• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.107-115
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• 2005

ATC (Abnormal traffic controller) is presented as next generation security technology to securely support reliable Internet service and to guarantee network survivability, which is deployed in Internet access point. The key concept of the ATC is abnormal traffic monitoring and traffic control technology. When fault factors exist continuously and/or are repeated, abnormal traffic control guarantees service completeness as much as possible. The ATC with control policy on abnormal traffic is superior to the ATC with blocking policy as well as conventional network node, when the ratio of effective traffic to abnormal traffic is higher than $30{\%}.$ When traffic intended unknown attack occurs, network IDS is high false positive probability and so is limited to apply. In this environment, the ATC can be a key player to help the network node such as router to control abnormal traffic.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.337-346
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• 2021

Detection of abnormal signal generally can be done by using features of normal signals as main information because of data imbalance. This paper propose an efficient method for abnormal signal detection using parallel AutoEncoder (AE) which can use features of abnormal signals as well. The proposed Parallel AE (PAE) is composed of a normal and an abnormal reconstructors having identical AE structure and train features of normal and abnormal signals, respectively. The PAE can effectively solve the imbalanced data problem by sequentially training normal and abnormal data. For further detection performance improvement, additional binary classifier can be added to the PAE. Through experiments using public acoustic data, we obtain that the proposed PAE shows Area Under Curve (AUC) improvement of minimum 22 % at the expenses of training time increased by 1.31 ~ 1.61 times to the single AE. Furthermore, the PAE shows 93 % AUC improvement in detecting abnormal underwater acoustic signal when pre-trained PAE is transferred to train open underwater acoustic data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.4C
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• pp.296-304
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• 2005

ATCoP(Abnormal traffic controller based on prediction) is presented to securely support reliable Internet service and to guarantee network survivability, which is deployed in Internet access point. ATCoP is a method to control abnormal traffic that is entering into the network When unknown attack generates excessive traffic, service survivability is guaranteed by giving the priority to normal traffic than abnormal traffic, that is reserving some channels for normal traffic. If the reserved channel number increases, abnormal traffic has lower quality service by ATCoP system and then its service survivability becomes worse. As an analytic result, the proposed scheme maintains the blocking probability of normal traffic on the predefined level in the specific interval of input traffic.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.04c
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• pp.10-14
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• 2008

In this paper, abnormal heating of circuit breaker in panel board by poor connection was studied. Firstly, calculation method of contact resistance shown in IEC 60943 was examined. Secondly, concerning abnormal heating of circuit breaker in panel board, field measurements were performed. Field measurements showed that locally abnormal heating by poor connection could be detected, therefore, in that case, immediate action was needed. Finally, experiments regarding abnormal heating between terminal of circuit breaker and electric wire were conducted. The results showed that abnormal heating above $180^{\circ}C$ was measured. Also, in the results of analysis of electrical waveforms according to load current, voltage drop occurred around poor connection, and the oxide was generated according to mechanical vibration. In order to prevent electrical disaster caused by poor connection, adequate torque should be applied to electrical connection, and periodic check-ups are needed.

• Korean Journal of Veterinary Service
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• v.17 no.2
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• pp.122-129
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• 1994

Teratogenic effects of diazinon were assessed morphologically and cholinergic blocking agents. Diazinon at doses ranging from 25 to 2000 ug /egg, was Injected on day 3 of incubation. TD50s were different for the various teratogenic signs (wry neck, micromelia, abnormal feathering, abnormal beak and curled claws). The threshould dose for wry neck was higher than threshould dose for other signs； 40 ug/egg produced substantial micromelia, abnormal feathering. abnormal beak and curled claws, but gave no signs of wry neck. In contrast to the teratogenic doses, the LD50 of diazinon was very high (above 2000 ug /egg). One of the characteristics of diazinon-induced teratogenesis was reduced body weight (78.7％) and body length (73.8%). Maximal teratogenic effects, scored as signs of retarded growth, wry neck micromelia, abnormal feathering, abnormal beak, and curled claws, were produced when the insectcide was administered on the third or fourth day. The threshold dose for type II teratogenic signs(such as wry neck and short neck) was higher than for type I (such as micromelia and abnormal feathering). Morphological studies, using atropine and gallamine, suggested that nicotine but not muscarinic receptors may be involved in the mechanism of diazinon induced type II malformations.

• Journal of the Korean Society of Safety
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• v.29 no.3
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• pp.129-135
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• 2014

The number of abnormal operation procedures (AOPs) for mitigating a plant abnormal status amounts to about one hundreds for the most of 1000MWe optimized power plant (OPR1000) and it is expected that the number of AOPs would be increased to cope with an abnormal status occurred newly. However, it is not well organized for operators to select a proper AOP from alarms occurred in main control room (MCR) during a plant abnormal status. It may be a burden to operators since the selection of AOP to respond an abnormal status is authorized by operators. When multiple alarms occur in MCR, it would take more time to respond them than a single alarm. To reduce the efforts, various MCR operation support systems have been developed. The purpose of this study was to develop a multi-alarm pattern card to select an appropriate AOP effectively when multiple alarms occurs in a single upper layout (UL) of MCR. It can be applied for an operation support tool as well as an education tool.

• International Commerce and Information Review
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• v.9 no.3
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• pp.305-320
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• 2007

With expanded use of B2B(between enterprises), B2G(between enterprises and government) and EDI(Electronic Data Interchange), and increased amount of available network information and information protection threat, as it was judged that security can not be perfectly assured only with security technology such as electronic signature/authorization and access control, Bayesian networks have been developed for protection of information. Therefore, this study speculates Bayesian networks system, centering on ERP(Enterprise Resource Planning). The Bayesian networks system is one of the methods to resolve uncertainty in electronic data interchange and is applied to overcome uncertainty of abnormal invasion detection in ERP. Bayesian networks are applied to construct profiling for system call and network data, and simulate against abnormal invasion detection. The host-based abnormal invasion detection system in electronic trade analyses system call, applies Bayesian probability values, and constructs normal behavior profile to detect abnormal behaviors. This study assumes before and after of delivery behavior of the electronic document through Bayesian probability value and expresses before and after of the delivery behavior or events based on Bayesian networks. Therefore, profiling process using Bayesian networks can be applied for abnormal invasion detection based on host and network. In respect to transmission and reception of electronic documents, we need further studies on standards that classify abnormal invasion of various patterns in ERP and evaluate them by Bayesian probability values, and on classification of B2B invasion pattern genealogy to effectively detect deformed abnormal invasion patterns.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.355-364
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• 2022

In this study a tendency of abnormal sea surface temperature (SST) occurrence in the seas around South Korea is analyzed from daily SST data from satellite and 14 buoys from August 2020 to July 2021. As thresholds 28℃ and 4℃ are used to determine marine heatwaves(MHWs) and abnormal low water temperature (ALWT), respectively, because those values are adopted by the National Institute of Fisheries Science for the breaking news of abnormal temperature. In order to calculate frequency of abnormal SST occurrence spatially by using satellite SST, research area was divided into six areas of coast and three open seas. ALWT dominantly appeared over a wide area (7,745 km²) in Gyeonggi Bay for total 94 days and it was also confirmed from buoy temperature showing an occurrence number of 47 days. MHWs tended to be high in frequency in the coastal areas of Chungcheongdo and Jeollabukdo and the south coastal areas while in case of buoy temperature Jupo was the place of high frequency (32 days). This difference was supposed to be due to the low accuracy of satellite SST at the coasts. MHWs are also dominant in offshore waters around Korean Peninsula. Although detecting abnormal SST by using satellite SST has advantage of understanding occurrence from a spatial point of view, we also need to perform detection using buoys to increase detection accuracy along the coast.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3996-4004
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• 2022

The data collected by the In-Situ Decommissioning (ISD) sensors are time-specific, age-specific, and developmental stage-specific. Research has been done on the stream data collected by ISD testbed in the recent few years to seek both frequent episodes and abnormal frequent episodes. Frequent episodes in the data stream have confirmed the daily cycle of the sensor responses and established sequences of different types of sensors, which was verified by the experimental setup of the ISD Sensor Network Test Bed. However, the discovery of abnormal frequent episodes remained a challenge because these abnormal frequent episodes are very small signals and may be buried in the background noise of voltage and current changes. In this work, we proposed Advanced Data Analytics (ADA) methods that are applied to the baseline data to identify frequent episodes and extended our approach by adding more features extracted from the baseline data to discover abnormal frequent episodes, which may lead to the early indicators of ISD system failures. In the study, we have evaluated our approach using the baseline data, and the performance evaluation results show that our approach is able to discover frequent episodes as well as abnormal frequent episodes conveniently.

• Journal of the Korean Geotechnical Society
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• v.26 no.10
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• pp.69-79
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• 2010

To detect abnormal events in slopes, Principal Component Analysis (PCA) is applied to the slope that was collapsed during monitoring. Principal component analysis is a kind of statical methods and is called non-parametric modeling. In this analysis, principal component score indicates an abnormal behavior of slope. In an abnormal event, principal component score is relatively higher or lower compared to a normal situation so that there is a big score change in the case of abnormal. The results confirm that the abnormal events and collapses of slope were detected by using principal component analysis. It could be possible to predict quantitatively the slope behavior and abnormal events using principal component analysis.