• Computers and Concrete
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• v.18 no.3
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• pp.405-420
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• 2016

Recently laser scanning technologies become widely used in many areas of the modern economy. In the following paper authors show a potential spectrum of use Terrestrial Laser Scanning (TLS) in diagnostics of reinforced concrete elements. Based on modes of failure analysis of reinforcement concrete beam authors describe downsides and advantages of adaptation of terrestrial laser scanning to this purpose, moreover reveal under which condition this technology might be used. Research studies were conducted by Faculty of Civil and Environmental Engineering at Gdansk University of Technology. An experiment involved bending of reinforced concrete beam, the process was registered by the terrestrial laser scanner. Reinforced concrete beam was deliberately overloaded and eventually failed by shear. Whole failure process was tracing and recording by scanner Leica ScanStation C10 and verified by synchronous photographic registration supported by digital photogrammetry methods. Obtained data were post-processed in Leica Cyclone (dedicated software) and MeshLab (program on GPL license). The main goal of this paper is to prove the effectiveness of TLS in diagnostics of reinforced concrete elements. Authors propose few methods and procedures to virtually reconstruct failure process, measure geometry and assess a condition of structure.

• Tribology and Lubricants
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• v.11 no.5
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• pp.15-19
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• 1995

Learning from the failure of mechanical systems is a necessity, given that it is the understanding of how and why things fail that generates effective redesign. This subsequently enables the technology that surrounds us to become more reliable, safer, and more economical by extending component life and minimizing the wasteful decisions made to replace systems that am either sound for continued operation could be easily repaired. Considerations for cost-effective decision making, so as to promote healthy machinery, equipment, and structures, are discussed in terms of learning from failure analysis, improving via reliability engineering, and achieving longevity through integrated diagnostics.

• Journal of Gastric Cancer
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• v.21 no.4
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• pp.335-351
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• 2021

Purpose: An underlying factor for the failure of several clinical trials of anti-epidermal growth factor receptor (EGFR) therapies is the lack of an effective method to identify patients who overexpress EGFR protein. The quantitative dot blot method (QDB) was used to measure EGFR protein levels objectively, absolutely, and quantitatively. Its feasibility was evaluated for the prognosis of overall survival (OS) of patients with gastric cancer. Materials and Methods: Slices of 2×5 ㎛ from formalin-fixed paraffin-embedded gastric cancer specimens were used to extract total tissue lysates for QDB measurement. Absolutely quantitated EGFR protein levels were used for the Kaplan-Meier OS analysis. Results: EGFR protein levels ranged from 0 to 772.6 pmol/g (n=246) for all gastric cancer patients. A poor correlation was observed between quantitated EGFR levels and immunohistochemistry scores with ρ=0.024 and P=0.717 in Spearman's correlation analysis. EGFR was identified as an independent negative prognostic biomarker for gastric cancer patients only through absolute quantitation, with a hazard ratio of 1.92 (95% confidence interval, 1.05-3.53; P=0.034) in multivariate Cox regression OS analysis. A cutoff of 208 pmol/g was proposed to stratify patients with a 3-year survival probability of 44% for patients with EGFR levels above the cutoff versus 68% for those below the cutoff based on Kaplan-Meier OS analysis (log rank test, P=0.002). Conclusions: A QDB-based assay was developed for gastric cancer specimens to measure EGFR protein levels absolutely, quantitatively, and objectively. This assay should facilitate clinical trials aimed at evaluation of anti-EGFR therapies retrospectively and prospectively for gastric cancer.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.244-250
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• 1997

The mean time to failure (MTTF) expressing the mean value of the system life is a measure of system effectiveness. To estimate the remaining life of component and/or system, the dynamic mean time to failure concept is suggested. It is the time-dependent Property depending on the status of components. The Kalman filter is used to estimate the reliability of components using the on-line information (directly measured sensor output or device-specific diagnostics in the intelligent sensor) in form of the numerical value (state factor). This factor considers the persistency of the fault condition and confidence level in measurement. If there is a complex system with many components, each calculated reliability's or components are combined, which results in the dynamic MTTF or system. The illustrative examples are discussed. The results show that the dynamic MTTF can well express the component and system failure behaviour whether any kinds of failure are occurred or not.

• Structural Engineering and Mechanics
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• v.58 no.5
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• pp.757-781
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• 2016

The results of diagnostics and analysis of an industrial hall located on the premises of a thermal power plant severely damaged by fire are presented in the paper. The comprehensive failure-related diagnostics, non-destructive and destructive tests of steel and concrete materials, geodetic surveying of selected structural members, numerical modelling, static analysis and reliability assessment were focused on two basic goals: The determination of the current technical condition of the load bearing structure and the assessment of its post fire resistance as well as assessing the degree of damage and subsequent design of reconstruction measures and arrangements which would enable the safe and reliable use of the building. The current mechanical properties of the steel material obtained from the tests and measured geometric characteristics of the structural members with imperfections were employed in finite element models to study the post-fire behaviour of the structure. In order to compare the behaviour of the numerically modelled steel roof truss, subjected to the effects of fire, with the real post-fire response of the damaged structure theoretically obtained resistance, critical temperature and the time at which the structure no longer meets the required reliability criteria under its given loading are compared with real values. A very good agreement between the simulated results and real characteristics of the structure after the fire was observed.

• Korean Journal of Computational Design and Engineering
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• v.18 no.4
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• pp.250-257
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• 2013

This paper introduces a failure analysis procedure that underpins real-time fault prognosis. In the previous study, we developed a systematic eventization procedure which makes it possible to reduce the original data size into a manageable one in the form of event logs and eventually to extract failure patterns efficiently from the reduced data. Failure patterns are then extracted in the form of event sequences by sequence-mining algorithms, (e.g. FP-Tree algorithm). Extracted patterns are stored in a failure pattern library, and eventually, we use the stored failure pattern information to predict potential failures. The two practical case studies (marine diesel engine and SIRIUS-II car engine) provide empirical support for the performance of the proposed failure analysis procedure. This procedure can be easily extended for wide application fields of failure analysis such as vehicle and machine diagnostics. Furthermore, it can be applied to human health monitoring & prognosis, so that human body signals could be efficiently analyzed.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.3
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• pp.789-791
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• 2004

We study on the video-capillaroscopy system for look a.t the nail fold capillary by the computer. This system is composed of the luminous source-producer, image-input, phase microscope. The method and contents of observation on this system are seperated from the image-analysis of nail fold capillary form-patterns and the currents of hematocytes. Therefore we think this system is possible to the practical clinic use for the basic model on the cardiac failure and the deficiency(血虛), stagnant(血瘀) of blood in human.

• The KSFM Journal of Fluid Machinery
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• v.15 no.6
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• pp.31-38
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• 2012

Rotor of rotating machinery is the highly damaged part. Fault of 7 different types was confirmed as the main causes of rotor damage from the pump failure history data in domestic and U.S. nuclear. For each fault types, simulation testing was performed and fault signals were collected form the sensors. To calculate the statistical parameters of time-domain & frequency-domain, measured signals were analyzed by using the discrete wavelet transform, fast fourier transform, statistical analysis. Total 84 parameters were obtained. And Effectiveness factor were used to evaluate the discrimination capacity of each parameter. From the effectiveness factor, RAW-P4/RAW-P7/WT2-NNL/WT2-EE/WT1-P1 showed high ranking. Finally, these parameters were selected as the feature parameters of intelligent fault diagnostics for rotor.

• Communications for Statistical Applications and Methods
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• v.25 no.5
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• pp.523-544
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• 2018

Bivariate distributions play a fundamental role in survival and reliability studies. We consider a regression model for bivariate survival times under right-censored based on the bivariate Kumaraswamy Weibull (Cordeiro et al., Journal of the Franklin Institute, 347, 1399-1429, 2010) distribution to model the dependence of bivariate survival data. We describe some structural properties of the marginal distributions. The method of maximum likelihood and a Bayesian procedure are adopted to estimate the model parameters. We use diagnostic measures based on the local influence and Bayesian case influence diagnostics to detect influential observations in the new model. We also show that the estimates in the bivariate Kumaraswamy Weibull regression model are robust to deal with the presence of outliers in the data. In addition, we use some measures of goodness-of-fit to evaluate the bivariate Kumaraswamy Weibull regression model. The methodology is illustrated by means of a real lifetime data set for kidney patients.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.503-512
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• 2012

Detecting outliers in a linear regression model eventually fails when similar observations are classified differently in a sequential process. In such circumstances, identifying clusters and applying certain methods to the clustered data can prevent a failure to detect outliers and is computationally efficient due to the reduction of data. In this paper, we suggest to implement a clustering procedure for this purpose and provide examples that illustrate the suggested procedure applied to the Hadi-Simonoff (1993) method, reverse Hadi-Simonoff method, and Gentleman-Wilk (1975) method.