• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.27-30
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• 2001

A inventory management system of the manufacturing industry has a model of different kinds according to the objective and the situation. A inventory management system needs superior system technique in demand forecast, economical efficiency, reliability and application for stable supply of the finished goods, the raw materials and the parts. This paper proposes a demand forecast method based on fuzzy structured neural network, which uses min-operation and trapezoid membership function of fuzzy rules. So we can have an intelligent inventory management system for optimized decision-making of forecasting data with expert's opinion in fuzzy environment. This inventory management system used an intelligence agent and it could be adapted to asystemenvironmentchangeinorder.

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.35-38
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• 2005

This paper suggests the method that forecast Dynamic Line Rating (DLR). Thermal Overload Risk (TOR) of next time is forecasted based on current weather condition and DLR value by Monte Carlo Simulation (MCS). To model weather element of transmission line for MCS, we will propose the use of weather forecast system and statistical models that time series law is applied. Also, through case study, forecasted TOR probability confirmed can utilize by standard that decide DLR of next time. In short, proposed method may be used usefully to keep safety of transmission line and reliability of supply of electric Power by forecasting transmission capacity of next time.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.584-590
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• 2001

An inventory management system of manufacturing industry has a model of different kinds according to the objective and the situation. An inventory management system needs superior system technique in demand forecast, economical efficiency, reliability and application for stable supply of the finished goods, the raw materials and the parts. This paper proposes a demand forecast method based on fuzzy structured neural network, which uses min-operation and trapezoid membership function of fuzzy rules. So we can construct an intelligent inventory management system that make optimized decision-making for forecasting data with expert s opinion in fuzzy environment. The inventory management system uses intelligence agent and it could be adapted to a system environment change in order.

• Proceedings of the KIEE Conference
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• 2004.11b
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• pp.91-93
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• 2004

Load forecasting is essential in order to supply electrical energy stably and economically in power systems. ANNs have flexibility to predict a nonlinear feature of load profiles. In this paper, we selected just the necessary input variables used in the paper(2) which is based on the phase-space embedding of a load time-series and reviewing others. So only 5 input variables were selected to forecast for spring, fall and winter season and another input considering temperature sensitivity is added during the summer season. The training cases are also selected from all previous data composed training cases of a 7-day, 14-day and 30-day period. Finally, we selected the training case of a 7-day period because it can be used in STLF without sacrificing the accuracy of the forecast. This allows more compact ANNs, smaller training cases. Consequently, test results show that compact neural networks can be forecasted without sacrificing the accuracy.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.147-159
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• 2011

In this paper the demand and supply of labor are forecasted over the next 10 years for customized nurturing human resource focused on Manufacturing Industry in Chung-Nam Province. Despite that the industrial structure is rapidly changing, industrial labors are nurturing on the basis of past industrial structure. This research is conducted for reducing mismatched labors throughout forecasting human resources until 2020. As a practical approach, the BLS Methodology is partially utilized. And the previous researches and official statistics data are reviewed. In conclusion, this study presents that more human resources on Manufacturing Industry than other Industries will be needed in Chung-Nam province. In details, it shows that there will be required more Industrial labors for strategic industries for examples, Audio and Video related industry, and Car related industry which is propelling by overall local government. In additions, policy implications are developed by analyzing current status and forecasting the labor demand and supply in the Chung-Nam Manufacturing sector.

• The Korean Journal of Food & Health Convergence
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• v.6 no.2
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• pp.9-16
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• 2020

The global halal market is forecast to grow at an annual average of 5.2 percent from 2017 to $3.07 trillion in 2023 due to the high growth rate of the world's Muslim population, the spread of halal-certified food consumption and the economic growth of the Muslim world. Through this study, the difficulty of obtaining halal certification can be overcome through accurate understanding of the general supply chain and other halal supply chain. Also, by examining the trends and requirements of halal logistics standards in countries with advanced halal logistics systems, halal logistics certification agencies, and halal port logistics, we can help establish our own halal logistics system by finding areas that can be benchmarked in Korea and differentiated from those that can be found. For the safe supply chain management of halal products between logistics Supply Chain, an integrated logistics system shall be developed to manage customs and customs as one-stop, while maintaining a complete halal condition on a series of logistics processes such as storage, transportation, customs clearance, etc. Korea, entry into the halal logistics market through halal integrity guarantee solution or platform development can also be considered, taking advantage of the strength of IT and packaging.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.147-154
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• 2024

The continuous increase in urban population due to migration of mases from rural areas to big cities has set urban water supply under serious stress. Urban water resources face scarcity of available water quantity, which ultimately effects the water supply. It is high time to address this challenging problem by taking appropriate measures for the improvement of water utility services linked with better understanding of demand side management (DSM), which leads to an effective state of water supply governance. We propose a dynamic framework for preventive DSM that results in optimization of water resource management. This paper uses Agent Based Modeling (ABM) with Digital Twin (DT) to model water consumption behavior of a population and consequently forecast water demand. DT creates a digital clone of the system using physical model, sensors, and data analytics to integrate multi-physical quantities. By doing so, the proposed model replicates the physical settings to perform the remote monitoring and controlling jobs on the digital format, whilst offering support in decision making to the relevant authorities.

• Journal of Korean Port Research
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• v.13 no.1
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• pp.27-40
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• 1999

This study aims to apply and examine the stochastic approach for empty container repositioning and leasing problem. For this a case study has been carried out on actual data such as various cost components and traffic flow. The results reveal that the proposed methodology produces more realistic results than the conventional deterministic approaches. It is also found that the results are significantly affected by the accuracy of demand and supply forecast.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.187-192
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• 2000

The severity of drought could be evaluated by the accumulative rainfall method, soil moisture condition method, storage ratio method, and water supply restriction intensity method, etc. The pattern of drought could be forecast with the most similar pattern of accumulative rainfall out of the file of past rainfall history. The information that how much rainfall should be expected to overcome the present drought could be obtained from the reservoir storage ratio and soil moisture condition.