• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.141-150
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• 2012

The present study examines an efficient congestion management system compatible with the evolving environment. The key is to build an information model shared and exchanged for marketbased solutions to alleviate congestion. Traditional methods for congestion management can be classified into two categories, i.e., the centralized scheme and the decentralized scheme, depending on the extent to which the independent system operator (ISO) is involved in market participants' (MPs) activities. Although the centralized scheme is more appropriate for providing reliable system operation and relieving congestion in near real-time, the decentralized scheme is preferred for supporting efficient market operation. The minimum set of information between the ISO and MPs for decentralized scheme is identified: i) congestion-based zone, ii) Power Transfer Distribution Factors, and iii) transmission congestion cost. The mathematical modeling of the proposed information is expressed, considering its process of making effective use of information. Numerical analysis is conducted to demonstrate both cost minimization from the MP perspective and the reliability enhancement from the ISO perspective based on the proposed information exchange scheme.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.386-387
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• 2006

전력시스템은 기존에 수직통합체제에서 벗어나 시장이라는 운영체제로 발전되고 있다. 이는 시스템의 불안정한 요소를 증가시키고 있으며, 이에 따라 시장체제 하에서 송전선로의 혼잡관리는 시스템을 안정적으로 운영될 수 있게 하는 중요한 요소 중의 하나로 간주되고 있다. 현재 사용되고 있는 최적조류계산(OPF) 방법은 송전선로 혼잡을 직접적으로 해결할 수 있는 방법이지만, 그 계산에 있어 시장 참여자들의 세부적인 정보의 공개를 요구하기 때문에 경쟁 시장체제에는 적당한 방법이 아니다. 따라서 본 논문에서는 시장 체제에 적합토록 제한된 정보 하에서 분권화 된 계산을 통한 혼잡관리 방식과 이에 따라 필요한 정보공유 프로토콜을 제안하겠다. 제안된 방법은 간단한 3모선 시뮬레이션을 통해 검증토록 하겠다.