• Geomechanics and Engineering
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• v.36 no.5
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• pp.465-474
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• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• Korean journal of applied entomology
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• v.52 no.4
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• pp.415-425
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• 2013

The yellow tea thrips, Scirtothrips dorsalis (Thysanoptera: Thripidae), has been regarded as a minor pest on citrus on Jeju Island. However, the damage of yellow tea thrips has gradually increased since 2007. This study was conducted to develop a forecasting model for generation peak time of S. dorsalis by using degree-days. Simple linear regression analysis was applied to determine the relationship between the generation number (x, dependent variable) and degree-days (y, independent variable). As a result, two regression models were established: citrus-based model (y = 310.9x + 69.0, $r^2$=0.99) and green tea-based model (y = 285.7x + 84.1, $r^2$=0.99). The models was fitted by independent data sets obtained from 2013 and evaluated using the technique of RSS (residual sum of square) and ${\chi}^2$-test. The green tea based-model showed a good fitting ability. The discrepancy between model outputs and actual data, and the practical application of models were discussed.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5B
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• pp.405-414
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• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• Economic Analysis
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• v.25 no.1
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• pp.1-33
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• 2019

This paper examines the economic factors that are related to the dynamics of the variance risk premium, and specially, which economic factors are related to the forecasting power of the variance premium regarding future index returns. Eleven general economic variables, eight interest rate variables, and eleven sentiment-associated variables are used to figure out the relevant economic variables that affect the variance risk premium. According to our empirical results, the won-dollar exchange rates, foreign reserves, the historical/implied volatility, and interest rate variables all have significant coefficients. The highest adjusted R-squared is more than 65 percent, indicating their significant explanatory power of the variance risk premium. Next, to verify the economic variables associated with the predictability of the variance risk premium, we conduct forecasting regressions to predict future stock returns and volatilities for one to six months. Our empirical analysis shows that only the won-dollar exchange rate, among the many variables associated with the dynamics of the variance risk premium, has a significant forecasting ability regarding future index returns. These results are consistent with results found in previous studies, including Londono (2012) and Bollerslev et al. (2014), which show that the variance risk premium is related to global risk factors.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Ocean and Polar Research
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• v.42 no.3
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• pp.211-223
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• 2020

With the increasing demand for improved marine environments and safety, greater ability to minimize damages to coastal areas from harmful organisms, ship accidents, oil spills, etc. is required. In this regard, an accurate assessment and understanding of current systems is a crucial step to improve forecasting ability. In this study, we examine spatial and temporal characteristics of current systems in the adjacent seas of Jeju Island using a high-resolution regional ocean circulation model. Our model successfully captures the features of tides and tidal currents observed around Jeju Island. The tide form number calculated from the model result ranges between 0.3 and 0.45 in the adjacent seas of Jeju Island, indicating that the dominant type of tides is a combination of diurnal and semidiurnal, but predominantly semidiurnal. The spatial pattern of tidal current ellipses show that the tidal currents oscillate in a northwest-southeast direction and the rotating direction is clockwise in the adjacent seas of Jeju Island and counterclockwise in the Jeju Strait. Compared to the mean kinetic energy, the contribution of tidal current energy prevails the most parts of the region, but largely decreases in the eastern seas of Jeju Island where the Tsushima Warm Current is dominant. In addition, a Lagrangian particle-tracking experiment conducted suggests that particle trajectories in tidal currents flowing along the coast may differ substantially from the mean current direction. Thus, improving our understanding of tidal currents is essential to forecast the transport of marine pollution and harmful organisms in the adjacent seas of Jeju Island.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.51-58
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• 2024

This paper examines the differences between the Korean and American stock markets using the Kospi and S&P 500 indices and discusses policy implications through them. To this end, in addition to the existing time series analysis method, a deep learning method was used to compare markets, and the comparison was made in terms of stock price forecasting ability and data generation ability. In monthly data, the difference between time series was not large, and in daily data, the difference in terms of stability was weak, and there was no significant difference in predictive power or simulation data generation. As shown in the results of this study, if there is not much difference in market price movement patterns between Korea and the United States, tax benefits for long-term stocks investment will be effective against the side effects of short selling.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.1-15
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• 2007

Aircraft operation doesn't consider the fact which can became obstacles. Forecasting, Preparing and Planning can make effective aircraft operation. This study is the find out the method of the effective Aircraft military power management. The force try to analysis the obstancles, aircraft operation ability and prepared military power. This study was analysis by ARENA 10.0. The obstacles consider to four category; weather, CBR warfare. aircraft accident and battle damage. And This study conduct the sensibility which can influence the aircraft operation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2005.11a
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• pp.137-140
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• 2005

Given importation of After-Sale System, we may assume that it comes to be changed on housing industry Construction companies have applied installment sale to fund of building, but now they have to take it upon themselves to borrow from PF(Project Financing) on the banking system. The purpose of this study is to assume the change and influence in the construction fund after adopting After-Sale system and suggests the appropriate strategy in PF for providing fund. Construction Companies can produce their Profits by concentrating on reconstruction part especially in the metropolitan area. And They make their own brands and have their competitive power. In this housing market, if After-sale system comes publically, there will be big changes. Until now many companies have no difficulties in making funds to manage construction activity. But After-sale system gives difficulties to many companies. Therefore they have to prepare their own funds under their responsibilities from banks of Project financing.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.203-213
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• 2016

We investigate volatility spillover aspects of realized volatilities (RVs) for the log returns of the Korea Composite Stock Price Index (KOSPI) and the Hang Seng Index (HSI) from 2009-2013. For all RVs, significant long memories and asymmetries are identified. For a model selection, we consider three commonly used time series models as well as three models that incorporate long memory and asymmetry. Taking into account of goodness-of-fit and forecasting ability, Leverage heteroskedastic autoregressive realized volatility (LHAR) model is selected for the given data. The LHAR model finds significant decompositions of the spillover effect from the HSI to the KOSPI into moderate negative daily spillover, positive weekly spillover and positive monthly spillover, and from the KOSPI to the HSI into substantial negative weekly spillover and positive monthly spillover. An interesting result from the analysis is that the daily volatility spillover from the HSI to the KOSPI is significant versus the insignificant daily volatility spillover of the KOSPI to HSI. The daily volatility in Hong Kong affects next day volatility in Korea but the daily volatility in Korea does not affect next day volatility in Hong Kong.