• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.293-301
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• 2023

With the advancement of artificial intelligence and its various use cases, accessing it through edge computing environments is gaining traction. However, due to the nature of edge computing environments, efficient management and optimization of clusters distributed in different geographical locations is considered a major challenge. To address these issues, this paper proposes a centralization and automation tool called KubEVC-Agent based on Kubernetes. KubEVC-Agent centralizes the deployment, operation, and management of edge clusters and presents a use case of the data transformation for optimizing intra-cluster communication. This paper describes the components of KubEVC-Agent, its working principle, and experimental results to verify its effectiveness.

• Journal of the Korean Society of Visualization
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• v.13 no.1
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• pp.43-48
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• 2015

Collective cell migration is a fundamental phenomenon observed in various biological processes such as development, wound healing, and cancer metastasis. During the collective migration, cells undergo changes in their phenotypes from those of stable to the migratory state via the process called epithelial-mesenchymal transition (EMT). Recent findings in biology and biochemistry have shown that EMT is closely related to the cancer invasion or metastasis, but not much of the correlations in kinematics and physical forces between the neighboring cells are known yet. In this study, we aim to understand the cell migration and stress distribution within the expanding cell cluster. We constructed the in vitro cell cluster on the hydrogel, employed traction force microscopy (TFM) and monolayer stress microscopy (MSM) to visualize the physical forces within the expanding cell monolayer. During the expansion, cells at the cluster edge exhibited enhanced motility and developed focal adhesions that are the essential features of EMT while cells at the core of the cluster maintained the epithelial characteristics. In the aspect of mechanical stress, the cluster edge had the highest traction force of ~90 Pa directed toward the cluster core, which means that cells at the edge actively pull the substrate to make the cluster expansion. The cluster core of the tightly confined cells by neighboring cells had a lower traction force value (~60 Pa) but the highest intercellular normal stress of ~800 Pa because of the accumulation of traction from the edge of the monolayer.

• Journal of information and communication convergence engineering
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• v.18 no.3
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• pp.183-187
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• 2020

The general Wi-Fi network connection structure is that a number of IoT (Internet of Things) sensor nodes are directly connected to one AP (Access Point) node. In this structure, the range of the network that can be established within the specified specifications such as the range of signal strength (RSSI) to which the AP node can connect and the maximum connection capacity is limited. To overcome these limitations, multiple middleware bridge technologies for dynamic scalability and load balancing were studied. However, these network expansion technologies have difficulties in terms of the rules and conditions of AP nodes installed during the initial network deployment phase In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road danger context aware and notification system through an intelligent risk situation recognition algorithm.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• Asian Journal of Innovation and Policy
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• v.9 no.2
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• pp.164-186
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• 2020

It is generally known that a Science and Technology Park - as a representative example of an Innovation Cluster - produces network synergy among industry, university, research institutes, and other innovation actors in a specific area, so that it has a competitive edge over other regions in technological innovation. However, as the novel Coronavirus (COVID-19) pandemic unfolds, it has become necessary to reduce face-to-face contacts and this could lead to lesser network synergy being produced in an Innovation Cluster. With this background, this research was designed and conducted to evaluate how COVID-19 has changed the activities in Innovation Clusters and explore future development scenarios. In order to find out the changes occurring in an Innovation Cluster, a survey was conducted among the people in Science and Technology Parks. The survey result shows that people are experiencing difficulties in technological innovation and support activities, and face-to-face contacts have been reduced in the Innovation Cluster. A scenario planning sought to explore the future development of the Innovation Cluster. It suggests that the transformation into a Digital Innovation Cluster, which is less affected by physical distance, but can still maintain the effectiveness of the networks, can be the key strategy for the future Innovation Cluster.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.5-6
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• 2019

Edge Computing에서 발생하는 데이터 분석에 대한 알고리즘의 성능 평가나 검증은 필수적이다. 이러한 평가 및 검증을 위해서는 비교 가능한 데이터가 필요하다. 본 논문에서는 Edge Computing에서 발생하는 데이터에 대한 분석 결과 및 Computing Resource에 대한 성능평가를 위해 Cloud 기반의 빅 데이터 분석시스템을 구축한다. Edge Computing 비교분석 빅 데이터 시스템은 실제 IoT 노드에서 Edge Computing을 수행할 때와 유사한 환경을 Cloud 상에 구축하고 연구되는 Edge Computing 알고리즘을 Data Analysis Cluster Container에 탑재해 분석을 시행한다. 그리고 분석 결과와 Computing Resource 사용률 데이터를 기존 IoT 노드 Edge Computing 데이터와 비교하여 개선점을 도출하는 것이 본 논문의 목표이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5323-5343
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• 2018

Mobile applications and social networks tend to enhance the need for high-quality content access. To address the rapid growing demand for data services in mobile networks, it is necessary to develop efficient content caching and distribution techniques, aiming at significantly reduction of redundant content transmission and thus improve content delivery efficiency. In this article, we develop optimal cooperative content cache and distribution policy, where a geographical cluster model is designed for content retrieval across the collaborative small cell base stations (SBSs) and replacement of cache framework. Furthermore, we divide the SBS storage space into two equal parts: the first is local, the other is global content cache. We propose an algorithm to minimize the content caching delay, transmission cost and backhaul bottleneck at the edge of networks. Simulation results indicates that the proposed neighbor SBSs cooperative caching scheme brings a substantial improvement regarding content availability and cache storage capacity at the edge of networks in comparison with the current conventional cache placement approaches.

• Applied Microscopy
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• v.50
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• pp.27.1-27.6
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• 2020

Transition metal dichalcogenides (TMD), which is composed of a transition metal atom and chalcogen ion atoms, usually form vacancies based on the knock-on threshold of each atom. In particular, when electron beam is irradiated on a monolayer TMD such as MoS₂ and WS₂, S vacancies are formed preferentially, and they are aligned linearly to constitute line defects. And then, a hole is formed at the point where the successively formed line defects collide, and metal clusters are also formed at the edge of the hole. This study reports a process in which the line defects formed in a monolayer WS₂ sheet expends into holes. Here, the process in which the W cluster, which always occurs at the edge of the formed hole, goes through a uniform intermediate phase is explained based on the line defects and the formation behavior of the hole. Further investigation confirms the atomic structure of the intermediate phase using annular dark field scanning transition electron microscopy (ADF-STEM) and image simulation.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.44-57
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.46.1-46.1
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• 2017

Late-type galaxies falling into a cluster would evolve being influenced by the interactions with both the cluster and the nearby cluster member galaxies. Most numerical studies, however, tend to focus on the effects of the former with little work done on those of the later. We thus perform numerical study on the evolution of a late-type galaxy falling radially toward the cluster center interacting with neighbouring early-type galaxies, using N-body, hydrodynamical simulations. Based on the information about the typical galaxy encounters obtained by using the galaxy catalog of Coma cluster, we run the simulations for the cases where a Milky Way Galaxy-like late-type galaxy, flying either edge-on or face-on, experiences six consecutive collisions with twice more massive early-type galaxies having hot gas in their halos. Our simulations show that the evolution of the late-type galaxy can be significantly affected by the high-speed multiple collisions with the early-type galaxies, such as on the cold gas content and the star formation activity, particularly through the hydrodynamic interactions between the cold disk and the hot gas halos. By comparing our simulation results with those of others, we claim that the role of the galaxy-galaxy interactions on the evolution of late-type galaxies in clusters could be comparable with that of the galaxy-cluster interactions, depending on the dynamical history.