• Korean Journal of Microbiology
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• v.55 no.3
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• pp.242-247
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• 2019

Biological factors (e.g. microorganism activity) in wastewater treatment plant (WWTP) play essential roles for degradation and/or removal of organic matters. In this study, to understand the microbial functional roles in WWTP, we tried to isolate and characterize a bacterial strain from activated sludge sample. Strain S16 was isolated from the activated sludge of a municipal WWTP in Daejeon metropolitan city, the Republic of Korea. The cells were a Gram-stain-positive, non-motile, facultative anaerobe, and rod-shaped. Strain S16 grew at a temperature of $15{\sim}40^{\circ}C$ (optimum, $30^{\circ}C$), with 0~9.0% (w/v) NaCl (optimum, 1.0~2.0%), and at pH 5.5~9.0 (optimum, pH 7.0~7.5). Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain S16 was most closely related to the unique species Miniimonas arenae NBRC $106267^T$ (99.79%, 16S rRNA gene sequence similarity) of the genus Miniimonas. The cell wall contained alanine, glutamic acid, serine, and ornithine. Although the isolation source of the type strain NBRC $106267^T$ which considered as a marine microorganism is sea sand, that of strain S16 is terrestrial environment. It might raise an ecological question for habitat transition. Therefore, comparative genome analysis will be valuable investigation for shedding light on their potential metabolic traits and genomic streamlining.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.1
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• pp.22-28
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• 2021

Multivariate processes, such as chemical and mechanical process, power plants are operated in a state where several facilities are complexly connected, the fault of a particular system can also have fatal consequences for the entire process. In addition, since process data is measured in an unstable environment, outlier is likely to be include in the data. Therefore, monitoring technology is essential, which can remove outlier from measured data and detect failures in advance. In this paper, data obtained from dynamic and multivariate process models was used to detect fault in various type of processes. The dynamic process is a simulation of a process with autoregressive property, and the multivariate process is a model that describes a situation when a specific sensor fault. Mahalanobis distance was used to remove outlier contained in the data generated by dynamic process model and multivariate process model, and fault detection was performed using ICA. For comparison, we compared performance with and a conventional single ICA method. The proposed fault detection method improves performance by 0.84%p for bias data and 6.82%p for drift data in the dynamic process. In the case of the multivariate process, the performance was improves by 3.78%p, therefore, the proposed method showed better fault detection performance.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.273-282
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• 2023

Probabilistic safety assessment (PSA) has been widely used to evaluate the seismic risk of nuclear power plants (NPPs). However, studies on seismic PSA for process plants, such as gas plants, oil refineries, and chemical plants, have been scarce. This is because the major disasters to which these process plants are vulnerable include explosions, fires, and release (or dispersion) of toxic chemicals. However, seismic PSA is essential for the plants located in regions with significant earthquake risks. Seismic PSA entails probabilistic seismic hazard analysis (PSHA), event tree analysis (ETA), fault tree analysis (FTA), and fragility analysis for the structures and essential equipment items. Among those analyses, ETA can depict the accident sequence for core damage, which is the worst disaster and top event concerning NPPs. However, there is no general top event with regard to process plants. Therefore, PSA cannot be directly applied to process plants. Moreover, there is a paucity of studies on developing fragility curves for various equipment. This paper introduces PSA for gas plants based on FTA, which is then transformed into Bayesian network, that is, a probabilistic graph model that can aid risk-informed decision-making. Finally, the proposed method is applied to a gas plant, and several decision-making cases are demonstrated.

• Economic Analysis
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• v.26 no.2
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• pp.72-113
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• 2020

This study investigates the relationship between mineral exports to China, North Korea's most important source of foreign currency acquisition, and its imports of various items from China from the first quarter of 1995 through to the third quarter of 2019. The results from a cointegration analysis suggest that there exists a long-run equilibrium relationship between mineral exports and imports of food, fuel, and some intermediate goods, such as industrial supplies, parts, and accessories. The results from a vector autoregression using first-differenced variables indicate that the short-run relationship between mineral exports and imports is different between the period before and after the third quarter of 2010. Prior to structural changes, i.e., before the third quarter of 2010, import shocks affected mineral exports. However, after the third quarter of 2010, an increase in mineral exports led to an increase in the import of vehicles, intermediate goods, and luxury goods. This paper shows both the possibilities and the limits that mineral exports can contribute to North Korea's economic growth. The results, which show that mineral exports have a long-run relationship with intermediate goods, such as industrial supplies, parts and accessories, imply that mineral exports to China could have a positive effect on the North Korean economy. However, the fact that mineral exports do not have any significant effect on the import of machinery and equipment, which helps the accumulation of capital formation, shows that mineral exports have a limited effect on inducing long-term growth in the North Korean economy.

• Journal of Advanced Navigation Technology
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• v.28 no.3
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• pp.348-355
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• 2024

A current source capable of stably supplying current as a measurement medium is required in order to measure and test important facilities that require large-scale measurement current, such as a control element drive mechanism control system(CEDMCS), in case of dismantling a nuclear power plant. However, it can provides only voltage power as a source, not current, although direct voltage controlled constant current source is essential to test major equipment. That kind of source is not available to supply stable constant current regardless of load variation. It is just voltage supplier. Developing current source is not easy other than voltage source. Very large-scale current source up to ampere class more than such ten times of normal current is inevitable to test above mentioned equipment. So, we developed large-scale current source which is controlled by input DC voltage and supplies constant stable current to object equipment according to this requirement. We measured and tested nuclear power plant equipment using given real site data for a long time and afforded long period load test, and then proved its validity and verification. The developed invetion will be used future installed important equipment measuring and testing.

• 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.

• The KIPS Transactions:PartD
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• v.14D no.1 s.111
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• pp.121-130
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• 2007

in a software development, the design or architecture prior to implementing the software is essential for the success. This paper presents a case that we successfully designed a software architecture of radiation monitoring system (RMS) for HANARO research reactor currently operating in KAERI by applying the quality attribute-driven design method which is modified from the attribute-driven design (ADD) introduced by Bass[1]. The quality attribute-driven design method consists of following procedures: eliciting functionality and quality requirements of system as architecture drivers, selecting tactics to satisfy the drivers, determining architectures based on the tactics, and implementing and validating the architectures. The availability, maintainability, and interchangeability were elicited as duality requirements, hot-standby dual servers and weak-coupled modulization were selected as tactics, and client-server structure and object-oriented data processing structure were determined at architectures for the RMS. The architecture was implemented using Adroit which is a commercial off-the-shelf software tool and was validated based on performing the function-oriented testing. We found that the design method in this paper is an efficient method for a project which has constraints such as low budget and short period of development time. The architecture will be reused for the development of other RMS in KAERI. Further works are necessary to quantitatively evaluate the architecture.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.267-275
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• 2010

Quality management of an x-ray unit drastically differs according to the type of establishment of medical institutions. Many primary medical institutions have it, but they do not pay much attention to quality management. In the study, Gyeongbuk area has been divided into four zones from January 4, 2010 to September 3, 2010, and four places were designated by city. Among medical institutions located at a total of 16 sites, the target was 8 places with X-ray emission equipment 10 years or more in use as well as 8 places with X-ray emission equipment less than 10 years in use. The 5 essential items of quality control were tested. In the test that checked for equipment it was found that sites with X-ray emission equipment 10 years or more in use didn't have ground connection (6.25%) while all of them passed the current leak test. In the exposure dose reproducibility test 4 sites with X-ray emission equipment 10 years or more in use (25%) and 1 site with X-ray emission equipment less than 10 years in use didn't pass the test. In the KVp accuracy test 5 sites with X-ray emission equipment 10 years or more in use (31.25%) and 2 sites with X-ray emission equipment less than 10 years in use (12.5%) didn't pass the test. In the tube current and tube current amount test 3 sites with X-ray emission equipment 10 years or more in use (18.75%) and 1 site with less than 10 years in use (6.25%) didn't pass the test. According to the findings of the present research, quality control at medical institutions with X-ray equipment 10 years or more in use was poorer than medical institutions with X-ray equipment less than 10 years in use. In this regard, administrative and technical measures need to be taken as soon as possible. In addition, owners of medical clinics or unit managers need to raise awareness, and it is necessary to revise a regular test cycle every year or every two years if they have old equipment or if the equipment is not used on a frequent basis. And it is also important to provide regular educational programs for quality management.

• Korean Journal of Food Science and Technology
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• v.52 no.1
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• pp.1-10
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• 2020

The present study identified the restrictions on the use of sous-vide products in the Korean HMR market for small and medium-sized manufacturing companies. A detailed literature review revealed that the HMR market in Korea is close to saturation. Notably, the technologically advanced products produced using sous-vide seem to display significant potential to overcome market saturation. The sous-vide method differs from conventional cooking techniques and is characterized by maintenance of food texture along with flavor enhancement. However, due to the unfamiliarity of the manufacturers with this method and the unclear food safety regulations, mass food manufacturing companies do not agree on using this method; hence, sous-vide production is usually undertaken by small/medium sized companies catering primarily through online marketing portals. This study highlights the various restrictions to the implementation of sous-vide production, and discusses several practical implications of sous-vide production that would help users of this technique enter the HMR market.

• Fire Science and Engineering
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• v.31 no.5
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• pp.53-62
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• 2017

Electric power which is the energy source of economy and industries requires long distance transportation due to regional difference between its production and consumption, and it is supplied through the multi-loop transmission and distribution system. Prior to its actual use, electric power flows through several transformations by voltage transformers in substations depending on the characteristics of each usage, and a transformer has the structure consisting of the main body, winding wire, insulating oil and bushings. A transformer fire that breaks out in substations entails the primary damage that interrupts the power supply to houses and commercial facilities and causes various safety accidents as well as the secondary economic losses. It is considered that causes of such fire include the leak of insulating oil resulting from the destruction of bottom part of bushings, and the chain reaction of fire due to insulating oil that reaches its ignition point within 1 second. The smoke detector and automatic fire extinguishing system are established in order to minimize fire damage, but a difficulty in securing golden time for extinguishing fire due to delay in the operation of detector and release of gas from the extinguishing system has become a problem. Accordingly, this study was carried out according to needs of active mechanism to prevent the spread of fire and block the leak of insulating oil, in accordance with the importance of securing golden time in extinguishing a fire in its early stage. A bushings fireproof structure was developed by applying the high temperature shape retention materials, which are expanded by flame, and mechanical flame cutoff devices. The bushings fireproof structure was installed on the transformer model produced by applying the actual standards of bushings and flange, and the full scale fire test was carried out. It was confirmed that the bushings fireproof structure operated at accurate position and height within 3 seconds from the flame initiation. It is considered that it could block the spread of flame effectively in the event of actual transformer fire.