• Journal of information and communication convergence engineering
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• v.19 no.4
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• pp.221-227
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• 2021

An early warning system was developed to help identify stock status as early as possible. For performance to improve, there needs to be a feature to predict the amount of stock that must be provided and a feature to estimate when to buy goods. This research was conducted to improve the inventory early warning system and optimize the Reminder Block's performance in minimum stock settings. The models used in this study are the single exponential smoothing (SES) method for prediction and the economic order quantity (EOQ) model for determining the quantity. The research was conducted by analyzing the Reminder Block in the early warning system, identifying data needs, and implementing the SES and EOQ mathematical models into the Reminder Block. This research proposes a new Reminder Block that has been added to the SES and EOQ models. It is hoped that this study will help in obtaining accurate information about the time and quantity of repurchases for efficient inventory management.

• Economic and Environmental Geology
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• v.56 no.1
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• pp.65-73
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• 2023

As one of the most vulnerable countries to floods, there should be an increased necessity for accurate and reliable flood forecasting in Indonesia. Therefore, a new prediction model using a machine learning algorithm is proposed to provide daily flood prediction in Indonesia. Data crawling was conducted to obtain daily rainfall, streamflow, land cover, and flood data from 2008 to 2021. The model was built using a Random Forest (RF) algorithm for classification to predict future floods by inputting three days of rainfall rate, forest ratio, and stream flow. The accuracy, specificity, precision, recall, and F1-score on the test dataset using the RF algorithm are approximately 94.93%, 68.24%, 94.34%, 99.97%, and 97.08%, respectively. Moreover, the AUC (Area Under the Curve) of the ROC (Receiver Operating Characteristics) curve results in 71%. The objective of this research is providing a model that predicts flood events accurately in Indonesian regions 3 months prior the day of flood. As a trial, we used the month of June 2022 and the model predicted the flood events accurately. The result of prediction is then published to the website as a warning system as a form of flood mitigation.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.11
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• pp.600-605
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• 2013

Ground source heat pump systems (GSHP) can achieve higher performance of the system, by supplying more efficient heat source to the heat pump, than the conventional air-source heat pump system. But building clients and designers have hesitated to use GSHP systems, due to expensive initial cost, and uncertain economic feasibility. In order to reduce the initial cost, many researches have focused on the energy-pile system, using the structure of the building as a heat exchanger. Even though several experimental studies for the energy-pile system have been conducted, there was not enough data of quantitative evaluation with economic analysis and comprehensive analysis for the energy-pile. In this study, a prediction method has been developed for the energy pile system with barrette pile, using the ground heat transfer model and ground heat exchanger model. Moreover, a feasibility study for the energy pile system with barrette pile was conducted, by performance analysis and LCC assessment. As a result, it was found that the heat exchange rate of a barrette pile was 2.55 kW, and the payback period using LCC analysis was 8.8 years.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.41-47
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• 2022

A reliable prediction model of national occupational accident fatality rate can be used to evaluate level of safety and health protection for workers in a country. Moreover, the socio-economic aspects of occupational accidents can be identified through interpretation of a well-organized prediction model. In this paper, we propose a machine learning based relative comparison methods to predict and interpret a national occupational accident fatality rate based on socio-economic indicators. First, we collected 29 years of the relevant data from 11 developed countries. Second, we applied 4 types of machine learning regression models and evaluate their performance. Third, we interpret the contribution of each input variable using Shapley Additive Explanations(SHAP). As a result, Gradient Boosting Regressor showed the best predictive performance. We found that different patterns exist across countries in accordance with different socio-economic variables and occupational accident fatality rate.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.1-9
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• 2020

Interest rate spreads indicate the conditions of the economy and serve as an indicator of the recession. The purpose of this study is to predict Korea's interest rate spreads using US data with long-term continuity. To this end, 27 US economic data were used, and the entire data was reduced to 5 dimensions through principal component analysis to build a dataset necessary for prediction. In the prediction model of this study, three RNN models (BasicRNN, LSTM, and GRU) predict the US interest rate spread and use the predicted results in the SVR ensemble model to predict the Korean interest rate spread. The SVR ensemble model predicted Korea's interest rate spread as RMSE 0.0658, which showed more accurate predictive power than the general ensemble model predicted as RMSE 0.0905, and showed excellent performance in terms of tendency to respond to fluctuations. In addition, improved prediction performance was confirmed through period division according to policy changes. This study presented a new way to predict interest rates and yielded better results. We predict that if you use refined data that represents the global economic situation through follow-up studies, you will be able to show higher interest rate predictions and predict economic conditions in Korea as well as other countries.

• Disaster Prevention Review
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• v.14 no.4
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• pp.124-125
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• 2012

• Journal of Digital Convergence
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• v.11 no.10
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• pp.305-311
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• 2013

The size of the market as the economy continues to evolve, in order to make the right decisions to accurately predict the economic problems the market has emerged as an important issues. To express the modern economic system, the largest of the various economic indicators, pillars stock indicators analysis and decision-making with a proper understanding of the problem for the application of the model is suitable for time-series data concealment HMM. Based on this time series model and the calculation of the time and cost savings dimension decrease techniques for the estimation and prediction of the model was applied to the problem was to verify the validity. As a result, the model predictions in both the short term rather than long-term predictions of the model estimates the optimal predictive value similar pattern very similar to both the actual data and was able to confirm that.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.271-283
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• 2012

In this paper, we predicted the rise or the fall in eleven major industrial stock price indices unlike existing studies dealing with the prediction of KOSPI that combines all industries. We used as input variables not only domestic economic indices but also foreign economic indices including the U.S.A, Japan, China and Europe that have affected korean stock market. Numerical analysis through SAS E-miner showed above or below about 60% accuracy using the logistic regression and neural network model.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.211-218
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• 1998

The demand prediction becomes an essential mean to utilize efficiently finite traffic facilities and to provide the optimized schedules for transportation system. The demand prediction is one of the critical complex management schemes for distibuting resources of transportation service by means of computer system. The construction of a prediction model is based on data granulization, followed by processing the raw input data and evaluating the predicted output values. A large number of economic-social parameters are also to be implemented in conventional prediction models which are only based on a sequence of past data. The proposed prediction models are classified by static and dynamic characteristics and its performances are evaluated utilizing computer simulation.