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Hierarchical Attention Network를 이용한 복합 장애 발생 예측 시스템 개발
박영찬(Youngchan Park),안상준(Sangjun An),김민태(Mintae Kim),김우주(Wooju Kim) 한국지능정보시스템학회 2020 지능정보연구 Vol.26 No.4
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
측면 충돌 시 센터에어백이 승객의 거동 및 머리상해에 미치는 영향
박지양,김동섭,곽영찬,손창기,윤영한,Park, Jiyang,Kim, Dongseop,Kwak, Youngchan,Son, Changki,Youn, Younghan 한국자동차안전학회 2018 자동차안전학회지 Vol.10 No.3
The Korean New Car Assessment Program (KNCAP) is a program to evaluate the safety of automobiles. In the safety assessment method, there are frontal collision, partial frontal collision, side collision, pillar collision, and left stability in the collision safety category. Among them, Korean in-depth analysis data shows that there are a lot of side collision accidents and it is necessary to protect them. This study will analyze the side collision accident that occurred in actual traffic accident based on Korea In-Depth Accident Study (KIDAS) and investigate the effect of center airbag on passenger in under side collision. In addition, with simulated side collision scenarios in the various side impact directions, it was investigated how the center airbag affects the driver and passenger in terms of kinematic and injury levels.
박지양,윤영한,곽영찬,손창기,Park, Jiyang,Youn, Younghan,Kwak, Youngchan,Son, Changki 한국자동차안전학회 2018 자동차안전학회지 Vol.10 No.3
Recently, the driver can be assisted by the advanced active safety devices such as ADAS from road traffic risks. With this system, driver and passenger may freed from can driving tasks or kept eyes on forward direction while on the road. Help from adoptive cruise control, auto parking and newly develped automated driving vehicles technologies, the driver positions will vary significantly from the current standard driver position during the travel time. On this hypothesis, the objective of this study is analyze the behavior and injuries of drivers in the event of frontal impact under these abnormal driver position. Based on the KNCAP frontal impact testing method, this simulation matrix was set-up with dummies of 5 th tile female Hybrid III dummy and 50 th tile male Hybrid III dummy. The small sedan type passenger car was modeled in this simulation. The series of simulation was performed to compare the injuries and behaviour of each dummy, varying the seating status and seat position of each dummy.
정경진,윤영한,박지양,김동섭,오명진,곽영찬,손창기,신재곤,이은덕,권해붕,Jung, Kyungjin,Youn, Younghan,Park, Jiyang,Kim, Dongseup,Oh, Myoungjin,Kwak, Youngchan,Son, Changki,Shin, Jaekon,Lee, Eundok,Kwon, Hae Boung 한국자동차안전학회 2017 자동차안전학회지 Vol.9 No.2
KNCAP is a program to evaluate the automobile safety, providing consumer vehicle safety assessment results. The safety evaluation tests are Frontal Impact, Offset Frontal Crash, Side Crash, Side Pole Crash, Rear Impact. This is the study of the offset frontal impact safety evaluation. Currently, IIHS is performing a small overlap test. NHTSA plans to implement the oblique moving deformable barrier test. Euro-NCAP plans to implement a mobile frontal impact test. Simulation is used to compare occupant behavior and injury. We have investigated whether the introduction of the test at KNCAP is necessary. The dummy model used in the simulation was the 50th percentile male Hybrid III dummy.
한국식품의 GCC 시장진출을 위한 수출경쟁력 조사연구 - 사우디아라비아와 아랍에미리트를 중심으로 -
엄익란 ( Eum Ikran ),박유경 ( Park Yukyong ),이병서 ( Lee Byongseo ),조영찬 ( Jo Youngchan ) 한국외국어대학교 중동연구소 2018 중동연구 Vol.37 No.1
The purpose of this study is to identify promising Korean agricultural products for advancement in the Gulf Cooperation Council(GCC) food market with a focus on Saudi Arabia and the UAE, and to analyze the export competitiveness for the selected items based on KANO and TIMKO model. To this end, this study employed a focus group interview, participation in Dubai Food Festivals, face to face consumer sensory evaluation, and in-depth interviews with distributors from August 2017 to December 2017. The study found that dried fruit/vegetable chips, Korean traditional beverages and fresh food were competitive while Korean ethnic foods such as gochujang and tteokbokki were not well known. The study result suggests that the improvement of product favorability and purchase intention should be encouraged through food tasting, promotion and advertisement, and development of various recipes using ingredients familiar to local consumers.