• Journal of the Korean Society for Precision Engineering
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• v.20 no.2
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• pp.146-153
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• 2003

Ultrasonic inspection of defects has been focused on the existence of defect in structural material and need has much time and expenses in inspecting all the coordinates (x, y) on material surface. Neural networks can have an application to coordinates (x, y) of defects by multi-point inspection method. Ultrasonic inspection modeling is optimized by neural networks Neural networks has trained training example of absolute and relative coordinate of defects, and defect pattern. This method can predict coordinates (x, y) of defects within engineering estimated mean error $\psi$.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.8 s.350
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• pp.1-5
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• 2006

At-wavelength inspection system of extreme Ultra-violet lithography was developed and the inspection results were compared with the optical mask inspection system by cross correlation experiments. In at-wavelength EUV mask inspection system, a raster scan of focused euv light is used to illuminate euv light to mask blank and specularly and non-specularly reflected euv light are detected by photo diode and microchannel plate. The cross correlation results between at-wavelength inspection tool and optical inspection tool shows strong correlation. Far-field scattering fringe pattern from programmed phase and opqque defect, which were detected by phosphor plate and CCD camera shows that distinct diffraction fringes were observed with fringe spacing dependent on the defect size.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.946-949
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• 2005

This paper deals with the algorithm development that inspects defects such as Bright Defect Dots, Dark Defect Dots, and Line Defect caused by the process of LCD(Liquid Crystal Display). While most of LCD production process is automated, the inspection of LCD panel and its appearance depends on manual process. So, the quality of the inspection is affected by the condition of worker. Especially, the more LCD size increases, the more the worker feels fatigued, which causes the probability of miss judgement. So, the automated inspection is required to manage the consistent quality of the product and reduce the production costs. In this paper, to solve these problems, we developed the imaging processing algorithm to inspect the defects in captured image of LCD. Experimental results reveal that we can recognize various types of defect of LCD with good accuracy and high speed.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1240-1242
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• 1996

In a still mill, the effective surface defect inspection algorithm is necessary. For this purpose, this paper proposed the preprocessing algorithm for surface defect inspection of cold mill strip. This consists of live steps. They are edge detection, binarizing, noise deletion, combining of fragmented defect and selecting the largest defect. Especially, binarizing is a critical problem. Bemuse the performance of the preprocessing is largely depend on the binarized image. So, we develope the adaptive thresholding method, which is multilevel thresholding. The thresholding value is varied according to the mean graylevel value of each test image. To investigate the performance of the proposed algorithm, we classified the detected defect using neural network. The test image is 20 defect images captured at German Sick Co. This algorithm is proved to have good property in cold mill strip surface inspection.

• Journal of the Korea Institute of Building Construction
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• v.15 no.5
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• pp.491-500
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• 2015

This study investigated defect status by work type, based on the report data of defect inspection results, acquired by consumers' request to safety inspection agencies, before the expiration of legal defect repair warranty period. In fact, the data was not acquired by centering on suppliers, namely, construction companies in relation with the defects becoming causes to increase construction cost of apartments. This study aims to provide objective and basic data for quality improvement at construction stage and for solution to defect disputes. The study results are presented below: (1) The number of defect cases occurring from architectural work among total work types were 1,986, defect occurrence rate was 62.5%, and defect repair cost was KRW $25,851/m^2$, which stood at 78.2% of the total work types. This means the defect occurrence rate and defect repair cost in architectural work are bigger than those of other work types. (2) Major defects in architectural work were revealed in the following order: cracks from frame work, inferior interior finishing work, inferior finishing work of plaster/masonry works, water leak/damage from waterproof work and withering/omission from landscape work. The total repair cost of the major selected defects was KRW $12,220/m^2$, and was analyzed to take up 37% of the total defect repair cost.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.12
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• pp.92-99
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• 2004

The formation degree of sustain (ITO pattern) decides quality of PDP (Plasma Display Panel). For this reason, it makes efforts in searching defects more than 30 un as 100%. Now, the existing inspection is dependent upon naked eye or microscope in off-line PDP manufacturing process. In this study developed prototype inspection system of PDP 170 glass is based on line-scan mechanism. Developed system creates information that detects and sorts kinds of defect automatically. Designed inspection technology adopts multi-vision method by slip-beam formation for the minimum of inspection time and detection algorithm is embodied in detection ability of developed system. Designed algorithm had to make good use of kernel matrix that draws up an approach to geometry. A characteristic of defects, as pin hole, substance, protrusion, are extracted from blob analysis method. Defects, as open, short, spots and et al, are distinguished by line type inspection algorithm. In experiment, we could have ensured ability of inspection that can be detected with reliability of up to 95% in about 60 seconds.

• Korean Journal of Construction Engineering and Management
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• v.6 no.1 s.23
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• pp.140-150
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• 2005

Recently, quality inspection and defect management have been considered as one of the major issues for increasing customer satisfaction and corporate image in domestic construction industry. However, the quality inspection and defect management have not been performed systematically because of insufficient field managers, the excessive amount of documents, complicated work process and difficulty in communicating construction information. Therefore, the field manager could not perform the quality inspection and defect management work in time as well as the reliability of recorded quality and defect data was decreased. The primary objective of this study is to propose a quality inspection and defect management system using information technology which enables field managers to efficiently gather the information of defection in apartment housing. It is anticipated that the effective use of the proposed system would be able to imp개ve communication among the related participants and systematically accumulate data that might be used in similar construction projects.

• Journal of the Korea Institute of Building Construction
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• v.13 no.3
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• pp.209-217
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• 2013

The execution of a construction project involves the engagement of many participants. Generally, the Authority uses CM to certify that the Work is built according to the contract documents. The CM work scope is expressed in the Construction Technology Management Act and its Regulation. The mandated and delegated CM services are limited to the construction period. If Contractor is required to repair a construction defect, the Contractor should bear the burden of the inspection service for the defect repair, because it is associated with him. Nevertheless, CM should submit a bond to provide the inspection and supervision service for the defect repair. These may result in conflict with each liability. Therefore, CM service in the law and regulation was investigated and analyzed in this study to classify the characteristics of CM contracts, and it was suggested that the CM liability for the inspection and supervision service for the defect repair should be reconsidered.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.149-154
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• 2022

In recent years, the application of deep learning method to computer vision has shown to achieve great performances. Thus, many research projects have also applied deep learning technology to railroad defect detection. In this paper, we have reviewed the researches that applied computer vision based deep learning method to railroad defect detection and inspection, and have discussed the current trend and the direction of those researches. Many research projects were targeted to operate automatically without visual inspection of human and to work in real-time. Therefore, methods to speed up the computation were also investigated. The reduction of the number of learning parameters was considered important to improve computation efficiency. In addition to computation speed issue, the problem of annotation was also discussed in some research projects. To alleviate the problem of time consuming annotation, some kinds of automatic segmentation of the railroad defect or self-supervised methods have been suggested.

• 한국정보디스플레이학회:학술대회논문집
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• 2007.08a
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• pp.286-289
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• 2007

We have developed an automated circuit defect inspection and repair method that can be used to improve the yield ratio of TFT-LCD. The method focuses on correcting resist patterns after the development process to ensure shape regularity. We built a prototype system and confirmed that the method is valid.