• Journal of Korean Port Research
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• v.14 no.3
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• pp.321-329
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• 2000

Welding is essential for the manufacture of a range of engineering components which may vary from very large structures such as ships and bridges to very complex structures such as aircraft engines, or miniature components for microelectronic applications. Especially, a domestic situation of the welding automation is still depend on the arc sensing system in comparison to the vision sensing system. Specially, the gap-detecting of workpiece using conventional arc sensor is proposed in this study. As a same principle, a welding current varies with the size of a welding gap. This study introduce to the fuzzy membership filter to cancel a high frequency noise of welding current, and ART2 which has the competitive learning network classifies the signal patterns the filtered welding signal. A welding current possesses a specific pattern according to the existence or the size of a welding gap. These specific patterns result in different classification in comparison with an occasion for no welding gap. The patterns in each case of 1mm, 2mm, 3mm and no welding gap are identified by the artificial neural network.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.699-707
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• 2015

Although digit character recognition has got a significant improvement in recent years, it is still challenging to achieve satisfied result if the data contains an amount of distracting factors. This paper proposes a novel digit character recognition approach using a multi-layer hierarchical model, Hybrid Restricted Boltzmann Machines (HRBMs), which allows the learning architecture to be robust to background distracting factors. The insight behind the proposed model is that useful high-level features appear more frequently than distracting factors during learning, thus the high-level features can be decompose into hybrid hierarchical structures by using only small label information. In order to extract robust and compact features, a stochastic 0-1 layer is employed, which enables the model's hidden nodes to independently capture the useful character features during training. Experiments on the variations of Mixed National Institute of Standards and Technology (MNIST) dataset show that improvements of the multi-layer hierarchical model can be achieved by the proposed method. Finally, the paper shows the proposed technique which is used in a real-world application, where it is able to identify digit characters under various complex background images.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.91-114
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• 2016

In this paper, recent research trends and activities on structural health monitoring (SHM) of civil infrastructure in Korea are reviewed. Recently, there has been increasing need for adopting smart sensing technologies to SHM, so this review focuses on smart sensing, monitoring, and assessment for civil infrastructure. Firstly, the research activities on smart sensor technology is reviewed including optical fiber sensors, piezoelectric sensors, wireless smart sensors, and vision-based sensing system. Then, a brief overview is given to the recent advances in smart monitoring and assessment techniques such as vibration-based global monitoring techniques, local monitoring with piezoelectric materials, decentralized monitoring techniques for wireless sensors, wireless power supply and energy harvest. Finally, recent joint SHM activities on several test beds in Korea are discussed to share the up-to-date information and to promote the smart sensors and monitoring technologies for applications to civil infrastructure. It includes a Korea-US joint research on test bridges of the Korea Expressway Corporation (KEC), a Korea-US-Japan joint research on Jindo cable-stayed bridge, and a comparative study for cable tension measurement techniques on Hwamyung cable-stayed bridge, and a campaign test for displacement measurement techniques on Sorok suspension bridge.

• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.216-220
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• 2018

In this paper, we present visual sensing techniques that can be used to teach a robot using a laser pointer. The light spot of an off-the-shelf laser pointer is detected and its movement is tracked on consecutive images of a camera. The three-dimensional position of the spot is calculated using stereo cameras. The light spot on the image is detected based on its color, brightness, and shape. The detection results in a binary image, and morphological processing steps are performed on the image to refine the detection. The movement of the laser spot is measured using two methods. The first is a simple method of specifying the region of interest (ROI) centered at the current location of the light spot and finding the spot within the ROI on the next image. It is assumed that the movement of the spot is not large on two consecutive images. The second method is using a Kalman filter, which has been widely employed in trajectory estimation problems. In our simulation study of various cases, Kalman filtering shows better results mostly. However, there is a problem of fitting the system model of the filter to the pattern of the spot movement.

• Journal of Construction Engineering and Project Management
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• v.5 no.4
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• pp.16-22
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. Several case studies were conducted to experimentally evaluate their impact on BIM-based energy analysis to calculate energy load. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• Journal of Sensor Science and Technology
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• v.14 no.2
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• pp.116-124
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• 2005

In this paper range measurement systems are designed using an ultrasonic sensor and a camera. An ultrasonic sensor provides the range measurement to a target quickly and simply but its low resolution is a disadvantage. We tackle this problem by employing a camera. Instead using a stereoscopic sensor, which is widely used for 3D sensing but requires a computationally intensive stereo matching, the range is measured by focusing and structured lighting. In focusing a straightforward focusing measure named as MMDH(min-max difference in histogram) is proposed and compared with existing techniques. In the method of structure lighting, light stripes projected by a beam projector are used. Compared to those using a laser beam projector, the designed system can be constructed easily in a low-budget. The system equation is derived by analysing the sensor geometry. A sensing scenario using the systems designed is in two steps. First, when better accuracy is required, measurements by ultrasonic sensing and focusing of a camera are fused by MLE(maximum likelihood estimation). Second, when the target is in a range of particular interest, a range map of the target scene is obtained by using structured lighting technique. The systems designed showed measurement accuracy up to 0.3[mm] approximately in experiments.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.395-407
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• 2023

Super-resolution (SR) has great significance in image processing because it enables downstream vision tasks with high spatial resolution. Recently, SR studies have adopted deep learning networks and achieved remarkable SR performance compared to conventional example-based methods. Deep-learning-based SR models generally require low-resolution (LR) images and the corresponding high-resolution (HR) images as training dataset. Due to the difficulties in obtaining real-world LR-HR datasets, most SR models have used only HR images and generated LR images with predefined degradation such as bicubic downsampling. However, SR models trained on simple image degradation do not reflect the properties of the images and often result in deteriorated SR qualities when applied to real-world images. In this study, we propose an image degradation model for HR satellite images based on the modulation transfer function (MTF) of an imaging sensor. Because the proposed method determines the image degradation based on the sensor properties, it is more suitable for training SR models on remote sensing images. Experimental results on HR satellite image datasets demonstrated the effectiveness of applying MTF-based filters to construct a more realistic LR-HR training dataset.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.532-536
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1325-1332
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• 2023

Recently, much research is being conducted based on point cloud data for the growth of innovations such as construction automation in the transportation field and virtual national space. This data is often measured through remote control in terrain that is difficult for humans to access using devices such as UAVs and UGVs. Drones, one of the UAVs, are mainly used to acquire point cloud data, but photogrammetry using a vision camera, which takes a lot of time to create a point cloud map, is difficult to apply in construction sites where the terrain changes periodically and surveying is difficult. In this paper, we developed a point cloud mapping system by adopting non-repetitive scanning LiDAR and attempted to confirm improvements through field application. For accuracy analysis, a point cloud map was created through a 2 minute 40 second flight and about 30 seconds of software post-processing on a terrain measuring 144.5 × 138.8 m. As a result of comparing the actual measured distance for structures with an average of 4 m, an average error of 4.3 cm was recorded, confirming that the performance was within the error range applicable to the field.

• ETRI Journal
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• v.30 no.5
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• pp.627-633
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• 2008

The technologies for wireless communication, sensing, and computation are each progressing at faster and faster rates. Notably, they are also being combined for an amazingly large multiplicative effect. It can be envisioned that the world will eventually be covered by networks of networks of smart sensors and actuators. This fact will give rise to revolutionary applications. However, to make this vision a reality, many research challenges must be overcome. This paper describes a representative set of new applications and identifies several key research challenges.