• Korean Management Science Review
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• v.20 no.2
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• pp.135-149
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• 2003

Developing a proper program for customer evaluation is one of the most imminent tasks to implement CRM (Customer Relationship Management). Design of the Customer Value model is an important key to the customer evaluation progrgm. This paper proposes two models for estimating Customer Value. The first one is a Description Model for Customer Value based on customer CSI (Customer Satisfaction Index) data. This model represents as quantitative numbers what customers feel from the company or the service. The second one is a Prediction Model which employs factor analysis and regression to predict customer value. This paper exploits the two models to evaluate Customer Value as well as for customer behavior prediction.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.107-115
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• 2002

We propose a new hybrid value predictor which achieves high performance by combining several predictors. Because the proposed hybrid value predictor can update the prediction table speculatively, it efficiently reduces the number of mispredicted instructions due to stale data. Also, the proposed predictor can enhance the prediction accuracy and efficiently decrease the hardware cost of predictor, because it allocates instructions into the best-suited predictor during instruction fetch stage by using the information of static classification which is obtained from the profile-based compiler implementation. For the 16-issue superscalar processors, simulation results based on the SimpleScalar/PISA tool set show that we achieve the average prediction rates of 73% by using speculative update and the average prediction rates of 88% by adding static classification for the SPECint95 benchmark programs.

• Korean Institute of Interior Design Journal
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• no.32
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• pp.97-104
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• 2002

The purpose of this study is to suggest the prediction model for the color design by the image types in the office interior. This prediction model of the color design is for the more comfortable environment by using suitable, various colors fitted with business functions. In this research, we carried out the evaluation experiment with the variables such as the color on ceiling, wall, floor and the harmonies of color schemes. We set the prediction index through the multi-regression analysis. And the prediction model was made by these results. The design methods by the prediction model are as follows. 1) The $\ulcorner$variable$\lrcorner$ image was deeply influenced by the value and chroma and it was marked high in low value and high chroma and the harmonies of contrast and different color. 2) The $\ulcorner$comfortable$\lrcorner$ image was related to the value and chroma and it was marked high in high value and low chroma and harmonies of homogeneity and similar. 3) The $\ulcorner$warm$\lrcorner$ image was greatly influenced by the hue and the harmony of color schemes, and it was marked high in the warm colors and harmonies of homogeneity.

• Korea Trade Review
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• v.46 no.6
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• Proceedings of the IEEK Conference
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• 2001.06b
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• pp.265-268
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• 2001

In this paper we investigate the implementation cost of value prediction methods for high performance micro-processors, and propose a new value prediction microarchitecture with low cost. After simulation, we found that the proposed microarchitecture can decrease the implementation cost by 36% to 50% and with slight performance degradation (less than 5%) .

• Journal of the Korean Data and Information Science Society
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• v.21 no.1
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• pp.185-194
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• 2010

In recommender systems which are used widely at e-commerce, collaborative filtering needs the information of user-ratings and neighbor user-ratings. These are an important value for recommendation in recommender systems. We investigate the in-formation of rating in NBCFA (neighbor Based Collaborative Filtering Algorithm), we suggest new algorithm that improve prediction accuracy of recommender system. After we analyze relations between two variable and Error Value (EV), we suggest new algorithm and apply it to fitted line. This fitted line uses Least Squares Method (LSM) in Exploratory Data Analysis (EDA). To compute the prediction value of new algorithm, the fitted line is applied to experimental data with fitted function. In order to confirm prediction accuracy of new algorithm, we applied new algorithm to increased sparsity data and total data. As a result of study, the prediction accuracy of recommender system in the new algorithm was more improved than current algorithm.

• Journal of the Korean Society of Industry Convergence
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• v.5 no.2
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• pp.103-110
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• 2002

An artificial neural network is applied to the prediction of compressive strength, slump value of concrete. Standard mixed tables arc trained and estimated, and the results are compared with those of experiments. To consider the varieties of material properties, the standard mixed tables of two companies of Ready Mixed Concrete are used. And they are trained with the neural network. In this paper, standard back propagation network is used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of 210, $240kgf/cm^2$ and target slump value of 12, 15cm are used because the amount of production of that range arc the most at ordinary companies. In results, in the prediction of compressive strength and slump value, the predicted values are converged well to those of standard mixed tables at the target error of 0.10, 0.05, 0.001 regardless of two companies.

• Journal of the Korea Computer Industry Society
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• v.7 no.3
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• pp.171-178
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• 2006

Value prediction of instruction issue width in superscalar processor is a technique to obtain performance gains by supplying earlier source values of its data dependent instructions using predicted value of a instruction. In this paper, the mean performance improvement by predictor as well as prediction accuracy and prediction rate are moaned and assessed by comparison and analysis of value predictor that instruction issue width(4,8,16) in parallel and run by predicting value, which is for performance improvements of ILP[4]. The experiment result show the superiority hight performance of 8-issue.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.67-77
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• 2004

Value prediction is a technique to obtain performance gains by supplying earlier source values of its data dependent instructions using predicted value of a instruction. To fully exploit the potential of value speculation, however, the efficient recovery mechanism is necessary in case of value misprediction. In this paper, we propose a sequential and selective recovery mechanism for value misprediction. It searches data dependency chain of the mispredicted instruction sequentially without pipeline stalls and adverse impact on clock cycle time. In our scheme, only the dependent instructions on the predicted instruction is selectively squashed and reissued in case of value misprediction.

• International Journal of Air-Conditioning and Refrigeration
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• v.13 no.2
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• pp.89-98
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• 2005

In this study, indoor VOCs concentration emitted from floor and furniture was measured after the installation of floor and furniture in a real residence. With the measured data, prediction method and predication equations for indoor concentration of each VOCs and BTEX were developed. The following conclusions were drawn from this study. First, according to the predicted results of concentration decrease of BTEX (benzene, toluene, ethylbenzene, m,p,o-xylene) after the installation of floor in a real residence, prediction equation can be expressed using exponential function. Second, in case of floor, more reliable prediction equation can be obtained by using cumulative value of indoor concentration than by using just hourly measured value directly. Indoor concentration of benzene can be expressed as $y=408.52(1­e^{-00031{\times}time})$ with $R^2$ of 0.94 which is significantly high value. Third, toluene showed the highest concentration in case of furniture installation indoors, and it needed the longest time for concentration decrease. However, other substances except toluene showed constant concentration throughout the measurement period. Fourth, in case of furniture installation indoors, prediction equation of toluene concentration decrease is estimated to be $y= 3616.3{\times}e^{(-0.1091{\times}time)}+513.96{\times}e^{(-0.0006{\times}time)}\;with\; R^2$ of 0.95 which is significantly high value.