• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.568-573
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• 2016

Since the existing analog village broadcasting system has some technical problems in applying and degradations in performance due to its old equipments, it had been required to be changed to a digital system and to develop the standardization from now. Therefore, we have analyzed the service coverage in various environments in order to construct an effective digital wireless village broadcasting system. Also, a precise prediction of field strength should be set up in various propagation environments to design a digital radio stations with reliable transmit power. Using these results the received power and the propagation characteristics in various environments can be predicted to establish a standard and the testing service will be deployed in the near future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.7
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• pp.1187-1192
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• 2008

This paper proposes a fast motion estimation using an adaptive search region and a new three step search. The proposed method improved in the quality of motion compensation image as $0.43dB{\sim}2.19dB$, according as it predict motion of current block from motion vector of neigher blocks, and adaptively set up search region using predicted motion information. We show that the proposed method applied a new three step search pattern is able to fast motion estimation, according as it reduce computational complexity per blocks as $1.3%{\sim}1.9%$ than conventional method.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.237-262
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• 2022

There has been a growing interest in IPOs (Initial Public Offerings) due to the profitable returns that IPO stocks can offer to investors. However, IPOs can be speculative investments that may involve substantial risk as well because shares tend to be volatile, and the supply of IPO shares is often highly limited. Therefore, it is crucially important that IPO investors are well informed of the issuing firms and the market before deciding whether to invest or not. Unlike institutional investors, individual investors are at a disadvantage since there are few opportunities for individuals to obtain information on the IPOs. In this regard, the purpose of this study is to provide individual investors with the information they may consider when making an IPO investment decision. This study presents a model that uses machine learning and text analysis to predict whether an IPO stock price would move up or down after the first 5 trading days. Our sample includes 691 Korean IPOs from June 2009 to December 2020. The input variables for the prediction are three tone variables created from IPO prospectuses and quantitative variables that are either firm-specific, issue-specific, or market-specific. The three prospectus tone variables indicate the percentage of positive, neutral, and negative sentences in a prospectus, respectively. We considered only the sentences in the Risk Factors section of a prospectus for the tone analysis in this study. All sentences were classified into 'positive', 'neutral', and 'negative' via text analysis using TF-IDF (Term Frequency - Inverse Document Frequency). Measuring the tone of each sentence was conducted by machine learning instead of a lexicon-based approach due to the lack of sentiment dictionaries suitable for Korean text analysis in the context of finance. For this reason, the training set was created by randomly selecting 10% of the sentences from each prospectus, and the sentence classification task on the training set was performed after reading each sentence in person. Then, based on the training set, a Support Vector Machine model was utilized to predict the tone of sentences in the test set. Finally, the machine learning model calculated the percentages of positive, neutral, and negative sentences in each prospectus. To predict the price movement of an IPO stock, four different machine learning techniques were applied: Logistic Regression, Random Forest, Support Vector Machine, and Artificial Neural Network. According to the results, models that use quantitative variables using technical analysis and prospectus tone variables together show higher accuracy than models that use only quantitative variables. More specifically, the prediction accuracy was improved by 1.45% points in the Random Forest model, 4.34% points in the Artificial Neural Network model, and 5.07% points in the Support Vector Machine model. After testing the performance of these machine learning techniques, the Artificial Neural Network model using both quantitative variables and prospectus tone variables was the model with the highest prediction accuracy rate, which was 61.59%. The results indicate that the tone of a prospectus is a significant factor in predicting the price movement of an IPO stock. In addition, the McNemar test was used to verify the statistically significant difference between the models. The model using only quantitative variables and the model using both the quantitative variables and the prospectus tone variables were compared, and it was confirmed that the predictive performance improved significantly at a 1% significance level.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• Journal of the Korean Wood Science and Technology
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• v.29 no.2
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• pp.100-108
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• 2001

An understanding of potential lumber cutting yields may lead to increased utilization of the lower grades of lumber in wood industry. Computer simulations of rough-mill operations require a lumber database as input to give reasonable estimates of such yields. The lumber database must contain detailed information regarding the location and type of defects, and then all manufacturing sequences can be tested with the same raw material. However, no suitable lumber database with mapped defects exists for Korean wood industry. In this study lumber databases of Pinus densiflora S. et Z and Quercus acutissima which are the main Korean domestic species were developed to prepare for coming era of "utilization of domestic species" in the near future. These databases were put into lumber cut-up simulation model(gang-rip-first simulator) to investigate the part yields. Gang-rip first simulation showed average part yields of 44.75% and 63.10% for Quercus acutissima and Pinus densiflora lumber database developed, respectively. In most cases process set-up of fixed blade best feed showed the best part yields and the level of acceptable defects could not make significant differences in part yields.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.6
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• pp.113-119
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• 2016

In this study, the method to predict the lateral response by using strain data is presented on the steel moment frame. For this, the reliability of the proposed method by applying the example of five-story frame structure were verified. Using the strain value of columns, it predicted the lateral response of structure. It is assumed that all of four strain sensors for one column set up and the strain responses of both end of the column are utilized. The lateral response of member is calculated by using the slope deflection method. Also, using the acceleration response of the one layer, the stiffness of the rotation spring located in the supporting point is predicted. As a result, it was effective to understand the lateral displacement and acceleration responses and to predict local damage and location.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.673-678
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• 2012

Accurate demand estimating process in the construction of urban railway is very important, and precise validation is required. Existing model formula in the 4 phase model is limited in the estimation of the demand the administrative boundary-based zone system reflects no spatial railway demand characteristics around railway stations. The purpose of this study is improving the accuracy of urban rail demand estimation through segmentations of station influence areas and modal split characteristics within the areas. According to the case analysis, it is possible to set up the ststion influence area with a radius of 500m in the urban region and 1,000m in the suburban. And eastablishing proper segmentations of the ststion influence area shows more accurate results to the real demand of railway stations.

• Journal of Advanced Navigation Technology
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• v.17 no.6
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• pp.638-645
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• 2013

For the efficient use of the insufficient frequency resources, a precise prediction of field strength based on various propagation environments should be set up to design of radio stations with reliable transmit power and service coverage. Therefore, many countries have tried to secure the propagation models suitable for their each geographical environments, and also, some models like BCAST were developed by Korea, but these models give the different results compared to measured values. In this paper, based on the measurements of DTV broadcasting services in domestic rural area, analysis and comparison of ITU-R P.1546 and BCAST models provide errors between measured and predicted values, and some points for improving SMI system has been proposed. As a result, P.1546 model provides the valid predicted data similar to measured data, but BCAST model has some problems of large deviation and higher prediction to measured data. In future, these problems and fading loss due to a forest or group of trees, and reflection loss due to a lake or sea need to be studied carefully.

• Fire Science and Engineering
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• v.20 no.1 s.61
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• pp.6-14
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• 2006

The data of city gas accidents has been collected and analysed for not only predictions of the fire and explosion accidents but also the criteria of societal risk. The accidents of the recent 11 years have been broken up into such 3 groups roughly as "release", "explosion", "fire" d 16 groups in detail. Owing to the Poisson probability distribution functions, 'careless work-explosion-pipeline' and 'joint loosening & erosion-release-pipeline' items respectively have turned out to record the lowest and most frequency among the recent 11-years accidents. And thus the proper counteractions must be carried out. In order to assess the societal risks tendency of the fatal gas accidents and set the more obvious safety policies up, the D. O. Hogon equation and the regression method has been used to range the acceptable range in the F-N curve of the cumulative casualties. The further works requires setting up successive database on the fire and explosion accidents systematically to obtain reliable analyses. Also the standard codification will be demanded.

• Korean Chemical Engineering Research
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• v.50 no.1
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• pp.66-71
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• 2012

We deal with a problem to determine experimentally as well as theoretically permeability of incompressible viscous flow through packed bed of bidisperse hard spheres in size. For the size ratios of large to small spheres ${\lambda}$=1.25 and 2, we set up bidisperse packing and measured porosity and permeability at various volumetric ratios of small to large spheres ${\gamma}$. Bidisperse packing shows lower porosity and permeability than monodisperse packing does. Variation of porosity as a function of ${\gamma}$ does not match with that of permeability. A theoretical expression for predicting permeability of a viscous flow for packed bed of bidisperse packing is derived based on calculation of drag force acting on each sphere and its predictions are compared with the experimental data and those from some relations previously suggested. It is found that our theory shows better agreement with experimental results than the previous studies and is proved to be quite simple and accurate in estimating the permeability.