• 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.715-720
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• 2023

The military is driving innovative changes such as AI, cloud computing, and drone operation through the Fourth Industrial Revolution. It is expected that such changes will lead to a rapid increase in the demand for information exchange requirements, reaching all lower-ranking soldiers, as networking based on IoT occurs. The flow of such information must ensure efficient information distribution through various infrastructures such as ground networks, stationary satellites, and low-earth orbit small communication satellites, and the demand for information exchange that is distributed through them must be appropriately dispersed. In this study, we redefined the DSCP, which is closely related to QoS (Quality of Service) in information dissemination, into 11 categories and performed research to map each cluster group identified by cluster analysis to the defense "information exchange requirement list" on a one-to-one basis. The purpose of the research is to ensure efficient information dissemination within a multi-layer integrated network (ground network, stationary satellite network, low-earth orbit small communication satellite network) with limited bandwidth by re-establishing QoS policies that prioritize important information exchange requirements so that they are routed in priority. In this paper, we evaluated how well the information exchange requirement lists classified by cluster analysis were assigned to DSCP through M&S, and confirmed that reclassifying DSCP can lead to more efficient information distribution in a network environment with limited bandwidth.

• Journal of the Korean association of regional geographers
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• v.4 no.1
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• pp.43-56
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• 1998

The purpose of this study is to identify distributional characteristics of climatic elements and to analyze synoptic characteristics on the pressure fields for spring droughts in Korea. In the distributions of minimum temperature during the spring droughts, positive anomalies and negative anomalies are mixed up, but in March the negative anomaly areas are widely distributed in Korea. It implies that the droughts of March have more frequent occurrences of the west-high, east-low pressure patterns. In the maximum air temperatures, the positive anomalies appear in Korea. It indicates that the spring droughts have rain days, cloud amount and humidities less than normal. As a result, the amount of evaporation is increased in Korea. In the pressure anomaly of surface pressure fields, the positive anomalies appear in the west, negative anomalies in the east in March, but in May the positive anomalies appeared zonally around the Korean peninsula. It indicates that March droughts have more frequent occurrences of the west-high. east-low patterns, but in May the Korean Peninsula has more frequent recurrences of the migratory anticyclone patterns. The height anomaly patterns of 500hPa pressure surface in spring droughts are similarly shown to those of surface fields. In March droughts, the positive height anomalies appear in the west, the negative height anomalies in the east, but in April the negative height anomaly areas are extended to the west part. In May the positive anomalies appear zonally around the Korean Peninsula, and strong positive height anomalies appear around the Kamchatka Peninsula and the sea of Okhotsk. These are the result of circulations that inhibit the eastward movement of westerlies and that has persistent anticyclone circulation patterns around the Korean Peninsula. As a result, the zonal indices of westerlies during March and April droughts are lower than normal, but higher in May. These data indicate that early spring droughts are associated with weak zonal flow, but the late spring droughts are obviously related with strong zonal flow. In addition, during early spring droughts the abnormally deep trough over the west coast of the North Pacific Ocean that accompanied the anticyclone was associated with frequent advection of air from the dry regions in the Central Asia into the Korean Peninsula. The atmospheric circulation patterns at the height of the 500hPa pressure surface in May was quite different from March and April circulation patterns. Instead of the abnormal ridge in the west and trough in the east, the circulation pattern in May was characterized by a much stronger than normal anticyclone over the Korean Peninsula. Also, the zonal indices of westerlies in May are higher than normal. The occurrences of drought in early spring, therefore, have mechanism different from those of late spring.