• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.475-478
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• 2003

This study was conducted to compare the design rainfall derived by the at-site and regional frequency analysis based on the regionalization of the precipitation. The regional and at-site design rainfalls were calculated by Log-Pearson type III distribution using Indirect Methods of Moments(WRC). The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error(RRMSE), Relative bias(RBIAS) and Relative reduction(RR) in RRMSE were computed and compared between design rainfalls resulted from observed and simulated data using the regional and at-site analysis. It was shown that the regional analysis procedure can substantially reduce the RRMSE, RBIAS in comparison with those of at-site analysis. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.651-657
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• 2015

The amount of web traffic has increased as a result of the rapid growth of the use of web-based applications. In order to obtain valuable information from web logs, we need to develop systems that can support interactive, flexible, and efficient ways to analyze and handle large amounts of data. In this paper, we present CERES, a log-based, interactive web analytics system for backbone networks. Since CERES focuses on analyzing web log records generated from backbone networks, it is possible to perform a web analysis from the perspective of a network. CERES is designed for deployment in a server cluster using the Hadoop Distributed File System (HDFS) as the underlying storage. We transform and store web log records from backbone networks into relations and then allow users to use a SQL-like language to analyze web log records in a flexible and interactive manner. In particular, we use the data cube technique to enable the efficient statistical analysis of web log. The system provides users a web-based, multi-modal user interface.

• Journal of Food Hygiene and Safety
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• v.24 no.1
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• pp.102-109
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• 2009

This study was carried out to analyse the microbio\logical contamination and biogenic amines(BA) content in Korea traditional soybean paste and commercial soybean paste. The results of microbio\logical analysis through Korean traditional soybean pastes($L1{\sim}L4$) were $7.8{\pm}0.1\;\log\;CFU/g{\sim}7.9{\pm}0.1\;\log\;CFU/g$, commercial soybean pastes($H1{\sim}H6$) were $6.2{\pm}0.1\;\log\;CFU/g{\sim}7.4{\pm}0.1\;\log\;CFU/g$ for APC (Aerobic Plate Count), and $L1{\sim}L4$, H5, H6 soybean pastes were $2.3{\pm}0.4\;\log\;CFU/g{\sim}2.6{\pm}0.1\;\log\;CFU/g$ for Bacillus cereus. But other microorganism was not dectected. Among biogenic amines, PUT(putrescine), TYR(tyramine), HIS(histamine), PHE(2-Phenylethylamine) were dectected high level and CAD(cadaverine), TRY(trypramine), AGM(agmatine) were dectected medium level and SPD(spermidine), SPM(spermine), NOR(noradrenaline), SER(serotonin) were dectected low level. Dectected contents of biogenic amines were higher in commercial soybean paste compared to the traditional soybean paste.

• The Journal of Engineering Geology
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• v.27 no.3
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• pp.323-330
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• 2017

For the formation identification, surface geological survey, drill core analysis, core description and well log analysis are widely used. Among them well log analysis is a popular method with drill core analysis, since it measures continuously physical properties at in-situ. In this study we calculated Thomsen anisotropic parameters (${\varepsilon},\;{\delta},\;{\eta}$) after applying Backus averaging method to the P wave velocity, S wave velocity, and density logs. The well log data application of Blackfoot, Canada, shows the formation could be divided by 12 layers. This shows that Thomsen anisotropic parameters for identifying formation using anisotropic parameters is useful if there is no natural gamma log that is widely used for the formation identification.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.129-137
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• 2016

To identify the cause of the error and maintain the health of system, an administrator usually analyzes event log data since it contains useful information to infer the cause of the error. However, because today's systems are huge and complex, it is almost impossible for administrators to manually analyze event log files to identify the cause of an error. In particular, as OpenStack, which is being widely used as cloud management system, operates with various service modules being linked to multiple servers, it is hard to access each node and analyze event log messages for each service module in the case of an error. For this, in this paper, we propose a novel message-based log analysis method that enables the administrator to find the cause of an error quickly. Specifically, the proposed method 1) consolidates event log data generated from system level and application service level, 2) clusters the consolidated data based on messages, and 3) analyzes interrelations among message groups in order to promptly identify the cause of a system error. This study has great significance in the following three aspects. First, the root cause of the error can be identified by collecting event logs of both system level and application service level and analyzing interrelations among the logs. Second, administrators do not need to classify messages for training since unsupervised learning of event log messages is applied. Third, using Dynamic Time Warping, an algorithm for measuring similarity of dynamic patterns over time increases accuracy of analysis on patterns generated from distributed system in which time synchronization is not exactly consistent.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.11
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• pp.1781-1794
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• 2018

Objective: This meta-analysis was conducted to evaluate the overall effect of direct-fed microbial (DFM) or probiotic supplementation on the log concentrations of culturable gut microbiota in broiler chickens. Methods: Relevant studies were collected from PubMed, SCOPUS, Poultry Science Journal, and Google Scholar. The studies included controlled trials using DFM supplementation in broiler chickens and reporting log concentrations of the culturable gut microbiota. The overall effect of DFM supplementation was determined using standardized mean difference (SMD) with a random-effects model. Subgroups were analyzed to identify pre-specified characteristics possibly associated with the heterogeneity of the results. Risk of bias and publication bias were assessed. Results: Eighteen taxa of the culturable gut microbiota were identified from 42 studies. The overall effect of DFM supplementation on the log concentrations of all 18 taxa did not differ significantly from the controls (SMD = -0.06, 95% confidence interval [-0.16, 0.04], p = 0.228, $I^2=85%$, n = 699 comparisons), but the 18 taxa could be further classified into three categories by the direction of the effect size: taxa whose log concentrations did not differ significantly from the controls (category 1), taxa whose log concentrations increased significantly with DFM supplementation (category 2), and taxa whose log concentrations decreased significantly with DFM supplementation (category 3). Category 1 comprised nine taxa, including total bacterial counts. Category 2 comprised four taxa: Bacillus, Bifidobacterium, Clostridium butyricum, and Lactobacillus. Category 3 comprised five taxa: Clostridium perfringens, coliforms, Escherichia coli, Enterococcus, and Salmonella. Some characteristics identified by the subgroup analysis were associated with result heterogeneity. Most studies, however, were present with unclear risk of bias. Publication bias was also identified. Conclusion: DFM supplementation increased the concentrations of some beneficial bacteria (e.g. Bifidobacterium and Lactobacillus) and decreased those of some detrimental bacteria (e.g. Clostridium perfringens and Salmonella) in the guts of broiler chickens.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.863-864
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• 2006

The aim of this study is to acquire useful information of lower urinary tract symptom(LUTS) diagnosis through urophonogaphy signal as noninvasive method. The system was implemented to measure urophonography signal, urinary volume and uroflow. The slope of the log-log plot of power spectrum density changed rapidly from $2.01{\sim}2.07$ to over 3.32 when the occlusion rate was 2 mm to 3.8 mm. This information combined with the uroflow parameters could give a useful prediction of lower urinary tract symptoms.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.3
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• pp.329-339
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• 1999

LPXDA(Log-Periodic X Dipole Antenna), consisting of X dipole elements, was proposed as a Log-periodic antenna with short boom length and good Performance relative to LPDA(Log-Periodic Dipole Antenna) . LPXDA for operating frequency band of 500 MHz~l,000 MHz was designed and radiation characteristics of the designed antenna was analyzed by moment method, and compared with LPDA characteristics. The proposed LPXDA, in this paper, was found to have shorter boom length by 20% and better radiation characteristics with variations of frequency relative to the LPDA. The calculated results show good agreement with measurements.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.591-603
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• 2018

Windows Event Log is a format that records system log in Windows operating system and methodically manages information about system operation. An event can be caused by system itself or by user's specific actions, and some event logs can be used for corporate security audits, malware detection and so on. In this paper, we choose actions related to corporate security audit and malware detection (External storage connection, Application install, Shared folder usage, Printer usage, Remote connection/disconnection, File/Registry manipulation, Process creation, DNS query, Windows service, PC startup/shutdown, Log on/off, Power saving mode, Network connection/disconnection, Event log deletion and System time change), which can be detected through event log analysis and classify event IDs that occur in each situation. Also, the existing event log tools only include functions related to the EVTX file parse and it is difficult to track user's behavior when used in a forensic investigation. So we implemented new analysis tool in this study which parses EVTX files and user behaviors.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.1
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• pp.157-167
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• 2020

The big data and artificial intelligence technology, which has provided the foundation for the recent 4th industrial revolution, has become a major driving force in business innovation across industries. In the field of information security, we are trying to develop and improve an intelligent security system by applying these techniques to large-scale log data, which has been difficult to find effective utilization methods before. The quality of security log big data, which is the basis of information security AI learning, is an important input factor that determines the performance of intelligent security system. However, the difference and complexity of log data by various product has a problem that requires excessive time and effort in preprocessing big data with poor data quality. In this study, we research and analyze the cases related to log data collection of various firewall. By proposing firewall log data collection format standard, we hope to contribute to the development of intelligent security systems based on security log big data.