• Title/Summary/Keyword: Regional prediction

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Numerical Simulation of the Water Temperature in the Al-Zour Area of Kuwait

  • Lee, Myung Eun;Kim, Gunwoo
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.25 no.3
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    • pp.334-343
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    • 2019
  • The Al-Zour coastal area, located in southern Kuwait, is a region of concentrated industrial water use, seawater intake, and the outfall of existing power plants. The Al-Zour LNG import facility project is ongoing and there are two issues regarding the seawater temperature in this area that must be considered: variations in water temperature under local meteorology and an increase in water temperature due to the expansion of the thermal discharge of expanded power plant. MIKE 3 model was applied to simulate the water temperature from June to July, based on re-analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the thermal discharge input from adjacent power plants. The annual water temperatures of two candidate locations of the seawater intake for the Al-Zour LNG re-gasification facility were measured in 2017 and compared to the numerical results. It was determined that the daily seawater temperature is mainly affected by thermal plume dispersion oscillating with the phase of the tidal currents. The regional meteorological conditions such as air temperature and tidal currents, also contributed a great deal to the prediction of seawater temperature.

Application of KORSLE to Estimate Soil Erosion at Field Scale (한국형 토양유실공식에 의한 토양유실량 현장예측)

  • Song, Jae Min;Yang, Jae E;Lim, Kyoung Jae;Park, Youn Shik
    • Journal of Soil and Groundwater Environment
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    • v.24 no.5
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    • pp.31-41
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    • 2019
  • In 2013, the Ministry of Environment in South Korea promulgated a new regulatory bulletin that contained revised enforcement ordinance on soil management protocols. The bulletin recommends the use of Universal Soil Loss Equation (USLE) for the soil erosion estimation, but USLE has limited applicability in prediction of soil erosion because it does not allow direct estimation of actual mass of soil erosion. Therefore, there is a great need of revising the protocol to allow direct comparison between the measured and estimated values of soil erosion. The Korean Soil Loss Equation (KORSLE) was developed recently and used to estimate soil loss in two fields as an alternative to existing USLE model. KORSLE was applied to estimate monthly rainfall erosivity indices as well as temporal variation in potential soil loss. The estimated potential soil loss by KORSLE was adjusted with correction factor for direct comparison with measured soil erosion. The result was reasonable since Nash-Stucliff efficiency were 0.8020 in calibration and 0.5089 in validation. The results suggest that KORSLE is an appropriate model as an alternative to USLE to predict soil erosion at field scale.

Proposed ICT-based New Normal Smart Care System Model to Close Health Gap for Older the Elderly

  • YOO, Chae-Hyun;SHIN, Seung-Jung
    • International journal of advanced smart convergence
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    • v.10 no.2
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    • pp.37-44
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    • 2021
  • At the time of entering the super-aged society, the health problem of the elderly is becoming more prominent due to the rapid digital era caused by COVID-19, but the gap between welfare budgets and welfare benefits according to regional characteristics is still not narrowed and there is a significant difference in emergency medical access. In response, this study proposes an ICT-based New Normal Smart Care System (NNSCS) to bridge the gap I n health and medical problems. This is an integrated system model that links the elderly themselves to health care, self-diagnosis, disease prediction and prevention, and emergency medical services. The purpose is to apply location-based technology and motion recognition technology under smartphones and smartwatches (wearable) environments to detect health care and risks, predict and diagnose diseases using health and medical big data, and minimize treatment latency. Through the New Normal Smart Care System (NNSCS), which links health care, prevention, and rapid emergency treatment with easy and simple access to health care for the elderly, it aims to minimize health gaps and solve health problems for the elderly.

Deep Learning Based Electricity Demand Prediction and Power Grid Operation according to Urbanization Rate and Industrial Differences (도시화율 및 산업 구성 차이에 따른 딥러닝 기반 전력 수요 변동 예측 및 전력망 운영)

  • KIM, KAYOUNG;LEE, SANGHUN
    • Journal of Hydrogen and New Energy
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    • v.33 no.5
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    • pp.591-597
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    • 2022
  • Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

A Study on the Real-Time Risk Analysis of Heavy-Snow according to the Characteristics of Traffic and Area (교통과 지역의 특성에 따른 대설의 실시간 피해 위험도 분석 연구)

  • KwangRim, Ha;YongCheol, Jung;JinYoung, Yoo;JunHee, Lee
    • Journal of Korea Society of Industrial Information Systems
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    • v.27 no.6
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    • pp.77-93
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    • 2022
  • In this study, we present an algorithm that analyzes the risk by reflecting regional characteristics for factors affected by direct and indirect damage from heavy-snow. Factors affected by heavy-snow damage by 29 regions are selected as influencing variables, and the concept of sensitivity is derived through the relationship with the amount of damage. A snow damage risk prediction model was developed using a machine learning (XGBoost) algorithm by setting weather conditions (snow cover, humidity, temperature) and sensitivity as independent variables, and setting the risk derived according to changes in the independent variables as dependent variables.

Practical strategies for the prevention and management of chronic postsurgical pain

  • Bo Rim Kim;Soo-Hyuk Yoon;Ho-Jin Lee
    • The Korean Journal of Pain
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    • v.36 no.2
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    • pp.149-162
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    • 2023
  • Chronic postsurgical pain (CPSP) is a multifactorial condition that affects a significant proportion of patients undergoing surgery. The prevention and management of CPSP require the identification of preoperative risk factors to screen high-risk patients and establish appropriate perioperative pain management plans to prevent its development. Active postoperative pain management should be provided to prevent CPSP in patients with severe pain following surgery. These tasks have become important for perioperative team members in the management of CPSP. This review article provides a comprehensive overview of the latest research on the role of perioperative team members in preventing and managing CPSP. Additionally, it highlights practical strategies that can be employed in clinical practice, covering the definition and risk factors for CPSP, including preoperative, intraoperative, and postoperative factors, as well as a risk prediction model. The article also explores various treatments for CPSP, as well as preventive measures, including preemptive analgesia, regional anesthesia, pharmacological interventions, psychoeducational support, and surgical technique modification. This article emphasizes the importance of a comprehensive perioperative pain management plan that includes multidisciplinary interventions, using the transitional pain service as an example. By adopting a multidisciplinary and collaborative approach, perioperative team members can improve patient outcomes, enhance patient satisfaction, and reduce healthcare costs. However, further research is necessary to establish targeted interventions to effectively prevent and manage CPSP.

Prediction of the daily-flow duration curve and streamflow using the regional flow duration curve creation technique (지역화 유황곡선을 작성기법을 이용한 유역의 일유황곡선 및 유량 예측)

  • Choo, Kyung Su;Jeung, Se Jin;Kim, Byung Sik
    • Proceedings of the Korea Water Resources Association Conference
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    • 2020.06a
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    • pp.132-132
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    • 2020
  • 유황곡선은 하천유량의 변동성을 함축적으로 나타내고 연간유량 분석방법(calendar-year method)과 전 자료기간유량분석방법(total-period method)을 이용하여 작성하고 분석할 수 있다. 본 연구는 유황곡선 상에서 유역특성인자들을 포함시켜 작성하는 방법을 제시하였고 지형 및 기상학적 인자를 통해 지역화 시킨 유황곡선을 통해 미계측 유역의 유황곡선을 추정할 수 있는 곡선을 개발하고자 한다. 이를 위해 유역의 특성인자자료를 수집하여 독립변수로 설정하였고 다중회귀분석을 실시하여 변수들을 지역화 시켰다. 지역화 시킨 변수들을 유황곡선에 반영하여 대상지역에서 하나의 유황곡선으로 나타내었다. 도출한 유황곡선을 자료가 있는 지역을 미계측유역이라 가정하고 검증하였다. 검증결과 실제자료와 유사하게 나타나는 것을 확인할 수 있었고 이를 통해 미계측 유역의 유출량 자료가 부족한 유역에 대한 예측과 과거 많은 부분이 결측된 유역에 대한 유출량 예측도 가능할 것이라 판단된다. 또한 강우시나리오를 통해 지형인자가 고려된 유황곡선을 이용한 다양한 자료분석을 실시할 수 있을 것이라 판단된다.

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The Variation of Yield-Related Traits of the QTL Pyramiding Lines for Climate-resilience and Nutrition Uptake in Rice

  • Joong Hyoun Chin
    • Proceedings of the Korean Society of Crop Science Conference
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    • 2022.10a
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    • pp.14-14
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    • 2022
  • Greenhouse gas emissions are one of the critical factors that drive change in rice cropping systems. Within this changing system, less water irrigation and chemical fertilizer are seriously considered, as well combining precision farming technologies with irrigation control. Water and phosphorus (P) fertilizer are two of the most critical inputs in rice cultivation. Due to the lack of water availability in the system, P fertilizer is not available, especially in acidic soil conditions. Moreover, the various types of abiotic stresses, such as drought, high temperature, salinity, submergence, and limited fertilizer result in significant yield loss in the system. Even in the late stage of growth, the waves caused by diseases and insects make the field more unfruitful. Therefore, agronomists and breeders need to identify the secondary phenotypes to estimate the yield loss of when stress appears. The prediction will be clearer if we have a set of markers tagging the causal variation and the associated precise phenotype indices. Although there have been various studies for abiotic stress tolerance, we still lack functional molecular markers and phenotype indices. This is due to the underlying challenges caused by environmental factors in highly unpredictable regional and yearly environmental conditions in the field system. Pupl (phosphorus uptake 1) is still known as the first QTL associated with phosphorus uptake and have been validated in different field crops. Interestingly, some pyramiding lines of Pupl and other QTLs for other stress tolerances showed preferable phenotypes in the yield. Precise physiological studies with the help of genomics are on-going and some results will be discussed.

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Prediction on Busan's Gross Product and Employment of Major Industry with Logistic Regression and Machine Learning Model (로지스틱 회귀모형과 머신러닝 모형을 활용한 주요산업의 부산 지역총생산 및 고용 효과 예측)

  • Chae-Deug Yi
    • Korea Trade Review
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    • v.47 no.2
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    • pp.69-88
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    • 2022
  • This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

Seismic vulnerability of reinforced concrete structures using machine learning

  • Ioannis Karampinis;Lazaros Iliadis
    • Earthquakes and Structures
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    • v.27 no.2
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    • pp.83-95
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    • 2024
  • The prediction of seismic behavior of the existing building stock is one of the most impactful and complex problems faced by countries with frequent and intense seismic activities. Human lives can be threatened or lost, the economic life is disrupted and large amounts of monetary reparations can be potentially required. However, authorities at a regional or national level have limited resources at their disposal in order to allocate to preventative measures. Thus, in order to do so, it is essential for them to be able to rank a given population of structures according to their expected degree of damage in an earthquake. In this paper, the authors present a ranking approach, based on Machine Learning (ML) algorithms for pairwise comparisons, coupled with ad hoc ranking rules. The case study employed data from 404 reinforced concrete structures with various degrees of damage from the Athens 1999 earthquake. The two main components of our experiments pertain to the performance of the ML models and the success of the overall ranking process. The former was evaluated using the well-known respective metrics of Precision, Recall, F1-score, Accuracy and Area Under Curve (AUC). The performance of the overall ranking was evaluated using Kendall's tau distance and by viewing the problem as a classification into bins. The obtained results were promising, and were shown to outperform currently employed engineering practices. This demonstrated the capabilities and potential of these models in identifying the most vulnerable structures and, thus, mitigating the effects of earthquakes on society.