• Title/Summary/Keyword: 측정 불확실성

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Analysis on the Water Footprint of Crystalline Silicon PV System (결정질 실리콘 태양광시스템의 물 발자국 산정에 대한 연구)

  • Na, Won-Cheol;Kim, Younghwan;Kim, Kyung Nam;Lee, Kwan-Young
    • Clean Technology
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    • v.20 no.4
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    • pp.449-456
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    • 2014
  • There has been increasing concerns for the problems of water security in countries, caused by the frequent occurrence of localized drought due to the climate change and uncertainty of water balance. The importance of fresh water is emphasized as considerable amount of usable fresh water is utilized for power generation sector producing electricity. PV power system, the source of renewable energy, consumes water for the every steps of life cycle: manufacturing, installation, and operation. However, it uses relatively less water than the traditional energy sources such as thermal power and nuclear power sources. In this study, to find out the use of water for the entire process of PV power system from extracting raw materials to operating the system, the footprint of water in the whole process is measured to be analyzed. Measuring the result, the PV water footprint of value chain was $0.989m^3/MWh$ and the water footprint appeared higher specially in poly-Si and solar cell process. The following two reasons explain it: poly-Si process is energy-intensive process and it consumes lots of cooling water. In solar cell process, deionized water is used considerably for washing a high-efficiency crystalline silicon. It is identified that PV system is the source using less water than traditional ones, which has a critical value in saving water. In discussing the future energy policy, it is vital to introduce the concept of water footprint as a supplementary value of renewable energy.

Analysis on Results and Changes in Recent Forecasting of Earthquake and Space Technologies in Korea and Japan (한국과 일본의 지진재해 및 우주이용 기술예측에 대한 최근의 변화 분석)

  • Ahn, Eun-Young
    • Economic and Environmental Geology
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    • v.55 no.4
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    • pp.421-428
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    • 2022
  • This study analyzes emerging earthquake and space use technologies from the latest Korean and Japanese scientific and technological foresights in 2022 and 2019, respectively. Unlike the earthquake prediction and early warning technologies presented in the 2017 study, the emerging earthquake technologies in 2022 in Korea was described as an earthquake/complex disaster information technology and public data platform. Many detailed future technologies were presented in Japan's 2019 survey, which includes largescale earthquake prediction, induced earthquake, national liquefaction risk, wide-scale stress measurement; and monitoring by Internet of Things (IoT) or artificial intelligence (AI) observation & analysis. The latest emerging space use technology in Korea and Japan were presented in more detail as robotic mining technology for water/ice, Helium-3, and rare earth metals, and manned station technology that utilizes local resources on the moon and Mars. The technological realization year forecasting in 2019 was delayed by 4-10 years from the prediction in 2015, which could be greater due to the Corona 19 epidemic, the declaration of carbon neutrality in Korea and Japan in 2020 and the Russo-Ukrainian War in 2022. However, it is required to more active research on earthquake and space technologies linked to information technology.

Development of a Water Quality Indicator Prediction Model for the Korean Peninsula Seas using Artificial Intelligence (인공지능 기법을 활용한 한반도 해역의 수질평가지수 예측모델 개발)

  • Seong-Su Kim;Kyuhee Son;Doyoun Kim;Jang-Mu Heo;Seongeun Kim
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.29 no.1
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    • pp.24-35
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    • 2023
  • Rapid industrialization and urbanization have led to severe marine pollution. A Water Quality Index (WQI) has been developed to allow the effective management of marine pollution. However, the WQI suffers from problems with loss of information due to the complex calculations involved, changes in standards, calculation errors by practitioners, and statistical errors. Consequently, research on the use of artificial intelligence techniques to predict the marine and coastal WQI is being conducted both locally and internationally. In this study, six techniques (RF, XGBoost, KNN, Ext, SVM, and LR) were studied using marine environmental measurement data (2000-2020) to determine the most appropriate artificial intelligence technique to estimate the WOI of five ecoregions in the Korean seas. Our results show that the random forest method offers the best performance as compared to the other methods studied. The residual analysis of the WQI predicted score and actual score using the random forest method shows that the temporal and spatial prediction performance was exceptional for all ecoregions. In conclusion, the RF model of WQI prediction developed in this study is considered to be applicable to Korean seas with high accuracy.

Estimation of Pollutant EMCs and Loadings in Highway Runoff (국내 고속도로 강우 유출수의 EMCs 및 유출 부하량 산정)

  • Kim, Lee-Hyung;Ko, Seok-Oh;Lee, Byung-Sik;Kim, Sunggil
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.2B
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    • pp.225-231
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    • 2006
  • The nonpoint source control is based on TPLMS (Total Pollution Load Management System) program. Recently, the Ministry of Environment in Korea has programed TPLMS for 4 major large rivers to improve the water quality in rivers by controling the total pollutant loadings from the watershed area. Usually the urbanization is the main pollutant sources, particularly for nonpoint pollutants, because of high imperviousness and high pollutant mass emissions. The stormwater runoff from urban areas is containing various pollutants such as sediments, metals and toxic chemicals due to human and vehicle activities. Of the various landuses, the highways are highly polluted landuses because of high pollutant accumulation rate by vehicle activities during dry periods. Therefore, this research is achieved to provide pollutant EMCs (Event Mean Concentrations) and mass loadings washed-off from highways during rainfall periods. Five monitoring locations were equipped with an automatic rainfall gage and an flow meter. The results show that the EMC ranges for 95% confidence intervals in highway land use are 45.52-125.76 mg/L for TSS, 52.04-95.48 mg/L for COD, 1.77-4.48 mg/L for TN, 0.29-0.54 mg/L for TP. The ranges of washed- off mass loading are $712.7-2,418.4mg/m^2$ for TSS and $684.1-1,779.6mg/m^2$ for COD.

The Precision Test Based on States of Bone Mineral Density (골밀도 상태에 따른 검사자의 재현성 평가)

  • Yoo, Jae-Sook;Kim, Eun-Hye;Kim, Ho-Seong;Shin, Sang-Ki;Cho, Si-Man
    • The Korean Journal of Nuclear Medicine Technology
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    • v.13 no.1
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    • pp.67-72
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    • 2009
  • Purpose: ISCD (International Society for Clinical Densitometry) requests that users perform mandatory Precision test to raise their quality even though there is no recommendation about patient selection for the test. Thus, we investigated the effect on precision test by measuring reproducibility of 3 bone density groups (normal, osteopenia, osteoporosis). Materials and Methods: 4 users performed precision test with 420 patients (age: $57.8{\pm}9.02$) for BMD in Asan Medical Center (JAN-2008 ~ JUN-2008). In first group (A), 4 users selected 30 patient respectively regardless of bone density condition and measured 2 part (L-spine, femur) in twice. In second group (B), 4 users measured bone density of 10 patients respectively in the same manner of first group (A) users but dividing patient into 3 stages (normal, osteopenia, osteoporosis). In third group (C), 2 users measured 30 patients respectively in the same manner of first group (A) users considering bone density condition. We used GE Lunar Prodigy Advance (Encore. V11.4) and analyzed the result by comparing %CV to LSC using precision tool from ISCD. Check back was done using SPSS. Results: In group A, the %CV calculated by 4 users (a, b, c, d) were 1.16, 1.01, 1.19, 0.65 g/$cm^2$ in L-spine and 0.69, 0.58, 0.97, 0.47 g/$cm^2$ in femur. In group B, the %CV calculated by 4 users (a, b, c, d) were 1.01, 1.19, 0.83, 1.37 g/$cm^2$ in L-spine and 1.03, 0.54, 0.69, 0.58 g/$cm^2$ in femur. When comparing results (group A, B), we found no considerable differences. In group C, the user_1's %CV of normal, osteopenia and osteoporosis were 1.26, 0.94, 0.94 g/$cm^2$ in L-spine and 0.94, 0.79, 1.01 g/$cm^2$ in femur. And the user_2's %CV were 0.97, 0.83, 0.72 g/$cm^2$ L-spine and 0.65, 0.65, 1.05 g/$cm^2$ in femur. When analyzing the result, we figured out that the difference of reproducibility was almost not found but the differences of two users' several result values have effect on total reproducibility. Conclusions: Precision test is a important factor of bone density follow up. When Machine and user's reproducibility is getting better, it’s useful in clinics because of low range of deviation. Users have to check machine's reproducibility before the test and keep the same mind doing BMD test for patient. In precision test, the difference of measured value is usually found for ROI change caused by patient position. In case of osteoporosis patient, there is difficult to make initial ROI accurately more than normal and osteopenia patient due to lack of bone recognition even though ROI is made automatically by computer software. However, initial ROI is very important and users have to make coherent ROI because we use ROI Copy function in a follow up. In this study, we performed precision test considering bone density condition and found LSC value was stayed within 3%. There was no considerable difference. Thus, patient selection could be done regardless of bone density condition.

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The Study on the Confidence Building for Evaluation Methods of a Fracture System and Its Hydraulic Conductivity (단열체계 및 수리전도도의 해석신뢰도 향상을 위한 평가방법 연구)

  • Cho Sung-Il;Kim Chun-Soo;Bae Dae-Seok;Kim Kyung-Su;Song Moo-Young
    • The Journal of Engineering Geology
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    • v.15 no.2 s.42
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    • pp.213-227
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    • 2005
  • This study aims to assess the problems with investigation method and to suggest the complementary solutions by comparing the predicted data from surface investigation with the outcome data from underground cavern. In the study area, one(NE-1) of 6 fracture zones predicted during the surface investigation was only confirmed in underground caverns. Therefore, it is necessary to improve the confidence level for prediction. In this study, the fracture classification criteria was quantitatively suggested on the basis of the BHTV images of NE-1 fracture zone. The major orientation of background fractures in rock mass was changed at the depth of the storage cavern, the length and intensity were decreased. These characteristics result in the deviation of predieted predicted fracture properties and generate the investigation bias depending on the bore hole directions and investigated scales. The evaluation of hydraulic connectivity in the surface investigation stage needs to be analyze by the groundwater pressures and hydrochemical properties from the monitoring bore hole(s) equipped with a double completion or multi-packer system during the test bore hole is pumping or injecting. The hydraulic conductivities in geometric mean measured in the underground caverns are 2-3 times lower than those from the surface and furthermore the horizontal hydraulic conductivity in geometric mean is six times lower than the vertical one. To improve confidence level of the hydraulic conductivity, the orientation of test hole should be considered during the analysis of the hydraulic conductivity and the methodology of hydro-testing and interpretation should be based on the characteristics of rock mass and investigation purposes.

Optimum Design of Soil Nailing Excavation Wall System Using Genetic Algorithm and Neural Network Theory (유전자 알고리즘 및 인공신경망 이론을 이용한 쏘일네일링 굴착벽체 시스템의 최적설계)

  • 김홍택;황정순;박성원;유한규
    • Journal of the Korean Geotechnical Society
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    • v.15 no.4
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    • pp.113-132
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    • 1999
  • Recently in Korea, application of the soil nailing is gradually extended to the sites of excavations and slopes having various ground conditions and field characteristics. Design of the soil nailing is generally carried out in two steps, The First step is to examine the minimum safety factor against a sliding of the reinforced nailed-soil mass based on the limit equilibrium approach, and the second step is to check the maximum displacement expected to occur at facing using the numerical analysis technique. However, design parameters related to the soil nailing system are so various that a reliable design method considering interrelationships between these design parameters is continuously necessary. Additionally, taking into account the anisotropic characteristics of in-situ grounds, disturbances in collecting the soil samples and errors in measurements, a systematic analysis of the field measurement data as well as a rational technique of the optimum design is required to improve with respect to economical efficiency. As a part of these purposes, in the present study, a procedure for the optimum design of a soil nailing excavation wall system is proposed. Focusing on a minimization of the expenses in construction, the optimum design procedure is formulated based on the genetic algorithm. Neural network theory is further adopted in predicting the maximum horizontal displacement at a shotcrete facing. Using the proposed procedure, various effects of relevant design parameters are also analyzed. Finally, an optimized design section is compared with the existing design section at the excavation site being constructed, in order to verify a validity of the proposed procedure.

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Monte-Carlo Simulations of Non-ergodic Solute Transport from Line Sources in Isotropic Mildly Heterogeneous Aquifers (불균질 등방 대수층 내 선형오염원으로부터 기원된 비에르고딕 용질 이동에 관한 몬테카를로 시뮬레이션)

  • Seo Byong-min
    • Journal of Soil and Groundwater Environment
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    • v.10 no.6
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    • pp.20-31
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    • 2005
  • Three dimensional Monte-Carlo simulations of non-ergodic transport of a lion-reactive solute plume by steady-state groundwater flow under a uniform mean velocity in isotropic heterogeneous aquifers were conducted. The log-normally distributed hydraulic conductivity, K(x), is modeled as a random field. Significant efforts are made to reduce tile simulation uncertainties. Ensemble averages of the second spatial moments of the plume and plume centroid variances were simulated with 1600 Monte Carlo runs for three variances of log K, ${\sigma}_Y^2=0.09,\;0.23$, and 0.46, and three dimensionless lengths of line plume sources normal to the mean velocity. The simulated second spatial moment and the plume centroid variance in longitudinal direction fit well to the first order theoretical results while the simulated transverse moments are generally larger than the first order results. The first order theoretical results significantly underestimated the simulated dimensionless transverse moments for the aquifers of large ${\sigma}_Y^2$ and large dimensionless time. The ergodic condition for the second spatial moments is far from reaching in all cases simulated, and transport In transverse directions may reach ergodic condition much slower than that in longitudinal direction. The evolution of the contaminant transported in a heterogeneous aquifer is not affected by the shape of the initial plume but affected mainly by the degree of the heterogeneity and the size of the initial plume.

Study on the Possibility of Estimating Surface Soil Moisture Using Sentinel-1 SAR Satellite Imagery Based on Google Earth Engine (Google Earth Engine 기반 Sentinel-1 SAR 위성영상을 이용한 지표 토양수분량 산정 가능성에 관한 연구)

  • Younghyun Cho
    • Korean Journal of Remote Sensing
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    • v.40 no.2
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    • pp.229-241
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    • 2024
  • With the advancement of big data processing technology using cloud platforms, access, processing, and analysis of large-volume data such as satellite imagery have recently been significantly improved. In this study, the Change Detection Method, a relatively simple technique for retrieving soil moisture, was applied to the backscattering coefficient values of pre-processed Sentinel-1 synthetic aperture radar (SAR) satellite imagery product based on Google Earth Engine (GEE), one of those platforms, to estimate the surface soil moisture for six observatories within the Yongdam Dam watershed in South Korea for the period of 2015 to 2023, as well as the watershed average. Subsequently, a correlation analysis was conducted between the estimated values and actual measurements, along with an examination of the applicability of GEE. The results revealed that the surface soil moisture estimated for small areas within the soil moisture observatories of the watershed exhibited low correlations ranging from 0.1 to 0.3 for both VH and VV polarizations, likely due to the inherent measurement accuracy of the SAR satellite imagery and variations in data characteristics. However, the surface soil moisture average, which was derived by extracting the average SAR backscattering coefficient values for the entire watershed area and applying moving averages to mitigate data uncertainties and variability, exhibited significantly improved results at the level of 0.5. The results obtained from estimating soil moisture using GEE demonstrate its utility despite limitations in directly conducting desired analyses due to preprocessed SAR data. However, the efficient processing of extensive satellite imagery data allows for the estimation and evaluation of soil moisture over broad ranges, such as long-term watershed averages. This highlights the effectiveness of GEE in handling vast satellite imagery datasets to assess soil moisture. Based on this, it is anticipated that GEE can be effectively utilized to assess long-term variations of soil moisture average in major dam watersheds, in conjunction with soil moisture observation data from various locations across the country in the future.

A Study on the Factors Influencing Technology Innovation Capability on the Knowledge Management Performance of the Company: Focused on Government Small and Medium Venture Business R&D Business (기술혁신역량이 기업의 지식경영성과에 미치는 요인에 관한 연구: 정부 중소벤처기업 R&D사업을 중심으로)

  • Seol, Dong-Cheol;Park, Cheol-Woo
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.15 no.4
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    • pp.193-216
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    • 2020
  • Due to the recent mid- to long-term slump and falling growth rates in the global economy, interest in organizational structures that create new products or services as a new alternative to survive and develop in an opaque environment both internally and externally, and enhance organizational sustainability through changes in production methods and business innovation is increasing day by day. In this atmosphere, we agree that the growth of small and medium-sized venture companies has a significant impact on the national economy, and various efforts are being made to enhance the technological innovation capabilities of the members so that these small and medium-sized venture companies can enhance and sustain their performance. The purpose of this study is also to investigate how the technological innovation capabilities of small and medium-sized venture companies correlate with the performance of knowledge management and to analyze the role of network capabilities to organize the strategic activities of enterprise to obtain the resources and organizational capabilities to be used for value creation from external networks. In other words, research was conducted on the impact of technological innovation capabilities of small and medium venture companies on knowledge management performance by using network capabilities as parameters. Therefore, in this study, we would like to verify the hypothesis that innovation capabilities will have a positive impact on knowledge management performance by using network capabilities of small and medium venture companies. Economic activities based on technological innovation capabilities should respond quickly to new changes in an environment where uncertainty has increased, and lead to macro-economic growth and development as well as overcoming long-term economic downturns so that they can become the nation's new growth engine as well as sustainable growth and survival of the organization. In addition, this study was conducted by setting the most important knowledge management performance within the organization as a dependent variable. As a result, R&D and learning capabilities among technological innovation capabilities have no impact on financial performance. In contrast, it was shown that corporate innovation activities have a positive impact on both financial and non-financial performance. The fact that non-financial factors such as quality and productivity improvement are identified in the management of small and medium-sized venture companies utilizing their technological innovation capabilities is contrary to a number of studies by those corporate innovation activities affect financial performance during prior research. The reason for this result is that research companies have been out of start-up companies for more than seven years, but sales are less than 10 billion won, and unlike start-up companies, R&D and learning capabilities have more positive effects on intangible non-financial performance than financial performance. Corporate innovation activities have been shown to have a positive (+) impact on both financial and non-financial performance, while R&D and learning capabilities have a positive (+) impact on financial performance by parameters of network capability. Corporate innovation activities have been shown to have no impact on both financial and non-financial performance, and R&D and learning capabilities have no impact on non-financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance. It could be seen that the parameter effects of network competency are limited to when R&D and learning competencies are derived from quantitative financial performance.