• Smart Media Journal
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• v.7 no.1
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• pp.37-44
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• 2018

Utilizing sequence control and numerical computing, embedded devices are used in a variety of automated systems, including those at industrial sites, in accordance with their control program. Since embedded devices are used as a control system in corporate industrial complexes, nuclear power plants and public transport infrastructure nowadays, deliberate attacks on them can cause significant economic and social damages. Most attacks aimed at embedded devices are data-coded, code-modulated, and control-programmed. The control programs for industry-automated embedded devices are designed to represent circuit structures, unlike common programming languages, and most industrial automation control programs are designed with a graphical language, LAD, which is difficult to process static analysis. Because of these characteristics, the vulnerability analysis and security related studies for industry automation control programs have only progressed up to the formal verification, real-time monitoring levels. Furthermore, the static analysis of industrial automation control programs, which can detect vulnerabilities in advance and prepare for attacks, stays poorly researched. Therefore, this study suggests a method to present a discussion on an industry automation control program designed to represent the circuit structure to increase the efficiency of static analysis of embedded industrial automation programs. It also proposes a medium term translation technology exploiting LLVM IR to comprehensively analyze the industrial automation control programs of various manufacturers. By using LLVM IR, it is possible to perform integrated analysis on dynamic analysis. In this study, a prototype program that converts to a logical expression type of medium language was developed with regards to the S company's control program in order to verify our method.

• Journal of The Korean Astronomical Society
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• v.49 no.5
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• pp.209-223
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• 2016

We present analyses of ~1250 variable sources identified in a 20 square degree field toward NGC 2784 by the KMTNet Supernova Program. We categorize the variable sources into three groups based on their B-band variability. The first group consists of 31 high variability sources with their B-band RMS variability greater than 0.3 magnitudes. The second group of medium variability contains 265 sources with RMS variability between 0.05 and 0.3 magnitudes. The remaining 951 sources belong to the third group of low variability with an RMS variability smaller than 0.05 magnitudes. Of the entire ~1250 sources, 4 clearly show periods of variability greater than 100 days, while the rest have periods shorter than ~51 days or no reliable periods. The majority of the sources show either rather irregular variability or short periods faster than 2 days. Most of the sources with reliable period determination between 2 and 51 days belong to the low-variability group, although a few belong to the medium-variability group. All the variable sources with periods longer than 35 days appear to be very red with B - V > 1.5 and V - I > 2.1 magnitudes. We classify candidates of 51 Cepheids, 17 semi-regular variables, 3 Mira types, 2 RV(B) Tauri stars, 26 eclipsing binary systems and 1 active galactic nucleus. The majority of long-term variables in our sample belong to either Mira or semi-regular types, indicating that long-term variability may be more prominent in post-main sequence phases of late-type stars. The depth of the eclipsing dips of the 26 candidates for eclipsing binaries is equivalent to ~0.61 as the average relative size of the two stars in the binary system. Our results illustrate the power of the KMTNet Supernova Program for future studies of variable objects.

• Korea Journal of Hospital Management
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• v.4 no.2
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• pp.242-265
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• 1999

Our study attempt is to see organizational performance according to the differences between types of hospital cultures. To determine theoretical relationship between the organizational culture and the performance, we select two hospitals in Pusan which are different in the purposes and shapes of establishment. We surveyed their members on a questionnaire based on the type of the organizational culture of the two institutions, analyzed, and review its organizational effectiveness. For the survey with questionnaires, which had been preliminary studied to raise its validity, question forms were distributed to 528 persons in April, 1999 based on the self-responses and recollected within 48 hours. The recollection rate was high(89.96%) and the quantity of questionnaires used for our final analysis was 430(81.44%). The Cronbach Coefficient Alpha of the questionnaires was 0.742. Regarding statistical techniques for analysis of the written materials, dispersion analysis(ANOVA) was adapted to test the organizational effectiveness of the two hospitals having the different organizational cultures, and Pearson Correlation was applied to determine correlations was among all variables. T-test was performed to test organizational effectiveness based on the differences in the extent of sharing the culture, organizational committment and work satisfaction between the two health institutions. From our analysis, we obtain the following conclusions. First, concerning with organizational culture of the two hospital, one of which is a christian hospital and the other is a private foundation hospital, the former is conservative and human-oriented but the latter focuses on renovation and accomplishment. Second, the private establishment has a relatively higher organizational effectiveness that the religious hospital as a result of analyzing the extent of sharing culture, organizational committment and work satisfaction. Third, it has been found that the correlations between the extent of the sharing culture and the organizational committment, the extent and work satisfaction, and the committment and the satisfaction are respectively positive influencing organizational effectiveness, especially work satisfaction. Fourth, cultural factors by which the christian hospital is affected more positively including human relations among its members, belief, its idea of establishment, tradition, work responsibility, power, and wage. On the other hand, factors such as director's leadership, personnel management, wage, hospital regulations and department managers' management ability have been seen as negative influences in order. And fifth, for the private foundation hospital human relations among its members, wages, work responsibility, director's leadership and department managers' management ability were positive in their sequence while wages, personnel management, hospital regulations, welfare and department managers' management ability were considered as negative influences in order. As these results of this study, the higher extent of sharing organizational culture, the more increasing in both organization committment and work satisfaction, the higher the effectiveness. Although it was somewhat difficult to generalize the results whose subjects were the two hospitals only, it was obvious that organizational culture was an important influential factor of organizational effectiveness. It is questionable that the extent of sharing organizational culture, organizational committment and work satisfaction as variables affecting the effectiveness have their validity, but this study has its significance in that it provided an approaching to evaluate the organizational culture of individual hospitals making allowances for such variables related to the general activities in its hospital. We hope the results of the study could be useful for the managerial strategies of the institutions.

• Economic and Environmental Geology
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• v.17 no.2
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• pp.115-139
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• 1984

The Cu-Pb-Zn-Ag hydrothermal vein-type deposits which comprise the Sambong mine occur within calc-alkaline volcanics of the Cretaceous Gyeongsang Basin. The ore mineralization took place through three distinct stages of quartz (I and II stages) and calcite veins (III stage) which fill the pre-existing fault breccia zones. These stages were separated in time by tectonic fracturing and brecciation events. The reflection variations of one mineral depending on mineralization sequence are considered to be resulted from variation in its chemical composition due to different physico-chemical conditions in the hydrothermal system. The reflection power of sphalerite increases with the content of Fe substituted for Zn. Reflectances of the sphalerite grain are lower on (111) than on (100) surface. The spectral profiles depend on the internal reflection color. Sphalerite, showing green, yellow and reddish brown internal reflection, have the highest reflection power at $544m{\mu}$ (green), $593m{\mu}$ (yellow) and $615m{\mu}$ (red) wavelength, respectively. Chalcopyrite is recognized as biaxial negative from the reflectivity data of randomly oriented grains measured at the most sensitivity at $544m{\mu}$. The microindentation hardness against the Fe content (wt. %) for the sphalerite increases to 8.05% Fe and then decreases toward 9.5% Fe content. Vickers hardness of the sphalerite is considerably higher on surface of (100) than on (111). The relationship between Vickers hardness and crystal orientation of the galena was determined to be $VHN_{(111)}$ > $VHN_{(210)}$ > $VHN_{(100)}$. The softer sulfides have the wider variation of the diagonal length in the indentation. Diagonal length in the indentation is pyrite

• 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.