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DIDO: Deep Inertial Quadrotor Dynamical Odometry

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arXiv 2022年

作者： Zhang, Kunyi Jiang, Chenxing Li, Jinghang Yang, Sheng Ma, Teng Xu, Chao Gao, Fei State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute Zhejiang University Huzhou313000 China Alibaba DAMO Academy Autonomous Driving Lab Hangzhou311121 China

—In this work, we propose an interoceptive-only state estimation system for a quadrotor with deep neural network processing, where the quadrotor dynamics is considered as a perceptive supplement of the inertial kinematics. To improve the precision of multi-sensor fusion, we train cascaded networks on real-world quadrotor flight data to learn IMU kinematic properties, quadrotor dynamic characteristics, and motion states of the quadrotor along with their uncertainty information, respectively. This encoded information empowers us to address the issues of IMU bias stability, quadrotor dynamics, and multi-sensor calibration during sensor fusion. The above multisource information is fused into a two-stage Extended Kalman Filter (EKF) framework for better estimation. Experiments have demonstrated the advantages of our proposed work over several conventional and learning-based methods. © 2022, CC BY.

关键词： Extended Kalman filters

Evaluating the Moderated Mediation Effects Of Switching Costs in the Link between Social Capital and NPD Performance

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SSRN

SSRN 2023年

作者： Li, Yi Ndubisi, Nelson Oly Li, Gang Xu, Jinpeng School of Economics & Management Xidian University Xi’an China College of Business & Economics Qatar University Doha Qatar School of Management Xi’an Jiao tong University Xi'An China The State Key Lab for Manufacturing Systems Engineering Xi’an China The Key Laboratory of the Ministry of Education for Process Control and Efficiency Projects Xi’an China

In this study, we explore the causes of switching costs from both supplier and customer side, discuss the performance outcomes in the setting of new product development, and investigate the role that switching costs play as moderators and mediators in social capital-new product development performance *** on data from 214 Chinese manufacturers, we employ the structural equation model to examine our conceptual model and hypotheses. Our results indicate that relationship quality and customer involvement separately positively and negatively affect switching costs, respectively, which negatively affects new product development performance. Switching costs mediate and moderate the relationship between social capital and new product development performance. Our findings indicate that switching costs significantly and positively moderate the relationship between relationship quality and new product development performance. On the other hand, switching costs insignificantly and negatively moderate the relationship between customer involvement and new product development performance. © 2023, The Authors. All rights reserved.

关键词： Product development

Unexpected Role of Singlet Oxygen Generated in Urea-Modulated Lafeo3 Perovskite Heterogeneous Fenton System in Denitrification: Mechanism and Theoretical Calculations

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SSRN

SSRN 2023年

作者： Qian, Zhen Luo, Mengchao Feng, Xiaohe Yang, Xiaojie Guo, Yongxue Yang, Lijuan Yuan, Bo Zhao, Yi Hao, Runlong Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China

This study appears an unprecedented phenomenon that 1O2, generated in urea-modulated LaFeO3 (U-LaFeO3) perovskite heterogeneous Fenton system, is superior to OH• and O2•- in NO removal. The 2.5U-LaFeO3/H2O2 system can remove 82.9% of NO, with a low H2O2/NO molar ratio (4.7) and high GHSV (30,800 h-1), along with good environmental adaptability and reusability. Urea modulation induced the formations of abundant La defects, O vacancies and Fe(IV), also improved the pore structure and surface area. The contribution order of different free radicals to NO removal is 1O2 > O2•- > •OH. Characterization combined with theoretical calculation clarify that (i) H2O2 is inclined to derive HO2• on Fe sites;(ii) 1O2 is mainly generated from O2•- reaction with Fe(IV)/Fe(III);(iii) •OH is mainly produced by splitting of H2O2 on Fe site and decomposition of HO2• on OV site. The main denitrification paths are NO+1O2→NO2, NO2+•OH→NO3- and NO+•OH→NO2-. © 2023, The Authors. All rights reserved.

关键词： Urea

What Matters for 3D Scene Flow Network

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arXiv 2022年

作者： Wang, Guangming Hu, Yunzhe Liu, Zhe Zhou, Yiyang Tomizuka, Masayoshi Zhan, Wei Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University China Mechanical Systems Control Laboratory University of California Berkeley United States

3D scene flow estimation from point clouds is a low-level 3D motion perception task in computer vision. Flow embedding is a commonly used technique in scene flow estimation, and it encodes the point motion between two consecutive frames. Thus, it is critical for the flow embeddings to capture the correct overall direction of the motion. However, previous works only search locally to determine a soft correspondence, ignoring the distant points that turn out to be the actual matching ones. In addition, the estimated correspondence is usually from the forward direction of the adjacent point clouds, and may not be consistent with the estimated correspondence acquired from the backward direction. To tackle these problems, we propose a novel all-to-all flow embedding layer with backward reliability validation during the initial scene flow estimation. Besides, we investigate and compare several design choices in key components of the 3D scene flow network, including the point similarity calculation, input elements of predictor, and predictor & refinement level design. After carefully choosing the most effective designs, we are able to present a model that achieves the state-of-the-art performance on FlyingThings3D and KITTI Scene Flow datasets. Our proposed model surpasses all existing methods by at least 38.2% on FlyingThings3D dataset and 24.7% on KITTI Scene Flow dataset for EPE3D metric. We release our codes at https://***/IRMVlab/3DFlow. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

Biochar Doped Carbon Nitride to Enhance the Photocatalytic Hydrogen Evolution: Synergy of Nitrogen Vacancies and Bridging Carbon Structure and First-Principles Calculation

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SSRN

SSRN 2023年

作者： Sun, Juan Zhang, Bin Chen, Wenxin Tao, Zichen Liu, Jie Wang, Lidong Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China

Enhancing the separation and transfer of photogenerated carries is vital for improving the efficiency of photocatalytic hydrogen evolution reaction (HER) of graphitic carbon nitride (g-C3N4). Herein, a carbon-rich g-C3N4 with nitrogen vacancies (CCN4) was constructed by incorporating the biochar into g-C3N4 during a thermal polymerization in N2 atmosphere. The doped carbon atoms replaced the bridged nitrogen and increased the delocalization π states. The synergy of the enhanced π states and nitrogen vacancy favored to narrow the bandgap and accelerate the charge separation and migration in catalyst. Meanwhile, the CCN4 catalyst had a favorable kinetics for HER with simpler steps and lower energy barrier than the PCN sample in which H2 production from *H is prior to OH generation. The CCN4 catalyst exhibited excellent HER activity with a hydrogen evolution rate of 3342 μmol·g-1·h-1 with 0.5wt% cocatalyst Pt, which was 5.06 times higher than that of pristine g-C3N4 (PCN). Additionally, CCN4 exhibited good resistance to salt with the concentration near natural seawater, showing the further improved hydrogen evolution rate of 3809 μmol·g-1·h-1. The findings reported in this work shed light on a biochar-tailored protocol to control the defects of g-C3N4 and optimize its intrinsic electronic properties and photocatalytic HER. © 2023, The Authors. All rights reserved.

关键词： Electronic properties

Wind Power Generation Capacity Evaluation Based on Correlation Analysis of Adjacent Wind Farms

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Wind Power Generation Capacity Evaluation Based on Correlati...

2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Sun, Shuxin Qiao, Ying Lu, Zongxiang Ma, Yanhong Tsinghua University State Key Lab of Control and Simulation of Power Systems and Generation Equipments Dept. of Electrical Engineering Beijing100084 China State Grid Gansu Electric Power Company Wind Power Technology Center Lanzhou730050 China

ISBN: (纸本)9781665414395

Accurate assessment of the power generation capacity of the wind farm is the key basis for incorporating it into power system scheduling and other optimizing operation activities. Traditional evaluation methods, such as benchmarking wind turbine method, have higher requirements for input data, but many wind farms in operation for many years are difficult to meet such data conditions. A new power generation capacity evaluation method based on correlation analysis of adjacent wind farms is proposed in this paper. Meteorological data and power of adjacent wind farms are used as replaced input data based on correlation analysis between the target farm with its adjacent farms. First, anticipate wind speed data and then construct the wind power curve with three different neural networks to evaluate power generation capacity. Afterwards, adjacent wind farms are used as data sources to fit the power data of the target wind farm with the application of influence radius method, inverse square distance method and bilinear interpolation method. Finally, combined with data in the actual case, investigate the advantages and disadvantages of different methods in the evaluation of power generation capacity. © 2021 IEEE.

关键词： Time delay

Robust control of Unknown Switched Linear systems from Noisy Data

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arXiv 2023年

作者： Liu, Wenjie Li, Yifei Sun, Jian Wang, Gang Chen, Jie National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chonqing401120 China Department of Control Science and Engineering Tongji University Shanghai201804 China

This paper investigates the problem of data-driven stabilization for linear discrete-time switched systems with unknown switching dynamics. In the absence of noise, a data-based state feedback stabilizing controller can be obtained by solving a semi-definite program (SDP) on-the-fly, which automatically adapts to the changes of switching dynamics. However, when noise is present, the persistency of excitation condition based on the closed-loop data may be undermined, rendering the SDP infeasible. To address this issue, an auxiliary function-based switching control law is proposed, which only requires intermittent SDP solutions when its feasibility is guaranteed. By analyzing the relationship between the controller and the system switching times, it is shown that the proposed controller guarantees input-to-state practical stability (ISpS) of the closed-loop switched linear system, provided that the noise is bounded and the dynamics switches slowly enough. Two numerical examples are presented to verify the effectiveness of the proposed controller. © 2023, CC BY.

关键词： controllers

NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations

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arXiv 2022年

作者： Ran, Yunlong Zeng, Jing He, Shibo Chen, Jiming Li, Lincheng Chen, Yingfeng Lee, Gimhee Ye, Qi The College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China Fuxi AI Lab NetEase Inc. Hangzhou China The Department of Computer Science National University of Singapore Singapore

Implicit neural representations have shown compelling results in offline 3D reconstruction and also recently demonstrated the potential for online SLAM systems. However, applying them to autonomous 3D reconstruction, where a robot is required to explore a scene and plan a view path for the reconstruction, has not been studied. In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one. To solve the challenges, firstly, a proxy of Peak Signal-to-Noise Ratio (PSNR) is proposed to quantify a viewpoint quality;secondly, the proxy is optimized jointly with the parameters of an implicit neural network for the scene. With the proposed view quality criterion from neural networks (termed as Neural Uncertainty), we can then apply implicit representations to autonomous 3D reconstruction. Our method demonstrates significant improvements on various metrics for the rendered image quality and the geometry quality of the reconstructed 3D models when compared with variants using TSDF or reconstruction without view planning. Project webpage https://***/NeurAR/. Copyright © 2022, The Authors. All rights reserved.

关键词： Computer vision

Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization

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arXiv 2023年

作者： Wang, Yabo Qi, Bo Ferrie, Chris Dong, Daoyi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Center for Quantum Software and Information University of Technology Sydney UltimoNSW2007 Australia CIICADA Lab School of Engineering Australian National University CanberraACT2601 Australia

Parameterized quantum circuits (PQCs) have been widely used as a machine learning model to explore the potential of achieving quantum advantages for various tasks. However, training PQCs is notoriously challenging owing to the phenomenon of plateaus and/or the existence of (exponentially) many spurious local minima. To enhance trainability, in this work we propose an efficient parameter initialization strategy with theoretical guarantees. We prove that by reducing the initial domain of each parameter inversely proportional to the square root of circuit depth, the magnitude of the cost gradient decays at most polynomially with respect to qubit count and circuit depth. Our theoretical results are substantiated through numerical simulations of variational quantum eigensolver tasks. Moreover, we demonstrate that the reduced-domain initialization strategy can protect specific quantum neural networks from exponentially many spurious local minima. Our results highlight the significance of an appropriate parameter initialization strategy, offering insights to enhance the trainability and convergence of variational quantum algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum electronics