• Journal of the Korean GEO-environmental Society
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• v.24 no.12
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• pp.61-67
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• 2023

Heat pipes located underground in urban areas and operated under high temperature and pressure conditions can cause large-scale human and economic damage if damaged. In order to predict damage in advance, damage and construction information of heat pipe are analyzed to derive independent variables that have a correlation with frequency of damage, and a simple regression analysis modified model using each variable is applied to the field. However, as the correlation between independent variables applied to the model increases, the independence between variables is harmed and the reliability of the model decreases. In this study, the independence of the pipe diameter, burial depth, insulation level of monitoring system, and disconnection or short circuit of the detection line, which are judged to be interrelated, was tested to derive a method for combining variables and setting categories necessary to apply to the frequency of damage estimation model. For the test of independence, the continuous variables pipe diameter and burial depth were each converted into three categories, insulation level of monitoring system was converted into two categories, and the categorical variable disconnection or short circuit of the detection line status was kept as two categories. As a result of the test of independence, p-value between pipe diameter and burial depth, level of monitoring system and disconnection or short circuit of the detection line was lower than the significance level (α = 0.05), indicating a large correlation between them. Therefore, the pipe diameter and burial depth were combined into one variable, and the categories of the combined variable were set to 9 considering the previously set categories. The insulation level of monitoring system and the disconnection or short circuit of the detection line were also combined into one variable. Since the insulation level is unreliable when the detection line status is disconnection or short circuit, the categories of the combined variable were set to 3.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Journal of the Korean Society of Food Science and Nutrition
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• v.41 no.6
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• pp.861-869
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• 2012

This study was conducted to investigate the purchasing status and to compare supplier performance evaluations between competitive bidding and negotiated contracts in school foodservice in Changwon, Korea. A total of 190 questionnaires were distributed and 167 (return rate 87.9%) were collected from June 29 to September 28, 2010, and then a total of 151 (analysis rate 79.5%) were used for the final analysis. First, 91.4% of food product purchases for school meals were contracted through competitive bidding, especially limited competitive bidding. It mainly consisted of agricultural products, processed food, and eco-friendly agricultural products (fruit). Second, 78.8% of schools purchased food products by negotiated contracts, while single negotiation accounted for 59.7%. Food products by negotiated contract consisted of meat, kimchi, and fish. Third, the purchase status of competitive bidding and negotiated contracts showed a significant difference in agricultural products (p<0.001), fish (p<0.001), meats (p<0.001), poultry (p<0.001), antibiotic-free poultry (p<0.001), eco-friendly grain (p<0.001), eco-friendly agricultural products (fruit) (p<0.001), eco-friendly processed food (p<0.001), processed products (p<0.001), milk (p<0.001) and general grain (p<0.001) except for kimchi. Fourth, comparative analysis of supplier performance evaluation (on a 5-point Likert scale) of school foodservice showed that price of product of competitive bidding (3.73) was significantly higher than that of negotiated contract (2.95) (p<0.001), and the overall performance level of the negotiated contract (3.85) was significantly higher than that of competitive bidding (3.61) (p<0.01). The supplier performance evaluation levels of product packaging (p<0.01), product quality at the time of delivery (p<0.001), hygiene of products (p<0.001), consistency to specification (p<0.001), swiftness of return and exchange (p<0.001), emergency delivery (p<0.001), service of delivery staff (p<0.05), and handling of complaints (p<0.001) of negotiated contracts were significantly higher than those of competitive bidding of school foodservice. In conclusion, school foodservice selected food suppliers both by adopting competitive bidding and negotiated contracts. And there was a significant difference of school foodservice supplier performance between competitive bidding and negotiated contracts in Changwon, Korea.