• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1025-1032
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• 2018

Snow Cover is a form of precipitation that is defined by snow on the surface and is the single largest component of the cryosphere that plays an important role in maintaining the energy balance between the earth's surface and the atmosphere. It affects the regulation of the Earth's surface temperature. However, since snow cover is mainly distributed in area where human access is difficult, snow cover detection using satellites is actively performed, and snow cover detection in forest area is an important process as well as distinguishing between cloud and snow. In this study, we applied the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to the geostationary satellites for the snow detection of forest area in existing polar orbit satellites. On the rest of the forest area, the snow cover detection using $R_{1.61{\mu}m}$ anomaly technique and NDSI was performed. As a result of the indirect validation using the snow cover data and the Visible Infrared Imaging Radiometer (VIIRS) snow cover data, the probability of detection (POD) was 99.95 % and the False Alarm Ratio (FAR) was 16.63 %. We also performed qualitative validation using the Himawari-8 Advanced Himawari Imager (AHI) RGB image. The result showed that the areas detected by the VIIRS Snow Cover miss pixel are mixed with the area detected by the research false pixel.

• Convergence Security Journal
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• v.18 no.1
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• pp.19-31
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• 2018

As the ICT technologies advance, the unprecedently large amount of digital data is created, transferred, stored, and utilized in every industry. With the data scale extension and the applying technologies advancement, the new services emerging from the use of large scale data make our living more convenient and useful. But the cybercrimes such as data forgery and/or change of data generation time are also increasing. For the data security against the cybercrimes, the technology for data integrity and the time verification are necessary. Today, public key based signature technology is the most commonly used. But a lot of costly system resources and the additional infra to manage the certificates and keys for using it make it impractical to use in the large-scale data environment. In this research, a new and far less system resources consuming signature technology for large scale data, based on the Hash Function and Merkle tree, is introduced. An improved method for processing the distributed hash trees is also suggested to mitigate the disruptions by server failures. The prototype system was implemented, and its performance was evaluated. The results show that the technology can be effectively used in a variety of areas like cloud computing, IoT, big data, fin-tech, etc., which produce a large-scale data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.648-655
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• 2019

This paper proposes the differential development of a Taekwondo electronic body protector. For this development, the most suitable sensor system was selected after analyzing and testing various sensor methods (magnetic sensors, electric capacity sensors, contact switch sensors, and piezo-film sensors) that could be applied in the electronic body protector, the selected sensors were distributed to the body and feet to make a more precise hit score, unlike the existing system in which all sensors are centralized on the body. Furthermore, it aims to illuminate using a lightweight film-type piezoelectric sensor on the body protector. In the case of an existing electronic body protector, all sensors and network device were concentrated on the body protector, so users need to purchase a set if they want it. On the other hand, the proposed system cloud can be used individually using a smart scoring WEP program. The effects of decreasing weight by up to 20% were compared with those of the existing system. Setting up a test facility is very difficult, so more study will be needed to analyze the effects of a hit.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.89-92
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• 2020

According to the various IoT(Internet of Things) services, there have been lots of task offloading researches for IoT devices. Since there are service response delay and core network load issues in conventional cloud computing based offloadings, fog computing based offloading has been focused whose location is close to the IoT devices. However, even in the fog computing architecture, the load can be concentrated on the for computing node when the number of requests increase. To solve this problem, the opportunistic fog computing concept which offloads task to available computing resources such as cars and drones is introduced. In previous fog and opportunistic fog node researches, the offloading is performed immediately whenever the service request occurs. This means that the service requests can be offloaded to the opportunistic fog nodes only while they are available. However, if the service response delay requirement is satisfied, there is no need to offload the request immediately. In addition, the load can be distributed by making the best use of the opportunistic fog nodes. Therefore, this paper proposes a delayed offloading scheme to satisfy the response delay requirements and offload the request to the opportunistic fog nodes as efficiently as possible.

• 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.