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arXiv 2023年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Gonçalves, Vinícius Tzes, Anthony Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States Electrical Engineering New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates

Control barrier functions (CBFs) have been widely applied to safety-critical robotic applications. However, the construction of control barrier functions for robotic systems remains a challenging task. Recently, collision detection using differentiable optimization has provided a way to compute the minimum uniform scaling factor that results in an intersection between two convex shapes and to also compute the Jacobian of the scaling factor. In this paper, we propose a framework that uses this scaling factor, with an offset, to systematically define a CBF for obstacle avoidance tasks. We provide theoretical analyses of the continuity and continuous differentiability of the proposed CBF. We empirically evaluate the proposed CBF's behavior and show that the resulting optimal control problem is computationally efficient, which makes it applicable for realtime robotic control. We validate our approach, first using a 2D mobile robot example, then on the Franka-Emika Research 3 (FR3) robot manipulator both in simulation and experiment. Index Terms-Robot safety, collision avoidance. © 2023, CC BY.

关键词： Robot applications

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SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localization

SubPipe: A Submarine Pipeline Inspection Dataset for Segment...

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OCEANS

作者： Olaya Álvarez-Tuñón Luiza Ribeiro Marnet Martin Aubard László Antal Maria Costa Yury Brodskiy Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR-Lab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark OceanScan Marine Systems & Technology Matosinhos Portugal RWTH Aachen University Aachen Germany

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a lightweight autonomous underwater vehicle (LAUV), operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first anno-tated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly avail-able online at https://***/remaro-network/SubPipe-dataset.

关键词： Location awareness Image segmentation Simultaneous localization and mapping Oceans Pipelines Object detection Inertial navigation

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A Method to Control Distributed Energy Resources in Distribution Networks Using Smart Meter Data

SSRN

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SSRN 2023年

作者： Zabihinia Gerdroodbari, Yasin Pengwah, Abu Bakr Razzaghi, Reza Heidari, Rahmat Andrew, Lachlan L.H. Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC Australia 10 Murray Dwyer Circuit Mayfield West NewcastleNSW Australia School of Computing and Information Systems University of Melbourne CarltonVIC Australia

Voltage regulation has been one of the major challenges for distribution network operators (DNOs) due to the integration of distributed energy resources in the recent years. Control approaches mitigating over-voltage (OV) generally require full network model, which for low-voltage distribution networks (LVDNs), can be inaccessible to DNOs. This paper presents a novel datadriven model-free control framework for improving voltage regulation in 4wire 3-phase LVDNs. In the proposed approach, first, using smart meter data, the aggregated resistance and reactance matrices of the network are estimated. Then, these matrices serve as an input for the proposed optimal power flow (OPF) method to control the DERs’ active and reactive power outputs. This method also lets the DNO adjust the fairness in prosumers’ generation curtailment. The performance of the proposed approach is evaluated in various case studies conducted on a real-world low-voltage test feeder. The simulation results exhibit significant effectiveness in solving the OV problem associated with DERs. © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Improved Radiation Estimation at High Energy Facility for Public Health

Improved Radiation Estimation at High Energy Facility for Pu...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Jerry Cao-Xue Hongfang Zhang Adam Stavola Bence Budavari Chiman Kwan Hongyi Wu Jiang Li William G. Enloe High School Raleigh NC Department of Electrical and Computer Engineering Old Dominion University Norfolk VA Thomas Jefferson National Accelerator Facility Newport News VA Applied Research LLC Rockville MD Force Projection Sector Johs Hopkins University Applied Physics Laboratory Laurel MD Department of Electrical & Computer Engineering University of Arizona Tuscon AZ

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

Long-term exposure to ionizing radiation can lead to the development of cancer and other potentially fatal diseases. Estimating radiation levels at high-energy particle accelerators is crucial for ensuring the safety of personnel, the public, and the environment. This paper explores the application of transformer neural networks, focusing on five different attention mechanisms—Full Attention, Prob Attention, AutoCorrelation, Fourier Attention, and Wavelet Attention—for estimating radiation levels at the Thomas Jefferson National Accelerator Facility (JLab). The goal is to improve the accuracy of radiation estimation by training models using data from key radiation sensors. Four hundred trials were conducted for each attention mechanism, and performance was evaluated using metrics including R 2 . The results demonstrated that Prob Attention and AutoCorrelation outperformed the other mechanisms, offering substantial improvements over previous models. These findings highlight the importance of selecting and fine-tuning attention mechanisms in transformers for complex time-series analysis tasks in radiation monitoring.

关键词： Wavelet transforms Training Radiation monitoring Attention mechanisms Time series analysis Estimation Transformers Sensors Safety Autocorrelation

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Abstraction-Free Control Synthesis to Satisfy Temporal Logic Constraints under Sensor Faults and Attacks

Abstraction-Free Control Synthesis to Satisfy Temporal Logic...

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IEEE Conference on Decision and Control

作者： Luyao Niu Zhouchi Li Andrew Clark Department of Electrical and Computer Engineering Network Security Lab University of Washington Seattle WA Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester MA Department McKelvey School of Engineering Electrical and Systems Engineering Washington University in St. Louis St. Louis MO

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We study the problem of synthesizing a controller to satisfy a complex task in the presence of sensor faults and attacks. We model the task using Gaussian distribution temporal logic (GDTL), and propose a solution approach that does not rely on computing any finite abstraction to model the system. We decompose the GDTL specification into a sequence of reach-avoid sub-tasks. We develop a class of fault-tolerant finite time convergence control barrier functions (CBFs) to guarantee that a dynamical system reaches a set within finite time almost surely in the presence of malicious attacks. We use the fault-tolerant finite time convergence CBFs to guarantee the satisfaction of ‘reach’ property. We ensure ‘avoid’ part in each sub-task using fault-tolerant zeroing CBFs. These fault-tolerant CBFs formulate a set of linear constraints on the control input for each sub-task. We prove that if the error incurred by system state estimation is bounded by a certain threshold, then our synthesized controller fulfills each reach-avoid sub-task almost surely for any possible sensor fault and attack, and thus the GDTL specification is satisfied with probability one. We demonstrate our proposed approach using a numerical study on the coordination of two wheeled mobile robots.

关键词： Fault tolerance Sufficient conditions Computational modeling Fault tolerant systems Gaussian distribution Robot sensing systems Mobile robots

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GCFormer: Granger Causality based Attention Mechanism for Multivariate Time Series Anomaly Detection

GCFormer: Granger Causality based Attention Mechanism for Mu...

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IEEE International Conference on Data Mining (ICDM)

作者： Shiwang Xing Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences China

Multivariate time series anomaly detection, crucial for ensuring the safety of real-world systems, primarily focuses on extracting characteristics from time series under normal condition, and identifying potential anomalies throughout the evaluation process. Recent studies have achieved fruitful progress through mining the spatio-temporal relationships from multivariate time series, however, these approaches mostly neglect the latency among series which could lead to higher false alarm. Granger causality presents a promising solution to extract these inherent time-lagged relationships. Nonetheless, the intricate and dynamic relationships among numerous time series in real-world systems surpass the ability of linear Granger causality. To address this, we extend the linear Granger causality and propose the Granger Causal Former (GCFormer), a novel approach that leverages attention mechanisms to learn the inherent causal spatio-temporal relationships between historical and current timestamps across multiple time series. Specifically, GCFormer develops a Spatio-Mask (SM) to select the top-k most relevant series and a Temporal-Mask (TM) to concentrate attention on more recent historical timestamps. Moreover, to mitigate overfitting and ensure a smooth training process, GCFormer introduces an adjust top-k method and a TM penalty term. We evaluated GCFormer on four real-world benchmark datasets, demonstrating its superior performance over state-of-the-art approaches. Further analysis and a case study highlight the model’s novelty and interpretability.

关键词：

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Beamforming Design for Multi-Mode STAR-RIS Empowered Multi-Cell systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年

作者： Zhai, Xiongfei Cai, Yunlong Kang, Kai Han, Guojun Xu, Weiqiang Al-Dhahir, Naofal Guangdong University of Technology School of Information Engineering Guangzhou510006 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Peng Cheng Laboratory Shenzhen518071 China Zhejiang Sci-Tech University School of Information Science and Engineering Hangzhou China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

We investigate multi-mode simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted multi-cell systems, consisting of multiple base stations (BSs), their own users (UEs), and one STAR-RIS which can operate in energy splitting (ES), function switching (FS), and time switching (TS) modes. Under these different modes, we formulate three optimization problems to maximize the sum rate performance. To solve these challenging problems, we first transform the original sum-rate maximization problems into more tractable weighted mean-square-error (MSE) minimization problems. Then, by exploiting the properties of different modes, auxiliary variables are introduced for decoupling variables. We propose a double-loop algorithm based on the penalty dual decomposition (PDD) framework to solve their corresponding augmented Lagrangian problems, which optimizes the primal/dual variables in the inner/outer loops. The convergence and complexity of the proposed algorithms are analyzed. Our simulation results demonstrate that the ES mode outperforms the FS mode due to the continuous power allocation, and the TS mode achieves the highest sum-rate performance due to its decoupled phase shifts and degrees of freedom in the time domain at the cost of more convergence iterations, higher implementation complexity, and increased latency. Moreover, all STAR-RIS assisted schemes outperform the conventional RIS assisted scheme. © 2017 IEEE.

关键词： Mean square error

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Gate Optimization of NEQR Quantum Circuits via PPRM Transformation

arXiv

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arXiv 2024年

作者： Iranmanesh, Shahab Aghababa, Hossein Fouladi, Kazim School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Engineering Loyola University Maryland MD United States Founder of Quantum Computation and Communication Laboratory University of Tehran Tehran Iran Faculty of Engineering College of Farabi University of Tehran Tehran Iran

Quantum image representation (QIR) is a key challenge in quantum image processing (QIP) due to the large number of pixels in images, which increases the need for quantum gates and qubits. However, current quantum systems face limitations in run-time complexity and available qubits. This work aims to compress the quantum circuits of the Novel Enhanced Quantum Representation (NEQR) scheme by transforming their Exclusive-Or Sum-of-Products (ESOP) expressions into Positive Polarity Reed-Muller (PPRM) equivalents without adding ancillary qubits. Two cases of run-time complexity-exponential and linear- are considered for NEQR circuits with m controlling qubits (m → ∞), depending on the decomposition of multi-controlled NOT gates. Using nonlinear regression, the proposed transformation is estimated to reduce the exponential complexity from O(2m) to O(1.5m), with a compression ratio approaching 100%. For linear complexity, the transformation is estimated to halve the run-time, with a compression ratio approaching 52%. Tests on six 256 × 256 images show average reductions of 105.5 times for exponential cases and 2.4 times for linear cases, with average compression ratios of 99.05% and 58.91%, respectively. © 2024, CC BY.

关键词： Qubits

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Frequency-dependent selectively oriented edge state topological transport

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Advanced Photonics Nexus 2024年第3期3卷 32-39页

作者： Jiajun Ma Chunmei Ouyang Yuting Yang Xinyue Qian Li Niu Yi Liu Quan Xu Yanfeng Li Zhen Tian Jianqiang Gu Jiaguang Han Weili Zhang Tianjin University Center for Terahertz Waves and College of Precision Instrument and Optoelectronics Engineeringand Key Laboratory of Optoelectronics Information and TechnologyTianjinChina China University of Mining and Technology School of Materials Science and PhysicsXuzhouChina Guilin University of Electronic Technology Guangxi Key Laboratory of Optoelectronic Information ProcessingSchool of Optoelectronic EngineeringGuilinChina Oklahoma State University School of Electrical and Computer EngineeringStillwaterOklahomaUnited States

Valley topological photonic crystals(TPCs),which are robust against local disorders and structural defects,have attracted great research interest,from theoretical verification to technical ***,previous works mostly focused on the robustness of topologically protected edge states and little attention was paid to the importance of the photonic bandgaps(PBGs),which hinders the implementation of various multifrequency functional topological photonic ***,by systematically studying the relationship between the degree of symmetry breaking and the working bandwidth of the edge states,we present spoof surface plasmon polariton valley TPCs with broadband edge states and engineered PBGs,where the operation frequency is easy to ***,by connecting valley TPCs operating at different frequencies,a broadband multifunctional frequency-dependent topological photonic device with selectively directional light transmission is fabricated and experimentally demonstrated,achieving the functions of wavelength division multiplexing and add–drop *** provide an effective and insightful method for building multi-frequency topological photonic devices.

关键词： multi-frequency topological device photonic valley Hall effect valley edge state photonic bandgap.

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Efficient Model Training in Decentralized systems with Federated Learning 2

Efficient Model Training in Decentralized Systems with Feder...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Rao, M.S. Srinivasa Saraswathy, S. Neela, V. Arun, M.R. Gupta, Sandeep Taqui, Syed Noeman Vignana Jyothi Institute of Engineering & Technology Vnr Department of Mechanical Engineering Telangana Hyderabad India Sri Sairam Engineering College Department of Computer Science and Business Systems Chennai India School of Computing and Information Technology Reva University Karnataka Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Department of Electrical Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

In the fast-changing field of machine learning, data privacy and model training efficiency are crucial. Federated Learning (FL) is a breakthrough method for training models on several decentralized devices or servers without centralizing raw data. This study examines Federated Learning's challenges in optimizing model training in dispersed systems. The first stage is to examine the Federated Learning (FL) framework and its merits, particularly in protecting data privacy and reducing server dependence. The next section will discuss communication overhead, stragglers, and non-IID (independent and identically distributed) data issues in a decentralized training model. We developed novel optimization methodologies and adaptive algorithms to improve Federated Learning (FL) efficiency. This includes updating asynchronous models, adaptability-based communication frequency, and non-IID data management. We also present a resilient aggregation method that mitigates Byzantine failures in participating nodes. We demonstrate that our methods improve convergence rate, communication costs, and model performance trained with Federated Learning (FL) by conducting a series of comprehensive experiments and analyzing real-world case studies. We show that these enhancements do not compromise data privacy, preserving Federated Learning (FL) principles. In conclusion, this study advances Federated Learning in decentralized systems. In an age of data sensitivity, this approach could help implement efficient, scalable, and privacy-preserving machine-learning methods. © 2023 IEEE.

关键词： Byzantine Failures Communication Efficiency Data Privacy Decentralized systems Federated Learning Model Training Non-IID Data

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