• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.119-129
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• 2022

In this paper, we predict the earthquake magnitudes which were recently occurred in Gyeongju and Pohang, using statistical methods based on historical data. For this purpose, we use the five-year block maximum data of 1392~1771 period, which has a relatively high annual density, among the historical earthquake magnitude data of the Chosun Dynasty. Then, we present the prediction and analysis of earthquake magnitudes for the return level over return period in the Chosun Dynasty using the extreme value theory based on the distribution of generalized extreme values (GEV). We use maximum likelihood estimation (MLE) and L-moments estimation for parameters of GEV distribution. In particular, this study also demonstrates via the goodness-of-fit tests that the GEV distribution can be an appropriate analytical model for these historical earthquake magnitude data.

• International Journal of Contents
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• v.10 no.4
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• pp.75-82
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• 2014

The purpose of this paper was to identify suitable variables for financial distress prediction models and to compare the accuracy of MDA and LA for early warning signals for wind energy companies in Korea. The research methods, discriminant analysis and logit analysis have been widely used. The data set consisted of 15 wind energy SMEs in KOSDAQ with financial statements in 2012 from KIS-Value. We found that five financial ratio variables were statistically significant and the accuracy of MDA was 86%, while that of LA is 100%. The importance of this study is that it demonstrates empirically that financial distress prediction models are applicable to the wind energy industry in Korea as an early warning signs of impending bankruptcy.

• Journal of Environmental Science International
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• v.9 no.6
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• pp.469-474
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• 2000

The traffic accidents in large cities such as Pusan metropolitan city have been increased every year due to increasing of vehicles numbers as well as the gravitation of the population. In addition to the carelessness of drivers, many meteorological factors have a great influence on the traffic accidents. Especially, the number of traffic accidents is governed by precipitation, visibility, cloud amounts temperature, etc. In this study, we have analyzed various data of meteorological factors from 1992 to 1997 and determined the standardized values for contributing to each traffic accident. Using the relationship between meteorological factors(visibility, precipitation, relative humidity and cloud amounts) and the total automobile mishaps, and experimental prediction formula for their traffic accident rates was seasonally obtained at Pusan city in 1997. Therefore, these prediction formulas at each meteorological factor may by used to predict the seasonal traffic accident numbers and contributed to estimate the variation of its value according to the weather condition it Pusan city.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.39-57
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• 2021

Time series prediction is an area of great interest to many people. Algorithms for time series prediction are widely used in many fields such as stock price, temperature, energy and weather forecast; in addtion, classical models as well as recurrent neural networks (RNNs) have been actively developed. After introducing the attention mechanism to neural network models, many new models with improved performance have been developed; in addition, models using attention twice have also recently been proposed, resulting in further performance improvements. In this paper, we consider time series prediction by introducing attention twice to an RNN model. The proposed model is a method that introduces H-attention and T-attention for output value and time step information to select useful information. We conduct experiments on stock price, temperature and energy data and confirm that the proposed model outperforms existing models.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.503-509
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• 2022

The purpose of this study is to set the rate of change between the market price of the next day and the previous day to be predicted as the predicted value, and the market price for each section is generated by dividing the stock price ranking of the next day to be predicted at regular intervals, which is different from the previous papers that predict the market price. We would like to propose a new time series data prediction method that predicts the market price change rate of the final next day through a model using the rate of change as the predicted value. The change in the performance of the model according to the degree of subdivision of the predicted value and the type of input data was analyzed.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Korean Journal of Materials Research
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• v.33 no.4
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.12
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• pp.724-735
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• 2003

In recent high-performance superscalar processors, the result value of an instruction is predicted to improve instruction-level parallelism by breaking data dependencies. Using those predicted values, instructions are speculatively executed and substantial performance can be gained. It, however, requires additional power consumption due to the frequent access and update of the value prediction table. In this paper, first, the trade-off between the performance improvement and the increased power consumption for value prediction is measured and analyzed. And, in order to reduce additional power consumption without performance loss, the technique of controlling speculative execution with confidence counter and predicting useful instructions is developed. Also, in order to prove the validity, a tool is developed that can simulate processor behavior at cycle-level and measure total energy consumption and power consumption per cycle.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.681-691
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• 2021

The estimated breeding value (EBV) and accuracy of Hanwoo steer (Korean cattle) is an indicator that can predict the slaughter time in the future and carcass performance outcomes. Recently, studies using pedigrees and genotypes are being actively conducted to improve the accuracy of the EBV. In this study, the pedigree and genotype of 46 steers obtained from livestock farm A in Gyeongnam were used for a pedigree best linear unbiased prediction (PBLUP) and a genomic best linear unbiased prediction (GBLUP) to estimate and analyze the breeding value and accuracy of the carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS). PBLUP estimated the EBV and accuracy by constructing a numeric relationship matrix (NRM) from the 46 steers and reference population I (545,483 heads) with the pedigree and phenotype. GBLUP estimated genomic EBV (GEBV) and accuracy by constructing a genomic relationship matrix (GRM) from the 46 steers and reference population II (16,972 heads) with the genotype and phenotype. As a result, in the order of CWT, EMA, BFT, and MS, the accuracy levels of PBLUP were 0.531, 0.519, 0.524 and 0.530, while the accuracy outcomes of GBLUP were 0.799, 0.779, 0.768, and 0.810. The accuracy estimated by GBLUP was 50.1 - 53.1% higher than that estimated by PBLUP. GEBV estimated with the genotype is expected to show higher accuracy than the EBV calculated using only the pedigree and is thus expected to be used as basic data for genomic selection in the future.