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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Hassan, Kamil Vettoretti, Denis Selvaratnam, Daniel Sandberg, Henrik Andrén, Filip Pröstl School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden AIT Austrian Institute of Technology Vienna Austria

ISBN: (纸本)9781665464543

In this article, we study the voltage regulation problem for an inverter-interfaced power distribution network that is subject to possible adversarial injections and/or severe fault scenarios. Different from the current literature, our aim is not just to regulate the voltage levels on the gridline within the prescribed range, but also to provide a measure of reliability for the safe operation of the grid with respect to such adversarial events. In that respect, we propose the framework of resilient control design which ensures safe operation of the grid under nominal conditions (with regard to the given voltage regulation objective) and, additionally, offers theoretical guarantees of finite-time recovery following intermittent fault/ safety violations. To validate these claims, we test the proposed resilient controller on a dedicated testbed for power inverter networks and, through hardware-in-the-loop (HIL) simulation case studies, showcase the efficacy of the suggested control scheme. © 2024 IEEE.

关键词： Hardware-in-the-loop simulation

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WOMN: WOrMhole Attack DetectioN and Mitigation Using Lightweight Distributed IDS in IoT Network

WOMN: WOrMhole Attack DetectioN and Mitigation Using Lightwe...

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2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Bhale, Pradeepkumar Ray, Dipojjwal Biswas, Santosh Nandi, Sukumar Indian Institute of Technology Guwahati Department of Computer Science and Engineering Guwahati781039 India Indian Institute of Technology Bhilai Department of Electrical Engineering and Computer Science Bhilai492015 India

ISBN: (纸本)9798350337785

Nowadays, the Internet of Things (IoT) is an emerging technology with a wide range of IoT devices. However, the diverse, energy-dependent, and resource-limited nature of IoT devices poses significant challenges to implementing security measures, making them susceptible to cyber threats. A particularly debilitating attack at the network layer of the IoT protocol stack is the WormHole Attack (WHA), which disrupts routing algorithms by feeding the network incorrect route information and compromising location-dependent protocols. This paper presents the development and application of a novel intrusion detection system (IDS) agent designed specifically for IoT environments, aimed at detecting and identifying both the occurrence of a WHA and the attacker nodes. The system uses the path of the data packet, time delay, and hop count to detect the presence of a WHA. Given that distributed IDS agents tend to generate substantial network traffic in an IoT context, this study introduces the utilization of the MapReduce technique to enhance the efficacy of the approach. The map and reduce functions work in tandem to scrutinize traffic and establish thresholds for detecting wormhole attacks in the IoT network. This proposed security solution is not only energy-efficient and scalable, making it ideal for IoT environments with limited resources, but it also boasts a rapid attack detection time (18-26.2 Sec.), high accuracy, and enhanced network performance. © 2023 IEEE.

关键词： MapReduce

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Machine Speech Chain with Emotion Recognition 11

Machine Speech Chain with Emotion Recognition

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11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Naufal, Akeyla Pradia Lestari, Dessi Puji Purwarianti, Ayu Azizah, Kurniawati Tanaya, Dipta Sakti, Sakriani School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798331520311

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics. © 2024 IEEE.

关键词： Chains

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Lightweight Tunnel Leakage Water Detection Algorithm Based on YOLOv8n 25

Lightweight Tunnel Leakage Water Detection Algorithm Based o...

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25th IEEE China Conference on System Simulation technology and its Application, CCSSTA 2024

作者： Wang, Shiwen Mo, Jiaqing Xu, Liang Zheng, Xinqi Xinjiang University School of Computer Science and Technology Urumqi China Tianjin University of Technology School of Electrical Engineering and Automation Tianjin China

ISBN: (纸本)9798350366600

Tunnel leakage is one of the biggest hazards to shield tunnels, this paper proposes a lightweight tunnel water leakage target detection algorithm that improves YOLOv8n for the shortcomings of the existing tunnel water leakage target detection algorithms, such as poor precision and too many model parameters. To make feature fusion richer and reduce the model parameters, this paper proposes to use a bi-directional feature pyramid network on the head network. In addition, this paper uses multi-scale convolutional attention to adapt to the complex features of the leakage area in the tunnel. Finally, use the dilated reparam block to reduce the computational costs and expand the receptive field. The algorithm in this paper not only improves the detection accuracy compared to the original algorithm, but also significantly reduces the model parameters and computational costs. In addition, the superiority of this paper's algorithm is verified through ablation experiments and comparisons with other mainstream target detection algorithms. © 2024 IEEE.

关键词： Target detection Tunnel leakage YOLOv8n

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Real-Time Edge Computing Services for Internet of Things-based Cloud Networks 15

Real-Time Edge Computing Services for Internet of Things-bas...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kashyap, Sneha Seth, Jyoti Hannah Jessie Rani, R. Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

the emergence of the Internet of Things (IoT) gadgets and cloud-enabled networks has brought about a dramatic growth in the quantity and kinds of programs that require real-time facet computing offerings. Aspect computing is a rising location of allotted computing that methods facts toward the cease person or tool, supplying low-latency responses to operations and analytics. It enables quicker and more dependable facts-extensive applications that can be personalized to consumer alternatives and needs. This paper discusses the design and implementation of real-time edge computing offerings for IoT-primarily based cloud networks, focusing on key components of the device structure, including carrier deployment, aid management, protection, and scalability. Furthermore, the paper describes the usage of area computing for IoT programs, which include video surveillance, domestic automation, and smart town solutions. Subsequently, the paper presents a survey of cutting-edge area computing companies, summarizing their offerings, business models, and services. © 2024 IEEE.

关键词： Analytics Applications Automation Business Models Computing Real-Time Services

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A Segmentation-Based Scheme to Expedite Block Propagation in Blockchain Networks 21

A Segmentation-Based Scheme to Expedite Block Propagation in...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Qiu, Zhihan Zhao, Qinglin Feng, Li Zhou, MengChu Shen, Meng Liu, Yi Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology Taipa China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science Guangdong University of Technology Guangzhou510006 China Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China

ISBN: (纸本)9798350365221

Blockchain technology, the foundation of cryptocurrencies like Bitcoin, has utility beyond finance due to its decentralized and secure transactional nature. However, today's blockchain networks face the challenge of low transaction throughput due to block propagation delays. In this study, we propose a novel approach that divides large blocks into segments and leverages multi-origin pipelining for segment dissemination, thereby expediting block propagation. The scheme also integrates the use of a Bloom filter for lightweight segment integrity verification to prevent the reception of tampered segments. Simulation results reveal that our scheme outperforms Bitcoin's legacy approach, demonstrating significant improvement in propagation speed, particularly for large blocks, while maintaining a low fork rate. © 2024 IEEE.

关键词： Blockchain

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Deciphering DNA variant-associated aberrant splicing with the aid of RNA sequencing (vol 55, pg 732, 2023)

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NATURE GENETICS 2023年第7期55卷 1251-1251页

作者： Zhang, Bin Gao, Xin Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia KAUST Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia

Aberrant RNA splicing events resulting from DNA variations are common causes of genetic disorders. Two studies published in Nature Genetics independently describe methods to decipher DNA-variant-associated aberrant sp... 详细信息

关键词： Data mining High-throughput screening

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Complex Double Integration Enhanced ZNN with Rejection of Linear Noise for Computing Time-Dependent Sylvester Equation 25

Complex Double Integration Enhanced ZNN with Rejection of Li...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Xu, Jinsha Hua, Cheng Li, Shuai Liao, Bolin College Of Computer Science And Engineering Jishou University Jishou China University Of Oulu Faculty Of Information Technology And Electrical Engineering Oulu Finland

ISBN: (纸本)9798350340266

Recurrent neural networks (RNN) are highly effective in solving the inverse problem of time-dependent matrices. However, in real-world engineering applications, noise interference is inevitable. The zeroing neural network (ZNN) is a special class of RNN that exhibit strong noise tolerance in solving the time-dependent Sylvester equation (TDSE). Despite the usefulness of the previously proposed original ZNN (OZNN) model and integral enhanced ZNN (IEZNN) model, they lack the ability to suppress linear and quadratic noise when solving the Sylvester equation. In this paper, a complex double integration enhanced ZNN (CDIEZNN) model is introduced for the first time to address the problem of TDSE. The theoretical basis for the convergence and robustness of the double integral model is described in detail. Two experiments are conducted to verify the convergence and robustness of the CDIEZNN model, and the digital simulation results demonstrate that in solving the TDSE, the residual error of the CDIEZNN model rapidly and stably converges to 3.82 × 10-6in a linear noise environment, and even in complex quadratic noise environments, the residual error can still converge to 1.99 × 10-2• In contrast, the OZNN and IEZNN models have weak suppression of linear noise and the residual errors cannot converge in a quadratic noise environment and the results show divergence. © 2024 IEEE.

关键词： Neural network models

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Towards Enhancing Child Safety: A Deep Learning Approach to Detect Child Safe and Unsafe Objects

Towards Enhancing Child Safety: A Deep Learning Approach to ...

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2024 IEEE International Women in engineering (WIE) Conference on electrical and computer engineering, WIECON-ECE 2024

作者： Khan, Fairuz Areefin Dey, Ashim Chittagong University of Engineering and Technology Department of Electrical and Electronic Engineering Chittagong4349 Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong4349 Bangladesh

ISBN: (纸本)9798331535476

The safety and well-being of children are a paramount concern, and the accurate detection of potentially haz-ardous objects is crucial in ensuring their protection, especially in indoor situations. In the Artificial Intelligence (AI) age, the future belongs to domestic robots;they will be used for child education, surveillance, and healthcare support. Early identification of unsafe objects and intervention by these next-generation social family robots can play an essential role in providing a safer environment for children. Keeping these issues in mind, this study aims to tackle daily risks and accidents that endanger children by using computer vision to detect unsafe objects. To achieve this goal, a unique dataset namely ChildSUn is introduced containing approximately 5350 images of child-safe and unsafe objects of ten categories. Various CNN architectures, including custom CNN, VGG16, VGG19, ResNet50, Inception-V3, EfficientNet-B0, and MobileNetV2, are implemented on the prepared dataset. A deep dive analysis is performed on the implemented models. The EfficientNet-B0 model showed exceptional performance, reaching a remarkable level of accuracy of 99.63% on the test dataset. These findings underscore the potential of EfficientNet-B0 as an effective solution for real-world child safety applications and encourage further exploration of deep learning techniques to enhance child protection globally. We hope this work will pave the way for future social household robot development, which will ensure child safety in any place. © 2024 IEEE.

关键词： Deep learning

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Profit Maximization of Delay-Sensitive, Differential Accuracy Inference Services in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6209-6224页

作者： Zhang, Yuncan Liang, Weifa Xu, Zichuan Jia, Xiaohua Yang, Yuanyuan City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Dalian University of Technology School of Software Dalian China Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY United States

The integration of Artificial Itelligence (AI) and edge computing has sparked significant interest in edge inference services. In this paper, we consider delay-sensitive, differential accuracy inference services in a Mobile Edge Computing (MEC) network while meeting user stringent delay and accuracy requirements. We formulate two novel profit maximization problems under static and dynamic settings of service request arrivals, with the aim of maximizing the accumulative profit of admitted requests. We assign differential accuracy service requests to the corresponding resolution instances of their requested service models, assuming that each resolution instance can serve up to L≥ 1 the same type of service requests. Since the profit maximization problem is NP-hard, we first formulate an Integer Linear Program (ILP) solution if the problem size is small or medium;otherwise, we devise a constant randomized algorithm with high probability. Then, we consider dynamic service request admissions without the knowledge of future request arrivals for a given finite time horizon, for which we develop a simple yet effective prediction mechanism to accurately predict the number of different resolution instances of each model needed, and pre-deploy the predicted number of resolution instances into cloudlets to reduce instantiating delays. We then devise an online algorithm with a provable competitive ratio for the dynamic profit maximization problem by leveraging the primal-dual dynamic updating technique. Finally, we evaluate the performance of the proposed algorithms by simulations. The simulation results demonstrate that the proposed algorithms are promising. © 2002-2012 IEEE.

关键词： Mobile edge computing

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