• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.845-859
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• 2020

Climate change and recent heat waves have drawn public attention toward other environmental issues, such as water pollution in the form of algal blooms, chemical leaks, and oil spills. Water pollution by the leakage of chemicals may severely affect human health as well as contaminate the air, water, and soil and cause discoloration or death of crops that come in contact with these chemicals. Chemicals that may spill into water streams are often colorless and water-soluble, which makes it difficult to determine whether the water is polluted using the naked eye. When a chemical spill occurs, it is usually detected through a simple contact detection device by installing sensors at locations where leakage is likely to occur. The drawback with the approach using contact detection sensors is that it relies heavily on the skill of field workers. Moreover, these sensors are installed at a limited number of locations, so spill detection is not possible in areas where they are not installed. Recently hyperspectral images have been used to identify land cover and vegetation and to determine water quality by analyzing the inherent spectral characteristics of these materials. While hyperspectral sensors can potentially be used to detect chemical substances, there is currently a lack of research on the detection of chemicals in water streams using hyperspectral sensors. Therefore, this study utilized remote sensing techniques and the latest sensor technology to overcome the limitations of contact detection technology in detecting the leakage of hazardous chemical into aquatic systems. In this study, we aimed to determine whether 18 types of hazardous chemicals could be individually classified using hyperspectral image. To this end, we obtained hyperspectral images of each chemical to establish a spectral library. We expect that future studies will expand the spectral library database for hazardous chemicals and that verification of its application in water streams will be conducted so that it can be applied to real-time monitoring to facilitate rapid detection and response when a chemical spill has occurred.

• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.4
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• pp.307-319
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• 2009

Carbon dioxide capture and storage (CCS) technology has been regarded as one of the most possible and practical option to reduce the emission of carbon dioxide ($CO_2$) and consequently to mitigate the climate change. Korean government also have started a 10-year R&D project on $CO_2$ storage in sea-bed geological structure including gas field and deep saline aquifer since 2005. Various relevant researches are carried out to cover the initial survey of suitable geological structure storage site, monitoring of the stored $CO_2$ behavior, basic design of $CO_2$ transport and storage process and the risk assessment and management related to $CO_2$ leakage from engineered and geological processes. Leakage of $CO_2$ to the marine environment can change the chemistry of seawater including the pH and carbonate composition and also influence adversely on the diverse living organisms in ecosystems. Recently, IMO (International Maritime Organization) have developed the risk assessment and management framework for the $CO_2$ sequestration in sub-seabed geological structures (CS-SSGS) and considered the sequestration as a waste management option to mitigate greenhouse gas emissions. This framework for CS-SSGS aims to provide generic guidance to the Contracting Parties to the London Convention and Protocol, in order to characterize the risks to the marine environment from CS-SSGS on a site-specific basis and also to collect the necessary information to develop a management strategy to address uncertainties and any residual risks. The environmental risk assessment (ERA) plan for $CO_2$ storage work should include site selection and characterization, exposure assessment with probable leak scenario, risk assessment from direct and in-direct impact to the living organisms and risk management strategy. Domestic trial of the $CO_2$ capture and sequestration in to the marine geologic formation also should be accomplished through risk management with specified ERA approaches based on the IMO framework. The risk assessment procedure for $CO_2$ marine storage should contain the following components; 1) prediction of leakage probabilities with the reliable leakage scenarios from both engineered and geological part, 2) understanding on physio-chemical fate of $CO_2$ in marine environment especially for the candidate sites, 3) exposure assessment methods for various receptors in marine environments, 4) database production on the toxic effect of $CO_2$ to the ecologically and economically important species, and finally 5) development of surveillance procedures on the environmental changes with adequate monitoring techniques.

• The Journal of Engineering Geology
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• v.23 no.4
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• pp.477-491
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• 2013

Natural radionuclides in groundwater in the Danyang area were investigated to characterize the behaviors of uranium and radon with respect to lithology and physico-chemical components, which can aid our understanding of their occurrence, properties, and origins. To this end, a total of 100 groundwater samples were collected and analyzed, and radionuclide levels were used to construct detailed concentration maps. The water type of the groundwater, assessed using a Piper diagram, is mainly Ca-Na-$HCO_3$. The concentrations of uranium range from 0.02 to $251.0{\mu}g/L$ (average, $3.85{\mu}g/L$) and only 1% exceed USEPA's MCL (Maximum Contaminant Level). Uranium is enriched in groundwaters of Cretaceous granites and Precambrian metamorphic rocks, whereas it is depleted in groundwaters of sedimentary rocks. The concentrations of radon range from 13 to 28,470 pCi/L (average, 2397 pCi/L). Only 15% of the samples exceed AMCL (Alternative Maximum Contaminant Level) of 4000 pCi/L. The radon concentration is highest in groundwater of Cretaceous granites and lowest in groundwater of sedimentary rocks. In conclusion, the distribution and occurrence of radionuclides are intimately related to the basic geological characteristics of the rocks in which the radiogenic minerals are primarily contained. The behavior of uranium is only weakly related to that of radon (correlation coefficient = 0.15). There are also weak correlations between radionuclides and the main chemical components, pH, EC, Eh, and well depth. Of note, the correlation coefficient between radon and $SiO_2$ is 0.68, and that between radon and $HCO_3$ is -0.48. Factor analysis shows that radionuclides behave somewhat independently of each other because there are no significant factors that control the behavior of chemical components as well as radionuclides. The detailed concentration maps during this study will be used to establish useful database of radionuclide distribution and geological properties throughout Korea.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.1-15
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• 2006

In Korea, a resident registration card has various personal information such as a present address, a resident registration number, a face picture and a fingerprint. A plastic-type resident card currently used is easy to forge or alter and tricks of forgery grow to be high-degree as time goes on. So, whether a resident card is forged or not is difficult to judge by only an examination with the naked eye. This paper proposed an automatic recognition method of a resident card which recognizes a resident registration number by using a refined ART2-based RBF network newly proposed and authenticates a face picture by a template image matching method. The proposed method, first, extracts areas including a resident registration number and the date of issue from a resident card image by applying Sobel masking, median filtering and horizontal smearing operations to the image in turn. To improve the extraction of individual codes from extracted areas, the original image is binarized by using a high-frequency passing filter and CDM masking is applied to the binaried image fur making image information of individual codes better. Lastly, individual codes, which are targets of recognition, are extracted by applying 4-directional contour tracking algorithm to extracted areas in the binarized image. And this paper proposed a refined ART2-based RBF network to recognize individual codes, which applies ART2 as the loaming structure of the middle layer and dynamicaly adjusts a teaming rate in the teaming of the middle and the output layers by using a fuzzy control method to improve the performance of teaming. Also, for the precise judgement of forgey of a resident card, the proposed method supports a face authentication by using a face template database and a template image matching method. For performance evaluation of the proposed method, this paper maked metamorphoses of an original image of resident card such as a forgey of face picture, an addition of noise, variations of contrast variations of intensity and image blurring, and applied these images with original images to experiments. The results of experiment showed that the proposed method is excellent in the recognition of individual codes and the face authentication fur the automatic recognition of a resident card.

• Applied Biological Chemistry
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• v.48 no.1
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• pp.23-33
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• 2005

Agricultural sciences field in South Korea has many strong points such as numerous researchers, establishment of research infra-structure, excellence in research competitiveness and high technological level. However, there are also many weaknesses including insufficient leadership at related societies and institutes, deficiency of the next generation research group, and insufficiency in research productivity. There are many opportunities including increasing the importance of the biotechnological industry, activating international cooperation researches, and exploring the multitude of possible research areas to be studied. However, some threats still exist, such as pressure from the government of developed countries to open the agricultural market, the decrease of specialized farms, and intensification for researches to gratify economic and social demands. To encourage research activities in the agricultural sciences field in Korea, the following actions and systems are required: 1) formulation of a mid- and a long-term research master plan, 2) development of a database on the man power in related fields, 3) activation of top-down research topics, and associated increase of individual research grants, 4) development of special national programs for basic researches in agricultural sciences, 5) organization of a committee for policy and planning within the related societies, and 6) system development for the fair evaluation of the research results.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.304-313
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• 2016

One of the issues currently facing nuclear power plants is how to store spent nuclear waste materials which are contaminated with radionuclides such as $^{134}Cs$, $^{135}Cs$, and $^{137}Cs$. Bioremediation processes may offer a potent method of cleaning up radioactive cesium. However, there have only been limited reports on $Cs^+$ tolerant bacteria. In this study, we report the isolation and identification of $Cs^+$ tolerant bacteria in environmental soil and sediment. The resistant $Cs^+$ isolates were screened from enrichment cultures in R2A medium supplemented with 100 mM CsCl for 72 h, followed by microbial community analysis based on sequencing analysis from 16S rRNA gene clone libraries(NCBI's BlastN). The dominant Bacillus anthracis Roh-1 and B. cereus Roh-2 were successfully isolated from the cesium enrichment culture. Importantly, B. cereus Roh-2 is resistant to 30% more $Cs^+$ than is B. anthracis Roh-1 when treated with 50 mM CsCl. Growth experiments clearly demonstrated that the isolate had a higher tolerance to $Cs^+$. In addition, we investigated the adsorption of $0.2mg\;L^{-1}$ $Cs^+$ using B. anthracis Roh-1. The maximum $Cs^+$ biosorption capacity of B. anthracis Roh-1 was $2.01mg\;g^{-1}$ at pH 10. Thus, we show that $Cs^+$ tolerant bacterial isolates could be used for bioremediation of contaminated environments.

• The Journal of Engineering Geology
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• v.9 no.2
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• pp.161-176
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• 1999

The objectives of this study are to analyze the changes of geo-surface topography in the Siwha embankment and the Ahsan city area by the image processing of Landsat Thematic Mapper data, and to estimate the reclaimed amount of the exposed tidal flat in the Siwha area using the GIS. False color composite, Tasseled cap, NVDI(normalized difference vegetation index), and supervised classification techniques were used to analyze the distribution of sediments and the aspect of topographical variations caused by artificial human actions. The total amount of the exposed tidal flat was estimated on the basis of the database snch as aerial photography, hydrographic chart, geological map, and scheme drawing in the Siwha area. The possible excavation regions for a seawall were predicted analyzing the supervised classification image of Landsat TM data. Tasseled cap images were used to observe the distribution of sediments. The difference of the NDVI images between spring and summer seasons indicates that deciduous and coniferous forests were distributed over the whole areas. The total fill-volume of the exposed Siwha tidal flat and the fill-volume of the construction planning seawall were calculated as $581,485,354\textrm{m}^3{\;}and{\;}3,387,360\textrm{m}^3$, respectively, from the digital terrain analysis. Daebu Island, Sunkam Island, and the part of Songsan-myeon were chosen as the cut area to make the seawall, and their cut-volumes were estimated as $5,229,576\textrm{m}^3,{\;}79,227,072\textrm{m}^3,{\;}and{\;}47,026,008\textrm{m}^3$, respectively. Therefore, the cut-volume of Daebu Island alone among three areas was sufficient to make the seawall.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.109-115
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• 2015

The new medical device technologies for bio-signal information and medical information which developed in various forms have been increasing. Information gathering techniques and the increasing of the bio-signal information device are being used as the main information of the medical service in everyday life. Hence, there is increasing in utilization of the various bio-signals, but it has a problem that does not account for security reasons. Furthermore, the medical image information and bio-signal of the patient in medical field is generated by the individual device, that make the situation cannot be managed and integrated. In order to solve that problem, in this paper we integrated the QR code signal associated with the medial image information including the finding of the doctor and the bio-signal information. bio-signal. System implementation environment for medical imaging devices and bio-signal acquisition was configured through bio-signal measurement, smart device and PC. For the ROI extraction of bio-signal and the receiving of image information that transfer from the medical equipment or bio-signal measurement, .NET Framework was used to operate the QR server module on Window Server 2008 operating system. The main function of the QR server module is to parse the DICOM file generated from the medical imaging device and extract the identified ROI information to store and manage in the database. Additionally, EMR, patient health information such as OCS, extracted ROI information needed for basic information and emergency situation is managed by QR code. QR code and ROI management and the bio-signal information file also store and manage depending on the size of receiving the bio-singnal information case with a PID (patient identification) to be used by the bio-signal device. If the receiving of information is not less than the maximum size to be converted into a QR code, the QR code and the URL information can access the bio-signal information through the server. Likewise, .Net Framework is installed to provide the information in the form of the QR code, so the client can check and find the relevant information through PC and android-based smart device. Finally, the existing medical imaging information, bio-signal information and the health information of the patient are integrated over the result of executing the application service in order to provide a medical information service which is suitable in medical field.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.