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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
운동심상이 만성 경수 손상 환자의 근활성도와 일상생활에 미치는 영향
박영찬,김정연,박희수,Park, Young-Chan,Kim, Jung-Yeon,Park, Hee-Su 대한물리치료학회 2013 대한물리치료학회지 Vol.25 No.5
Purpose: The purpose of this study is to determine the effect of motor imagery training on residual upper extremity strength and activities of daily living of chronic cervical spinal cord injury patients. Methods: Twelve ASIA A B patients, who had more than a 12-month duration of illness and C5 or 6 motor nerve injury level, were randomly divided into experimental group (n=6) and control group (n=6). Patients in the experimental group performed motor imagery training for five minutes prior to general muscle strengthening training, while those in the control group performed general muscle strengthening training only. The training was performed five times per week, 30 minutes per day, for a period of four weeks. General muscle strengthening training consisted of a progressive resistive exercise for residual upper extremity. Motor imagery training consisted of imagining this task performance. Before and after the training, EMG activity using BTS Pocket Electromyography and Spinal Cord Independent Measure III(SCIM III) were compared and analyzed. Results: The residual upper extremity muscle strengths showed improvement in both groups after training. Comparison of muscle strength improvement between the two groups showed a statistically significant improvement in the experimental group compared to the control group (p<0.05). SCIM III measurements showed significant improvement in the scores for Self-care and Transfer items in the experimental group. Conclusion: Motor imagery training was more effective than general muscle strengthening training in improving the residual upper extremity muscle strength and activities of daily living of patients with chronic cervical spinal cord injury.
지도자의 리더십 유형이 체육고등학교 운동선수들의 성취목표성향에 미치는 영향
박영찬(Park, Young-Chan),고의석(Ko, Wi-Sug) 한국산학기술학회 2012 한국산학기술학회논문지 Vol.13 No.11
지도자의 리더십 유형이 체육고교 운동선수들의 성취목표성향에 미치는 영향을 조사하기 위하여 총 290명 의 체육고등학교 운동선수들을 대상으로 다요인 리더십 질문지(MLQ)와 성취목표검사지(TEOSQ)가 사용되었다. 수집 된 자료에 대하여 t-test, One-way ANOVA, Scheffe 검증, 다중회기분석을 실시하여 다음과 같은 결과가 도출되었다. 첫째, 성별에서 남학생이, 종목별에서 대인종목, 운동경력에서 2년 이하, 주전/비주전에서 주전선수 요인이 리더십을 높게 지각했다. 둘째, 성별에선 남학생, 주전/비주전에서 주전선수가 성취목표성향을 높게 인식하였다. 셋째, 자아목표 성향의 선수에게 지도자의 리더십 유형의 개별적 배려, 지적자극 요인, 조건적 보상 요인에서 유의한 차이가 나타났 다. 과제목표성향의 선수는 카리스마와 조건적 보상 부분에서 유의한 차이를 나타내었다. 덧붙여서, 체육고등학교 선 수들은 인구통계학적 특성에 따라 리더십 유형과 성취목표성향을 다르게 인식했으며, 선수의 성취목표성향에 따른 리더십 유형의 구성요인 별도로 각각 다르게 인식하는 경향성을 보였다. To study the influence of leadership types on achievement goal orientation of high school athletes, Multifactor Leadership Questionnaire (MLQ) and Task and Ego Orientation in Sport Questionnaire (TEOSQ) were used to two-hundred-ninety athletes in Physical Education High Schools. The data from the survey were analyzed by SPSS 19.0 and the results of the analysis were as in the following. First, there were meaningful differences in the leadership factors according to the demographic characteristics. Male athletes tended to be more conscious of the individualized consideration(transformation leadership), contingent reward, management by exception(transactional leadership) of the leadership factors. Second, there were differences in athletes’ achievement goal orientation. Comparing to the female athletes, male athletes gave more importance on the task goal orientation. Third, there were differences in the leader’s leadership types on the athlete’s achievement goal orientation. The athletes with self goal orientation and the individualized consideration (transformational leadership) of leadership types showed statistically meaningful differences. The athletes with high self goal orientation had a negative relationship to the individualized consideration and a positive relationship to the intellectual stimulation. The athletes with task goal orientation showed differences in the transactional leadership as well as in the contingent reward.