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4th International Conference on Power engineering, ICPE 2023

作者： Cheng, Xu Chen, Jianfu Chen, Yong Zhao, Xiaoyan Zhou, Zhipeng Yang, Qifan Dai, Ning Yi Zhuhai Power Supply Bureau and DC Power Distribution and Consumption Technology Research Center Guangdong Power Grid Co. Ltd. Zhuhai China Zhuhai Power Supply Bureau Guangdong Power Grid Co. Ltd. Zhuhai China University of Macau SKL of Internet of Things for Smart City Department of Electrical and Computer Engineering Macao China University of Macau and Zhuhai UM Science & Technology Research Institute SKL of Internet of Things for Smart City Department of Electrical and Computer Engineering Macao China

ISBN: (纸本)9798350340020

Digital twin technology describes and models the characteristics, behavior, formation process and real-world performance of physical objects by using digital technologies such as sensors, internet of things (IoT), and artificial intelligence (AI). It is one of the key technologies to realize real-time perception and operation optimization of electric power equipment. Proportional-integral (PI) modules are the most commonly used blocks in the control system of power converters. But choosing the parameters with the best control effect requires repeated experiments. Therefore, this paper proposes a data-driven model that can evaluate the control performance of different PI parameters accurately and efficiently. First, the concept of digital twins and a general architecture applicable to the key power electronic equipment are demonstrated. Then, taking the grid-connected converter as an example, a data-driven model based on artificial neural network (ANN) and Levenberg-Marquardt (LM) algorithm is built to realize the evaluation of PI controller parameters. Comparisons with simulation models are also provided to show the validity of the digital-twin driven control parameter evaluation method. © 2023 IEEE.

关键词： Neural networks

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Guest Editorial: internet of Drones: Novel Applications, Recent Deployments, and Integration

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IEEE internet of things Magazine 2021年第4期4卷 8-10页

作者： Chamola, Vinay Yu, F. Richard Sikdar, Biplab Kanhere, Salil Guizani, Mohsen Internet of Things Lab at BITS-Pilani India Carleton University Ottawa Canada Department of Electrical and Computer Engineering Singapore Singapore School of Computer Science and Engineering University of New South Wales Sydney Australia Qatar University Computer Science and Engineering Department Qatar

Unmanned aerial vehicles (UAVs), also called drones, are extensively being adopted for a wide variety of applications such as traffic surveillance, disaster management, rescue operations, and environment monitoring. With the adoption of the internet of things (IoT), UAV networks are quickly transforming into the internet of Drones (IoD) paradigm. This has opened up opportunities for the exploration of more sophisticated UAV applications. © 2018 IEEE.

关键词： Drones

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Red Piranha Optimization Based Fast and Robust Fuzzy C-Means Clustering for Precise Glioma Segmentation in Therapeutic Applications 10

Red Piranha Optimization Based Fast and Robust Fuzzy C-Means...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sophia, G. Ramaraj, Kottaimalai Karthikeyan, K. Prasath Alias Surendhar, S. Ambika, B. Thilagaraj, M. Mv J College of Engineering Department of Computer Science and Engineering Bengaluru India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Krishnankoil India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalayam India Aarupadai Veedu Institute of Technology Department of Biomedical Engineering Tamil Nadu Chennai India Thiagarajar College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Madurai India Mvj College of Engineering Department of Industrial Internet of Things Bengaluru India

ISBN: (纸本)9798350384369

Gliomas have cells that resemble glial cells., which are normal brain cells. They are the prevalent kind of tumors;however., in patients with several years to live., they may be a lesser threat., or in patients with only two years to live., they may be more deadly. Chemotherapy and radiation are able to be utilized to decrease the development of brain tumors which are unable to be practically taken away even though medical intervention is still the preferred therapy for these tumors. Magnetic resonance imaging (MRI) for tumor diagnosis because they offer comprehensive images of the structure of the brain. Accurately segmenting tumors from MRls is crucial for both preparing for surgery and diagnostic purposes. Further investigations may not be able to justify manual tumor segmentation. Furthermore., tumor delineation can significantly influence the scheduling of treatment., rate of growth forecasting., and better diagnosis. The present study uses an integrated segmentation algorithm that combines optimization and clustering techniques. One method for creating clusters or groups of identical pixels is fuzzy C-means (FCM) clustering., an unsupervised learning technique. Fast and Robust FCM (FRFCM) addresses the drawbacks of traditional method. Red Piranha Optimization (RPO) is a recently introduced optimization technique that draws inspiration from nature and emulates the community-based hunting act of red piranhas. The suggested RPO-based FCM method was evaluated on a number of BraTS challenge datasets to guarantee its effectiveness. The results of the dissection are contrasted with the corresponding ground truth images. The enhanced dice score value of 92 % is obtained using the developed method. Medical professionals can use findings to help schedule surgical procedures., diagnose patients promptly, and treat them. © 2024 IEEE.

关键词： Chemotherapy

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Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

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computers, Materials & Continua 2022年第12期73卷 4465-4487页

作者： Zia Ullah Muhammad Ismail Mohmand Sadaqat ur Rehman Muhammad Zubair Maha Driss Wadii Boulila Rayan Sheikh Ibrahim Alwawi Department of Computer Science The Brains InstitutePeshawar25000Pakistan School of Natural and Computing Sciences University of AberdeenAberdeenUK Department of Neurosciences KU Leuven Medical SchoolLeuven3000Belgium Security Engineering Laboratory CCISPrince Sultan UniversityRiyadh12435Saudi Arabia Robotics and Internet of Things Lab Prince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science Robert Gordon UniversityAberdeenUK

Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it *** overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in which the proposed model first extract features followed by feature ***,RestNet-50,VGG-19,and Inception-V3 is used to ensure feature learning followed by feature ***,the three feature extraction models are utilized using Ensemble Learning techniques for final expression *** representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising *** evaluate the efficiency of the proposed model,we use two wild benchmark datasets Real-world Affective Faces Database(RAF-DB)and AffectNet for facial expression *** proposed model classifies the emotions into seven different categories namely:happiness,anger,fear,disgust,sadness,surprise,and ***,the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost,convergence and accuracy based on a standard problem specific to classification applications.

关键词： Ensemble learning emotion recognition feature fusion occlusion

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Quicsdn: Transitioning from Tcp to Quic for Southbound Communication in Sdns

SSRN

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SSRN 2022年

作者： Kumar, Puneet Dezfouli, Behnam Internet of Things Research Lab Department of Computer Science and Engineering Santa Clara University United States

In Software-Defined Networks (SDNs), the control plane and data plane communicate for various purposes, such as applying configurations and collecting statistical data. While various methods have been proposed to reduce the overhead and enhance the scalability of SDNs, the impact of the transport layer protocol used for southbound communication has not been investigated. Existing SDNs rely on TCP (and TLS) to enforce reliability and security. In this paper, we show that the use of TCP imposes a considerable overhead on southbound communication, identify the causes of this overhead, and demonstrate how replacing TCP with QUIC can enhance the performance of this communication. We introduce the quicSDN architecture, enabling southbound communication in SDNs via the QUIC protocol. We present a reference architecture based on the standard, most widely used protocols by the SDN community and show how the controller and switch are revamped to facilitate this transition. We compare, both analytically and empirically, the performance of quicSDN versus the traditional SDN architecture and confirm the superior performance of quicSDN. © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Delay Minimization for Intelligent Reflecting Surface Assisted Federated Learning

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China Communications 2022年第4期19卷 216-229页

作者： Ning Huang Tianshun Wang Yuan Wu Suzhi Bi Liping Qian Bin Lin State Key Laboratory of Internet of Things for Smart City University of MacaoMacaoChina Department of Computer and Information Science University of MacaoMacaoChina Zhuhai-UM Science and Technology Research Institute Zhuhai 519031China College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518060China College of Information Engineering Zhejiang University of TechnologyHangzhou 310014China Department of Communication Engineering Dalian Maritime UniversityDalian 116026China

Federated learning(FL), which allows multiple mobile devices to cooperatively train a machine learning model without sharing their data with the central server, has received widespread ***, the process of FL involves frequent communications between the server and mobile devices,which incurs a long latency. Intelligent reflecting surface(IRS) provides a promising technology to address this issue, thanks to its capacity to reconfigure the wireless propagation environment. In this paper, we exploit the advantage of IRS to reduce the latency of FL. Specifically, we formulate a latency minimization problem for the IRS assisted FL system, by optimizing the communication resource allocations including the devices’ transmit-powers, the uploading time, the downloading time, the multi-user decomposition matrix and the phase shift matrix of IRS. To solve this non-convex problem, we propose an efficient algorithm which is based on the Block Coordinate Descent(BCD) and the penalty difference of convex(DC) algorithm to compute the solution. Numerical results are provided to validate the efficiency of our proposed algorithm and demonstrate the benefit of deploying IRS for reducing the latency of FL. In particular, the results show that our algorithm can outperform the baseline of Majorization-Minimization(MM) algorithm with the fixed transmit-power by up to 30%.

关键词： federated learning intelligent reflecting surface latency minimization

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Double-Blockchain Assisted Secure and Anonymous Data Aggregation for Fog-Enabled Smart Grid

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engineering 2022年第1期8卷 159-169页

作者： Siguang Chen Li Yang Chuanxin Zhao Vijayakumar Varadarajan Kun Wang Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal UniversityWuhu 241000China School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

As a future energy system,the smart grid is designed to improve the efficiency of traditional power systems while providing more stable and reliable ***,this efficient and reliable service relies on collecting and analyzing users’electricity consumption data frequently,which induces various security and privacy *** address these challenges,we propose a double-blockchain assisted secure and anonymous data aggregation scheme for fog-enabled smart grid named ***,we design a three-tier architecture-based data aggregation framework by integrating fog computing and the blockchain,which provides strong support for achieving efficient and secure data collection in smart ***,we develop a secure and anonymous data aggregation mechanism with low computational overhead by jointly leveraging the Paillier encryption,batch aggregation signature and anonymous *** particular,the system achieves fine-grained data aggregation and provides effective support for power dispatching and price adjustment by the designed double-blockchain and two-level data ***,the superiority of the proposed scheme is illustrated by a series of security and computation cost analyses.

关键词： Blockchain Fog computing Homomorphic encryption Smart grid Anonymity

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HIL-based Distributed Control of Inverter-Air-Conditioner for Power System Frequency Regulation 7

HIL-based Distributed Control of Inverter-Air-Conditioner fo...

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7th IEEE Conference on Energy internet and Energy System Integration, EI2 2023

作者： Zhao, Yuming Wu, Tong Chen, Silin Hui, Hongxun Shenzhen Power Supply Company China Southern Power Grid Shenzhen China Zhuhai Um Science & Technology Research Institute University of Macau Zhuhai China University of Macau State Key Laboratory of Internet of Things For Smart City Department of Electrical and Computer Engineering Macau China

ISBN: (纸本)9798350345094

The goals of carbon peaking and carbon neutrality goals bring a fast extension of renewable energy such as photovoltaic power(PV) and wind power. While providing abundant clean power, the intermittent output also brings power imbalance problems and results in frequency fluctuation and power shortage. Since the demotion of traditional power generators reduces their regulation ability in power balancing, demand side load is considered a satisfactory regulation resource. Inverter air conditioners(IAC) are a widely approved demand response resource because of their flexibility, universality, and ability to maintain customers' comfort. With a great amount of IAC implemented, adopting a reasonable control strategy is important. Centralized control is a commonly used method;however, to develop the communication efficiency and extensibility potential of IAC in larger city scenarios, this paper proposed a Hardware-In-the-Loop(HIL) distributed control method for IAC participating frequency regulation demand response service with Breadth First Search(BFS) strategy based on the Realtime Digital Simulator(RTDS) platform. The HIL clients are Raspberry Pi- 4B and ESP8266 chips, implemented with the IAC model for demand response service. © 2023 IEEE.

关键词： Graph theory

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Intelligent Hybrid Heat Management System: Overcoming Challenges and Improving Efficiency

Intelligent Hybrid Heat Management System: Overcoming Challe...

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Annual IEEE Systems Conference

作者： Vira Shendryk Andriy Perekrest Yuliia Parfenenko Reza Malekian Olha Boiko Paul Davidsson Department of Computer Science and Media Technology Internet of Things and People Research Centre Malmö University Malmö Sweden Department of Information Technologies Sumy State University Sumy Ukraine Department of Computation Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Department of Computer Science and Media Technology Malmö University Internet of Things and People Research Centre Malmö University Malmo Sweden

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

The research delves into intelligent hybrid heat management Systems, exploring the challenges faced and solutions for enhancing efficiency. Hybrid heating systems are complex cyber-technical systems that combine city heating networks with renewable energy sources, such as heat pumps and solar panels. Traditional heating systems often lack adaptability to internal and external conditions, leading to suboptimal performance and user expectations. This paper proposes a new approach by integrating smart technologies, the internet of things, Artificial Intelligence, Machine Learning, optimization techniques, and trade-offs into the management of hybrid heat systems. The emphasis is also placed on the fact that the introduction of smart technologies makes it possible to make hybrid heating systems human-oriented and meet individual needs. Energy efficiency improvement is achievable by combining solutions, such as actual forecasting, with intelligent management that adapts to changing climates and user behaviors. The challenges addressed include inadequate responsiveness to load changes, inaccurate heat consumption forecasting, and inefficient data management. The paper emphasizes the need for intelligent systems that comply with the current standards, providing cost optimization, socializing and ensuring resilience, customer orientation, reliability, safety, and trustworthiness. This exploration of intelligent hybrid heat management systems seeks to overcome existing challenges and pave the way for a sustainable, digitally optimized future in district heating systems.

关键词： Heating systems Energy consumption Renewable energy sources Heat pumps Machine learning internet of things Intelligent agents

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Primeval Identification and Diagnosis of Alzheimer's Disease Utilizing 3D CNN Models

Primeval Identification and Diagnosis of Alzheimer's Disease...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： K. L. Sujitha Lynsha Helena Pratheeba HP Rajeshwari R Kottaimalai Ramaraj Pallikonda Rajasekaran Murugan Thilagaraj M Department of Computer Science and Engineering MVJ College of Engineering Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

An early-onset neurocognitive impairment, Alzheimer's disease (AD) affects individuals. The symptoms of this illness include diminished memory, strange conduct, and linguistic difficulties. Since AD is the source of 60-80% of instances of dementia, it is the single most prevalent cause of dementia. An AD that is neurodegenerative begins with moderate cognitive impairment (MCI) and progressively worsens. It damages brain tissue, impairs ability to think and remember, and makes it difficult to carry out basic duties. The development of more sophisticated therapies for AD depends on swift detection. For this reason, stopping and managing the progression of AD depends greatly on early detection. The primary goal is to create a comprehensive framework for medical image categorization according to different phases of Alzheimer's disease and early identification of the condition. This work employs a deep learning approach, more precisely convolutional neural networks (CNN). Simple CNN architectures that handle 2D and 3D structural brain magnetic resonance imaging (MRI) form the basis of the suggested methodology. The limited quantity of datasets presents the primary obstacles for medical images. Thus, in order to optimize the size of the dataset and avoid the issue of overfitting, methods to augment data are used. Techniques for resampling, such as downsampling and oversampling are employed to address imbalanced dataset collections.

关键词： Solid modeling Three-dimensional displays Parkinson's disease Magnetic resonance imaging Resonance Convolutional neural networks Proposals Alzheimer's disease Older adults Medical diagnostic imaging

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