• 응용통계연구
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• 제27권3호
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• pp.419-430
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• 2014

전력수급의 정확한 예측은 국민들의 일상적 생활 유지, 산업활동, 그리고 국가경영을 위하여 매우 중요하다. 본 연구에서는 시계열모형화에 의해 전력판매량을 예측한다. 실제 자료분석을 통하여 입력시계열로서 냉난방도일과 개입변수로 펄스함수를 사용한 전이함수모형이 다른 시계열모형에 비해서 제곱근평균제곱오차 및 평균절대오차의 의미에서 더 우수하였다.

• Asia pacific journal of information systems
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• 제9권3호
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• pp.93-110
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• 1999

The previous studies regarding the stock returns have advocated that industry effects exist over entire industry. As the industry categories are more rigid, the demand for predicting the industry sectors is rapidly increasing. The advances in Artificial Intelligence and Neural Networks suggest the feasibility of a valuable computational model for stock returns prediction. We propose a sector-factor model for predicting the return on industry stock index using neural networks. As a substitute for the traditional models, neural network model may be more accurate and effective alternative when the dynamics between the underlying industry features are not well known or when the industry specific asset pricing equation cannot be solved analytically. To assess the potential value of neural network model, we simulate the resulting network and show that the proposed model can be used successfully for banks and general construction industry. For comparison, we estimate models using traditional statistical method of multiple regression. To illustrate the practical relevance of neural network model, we apply it to the predictions of two industry stock indexes from 1980 to 1995.

• 한국항행학회논문지
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• 제10권4호
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• pp.356-363
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• 2006

본 논문에서는 위성통신을 이용한 방송 및 디지털 멀티미디어 서비스의 요구증대를 앞두고 20 GHz 이상의 높은 주파수 대역의 강우에 의한 전파 손실 예측 모델을 제안하고 강우량에 따른 감쇠 정도를 기존의 모델과 비교 분석 하였다. 본 논문에서는 Ka 대역에서 강우 감쇠 특성에서 이론치와 실험치가 접근함을 보이고 강우감쇠에 효과적인 기법을 제안하여 적응변조 시스템의 성능을 분석하였다. 제안 예측모델에서는 강우 강도를 기준으로 위성링크 설계를 위한 안테나 크기, 시스템의 불작동율 등에 대한 기본 자료를 Ka 대역 위성통신 시스템의 원활한 운영을 위해 제공한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 A
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• pp.210-212
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• 1999

This paper proposes a novel wavelet transform based technique for prediction of System Marginal Price(SMP). In this paper, Daubechies D1(haar), D2, D4 wavelet transforms are adopted to predict SMP and the numerical results reveal that certain wavelet components can effectively be used to identify the SMP characteristics with relation to the system demand in electric power systems. The wavelet coefficients associated with certain frequency and time localisation are adjusted using the conventional multiple regression method and then reconstructed in order to predict the SMP on the next scheduling day through a five-scale synthesis technique. The outcome of the study clearly indicates that the proposed wavelet transform approach can be used as an attractive and effective means for the SMP forecasting.

• 재무관리논총
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• 제3권2호
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• pp.267-279
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• 1996

The prediction of future demand is a vital task in managing business operations. To this end, traditional approaches often focused on statistical techniques such as exponential smoothing and moving average. The need for better accuracy has led to nonlinear techniques such as neural networks and case based reasoning. In addition, experimental design techniques such as orthogonal arrays may be used to assist in the formulation of an effective methodology. This paper investigates a multistrategy approach involving neural nets, case based reasoning, and orthogonal arrays. Neural nets and case based reasoning are employed both separately and in combination, while orthoarrays are used to determine the best architecture for each approach. The comparative evaluation is performed in the context of an application relating to the prediction of Treasury notes.

• International journal of advanced smart convergence
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• 제10권4호
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• pp.98-103
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• 2021

Recently, as the need for food resources has increased both domestically and internationally, support for the agricultural sector for stable food supply and demand is expanding in Korea. However, according to recent media articles, the biggest problem in rural communities is the unstable profit structure. In addition, in order to confirm the profit structure, profit forecast data must be clearly prepared, but there is a lack of auxiliary data for farmers or future returnees to predict farm income. Therefore, in this paper we analyzed data over the past 15 years through time series analysis and proposes an artificial intelligence farm income prediction algorithm that can predict farm household income in the future. If the proposed algorithm is used, it is expected that it can be used as auxiliary data to predict farm profits.

• 전자통신동향분석
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• 제34권1호
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• pp.111-122
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• 2019

As society develops, the demand for safety and security services increases. Developed nations such as the United States use advanced technology to lower crime rate and promote intelligent security services. First, this article examines intelligent systems that are used for monitoring and detecting crimes and dangerous situations. Recently, we have been studying technologies that enable preemptive responses through prediction of crime and hazardous situations. In this paper, we examine the cases of security services based on a crime/risk prediction model and explain the structure and major technologies of an intelligent security system. In addition, we propose a direction for technological development for achieving future security services.

• 방송공학회논문지
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• 제25권7호
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• pp.1052-1062
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• 2020

As the market demand for immersive media increases, an efficient streaming method is required in consideration of network conditions while maintaining the user's immersive experience. Accordingly, transmitting a viewport with relatively high-quality, such as tile-based streaming, is mainly used. But there still remains a lot of technical challenges, such as quickly providing a new viewport in high-quality according to the gaze. To solve the aforementioned problem, in this paper, we propose a method of configuring and transmitting a supplemental tile set through the predicted direction, and a range of stable utilization of the transmitted supplemental tile set.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.118-121
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• 2023

Recently, green energy support policies have been announced around the world in accordance with environmental regulations, and asthe market grows rapidly, demand for batteries is also increasing. Therefore, various methodologies for battery diagnosis and recycling methods are being discussed, but current accurate life prediction of batteries has limitations due to the nonlinear form according to the internal structure or chemical change of the battery. In this paper, CS2 lithium-ion battery measurement data measured at the A. James Clark School of Engineering, University of Marylan was used to predict battery performance with high accuracy using a convolutional neural network (CNN) model among deep learning-based models. As a result, the battery performance was predicted with high accuracy. A data structure with a matrix of total data 3,931 ☓ 19 was designed as test data for the CS2 battery and checking the result values, the MAE was 0.8451, the RMSE was 1.3448, and the accuracy was 0.984, confirming excellent performance.

• ETRI Journal
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• 제46권3호
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• pp.379-391
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• 2024

To meet increasing traffic requirements in mobile networks, small base stations (SBSs) are densely deployed, overlapping existing network architecture and increasing system capacity. However, densely deployed SBSs increase energy consumption and interference. Although these problems already exist because of densely deployed SBSs, even more SBSs are needed to meet increasing traffic demands. Hence, base station (BS) switching operations have been used to minimize energy consumption while guaranteeing quality-of-service (QoS) for users. In this study, to optimize energy efficiency, we propose the use of deep reinforcement learning (DRL) to create a BS switching operation strategy with a traffic prediction model. First, a federated long short-term memory (LSTM) model is introduced to predict user traffic demands from user trajectory information. Next, the DRL-based BS switching operation scheme determines the switching operations for the SBSs using the predicted traffic demand. Experimental results confirm that the proposed scheme outperforms existing approaches in terms of energy efficiency, signal-to-interference noise ratio, handover metrics, and prediction performance.