• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.12
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• pp.1027-1034
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• 2014

The shear stress on a surface due to the thin film fluid flow is an important issue. In case of a rotating disk, the fluid is delivered to the edge of the disk by centrifugal force, which acts as a body force on the fluid. Wear of a surface is affected by the shear stress acting on the surface and curvature. In this study, we utilize computational fluid dynamics software to model the ratio of curvature and local shear stress on solid surfaces. The key goal of the study is to determine an optimized curvature for the thin film fluid flow on a solid surface in order to minimize the local shear stress affecting the wear of this surface. Our results on the effects of curvature will be utilized for the design of devices that utilize thin film fluid flow on a solid surface, such as rotating-disk spray systems and thin film coating.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.4
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• pp.55-63
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• 1995

This paper describes the procedure to analyze the performance of the end-plated propeller(EPP) by a boundary integral method. The screw blade(SB) and end-plate(EP) are represented by a set of quadrilateral panels, where the source and normal dipole of uniform strength are distributed. The perturbation velocity potential, being the only unknown via the potential-based formulation, is determined by satisfying the flow tangency condition on the blade and the end-plate at the same time. The Kutta condition is satisfied through an iterative process by requiring the null pressure jump across the upper and lower sides of the trailing edges of both the SH and the EP. Sample calculations indicate that the EP increases the loading near the tip of the SB while spreading the trailing vortices along the trailing edge of the EP, thus avoiding the strong tip-vortex formation. Predicted performance of the EPP shows good correlations with the experimental results. The method is therefore considered applicable in designing and analyzing the EPP which may be an alternative for energy-saving propulsive devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1571-1576
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• 2018

Recently, the attractive changes of mobile device are a improvement of the computing performance, dramatic improvement of storage capacity, constant connection to the internet, and sophisticated development of display technology. As a result, database applications utilizing mobile devices are emerging. These applications include databases for mobile servers, databases for edge computing, and fog computing. Therefore, it is important to pay attention to the current mobile database and pay attention to whether it has suitable performance for the applications. In this paper, the most common mobile database, SQLite3 is selected and experimented to test and understand the update performance and characteristics. The results of experiment are compared with the one of Oracle database at the same condition to evaluate the experiment. As a result, Insert Performance of SQLite3 has a lot of points to be improved and Update performance is very good. Especially, the performance of Range Query is excellent.

• Current Applied Physics
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• v.18 no.11
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• pp.1422-1425
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• 2018

$NaTa_{1-x}Fe_xO_3$ ($0{\leq}x{\leq}0.40$) nanocubes were synthesized by a relatively low temperature hydrothermal method, using $Ta_2O_5$, $FeCl_3$ and NaOH as the precursors. The UV-vis diffuse reflectance spectra showed that $NaTa_{1-x}Fe_xO_3$ had significant visible-light-absorbing capability, and the absorption edge of $NaTaO_3$ shifted to longer wavelength with the increase of Fe dopants. Moreover, $NaTa_{1-x}Fe_xO_3$ exhibited room-temperature ferromagnetism when $Fe^{3+}$ occupied $Ta^{5+}$ sites in $NaTaO_3$ crystal lattice. The ferromagnetism is mainly attributed to the superexchange interactions between doped $Fe^{3+}$, rather than the contribution of oxygen vacancies caused by Fe doping. Therefore, Fe doping can simultaneously regulate the optical and magnetic properties of $NaTaO_3$ semiconductor, which will enable its potential applications in multifunctional optical-electronics and opticalspintronics devices.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1145-1150
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• 2020

After the Davos Forum, the recent 4th Industrial Revolution has become an area of interest to countries around the world. Among the technologies of the 4th industrial revolution, the ubiquitous computing environment requires a convergence environment of various devices, networks, and software technologies, and the RFID technology that identifies objects among the IoT technology fields is applied to all industries and has a competitive edge. Systems to which RFID technology is applied are being used in various industrial fields, especially! It is efficiently used for accurate inventory management and SCM management in the field of distribution and logistics. If the RFID system is built in a cloud-based environment, it will be possible to secure reliability in distribution management in consideration of an effective logistics management system and economic feasibility. This study is a study on the RFID system in a cloud computing environment to reduce the cost of operating or maintaining an application server to improve the economy and reliability.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.5
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• pp.139-146
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• 2022

With the recent development of the Internet of Things (IoT) and the convergence of vehicles and IT technologies, high-performance applications such as autonomous driving are emerging, and multi-access edge computing (MEC) has attracted lots of attentions as next-generation technologies. In order to provide service to these computation-intensive tasks in low latency, many methods have been proposed to partition tasks so that they can be performed through cooperation of multiple MEC servers(MECSs). Conventional methods related to task partitioning have proposed methods for partitioning tasks on vehicles as mobile devices and offloading them to multiple MECSs, and methods for offloading them from vehicles to MECSs and then partitioning and migrating them to other MECSs. In this paper, the performance of task partitioning methods using offloading and migration is compared and analyzed in terms of service delay, blocking rate and energy consumption according to the method of selecting partitioning targets and the number of partitioning. As the number of partitioning increases, the performance of the service delay improves, but the performance of the blocking rate and energy consumption decreases.

• KNOM Review
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• v.24 no.1
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• pp.20-28
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• 2021

Extensive use of social media applications and mobile devices continues to increase data traffic. Social media applications generate an endless and massive amount of multimedia traffic, specifically video traffic. Many social media platforms such as YouTube, Daily Motion, and Netflix generate endless video traffic. On these platforms, only a few popular videos are requested many times as compared to other videos. These popular videos should be cached in the user vicinity to meet continuous user demands. MEC has emerged as an essential paradigm for handling consistent user demand and caching videos in user proximity. The problem is to understand how user demand pattern varies with time. This paper analyzes three publicly available datasets, MovieLens 20M, MovieLens 100K, and The Movies Dataset, to find the user request pattern over time. We find hourly, daily, monthly, and yearly trends of all the datasets. Our resulted pattern could be used in other research while generating and analyzing the user request pattern in MEC-based video caching scenarios.

• KNOM Review
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• v.23 no.1
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• pp.1-9
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• 2020

With the rapid development of science and technology in recent years, the network environment are growing, and a huge amount of traffic is generated. In particular, the development of 5G networks and edge computing will accelerate this phenomenon. However, according to these trends, network malicious behaviors and traffic overloads are also frequently occurring. To solve these problems, network administrators need to build a network management system to implement a high-speed network and should know exactly about the connection topology of network devices through the network management system. However, the existing network topology discovery method is inefficient because it is passively managed by an administrator and it is a time consuming task. Therefore, we proposes a method of network topology discovery according to the amount of in and out network traffic. The proposed method is applied to a real network to verify the validity of this paper.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.175-182
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• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

• Smart Media Journal
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• v.12 no.7
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• pp.27-42
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• 2023

The development of cloud services and IoT technology has radically changed the cloud environment, and has evolved into a new concept called fog computing and F2C (fog-to-cloud). However, as heterogeneous cloud/fog layers are integrated, problems of access control and security management for end users and edge devices may occur. In this paper, an F2C-based IoT smart health monitoring system architecture was designed to operate a medical information service that can quickly respond to medical emergencies. In addition, a role-based service access control technology was proposed to enhance the security of user's personal health information and sensor information during service interoperability. Through simulation, it was shown that role-based access control is achieved by sharing role registration and user role token issuance information through blockchain. End users can receive services from the device with the fastest response time, and by performing service access control according to roles, direct access to data can be minimized and security for personal information can be enhanced.