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

• The Plant Pathology Journal
• /
• v.24 no.1
• /
• pp.46-51
• /
• 2008

With the goal of achieving better integrated pest management for hot pepper, a disease-forecasting system was compared to a conventional disease-control method. Experimental field plots were established at Asan, Chungnam, in 2005 to 2006, and hourly temperature and leaf wetness were measured and used as model inputs. One treatment group received applications of a protective fungicide, dithianon, every 7 days, whereas another received a curative fungicide, dimethomorph, when the model-determined infection risk (IR) exceeded a value of 3. In the unsprayed plot, fruits showed 18.9% (2005) and 14.0% (2006) anthracnose infection. Fruits sprayed with dithianon at 7-day intervals had 4.7% (2005) and 15.4% (2006) infection. The receiving model-advised sprays of dimethomorph had 9.4% (2005) and 10.9% (2006) anthracnose infection. Differences in the anthracnose levels between the conventional and model-advised treatments were not statistically significant. The efficacy of 10 (2005) and 8 (2006) applications of calendar-based sprays was same as that of three (2005 and 2006) sprays based on the disease-forecast system. In addition, we found much higher the IRs with the leaf wetness sensor from the field plots comparing without leaf wetness sensor from the weather station at Asan within 10km away. Since the wetness-periods were critical to forecast anthracnose in the model, the measurement of wetness-period in commercial fields must be refined to improve the anthracnose-forecast model.

• International journal of advanced smart convergence
• /
• v.7 no.2
• /
• pp.86-94
• /
• 2018

This study focuses on presenting the IT program module provided by real - time forecasting and database of the voluntary disclosure quality measure in order to solve the problem of capital cost due to information asymmetry of external investors and corporate executives. This study suggests a model of the algorithm that the quality of real - time voluntary disclosure can be provided to all investors immediately by IT program in order to deliver the meaningful value in the domestic capital market. This is a method of generating and analyzing real-time or non-real-time prediction models by transferring the predicted estimates delivered to the Big Data Log Analysis System through the statistical DB to the statistical forecasting engine.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• autumn
• /
• pp.478-481
• /
• 2002

In these days, Cost and Scheduling was managed effectively because of introduction of EVMS to construction project. However the EVMS is appropriate methods to advanced country, so it is difficult to apply into domestic construction project. in this paper weighted value n, m was used of compositive index(CI) to forecast Estimate At Completion (EAC) using statistical analysis in credible interval the objective of this paper is to verify compositive index(CI) and to forecast Estimate At Completion (EAC).

• Journal of Information Technology Applications and Management
• /
• v.13 no.4
• /
• pp.303-319
• /
• 2006

Business Information Systems are strategic applications to achieve companies' goals and innovation. This idea make companies invest their time and budget in the Information Systems. However, it is difficult to forecast effects of the investment in Information Systems and it causes hesitation of making decision. Thus, I researched a case so that I could forecast the effect of the information systems using AHP(analytic hierarchical process). In this study, I approached this matter with three views such as intelligence(review of prior literature), design(methodology development), and application. This study is significant in terms of practicality rather than theoretical dimension. Particularly, I suggested a way of quantifying in monetary value the quality aspects through inverting qualitative facts to quantitative facts and calculated the investment feasibility with it.

• Journal of the Korea Computer Industry Society
• /
• v.6 no.2
• /
• pp.241-248
• /
• 2005

Conventional estimating methods forecast the future that it usually using the past statistical numerical value. In order to forecast the farming price, it must need many effort and accuracy knowledge. Therefore, to solve the these problems, this paper to improve forecasting farming price using fuzzy rules and neural network as a preprocessing. Also, we developed an intelligent farming expert system for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results proved reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.

• Communications for Statistical Applications and Methods
• /
• v.16 no.5
• /
• pp.791-801
• /
• 2009

Value-at-Risk(VaR) has been used as an important tool to measure the market risk. However, the selection of the VaR models is controversial. This paper proposes VaR forecast combinations using support vector machine quantile regression instead of selecting a single model out of historical simulation and GARCH.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.10
• /
• pp.4997-5013
• /
• 2016

This paper proposes and then verifies a model that can be used to forecast the effects of behavior on mobile advertising based on the Technology Acceptance Model (TAM) and Web Advertising Effect. The objective of this research is to probe the relationship between the cause and effect of the entertainment, informativeness, usefulness, capacity to accommodate smart-environment technologies, Hedonic Adaptation Model (HAM), etc. that mobile advertisements provide, as well as the attitudes toward advertisements in general. In order to accomplish this goal, the research was verified using Structural Equation Modeling (SEM), and the results are as follows. First, the informativeness of mobile advertising has a positive effect on the recognized ease of use. Second, the entertainment and informativeness of mobile advertising has positive effects on the recognized usefulness. Third, the recognized ease of use has a positive effect on the recognized usefulness. Fourth, the informativeness of mobile advertising causes a positive effect on smart-environment technologies. Fifth, the entertainment and informativeness of mobile advertising cause positive effects on the HAM. Sixth, smart-environment technologies cause positive effects on the HAM. Seventh, the recognized usefulness causes a positive effect on the value of mobile advertising and the intention of use. Eighth, the HAM has a positive effect on the value of mobile advertising and the general attitudes toward it. Ninth, the value of mobile advertising has a positive effect on the attitudes toward advertising. Tenth, the attitudes toward mobile advertising have a positive effect on the intention of use.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.7
• /
• pp.307-314
• /
• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

• Korean Business Review
• /
• v.22 no.2
• /
• pp.61-86
• /
• 2009

The purpose of this article is to investigate the effects of cash flow and accrual, which are the elements of earnings, and those of the elements of cash flow and accrual on the consistency of cash flow and firm's value. We analyzed 4 kinds of regression models, of which the independent variables are this period's cash flow, the elements of cash flow, accrual, and the elements of accrual, and the dependent variables are the next period's cash flow, and the stock price at the end of financial statements disclosure months, respectively. The sample firms were the manufacturing companies listed on the Stock Exchange 1980 through 2006, of which the fiscal year ended in December. And, the results of the analyses are as follows: Cash flow and accrual are shown to have significant relationships with cash flow consistency and the evaluations of firms' value. And, the elements of cash flow or accrual proved to have more influence than the total amount of them, on cash flow consistency and the evaluation of firms' value. Also, the results present that some of the elements of cash flow and accrual differently affect cash flow consistency and the evaluation of firms' value. Accordingly, this study indicates that each of the elements of cash flow and accrual needs to be considered respectively rather than the total amount of them, in the case that cash flow and accrual are used in the decision-making concerned with the forecast of cash flow and the evaluation of firms' value. This study also shows that each element of cash flow and accrual needs to be used differently for cash flow forecast and the evaluation of firms' value.