• Title/Summary/Keyword: 복합 의사결정요소

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Forecasting the Busan Container Volume Using XGBoost Approach based on Machine Learning Model (기계 학습 모델을 통해 XGBoost 기법을 활용한 부산 컨테이너 물동량 예측)

  • Nguyen Thi Phuong Thanh;Gyu Sung Cho
    • Journal of Internet of Things and Convergence
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    • v.10 no.1
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    • pp.39-45
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    • 2024
  • Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.

Determination of Target Clean-up Level and Risk-Based Remediation Strategy (위해성에 근거한 정화목표 산정 및 복원전략 수립)

  • Ryu, Hye-Rim;Han, Joon-Kyoung;Nam, Kyoung-Phile
    • Journal of Soil and Groundwater Environment
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    • v.12 no.1
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    • pp.73-86
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    • 2007
  • Risk-based remediation strategy (RBRS) is a consistent decision-making process for the assessment and response to chemical release based on protecting human health and the environment. The decision-making process described integrates exposure and risk assessment practices with site assessment activities and remedial action selection to ensure that the chosen actions are protective of human health and the environment. The general sequences of events in Tier 1 is as follows: initial site assessment, development of conceptual site model with all exposure pathways, data collection on pollutants and receptors, and identification of risk-based screening level (RBSL). If site conditions do not meet RBSL, it needs further site-specific tier evaluation, Tier 2. In most cases, only limited number of exposure pathways, exposure scenarios, and chemicals of concern are considered the Tier 2 evaluation since many are eliminated from consideration during the Tier 1 evaluation. In spite of uncertainties due to the conservatism applied to risk calculations, limitation in site-specific data collections, and variables affecting the selection of target risk levels and exposure factors, RBRS provides us time- and cost-effectiveness of the remedial action. To ensure reliance of the results, the development team should consider land and resource use, cumulative risks, and additive effects. In addition, it is necessary to develop appropriate site assessment guideline and reliable toxicity assessment method, and to study on site-specific parameters and exposure parameters in Korea.

School Dietitians' Perception and Performance on a School Foodservice Menu Evaluation (학교급식 영양(교)사의 메뉴평가에 대한 인식과 시행 현황)

  • Choi, Mi-Kyung;Ahn, Sun-Woo
    • Journal of the Korean Society of Food Science and Nutrition
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    • v.40 no.8
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    • pp.1172-1178
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    • 2011
  • The purpose of this study was to investigate the status of a school foodservice menu evaluation and the perception of the school's dietitian on menu evaluation. Questionnaires were distributed to 448 school dietitians with an official letter, and a total of 292 responses were used for analysis. More than 90% of the respondents stated that a menu evaluation for the school foodservice was necessary. The major barriers to menu evaluation were "excessive workload" and a "lack of know-how", and the expected benefits were "increased satisfaction of customers" and "increased foodservice efficiency". The menu evaluation for "student preferences", "health improvement", and "ease of quality management" categories were performed in more than 45% of schools. The proportion of subjects who answered that "customer satisfaction" and "increased efficiency of foodservice" were expected benefits of menu evaluation were significantly higher in the menu evaluation group (p<0.05).