• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.209-220
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• 2023

Recently, defects that occurred during the construction of apartment houses have become a social issue. Defects in apartment houses lead to waste of resources and economic loss, causing psychological and physical damage to customers, and a decrease in reliability and financial loss to construction companies. The purpose of this study was to analyze the impact of defect occurrence according to the difference in project management level in the apartment housing construction stage, and to investigate the extent to which the project manager's experience affects the defect occurrence rate. For the empirical analysis of the study, statistical analysis was conducted using data collected from 130 actual projects. The results of the analysis showed that schedule management, cost management, and quality management had a positive effect on reducing the defect occurrence rate in the execution stage of apartment housing construction, while human resource management productivity had a negative effect. This study theoretically demonstrated the importance of project management, and in practice, showed that schedule management, cost management, and quality management should be faithfully performed in the execution stage to reduce the rate of defects after project completion. It was also suggested that hiring an experienced project manager would help reduce project defects.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.785-796
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• 2023

In this study, a methodology for real-time classification and prediction of defects that may appear in PVDF(Polyvinylidene fluoride) sensors, which are widely used for structural integrity monitoring, is proposed. The types of sensor defects appearing according to the sensor attachment environment were classified, and an impact test using an impact hammer was performed to obtain an output signal according to the defect type. In order to cleary identify the difference between the output signal according to the defect types, the time domain statistical features were extracted and a data set was constructed. Among the machine learning based classification algorithms, the learning of the acquired data set and the result were analyzed to select the most suitable algorithm for detecting sensor defect types, and among them, it was confirmed that the highest optimization was performed to show SVM(Support Vector Machine). As a result, sensor defect types were classified with an accuracy of 92.5%, which was up to 13.95% higher than other classification algorithms. It is believed that the sensor defect prediction technique proposed in this study can be used as a base technology to secure the reliability of not only PVDF sensors but also various sensors for real time structural health monitoring.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.04a
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• pp.67-70
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• 2005

The carbon fiber reinforced/epoxy laminated composites were fabricated with initial fiber failures within the unidirectional fiber pre-pregnated ply. The fiber failures were made intentionally either parallel to and/or perpendicular to the unidirectional fibers within the ply. The pre-made clear-cut cracks were found to be healed partially after laminating process. The laminates were impacted with or without initial fiber failures within the laminates. The force versus deflection curves were compared. The partially healed laminates showed the reduced laminate stiffness as compared to those without any intentional fiber failures. The impact curves were compared with size and the location of the initial failures varied.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.25-30
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• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• Structural Engineering and Mechanics
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• v.90 no.4
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• pp.357-370
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• 2024

Due to the fact that the mechanism of the effects of temperature and initial geometric imperfection on low-velocity impact problem of axially moving plates is not yet clear, the present paper is to fill the gap. In the present paper, the nonlinear dynamic behavior of axially moving imperfect graphene platelet reinforced metal foams (GPLRMF) plates subjected to lowvelocity impact in thermal environment is analyzed. The equivalent physical parameters of GPLRMF plates are estimated based on the Halpin-Tsai equation and the mixing rule. Combining Kirchhoff plate theory and the modified nonlinear Hertz contact theory, the nonlinear governing equations of GPLRMF plates are derived. Under the condition of simply supported boundary, the nonlinear control equation is discretized with the help of Gallekin method. The correctness of the proposed model is verified by comparison with the existing results. Finally, the time history curves of contact force and transverse center displacement are obtained by using the fourth order Runge-Kutta method. Through detailed parameter research, the effects of graphene platelet (GPL) distribution mode, foam distribution mode, GPL weight fraction, foam coefficient, axial moving speed, prestressing force, temperature changes, damping coefficient, initial geometric defect, radius and initial velocity of the impactor on the nonlinear impact problem are explored. The results indicate that temperature changes and initial geometric imperfections have significant impacts.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.63-69
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• 1996

Recently, vibrational signal processing as a tool of machinery diagnosis has been actively studying. In this study, a new scheme for detection and diagnosis of localized defects in ball bearings, using unwrapped phase spectrum of FFT is described. The characteristic phase spectra for such defects shows linearly varying patterns due to the repetitive impact signals generated by localized defects, i.e., one linear line for single defect and various linearly changing shape according to angle between the two defect located points. The effectiveness of this method is confirmed by computer simulation and experiments on bearing with single or double defects at different locations.

• Journal of Ocean Engineering and Technology
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• v.19 no.1 s.62
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• pp.39-43
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• 2005

The study investigated the nondestructive characteristics of damage, caused by law-velocity impact, on symmetric cross-ply laminates, composed of [0o/90o]16s, 24s, 32s, 48s. The thickness of the laminates was 2, 3, 4 and 6 mm, respectively. The impact machine used, Model 8250 Dynatup Instron, was a drop-weight type that employed gravity. The impact velocities used in this experiment were 0.75, 0.90, 1.05, 1.20 and 1.35 m/sec, respectively. Both the load and the deformation increased when the impact velocity was increased. Further, when the load increased with the laminate thickness in the same impact velocity, the deformation still decreased. The extensional velocity was quick, as the laminate thickness increased in the same impact velocity and the impact velocity increased in the same laminate thickness. In the ultrasonic scans, the damaged area represented a dimmed zone. This is due to the fact that the wave, after the partial reflection by the deflects, does not have enough energy to touch the opposite side or to come back from it. The damaged laminate areas differed, according to the laminate thickness and the impact velocity. The extensional velocities are lower in the 0o direction and higher in the 90o direction, when the size of the defect increases. However, it was difficult to draw any conclusion for the extensional velocities in the 45o direction.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.3
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• pp.246-252
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• 2004

Electronic speckle pattern interferometry (ESPI) for quantitative evaluation of delaminations inside of a composite material plate is described. Delaminations caused by the impact on composite materials are difficult to detect visual inspection and ultrasonic testing due to non-homeogenous structure. This paper proposes the quantitative evaluation technique of the defects made in the composite plates by impact load. Artificial defects are introduced inside of the composite plate for the development of a reliable ESPI inspection technique. Real defects produced by impact tester are inspected and compared with the results of visual inspection which shows a good agreement within 5% error.

• Smart Structures and Systems
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• v.21 no.5
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.463-469
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• 2020

People enjoy more leisure time these days as the government introduced the workweek last year and there is a growing interest on living healthy life. Amidst such change in society, trekking in forest has gained popularity along more people spending leisure time by walking for health. As a result, there are many scenic trails being built using wood deck. Usually certain parts of trails are built with wood deck where the path maybe too dangerous for walking or where nature needs to be protected. However wood material used for deck is easily prone to defects which can lead to safety issues. This study aims to understand the differences by materials used for wood deck and to investigate a link between different materials used and causes for defect occurrence. And further, the study aims to identify materials and construction methods for high workability and safety. The study was able to conclude that different materials used and different construction methods of applied for wood deck yield different results in terms of workability and defect occurrence.