• The Journal of the Convergence on Culture Technology
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• v.6 no.2
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• pp.321-325
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• 2020

The purpose of this study is to re-concept the future battle of the Army, which combines the effects of advanced technology on the concept of combat and the AICBM technology. The "war concept" changes with the times, and can be seen through the following two examples. First, it is a concept that achieves relative superiority by analyzing enemies. A case in point is the U.S. military's development of a "public joint battle" into a "multi-domain operation." Second, it is 'science and technology' that leads to a change in the concept of combat. A case in point is that the firepower warfare on land and sea in World War I developed from World War II to "air warfare" due to the emergence of aircraft. In this regard, the U.S. military is focusing on the concept of fighting in line with the future operational environment based on high-tech science and technology and the construction of the future military through the creation of the "Future Command." Therefore, our military needs to utilize the major technologies of the fourth industrial revolution as an opportunity to develop the concept of future combat, and the future war will greatly affect the development of the concept of advanced science and technology carrying out war, as AIC technology based on the fourth industrial revolution will promote innovation in defense operations in the form of super-connected, super-intelligence and super-integration. Therefore, this study will present the impact of advanced technology on the concept of combat and the concept of battle of the future Army incorporating the technology of AICBM.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.8-15
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• 2020

A forecasting method using deep learning does not have consistent results due to the differences in the characteristics of the dataset, even though they have the same forecasting models and parameters. For example, the forecasting model X optimized with dataset A would not produce the optimized result with another dataset B. The forecasting model with the characteristics of the dataset needs to be optimized to increase the accuracy of the forecasting model. Therefore, this paper proposes novel optimization steps for outlier removal, dataset classification, and a CNN-LSTM-based hyperparameter tuning process to forecast the daily power usage of a university campus based on the hourly interval. The proposing model produces high forecasting accuracy with a 2% of MAPE with a single power input variable. The proposing model can be used in EMS to suggest improved strategies to users and consequently to improve the power efficiency.