• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.9-17
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• 2019

In this paper, we propose an attribute-based broadcast encryption algorithm that can be applied to satellite broadcasting network. The encryption algorithm is a cryptographic method by which a carrier(sender) can transmit contents efficiently and securely to a plurality of legitimate users through satellites. An attribute-based encryption algorithm encrypts contents according to property of contents or a user, In this paper, we combine effectively two algorithms to improve the safety and operability of satellite broadcasting network. That is, it can efficiently transmit ciphertexts to a large number of users, and has an advantage in that decoding can be controlled by combining various attributes. The proposed algorithm reduces the network load by greatly reducing the size of the public key, the private key and the cipher text in terms of efficiency, and the decryption operation amount is reduced by half to enable fast decryption, thereby enhancing the operability of the user.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.535-544
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• 2018

Autonomous driving can be limited by only using sensors if the sensor is blocked by sudden changes in surrounding environments or large features such as heavy vehicles. In order to overcome the limitations, the precise road-map has been used additionally. This study was conducted to segment and classify road objects using 3D point cloud data acquired by terrestrial mobile mapping system provided by National Geographic Information Institute. For this study, the original 3D point cloud data were pre-processed and a filtering technique was selected to separate the ground and non-ground points. In addition, the road objects corresponding to the lanes, the street lights, the safety fences were initially segmented, and then the objects were classified using the support vector machine which is a kind of machine learning. For the training data for supervised classification, only the geometric elements and the height information using the eigenvalues extracted from the road objects were used. The overall accuracy of the classification results was 87% and the kappa coefficient was 0.795. It is expected that classification accuracy will be increased if various classification items are added not only geometric elements for classifying road objects in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1474-1480
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• 2018

When fires occur in high-rise buildings, it is difficult to determine whether each escape route is safe because of complex structure. Therefore, it is necessary to provide residents with escape routes quickly after determining their safety. We propose a method to measure the visibility of the escape route due to the smoke generated in the fire by analyzing the images. The visibility can be easily measured if the density of smoke detected in the input image is known. However, this approach is difficult to use because there are no suitable methods for measuring smoke density. In this paper, we use principal component analysis by extracting a background image from input images and making it training data. Background images and smoke images are extracted from images given as inputs, and then the learned principal component analysis is applied to map of as a new feature space, and the change is calculated and the visibility due to the smoke is measured.

• Progress in Medical Physics
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• v.29 no.4
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• pp.123-136
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• 2018

The complex dose distribution and dose transfer characteristics of intensity-modulated radiotherapy increase the importance of precise beam data measurement and review in the acceptance inspection and preparation stages. In this study, we propose a process map for the introduction and installation of high-precision radiotherapy devices and present items and guidelines for risk management at the acceptance test procedure (ATP) and commissioning stages. Based on the ATP of the Varian and Elekta linear accelerators, the ATP items were checked step by step and compared with the quality assurance (QA) test items of the AAPM TG-142 described for the medical accelerator QA. Based on the commissioning procedure, dose quality control protocol, and mechanical quality control protocol presented at international conferences, step-by-step check items and commissioning guidelines were derived. The risk management items at each stage were (1) 21 ionization chamber performance test items and 9 electrometer, cable, and connector inspection items related to the dosimetry system; (2) 34 mechanical and dose-checking items during ATP, 22 multileaf collimator (MLC) items, and 36 imaging system items; and (3) 28 items in the measurement preparation stage and 32 items in the measurement stage after commissioning. Because the items presented in these guidelines are limited in terms of special treatment, items and practitioners can be modified to reflect the clinical needs of the institution. During the system installation, it is recommended that at least two clinically qualified medical physicists (CQMP) perform a double check in compliance with the two-person rule. We expect that this result will be useful as a radiation safety management tool that can prevent radiation accidents at each stage during the introduction of radiotherapy and the system installation process.

• The Journal of Engineering Geology
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• v.28 no.4
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• pp.593-603
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• 2018

The most of groundwater with high U-concentration occur in the Jurassic granite of Gyeonggi massif and Ogcheon belt, and some of them occur in the Cretaceous granite of Ogcheon belt. On the contrary, they do not occur in the Jurassic granite of Yeongnam massif and the Cretaceou granite of Gyeongsang basin. The Jurassic and Cretacous granite, the host rock of high U-groundwater, were resulted from parental magma with high ratio of crustal material and highly differentiated product of fractional crystalization. These petrogenetic characteristics explain the geological evidence for preferential distribution of uraniferous groundwater in each host rock. It were reported recently that high U-content, low Th/U ratio and soluble mineral occurrence of uraninite in the two-mica granite of Daejeon area which have characteristics of S-type peraluminous and highly differntiated product. It is the mineralogical-geochemical evidences supporting the fact that the two-mica granite is the effective source of uranium in groundwater. The biotite granite and two-mica granite of Jurassic age were reported as biotite granite in many geological map even though two-mica granite occur locally. This fact suggest that the influence of two-mica granite can not be ignored in uraniferous groundwater hosted by biotite granite.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.345-352
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• 2020

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1124-1129
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• 2021

The application area based on UAV (Unmanned Aerial Vehicle) is continuously increasing as time passing by. In particular the UAVs which consist of more than four horizontal propellers and the functionality of VTOL (Vertical Take-Off and Landing) are utilized in diverse platforms and the application products due to their safety and aerodynamically simpler design and architectures. The most UAV missions are controlled by GCSs (Ground Control System). The GCSs are generally connected to the internet and get electrical map and environmental information such as temperature, humidity, wind speed, wind direction and so on. In this paper, we design a system for UAV system to have capability of approaching to a certain ship and monitoring her efficiently by using AIS (Auto Identification System) information. Furthermore we verify that adapting AIS on GCS side is more efficient through experiments.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.281-288
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• 2022

This study quantitatively analyzed the human casualties that can occur when a multicopter-type Urban Air Mobility (UAM) with a weight of about 1 ton and a speed of about 100 km/h falls in an urban area. Based on the population density and building database in Seoul, the population exposed to collisions in the event of a UAM crash was derived. Through the ballistic descent model, the accident impact radius of the UAM fall was calculated. In addition, the change in human casualties on the ground was analyzed when the accident impact radius increased. Finally, the ground risk map was created for Seoul, and it was confirmed that about 1 to 10 people could be injured when a UAM crash.

• Geomechanics and Engineering
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• v.27 no.6
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• pp.551-560
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• 2021

Due to the low strength and high compressibility characteristics, the loess deposits tunnels are prone to large deformations and collapse. An accurate stability evaluation for loess deposits is of considerable significance in deformation control and safety work during tunnel construction. 37 groups of representative data based on real loess deposits cases were adopted to establish the stability evaluation model for the tunnel project in Yan'an, China. Physical and mechanical indices, including water content, cohesion, internal friction angle, elastic modulus, and poisson ratio are selected as index system on the stability level of loess. The data set is randomly divided into 80% as the training set and 20% as the test set. Firstly, principal component analysis (PCA) is used to convert the five index system to three linearly independent principal components X1, X2 and X3. Then, the principal components were used as input vectors for probabilistic neural network (PNN) to map the nonlinear relationship between the index system and stability level of loess. Furthermore, Leave-One-Out cross validation was applied for the training set to find the suitable smoothing factor. At last, the established model with the target smoothing factor 0.04 was applied for the test set, and a 100% prediction accuracy rate was obtained. This intelligent classification method for loess deposits can be easily conducted, which has wide potential applications in evaluating loess deposits.