• 디지털산업정보학회논문지
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• 제9권2호
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• pp.139-149
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• 2013

As the importance of technology forecasting while countries and companies manage the R&D project is growing bigger, the methodology of technology forecasting has been diversified. One of the forecasting method is patent analysis. This research proposes quick forecasting process of emerging technology based on keyword approach using text mining. The forecasting process is following: First, the term-document matrix is extracted from patent documents by using text mining. Second, emerging technology keyword are extracted by analyzing the importance of word from utilizing mean values and standard deviation values of the term and the emerging trend of word discovered from time series information of the term. Next, association between terms is measured by using cosine similarity. finally, the keyword of emerging technology is selected in consequence of the synthesized result and we forecast the emerging technology according to the results. The technology forecasting process described in this paper can be applied to developing computerized technology forecasting system integrated with various results of other patent analysis for decision maker of company and country.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권10호
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• pp.4887-4907
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• 2017

Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1812-1821
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• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.156-163
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• 2001

Business forecasting is vital to the success of business. There has been an increasing demand for building business forecasting software system to assist human being to do forecasting. However, the uncertain and complex nature makes is a challenging work to analyze, design and implement software solutions for business forecasting. Traditional forecasting systems in which their models are trained based on small collection of historical data could not meet such challenges at the information explosion over the Internet. This paper presents an agent oriented business forecasting approach for building intelligent business forecasting software systems with high reusability. Although agents have been applied successfully to many application domains. little work has been reported to use the emerging agent oriented technology of this paper is that it explores how agent can be used to help human to manage various business forecasting processes in the whole business forecasting life cycle.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권1호
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• pp.31-50
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• 2023

In the midst of dynamic industrial changes, companies need data analysis considering the effects of integration of various technologies in order to establish innovative R & D strategies. However, the existing technology forecasting model evaluates individual technologies without considering relationship among them. To improve this problem, this study suggests a new methodology reflecting the integration of technologies. In the study, a technology forecasting indicator was developed using the technology integration index based on social network analysis. In order to verify the validity of the proposed methodology, 'drone task performance technology' based on patent data was applied to the research model. This study aimed to establish a theoretical basis to design a research model that reflects the degree of integration of technologies when conducting technology forecasting research. In addition, this study is meaningful in that it quantitatively verified the proposed methodology using actual patent data.

• 대한원격탐사학회지
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• 제37권4호
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• pp.815-831
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• 2021

Drought forecasting is crucial to minimize the damage to food security and water resources caused by drought. Satellite-based drought research has been conducted since 1980s, which includes drought monitoring, assessment, and prediction. Unlike numerous studies on drought monitoring and assessment for the past few decades, satellite-based drought forecasting has gained popularity in recent years. For successful drought forecasting, it is necessary to carefully identify the relationships between drought factors and drought conditions by drought type and lead time. This paper aims to provide an overview of recent research trends and challenges for satellite-based drought forecasts focusing on lead times. Based on the recent literature survey during the past decade, the satellite-based drought forecasting studies were divided into three groups by lead time (i.e., short-term, sub-seasonal, and seasonal) and reviewed with the characteristics of the predictors (i.e., drought factors) and predictands (i.e., drought indices). Then, three major challenges-difficulty in model generalization, model resolution and feature selection, and saturation of forecasting skill improvement-were discussed, which led to provide several future research directions of satellite-based drought forecasting.

• 융합보안논문지
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• 제6권4호
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• pp.121-132
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• 2006

세계 각국은 한정된 예산이지만, 우선순위, 선택과 집중을 통하여 원하는 미래를 만들어 나갈 수 있도록 기술예측방법론을 발전시키고 있다. 국방과학기술예측이 시작된 지도 20여년이 되었다. 그러나 공급자위주의 기술예측을 하고 있어 고객의 입장에 볼 때, 효용성이 미흡한 실태이다. 따라서 영국에서 시작된 기술전망(technology foresight) 방법론을 적용하여 고객이 원하는 기술수요를 찾아서 기술을 공급할 수 있도록 해야만 한다. 이것이 진정한 국방과학기술예측의 가치로서 주변국보다 작고 약한 우리나라의 미래와 번영을 지켜줄 수 있기 때문이다. 이를 위한 국방과학기술예측발전을 위한 8가지 발전방향을 제시하였다.

• 대한산업공학회지
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• 제40권4호
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• pp.415-423
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• 2014

In recent decades, RFID (Radio Frequency IDentification) technology has been recognized as one of the most core competencies in implementing ubiquitous society. However, Korea has not seen good success in diffusion of RFID even though Korean government continues funding many projects to diffuse the technology in industries. Most previous researches overestimate the growth of Korean RFID market in contrary to real market situation. This study aims to analyze the Korean RFID market and find a reasonable forecasting model for it. Our experimental results show that Bass forecasting model provides the more realistic estimates than any other models and the analyses of forecasting error provide useful information for the better forecasting. We also observed that government policy plays a crucial role in the diffusion of RFID technology in Korea.

• International Journal of Control, Automation, and Systems
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• 제6권5호
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• pp.639-650
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• 2008

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain variables to form the set of input variables for the proposed forecasting model. The behavior of the wavelet domain constitutive series has been studied based on statistical analysis. It has been observed that forecasting accuracy can be improved by the use of WT in a forecasting model. Multi-scale analysis from one to seven levels of decomposition has been performed and the empirical evidence suggests that accuracy improvement is highest at third level of decomposition. Forecasting performance of the proposed model has been compared with (i) a heuristic technique, (ii) a simulation model used by Ontario's Independent Electricity System Operator (IESO), (iii) a Multiple Linear Regression (MLR) model, (iv) NN model, (v) Auto Regressive Integrated Moving Average (ARIMA) model, (vi) Dynamic Regression (DR) model, and (vii) Transfer Function (TF) model. Forecasting results show that the performance of the proposed WT based NN model is satisfactory and it can be used by the participants to respond properly as it predicts price before closing of window for submission of initial bids.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권9호
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• pp.355-360
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• 2014

오늘날 국가와 기업의 연구 개발 투자 및 경영 정책 전략 수립에서 미래 부상 기술 예측은 매우 중요한 역할을 한다. 기술 예측을 위한 다양한 방법들이 사용되고 있으며 특허를 이용한 기술 예측 또한 활발히 진행되고 있다. 특허를 이용한 기술 예측에는 전문가들의 평가와 견해를 통한 정성적인 방법이 주로 사용되어 왔다. 정성적인 방법은 분석 결과의 객관성을 보장하지 못하고 분석에 많은 비용 및 시간이 요구된다. 이런 문제점을 보완하기 위해 최근에는 텍스트 마이닝을 이용한 특허 데이터의 정량적인 분석이 이루어지고 있다. 텍스트 마이닝 기법을 적용함으로써 특허 문서의 통계적 분석이 가능하다. 본 논문에서는 텍스트 마이닝과 ARIMA 분석을 이용한 기술 예측 방법을 제안한다.