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Innovative Smart Grid Technologies (ISGT)

作者： Cecilia Klauber Hannah Burroughs Alec Zhou Cyber and Infrastructure Resilience Lawrence Livermore National Laboratory Livermore CA USA Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA

As the number of and capabilities of distributed and inverter-based energy resources increase, there is also an increasing opportunity to utilize such devices to provide resilience to large-scale power outages. During traditional power system restoration, natural hazard disruption or cyber attack of a central controller could cause the necessary processes to halt, but self-determining, decentralized schemes provide new opportunities for robustness. Therefore, a collaborative, autonomous restoration method is proposed and demonstrated in the field on a test microgrid with distributed energy resources. The methodology and test set-up is described and field demonstration results provided. The novel method is shown to effectively black start the microgrid using grid-forming inverter-based resources in a decentralized fashion, enabling future grid reliability even in the face of natural hazards and cyber disruptions.

关键词： Collaboration Process control Microgrids Reliability theory Hazards Robustness Power system reliability

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Improved Multistep Model Predictive Control for Six-Step Operation of Traction PMSM Drives

Improved Multistep Model Predictive Control for Six-Step Ope...

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Workshop on Predictive Control of electrical Drives and Power Electronics (PRECEDE)

作者： Zhenyao Sun Shuai Xu Guangtong Ma Juri Jatskevich State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong University Chengdu China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

Model predictive control (MPC) has recently been considered for many permanent magnet synchronous machine (PMSM) drive applications. However, the conventional MPC lacks accurate current tracking and fast dynamic responses in the overmodulation region and the six-step operation, which limits its application areas. This paper proposes a six-step operation scheme based on MPC for PMSM drives in traction applications. The current trajectory long-horizon prediction and the average current-based objective function are introduced to determine the optimal switching instants. To enhance the method’s precision, a trapezoidal integration rule has been used in the prediction step. The voltage angle control principle is introduced to clamp the voltage vectors. The effectiveness of the proposed method has been validated by detailed studies. The proposed method is demonstrated to have excellent current tracking accuracy and fast dynamic response in six-step operation, which is an advantage over existing methods.

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Multimodal Joint Prediction of Traffic Spatial-Temporal Data with Graph Sparse Attention Mechanism and Bidirectional Temporal Convolutional Network

arXiv

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arXiv 2024年

作者： Zhang, Dongran Yan, Jiangnan Polat, Kemal Alhudhaif, Adi Li, Jun Guangdong Provincial Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Department of Electrical and Electronics Engineering Faculty of Engineering Bolu Abant Izzet Baysal University Bolu Turkey Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia

Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is relatively limited research on joint prediction across different transportation modes. Furthermore, existing multimodal traffic joint modeling methods often lack flexibility in spatial-temporal feature extraction. To address these issues, we propose a method called Graph Sparse Attention Mechanism with Bidirectional Temporal Convolutional Network (GSABT) for multimodal traffic spatial-temporal joint prediction. First, we use a multimodal graph multiplied by self-attention weights to capture spatial local features, and then employ the Top-U sparse attention mechanism to obtain spatial global features. Second, we utilize a bidirectional temporal convolutional network to enhance the temporal feature correlation between the output and input data, and extract inter-modal and intra-modal temporal features through the share-unique module. Finally, we have designed a multimodal joint prediction framework that can be flexibly extended to both spatial and temporal dimensions. Extensive experiments conducted on three real datasets indicate that the proposed model consistently achieves state-of-the-art predictive performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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Data Association for Maneuvering Targets Through a Combined Siamese Network and Xgboost Model

SSRN

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SSRN 2023年

作者： Gao, Chang Yan, Junkun Chen, Bo Varshney, Pramod K. Jia, Tianyi Liu, Hongwei National Laboratory of Radar Signal Processing Xidian University Xian710071 China Department of Electrical Engineering and Computer Science Syracuse University NY13244 United States

Data association plays an important role in forming target tracks when false alarms exist. Its accuracy is key to reducing the computational burden of the combinatorial explosion problem inherent to target tracking in environments where false alarms are densely distributed. Traditional methods are usually developed according to some assumed motion models and suffer performance degradation when there is mismatch between assumed models and the actual motion, which may often be the case for maneuvering targets. To cope with this problem, we propose a data-driven method that learns the association criteria directly from data. By use of the domain knowledge and a convolutional Siamese network, features of different attributes present in the observations are extracted. Then the features are fused through the use of XGBoost. Simulation results show that the proposed method performs better than the traditional model-based methods in correlating observations of maneuvering targets and avoiding correlations of false alarms. By visualizing the decision process of the feature fusion model, it is found that the proposed method learns from the data to make comprehensive use of multiple features and does not simply set hard thresholds for the features. The computational complexity is also analyzed both theoretically and experimentally. © 2023, The Authors. All rights reserved.

关键词： Target tracking

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MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutional Networks for 3D Human Pose Estimation

MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutiona...

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IEEE International Conference on Robotics and Biomimetics

作者： Shanshan Ji Qiwei Meng Wen Wang Zheyuan Lin Te Li Minhong Wan Chunlong Zhang Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive actions. Recently, graph convolutional networks (GCNs) have been increasingly used for 2D to 3D pose estimation tasks since the skeleton topologies can be viewed as graph structures. In this paper, we propose a novel graph convolutional network architecture, Multi-scale Multi-branch Fusion Graph Convolutional Networks (MSMB-GCN), for 3D Human Pose Estimation(3D HPE) task. The proposed model consists of multiple GCN blocks with a multi-branch architecture. This multi-branch architecture enables the model to get multi-scale features for human skeletal representations. The group of GCN blocks, which has strong multi-level feature extraction capabilities, allows the model to learn global and local features, lower-level and higher-level features. Experiment results on the HumanPose benchmark demonstrate that our model outperforms the state-of-the-art and ablation studies validate the effectiveness of our approach.

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Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training

arXiv

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arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Das, Rohan Kumar Gao, Xiaoxue Wang, Jiadong Li, Haizhou The Department of Electrical and Computer Engineering National University of Singapore Singapore The Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Fortemedia Singapore138637 Singapore Institute for Infocomm Research A*STAR Singapore The Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China The Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore The University of Bremen 28359 Germany

Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However, there is a noticeable gap in addressing the challenges from real-world AV-ASD scenarios. Due to the presence of low-quality noisy videos in such cases, AV-ASD systems without a selective listening ability are short of effectively filtering out disruptive voice components from mixed audio inputs. In this paper, we propose a Multi-modal Speaker Extraction-to-Detection framework named ‘MuSED’, which is pre-trained with audio-visual target speaker extraction to learn the denoising ability, then it is fine-tuned with the AV-ASD task. Meanwhile, to better capture the multi-modal information and deal with real-world problems such as missing modality, MuSED is modelled on the time domain directly and integrates the multi-modal plus-and-minus augmentation strategy. Our experiments demonstrate that MuSED substantially outperforms the state-of-the-art AV-ASD methods and achieves 95.6% mAP on the AVA-ActiveSpeaker dataset, 98.3% AP on the ASW dataset, and 97.9% F1 on the Columbia AV-ASD dataset, respectively. We will publicly release the code in due course. © 2024, CC BY.

关键词： Extraction

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Multi-View 2D to 3D Lifting Video-Based Optimization: A Robust Approach for Human Pose Estimation with Occluded Joint Prediction*

Multi-View 2D to 3D Lifting Video-Based Optimization: A Robu...

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

作者： Daniela Rato Miguel Oliveira Vítor Santos Angel Sappa Bogdan Raducanu Department of Mechanical Engineering Institute of Electronics and Informatics Engineering of Aveiro Campus Universitário de Santiago University of Aveiro Aveiro Portugal LASI - Intelligent System Associate Laboratory Portugal Edificio O Computer Vision Center Barcelona Spain ESPOL Polytechnic University Guayaquil Ecuador

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In the context of robotics, accurate 3D human pose estimation is essential for enhancing human-robot collaboration and interaction. This manuscript introduces a multi-view 2D to 3D lifting optimization-based method designed for video-based 3D human pose estimation, incorporating temporal information. Our technique addresses key challenges, namely robustness to 2D joint detection error, occlusions, and varying camera perspectives. We evaluate the performance of the algorithm through extensive experiments on the MPI-INF-3DHP dataset. Our method demonstrates very good robustness up to 25 pixels of 2D joint error and shows resilience in scenarios involving several occluded joints. Comparative analyses against existing 2D to 3D lifting and multi-view methods showcase good performance of our approach.

关键词： Three-dimensional displays Accuracy Pose estimation Robot vision systems Human-robot interaction Cameras Robustness Intelligent robots Resilience

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Polarization-Space Modulation with Reconfigurable Intelligent Surfaces, Robust Estimation and Quaternion Neural Networks

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IEEE Transactions on Antennas and Propagation 2025年

作者： Buvarp, Anders M. Mili, Lamine Mishra, Kumar Vijay Zaghloul, Amir I. Virginia Tech Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States U.S. Army Research Laboratory AdelphiMD20783 United States

Contemporary communications systems use large arrays in order to exploit the spatial domain requiring multiple radio-frequency (RF) chains leading to prohibitive cost and power consumption. Spatial degrees of freedom are also achieved by utilizing the polarization states of an electromagnetic wave. To this end, we propose a polarization-state modulation system based on carrier-wave reflections from a reconfigurable intelligent surface (RIS), where a sequence of data bits is mapped to polarization states. We consider a system with an upper millimeter-wave or low-THz RF source so that the channel model is line-of-sight-dominant. The data symbol constellation consists of 16 quaternion-valued symbols, of which two correspond to linear polarizations and the remaining twelve represent elliptical polarizations. Our channel models consist of both additive white Gaussian noise and impulse noise. We utilize the Weiszfeld algorithm and generalized M-estimators (GM-estimators) to handle the impulse noise and robustly decode the polarization-space modulated (PSM) signals. Furthermore, we train and evaluate quaternion neural networks (QNN) for decoding PSM signals using seven different activation methods. Our numerical experiments indicate that the RIS is capable of directing signal power toward the location of the receiver. The bit error rate performance of our QNNs and robust decoders exceeds that of OFDM-BPSK. © 1963-2012 IEEE.

关键词： Impulse noise

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Spectrally-Divided Broadband Chirped Pulse Amplification using a Compact Zigzag Compressor

Spectrally-Divided Broadband Chirped Pulse Amplification usi...

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2024 Laser Applications Conference, LAC 2024

作者： Guo, Zh. Kaksis, E. Stummer, V. Pugžlys, A. Zeng, H. Baltuška, A. School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Photonics Institute Vienna University of Technology Gußhausstrasse 27-387 ViennaA-1040 Austria Center for Physical Sciences & Technology Savanoriu Ave. 231 VilniusLT-02300 Lithuania State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai200062 China

We propose a scalable folding geometry for CPA dispersion management that allows a 10-fold increase of the stretched pulse duration and a 3-fold increase of damage-limited extractable pulse energy compared to a standa... 详细信息

关键词： Compressors

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Coherent and incoherent laser pump on a five-level atom in a strongly coupled cavity-QED system

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Physical Review A 2022年第1期105卷 013701-013701页

作者： Asha Devi Sarath D. Gunapala Malin Premaratne Advanced Computing and Simulation Laboratory Department of Electrical and Computer Systems Engineering Monash University Clayton Victoria 3800 Australia Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 USA

We propose a cavity quantum electrodynamic system consisting of a five-level atom coupled to a single mode of the cavity electromagnetic field. The study is focused on the regime of strong coupling between the cavity and atom. Pump laser fields and cavity fields connect the split energy levels of the atom. Instead of the well-known two-level Dicke model obtained by adiabatic elimination of the high-energy levels, we consider the pump lasers' detunings to the atomic transitions to be very small such that we can examine the influence of the higher-energy states. We have studied the effect of an external coherent drive and incoherent pumping on these higher-energy levels and observed the enhancement of intracavity photon numbers due to quantum coherence effects. The amplification of intracavity photons is achieved even without a population inversion. However, the effect of the coherent and incoherent drive is negligible for very large detunings when the higher-energy states are adiabatically eliminated. At zero and small detunings, the system reaches the steady state at an earlier instant of time for higher incoherent pumping. We find an almost agreeable steady-state behavior of the system's exact full quantum dynamics model and its semiclassical approximation. Our model tries to accurately simulate the open system by considering the cavity decay, spontaneous decay, and dephasing of the system.

关键词： Atoms, ions, & molecules in cavities Cavity quantum electrodynamics Nanophotonics

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