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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liu, Jiaming Kong, Linghe Wu, Yue Gong, Maoguo Li, Hao Miao, Qiguang Ma, Wenping Qin, Can The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Computer Science and Technology Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Electronic Engineering Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The College of Mathematics Science Inner Mongolia Normal University Hohhot010022 China The School of Artificial Intelligence Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Existing 3D mask learning methods encounter performance bottlenecks under limited data, and our objective is to overcome this limitation. In this paper, we introduce a triple point masking scheme, named TPM, which serves as a scalable plug- and-play framework for MAE pre-training to achieve multi-mask learning for 3D point clouds. Specifically, we augment the baseline methods with two additional mask choices (i.e., medium mask and low mask) as our core insight is that the recovery process of an object can manifest in diverse ways. Previous high-masking schemes focus on capturing the global representation information but lack fine-grained recovery capabilities, so that the generated pre-training weights tend to play a limited role in the fine-tuning process. With the support of the proposed TPM, current methods can exhibit more flexible and accurate completion capabilities, enabling the potential autoencoder in the pre-training stage to consider multiple representations of a single 3D point cloud object. In addition, during the fine-tuning stage, an SVM-guided weight selection module is proposed to fill the encoder parameters for downstream networks with the optimal weight, maximizing linear accuracy and facilitating the acquisition of intricate representations for new objects. Extensive experimental results and theoretical analysis show that five baselines equipped with the proposed TPM achieve comprehensive performance improvements on various downstream tasks. © 2025 IEEE. All rights reserved.

关键词： 3D modeling

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A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

A Graph-Based Self-Calibration Technique for Cable-Driven Ro...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

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Validating a Chaos Based PRBG Under Different Chaotic Maps

Validating a Chaos Based PRBG Under Different Chaotic Maps

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Moysis, Lazaros Lawnik, Marcin Volos, Christos Baptista, Murilo S. Fragulis, George F. Goudos, Sotirios K. University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece Aristotle University of Thessaloniki Laboratory of Nonlinear Systems - Circuits and Complexity Physics Department Thessaloniki Greece University of Western Macedonia Department of Mechanical Engineering Kozani Greece Silesian University of Technology Faculty of Applied Mathematics Department of Mathematics Applications and Methods for Artificial Intelligence Gliwice44-100 Poland Supa University of Aberdeen Institute for Complex Systems and Mathematical Biology Department of Physics AberdeenAB24 3UX United Kingdom Aristotle University of Thessaloniki ELEDIA@AUTH Physics Department Thessaloniki Greece

ISBN: (纸本)9798350318845

Chaos-based random bit generators are abundantly used in chaos-based encryption and security applications, as a fast, deterministic source of randomness, to perform actions like permutation and substitution. The chaotic map used as a source of randomness though may heavily affect the bit generator's statistical randomness, key space, and speed. Thus, any given bit generator may not be equally efficient for all the chaotic maps used as a seed. Motivated by this, the present work considers a bit generator that utilizes a common modulo-based hashing technique, and studies its performance under a plethora of different chaotic maps. For each map, the generator is evaluated with respect to randomness, key space, and operations per bit ratio. This analysis can serve as a guideline for random bit generator usage in future chaos-based encryption designs. © 2024 IEEE.

关键词： Cryptography

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Cyber-Secure and Safe Operation of Solar Photovoltaic Power Distribution systems

Cyber-Secure and Safe Operation of Solar Photovoltaic Power ...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Jaewon Kim Hasan Ibrahim Shaozhe Wang Akshay Mete Le Xie Prasad Enjeti P. R. Kumar Laboratory for Information & Decision Systems (LIDS) Massachusetts Institute of Technology (MIT) Cambridge MA USA Department of Electrical & Computer Engineering Texas A&M University College Station TX USA

Solar photovoltaic (PV)-rich power distribution systems are networked Cyber-Physical systems (CPS). These are control systems where multiple computing nodes and diverse intelligent agents interact with the physical world in real-time. However, the presence of networked components renders them vulnerable to potential cyber-attacks, cyber-intrusions, and other malicious events. This is because these systems depend on the measurements reported from their heterogeneous sensors. This makes them vulnerable to potential cyber-attacks where malicious agents can compromise the sensors or the communication networks carrying the sensor measurements. This paper proposes a novel methodology for enhancing the cyber-security and cyber-resilient post-attack safe operation of solar PV-rich power distribution systems against potential cyber-attacks through the Dynamic Watermarking (DW), using online system identification. The resiliency of the proposed technique is tested and validated with several attack scenarios on both a lab-scale 3kW grid-connected PV inverter and a Hardware-in-the-Loop (HiL) system. The proposed approach can be applied to other types of power distribution systems to enhance their cyber-secure and cyber-resilient safe operation. This paper thereby contributes to the field of cyber-security of Cyber-Physical Energy systems (CPES).

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The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration

arXiv

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arXiv 2024年

作者： Greer, Ross Antoniussen, Bjørk Andersen, Mathias V. Møgelmose, Andreas Trivedi, Mohan M. The Laboratory for Intelligent and Safe Automobiles Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92092 United States The Faculty of Electronic Systems Aalborg University Denmark

Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data. We demonstrate the effectiveness of entropy querying to select informative samples, aiming to reduce annotation costs and improve model performance. We experiment using the BEVFusion model for 3D object detection on the nuScenes dataset, comparing active learning to random sampling and demonstrating that entropy querying outperforms in most cases. The method is particularly effective in reducing the performance gap between majority and minority classes. Class-specific analysis reveals efficient allocation of annotated resources for limited data budgets, emphasizing the importance of selecting diverse and informative data for model training. Our findings suggest that entropy querying is a promising strategy for selecting data that enhances model learning in resource-constrained environments. Copyright © 2024, The Authors. All rights reserved.

关键词： Entropy

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Assessment of Protection Schemes and their Security under Denial of Service Attacks

Assessment of Protection Schemes and their Security under De...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Kharchouf Ibtissam Mahmoud S. Abdelrahman Abdulmeen Alrashide Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Fl USA Department of Electrical &#x0026 Computer Engineering Florida International University Miami FL USA

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

The widespread use of information and communication technology in today's electrical grid makes it increasingly vulnerable to cyber-attacks resulting in major operational and security issues. The efficiency assessment of cyber threats and their negative impact on the system is important but challenging to detect, mitigate, or prevent. Conducting such evaluation on real systems infrastructure is impractical or even impossible. In this paper, we discuss the capabilities that we have in our laboratory facilities at the FIU's smart grid testbed research facility. The testbed was built as an integrated hardware-based AC/DC system where the hardware, software, and communication-based technology can be used to envision a complete Cyber-Physical smart grid framework. Our focus is on the substation automation system where we discuss the IEC 61850 security vulnerabilities and further assess the effect of Denial of Service (DoS) attacks on the physical system. The DoS is being performed in two forms flooding attack and GOOSE poisoning attack. A case study of a transmission line fault of the proposed protection scheme is being conducted. Comparison and evaluation of the impact of DoS cyber-attacks on the physical system are discussed. The experimental results showed the impact of DoS attacks on the protection scheme's dependability and security, where the need to develop detection and mitigation techniques to overcome the IEC 61850 vulnerabilities.

关键词： Power transmission lines Substation automation Software Hardware Real-time systems Smart grids Information and communication technology

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GNS3 Communication Network Emulation for Substation GOOSE Based Protection Schemes

GNS3 Communication Network Emulation for Substation GOOSE Ba...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Abdulmueen Alrashide Mahmoud S. Abdelrahman Ibtissam Kharchouf Osama A. Mohammed Department of Electrical and Computer Engineering Florida International University Energy Systems Research Laboratory Miami Fl USA Department of Electrical &#x0026 Computer Engineering Florida International University Miami FL USA

ISBN: (数字)9781665485371

ISBN: (纸本)9781665485388

This paper presents a communication network model used to study the impact of the communication layer on smart grid protection schemes. Communication failure due to hardware malfunction or cyber-attack could lead to major impacts on the smart grid. Some critical devices require real-time data or close to real-time to make the right decision at the right time and keep the system in a stable zone, such as a protection relay. Active man in the middle attack adds additional latency to the packet delivery. A false data injection attack could drive the system to an unstable point, eventually leading to a blackout. This paper designed and implemented communication emulation model for a local area network on the FIU smart grid testbed. Two cases were tested in the developed communication model. In the first case, two physical raspberry pi boards were used to represent two nodes in the smart grid and one traffic generated within the virtual network. In the second case, five ABB Protection Relays and COM600 substation automation devices were connected to GNS3. The two cases showed that GNS3 could be used to emulate the communications network and study the impact of the network performance on the real-world substation environment.

关键词： Performance evaluation LAN emulation Substation automation Emulation Europe Real-time systems Hardware

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High-Quality, Low-Quantity: A Data-Centric Approach to Deep Learning Performance Optimization in Digital X-Ray Radiography

SSRN

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SSRN 2024年

作者： Hena, Bata Wei, Ziang Castanedo, Clemente Ibarra Maldague, Xavier Department of Electrical and Computer Engineering Université Laval 1065 Ave de la Médecine Quebec CityQCG1V 0A6 Canada Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Université Laval 1065 Ave de la Médecine Quebec CityQCG1V 0A6 Canada

The accuracy of identifying defects using specialized deep learning models can be affected by the circumstances in which the training data is curated. This is especially evident in digital X-ray radiography, where the depiction of flaws is significantly impacted by the exposure conditions. This study examines the effect of curating high-quality data on deep learning models. The variation in contrast-to-noise ratio (CNR), which is a crucial metric of image quality between features of interest and an adjacent normal background, has been found to be a key factor in model generalization in digital X-ray radiography applications. By making systematic alterations to exposure conditions during data curation, it was possible to obtain several representations of flaws in each test component with varying contrast-to-noise ratios (CNR) in the resultant radiographs. To evaluate the efficacy of the model under various conditions, two distinct datasets were curated. Dataset 1 was obtained by acquiring images with a consistent exposure setting on 140 test samples. The samples contained 4 morphologically distinct classes of flat bottom holes with seven different depths and sizes. The contrast-to-noise ratio (CNR) representations of flaws in this dataset can be attributed only to differences in depth in Dataset 1. Additionally, Dataset 2 was curated with an expanded range of CNR values by methodically adjusting exposure settings during image acquisitions. Hence, only 42% of the test pieces from Dataset 1, which had three distinct depths of flat bottom holes, were used. Each of the two datasets was used to separately train YOLOv8 for instance segmentation and U-net for multi-class semantic segmentation. Each model was trained under the same conditions, and their performances were assessed using test sets from both dataset groups. The model trained on Dataset 1 exhibited a notable decline in performance when evaluated on test sets from Dataset 2, suggesting a lack of generalization ab

关键词： Image acquisition

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Not so griddy: internal representations of RNNs path integrating more than one agent 24

Not so griddy: internal representations of RNNs path integra...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： William T. Redman Francisco Acosta Santiago Acosta-Mendoza Nina Miolane Intelligent Systems Center Johns Hopkins Applied Physics Laboratory Department of Physics University of California Santa Barbara Dynamical Neuroscience University of California Santa Barbara Department of Electrical & Computer Engineering University of California Santa Barbara

ISBN: (纸本)9798331314385

Success in collaborative and competitive environments, where agents must work with or against each other, requires individuals to encode the position and trajectory of themselves and others. Decades of neurophysiological experiments have shed light on how brain regions [e.g., medial entorhinal cortex (MEC), hippocampus] encode the self's position and trajectory. However, it has only recently been discovered that MEC and hippocampus are modulated by the positions and trajectories of others. To understand how encoding spatial information of multiple agents shapes neural representations, we train a recurrent neural network (RNN) model that captures properties of MEC to path integrate trajectories of two agents simultaneously navigating the same environment. We find significant differences between these RNNs and those trained to path integrate only a single agent. At the individual unit level, RNNs trained to path integrate more than one agent develop weaker grid responses, stronger border responses, and tuning for the relative position of the two agents. At the population level, they develop more distributed and robust representations, with changes in network dynamics and manifold topology. Our results provide testable predictions and open new directions with which to study the neural computations supporting spatial navigation.

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