• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1031-1041
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• 2020

Cloud computing technology plays an important role in the deep learning industry as deep learning services are deployed frequently on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multi-tenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep-learning service with 92.875% accuracy and 1.325kB/s extraction speed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.4
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• pp.335-342
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• 2012

3D point cloud is widely used in Architecture, Civil Engineering, Medical, Computer Graphics, and many other fields. Due to the improvement of 3D laser scanner, a massive 3D point cloud whose gigantic file size is bigger than computer's memory requires efficient preprocessing and visualization. We suggest a data structure to solve the problem; a 3D point cloud is gradually subdivided by arbitrary-sized cube grids structure and corresponding point cloud subsets generated by the center of each grid cell are achieved while preprocessing. A massive 3D point cloud file is tested through two algorithms: QSplat and ours. Our algorithm, grid-based, showed slower speed in preprocessing but performed faster rendering speed comparing to QSplat. Also our algorithm is further designed to editing or segmentation using the original coordinates of 3D point cloud.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.98.2-98.2
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• 2012

We firstly present the unified Far-UV continuum map of the Taurus-Auriga-Perseus (TPA) complex, one of the largest local associations of dark cloud located in (l, b)=([154,180], [-28, -2]), by merging both FIMS and GALEX. The FUV continuum map shows that dust extinction correlate well with the FUV around the complex. It shows strong absorption in FUV toward the dense Taurus cloud while it does not in California cloud. It turned out that it is related to the relative location of each cloud and Perseus OB2 association. We also present some results of dust scattering simulation based on Monte Carlo Radiative Transfer technique (MCRT). Through this dust scattering simulation, we have derived the scattering parameter for this region, albedo(a)=$0.42^{+0.05}{_{-0.05}}$, asymmetry factor(g)=$0.47^{+0.11}{_{-0.27}}$. The optical parameters we obtained seem reasonable compared to the theoretical model values ~0.40 and ~0.65 for the albedo and the phase function though the phase function is rather small. Using the result of simulation, we figured out the geometries of each cloud in the complex region, especially their distances and thicknesses. Our predictions from the results are in good agreement with the previous studies related to the TPA complex. For example, the Taurus cloud is within ~200pc from the Sun and the Perseus seems to be multi-layered, at least two. The California cloud is more distant than the other cloud on average at ~350 pc and Auriga cloud seems to be between the Taurus cloud and the eastern end of the California cloud. We figured out that across the TPA complex region, there might be some correlation between the LSR velocity and the distance to each cloud in the complex.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.871-874
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• 2011

The fast emerging of network technology and the high demand of computing resources have prompted many organizations to outsource their storage and computing needs. Cloud based storage services such as Microsoft's Azure and Amazon's S3 allow customers to store and retrieve any amount of data, at anytime from anywhere via internet. The scalable and dynamic of the cloud storage services help their customer to reduce IT administration and maintenance costs. No doubt, cloud based storage services brought a lot of benefits to its customer by significantly reducing cost through optimization increased operating and economic efficiencies. However without appropriate security and privacy solution in place, it could become major issues to the organization. As data get produced, transferred and stored at off premise and multi tenant cloud based storage, it becomes vulnerable to unauthorized disclosure and unauthorized modification. An attacker able to change or modify data while data inflight or when data is stored on disk, so it is very important to secure data during its entire life-cycle. The traditional cryptography primitives for the purpose of data security protection cannot be directly adopted due to user's lose control of data under off premises cloud server. Secondly cloud based storage is not just a third party data warehouse, the data stored in cloud are frequently update by the users and lastly cloud computing is running in a simultaneous, cooperated and distributed manner. In our proposed mechanism we protect the integrity, authentication and confidentiality of cloud based data with the encrypt- then-upload concept. We modified and applied proxy re-encryption protocol in our proposed scheme. The whole process does not reveal the clear data to any third party including the cloud provider at any stage, this helps to make sure only the authorized user who own corresponding token able to access the data as well as preventing data from being shared without any permission from data owner. Besides, preventing the cloud storage providers from unauthorized access and making illegal authorization to access the data, our scheme also protect the data integrity by using hash function.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.143-156
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• 2023

To precisely and reliably analyze the contents of the satellite imagery, recognizing the clouds which are the obstacle to gathering the useful information is essential. In recent times, deep learning yielded satisfactory results in various tasks, so many studies using deep neural networks have been conducted to improve the performance of cloud detection. However, existing methods for cloud detection have the limitation on increasing the performance due to the adopting the network models for semantic image segmentation without modification. To tackle this problem, we introduced the multi-branch neural architecture search to find optimal network structure for cloud detection. Additionally, the proposed method adopts the soft intersection over union (IoU) as loss function to mitigate the disagreement between the loss function and the evaluation metric and uses the various data augmentation methods. The experiments are conducted using the cloud detection dataset acquired by Arirang-3/3A satellite imagery. The experimental results showed that the proposed network which are searched network architecture using cloud dataset is 4% higher than the existing network model which are searched network structure using urban street scenes with regard to the IoU. Also, the experimental results showed that the soft IoU exhibits the best performance on cloud detection among the various loss functions. When comparing the proposed method with the state-of-the-art (SOTA) models in the field of semantic segmentation, the proposed method showed better performance than the SOTA models with regard to the mean IoU and overall accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.8
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• pp.474-480
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• 2014

In this paper, we propose a study for the next generation terrestrial broadcasting technology based on SFN(Single Frequency Networks), which applies multiple receiving antenna to improve receiving performance of cloud transmission system. By applying multiple receiving antenna, the received broadcast signals at the boundary of different SFN broadcasting area could be modelled by distributed MIMO system. Due to the interference cancellation effect of the MIMO detector, the proposed scheme could suppress the adjacent area interference more efficiently compared to the single receiving antenna case. Simulation results show that receiving performance can be improved dramatically in overlapping area of SFN by applying multiple antenna receivers in cloud transmission system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.403-412
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• 2017

With the introduction of BIM and ICT technology, 'Cloud-based collaborative design support system' has been actively researched and new user interface is needed to support it. When developing the user interface, scenario - based design method is selected which based on the actual usage scenarios to anticipate the usage situation and reflect the user's requirements. However, since the existing scenario-based design technique is an inductive method, it is difficult to cover the usage situation of all users when creating the usage scenario. For this reason, there is a need for a new scenario creation method that can systematically create and modify scenarios instead of inductive methods. This study proposes a 'Multi-scenario-based design method' that complemented the existing method and verify its usability by applying it to the user interface development process of 'Cloud-based collaborative design support system'.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.976-995
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• 2016

Workflow scheduling is one of the challenging problems in cloud computing, especially when service reliability is considered. To improve cloud service reliability, fault tolerance techniques such as fault recovery can be employed. Practically, fault recovery has impact on the performance of workflow scheduling. Such impact deserves detailed research. Only few research works on workflow scheduling consider fault recovery and its impact. In this paper, we investigate the problem of workflow scheduling in clouds, considering the probability that cloud resources may fail during execution. We formulate this problem as a multi-objective optimization model. The first optimization objective is to minimize the overall completion time and the second one is to minimize the overall execution cost. Based on the proposed optimization model, we develop a heuristic-based algorithm called Min-min based time and cost tradeoff (MTCT). We perform extensive simulations with four different real world scientific workflows to verify the validity of the proposed model and evaluate the performance of our algorithm. The results show that, as expected, fault recovery has significant impact on the two performance criteria, and the proposed MTCT algorithm is useful for real life workflow scheduling when both of the two optimization objectives are considered.

• Journal of Communications and Networks
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• v.18 no.5
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• pp.725-734
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• 2016

For multi-mode radar signals in complex electromagnetic environment, different modes of one emitter tend to be deinterleaved into several emitters, called as "extension", when processing received signals by use of existing sorting methods. The "extension" problem inevitably deteriorates the sorting performance of multi-mode radar signals. In this paper, a novel method based on spatial data mining is presented to address above challenge. Based on theories of data field, we describe the distribution information of feature parameters using potential field, and makes partition clustering of parameter samples according to revealed distribution features. Additionally, an evaluation criterion based on cloud model membership is established to measure the relevance between different cluster-classes, which provides important spatial knowledge for the solution of the "extension" problem. It is shown through numerical simulations that the proposed method is effective on solving the "extension" problem in multi-mode radar signal sorting, and can achieve higher correct sorting rate.

• Journal of Environmental Science International
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• v.32 no.12
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• pp.899-914
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• 2023

Various seeding materials for cloud seeding are being used, and sodium chloride powder is one of them, which is commonly used. This study analyzed the experimental results of multi-aircraft cloud seeding in connection with Republic of Korea Air Force (CN235) and KMA/NIMS(Korea Meteorological Administration/National Institute of Meteorological Sciences) Atmospheric Research Aircraft. Powdered sodium chloride was used in CN235 for the first time in South Korea. The analysis of the cloud particle size distributions and radar reflectivity before and after cloud seeding showed that the growth efficiency of powdery seeding material in the cloud is slightly higher than that of hygroscopic flare composition in the distribution of number concentrations by cloud aerosol particle diameter (10 ~ 1000 ㎛). Considering the radar reflectivity, precipitation, and numerical model simulation, the enhanced precipitation due to cloud seeding was calculated to be a maximum of 3.7 mm for 6 hours. The simulated seeding effect area was about 3,695 km², which corresponds to 13,634,550 tons of water. In the precipitation component analysis, as a direct verification method, the ion equivalent concentrations (Na⁺, Cl^-, Ca²⁺) of the seeding material at the Bukgangneung site were found to be about 1000 times higher than those of other non-affected areas between about 1 and 2 hours after seeding. This study suggests the possibility of continuous multi-aircraft cloud seeding experiments to accumulate and increase the amount of precipitation enhancement.