• International Journal of Contents
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• v.15 no.4
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• pp.113-119
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• 2019

It has been reported that large amounts of information on agri-foods were delivered to consumers through television and social networks, and the information may influence consumers' behavior. The purpose of this paper was first to analyze relations of social network service and broadcasting program on paprika consumption in the aspect of amounts to purchase and identify potential factors that can promote paprika consumption; second, to develop prediction models of paprika consumption by using structured and unstructured big data. By using data 2010-2017, cross-correlation and time-series prediction algorithms (autoregressive exogenous model and vector error correction model), statistically significant correlations between paprika consumption and television programs/shows and blogs mentioning paprika and diet were identified with lagged times. When paprika and diet related data were added for prediction, these data improved the model predictability. This is the first report to predict paprika consumption by using structured and unstructured data.

• The Journal of the Korea Contents Association
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• v.18 no.12
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• pp.229-237
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• 2018

This paper considers effective timetable modules in order to easily satisfy various user requirements during scheduling timetables in universities. Noticing that the methods for allocating time blocks change according to the timetable modules, we suggest six models of symmetric timetable modules composed of 4 blocks, and show that our models have more benefits without any loss from the viewpoint of customers, if the suppliers consider the decreasing upper bound of ratio utilizing space resources. By adapting a concept of potentially determined blocks and suggesting their management strategies, finally, we accomplish a method for supporting flexibility and controllability when the universities timetables are scheduled.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.31-44
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• 2019

The main objective of this research is to develop a hydroclimate drought index (HCDI) using the gridded climate data inputs in a Variable Infiltration Capacity (VIC) modeling platform. Typical drought indices, including, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Self-calibrated Palmer Drought Severity Index (SC-PDSI) in South Korea are also used and compared. Inverse Distance Weighting (IDW) method is applied to create the gridded climate data from 56 ground weather stations using topographic information between weather stations and the respective grid cell ($12km{\times}12km$). R statistical software packages are used to visualize HCDI in Google Earth. Skill score (SS) are computed to evaluate the drought predictability based on water information derived from the observed reservoir storage and the ground weather stations. The study indicates that the proposed HCDI with the gridded climate data input is promising in the sense that it can help us to predict potential drought extents and to mitigate its impacts in a changing climate. The longer term drought prediction (e.g., 9 and 12 month) capability, in particular, shows higher SS so that it can be used for climate-driven future droughts.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.85-90
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• 2022

In modern conditions of the development of public relations, there is a continuous development of technologies. This not only reflects the convenience of service users, and new technology but also contributes to the emergence of new disputes to protect the rights of stakeholders. Therefore, it is urgent to study the distinctions between the jurisdiction of commercial, civil and administrative courts in resolving IT disputes. The work aims to study the peculiarities of delimitation of the jurisdiction of commercial, civil, and administrative courts through the prism of IT measurement. The research methodology consists of such methods as a historical, comparative-legal, formal-logical, empirical, method of analogy, method of synthesis, method of analysis, and systematic method. Examining the specifics of delimiting the jurisdiction of commercial, civil, and administrative courts through the IT dimension, it was concluded that there is a problem in determining the jurisdiction of the court. In addition, the judicial practice on this issue is quite variable, which negatively affects the predictability of technology in resolving potential disputes. In this regard, the criterion models for distinguishing between commercial, administrative, and civil proceedings according to the legal classification of the parties, as well as the nature of the claim are identified. This separation will contribute to a more accurate application of legal norms and methods of application of administrative norms and reduce the number of cases of improper proceedings.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.437-437
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• 2022

The prediction of dam inflow rate is crucial for the management of the largest multi-purpose dam in South Korea, the Soyang Dam. The main issue associated with the management of water resources is the stochastic nature of the reservoir inflow leading to an increase in uncertainty associated with the inflow prediction. The Autoregressive (AR) model is commonly used to provide the simulation and forecast of hydrometeorological data. However, because its estimation is based solely on the time-series data, it has the disadvantage of being unable to account for external variables such as climate information. This study proposes the use of the Autoregressive Exogenous Stochastic Volatility (ARXSV) model within a Bayesian modeling framework for increased predictability of the monthly dam inflow by addressing the exogenous and stochastic factors. This study analyzes 45 years of hydrological input data of the Soyang Dam from the year 1974 to 2019. The result of this study will be beneficial to strengthen the potential use of data-driven models for accurate inflow predictions and better reservoir management.

• Journal of the Korean earth science society
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• v.30 no.3
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• pp.294-304
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• 2009

A statistical prediction model for the typhoon intensity and track in the Northwestern Pacific area was developed based on the artificial neural network scheme. Specifically, this model is focused on the 5-day prediction after tropical cyclone genesis, and used the CLIPPER parameters (genesis location, intensity, and date), dynamic parameters (vertical wind shear between 200 and 850hPa, upper-level divergence, and lower-level relative vorticity), and thermal parameters (upper-level equivalent potential temperature, ENSO, 200-hPa air temperature, mid-level relative humidity). Based on the characteristics of predictors, a total of seven artificial neural network models were developed. The best one was the case that combined the CLIPPER parameters and thermal parameters. This case showed higher predictability during the summer season than the winter season, and the forecast error also depended on the location: The intensity error rate increases when the genesis location moves to Southeastern area and the track error increases when it moves to Northwestern area. Comparing the predictability with the multiple linear regression model, the artificial neural network model showed better performance.

• IMMUNE NETWORK
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• v.6 no.1
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• pp.43-51
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• 2006

Background: The DR-$70^{TM}$ immunoassay is a newly developed cancer diagnostic test which quantifies the serum fibrin degradation products (FDP), produced during fibrinolysis, by antibody reaction. The purpose of this study was to evaluate the potential of DR-$70^{TM}$ Immunoassay in screening malignant tumor. Methods: Sample subjects were 4,169 adults, both male and female, who visited the health promotion center of a general hospital from March 2004 to April 2005 and underwent the DR-$70^{TM}$ immunoassay test and other tests for cancer diagnosis. The patient group was defined as 42 adults out of the sample subjects who were newly diagnosed with cancer during the same time period when the DR-$70^{TM}$ immunoassay test was performed. Final confirmation of a malignant tumor was made by pathological analysis. Results: The mean DR-$70^{TM}$ level was $0.83{\pm}0.65{\mu}g/ml$ (range: 0.00 (0.0001)${\sim}7.42{\mu}g/ml)$ in the control group (n=4,127) as opposed to $2.70{\pm}2.33{\mu}g/ml$ (range: $0.12{\sim}9.30{\mu}g/ml)$ in the cancer group (n=42), and statistical significance was established (p<0.0001, Student t-test). When categorized by the type of malignant tumor, all cancer patients with the exception of the subgroups of colon and rectal cancer showed significantly higher mean DR-$70^{TM}$ levels compared with the control group (p<0.0001, Kruscal-Wallis test). The receiver operating characteristic (ROC) curve analysis revealed ${\geq}1.091{\mu}g/ml$ as the best cut-off value. Using this cut-off value, the DR-$70^{TM}$ immunoassay produced a sensitivity of 71.4%, a specificity of 70.1%, a positive predictability of 69.4%, and a negative predictability of 69.2% (1). Conclusion: A significant increase in the mean DR-$70^{TM}$ value was observed in the cancer group (thyroidal, gastric, breast, hepatic and ovarian) com pared with the control group. In particular, the specificity and sensitivity of the DR-$70^{TM}$ immunoassay was relatively high in the subgroups of breast, gastric, and thyroidal cancer patients. There is need for further studies on a large number of malignant tumor patients to see how the DR-$70^{TM}$ level might be changed according to the differentiation grade and postoperative prognosis of the malignant tumor.

• Development and Reproduction
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• v.8 no.1
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• pp.1-9
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• 2004

The potential importance of proteomic approaches has been clearly demonstrated in other fields of human medical research, including liver and heart disease and certain forms of cancer. However, reproductive researches have been applied to proteomics poorly. Proteomics can be defined as the systematic analysis of proteins for their identity, quantity, and function. It could increase the predictability of early drug development and identify non-invasive biomarkers of toxicity or efficacy. Proteome analysis is most commonly accomplished by the combination of two-dimensional gel electrophoresis(2DE) and MALDI-TOF(matrix-assisted laser desorption ionization-time of flight) MS(mass spectrometry) or protein chip array and SELDI-TOF(surface-enhanced laser desorption ionization-time of flight) MS. In addition understanding the possessing knowledge of the developing biomarkers used to assess reproductive biology will also be essential components relevant to the topic of reproduction. The continued integration of proteomic and genomic data will have a fundamental impact on our understanding of the normal functioning of cells and organisms and will give insights into complex cellular processes and disease and provides new opportunities for the development of diagnostics and therapeutics. The challenge to researchers in the field of reproduction is to harness this new technology as well as others that are available to a greater extent than at present as they have considerable potential to greatly improve our understanding of the molecular aspects of reproduction both in health and disease.

• Journal of Periodontal and Implant Science
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• v.48 no.2
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• pp.114-123
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• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.18-26
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• 2015

This study is a fundamental research to suggest a forecasting model for short-term railway passenger demand focusing on major lines (Gyeungbu, Honam, Jeonla, Janghang, Jungang) of Saemaeul rail and Mugunghwa rail. Also the author tried to verify the potential application of the proposed models. For this study, SARIMA model considering characteristics of seasonal trip is basically used, and daily mean forecasting models are independently constructed depending on weekday/weekend in order to consider characteristics of weekday/weekend trip and a legal holiday trip. Furthermore, intervention events having an impact on using the train such as introduction of new lines or EXPO are reflected in the model to increase reliability of the model. Finally, proposed models are confirmed to have high accuracy and reliability by verifying predictability of models. The proposed models of this research will be expected to utilize for establishing a plan for short-term operation of lines.