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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Delbruck, Tobi Wang, Zuowen Mei, Haiyang Haessig, Germain Joubert, Damien Haque, Justin Chen, Yingkai Milde, Moritz B. Gruev, Viktor Univ. of Zurich and ETH Zurich Sensors Group Institute of Neuroinformatics Switzerland Center for Vision Automation & Control High-Performance Vision Systems AIT Austrian Institute of Technology Vienna Austria Western Sydney University Intl. Centre for Neuromorphic Systems The MARCS Institute Sydney Australia University of Illinois at Urbana-Champaign Dept. of Electrical and Computer Engineering UrbanaIL United States

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [... 详细信息

ISBN: (纸本)9798350302493

关键词： Polarization

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Adaptive Admittance Control for Safety-Critical Physical Human Robot Collaboration

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IFAC-PapersOnLine 2023年第2期56卷 1313-1318页

作者： Yuzhu Sun Mien Van Stephen McIlvanna Nhat Nguyen Minh Seán McLoone Dariusz Ceglarek Centre for Intelligent Autonomous Manufacturing Systems School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Northern Ireland UK Warwick Manufacturing Group University of Warwick Coventry UK

Physical human-robot collaboration requires strict safety guarantees, due to the fact that robots and humans work in a shared workspace. This paper presents a novel control framework to handle safety-critical position-based constraints for human-robot physical interaction. The proposed methodology is based on admittance control, exponential control barrier functions (ECBFs), and quadratic program (QP) to achieve compliance during the force interaction between human and robot, while simultaneously guaranteeing safety constraints. In particular, the formulation of admittance control is formulated as a second-order nonlinear control system, and the interaction forces between humans and robots are regarded as the control input. A virtual force feedback for admittance control is provided in real-time by using the ECBFs-QP framework as a compensator of the external human forces. A safe trajectory is therefore derived from the proposed adaptive admittance control scheme for a low-level controller to track. The main innovation of the proposed approach is the ability to enable the robot to naturally comply with human forces without violating any safety constraints, even when external human forces incidentally force the robot to do so. The effectiveness of our approach is demonstrated in simulation studies on a two-link planar robot manipulator.

关键词： Robotics physical human-robot collaboration safety-critical control admittance control control barrier functions robot manipulator

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Feature Correlation Aggregation: on the Path to Better Graph Neural Networks

Feature Correlation Aggregation: on the Path to Better Graph...

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Jieming Zhou Tong Zhang Pengfei Fang Lars Petersson Mehrtash Harandi College of Engineering and Computer Science Australian National University Canberra Australia Image and Visual Representation Laboratory EPFL Lausanne Switzerland Imaging and Computer Vision Group CSIRO Canberra Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia

Prior to the introduction of Graph Neural Networks (GNNs), modelling and analyzing irregular data, particularly graphs, was thought to be the Achilles’ heel of deep learning. The core concept of GNNs is to find a representation by recursively aggregating the representations of a central node and those of its neighbor, and many GNNs’ designs have demonstrated its success. However, most only focus on using the first-order information between a node and its neighbors. In this paper, we introduce a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN, namely the Feature cOrrelation aGgregation (FOG) module, which learns the second-order information from feature correlation between a node and its neighbors in the pipeline. By adding FOG into existing variants of GNNs, we empirically verify this second-order information complements the features generated by original GNNs across a broad set of benchmarks. A tangible boost in the model’s performance is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters. e.g., 26.202% improvement on a real-world molecular dataset using graph convolutional networks.

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Polarization-controlled dual resonant lattice Kerker effects

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Nano Research 2023年第2期16卷 3195-3200页

作者： Lei Xiong Xueqian Zhao Xiang Du Shuai Chen Yuanfu Lu Hongwei Ding Guangyuan Li School of Information Science and Engineering Yunnan UniversityKunming 650500China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Electronic and InformationGuangxi UniversityNanning 530004China College of Physics and Electronic Technology Anhui Normal UniversityWuhu 241000China Tianjin H-Chip Technology Group Corporation Tianjin 300467China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhen 518055China

The generalized Kerker effects have attracted increasing interests in recent years due to their abilities to manipulate the far-field properties of ***,the dual-polarized generalized Kerker effect enabling different tailoring of orthogonally-polarized electromagnetic waves has not yet been ***,we demonstrate polarization-controlled dual resonant lattice Kerker effects in periodic silicon *** varying the incident angle,the electric dipole and magnetic dipole surface lattice resonances can spectrally overlap,causing zero reflectance and unitary transmittance,i.e.,the resonant lattice Kerker *** incident angle for achieving this effect can be tuned differently for s-and p-polarizations over large regions by varying the nanodisk size or the lattice *** proposed dual-polarized resonant lattice Kerker effects open up avenues for polarization-controlled manipulation of the phase and wavefront of light with metasurfaces.

关键词： Kerker effect surface lattice resonances dual-polarized silicon metasurface

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A Data-Driven Distributed Control Scheme: Learning Multi-Objective Agent-Based MPC for Path-Tracking

A Data-Driven Distributed Control Scheme: Learning Multi-Obj...

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International Conference on Intelligent Transportation

作者： Jiaming Zhong Reza Valiollahi Mehrizi Yash Vardhan Pant Amir Khajepour Department of Mechanical and Mechatronics Engineering Mechatronic Vehicle Systems (MVS) Lab University of Waterloo Waterloo ON Canada Department of Electrical and Computer Engineering Control Learning and Logic (CL2) group University of Waterloo Waterloo ON Canada

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Agent-based model predictive control (AMPC) has recently been proposed for vehicle systems with various controllers, such as differential braking and torque vectoring, where controllers are regarded as distributed agents contributing to the same objective. However, this scheme is challenging in handling multiple conflicting objectives with coupled agents. A common approach for such tasks is the integrated MPC, where all objectives and agents are stacked together in one optimization. Nevertheless, as more agents and objectives are involved, the integrated MPC will face challenges like computational burdens and maintenance difficulties in practice. To this end, this paper proposes a learning multi-objective AMPC that can improve design flexibility and computing efficiency. First, under the assumption of information exchange, a multi-objective AMPC tailored from the alternating direction method of multipliers (ADMM) is proposed to decouple the system and achieve the same performance as the integrated scheme iteratively. Second, a learning-based method for initializing iterations is proposed to accelerate convergence. In addition, a data management method is proposed for real-time efficiency, and an authentication module is designed for learning reliability. We compare the proposed scheme against the integrated scheme via a combined path-tracking simulation for autonomous vehicles with various controllers. The proposed scheme achieves the same control performance as the integrated one while reducing the computational time by 43.5 %. Furthermore, the learning-based method saves 88.6% more computational time than without learning, making it suitable for real-time implementation.

关键词： Learning systems Torque Decentralized control Reliability engineering Real-time systems Convex functions Planning Information exchange Convergence Predictive control

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Decision-Focused Learning for Complex System Identification: HVAC Management System Application

arXiv

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arXiv 2025年

作者： Favaro, Pietro Toubeau, Jean-François Vallée, François Dvorkin, Yury Power Systems and Markets Research Group University of Mons Mons Belgium Ralph O’Connor Sustainable Energy Institute Department of Electrical and Computer Engineering Department of Civil and System Engineering Johns Hopkins University BaltimoreMD United States

As opposed to conventional training methods tailored to minimize a given statistical metric or task-agnostic loss (e.g., mean squared error), Decision-Focused Learning (DFL) trains machine learning models for optimal performance in downstream decision-making tools. We argue that DFL can be leveraged to learn the parameters of system dynamics, expressed as constraint of the convex optimization control policy, while the system control signal is being optimized, thus creating an end-to-end learning framework. This is particularly relevant for systems in which behavior changes once the control policy is applied, hence rendering historical data less applicable. The proposed approach can perform system identification — i.e., determine appropriate parameters for the system analytical model — and control simultaneously to ensure that the model’s accuracy is focused on areas most relevant to control. Furthermore, because black-box systems are non-differentiable, we design a loss function that requires solely to measure the system response. We propose pre-training on historical data and constraint relaxation to stabilize the DFL and deal with potential infeasibilities in learning. We demonstrate the usefulness of the method on a building Heating, Ventilation, and Air Conditioning day-ahead management system for a realistic 15-zone building located in Denver, US. The results show that the conventional RC building model, with the parameters obtained from historical data using supervised learning, underestimates HVAC electrical power consumption. For our case study, the ex-post cost is on average six times higher than the expected one. Meanwhile, the same RC model with parameters obtained via DFL underestimates the ex-post cost only by 3%. © 2025, CC BY-NC-ND.

关键词： Convex optimization

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Long-Range Low-Power Wireless Sensor to Monitor Temperature and Humidity of Dairy Calves

Long-Range Low-Power Wireless Sensor to Monitor Temperature ...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Jia Xue Kow Johan Osorio Estevez Ana Flavia Souza Lima Yuezhong Xu Dong Sam Ha Bradley Department of Electrical and Computer Engineering Multifunctional Integrated Circuits and Systems (MICS) Group Virginia Tech Blacksburg Virginia USA School of Animal Sciences Virginia Tech Blacksburg Virginia USA

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

This paper presents a low-power wireless sensor for monitoring the temperature and humidity of dairy calves over extended distances. The wireless sensor consists of a microcontroller with a LoRa ® radio, temperature and humidity sensors, and a power controller. The sensor samples data once every minute and remains in sleep mode most of the time. To save power during sleep, the power controller shuts off power to the sensor. The sensor was prototyped, and its operation was verified in a field test with three calves. The sensor is active for data sensing, processing, and transmission for about 458 ms in each cycle and dissipates an average of 16.13 mW during the active period. It is estimated that the sensor would operate for approximately 33 months using a CR2477 coin cell battery with a sampling rate of one data point per minute.

关键词： Temperature sensors Wireless communication Temperature measurement Wireless sensor networks Temperature distribution Humidity Sensors Batteries Biomedical monitoring Monitoring

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Next-Gen Mental Health Detection through Deep Learning and NLP in Social Media

Next-Gen Mental Health Detection through Deep Learning and N...

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Innovations in Communications, Electrical and computer engineering (ICICEC), International Conference on

作者： Jagan. A Senthil Kumar D S. Karunakaran Nageswari D Regina Shoba Dass J. Seetha Computer Science and Engineering Surya Group of Institutions Vikiravandi Villupuram Artificial Intelligence and Data Science St. Joseph's College of Engineering OMR Chennai Department of Computer Technology Kongu Engineering College Perundurai Erode Department of Science and Humanities R.M.K. College of Engineering and Technology Psychology CMR University Bangalore Department of Computer Science and Business Systems Panimalar Engineering College Chennai

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376524

Social media proliferation promotes new modalities of understanding and intervening in mental health. This paper presents a new approach to mental health detection using some deep learning and natural language processing techniques over social media data. Enriched by extensive textual content shared across social media platforms such as Twitter, Reddit, and Facebook, we propose to design an automated system that can detect traces of mental health conditions like depression, anxiety, and stress. In line with the proposed methodology, Bi-LSTM networks and attention mechanisms shall be applied to exploit linguistic patterns and semantic features from user posts. Such techniques involve the integration of sentiment analysis and topic modelling, which increase the accuracy and contextual comprehension of detected signals. Either way, the system is trained against a curated dataset of annotated social media posts validated further against clinical assessments for strength and reliability, with quite promising preliminary results— Rubin et al.'s model achieves high precision and recall in mental health indicator detection. The results reinforce that AI-driven tools have an assay of potential in rendering early detection and intervention support, especially for mental illness, which has contributed to improved mental health outcomes globally.

关键词： Deep learning Technological innovation Sentiment analysis Accuracy Social networking (online) Blogs Semantics Mental health Web sites Synthetic data

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Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids

Latent Neural ODE for Integrating Multi-timescale measuremen...

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Innovative Smart Grid Technologies (ISGT)

作者： Shweta Dahale Sai Munikoti Balasubramaniam Natarajan Rui Yang Electrical and Computer Engineering Kansas State University Manhattan USA Sensing and Predictive Analytics Group in the Power Systems Engineering Center of the National Renewable Energy Laboratory (NREL) Golden CO

Under a smart grid paradigm, there has been an increase in sensor installations to enhance situational awareness. The measurements from these sensors can be leveraged for real-time monitoring, control, and protection. However, these measurements are typically irregularly sampled. These measure-ments may also be intermittent due to communication bandwidth limitations. To tackle this problem, this paper proposes a novel latent neural ordinary differential equations (LODE) approach to aggregate the unevenly sampled multivariate time-series measurements. The proposed approach is flexible in performing both imputations and predictions while being computationally efficient. Simulation results on IEEE 37 bus test systems illustrate the efficiency of the proposed approach.

关键词： Simulation Aggregates Energy measurement Bandwidth Ordinary differential equations Real-time systems Smart grids

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To Impute or Not: Recommendations for Multibiometric Fusion

arXiv

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arXiv 2024年

作者： Dale, Melissa R. Singer, Elliot Borgström, Bengt J. Ross, Arun Dept of Computer Science and Engineering Michigan State University East LansingMI United States Artificial Intelligence Technology and Systems Group MIT Lincoln Laboratory LexingtonMA United States

Combining match scores from different biometric systems via fusion is a well-established approach to improving recognition accuracy. However, missing scores can degrade performance as well as limit the possible fusion techniques that can be applied. Imputation is a promising technique in multibiometric systems for replacing missing data. In this paper, we evaluate various score imputation approaches on three multimodal biometric score datasets, viz. NIST BSSR1, BIOCOP2008, and MIT LL TRIMODAL, and investigate the factors which might influence the effectiveness of imputation. Our studies reveal three key observations: (1) Imputation is preferable over not imputing missing scores, even when the fusion rule does not require complete score data. (2) Balancing the classes in the training data is crucial to mitigate negative biases in the imputation technique towards the under-represented class, even if it involves dropping a substantial number of score vectors. (3) Multivariate imputation approaches seem to be beneficial when scores between modalities are correlated, while univariate approaches seem to benefit scenarios where scores between modalities are less correlated. © 2024, CC BY.

关键词： Biometrics

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