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第41届中国控制会议

作者： Yun Zhang Yunsheng Fan Yuanyuan Yu Dongdong Mu Xiaojie Sun College of Marine Electrical Engineering Dalian Maritime University Key Laboratory of Technology and System for Intelligent Ships of Liaoning Province College of Information Engineering Henan University of Science and Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Autonomous planning of navigation trajectory is a key research element of USV and a basic condition for safe and autonomous *** this paper,an autonomous planning collision avoidance control algorithm for USV is *** is used to trajectory planning and dynamic window method for real-time collision avoidance,a hybrid planning algorithm of dynamic path planning algorithm and parallel planning of autonomous collision avoidance control algorithm is built to achieve autonomous navigation control of *** setting method of local target points has been improved to shorten the running time of the control *** validation was carried out in different static and dynamic environments.A realistic collision avoidance environment model is established by extracting the environmental data from the port map of Dalian *** result demonstrates that the method is effective in controlling the USV to sail safely and quickly in complex real scenarios with various dynamic impediments.

关键词： Dynamic window method Unmanned Surface Vehicle Autonomous obstacle avoidance

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Enhanced LOCA model for accurate few-shot counting of warehouse goods 11

Enhanced LOCA model for accurate few-shot counting of wareho...

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Optical Metrology and Inspection for Industrial Applications XI 2024

作者： Cui, Kaibin Sun, Ting Deng, Lei Chen, Baohua School of Instrument Science and Opto-Electronics Engineering Beijing Information Science and Technology University Beijing China Laboratory of Intelligent Microsystems Beijing Information Science and Technology University Beijing China Department of Automation Tsinghua University Beijing China

ISBN: (纸本)9781510682108

This paper aims to address the limitations of traditional methods in adaptability and reliability by enhancing the precision of goods counting in logistics warehousing environments. Recent advancements in deep learning offer promising solutions to these challenges. Leveraging visual technology, our study proposes enhancements to the A Low-Shot Object Counting Network With Iterative Prototype Adaptation (LOCA) counting network. Specifically, we introduce the aspect ratio as a new feature for extraction, which enhances the model’s capability to capture object characteristics effectively. In the image feature extraction module, vision mamba is introduced to compress visual representations using a bidirectional state space model(SSM). Furthermore, we augment the loss function by integrating the Structural Similarity Index (SSIM) alongside the existing Mean Squared Error (MSE) loss. This augmentation enables the model to maintain pixel-level accuracy while preserving crucial structural information within the images. The experimental results on the test set demonstrate a significant enhancement in Mean Absolute Error (MAE) metrics(over 30%) and Root of Mean Squared Error(RMSE)(over 30%), thereby validating the effectiveness and generalization capability of the enhanced model. Notably, the introduction of vision mamba, aspect ratios and the SSIM loss function contributes to the model’s improved performance, facilitating more accurate and reliable goods counting. The dataset used in this study originates from real-world warehouse environments, comprising over 1000 annotated images. These annotations encompass two types: points and bounding boxes, which play a crucial role in the development of few-shot counting models. By integrating these innovative features and loss functions, the enhanced model offers a more accurate counting solution for warehouse goods, showcasing potential applications in achieving high-precision inventory audits. © 2024 SPIE.

关键词： Medical imaging

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Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

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Plant Phenomics 2023年第4期5卷 779-789页

作者： Jianqing Zhao Yucheng Cai Suwan Wang Jiawei Yan Xiaolei Qiu Xia Yao Yongchao Tian Yan Zhu Weixing Cao Xiaohu Zhang National Engineering and Technology Center for Information Agriculture Nanjing Agricultural UniversityNanjing 210095China Key Laboratory for Crop System Analysis and Decision Making Ministry of Agriculture and Rural AffairsNanjing 210095China Jiangsu Key Laboratory for Information Agriculture Nanjing 210095China Jiangsu Collaborative Innovation Center for Modern Crop Production Nanjing 210095China

Accurate wheat spike detection is crucial in wheat field phenotyping for precision *** in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat ***,wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the *** models for wheat spike detection are typically designed for a specific growth *** adaptability to other growth stages or field scenes is *** models cannot detect wheat spikes accurately caused by the difference in color,size,and morphological features between growth *** paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity *** constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection ***,a Detection Network is designed to improve wheat spike detection capability.A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery.A new micro-scale detection layer is added to the network to extract the features of small *** loss is improved by Complete Intersection over Union to reduce the impact of the *** results show that WheatNet can achieve greater accuracy than classical detection *** detection accuracy with average precision of spike detection at the filling stage is 90.1%,while it is 88.6%at the maturity *** suggests that WheatNet is a promising tool for detection of wheat spikes.

关键词： Wheat maturity wheat

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SnP entangled by carbon nanotube networks as anode for pseudocapacitive half/full battery

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Journal of Materiomics 2023年第2期9卷 362-369页

作者： Qian Zhao Dan Zhao Lan Feng Jian Yu Yi Liu Shouwu Guo School of Materials Science and Engineering Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic MaterialsShaanxi University of Science and TechnologyXi'an710021China Department of Electronic Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai200240China

Due to the lower operating voltage and higher theoretical specific capacity,tin phosphide is considered a class of materials with prospects as an anode material for lithium-ion batteries(LIBs).Among them,tin monophosphide has attracted people's attention due to its unique layered ***,because of the challenging synthesis method and metastable nature,the application of SnP is *** this work,tin phosphide/carbon nanotubes(SnP/CNTs)are prepared by controlling the nucleation and adjusting the ratio of phosphorus/carbon using carbon nanotube as ***-MOF is used as a template to make the morphology of SnP more evenly,and carbon nanotubes can also be used as a conductive network to increase the speed of electron *** an anode material for LIBs,SnP/CNTs reveals superior rate performances(reversible capability of 610 mA·h·g^(-1)at 2000 mA·g^(-1)).The full-cell was assembled and tested,after 50 cycles at 0.1 C,the capacity can maintain 292 mA·h·g^(-1),and its capacity retention rate can reach 80.5%.After 230 cycles,its capacity can maintain at around 223 mA·h·g*** addition,SnP/CNTs materials exhibit 89%pseudocapacitance contribution upon cycling,which indicates the robust Lit storage and satisfactory fast-charging ***,SnP/CNTs suggests a promising anode material for energy storage system.

关键词： SnP Lithium-ion storage Carbon nanotubes Sn4P3 Pseudocapacitive

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Flexible Inverse Design of Microwave Filter Customized on Demand With Wavelet Transform Deep Learning

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2025年第3期44卷 806-817页

作者： Xu, Kuiwen Wang, Jianguo Cai, Jialin Ma, Xuetiao Lv, Qinyi Chen, Shichang Liu, Jie Liu, Jun Hangzhou Dianzi University Innovation Center for Electronic Design Automation Technology The Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou310018 China Chang'an Campus Northwestern Polytechnical University School of Electronic Information Xi'an710060 China University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China Information Science Academy China Electronics Technology Group Corporation Beijing100846 China

Artificial intelligence (AI) techniques are increasingly being used for the inverse design of microwave devices. However, several challenges, including intensive computation costs for training samples, high-dimensional data, nonuniformity, and low-quality samples in the design space, can negatively impact the final modeling performance. To alleviate these issues, a high-quality sampling inverse design scheme incorporating wavelet transform deep learning (HQS-WTDL) is proposed to achieve customized, automated microwave filter design. In the forward simulation-based sampling, particle swarm optimization (PSO) is used to tentatively select rule-defined samples. Multilabel synthetic minority over-sampling technique (MLSMOTE) is then applied to enlarge the training samples and improve their uniformity in the design space. An inverse modeling approach using neural networks to map the nonlinear relationship between a given set of S-parameters and required filter structural parameters is presented. Given that the dimension of the S-parameters is much higher than that of the structure parameters, the corresponding neural network used in this approach is deep and complex, with multiple layers and neurons. To reduce the number of input variables, the S-parameters are subjected to wavelet transformation, allowing for more efficient representation by the neural network. The proposed method is validated using a band-pass microstrip hairpin filter as an example. Results demonstrate that the proposed approach achieves better modeling effectiveness and inverse design efficiency than conventional methods. In addition, the proposed method allows for fast customization of device parameters, such as center frequencies and bandwidths with good prediction accuracy. © 1982-2012 IEEE.

关键词： Inverse problems

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Sandwiched dual seed sources master oscillator power amplifier quantum cascade lasers

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Optics Express 2025年第1期33卷 1585-1593页

作者： Lu, Xiyu Tang, Jing Niu, Shan Cheng, Fengmin Zhuo, Ning Zhang, Jinchuan Zhai, Shenqiang Wang, Lijun Liu, Shuman Liu, Fengqi Liu, Junqi Laboratory of Solid-State Optoelectronics Information Technology Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China

To facilitate the development of compact multi-wavelengths lasers, we reported the experimental demonstration of dual seed sources master oscillator power amplifier (MOPA) QCLs. The devices were based on a sandwich configuration consisting of dual seed sources and a central power amplifier. Emission spectrum of devices was modulated by switching seed sources. By utilizing this design, two room temperature continuous-wave output power of 46 mW and 30 mW were obtained at modes 7.35 µm and 7.38 µm, respectively. The problem of power difference at the dual wavelengths have been improved. The two target modes were fine-tuning through current with tuning coefficients -0.0131 cm−1mA−1 and -0.0122 cm−1mA−1, respectively. The reported compact quantum cascade lasers with dual seed sources and fine-tuning characteristics are significant for developing miniaturized and on-chip sensing systems with multi-gas accurate detection. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Quantum cascade lasers

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Adversarial Attacks and Defenses in Image Classification: A Practical Perspective 7

Adversarial Attacks and Defenses in Image Classification: A ...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Chen, Yongkang Zhang, Ming Li, Jin Kuang, Xiaohui National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

The rapid and steady development of machine learning, especially deep learning, has promoted significant progress in the field of image classification. However, Machine learning models are demonstrated to be vulnerable to adversarial examples, which pose serious threats in security-critical applications. This paper summarizes the adversarial attacks and defenses from a practical perspective, facing the field of image classification. We further analyze and evaluate the characteristics and performance of various defense techniques from four aspects: gradient masking, adversarial training, adversarial examples detection and input transformations. We discuss the advantages and disadvantages of different defenses. Finally, the future development trend of defense techniques against adversarial examples is discussed. We hope our study will advance the research in machine learning security. © 2022 IEEE.

关键词： Image classification

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SolarDesign:An online photovoltaic device simulation and design platform

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Chinese Physics B 2025年第1期34卷 135-141页

作者： Wei E.I.Sha Xiaoyu Wang Wenchao Chen Yuhao Fu Lijun Zhang Liang Tian Minshen Lin Shudi Jiao Ting Xu Tiange Sun Dongxue Liu College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Integrated Optoelectronics Key Laboratory of Automobile Materials of MOEKey Laboratory of Material Simulation Methods&Software of MOESchool of Materials Science and EngineeringJilin UniversityChangchun 130012China ZJU-UIUC Institute International CampusZhejiang UniversityHaining 314400China Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China Science and Technology Research Institute China Three Gorges CorporationBeijing 101199China

Solar Design(https://***/)is an online photovoltaic device simulation and design platform that provides engineering modeling analysis for crystalline silicon solar cells,as well as emerging high-efficiency solar cells such as organic,perovskite,and tandem *** platform offers user-updatable libraries of basic photovoltaic materials and devices,device-level multi-physics simulations involving optical–electrical–thermal interactions,and circuit-level compact model simulations based on detailed balance *** internationally advanced numerical methods,the platform accurately,rapidly,and efficiently solves optical absorption,electrical transport,and compact circuit *** achieves multi-level photovoltaic simulation technology from“materials to devices to circuits”with fully independent intellectual property *** to commercial softwares,the platform achieves high accuracy and improves speed by more than an order of ***,it can simulate unique electrical transport processes in emerging solar cells,such as quantum tunneling,exciton dissociation,and ion migration.

关键词： photovoltaic device simulation silicon solar cells organic and perovskite solar cells multi-physics and circuit simulation

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Development of a Bio-inspired Tailless FWMAV with High-Frequency Flapping Wings Trajectory Tracking Control

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Journal of Bionic engineering 2024年第5期21卷 2145-2166页

作者： Qingcheng Guo Chaofeng Wu Yichen Zhang Feng Cui Wu Liu Xiaosheng Wu Junguo Lu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong UniversityShanghai200240China Department of Micro/Nano Electronics School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai200240China Department of Automation Shanghai Jiao Tong UniversityShanghai200240China The Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China

The development of a tailless Flapping Wing Micro Aerial Vehicle(FWMAV)inspired by the hummingbird is presented in this *** implementing mechanical simplifications,it is possible to use planar machining technology for manufacturing of the FWMAV’s body,greatly reducing assembly ***,studies on flapping wing aircraft are limited to open-loop wing kinematics *** this work,an instantaneous closed-loop wing trajectory tracking control system is introduced to minimize wings’trajectory tracking *** control system is based on Field-Oriented Control(FOC)with a loop shaping compensation technique near the flapping *** frequency analysis,the loop shaping compensator ensures the satisfactory bandwidth and performance for the closed-loop flapping *** implement the proposed controller,a compact autopilot board integrated with FOC hardware is designed,weighing only 2.5 *** utilizing precise wing trajectory tracking control,the hummingbird-inspired FWMAV demonstrates superior ability to resist external disturbances and exhibits reduced attitude tracking errors during hovering flight compared to the open-loop wing motion.

关键词： Flapping Wing Micro Aerial Vehicle(FWMAV) Bio-inspired robotics Trajectory control Loop shaping compensator Field-Oriented Control(FOC)

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Thermally induced in situ fabrication of TiO_(2)/CN heterojunction dopant for enhancement of hydrogen storage properties of LiAlH_(4)

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Journal of Materials science & technology 2024年第36期203卷 227-236页

作者： Guorong Zhang Jiaxi Liu Sheng Wei Fen Xu Lixian Sun Yongpeng Xia Hehui Wang Jinfan Wu Yuan Gao Qiwei Shao Yiting Bu Yanxun Guan Lumin Liao Taigen Liang Lina Qin Guangxi Key Laboratory of Information Materials&Guangxi Collaborative Innovation Center for Structure and Properties for New Energy and Materials School of Material Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Herein,a novel TiO_(2)/CN heterojunction material has been prepared by one-step bubble template-assisted calcination to enhance the hydrogen storage capability of LiAlH_(4).The TEM,XPS and UPS analysis confirm that a heterostructure is formed between TiO_(2)and g-C3 N4 *** experimental findings indi-cate that the TiO_(2)/CN significantly enhances the dehydrogenation performance of LiAlH_(4).For instance,the LiAlH_(4)-7 wt%TiO_(2)/CN starts to dehydrogenize at 76℃(94℃ less than pure LiAlH_(4))and releases 6.5 wt%H2 at 200℃.Meanwhile,LiAlH_(4)-7 wt%TiO_(2)/CN releases 4.9 wt%H2 at 120℃ within 50 *** mech-anism analysis illustrates that AlTi3 N is formed in situ during *** density functional theory(DFT)calculation results reveal that the AlTi3 N can weaken the Al-H bonds in LiAlH_(4)through interfacial charge ***,the TiO_(2)/CN heterostructure creates an internal electric field that generates an electron-rich *** a result,the negative electron layer at one end of the TiO_(2)/CN heterojunction has an increased affinity for H,which enhances the dehydrogenation reaction of LiAlH_(4).Clearly,both the TiO_(2)/CN heterostructure and AlTi3 N contribute to the improvement of the dehydrogenation properties of LiAlH_(4).

关键词： Hydrogen storage material LiAlH_(4) TiO_(2) Carbon nitride Heterojunction

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