• Proceedings of the KSME Conference
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• 2000.11b
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• pp.615-620
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• 2000

Manoeuvrability of ships has been receiving a great deal of attention both concerning navigation safety and the prediction of ship manoeuvring characteristics, especially at the preliminary design stage. Recently, in order to improve manoeuvrability of ships, High-lift devices could be applied to design of rudder at design stage. Now, among the them, we carried out the flow visualization and investigation of flow field around a flapped rudder(trailing-edge flap). A trailing-edge flap is simply a portion of the trailing-edge section of airfoil that is hinged and which can be deflected upward or downward. Flow visualization results of flap defection shown as follow Photos including main body and flap defection.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.300-300
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• 2011

Si oxidation is a key process in developing silicon devices, such as highly integrated metal-oxide-semiconductor (MOS) transistors and antireflection-coating (ARC) on solar cell substrate. Many experimental and theoritical studies have been carried out for elucidating oxidation processes and adsorption structure using ab initio total energy and electronic structure calcultaions. However, the initial oxidation processes at step edge on vicinal Si surface have not been studied using the ReaxFF reactive force field. In this work, strucutural change, charge distribution of oxidized Si throughout the depth from Si surface were observed during oxidation processes on vicinal Si(001) surface inclined by $10.5^{\circ}$ of miscut angle toward [100]. Adsorption energys of step edge and flat terrace were calculated to compare the oxidation reaction at step edge and flat terrace on Si surface.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.344-345
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• 2012

The Gate-Induced-Drain-Leakage (GIDL) current with channel doping and width dependence are characterized. The GIDL currents are found to increase in MOSFETs with higher channel doping levels and the observed GIDL current is generated by the band-to-band-tunneling (BTBT) of electron through the reverse-biased channel-to-drain p-n junction. A BTBT model is used to fit the measured GIDL currents under different channel-doping levels. Good agreement is obtained between the modeled results and experimental data. The increase of the GIDL current at narrower widths in mainly caused by the stronger gate field at the edge of the shallow trench isolation (STI). As channel width decreases, a larger portion of the GIDL current is generated at the channel-isolation edge. Therefore, the stronger gate field at the channel-isolation edge causes the total unit-width GIDL current to increases for narrow-width devices.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.321-328
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• 2013

In this paper, we present a technique for obtaining conflict-free schedules for real-time automation protocol for industrial Ethernet (RAPIEnet) switches. Mathematical model of the switch is obtained using graph theory. Initially network traffic entry and exit parts in a single RAPIEnet switch are identified, so that a bipartite conflict graph can be constructed. The obtained conflict graph is transformed to three kinds of matrices to be used as inputs for our simulation model, and selection of any of the matrix forms is application-specific. A greedy edge-coloring algorithm is used to schedule the network traffic and to solve the minimum coloring problem. After scheduling, empty slots are identified for forwarding the non real-time traffic of asynchronous devices. Finally, an algorithm for synchronizing the schedules of adjacent switches is proposed using edge-contraction and minors. All simulations were carried out using Matlab.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.145-148
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• 2002

A study on image searching and management techniques is actively developed by user requirements for multimedia information that are existing as images, audios, texts data from various information processing devices. We had been studied an automatical shape region segmentation method using color. distribution and edge characteristics of moving images for. contents-base description. The Proposed method uses a color information quantized on human visual system and extracts overlapped regions to be matched by using edge characteristics of the image frame. The performance of the proposed method is represented by similarity for comparison to a segmented image and original image.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.443-451
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• 2023

Machine Learning has introduced many solutions in data science, but its application in IoT faces significant challenges, due to the limitations in memory size and processing capability of constrained devices. In this paper we design an automatic gamma radiation detection and identification embedded system that exploits the power of TinyML in a SiPM micro radiation sensor leveraging the Edge Impulse platform. The model is trained using real gamma source data enhanced by software augmentation algorithms. Tests show high accuracy in real time processing. This design has promising applications in general-purpose radiation detection and identification, nuclear safety, medical diagnosis and it is also amenable for deployment in small satellites.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.803-812
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• 2022

With the increasing number of mobile device users worldwide, utilizing mobile edge computing (MEC) devices close to users for content caching can reduce transmission latency than receiving content from a server or cloud. However, because MEC has limited storage capacity, it is necessary to determine the content types and sizes to be cached. In this study, we investigate a caching strategy that increases the hit ratio from small base stations (SBSs) for mobile users in a heterogeneous network consisting of one macro base station (MBS) and multiple SBSs. If there are several SBSs that users can access, the hit ratio can be improved by reducing duplicate content and increasing the diversity of content in SBSs. We propose a Deep Q-Network (DQN)-based caching strategy that considers time-varying content popularity and content redundancy in multiple SBSs. Content is stored in the SBS in a divided form using maximum distance separable (MDS) codes to enhance the diversity of the content. Experiments in various environments show that the proposed caching strategy outperforms the other methods in terms of hit ratio.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.16-26
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• 2024

Mobile edge computing (MEC) plays a crucial role in improving the performance of resource-constrained mobile devices by offloading computation-intensive tasks to nearby edge servers. However, existing methods often neglect the critical consideration of future task requirements when making offloading decisions. In this paper, we propose an innovative approach that addresses this limitation. Our method leverages recurrent neural networks (RNNs) to predict task sizes for future time slots. Incorporating this predictive capability enables more informed offloading decisions that account for upcoming computational demands. We employ genetic algorithms (GAs) to fine-tune fitness functions for current and future time slots to optimize offloading decisions. Our objective is twofold: minimizing total processing time and reducing energy consumption. By considering future task requirements, our approach achieves more efficient resource utilization. We validate our method using a real-world dataset from Google-cluster. Experimental results demonstrate that our proposed approach outperforms baseline methods, highlighting its effectiveness in MEC systems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2199-2205
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• 2013

In modern society the image processing has been applied to various digital devices such as smartphone, digital camera, and digital TV. In the field of image processing the edge detection is one of the important parts in the image processing procedure. The image edge means point that the pixel value is changed between background and object rapidly, and includes the important information such as magnitude, location, and orientation. The performance of the existing edge detection method is insufficient for the image degraded by AWGN(additive white Gaussian noise) because it detects edges by using small weighted masks. Therefore, in this paper, to detect edge in AWGN environment effectively, we proposed an algorithm that detects edge as calculated gradient of sorting vector which is transformed by estimated mask from new pixel according to each region.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.77-84
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• 2024

Over the past few years, IoT edges have begun to emerge based on new low-latency communication protocols such as 5G. However, IoT edges, despite their enormous advantages, pose new complementary threats, requiring new security solutions to address them. In this paper, we propose a cloud environment-based IoT edge architecture model that complements IoT systems. The proposed model acts on machine learning to prevent security threats in advance with network traffic data extracted from IoT edge devices. In addition, the proposed model ensures load and security in the access network (edge) by allocating some of the security data at the local node. The proposed model further reduces the load on the access network (edge) and secures the vulnerable part by allocating some functions of data processing and management to the local node among IoT edge environments. The proposed model virtualizes various IoT functions as a name service, and deploys hardware functions and sufficient computational resources to local nodes as needed.