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IEEE International Conference on robotics and Biomimetics

作者： Yongxiong Xiao Shiqiang Zhu Wei Song Minhong Wan Jason Gu Te Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

The interference-plus-noise covariance matrix (INCM) is essential in improving an acoustic beamformer's interference and noise attenuation performance. In practical implementation, INCM reconstruction is required to remove the signal of interest (SOI) components from the sample covariance matrix. However, some of the interference and noise components are inevitably removed during the INCM reconstruction process to avoid distortion of the desired speech, which deteriorates the interference and noise attenuation performance of the beamformer. This paper proposes constructing an INCM with as much information on the interferences and noise as possible by adding covariance matrices of the spherically diffuse noise, background noise, and interferences. The final INCM is reconstructed by using the principal eigenvector and definition of INCM. The beamformer's weight coefficients are computed by the linearly constrained minimum variance (LCMV) formulation. The proposed method is validated by experiments using a circular microphone array mounted on a tour robot in an exhibition hall. The results show that the proposed beamformer improves the robustness of automatic speech recognition and the performance of robot audition.

关键词： Acoustic distortion Transfer functions Interference Attenuation Acoustics Robustness Covariance matrices

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Aerodynamic Simulation of a Typical Multirotor e VTOL Aircraft Configuration *

Aerodynamic Simulation of a Typical Multirotor e VTOL Aircra...

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IEEE International Conference on robotics and Biomimetics

作者： Menglong Ding Yanyan Zhang Shiqiang Zhu Wei Song Pengyu Zhao Xufei Yan Jiandong Cai Jason Gu Anhuan Xie Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

The aerodynamic interaction among rotors is a key phenomenon that influences the performance of the electric Vertical Takeoff and Landing (eVTOL) aircraft. This study applied Lattice-Boltzmann Method (LBM) to systematically investigate the aerodynamic interaction under typical flight conditions. Simulation of a single rotor was performed to validate the reliability of LBM on rotor aerodynamics and find the maximum acceptable grid size. A numerical study on the complex unsteady flowfield caused by the sixteen rotors was also performed when considering the eVTOL is in hovering, forward flight and hovering under sidewind. Moreover, the analysis of the flowfield provides a detailed insight into the flow physics involved in such regular flight conditions.

关键词： Visualization Atmospheric modeling Rotors Aerodynamics Numerical models Reliability Aircraft

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Motion Planning Integrated with Vehicle-Terrain Interactions for Off-Road Autonomous Ground Vehicles 27

Motion Planning Integrated with Vehicle-Terrain Interactions...

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27th IEEE International Conference on intelligent Transportation systems, ITSC 2024

作者： Yin, Jianhua Zhan, Xianyuan Ji, Tianying Xu, Bingrong He, Yi Zhang, Daqing Li, Lingxi Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan430063 China Engineering Research Center of Transportation Information and Safety Ministry of Education Wuhan430063 China Chongqing Research Institute Wuhan University of Technology Chongqing China Institute for Ai Industry Research Tsinghua University Beijing100084 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Tsinghua University Department of Computer Science and Technology Beijing100084 China School of Automation Wuhan University of Technology Wuhan430070 China College of Mechanical and Electrical Engineering Hunan Agricultural University Changsha410128 China Sunward Intelligent Equipment Co. Ltd. Changsha410100 China Elmore Family School of Electrical and Computer Engineering College of Engineering Purdue University IN46202 United States

ISBN: (纸本)9798331505929

Off-road autonomous ground vehicles (AGVs) work in unstructured environments with varied elevation and deformable ground, where vehicle-terrain interactions are more complex resulting in unpredictable dynamics behavior. Current sensor-based hazards/obstacle identification methods mainly focus on geometric hazards/obstacles with explicit geometry, which might be adequate for vehicles in on-road conditions. However, in the off-road environment, vehicle-terrain interactions could introduce non-geometric hazards that cannot be detected by current geometric hazards-oriented sensing techniques, e.g. vehicle stuck in mud. To enable AGVs to traverse the target terrain and meet the goal while avoiding non-geometric hazards, this work proposes a motion planning method integrated with coupled vehicle-terrain dynamics response. Specifically, terramechanics-based modeling and simulation (M&S) is utilized to characterize vehicle-terrain interactions. Based on the dynamics response, a reinforcement learning (RL) algorithm with a specialized reward function is used to navigate the AGV to find the goal with high mobility and safety. We demonstrate the proposed method with two case studies, 1) high mobility is the foremost concern with less safety requirement, and 2) both high mobility and safety are required. The results show that the proposed motion planning method is effective for navigating the AGV in off-road terrains. © 2024 IEEE.

关键词： Off road vehicles

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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IEEE International Conference on robotics and Biomimetics

作者： Tian Xiang Qiwei Meng Ji Zhang Beibei Zhang Wei Song Anhuan Xie Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong HKSAR China Research Center for Intelligent Computing Software Research Institute of Intelligent Computing Zhejiang Laboratory Hangzhou China Electrical and Computer Engineering Dalhousie University Halifax Canada

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model’s partitioning strategy. It also serves as an important metric influencing the model’s suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future.

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Interconnectedness of Circular Economy, Artificial Intelligence and Vertical Farming as a Technosignature Model for Sustainable Space Food Production 15

Interconnectedness of Circular Economy, Artificial Intellige...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Ii, Ronnie Concepcion Cabaccan, Carlo Ducut, Jullian Dominic Relano, R-Jay Alejandrino, Jonnel Agulto, Ronnel Bandala, Argel Mohamad, Roslina Dadios, Elmer Culaba, Alvin Department of Manufacturing Engineering and Management De la Salle University Manila Philippines De la Salle University Center for Engineering and Sustainability Development Research Intelligent Systems Research Unit Manila Philippines De la Salle University Bio-energy Environment and Renewables Laboratory Center for Engineering and Sustainability Development Research Manila Philippines Plant and Soil Health Research Unit Center for Natural Sciences and Environmental Research De la Salle University Manila Philippines School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines School of Electrical Engineering College of Engineering Universiti Teknologi Mara Shah Alam Malaysia De la Salle University Department of Electronics and Computer Engineering Manila Philippines De la Salle University Department of Mechanical Engineering Manila Philippines

ISBN: (纸本)9798350381177

The degradation of Earth's natural resources and environmental health due to improper anthropological activities, mainly through unoptimized food production, is manifested through climate change that negatively alters the productivity in the agricultural sector. Because of this challenge in open terrestrial agriculture, the concept of space farming is believed to be significant in delivering fresh and quality food for the people on top of providing for astronauts and making them healthy in space. However, another problem emerges in the view of circular economy (CE) which is a production and consumption idealism that avoids degradation of natural resources and environment in producing one food unit and despite the collaborative advances in space technology, particularly in astrobotany, the materialization of CE and the absolutism of artificial intelligence (AI) are still partly imaginary. This study employed a bibliometric, scientometric and systematic analysis to highlight the advantages, limitations, and connections of CE, AI, and vertical farming (VF) which led to establishing a technosignature model for sustainable space food production. Here, CE, AI and VF were singularized as a model for sustainable space food production and identified their potential interplay. Based on scientific correlations, it is undoubtedly true that CE, AI, and VF have existing mutualism in Earth-based systems, but the degree of its importance as a whole is not yet completely appreciated for space farming. The developed CE-AI-VF technosignature model is scalable and adaptable if the government and scientific community put it into action accompanied by science policies. © 2023 IEEE.

关键词： Climate change

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A Target-based Calibration Method for LiDAR-Visual-Thermal Multi-Sensor System

A Target-based Calibration Method for LiDAR-Visual-Thermal M...

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IEEE International Conference on robotics and Biomimetics

作者： Songyu Yuan Tian Xie Shiqiang Zhu Yeheng Chen Yuehua Li Tao Zheng Jason Gu Zhejiang Lab Research Center for Intelligent Robotics Research Institute of Interdis-ciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

As the basis of multi-sensor fusion, accurate extrinsic calibration among multi-sensors is vital for hetero-geneous information fusion. However, most existing methods only focus on the calibration between two specific heteroge-neous sensors, such as camera-LiDAR, LiDAR-thermal, and etc. which may cause inevitable accumulation when applied to a multi-sensor system. To address this problem, a novel calibration target and a high-accuracy method are proposed to simultaneously calibrate the intrinsic and extrinsic of a 3D LiDAR, a thermal camera and a visible camera without any user intervention. The proposed method only relies on one calibration board, to calibrate all the intrinsic and extrinsic parameters in one-step, without any strict pose requirement or long-time optimization. Furthermore, 2D-3D corresponding features can be extracted with higher precision by considering sensor model, comparing to the common feature extraction methods. Experiments with Velodyne VLP-16 LiDAR, ZED camera and D843NT thermal camera demonstrate that the proposed method can complete calibration among three of them automatically. Finally, this paper also presents a target-based method of uniformly expressing the calibration errors among multi-sensor system, with a competitive performance against most state-of-the-art methods.

关键词： Laser radar Three-dimensional displays Sensor phenomena and characterization Cameras Feature extraction Sensor systems Robustness

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A Data-and-Model-Driven Acceleration Approach for Large-Scale Network-Constrained Unit Commitment Problem With Uncertainty

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IEEE Transactions on Sustainable Energy 2025年

作者： Zhou, Yuzhou Han, Zhihan Zhai, Qiaozhu Wu, Lei Cao, Xiaoyu Guan, Xiaohong Xi'an Jiaotong University Systems Engineering Institute MOEKLINNS Lab Xi'an710049 China Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Xi'an Jiaotong University Systems Engineering Institute Xi'an710049 China Stevens Institute of Technology Department of Electrical and Computer Engineering Hoboken07030 United States Tsinghua University Department of Automation Center for Intelligent and Networked Systems Beijing100084 China

Numerous binary variables and constraints greatly affect the computation of large-scale network-constrained unit commitment (NCUC) problems. With the integration of massive renewable energy and distributed loads, binary variables and network power flows face more frequent changes to cope with uncertainties. How to ensure the calculation efficiency and accuracy of NCUC problems against uncertainties and frequent changes becomes a common challenge for academia and industry. Inspired by data-driven and model-driven technologies, this paper presents a hybrid reduction approach for variables and constraints to accelerate the solving of large-scale NCUC problems, which achieves "one plus one much greater than two" effect. Specifically, an offline database of nodal net load clusters and the corresponding NCUC solutions is established, which also captures the impacts of changes in renewables, loads, topology, etc. on UC solutions. With this database, a data-and-modeldriven reduction method is proposed to fix binary variables and reduce constraints, while considering the changes of prices and net loads in online NCUC instances. Moreover, the reduction method was further developed to accelerate various NCUC problems, including scenario-based methods, robust optimization methods, etc. Thus, the computation burden of online large-scale NCUC problems is greatly mitigated while ensuring good solution quality. Numerical tests implemented on IEEE 24-bus system, 118-bus system, and Polish 2383-bus system illustrate the efficiency of the proposed approach. © 2025 IEEE. All rights reserved.

关键词： Mendelevium

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Dual-beam photothermal material trace detection method based on self-mixing interference

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Applied Optics 2025年第16期64卷 4549-4558页

作者： Yan Zhao Yunlong Zhi Hui Zhao Daming Dong Jigang Tong Chunling Tu School of Electrical Engineering and Automation Tianjin University of Technology Tianjin 300384 China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems Tianjin 300384 China National Engineering Research Center for Intelligent Agricultural Equipment Beijing Academy of Agriculture and Forestry Sciences Beijing 100097 China Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria 0188 South Africa

In this paper, a dual-beam photothermal self-mixing substance trace detection system is proposed. The crystal violet (CV) solution of the sample undergoes a thermal lens effect under the action of pump photoperiod excitation, and the refractive index changes cause the deviation of the probe beam, and the self-mixing interference (SMI) effect occurs when the modulated light returns to the cavity. By combining theoretical and experimental results, the power fluctuation of the SMI signal caused by the thermo-optic effect is linearly related to CV concentration and other parameters. At an excitation power of 24 mW, the detection limit can reach $8.0 \times {10^{- 8}}\;{\rm mol/L}$ . This method does not require labeling or complex CV pretreatment and has high sensitivity and flexibility, providing a guide for CV characterization in biological, environmental, and pharmaceutical research.

关键词： Gaussian beams Laser beams Optical fields Phase shift Refractive index Spatial resolution

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σ-ZERO: GRADIENT-BASED OPTIMIZATION OF 0-NORM ADVERSARIAL EXAMPLES

arXiv

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arXiv 2024年

作者： Cinà, Antonio Emanuele Villani, Francesco Pintor, Maura Schönherr, Lea Biggio, Battista Pelillo, Marcello Department of Computer Science Bioengineering Robotics and Systems University of Genoa Italy Department of Electrical and Electronic Engineering University of Cagliari Italy CISPA Helmholtz Center for Information Security Germany Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Italy

Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider 2- and ∞-norm constraints to craft input perturbations, only a few investigate sparse 1- and 0-norm attacks. In particular, 0-norm attacks remain the least studied due to the inherent complexity of optimizing over a non-convex and non-differentiable constraint. However, evaluating adversarial robustness under these attacks could reveal weaknesses otherwise left untested with more conventional 2- and ∞-norm attacks. In this work, we propose a novel 0-norm attack, called σ-zero, which leverages a differentiable approximation of the 0 norm to facilitate gradient-based optimization, and an adaptive projection operator to dynamically adjust the trade-off between loss minimization and perturbation sparsity. Extensive evaluations using MNIST, CIFAR10, and ImageNet datasets, involving robust and non-robust models, show that σ-zero finds minimum 0-norm adversarial examples without requiring any time-consuming hyperparameter tuning, and that it outperforms all competing sparse attacks in terms of success rate, perturbation size, and efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Tapered CPW Transmission Line for Enlarging Dynamic Range of Transmission Spectroscopy

Tapered CPW Transmission Line for Enlarging Dynamic Range of...

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European Conference on Microwave

作者： Marie Mertens Raphaël Trouillon Ke Wu Bart Nauwelaers Tomislav Markovic Dominique Schreurs Department of Electrical Engineering Division WaveCoRE KU Leuven Belgium Department of Electrical Engineering Poly-Grames Research Center Polytechnique Montreal Canada Department of Electrical Engineering Polytechnique Montreal Canada TransMedTech Research Institute Canada Department of Electronics Microelectronics Computer and Intelligent Systems University of Zagreb Croatia

We propose a new coplanar waveguide (CPW) technique to enhance transmission efficiency and field interaction through a biological sample in two-port transmission line measurements. The aim is to overcome low dynamic range and enlarge parametric sensitivity in dielectric characterization. The new design consists of a CPW line with adapted dimensions in its sensing region to obtain a loaded characteristic impedance of 50 $\Omega$. Glucose measurements show improved distinguishability in concentration. Furthermore, the transmission shows lower ripple at low frequencies (1-4 GHz) and improvement of 4 to 7 dB at higher frequencies (5-10 GHz) for our board loaded with PC12 cell suspensions. In addition, lower reflection is observed.

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