• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.211-216
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• 2016

Worldwide casualties caused by earthquakes, floods, fire or other disaster has been increasing. So many researchers are being actively done technical studies to ensure golden-time. In this paper if a disaster occurs, use the IoT technologies in order to secure golden-time and transmits the message after to find the user of the accident area first. When the previous job is finished, gradually finds a user of the surrounding area and transmits the message. For national emergency information, OPEN API of Korea Meteorological Administration was used. To collect detailed information on a relevant area in real time, this study established the system that connects and integrates Crowd Sensing technology with BLE (Bluetooth Low Energy) Beacon technology. Up to now, the CBS based on base station has been applied. However, this study designed and mapped DB in the integration of Beacon based user positioning and national administrative address system in order to estimate local users. In this experiment, the accuracy and speed of information dif6fusion algorithm were measured with a rise in the number of users. The experiments were conducted in a manner that increases the number of users by one thousand and was measured the accuracy and speed of the message spread transfer algorithm. Finally, became operational in less than one second in 20,000 users, it was confirmed that the notification message is sent.

• Korean Journal of Remote Sensing
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• v.26 no.5
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• pp.593-603
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• 2010

Most spatial data acquisition systems employing a set of frame cameras may have suffered from their small fields of view and poor base-distance ratio. These limitations can be significantly reduced by employing an omni-directional camera that is capable of acquiring images in every direction. Bundle Block Adjustment (BBA) is one of the existing georeferencing methods to determine the exterior orientation parameters of two or more images. In this study, by extending the concept of the traditional BBA method, we attempt to develop a mathematical model of BBA for omni-directional images. The proposed mathematical model includes three main parts; observation equations based on the collinearity equations newly derived for omni-directional images, stochastic constraints imposed from GPS/INS data and GCPs. We also report the experimental results from the application of our proposed BBA to the real data obtained mainly in urban areas. With the different combinations of the constraints, we applied four different types of mathematical models. With the type where only GCPs are used as the constraints, the proposed BBA can provide the most accurate results, ${\pm}5cm$ of RMSE in the estimated ground point coordinates. In future, we plan to perform more sophisticated lens calibration for the omni-directional camera to improve the georeferencing accuracy of omni-directional images. These georeferenced omni-directional images can be effectively utilized for city modelling, particularly autonomous texture mapping for realistic street view.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.3
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• pp.182-192
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• 2000

The objective of this study was to determine the effects of various kinds of composts on the amount of maximum composts in upland soils. Field experiments were conducted in the loam and sandy loam soils. Various kinds of composts such as poultry manure compost(PMC), cow manure compost(CMC), human excrement sludge(HES), and food industrial sludge compost(FISC) were applied annually at rates of 0, 40, and $80Mg\;ha^{-1}$ to soils grown with soybean and maize plants for 4 years during 1994 to 1997. The results of this study were as follows : Maize dry matter production was related linearly positively with application amount of compost, but soybean dry matter production was showed an apex at $60Mg\;ha^{-1}$, maize produced 4.7 times dry matter as compared to soybean. Rate of nitrogen utilization of maize were 21~31% and that of soybean were 8~19%, rate of phosphorous utilization of maize were 5~7% and that of soybean were l~2%. On the basis of biomass maximum application rates of poultry manure compost were $39{\sim}47Mg\;ha^{-1}$ for maize cultivation and $8{\sim}13Mg\;ha^{-1}$ for soybean cultivation. On the other hand those amounts estimated by nitrogen requirement of crops were 39~47 and $8{\sim}13Mg\;ha^{-1}$ for maize and soybean cultivation, respectively. Based on phosphorous requirement of crops those were 29 and $14Mg\;ha^{-1}$ for maize and soybean cultivation, respectively. Considering the upper limit of the base saturation percentage to be 80%, maximum application rates were 63~93 and $49{\sim}69Mg\;ha^{-1}$ in loam and sandy loam soil, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.5
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• pp.901-909
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• 2021

Since mobile devices have limited computational resources, it tends to use the cloud to compute or store data. As real-time becomes more important due to 5G, many studies have been conducted on edge clouds that computes at locations closer to users than central clouds. The farther the user's physical distance from the edge cloud connected to base station is, the slower the network transmits. So applications should be migrated and re-run to nearby edge cloud for smooth service use. We run applications in docker containers, which is independent of the host operating system and has a relatively light images size compared to the virtual machine. Existing migration studies have been experimented by using network simulators. It uses fixed values, so it is different from the results in the real-world environment. In addition, the method of migrating images through shared storage was used, which poses a risk of packet content exposure. In this paper, Containers are migrated with Secure CoPy(SCP) method, a data encryption transmission, by establishing an edge computing environment in a real-world environment. It compares migration time with Network File System, one of the shared storage methods, and analyzes network packets to verify safety.

• Journal of the Korean Institute of Gas
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• v.25 no.3
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• pp.46-52
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• 2021

In this study, the on-site location identification and response system was proposed by accurately checking the location information of rescue requesters in the buildings using the smartphone Wi-Fi AP. The location server was requested to measure the strength of the Wi-Fi AP at least 25 times at 8 different building location points. And the accuracy of the position and the error range were checked by analyzing the coordinate values of the received positions. In addition, the response time was measured by changing the conditions of location information in three groups to compare the response time for saving lives with and without location information. The minimum and maximum error values for the eight cases were found to be at least 4.137 m and up to 14.037 m, respectively, with an average error of 9.525 m. Compared to the base transceiver station (BTS) based position error value of 263m, the range could be reduced by up to 93%. When the location information was given, it took 10 minutes and 50 seconds to save lives; however, when there was no location information at all, rescue process took more than 45 minutes. From this research effort, it was analyzed that the acquisition of the location information of rescuees in the building using the smartphone Wi-Fi AP approach is effective in reducing the life-saving time for on-site responses.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.8
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• pp.15-22
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• 2019

Awireless sensor network utilizes small sensors with a low cost and low power being deployed over a wide area. They monitor the surrounding environment and gather the associated information to transmit it to a base station via multi-hop transmission. Most of the research has mainly focused on static sensors that are located in a fixed position. Unlike a wireless sensor network based on static sensors, we can exploit drone-based technologies for more efficient wireless networks in terms of coverage and energy. In this paper, we introduce a transmission power model and a video encoding power model to design the network environment. We also explain a priority mapping scheme, and deploy drones oriented for network coverage and energy consumption. Through our simulations, this research shows coverage and energy improvements in adrone-based wireless sensor network with fewer sensors, compared to astatic sensor-based wireless sensor network. Concretely, coverage increases by 30% for thedrone-based wireless sensor network with the same number of sensors. Moreover, we save an average of 25% with respect to the total energy consumption of the network while maintaining the coverage required.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.859-865
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• 2018

A construction of infrastructures and base station on the moon could be undertaken by linking with the regions where construction materials and energy could be supplied on site. It is necessary to detect craters on the lunar surface and gather their topological information in advance, which forms permanent shaded regions (PSR) in which rich ice deposits might be available. In this study, an effective method for automatic detection of lunar craters on the moon surface is taken into consideration by employing a latest version of deep-learning algorithm. A training of a deep-learning algorithm is performed by involving the still images of 90000 taken from the LRO orbiter on operation by NASA and the label data involving position and size of partly craters shown in each image. the Faster RCNN algorithm, which is a latest version of deep-learning algorithms, is applied for a deep-learning training. The trained deep-learning code was used for automatic detection of craters which had not been trained. As results, it is shown that a lot of erroneous information for crater's positions and sizes labelled by NASA has been automatically revised and many other craters not labelled has been detected. Therefore, it could be possible to automatically produce regional maps of crater density and topological information on the moon which could be changed through time and should be highly valuable in engineering consideration for lunar construction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.434-439
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• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.63-71
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• 2020

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.53-61
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• 2010

In wireless sensor networks(WSNs) individual sensor nodes are subject to security compromises. An adversary can physically capture sensor nodes and obtain the security information. And the adversary injects false reports into the network using compromised nodes. If undetected, these false reports are forwarded to the base station. False reports injection attacks can not only result in false alarms but also depletion of the limited amount of energy in battery powered sensor nodes. To combat these false reports injection attacks, several filtering schemes have been proposed. The statistical en-routing filtering(SEF) scheme can detect and drop false reports during the forwarding process. In SEF, The number of the message authentication codes(threshold) is important for detecting false reports and saving energy. In this paper, we propose a dynamic threshold determination method for energy efficient SEF using fuzzy-logic in wireless sensor networks. The proposed method consider false reports rate and the number of compromised partitions. If low rate of false reports in the networks, the threshold should low. If high rate of false reports in networks, the threshold should high. We evaluated the proposed method’s performance via simulation.