• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.952-973
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• 2021

In recent years, mobile devices have become an essential part of daily life. More and more applications are being supported by mobile devices thanks to edge computing, which represents an emergent architecture that provides computing, storage, and networking capabilities for mobile devices. In edge computing, heavy tasks are offloaded to edge nodes to alleviate the computations on the mobile side. However, offloading computational tasks may incur extra energy consumption and delays due to network congestion and server queues. Therefore, it is necessary to optimize offloading decisions to minimize time, energy, and payment costs. In this article, different offloading models are examined to identify the offloading parameters that need to be optimized. The paper investigates and compares several optimization techniques used to optimize offloading decisions, specifically Swarm Intelligence (SI) models, since they are best suited to the distributed aspect of edge computing. Furthermore, based on the literature review, this study concludes that a Cuckoo Search Algorithm (CSA) in an edge-based architecture is a good solution for balancing energy consumption, time, and cost.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.59-65
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• 2023

As research on blockchain applications in various fields is actively increasing, management of private keys that prove users of blockchain has become important. If you lose your private key, you lose all your data. In order to solve this problem, previously, blockchain wallets, private key recovery using partial information, and private key recovery through distributed storage have been proposed. In this paper, we propose a safe private key backup and recovery method using Shamir's Secrete Sharing (SSS) scheme and biometric information, and evaluate its safety. In this paper, we propose a safe private key backup and recovery method using Shamir's Secrete Sharing (SSS) scheme and biometric information, and evaluate its safety against robustness during message exchange, replay attack, man-in-the-middle attack and forgery and tampering attack.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.297-307
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• 2022

The simple procedural segment selection algorithm commonly used in Dynamic Adaptive Streaming over HTTP (DASH) reveals severe weakness to provide high-quality streaming services in the integrated mobile networks of various wired and wireless links. A major issue could be how to properly cope with dynamically changing underlying network conditions. The key to meet it should be to make the segment selection algorithm much more adaptive to fluctuation of network traffics. This paper presents a system architecture that replaces the existing procedural segment selection algorithm with a deep reinforcement learning algorithm based on the Asynchronous Advantage Actor-Critic (A3C). The distributed A3C-based deep learning server is designed and implemented to allow multiple clients in different network conditions to stream videos simultaneously, collect learning data quickly, and learn asynchronously, resulting in greatly improved learning speed as the number of video clients increases. The performance analysis shows that the proposed algorithm outperforms both the conventional DASH algorithm and the Deep Q-Network algorithm in terms of the user's quality of experience and the speed of deep learning.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.281-288
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• 2022

According to statistics conducted by the Korea Coast Guard, the number of accidents on small boats under 5 tons is increasing every year. This is because only a small number of people are on board. The previously developed maritime distress and safety systems are not well distributed because passengers must be equipped with additional remote equipment. The purpose of this study is to develop a distress awareness system that recognizes man over-board situations in real time. This study aims to present the part of the passenger tracking system among the small ship's distress awareness situational system that can generate passenger's location information in real time using deep learning based object detection and tracking technologies. The system consisted of the following steps. 1) the passenger location information is generated in the form of Bounding box using its detection model (YOLOv3). 2) Based on the Bounding box data, Deep SORT predicts the Bounding box's position in the next frame of the image with Kalman filter. 3) When the actual Bounding Box is created within the range predicted by Kalman-filter, Deep SORT repeats the process of recognizing it as the same object. 4) If the Bounding box deviates the ship's area or an error occurs in the number of tracking occupant, the system is decided the distress situation and issues an alert. This study is expected to complement the problems of existing technologies and ensure the safety of individuals aboard small boats.

• New & Renewable Energy
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• v.19 no.2
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• pp.1-12
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• 2023

In this study, an efficient one-dimensional model was developed for predicting microchannel steam/methane reformers with thin washcoat catalyst layers with a focus on low-pressure reforming conditions suitable for distributed hydrogen production systems for fuel cell applications. The governing equations for steam/methane mixture gas flowing through the microchannel reformer were derived considering the species conservation with reforming reactions and energy conservation with external convective heat supply. The reaction rates for the developed model were simply determined through the catalyst effectiveness factor correlations instead of performing complicated calculations for the steam/methane reforming process occurring inside the washcoat catalyst layers. The accuracy of the developed was verified by comparing the results obtained herein with those obtained by the detailed computational fluid dynamics calculation for the same microchannel reformer.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.156-165
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• 2023

IEEE 802.15.4e-TSCH is recognized as a wireless industrial sensor network standard used in IoT systems. To ensure both power savings and reliable communications, the TSCH standard uses techniques including channel hopping and bandwidth reserve. In TSCH mode, scheduling is crucial because it allows sensor nodes to select when data should be delivered or received. Because a wide range of applications may necessitate energy economy and transmission dependability, we present a distributed approach that uses a cluster tree topology to forecast scheduling requirements for the following slotframe, concentrating on the Poisson model. The proposed Optimized Minimal Scheduling Function (OMSF) is interested in the details of the scheduling time intervals, something that was not supported by the Minimal Scheduling Function (MSF) proposed by the 6TSCH group. Our contribution helps to deduce the number of cells needed in the following slotframe by reducing the number of negotiation operations between the pairs of nodes in each cluster to settle on a schedule. As a result, the cluster tree network's error rate, traffic load, latency, and queue size have all decreased.

• Smart Structures and Systems
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• v.31 no.1
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• pp.69-78
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• 2023

The purpose of this paper is to develop a scalable grey predictive controller with unavoidable random delays. Grey prediction is proposed to solve problems caused by incorrect parameter selection and to eliminate the effects of dynamic coupling between degrees of freedom (DOFs) in nonlinear systems. To address the stability problem, this study develops an improved gray-predictive adaptive fuzzy controller, which can not only solve the implementation problem by determining the stability of the system, but also apply the Linear Matrix Inequality (LMI) law to calculate Fuzzy change parameters. Fuzzy logic controllers manipulate robotic systems to improve their control performance. The stability is proved using Lyapunov stability theorem. In this article, the authors compare different controllers and the proposed predictive controller can significantly reduce the vibration of offshore platforms while keeping the required control force within an ideal small range. This paper presents a robust fuzzy control design that uses a model-based approach to overcome the effects of modeling errors. To guarantee the asymptotic stability of large nonlinear systems with multiple lags, the stability criterion is derived from the direct Lyapunov method. Based on this criterion and a distributed control system, a set of model-based fuzzy controllers is synthesized to stabilize large-scale nonlinear systems with multiple delays.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.222-224
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• 2022

Deep learning-based computer vision applications in the field of object segmentation take a transfer learning method using hyperparameters and models pretrained and distributed by STOA techniques to improve performance. Custom datasets used in this process require a lot of resources, such as time and labeling, in labeling tasks to generate Ground Truth information. In this paper, we present an automatic labeling construction method using object segmentation techniques so that resources such as time and labeling can be used less to build custom datasets used in deep learning neural networks.

• Journal of Surface Science and Engineering
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• v.57 no.1
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• pp.1-7
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• 2024

The study investigated the impact of substrate pretreatment on depositing high-quality B-doped diamond (BDD) thin films using the HFCVD method. Films were deposited on Si and Nb substrates after sanding and seeding. Despite identical sanding conditions, BDD films formed faster on Nb due to even diamond seed distribution. Post-deposition, film average roughness (R_a) remained similar to substrate R_a, but higher substrate R_a led to decreased crystallinity. Nb substrate with 0.83 ㎛ R_a exhibited faster crystal growth due to dense, evenly distributed diamond seeds. BDD film on Nb with 0.83 ㎛ R_a showed a wide, stable potential window (2.8 eV) in CV results and a prominent 1332 cm^-1 diamond peak in R_aman spectroscopy, indicating high quality. The findings underscore the critical role of substrate pretreatment in achieving high-quality BDD film fabrication, crucial for applications demanding robust p-type semiconductors with superior electrical properties.

• Advances in nano research
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• v.16 no.1
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• pp.91-109
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• 2024

Carbon nanotubes are drawing wide attention of research communities and several industries due to their versatile capabilities covering mechanical and other multi-physical properties. However, owing to extreme operating conditions of the synthesis process of these nanostructures, they are often imposed with certain inevitable structural deformities such as single vacancy and nanopore defects. These random irregularities limit the intended functionalities of carbon nanotubes severely. In this article, we investigate the mechanical behaviour of double-wall carbon nanotubes (DWCNT) under the influence of arbitrarily distributed single vacancy and nanopore defects in the outer wall, inner wall, and both the walls. Large-scale molecular simulations reveal that the nanopore defects have more detrimental effects on the mechanical behaviour of DWCNTs, while the defects in the inner wall of DWCNTs make the nanostructures more vulnerable to withstand high longitudinal deformation. From a different perspective, to exploit the mechanics of damage for achieving defect-induced shape modulation and region-wise deformation control, we have further explored the localized longitudinal and transverse spatial effects of DWCNT by designing the defects for their regional distribution. The comprehensive numerical results of the present study would lead to the characterization of the critical mechanical properties of DWCNTs under the presence of inevitable intrinsic defects along with the aspect of defect-induced spatial modulation of shapes for prospective applications in a range of nanoelectromechanical systems and devices.