• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5534-5540
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• 2015

This In the railway signaling system, applications of axle counter in addition to track circuit goes on increasing for detecting train position. Consequently, this paper compares sensor methods of axle counter with between geo-magnetism method and proximity sensor method. And it presents differences and results, to improve reliabilities of train detection and axle counting. Also, this article presents an applied result which is based on field experience, with regard to installation, considering attachment condition of sensor part for accurate axle counting. This study acquires expandability that is able to perform not only axle counting function but also various other functions (direction detection of train, speed detection of train, and so on). It was a result of a change of design in order to judge phase difference of sensors, to improve reliability of axle counting. Furthermore, it does not subordinate to characteristics (type, weight of train). And it is confirmed that the omission of axle counting was not occurred in 350km/h. This was the result of Lab test after the construction of transfer equipment of trial axle and Test Bed for axle counting. Both of them are self-productions. Through this, it prepares foundation which is able to apply not only to train detection but also to speed of passing trains, formation number of trains, detector locking condition - when the train passes the section of switch point, and level crossing devices. Furthermore, it would be judged to contribute safety train operation if proximity sensor method applies to the whole railway signaling system from now on.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Journal of the Korean Geotechnical Society
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• v.39 no.12
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• pp.75-91
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• 2023

In this study, we explore the use of ground penetrating radar (GPR) for the nondestructive survey of subsurface conduits, focusing on the challenges posed by multichannel environments. A key concern is the shadow regions created by conduits, which significantly impact survey results. The shadow regions, which are influenced by conduit position and diameter, hinder signal propagation, thereby making detection within these regions challenging. Using finite-difference time-domain numerical analysis, we examined the characteristics of conduit signals, which typically manifest in hyperbolic patterns. Particularly, we investigated three conduit arrangements: horizontal, vertical, and diagonal. Automatic gain control was applied to amplify the signals, enabling the analysis of variations in shadow regions and signal characteristics for each arrangement. In the horizontal arrangement, the proximity of the two conduits resulted in the emergence of a new hyperbolic pattern between the existing conduits. In the vertical arrangement, the lower conduit could be detected using hyperbolic signals on either side, but the detection was challenging when the upper conduit diameter exceeded that of the lower conduit. In the diagonal arrangement, signal characteristics varied based on the position of shadow regions relative to the detection range of the equipment. Asymmetrical signal patterns were observed when the shadow regions fell within the detection range, whereas the signals of the two conduits were minimally impacted when the shadow regions were outside the detection range. This study provides vital insights into accurately detecting and characterizing subsurface multichannel conduits using GPR-a significant contribution to the field of subsurface exploration and management.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1353-1369
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• 2023

In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.