• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.175-194
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• 1997

The competitive power of technologies is required for survival of enterprises in the social environments that change rapidly. So the strategy of technology development becomes more and more important. For the establishment of strategy, the situational analysis and the forecasting analysis are executed and they include the technology assessment and the technological forecasting. The technology assessment is systematical examination and analysis of the present status of technology. Among the various methods of technology assessment, Matrix Method is one of the usual methods. This research is intended to find out the problems and the difficulties in the current Matrix Method, and to improve the method, finally to help the R&D departments of enterprises applying the method. This suggested matrix(TSM Technology Shift Matrix) method is designed so that one can judge the current situation of technology and future expectation, by moving the matrix which is placed to the upside of the basic matrix.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.4
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• pp.113-119
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• 2006

The importance of intellectual property is increasing in the knowledge and information era. An organization that embraces technology and strategy is faced with promising opportunities and managerial difficulties. Forecasting the performance of technology and the underlying costs to achieve such performance is even more difficult than before. Those organizations that employ technology as part of their strategic arsenal know that they are running serious risks, which clearly increases the uncertainty of organizational performance. Therefore forecasting the performance of technology is a difficult task since technology has been characterized by intangible and tacit factors and traded in a supplier's market. The decision makers usually face a complex system of interrelated components, such as resources, desired outcomes or objectives. This study intends to evaluate the contribution of technology in intangible assets by the Analytic Hierarchy Process.

• Journal of Korea Spatial Information System Society
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• v.7 no.1 s.13
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• pp.3-11
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• 2005

This paper proposes the method for the man power forecasting and performs mid-term(1994-1998) forecasting of telematics man power demands in Korea. Telematics technology has been selected as '839 New IT Growth Engine' by Ministry of Information and Communication (MIC) of Korean Government to boost Korean IT industry for the next 10 years. In order to meet the man power requirement in this telematics industry, accurate forecasting of the man power demand is necessary. The procedures for the forecasting includes study of man power forecasting models, deriving market size of the telematics industry, perform labor productivity analysis, derive the man power structure by the types of the work forces by the types of telematics industry, and finally derive annual man power demands by the worker types and the telematics industry types.

• Journal of Korean Society of Transportation
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• v.18 no.3
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• pp.55-76
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• 2000

We propose a general solution methodology for identifying the optimal aggregation interval sizes as a function of the traffic dynamics and frequency of observations for four cases : i) link travel time estimation, ii) corridor/route travel time estimation, iii) link travel time forecasting. and iv) corridor/route travel time forecasting. We first develop statistical models which define Mean Square Error (MSE) for four different cases and interpret the models from a traffic flow perspective. The emphasis is on i) the tradeoff between the Precision and bias, 2) the difference between estimation and forecasting, and 3) the implication of the correlation between links on the corridor/route travel time estimation and forecasting, We then demonstrate the Proposed models to the real-world travel time data from Houston, Texas which were collected as Part of the Automatic Vehicle Identification (AVI) system of the Houston Transtar system. The best aggregation interval sizes for the link travel time estimation and forecasting were different and the function of the traffic dynamics. For the best aggregation interval sizes for the corridor/route travel time estimation and forecasting, the covariance between links had an important effect.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.67-78
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• 2019

Accurate electric load forecasting is very important in the efficient operation of the smart grid. Recently, due to the development of IT technology, many works for constructing accurate forecasting models have been developed based on big data processing using artificial intelligence techniques. These forecasting models usually utilize external factors such as temperature, humidity and historical electric load as independent variables. However, due to diverse internal and external factors, historical electrical load contains many missing data, which makes it very difficult to construct an accurate forecasting model. To solve this problem, in this paper, we propose a random forest-based missing data recovery scheme and construct an electric load forecasting model based on multilayer perceptron using the estimated values of missing data and external factors. We demonstrate the performance of our proposed scheme via various experiments.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.75-92
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• 2020

In most cases of the water balance analysis, the return flow ratio for each water supply was uniformly determined and applied, so it has been contained a problem that the volume of available water would be incorrectly calculated. Therefore, sewage and wastewater among the return water were focused in this study and the data-driven model was developed to forecast the outflow from the sewage treatment plant. The forecasting results of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), and SVR (Support Vector Regression) models, which are mainly used for forecasting the time series data in most fields, were compared with the observed data to determine the optimal model parameters for forecasting outflow. As a result of applying the model, the root mean square error (RMSE) of the GRU model was smaller than those of the LSTM and SVR models, and the Nash-Sutcliffe coefficient (NSE) was higher than those of others. Thus, it was judged that the GRU model could be the optimal model for forecasting the outflow in sewage treatment plants. However, the forecasting outflow tends to be underestimated and overestimated in extreme sections. Therefore, the additional data for extreme events and reducing the minimum time unit of input data were necessary to enhance the accuracy of forecasting. If the water use of the target site was reviewed and the additional parameters that could reflect seasonal effects were considered, more accurate outflow could be forecasted to be ready for climate variability in near future. And it is expected to use as fundamental resources for establishing a reasonable river water management system based on the forecasting results.

• Proceedings of the Korea Technology Innovation Society Conference
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• 1999.11c
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• pp.47-64
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• 1999

• Proceedings of the Korea Technology Innovation Society Conference
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• 2002.05b
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• pp.137-147
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• 2002

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.143-152
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• 1999

The competitive advantage of technologies is required for survival of enterprises in the social environments which change rapidly. So the strategy of technology development becomes more and more important. For the establishment of strategy, the situational analysis and the forecasting analysis are executed and they include the technology assessment and the technology forecasting. This research is intended to find out the problems and the difficulties in the current Matrix Method, and to improve the method, finally to help the R&D departments of enterprises applying the method. The suggested matrix(TSM : Technology Shift Matrix) method is designed so that one can judge the current situation of technology and future trend, by moving the matrix which is placed to the upside of the basic matrix.

• Journal of Information Technology Applications and Management
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• v.19 no.1
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• pp.1-12
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• 2012

Data mining is the process of searching and analyzing large quantities of data for finding out meaningful patterns and rules. Artificial Neural Network (ANN) is one of the tools of data mining which is becoming very popular in forecasting the future values. Some of the areas where it is used are banking, medicine, retailing and fraud detection. In finance, artificial neural network is used in various disciplines including stock market forecasting. In the stock market time series, due to high volatility, it is very important to choose a model which reads volatility and forecasts the future values considering volatility as one of the major attributes for forecasting. In this paper, an attempt is made to develop two models - one using feed forward back propagation Artificial Neural Network and the other using Autoregressive Conditional Heteroskedasticity (ARCH) technique for forecasting stock market returns. Various parameters which are considered for the design of optimal ANN model development are input and output data normalization, transfer function and neuron/s at input, hidden and output layers, number of hidden layers, values with respect to momentum, learning rate and error tolerance. Simulations have been done using prices of daily close of Sensex. Stock market returns are chosen as input data and output is the forecasted return. Simulations of the Model have been done using MATLAB$^{(R)}$ 6.1.0.450 and EViews 4.1. Convergence and performance of models have been evaluated on the basis of the simulation results. Performance evaluation is done on the basis of the errors calculated between the actual and predicted values.