• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Korean Journal of Ecology and Environment
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• v.51 no.4
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• pp.268-286
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• 2018

The eutrophication and algal blooms by harmful cyanobacteria (CyanoHAs) and freshwater redtide (FRT) that severely experiencing in typical regulated weir system of the Nakdong River are one of the most rapidly expanding water quality problems in Korea and worldwide. To compare with the factors of rainfall, hydrology, and dominant algae, this study explored spatiotemporal variability of the major water environmental factors by weekly intervals in eight weir pools of the Nakdong River from January 2013 to July 2017. There was a distinct difference in rainfall distribution between upstream and downstream regions. Outflow discharge using small-scale hydropower generation, overflow and fish-ways accounted for 37.4%, 60.1% and 2.5%, respectively. Excluding the flood season, the outflow was mainly due to the hydropower release through year-round. These have been associated with the drawdown of water level, water exchange rate, and the significant impact on change of dominant algae. The mean concentration (maximum value) of chlorophyll-a was $17.6mg\;m^{-3}$ ($98.2mg\;m^{-3}$) in the SAJ~GAJ and $29.6mg\;m^{-3}$ ($193.6mg\;m^{-3}$) in the DAS~HAA weir pools reaches, respectively. It has increased significantly in the downstream part where the influence of treated wastewater effluents (TWEs) is high. Indeed, very high values (>50 or $>100mg\;m^{-3}$) of chlorophyll-a concentration were observed at low flow rates and water levels. Algal assemblages that caused the blooms of CyanoHAs and FRT were the cyanobacteria Microcystis and the diatom Stephanodiscus populations, respectively. In conclusion, appropriate hydrological management practices in terms of each weir pool may need to be developed.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.21-36
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• 2019

Dead fuel moisture content is a key variable in fire danger rating as it affects fire ignition and behavior. This study evaluates simple regression models estimating the moisture content of standardized 10-h fuel stick (10-h FMC) at three sites with different characteristics(urban and outside/inside the forest). Equilibrium moisture content (EMC) was used as an independent variable, and in-situ measured 10-h FMC was used as a dependent variable and validation data. 10-h FMC spatial distribution maps were created for dates with the most frequent fire occurrence during 2013-2018. Also, 10-h FMC values of the dates were analyzed to investigate under which 10-h FMC condition forest fire is likely to occur. As the results, fitted equations could explain considerable part of the variance in 10-h FMC (62~78%). Compared to the validation data, the models performed well with R2 ranged from 0.53 to 0.68, root mean squared error (RMSE) ranged from 2.52% to 3.43%, and bias ranged from -0.41% to 1.10%. When the 10-h FMC model fitted for one site was applied to the other sites, $R^2$ was maintained as the same while RMSE and bias increased up to 5.13% and 3.68%, respectively. The major deficiency of the 10-h FMC model was that it poorly caught the difference in the drying process after rainfall between 10-h FMC and EMC. From the analysis of 10-h FMC during the dates fire occurred, more than 70% of the fires occurred under a 10-h FMC condition of less than 10.5%. Overall, the present study suggested a simple model estimating 10-h FMC with acceptable performance. Applying the 10-h FMC model to the automatic mountain weather observation system was successfully tested to produce a national-scale 10-h FMC spatial distribution map. This data will be fundamental information for forest fire research, and will support the policy maker.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.51-60
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• 2019

The major purpose of this study is to construct an in-situ soil moisture verification network employing Frequency Domain Reflectometry (FDR) sensors for Cosmic-ray soil moisture observation system operation as well as long-term field-scale soil moisture monitoring. The test bed of Cosmic-ray and FDR verification network system was established at the Sulma Catchment, in connection with the existing instrumentations for integrated data provision of various hydrologic variables. This test bed includes one Cosmic-ray Neutron Probe (CRNP) and ten FDR stations with four different measurement depths (10 cm, 20 cm, 30 cm, and 40 cm) at each station, and has been operating since July 2018. Furthermore, to assess the reliability of the in-situ verification network, the volumetric water content data measured by FDR sensors were compared to those calculated through the core sampling method. The evaluation results of FDR sensors- measured soil moisture against sampling method during the study period indicated a reasonable agreement, with average values of $bias=-0.03m^3/m^3$ and RMSE $0.03m^3/m^3$, revealing that this FDR network is adequate to provide long-term reliable field-scale soil moisture monitoring at Sulmacheon basin. In addition, soil moisture time series observed at all FDR stations during the study period generally respond well to the rainfall events; and at some locations, the characteristics of rainfall water intercepted by canopy were also identified. The Temporal Stability Analysis (TSA) was performed for all FDR stations located within the CRNP footprint at each measurement depth to determine the representative locations for field-average soil moisture at different soil profiles of the verification network. The TSA results showed that superior performances were obtained at FDR 5 for 10 cm depth, FDR 8 for 20 cm depth, FDR2 for 30 cm depth, and FDR1 for 40 cm depth, respectively; demonstrating that those aforementioned stations can be regarded as temporal stable locations to represent field mean soil moisture measurements at their corresponding measurement depths. Although the limit on study duration has been presented, the analysis results of this study can provide useful knowledge on soil moisture variability and stability at the test bed, as well as supporting the utilization of the Cosmic-ray observation system for long-term field-scale soil moisture monitoring.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.2
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• pp.135-142
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• 2022

Purpose. This study aims to evaluate the combined effect of reduced thickness in different regions on the fracture resistance of monolithic zirconia crowns. Materials and methods. Seven nickel-chromium dies were generated from a 3D model of mandibular first molar using the digital scanner with the following geometries: 1.5 mm occlusal reduction, 1.0 mm deep chamfer. Based on the abutment model, Zirconia blocks (Luxen Zirconia) were selected to fabricate Sixty-three zirconia crowns with occlusal thicknesses of 0.3 mm, 0.5 mm, and 1.5 mm, and different axial thicknesses of 0.3 mm, 0.5 mm, and 1.0 mm. All crowns were cemented by resin cement. Next, the crowns were subjected to load-to-fracture test until fracture using an electronic universal testing machine. In addition, fracture patterns were observed with a scanning electron microscope (SEM). Two-way ANOVA and the Tuckey HSD test for post hoc analysis were used for statistical analysis (P < .05). Results. The mean values of fracture resistancerecorded was higher than the average biting force in the posterior region. The two-way ANOVA showed that the occlusal and axial thickness affected the fracture resistance significantly (P < .05). However, the effect of axial thickness on fracture resistance did not show a statistical difference when thicker than 0.5 mm. The observed failure modes were partial or complete fracture depending on the severity of crack propagation. Conclusion. Within the limitations of the present study, the CAD-CAM monolithic zirconia crown with extremely reduced thickness showed adequate fracture resistance to withstand occlusal load in molar regions. In addition, both occlusal and axial thickness affected the fracture resistance of the zirconia crown and showed different results as combined.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.35-46
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• 2021

Purpose : By comparing and analyzing treatment plans using abdominal compression and The Continuous Positive Air Pressure(CPAP) during SABR of lung cancer, we try to contribute to the improvement of radiotherapy effect. Materials & Methods : In two of the lung SABR patients(A, B patient), we developed a SABR plan using abdominal compression device(the Body Pro-Lok, BPL) and CPAP and analyze the treatment plan through homogeneity, conformity and the parameters proposed in RTOG 0813. Furthermore, for each phase, the X, Y, and Z axis movements centered on PTV are analyzed in all 4D CTs and compared by obtaining the volume and average dose of PTV and OAR. Four cone beam computed tomography(CBCT) were used to measure the directions from the center of the PTV to the intrathoracic contacts in three directions out of 0°, 90°, 180° and 270°, and compare the differences from the average distance values in each direction. Result : Both treatment plans obtained using BPL and CPAP followed recommendations from RTOG, and there was no significant difference in homogeneity and conformity. The X-axis, Y-axis, and Z-axis movements centered on PTV in patient A were 0.49 cm, 0.37 cm, 1.66 cm with BPL and 0.16 cm, 0.12 cm, and 0.19 cm with CPAP, in patient B were 0.22 cm, 0.18 cm, 1.03 cm with BPL and 0.14 cm, 0.11 cm, and 0.4 cm with CPAP. In A patient, when using CPAP compared to BPL, ITV decreased by 46.27% and left lung volume increased by 41.94%, and average dose decreased by 52.81% in the heart. In B patient, volume increased by 106.89% in the left lung and 87.32% in the right lung, with an average dose decreased by 44.30% in the stomach. The maximum difference of A patient between the straight distance value and the mean distance value in each direction was 0.05 cm in the a-direction, 0.05 cm in the b-direction, and 0.41 cm in the c-direction. In B patient, there was a difference of 0.19 cm in the d-direction, 0.49 cm in the e-direction, and 0.06 cm in the f-direction. Conclusion : We confirm that increased lung volume with CPAP can reduce doses of OAR near the target more effectively than with BPL, and also contribute more effectively to restriction of tumor movement with respiration. It is considered that radiation therapy effects can be improved through the application of various sites of CPAP and the combination with CPAP and other treatment machines.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Korean Journal of Poultry Science
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• v.48 no.2
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• pp.69-80
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• 2021

We conducted a 4 × 4 diallel cross-mating test using 934 chickens from four grandparent stock lines to develop a new synthetic breed of Korean native chicken. The mean values, combining ability, and reciprocal effects on survival rate, body weight, and hen-day egg production were analyzed. In phenotypes, most chickens have yellowish-brown, reddish-brown and mixed color feathers. The average survival rate was 86.8±12.3%, with the highest in YH combination. Specific combining ability (SCA) had a greater effect on survival rate than general combining ability (GCA), and the SCA of HY combination was the highest. The 16 cross-combinations were distinctly divided into three weight groups according to their genetic characteristics. At 12 weeks of age, GCA showed a greater effect on weight than SCA, and the SCA of FH combination was the highest. The age at first egg laying was 157 days, and the crosses reached sexual maturity faster than the pure lines. The egg production rate was highest in SY at 79.5±2.1%. The GCA and SCA for hen-day egg production were similar, and the SCA was highest in the HS and FY combinations. The reciprocal effect showed that the offspring's egg production rate was high when S and Y were maternal parents in almost all combinations. In conclusion, FH and HF combinations, which have excellent growth performance, are the most desirable paternal parent stock strains, and FY, FS, HY, and SY combinations, which have excellent laying performance with moderate weight, are the preferred maternal strains.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.205-212
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• 2023

This study was attempted to find out how the ecological niche and interspecies relationship of Quercus aliena and Q. serrata, which are the main constituents of potential natural vegetation along the riverside of mountains in Korea, under climate change conditions. To this end, soil moisture and soil nutrients were treated with 4 grad ients under climate change conditions with elevated CO₂ and temperature, plants we re harvested at the end of the growing season, growth responses of traits were measured, ecological niche breadth and overlap were calculated, and it was compared with that of the control group(ambient condition). In addition, the relationship between the two species was analyzed by principal component analysis using trait values. As a result, the ecological niche breadth of Q. aliena was wider than that of Q. serrata under the moisture environment conditions under climate change. Under nutrient conditions, the ecological niche of the two species were similar. In addition, the ecological overlap for soil moisture of Q. aliena and Q. serrata was wider than the soil nutrient gradient under climate change. The species with traits in which the increase in ecological niche breadth due to climate change occurred more than the decrease was Q. aliena in both water and nutrient gradients. And in the responses of the population level, due to climate change, the adaptability of Q. aliena was higher than that of Q. serrata under the soil moisture condition, but the two species were similar under the nutrient condition. These results mean that the competition between the two species occurs more severely in the water environment under climate change conditions, and at that time, Q. aliena has higher adaptability than Q. serrata.

• Journal of Practical Agriculture & Fisheries Research
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• v.4 no.1
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• pp.70-79
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• 2002

This study was conducted to determine correlations and combining abilities of leaf lengths, leaf widths, petiole lengths, number of branches, number of leaves, leaf areas, fresh weights, dry weights and number of flowers in F₁ crosses made with the partial seven-parent diallel cross in Viola tricolor. Leaf lengths and leaf widths showed highly positive correlation with petiole length and negative correlation with number of branches, number of leaves and number of flowers. There was positive correlation between the number of leaves and flowers as well as between leaf area and fresh weight. Mean squares of general combining ability(GCA) and specific combining ability(SCA) were highly significant for all the parameters. Variance component values of SCA were greater than those of GCA for all the parameters except leaf length, implying preponderance of non-additive gene actions for these characters. The lines C and G for leaf lengths and widths, the lines A and F for number of leaves, the lines A, B and G for leaf areas, and the lines B and F for number of flowers showed relatively high GCA effects. The crosses of A×B and B×D exhibited high SCA effects on increasing leaf lengths, leaf widths, number of leaves, fresh weights and number of flowers. And also the crosses of B×E and D×G exhibited high SCA effects on decreasing leaf lengths, leaf areas and fresh weights as well as increasing number of flowers. The broad sense heritabilities of most characters were high compared with the narrow sense one. Those of leaf length, leaf width, petiole length and number of leaves were high in both the broad and narrow sense heritabilities.