• Journal of Soil and Groundwater Environment
• /
• v.19 no.4
• /
• pp.31-44
• /
• 2014

In the present study, a few of recently developed geostatistical models are comparatively studied. The models are two-point statistics based sequential indicator simulation (SISIM) and generalized coupled Markov chain (GCMC), multi-point statistics single normal equation simulation (SNESIM), and object based model of FLUVSIM (fluvial simulation) that predicts structures of target object from the provided geometric information. Out of the models, SNESIM and FLUVSIM require additional information other than conditioning data such as training map and geometry, respectively, which generally claim demanding additional resources. For the comparative studies, three-dimensional fluvial reservoir model is developed considering the genetic information and the samples, as input data for the models, are acquired by mimicking realistic sampling (i.e. random sampling). For SNESIM and FLUVSIM, additional training map and the geometry data are synthesized based on the same information used for the objective model. For the comparisons of the predictabilities of the models, two different measures are employed. In the first measure, the ensemble probability maps of the models are developed from multiple realizations, which are compared in depth to the objective model. In the second measure, the developed realizations are converted to hydrogeologic properties and the groundwater flow simulation results are compared to that of the objective model. From the comparisons, it is found that the predictability of GCMC outperforms the other models in terms of the first measure. On the other hand, in terms of the second measure, the both predictabilities of GCMC and SNESIM are outstanding out of the considered models. The excellences of GCMC model in the comparisons may attribute to the incorporations of directional non-stationarity and the non-linear prediction structure. From the results, it is concluded that the various geostatistical models need to be comprehensively considered and comparatively analyzed for appropriate characterizations.

• Nuclear Engineering and Technology
• /
• v.53 no.10
• /
• pp.3275-3285
• /
• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• Journal of Trauma and Injury
• /
• v.34 no.3
• /
• pp.155-161
• /
• 2021

Purpose: The coronavirus disease 2019 (COVID-19) pandemic has necessitated a redistribution of resources to meet hospitals' service needs. This study investigated the impact of COVID-19 on a regional trauma center in South Korea. Methods: We retrospectively reviewed cases of polytrauma at a single regional trauma center in South Korea between January 20 and September 30, 2020 (the COVID-19 period) and compared them to cases reported during the same time frame (January 20 to September 30) between 2016 and 2019 (the pre-COVID-19 period). The primary outcome was in-hospital mortality, and secondary outcomes included the number of daily admissions, hospital length of stay (LOS), and intensive care unit (ICU) LOS. Results: The mean number of daily admissions decreased by 15% during the COVID-19 period (4.0±2.0 vs. 4.7±2.2, p=0.010). There was no difference in mechanisms of injury between the two periods. For patients admitted during the COVID-19 period, the hospital LOS was significantly shorter (10 days [interquartile range (IQR) 4-19 days] vs. 16 days [IQR 8-28 days], p<0.001); however, no significant differences in ICU LOS and mortality were found. Conclusions: The observations at Regional Trauma Center, Pusan National University Hospital corroborate anecdotal reports that there has been a decline in the number of patients admitted to hospitals during the COVID-19 period. In addition, patients admitted during the COVID-19 pandemic had a significantly shorter hospital LOS than those admitted before the COVID-19 pandemic. These preliminary data warrant validation in larger, multi-center studies.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.64 no.2
• /
• pp.159-164
• /
• 2019

Weed control is a crucial practice not only in organic farming, but also in modern agriculture because it can lead to loss in crop yield. In general, weed is distributed in patches heterogeneously in the field. These patches vary in size, shape, and density. Thus, it would be efficient if chemicals are sprayed on these patches rather than spraying uniformly in the field, which can pollute the environment and be cost prohibitive. In this sense, weed detection could be beneficial for sustainable agriculture. Studies have been conducted to detect weed patches in the field using remote sensing technologies, which can be classified into a method using image segmentation based on morphology and a method with vegetative indices based on the wavelength of light. In this study, the latter methodology has been used to detect the weed patches. As a result, it was found that the vegetative indices were easier to operate as it did not need any sophisticated algorithm for differentiating weeds from crop and soil as compared to the former method. Consequently, we demonstrated that the current method of using vegetative index is accurate enough to detect weed patches, and will be useful for farmers to control weeds with minimal use of chemicals and in a more precise manner.

• Smart Media Journal
• /
• v.7 no.4
• /
• pp.30-36
• /
• 2018

Single domain model like DataLake framework is in spotlight because it can improve data efficiency and process data smarter in big data environment, where large scaled business system generates huge amount of data. In particular, efficient operation of network, storage, and computing resources in logical single domain model is very important for physically partitioned multi-site data process. Based on the advantages of Data Lake framework, we define and extend the concept of Connected Data Architecture and functions of DataLake framework for integrating multiple sites in various domains and managing the lifecycle of data. Also, we propose the design of CDA-based AI service and utilization scenarios in various application domain.

• Journal of Climate Change Research
• /
• v.9 no.1
• /
• pp.1-12
• /
• 2018

There is an emphasis on the importance of adaptation against to climate change and related natural disasters. As a result, various climate information with different time-scale can be used for science-based climate change adaptation policy. From the aspects of Global Framework for Climate Services (GFCS), various time-scaled climate information in Korea is mainly produced by Korea Meteorological Administration (KMA) However, application of weather and climate information in different application sectors has been done individually in the fields of agriculture and water resources mostly based-on weather information. Furthermore, utilization of climate information including seasonal forecast and climate change projections are insufficient. Therefore, establishment of the Cooperation Center for Application of Weather and Climate Information is necessary as an institutional platform for the UIP (User Interface Platform) focusing on multi-model ensemble (MME) based climate service, seamless climate service, and climate service based on multidisciplinary approach. In addition, APCC Integrated Modeling Solution (AIMS) was developed as a technical platform for UIP focusing on user-centered downscaling of various time-scaled climate information, application of downscaled data into impact assessment modeling in various sectors, and finally producing information can be used in decision making procedures. AIMS is expected to be helpful for the increase of adaptation capacity against climate change in developing countries and Korea through the voluntary participation of producer and user groups within in the institutional and technical platform suggested.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.342-345
• /
• 2014

Network intrusion detection system based on machine learning methods is quite dependent on the selected features in terms of accuracy and efficiency. Nevertheless, choosing the optimal combination of features from generally used features to detect network intrusion requires extensive computing resources. For instance, the number of possible feature combinations from given n features is $2^n-1$. In this paper, to tackle this problem we propose a optimal feature selection algorithm. Proposed algorithm is based on the local search algorithm, one of representative meta-heuristic algorithm for solving optimization problem. In addition, the accuracy of clusters which obtained using selected feature components and k-means clustering algorithm is adopted to evaluate a feature assembly. In order to estimate the performance of our proposed algorithm, comparing with a method where all features are used on NSL-KDD data set and multi-layer perceptron.

• Asian-Australasian Journal of Animal Sciences
• /
• v.32 no.8
• /
• pp.1077-1083
• /
• 2019

Objective: Sow longevity is important for efficient and profitable pig farming. Recently, there has been an increasing interest in social genetic effect (SGE) of pigs on stress-tolerance and behavior. The present study aimed to estimate genetic correlations among average daily gain (ADG), stayability (STAY), and number of piglets born alive at the first parity (NBA1) in Korean Yorkshire pigs, using a model including SGE. Methods: The phenotypic records of ADG and reproductive traits of 33,120 and 11,654 pigs, respectively, were evaluated. The variances and (co) variances of the studied traits were estimated by a multi-trait animal model applying the Bayesian with linear-threshold models using Gibbs sampling. Results: The direct and SGEs on ADG had a significantly negative (-0.30) and neutral (0.04) genetic relationship with STAY, respectively. In addition, the genetic correlation between the social effects on ADG and NBA1 tended to be positive (0.27), unlike the direct effects (-0.04). The genetic correlation of the total effect on ADG with that of STAY was negative (-0.23) but non-significant, owing to the social effect. Conclusion: These results suggested that total genetic effect on growth in the SGE model might reduce the negative effect on sow longevity because of the growth potential of pigs. We recommend including social effects as selection criteria in breeding programs to obtain satisfactory genetic changes in both growth and longevity.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.22 no.5
• /
• pp.57-67
• /
• 2019

Water-Birds are the birds that occupy the highest proportion in Korea, inland wetlands and reservoirs provide them with a good environment as habitat, but their habitats have been losing because of thoughtless development. Therefore, artificial plant islands in reservoirs are important for improving habitat environment and providing food resources. However, there are no research and standards on the built and management of artificial plant islands. So this study is to find out the density of bird using artificial plant island as habitat through monitoring using UAV focus on the Cheonho-reservoirs located in Seobuk-gu, Cheonan-si(Middle Chungcheong Province). Further, the correlation analysis with environmental factors was conducted to determine the effect of artificial plant islands as habitats for water-birds. The supervised classification of the three-time images taken by the drone identified 244 white-billed ducks and 46 mandarin ducks. The utilization rate was different for each photographed date, and more individuals were identified in wet artificial plant islands than dry ones. As a result of analyzing the utilization follow environmental factors, the distance from the trail showed a significant correlation, and the other factors did not have a statistically significant effect. This study is the first case of the UAV monitoring method of the water-birds using artificial plant islands in the reservoir, and can be used as the basic data for the built and management.

• Journal of Korean Society on Water Environment
• /
• v.36 no.6
• /
• pp.636-650
• /
• 2020

A modeling system that can consider the overall water environment and be used to integrate hydrology, water quality, and aquatic ecosystem on a watershed scale is essential to support decision-making in integrated water resources management (IWRM). In adapting imported models for evaluating the unique water environment in Korea, a platform perspective is becoming increasingly important. In this study, a modeling platform is defined as an ecosystem that continuously grows and provides sustainable values through voluntary participation- and interaction-of all stakeholders- not only experts related to model development, but also model users and decision-makers. We assessed the conceptual values provided by the IWRM modeling platform in terms of openness, transparency, scalability, and sustainability. I We also reviewed the technical aspects of functional and spatial integrations in terms of socio-economic factors and user-centered multi-scale climate-forecast information. Based on those conceptual and technical aspects, we evaluated potential modeling platforms such as Source, FREEWAT, Object Modeling System (OMS), OpenMI, Community Surface-Dynamics Modeling System (CSDMS), and HydroShare. Among them, CSDMS most closely approached the values suggested in model development and offered a basic standard for easy integration of existing models using different program languages. HydroShare showed potential for sharing modeling results with the transparency expected by model user-s. Therefore, we believe that can be used as a reference in development of a modeling platform appropriate for managing the unique integrated water environment in Korea.