检索结果-内蒙古大学图书馆

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

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

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

Research and simulation on voltage stability control strategy for AC port of power electronic transformer 16

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Research and simulation on voltage stability control strateg...

16th IET International Conference on AC and DC Power Transmission, ACDC 2020

作者： Shen, Yu Nie, Haozhe Cai, Weiqian Yuan, Liqiang Zhang, Chunpeng Zhao, Zhengming State Key Lab of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China

ISBN: (纸本)9781839533914

With wide application of power electronic equipment in power system, voltage stability and power supply reliability need to be improved. In this paper, voltage sag caused by AC port load increase and the influencing factors are studies with a typical power electronic transformer topology as example. A calculation method which can estimate the port voltage sag performance according to the load impedance is proposed. In order to maintain the port voltage stability in the process of load increase, a voltage stability control strategy is proposed based on the double-loop control by increasing the voltage feedforward, which can improve the power supply quality and stability of power electronic transformer. Finally, the MATlab/Simulink simulation results show that the proposed scheme is accurate and effective. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： MATlab

Multi-chamber pneumatic actuator for peristaltic soft robot

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Multi-chamber pneumatic actuator for peristaltic soft robot

2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Gao, Sheng Wang, Yue Xiong, Rong Gong, Zhefeng Zheng, Nenggan Lu, Haojian Zhejiang University Department of Polytechnic Institute Hangzhou310058 China Key Laboratory of Medical Neurobiology of the Ministry of Health of China Key Laboratory of Neurobiology Zhejiang University School of Medicine Zhejiang Lab Department of Neurobiology Hangzhou310058 China Qiushi Academy for Advanced Studies Zhejiang University Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology Hangzhou310058 China Institute of Cyber-Systems and Control Zhejiang University State Key Laboratory of Industrial Control and Technology Hangzhou China

ISBN: (纸本)9781665436786

Soft robots have the characteristics of high flexibility and strong environmental adaptability compared with traditional robots, and the peristaltic soft robot is a critical class for this field because of the peristaltic creature's superior softness, various movement, and can through complex space. However, this soft robot has the problem of single motion or limited deformation ability at present. In this paper, a pneumatic corrugated structure driver with a multi-chamber was designed to squirmy soft robots, it can elongation, contraction and turning. We designed the actuator's structure and fabrication of the model. Simultaneously, a set of controller systems is built to control its deformation, we complete the action of forwarding and turning with the actuator on experiments. © 2021 IEEE.

关键词： Pneumatic actuators

Boosting Black-Box Adversarial Attacks with Meta Learning

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arXiv 2022年

作者： Fu, Junjie Sun, Jian Wang, Gang The State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

Deep neural networks (DNNs) have achieved remarkable success in diverse fields. However, it has been demonstrated that DNNs are very vulnerable to adversarial examples even in black-box settings. A large number of black-box attack methods have been proposed to in the literature. However, those methods usually suffer from low success rates and large query counts, which cannot fully satisfy practical purposes. In this paper, we propose a hybrid attack method which trains meta adversarial perturbations (MAPs) on surrogate models and performs black-box attacks by estimating gradients of the models. Our method uses the meta adversarial perturbation as an initialization and subsequently trains any black-box attack method for several epochs. Furthermore, the MAPs enjoy favorable transferability and universality, in the sense that they can be employed to boost performance of other black-box adversarial attack methods. Extensive experiments demonstrate that our method can not only improve the attack success rates, but also reduces the number of queries compared to other methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks