• Journal of Korean Society of Forest Science
• /
• v.110 no.4
• /
• pp.479-495
• /
• 2021

To improve on the efficient management of forest resources, it is necessary to create a forest soil map, which represents a comprehensive database of forest lands. Although a 1:25,000 scale forest site map has been used in Korea, the need for a large-scale forest soil map with high precision and information on forest lands that is specialized for individual purposes has been identified. Moreover, to keep pace with the advancement in forest management and transition to a digital society, it is essential to develop a method for constructing new forest soil maps that can diversify its use. Therefore, this paper presented a developmental process and used a 1:5,000 scale forest soil map to propose future directions. National maps showing the soil type, depth, and texture were produced based on the survey and analysis of forest soils, followed by the Forest Land Soil Map (1:5,000) Production Standard Manual. Alternatively, forest soil map data were the basis on which various other maps that can be used to prevent and predict forest disasters and evaluate environmental capacities were developed. Accordingly, ways to provide appropriate information to achieve the national forest plan, secure forestry big data, and accomplish sustainable forest management that corresponds to the national development plan are proposed based on results from the current study.

• Information Systems Review
• /
• v.10 no.2
• /
• pp.269-289
• /
• 2008

It is very difficult problem to estimate and evaluate the performance of e-government system which scope and size are large and its effectiveness can not be seen shortly but reveals after several years. It is because the previous offline processes can not be transformed to online ones fully and shortly. For such e-transformation cases, the performance evaluation model should be adjusted and modified gradually as time passes. This paper propose new EEM(E-transformation Evaluation Model) model and methodology to evaluate G2B system that is one of large e-government project. EEM model can derive monetary value of e-transformatized business process areas(online areas). It also estimate the expected effect of offline area that is not yet transformed to online. EEM model consists of standard model, verification model and estimation model with some variables such as evaluation year, evaluation area and data type. By using survey data and database data together it can validate the correctness of the model and derive the effect of the system introduction. This paper also propose EEM evaluation methodology consisting of 5 stages and 10 sub processes to evaluate online and offline effect efficiently. To show the usefulness of this study, we evaluate the performance of Korea G2B system named KONEPS which is famous as a successful e-government case in the world by using the proposed model and methodology. The proposed model and methodology can be applied to different similar areas including e-government projects and large scale information system introduction in private sectors. This study can be also used for establishing appropriate policies about e-government project and informatization issues.

• Journal of KIISE:Computer Systems and Theory
• /
• v.35 no.6
• /
• pp.237-247
• /
• 2008

A virtual screening is the process of reducing an unmanageable number of compounds to a limited number of compounds for the target of interest by means of computational techniques such as molecular docking. And it is one of a large-scale scientific application that requires large computing power and data storage capability. Previous applications or softwares for molecular docking such as AutoDock, FlexX, Glide, DOCK, LigandFit, ViSION were developed to be run on a supercomputer, a workstation, or a cluster-computer. However the virtual screening using a supercomputer has a problem that a supercomputer is very expensive and the virtual screening using a workstation or a cluster-computer requires a long execution time. Thus we propose a service-based virtual screening system using Grid computing technology which supports a large data intensive operation. We constructed 3-dimensional chemical molecular database for virtual screening. And we designed a resource broker and a data broker for supporting efficient molecular docking service and proposed various services for virtual screening. We implemented service based virtual screening system with DOCK 5.0 and Globus 3.2 toolkit. Our system can reduce a timeline and cost of drug or new material design.

• Journal of the Korean Institute of Gas
• /
• v.17 no.6
• /
• pp.83-89
• /
• 2013

Energy industry facilities can cause fatal damage for internal industry employee as well as external general people because handling various kinds of gas and harmful substance might be spread to large scale severe accident by fire, explosion, and toxic gas leakage. In order to prevent these accidents, quantification by damage effect on structure and human is tried by using quantitative risk assessment, but it is difficult to process instantly exceptional cases and requires knowledge of expert. This paper aims to minimize exceptional cases and knowledge of expert, and present risk with human perceptible. So, we designed and developed zone-base risk analysis system that can compute risk of zone in real time at that point using database and incremental model.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.3
• /
• pp.579-586
• /
• 2018

With the beginning of the new era of bigdata, information extraction or prediction are an important research area. Here, we present the acquisition of semi-automatically curated large-scale biological database and the prediction of enzyme reaction annotation for analyzing the pharmacological activities of drugs. Because the xenobiotic metabolism of pharmaceutical drugs by cellular enzymes is an important aspect of pharmacology and medicine. In this study, we apply and analyze similarity models to predict bimolecular reactions between human enzymes and their corresponding substrates. Thirteen models select to reflect the characteristics of each cluster in the similarity model. These models compare based on sensitivity and AUC. Among the evaluation models, the Simpson coefficient model showed the best performance in predicting the reactivity between the enzymes. The whole similarity model implement as a web service. The proposed model can respond dynamically to the addition of reaction information, which will contribute to the shortening of new drug development time and cost reduction.

• Journal of KIISE:Databases
• /
• v.37 no.5
• /
• pp.221-227
• /
• 2010

Web access sequence mining can discover the frequently accessed web pages pursued by users. Utility-based web access sequence mining handles non-binary occurrences of web pages and extracts more useful knowledge from web logs. However, the existing utility-based web access sequence mining approach considers web access sequences from the very beginning of web logs and therefore it is not suitable for mining data streams where the volume of data is huge and unbounded. At the same time, it cannot find the recent change of knowledge in data streams adaptively. The existing approach has many other limitations such as considering only forward references of web access sequences, suffers in the level-wise candidate generation-and-test methodology, needs several database scans, etc. In this paper, we propose a new approach for high utility web access sequence mining over data streams with a sliding window method. Our approach can not only handle large-scale data but also efficiently discover the recently generated information from data streams. Moreover, it can solve the other limitations of the existing algorithm over data streams. Extensive performance analyses show that our approach is very efficient and outperforms the existing algorithm.

• Journal of Environmental Health Sciences
• /
• v.44 no.5
• /
• pp.409-420
• /
• 2018

Objectives: An outbreak of humidifier disinfectant-related respiratory disease has not only been a massive disaster for end users, but it is also a concern for the workers of the manufacturers. This study presents the results of a questionnaire survey on the health effects among workers involved in the manufacture of humidifier disinfectants. Methods: Seven sites where humidifier disinfectants were manufactured were identified. A questionnaire survey was conducted to assess the physical symptoms experienced by workers related to humidifier disinfectants. Among a total of 177 workers, 42 subjects were available for the survey. Results: Twenty-one of the 42 respondents reported that they experienced respiratory or skin and mucosal irritation symptoms during work. Of the respondents who experienced symptoms, 14 believed that their symptoms were related to the work process and reported that the symptoms were experienced while working. However, no respondents reported ongoing symptoms or sequelae during the investigation period, and this result could not minimize selection bias due to low response rates. We then compared the characteristics of the group who experienced suspicious symptoms with those of the group without any symptoms. There was no statistically significant difference between two groups. Conclusions: We could not find significant health effects related to the humidifier disinfectant manufacturing process, although 21 respondents experienced stimulant symptoms and 14 respondents believed that the symptoms were related to the work process. Due to the long period of time after occupational exposure and the lack of data, there were many limitations to this study. However, this is one of the few follow-up investigations of workers related to this large-scale disaster in South Korea and the limitations of this study highlight the need to follow up with a nationwide database rather than an occasional survey.

• Journal of Korean Neurosurgical Society
• /
• v.54 no.3
• /
• pp.211-219
• /
• 2013

Objective : The objectives of the present study were to characterize the natural course of initially non-operated traumatic acute subdural hematoma (ASDH) and to identify the risk factors of hematoma progression. Methods : Retrospective analysis was performed using sequential computed tomography (CT) images maintained in a prospective observational database containing 177 ASDH cases treated from 2005 to 2011. Patients were allocated to four groups as followings; 136 (76.8%) patients to the spontaneous resolution group, 12 (6.8%) who underwent operation between 4 hours and 7 days to the rapid worsening group (RWG), 24 (13.6%) who experienced an increase of hematoma and that underwent operation between 7 and 28 days to the subacute worsening group (SWG), and 5 (2.8%) who developed delayed aggravation requiring surgery from one month after onset to the delayed worsening group (DWG). Groups were compared with respect to various factors. Results : No significant intergroup difference was found with respect to age, mechanism of injury, or initial Glasgow Coma Scale. The presence of combined cerebral contusion or subarachnoid hemorrhage was found to be a significant prognostic factor. Regarding CT findings, mixed density was common in the RWG and the SWG. Midline shifting, hematoma thickness, and numbers of CT slices containing hematoma were significant prognostic factors of the RWG and the SWG. Brain atrophy was more severe in the SWG and the DWG. Conclusion : A large proportion of initially non-operated ASDHs worsen in the acute or subacute phase. Patients with risk factors should be monitored carefully for progression by repeat CT imaging.

• Korean Journal of Cognitive Science
• /
• v.28 no.4
• /
• pp.299-314
• /
• 2017

Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.

• The Journal of the Korea Contents Association
• /
• v.12 no.3
• /
• pp.421-433
• /
• 2012

AMGA service, which is one of the EMI gLite middleware components, is widely used for analysis of distributed large scale experiments data as metadata repository by scientific and technological researchers and the use of AMGA is extended farther to include general industries needing metadata Catalogue as well. However AMGA, based unix and Grid UI, has the weakness of being absence of general-purpose user interfaces in comparison to other commercial database systems and that's why it's difficult to use and diffuse it although it has the superiority of the functionality. In this paper, we developed AMGA GUI toolkit to provide work convenience using object-oriented modeling language(UML). Currently, AMGA has been used as the main component among many user communities such as Belle II, WISDOM, MDM, and so on, but we expect that this development can not only lower the barrier to entry for AMGA beginners to use it, but lead to expand the use of AMGA service over more communities.