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5th International Conference on Information Communication and Signal Processing, ICICSP 2022

作者： Wang, Wei Wang, Gang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9781665485890

In this paper, the time-of-arrival (TOA) localization problem in the presence of non-line-of-sight (NLOS) errors is addressed. We propose a robust localization method based on the maximum correntropy criterion with variable center (MCC-VC), in which the center and bandwidth of the kernel function are jointly estimated with the source position. Particularly, we formulate two optimization problems based on the MCC-VC;one problem is to estimate the kernel center and bandwidth, and the other is to estimate the source position. The two problems are solved alternately to obtain the final source position estimation. Unlike the previous methods, the proposed MCC-VC method does not require any prior information. Simulation results show that the proposed method performs well both in sparse and dense NLOS environments. © 2022 IEEE.

关键词： Time of arrival

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A SECPE Circuit Based on Optimal Flipping Control for Weakly-Coupled Piezoelectric Energy Harvesters 4

A SECPE Circuit Based on Optimal Flipping Control for Weakly...

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4th IEEE International Conference on Circuits and Systems, ICCS 2022

作者： Xiong, Jin Xia, Huakang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (数字)9781665460361

ISBN: (纸本)9781665460361

Piezoelectric energy (PE) harvesters can harvest vibration energy and convert it into electrical energy, which provides a promising solution to supply sustainable power for wireless sensor networks (WSN) applications. Synchronous electric charge extraction (SECE) technique can optimize the extracted power from a PE harvester regardless of the connected load;however, its performance degrades seriously when the PE harvester is in weakly-coupled (k2 Qm © 2022 IEEE.

关键词： Wireless sensor networks

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Road Sign Classification Using Transfer Learning and Pre-trained CNN Models 1

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1st International Conference on Artificial Intelligence and Smart Vehicles, ICAISV 2023

作者： Hosseini, Seyed Hossein Ghaderi, Foad Moshiri, Behzad Norouzi, Mojtaba Human Computer Interaction Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran School of ECE University of Science and Technology Tehran Iran

ISBN: (数字)9783031437632

ISBN: (纸本)9783031437625

In this paper, we propose a transfer learning-based approach for road sign classification using pre-trained CNN models. We evaluate the performance of our fine-tuned VGG-16, VGG-19, ResNet50 and EfficientNetB0 models on the German Traffic Sign Recognition Benchmark (GTSRB) test dataset. Our work makes several contributions, including the utilization of transfer learning with pre-trained CNN models, the integration of augmentation techniques, and a comprehensive evaluation of multiple state-of-the-art architectures on the road sign classification task. Our experimental results show that the proposed approach achieves an average accuracy of 99.2% on the GTSRB test dataset, outperforming existing state-of-the-art approaches. The results confirm that transfer learning and pre-trained models can significantly improve the accuracy of road sign classification, even with a relatively small dataset. Our study contributes to the field of intelligent transportation systems by providing an efficient and accurate method for road sign classification. We believe that our approach can be easily extended to other computer vision tasks and applied to real-world scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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LSR-forest: An locality sensitive hashing-based approximate k-nearest neighbor query algorithm on high-dimensional uncertain data

LSR-forest: An locality sensitive hashing-based approximate ...

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作者： Wang, Jiagang Qian, Tu Yang, Anbang Wang, Hui Qian, Jiangbo Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

Uncertain data is widely used in many practical applications, such as data cleaning, location-based services, privacy protection, and so on. With the development of technology, data has a tendency to high-dimensionality. The most common indexes for nearest neighbor search on uncertain data are the R-Tree and the KD-Tree. These indexes will inevitably bring about "curse of dimension." Focus on this problem, article proposes a new hash algorithm, called the LSR-forest, which based on locality sensitive hashing and R-Tree, to solve the high-dimensional uncertain data approximate neighbor search problem. The LSR-forest can hash similar high-dimensional uncertain data into a same bucket with a high probability, and then constructs multiple R-Tree-based indexes for hashed buckets. When querying, it is possible to judge neighbors by checking the data in the hypercube which the query point is in. One can also adjust the query range automatically by different parameter of k. Many experiments on different datasets are presented in this article. The results show that LSR-forest has better effectiveness and efficiency than R-Tree on high-dimensional datasets. © 2020 John Wiley & Sons, Ltd.

关键词： Nearest neighbor search

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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Dropout regularization versus ℓ2-penalization in the linear model

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 9810-9857页

作者： Gabriel Clara Sophie Langer Johannes Schmidt-Hieber Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede The Netherlands

We investigate the statistical behavior of gradient descent iterates with dropout in the linear regression model. In particular, non-asymptotic bounds for the convergence of expectations and covariance matrices of the iterates are derived. The results shed more light on the widely cited connection between dropout and ℓ2-regularization in the linear model. We indicate a more subtle relationship, owing to interactions between the gradient descent dynamics and the additional randomness induced by dropout. Further, we study a simplified variant of dropout which does not have a regularizing effect and converges to the least squares estimator.

关键词： dropout algorithmic regularization gradient descent

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Effects of Bistatic Operation in Harmonic Radar 19

Effects of Bistatic Operation in Harmonic Radar

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19th European Radar Conference, EuRAD 2022

作者： Lavrenko, Anastasia Mathematics and Computer Science University of Twente Faculty of Electrical Engineering Netherlands

ISBN: (数字)9782874870712

ISBN: (纸本)9782874870712

Harmonic radar systems are used to interrogate, or track a location of, passive nonlinear targets in highly cluttered environments, and they are notorious for their poor power efficiency and low detection ranges. Due to harmonic operation, the received signal power close to maximum range becomes inversely proportional to the fourth power of the forward distance (from the radar transmitter to the harmonic target), compared against the inverse second power law for the return distance (from the harmonic target back to the radar receiver). This difference provides additional degrees of freedom for system design when a harmonic radar transmitter and receiver can be positioned at different distances to the target. This paper investigates the effect this has on detection range in harmonic radar. © 2022 European Microwave Association (EuMA).

关键词： Radar receivers

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Leveraging Information-Centric Edge Computing in Private 5G Cellular Networks via the W3C Web of Things 22

Leveraging Information-Centric Edge Computing in Private 5G ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Romann, Jan Damer, Hugo Hakim Gias, T. M. Rayhan Bergmann, Olaf University of Applied Sciences Emden/Leer Faculty of Technology Department of Electrical Engineering and Computer Science Constantiaplatz 4 Emden26723 Germany

ISBN: (纸本)9798331508050

In this paper, we present an architecture for the deployment of an edge computing system based on Named Data Networking (NDN) as part of a private 5G network. Our proposed architecture integrates IP-based and non-IP-based components using the W3C Web of Things (WoT) standards, including a Thing Description Directory (TDD). Our results highlight the potential of both edge computing and WoT concepts for private 5G deployments but also reveal several standardization gaps that need to be closed to make effective use of the new technologies. © 2025 IEEE.

关键词： Internet of things

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Robot Manipulator Anti-Disturbance Control based on PSO Multi-task Optimization 4

Robot Manipulator Anti-Disturbance Control based on PSO Mult...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Chen, Yifan Qu, Miaomiao Shi, Xuhua Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

A new optimal anti-disturbance sliding mode control approach for manipulators is proposed in this paper. Aiming at the difficulty of parameter selection of sliding mode controller for manipulators, instead of empirical trial and error design approach, it is proposed a multi-task transfer strategy of surrogate-assisted Particle Swarm Optimization (PSO) approach, to solve the problem of optimal control parameter selection in the time-consuming adjustment process. The experimental results show that compared with the traditional PSO algorithm, the approach in this paper can effectively improve the convergence speed and control effect. The performance of the controller based on this optimization approach is superior to that based on the traditional PSO algorithm in terms of dynamic and static performance. © 2022 IEEE.

关键词： Sliding mode control

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Detection and Localization of Stealth False Data Injection Attacks in Active Power Distribution Systems Using an Ensemble of Deep CNNs

Detection and Localization of Stealth False Data Injection A...

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2023 IEEE International Conference on Environment and electrical engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, EEEIC / I and CPS Europe 2023

作者： Dehbozorgi, Mohammad Reza Rastegar, Mohammad Arani, Mohammad Reza School of Electrical and Computer Engineering Shiraz University Shiraz Iran Toronto Metropolitan University The Faculty of Engineering and Architectural Science Toronto Canada

ISBN: (纸本)9798350347432

Cyber-physical power systems' reliance on cyberspace makes them vulnerable to cyber-attacks, particularly false data injection attacks (FDIAs), where the aim is to alter the state estimation (SE) results by changing meters' readings. Because of distribution systems' properties such as the lower measurement redundancy and varying loads, existing methods cannot be used to accurately detect and localize an FDIA. To fill these gaps and to deal with the rarity of FDIAs in distribution systems, we propose an ensemble of deep convolutional neural networks (CNNs) to detect and localize FDIAs in active balance, and unbalanced distribution systems. To this end, first, a dataset is created using different attacking scenarios and the possible reconfigurations and renewable generation scenarios in the system. The records are in the form of WLS-generated voltage estimation of PQ buses with different balancing ratios between attacked and normal records. Then, these datasets are used to train different CNNs. These CNNs' outputs are merged using a multilayer perceptron network. Finally, FDIA is detected and localized by the proposed ensemble model in a balanced and unbalanced power distribution system. Results of simulations on IEEE 33-bus and the modified IEEE 13-bus networks verify that the ensemble model can distinguish between normal and attacked records with great accuracy according to the area under the curve (AUC) criteria, thus giving the operators a powerful tool to defend against FDIAs. © 2023 IEEE.

关键词： Electric power distribution

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