of DTI can be presented, and it is possible to select a significant number of potentially severe drinkers. In other words, AUDIT was proved as useful tool in screening and discrimination of severe drunker through DTI.

Designing a Blockchain-based Smart Contract for Seafarer Wage Payment (블록체인 기반 선원 임금지불을 위한 스마트 컨트랙트 설계)

  • Yoo, Sang-Lok;Kim, Kwang-Il;Ahn, Jang-Young
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.27 no.7
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    • pp.1038-1043
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    • 2021
  • Guaranteed seafarer wage payment is essential to ensure a stable supply of seafarers. However, disputes over non-payment of wages to seafarers often occur. In this study, an automatic wage payment system was designed using a blockchain-based smart contract to resolve the problem of seafarers' wage arrears. The designed system consists of an information register, a matching processing unit, a review rating management unit, and wage remittance before deploying smart contracts. The matching process was designed to send an automatic notification to seafarers and shipowners if the sum of the weight of the four variables, namely wages, ship type/fishery, position, and license, exceeded a pre-defined threshold. In addition, a review rating management system, based on a combination of mean and median, was presented to serve as a medium to mutually fulfill the normal working conditions. The smart contract automatically fulfills the labor contract between the parties without an intermediary. This system will naturally resolve problems such as fraudulent advance payment to seafarers, embezzlement by unregistered employment agencies, overdue wages, and forgery of seafarers' books. If this system design is commercialized and institutionally activated, it is expected that stable wages will be guaranteed to seafarers, and in turn, the difficulties in human resources supply will be solved. We plan to test it in a local environment for further developing this system.

Development of SVM-based Construction Project Document Classification Model to Derive Construction Risk (건설 리스크 도출을 위한 SVM 기반의 건설프로젝트 문서 분류 모델 개발)

  • Kang, Donguk;Cho, Mingeon;Cha, Gichun;Park, Seunghee
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.43 no.6
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    • pp.841-849
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    • 2023
  • Construction projects have risks due to various factors such as construction delays and construction accidents. Based on these construction risks, the method of calculating the construction period of the construction project is mainly made by subjective judgment that relies on supervisor experience. In addition, unreasonable shortening construction to meet construction project schedules delayed by construction delays and construction disasters causes negative consequences such as poor construction, and economic losses are caused by the absence of infrastructure due to delayed schedules. Data-based scientific approaches and statistical analysis are needed to solve the risks of such construction projects. Data collected in actual construction projects is stored in unstructured text, so to apply data-based risks, data pre-processing involves a lot of manpower and cost, so basic data through a data classification model using text mining is required. Therefore, in this study, a document-based data generation classification model for risk management was developed through a data classification model based on SVM (Support Vector Machine) by collecting construction project documents and utilizing text mining. Through quantitative analysis through future research results, it is expected that risk management will be possible by being used as efficient and objective basic data for construction project process management.

Cost Analysis of the Recent Projects for Overseas Vanadium Metallurgical Processing Plants (해외 바나듐 제련 플랜트 관련 사업 비용 분석)

  • Gyuri Kim;Sang-hun Lee
    • Resources Recycling
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    • v.33 no.3
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    • pp.3-11
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    • 2024
  • This study addressed the cost structure of metallurgical plants for vanadium recovery or production, which were previously planned or implemented. Vanadium metallurgy consists of several sub-processes such as such as pretreatment, roasting, leaching, precipitation, and filtration, in order to finally produce vanadium pentoxide. Here, lots of costs should be spent for such plants, in which these costs are largely divided into CAPEX (Capital Expenditure) and OPEX (Operational Expenditure). As a result, the capacities (feed input rates) and vanadium contents are various along the target projects for this study. However, final production rates and grades of vanadium pentoxide showed relatively small differences. In addition, a noticeable correlation is found between capacities and specific operating costs, in that a steadily decreasing trend is described with a non-linear curve with around -0.3 power. Therefore, for the plant capacity below 100,000 tons per year, the specific operating cost rapidly decreases as the capacity increases, whereas the cost remains relatively stable in the range of 0.6 to 1.2 million tons per year of the capacity. From a technical perspective, effective optimization of the metallurgical process plant can be achieved by improving vanadium recovery rate in the pre-treatment and/or roasting-leaching processes. Finally, the results of this study should be updated through future research with on-going field verification and further detailed cost analysis.