• Title/Summary/Keyword: Anomaly detection

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Efficient Searching for Shipwreck Using an Integrated Geophysical Survey Techniques in the East Sea of Korea (동해에서 지구 물리 이종방법간의 결합시스템을 활용한 침선 수색의 효용성 연구)

  • Lee-Sun, Yoo;Nam Do, Jang;Seom-Kyu, Jung;Seunghun, Lee;Cheolku, Lee;Sunhyo, Kim;Jin Hyung, Cho
    • Ocean and Polar Research
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    • v.44 no.4
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    • pp.355-364
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    • 2022
  • When the 60-ton-class patrol boat '72' of the Korea Coast Guard (KCG) was on duty and she accidentally collided with another patrol boat ('207', 200-ton-class) and sank. A month-long search found a small amount of lost items, but neither the crew nor the ship was found. For the first time in 39 years since the accident, the Korea Institute of Ocean Science and Technology (KIOST) searched the boat 72 using the latest integrated geophysical techniques. A number of sonar images presumed to be of a sunken ship was acquired using a combined system of side scan sonar and marine magnetometer, operated at an altitude of approximately 30 m from the seabed. At the same time, a strong magnetic anomaly (100 nT) was detected in one place, indicating the presence of an iron ship. A video survey using a remotely operated underwater vehicle (ROV) confirmed the presence of a shielding part of a personal firearm at the stern of the sunken vessel. Based on these comprehensive data, the sunken vessel discovered in this exploration was assumed to be '72'. This result is meaningful in terms of future ocean exploration and underwater archaeology, as the integrated system of various geophysical methods is an efficient means of identifying objects present in the water.

A Design of Authentication Mechanism for Secure Communication in Smart Factory Environments (스마트 팩토리 환경에서 안전한 통신을 위한 인증 메커니즘 설계)

  • Joong-oh Park
    • Journal of Industrial Convergence
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    • v.22 no.4
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    • pp.1-9
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    • 2024
  • Smart factories represent production facilities where cutting-edge information and communication technologies are fused with manufacturing processes, reflecting rapid advancements and changes in the global manufacturing sector. They capitalize on the integration of robotics and automation, the Internet of Things (IoT), and the convergence of artificial intelligence technologies to maximize production efficiency in various manufacturing environments. However, the smart factory environment is prone to security threats and vulnerabilities due to various attack techniques. When security threats occur in smart factories, they can lead to financial losses, damage to corporate reputation, and even human casualties, necessitating an appropriate security response. Therefore, this paper proposes a security authentication mechanism for safe communication in the smart factory environment. The components of the proposed authentication mechanism include smart devices, an internal operation management system, an authentication system, and a cloud storage server. The smart device registration process, authentication procedure, and the detailed design of anomaly detection and update procedures were meticulously developed. And the safety of the proposed authentication mechanism was analyzed, and through performance analysis with existing authentication mechanisms, we confirmed an efficiency improvement of approximately 8%. Additionally, this paper presents directions for future research on lightweight protocols and security strategies for the application of the proposed technology, aiming to enhance security.

Neonatal hearing screening in a neonatal intensive care unit using distortion product otoacoustic emissions (변조 이음향방사(DPOAE)를 이용한 고위험군 신생아 청각선별검사)

  • Kim, Do Young;Kim, Sung Shin;Kim, Chang Hwi;Kim, Shi Chan
    • Clinical and Experimental Pediatrics
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    • v.49 no.5
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    • pp.507-512
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    • 2006
  • Purpose : Early detection and intervention of hearing impairment is believed to improve speech and language development and behavior of children. The aim of this preliminary study was to determine the prevalence of hearing impairments, and to identify the association of risk factors relating to refer response in high risk neonates who were screened using distortion product otoacoustic emissions (DPOAE). Methods : The subjects included 871 neonates who were admitted to the neonatal intensive care unit of the Pediatric Department in Soonchunhyang University Bucheon Hospital from May, 2001 to December, 2004. They were screened using DPOAE. Based on DPOAE, we divided the neonates in two groups : 'Pass' and 'Refer'. The differences in risk factors between the pass group and the refer group were analyzed. Results : The incidence of the refer group was 12.1 percent(106 out of 871). The bilateral refer rate was 5.4 percent(47 out of 871). And the unilateral refer rate was 6.7 percent(59 out of 871). Gender, birth place, family history of hearing loss, small/large for gestational age, obstetrical factor, hyperbilirubinemia and use of gentamicin were not statistically related to the refer rate. Statistically related to refer rate were birth weight, resuscitated neonates, Apgar score, craniofacial anomaly, mechanical ventilator application, sepsis, using of vancomycin(P<0.05). The prevalence of hearing impairment (${\geq}60dB$) in this study was 2 percent(18 out of 871). Conclusion : This study showed a higher prevalence of hearing impairment in high-risk neonates. Thus neonatal hearing screening should be carried out in high-risk neonates.

Development of a complex failure prediction system using Hierarchical Attention Network (Hierarchical Attention Network를 이용한 복합 장애 발생 예측 시스템 개발)

  • Park, Youngchan;An, Sangjun;Kim, Mintae;Kim, Wooju
    • 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.