• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.1-8
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• 2021

With the rapid growth of intelligent devices and communication technologies, 5G network environment has become more heterogeneous and complex in terms of service management and orchestration. 5G architecture requires supportive technologies to handle the existing challenges for improving the Quality of Service (QoS) and the Quality of Experience (QoE) performances. Among many challenges, traffic steering is one of the key elements which requires critically developing an optimal solution for smart guidance, control, and reliable system. Mobile edge computing (MEC), software-defined networking (SDN), network functions virtualization (NFV), and deep learning (DL) play essential roles to complementary develop a flexible computation and extensible flow rules management in this potential aspect. In this proposed system, an accurate flow recommendation, a centralized control, and a reliable distributed connectivity based on the inspection of packet condition are provided. With the system deployment, the packet is classified separately and recommended to request from the optimal destination with matched preferences and conditions. To evaluate the proposed scheme outperformance, a network simulator software was used to conduct and capture the end-to-end QoS performance metrics. SDN flow rules installation was experimented to illustrate the post control function corresponding to DL-based output. The intelligent steering for network communication traffic is cooperatively configured in SDN controller and NFV-orchestrator to lead a variety of beneficial factors for improving massive real-time Internet of Things (IoT) performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4545-4566
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• 2021

The evolution of IoT technology is having a significant impact on people's lives. Almost all areas of people's lives are benefiting from increased productivity and simplification made possible by this trending technology. On the downside, however, the application of IoT technology is posing some security challenges, among them, unauthorized access to IoT devices. This paper presents an Attribute-based Access Control Fog architecture that aims to achieve effective distribution, increase availability and decrease latency. In the proposed architecture, the main functional points of the Attribute-based Access Control are distributed to provide policy decision and policy information mechanisms in fog nodes, locating these functions near end nodes. To evaluate the proposed architecture, an access control engine based on the Attribute-based Access Control was built using the Balana library and simulated using EdgeCloudSim to compare it to the traditional cloud-based architecture. The experiments show that the fog-based architecture provides robust results in terms of reducing latency in making access decisions.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.92-93
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• 2022

As the importance of smart factories is emphasized, the use of industrial Ethernet-based devices is expected to increase to build smart factories. PROFINET is an industrial Ethernet protocol developed by SIEMENS, and a number of smart factories are currently being built as PROFINET-based products. Accordingly, in order to develop and utilize various industrial IoT-based services, an IIoT device capable of collecting various sensor data and information from PROFINET-based manufacturing equipment and transmitting data to an edge computer is required.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.133-135
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• 2022

With the development of IoT and AI technologies and media, various digital devices are being used, and unmanned and automation is progressing rapidly. In particular, high-level image processing technology is required in fields such as smart factories, autonomous driving technology, and intelligent CCTV. However, noise present in the image affects processes such as edge detection and object recognition, and causes deterioration of system accuracy and reliability. In this paper, we propose a filtering algorithm using fuzzy weights to reconstruct images damaged by complex noise. The proposed algorithm obtains a reference value using noise judgment and calculates the final output by applying a fuzzy weight. Simulation was conducted to verify the performance of the proposed algorithm, and the result image was compared with the existing filter algorithm and evaluated.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.1
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• pp.41-48
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• 2023

Over the past ten years, numerous industries have undergone upheavals that have significantly altered how businesses interact with their clients and how goods are created and produced bySMEs. Many cutting-edge technologies have recently been created and implemented to enhance business models, facilitate sustainability features for organizations, and boost business capabilities. This essay seeks to understand how digital entrepreneurship functions in developing nations. The results of this study show the effectiveness of digital transformation in the context of SMEs is greatly influenced by aspects including the change of managerial intensity and the involvement and perception of workers, customers, and shareholders. One of the needs that business owners showcased in this study in terms of digitization is infrastructure resources to support digitization such as devices, the Internet, and funds, but also the ability to use digital media for business development. Practical skills that business people want to learn such as product design and management of their social media accounts. There are also aspects of time and self-motivation of the business actor that can speed up or slow down the digitization process. Finally, government support that is structured in encouraging MSMEs is also one of the supporters and drivers of digitalization in the blue economy.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.723-726
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• 2024

단일 dataflow 를 지원하는 DNN 가속기는 자원 효율적인 성능을 보이지만, 여러 DNN 모델에 대해서 가속 효과가 제한적입니다. 반면에 모든 dataflow 를 지원하여 매 레이어마다 최적의 dataflow를 사용하여 가속하는 reconfigurable dataflow accelerator (RDA)는 굉장한 가속 효과를 보이지만 여러 dataflow 를 지원하는 과정에서 필요한 추가 하드웨어로 인하여 효율적이지 못합니다. 따라서 본 연구는 제한된 dataflow 만을 지원하여 추가 하드웨어 요구사항을 감소시키고, 중복되는 하드웨어의 재사용을 통해 최적화하는 새로운 가속기 설계를 제안합니다. 이 방식은 자원적 한계가 뚜렷한 엣지 디바이스에 RDA 방식을 적용하는데 필수적이며, 기존 RDA 의 단점을 최소화하여 성능과 자원 효율성의 최적점을 달성합니다. 실험 결과, 제안된 가속기는 기존 RDA 대비 32% 더 높은 에너지 효율을 보이며, latency 는 불과 1%의 차이를 보였습니다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1774-1794
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• 2024

In the field of environmental sensing, it is necessary to develop radio planning techniques for the next generation Internet of Things (IoT) networks over mixed terrains. Such techniques are needed for smart remote monitoring of utility supplies, with links situated close to but out of range of cellular networks. In this paper, a three-dimension (3-D) geometric optimization algorithm is proposed, considering the positions of edge IoT devices and antenna coupling factors. Firstly, a multi-level single linkage (MLSL) iteration method, based on geometric objectives, is derived to evaluate the data rates over ISM 915 MHz channels, utilizing optimized power-distance profiles of continuous waves. Subsequently, a federated learning (FL) data selection algorithm is designed based on the 3-D geometric positions. Finally, a measurement example is taken in a meadow biome of the Mexican Colima district, which is prone to fluvial floods. The empirical path loss model has been enhanced, demonstrating the accuracy of the proposed optimization algorithm as well as the possibility of further prediction work.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.768-779
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• 2017

Recently, the great number of mobile devices causes excessive data/control traffic problems in the centralized LTE/EPC network and such dramatically increased traffic is emerging as a critical issue. In the Centralized Mobility Management (CMM) based LTE/EPC network, the Packet Data Network Gateway (P-GW) plays the centralized mobility anchor role and it accommodates most of data traffic. To solve this problem, the IETF has proposed the Distributed Mobility Management (DMM) scheme, but it has only focused on the data traffic load balancing and could not solve the control traffic overload problem. In this paper, therefore, we propose a new SDN based hybrid CMM/DMM Mobility Management (C-DMM) architecture based on Packet Network Edge Gateway (P-EGW), and introduce a selection scheme between CMM and DMM according to a device's mobility and the number of PDN connections. In order to prove the efficiency of the proposed architecture and scheme, we compare the data traffic processing load and the control traffic processing load over each scheme by emulating them in the ONOS controller and the Mininet environment.