• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4043-4064
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• 2021

Night-time image quality evaluation is an urgent requirement in visual inspection. The lighting environment of night-time results in low brightness, low contrast, loss of detailed information, and colour dissonance of image, which remains a daunting task of delicately evaluating the image quality at night. A new blind quality assessment metric is presented for realistic night-time scenario through a comprehensive consideration of contrast, texture, and colour in this article. To be specific, image blocks' color-gray-difference (CGD) histogram that represents contrast features is computed at first. Next, texture features that are measured by the mean subtracted contrast normalized (MSCN)-weighted local binary pattern (LBP) histogram are calculated. Then statistical features in Lαβ colour space are detected. Finally, the quality prediction model is conducted by the support vector regression (SVR) based on extracted contrast, texture, and colour features. Experiments conducted on NNID, CCRIQ, LIVE-CH, and CID2013 databases indicate that the proposed metric is superior to the compared BIQA metrics.

• Structural Monitoring and Maintenance
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• v.10 no.2
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• pp.133-154
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• 2023

Obscured structural members are mostly under-evaluated during condition assessment due to lack of visual inspection capability. Insufficient information about the integrity of these structural members poses a significant risk for public safety. Time domain reflectometry (TDR) is a novel approach in structural health monitoring (SHM). Ordinary coaxial cables "as is" without a major modification are not suitable for SHM with TDR. The objective of this study is to propose a practical and cost-effective modification approach to commercially available coaxial cables in order to use them as a "cable sensor" for damage detection with the TDR equipment for obscured structural members. The experimental validation and assessment of the proposed modification approach was achieved by conducting 3-point bending tests of the model piles as a representative obscured structural member. It can be noted that the RG59/U-6 and RG6/U-4 cable sensors expose higher strain sensitivity in comparison with non-modified "as is" versions of the cables used. As a result, the cable sensors have the capability of sensing both the presence and the location of a structural damage with a maximum aberration of 3 cm. Furthermore, the crack development can be monitored by the RG59/U-6 cable sensor with a simple calibration.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.399-408
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• 2020

The construction industry is suffering from aging workers, frequent accidents, as well as low productivity. With the rapid development of information technologies in recent years, automatic construction, especially automatic cranes, is regarded as a promising solution for the above problems and attracting more and more attention. However, in practice, limited by the complexity and dynamics of construction environment, manual inspection which is time-consuming and error-prone is still the only way to recognize the search object for the operation of crane. To solve this problem, an image-processing-based automated object recognition approach is proposed in this paper, which is a fusion of Convolutional-Neutral-Network (CNN)-based and traditional object detections. The search object is firstly extracted from the background by the trained Faster R-CNN. And then through a series of image processing including Canny, Hough and Endpoints clustering analysis, the vertices of the search object can be determined to locate it in 3D space uniquely. Finally, the features (e.g., centroid coordinate, size, and color) of the search object are extracted for further recognition. The approach presented in this paper was implemented in OpenCV, and the prototype was written in Microsoft Visual C++. This proposed approach shows great potential for the automatic operation of crane. Further researches and more extensive field experiments will follow in the future.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.15-22
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• 2020

In this study, we propose an improvement method that can create U-Net model which detect fine concrete cracks by applying a weighted loss function. Because cracks in concrete are a factor that threatens safety, it is important to periodically check the condition and take prompt initial measures. However, currently, the visual inspection is mainly used in which the inspector directly inspects and evaluates with naked eyes. This has limitations not only in terms of accuracy, but also in terms of cost, time and safety. Accordingly, technologies using deep learning is being researched so that minute cracks generated in concrete structures can be detected quickly and accurately. As a result of attempting crack detection using U-Net in this study, it was confirmed that it could not detect minute cracks. Accordingly, as a result of verifying the performance of the model trained by applying the suggested weighted loss function, a highly reliable value (Accuracy) of 99% or higher and a harmonic average (F1_Score) of 89% to 92% was derived. The performance of the learning improvement plan was verified through the results of accurately and clearly detecting cracks.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.2
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• pp.159-164
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• 2013

It is well known that a single moving camera video is capable of extracting the 3-dimensional position of an object. With this in mind, current research performed image-based monitoring to establish a floating structure model using a camcorder system. Following this, the present study extracted frame images from digital camcorder video clips and matched the interest points to obtain relative 3D coordinates for both regular and irregular wave conditions. Then, the researchers evaluated the transformation accuracy of the modified SURF-based matching and image-based displacement estimation of the floating structure model in regular wave condition. For the regular wave condition, the wave generator's setting value was 3.0 sec and the cycle of the image-based displacement result was 2.993 sec. Taking into account mechanical error, these values can be considered as very similar. In terms of visual inspection, the researchers observed the shape of a regular wave in the 3-dimensional and 1-dimensional figures through the projection on X Y Z axis. In conclusion, it was possible to calculate the displacement of a floating structure module in near real-time using an average digital camcorder with 30fps video.

• Journal of Korean Society for Quality Management
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• v.48 no.2
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• pp.297-307
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• 2020

Purpose: The purpose of this study is to analyze the factors that affect ammunition performance by applying data mining techniques to the Ammunition Stockpile Reliability Program (ASRP) data of the 155mm propelling charge. Methods: The ASRP data from 1999 to 2017 have been utilized. Logistic regression and decision tree analysis were used to investigate the factors that affect performance of ammunition. The performance evaluation of each model was conducted through comparison with an artificial neural networks(ANN) model. Results: The results of this study are as follows; logistic regression and the decision tree analysis showed that major defect rate of visual inspection is the most significant factor. Also, muzzle velocity by base charge and muzzle velocity by increment charge are also among the significant factors affecting the performance of 155mm propelling charge. To validate the logistic regression and decision tree models, their classification accuracies have been compared with the results of an ANN model. The results indicate that the logistic regression and decision tree models show sufficient performance which conforms the validity of the models. Conclusion: The main contribution of this paper is that, to our best knowledge, it is the first attempt at identifying the significant factors of ASPR data by using data mining techniques. The approaches suggested in the paper could also be extended to other types ammunition data.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.41-55
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• 2021

To detect thunderstorms occurring in Korea, National Meteorological Satellite Center (NMSC) also introduced the rapid-development thunderstorm (RDT) algorithm developed by EUMETSAT. At NMCS, the H-RDT (HR) based on the Himawari-8 satellite and the K-RDT (KR) which combines the GK2A convection initiation output with the RDT were developed. In this study, we optimized the KR (KU) to improve the detection level of thunderstorms occurring in Korea. For this, we used all available data, such as GK2A/AMI, RADAR, lightning, and numerical model data from the recent two years (2019-2020). The machine learning of logistic regression and stepwise variable selection was used to optimize the KU algorithms. For considering the developing stages and duration time of thunderstorms, and data availability of GK2A/AMI, a total of 72 types of detection algorithms were developed. The level of detection of the KR, HR, and KU was evaluated qualitatively and quantitatively using lightning and RADAR data. Visual inspection using the lightning and RADAR data showed that all three algorithms detect thunderstorms that occurred in Korea well. However, the level of detection differs according to the lightning frequency and day/night, and the higher the frequency of lightning, the higher the detection level is. And the level of detection is generally higher at night than day. The quantitative verification of KU using lightning (RADAR) data showed that POD and FAR are 0.70 (0.34) and 0.57 (0.04), respectively. The verification results showed that the detection level of KU is slightly better than that of KR and HR.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.19-28
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• 2012

In this study, cause of longitudinal crack which is found on duct slab of road tunnel is studied. In-depth investigation, such as visual inspection, non-destructive testing and geometrical surveying of duct slab, is carried out. In order to perform cause analysis, the investigated results are compared to the results of numerical analysis. Many factors, which cause longitudinal crack, are classified as constrained condition of the duct slab, location of the rebar, temperature, shrinkage and so on. According to the classified causes of longitudinal crack, numerical analysis is performed considering construction stage of the tunnel lining. Especially, in order to predict shrinkage stain due to discrepancy of curing date, ACI-209 model, KCI structural design code and other researcher's shrinkage test results are compared. The results show that shrinkage strain is one of the main factors causing longitudinal crack. Other investigated tunnels are classified along with the construction method of duct slab and patterns of cracks. As a result, improving ways to construct duct slab are suggested.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.901-911
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• 2009

In this paper, we propose a construction technique of two-level composite gradient map to automatically track a face with large movement in successive frames. Our method is composed of three main steps. First, the gradient maps with two-level resolution are generated for fast convergence of active contour. Second, to recognize the variations of face between successive frames and remove the neighbor background, weighted composite gradient map is generated by combining the composite gradient map and difference mask of previous and current frames. Third, to prevent active contour from converging local minima, the energy slope is generated by using closing operation. In addition, the fast closing operation is proposed to accelerate the processing time of closing operation. For performance evaluation, we compare our method with previous active contour model-based face tracking methods using a visual inspection, robustness test and processing time. Experimental results show that our method can effectively track the face with large movement and robustly converge to the optimal position even in frames with complicated background.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.44.1-44.1
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• 2018

We present a spectroscopic study of the Galactic supernova remnant (SNR) Cygnus Loop using the fifth Data Release (DR5) of LAMOST. The LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope) features both a large field-of-view (about 20 deg2) and a large aperture (~4 m in diameter), which allow us to obtain 4000 spectra simultaneously. Its wavelength coverage ranges from ${\sim}3700{\AA}$ to $9000{\AA}$ with a spectral resolution of $R{\approx}1800$. The Cygnus Loop is a prototype of middle-aged SNRs, which has advantages of being bright, large in angular size (${\sim}3.8^{\circ}{\times}3^{\circ}$), and relatively unobscured by dust. Along the line of sight of the Cygnus Loop, 2747 LAMOST DR5 spectra are found in total, which are spatially distributed over the entire remnant. Among them, 778 spectra are selected based on the presence of emission lines (i.e., [O III]${\lambda}5007$, Ha, and [S II]${\lambda}{\lambda}$ 6717, 6731) for further visual inspection. About half of them (336 spectra) show clear spectral features to confirm their association with the remnant, 370 spectra show stellar features only, and 72 spectra are ambiguous and need further investigation. For those associated with the remnant, we identify emission lines and measure their intensities. Spectral properties considerably vary within the remnant, and we compare them with theoretical models to derive physical properties of the SNR such as electron density and temperature, and shock velocity. While some line ratios are in good agreement with model prediction, others cannot be explained by simple shock models with a range of shock velocities. We discuss these discrepancies between model predictions and the observations and finally highlight the powerfulness of the LAMOST data to investigate spatial variations of physical properties of the Cygnus Loop.