• Earthquakes and Structures
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• v.16 no.4
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• pp.375-389
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• 2019

During earthquakes, the performance of structures needs to be evaluated, which provides guidance for selecting suitable retrofitting schemes. The purpose of this paper is to accomplish seismic assessment of a simple steel residential building. Once the responses of the system are determined, the scope of the study extends to evaluate selected retrofitting strategies that are intended to rehabilitate the flaws of the structure under prescribed ground motions with high probability of occurrence at the site. After implementing the retrofits, seismic assessment of the upgraded structure is carried out to check if the remediation at various seismic performance levels is acquired or not. Outcomes obtained from retrofitted scenarios are compared to the results obtained from the initial un-retrofitted configuration of the structure. This paper presents the process for optimal selection of rehabilitation solutions considering the cost of implementation, downtime and disruption to property owners while improving the seismic performance level of the structure.

• ETRI Journal
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• v.41 no.5
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• pp.684-695
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• 2019

In a cloud environment, performance degradation, or even downtime, of virtual machines (VMs) usually appears gradually along with anomalous states of VMs. To better characterize the state of a VM, all possible performance metrics are collected. For such high-dimensional datasets, this article proposes a feature extraction algorithm based on unsupervised fuzzy linear discriminant analysis with kernel (UFKLDA). By introducing the kernel method, UFKLDA can not only effectively deal with non-Gaussian datasets but also implement nonlinear feature extraction. Two sets of experiments were undertaken. In discriminability experiments, this article introduces quantitative criteria to measure discriminability among all classes of samples. The results show that UFKLDA improves discriminability compared with other popular feature extraction algorithms. In detection accuracy experiments, this article computes accuracy measures of an anomaly detection algorithm (i.e., C-SVM) on the original performance metrics and extracted features. The results show that anomaly detection with features extracted by UFKLDA improves the accuracy of detection in terms of sensitivity and specificity.

• FOCUS: LIFE SCIENCE
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• no.1
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• pp.8.1-8.3
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• 2024

Ultra-high performance liquid chromatography (UHPLC) systems are integral to modern analytical laboratories, necessitating careful maintenance and operation protocols to ensure optimal performance. This document provides comprehensive guidelines for the proper shutdown and reactivation of UHPLC systems to prevent damage and maintain operational efficiency. • Shutdown: Remove the column and replace it with a union to avoid blockages. Flush the system with a compatible solvent mix, clean mobile phase reservoirs to prevent microbial growth, flush the pump with storage solvent, and clean the autosampler, including the needle and injection port. • Reactivation: Inspect the system for wear or damage, gradually reintroduce mobile phases starting with a weak solvent, reinstall the column securely, and perform system checks on baseline stability, pressure consistency, and detector performance. By adhering to these guidelines, laboratories can ensure the longevity and reliability of their UHPLC systems, maintaining high analytical performance and minimizing downtime. These procedures help prevent common issues such as blockages, contamination, and component wear, thereby supporting efficient and accurate analytical operations.

• The KIPS Transactions:PartA
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• v.12A no.5 s.95
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• pp.395-404
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• 2005

There have been large concerns about survivability defined as the capability of a system to perform a mission-critical role, in a timely manner, in the presence of attacks, failures. In particular, One of the most important core technologies required for the design of the ITS(Intrusion Tolerance System) that performs continuously minimal essential services even when the computer system is partially compromised because of intrusions is the survivability one of In included the dependability analysis of a reliability and availability etc. quantitative dependability analysis of the In. In this Paper, we applied self-healing mechanism utilizing two factors of self-healing mechanism (fault model and system response), the core technology of autonomic computing to secure the protection power of the ITS and consisted of a state transition diagram of the ITS composed of a primary server and a backup server. We also defined the survivability, availability, and downtime cost of the ITS, and then performed studies on simulation experiments and two cases of vulnerability attack. Simulation results show that intrusion tolerance capability at the initial state is more important than coping capability at the attack state in terms of the dependability enhancement.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.5
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• pp.21-27
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• 2014

Currently, wind power has become a major research field in the area of sustainable development. As one important component of a wind turbine transmission system, most instances of downtime due to a gearbox failure are caused by bearing failures. Gearboxes for wind turbines must have the highest levels of reliability over a period of approximately 20 years, withstanding high dynamic loads. At the same time, a lightweight design and cost minimization efforts are required. These demands can only be met with a well-thought-out design, high-quality materials, a high production quality and proper maintenance. In order to design a reliable and lightweight gearbox, it is necessary to analyze methods pertaining to the bearing rating lifetimes of the standard and of different companies, also including calculation methods for modification factors. This can determine the influence of the bearing lifetime.

• Journal of the Earthquake Engineering Society of Korea
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• v.17 no.6
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• pp.279-291
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• 2013

The seismic damage of non-structural components, such as communication facilities, causes direct economic losses as well as indirect losses which result from social chaos occurring with downtime of communication and financial management network systems. The current Korean seismic code, KBC2009, prescribes the design criteria and requirements of non-structural components based on their elastic response. However, it is difficult for KBC to reflect the dynamic characteristics of structures where non-structural components exist. In this study, both linear and nonlinear time history analyses of structures with various analysis parameters were carried out and floor acceleration spectra obtained from analyses were compared with both ground acceleration spectra used for input records of the analyses and the design floor acceleration spectrum proposed by National Radio Research Agency. Also, this study investigates to find out the influence of structural dynamic characteristics on the floor acceleration spectra. The analysis results show that the acceleration amplification is observed due to the resonance phenomenon and such amplification increases with the increase of building heights and with the decrease of structure's energy dissipation capacities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3865-3883
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• 2016

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.

• Journal of Advanced Research in Ocean Engineering
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• v.4 no.1
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• pp.16-24
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• 2018

This paper addresses the heading control of an offshore floating storage and regasification unit (FSRU) using a resolved motion and acceleration control (RMAC) algorithm. A turret moored vessel tends to have the slewing motion. This slewing motion may cause a considerable decrease in working time in loading and unloading operation because the sloshing in the LNG containment tank might happen and/or the collision between FSRU and LNGC may take place. In order to deal with the downtime problem due to this slewing motion, a heading control system for the turret moored FSRU is developed, and a series of model tests with azimuth thrusters on the FSRU is conducted. A Kalman filter is applied to estimate the low-frequency motion of the vessel. The RMAC algorithm is employed as a primary heading control method and modified I-controller is introduced to reduce the steady-state errors of the heading of the FSRU.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2004.11a
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• pp.365-370
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• 2004

The integration of intelligent devices, devices-level networks, and software into motor control systems can deliver improved diagnostics, fast warnings for increased system reliability, design flexibility, and simplified wiring. Remote access to motor-control information also affords an opportunity for reduced exposure to hazardous voltage and improved personnel safety during startup and trouble-shooting. This paper presents LonWorks fieldbus networked intelligent induction control system architecture. Experimental bed system with two inverter motor driving system for controlling 1.5kW induction motor is configured for LonWorks networked intelligent motor control. In recent years, MCCs have evolved to include component technologies, such as variable-speed drives, solid-state starters, and electronic overload relays. Integration was accomplished through hardwiring to a programmable logic controller (PLC) or distributed control system (DCS). Devicelevel communication networks brought new possibilities for advanced monitoring, control and diagnostics. This LonWorks network offered the opportunity for greatly simplified wiring, eliminating the bundles of control interwiring and corresponding complex interwiring diagrams. An intelligent MCC connected in device level control network proves users with significant new information for preventing or minimizing downtime. This information includes warnings of abnormal operation, identification of trip causes, automated logging of events, and electronic documentation. In order to show the application of the multi-motors control system, the prototype control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using LonWorks network.

• Journal of Korean Society for Quality Management
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• v.45 no.1
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.