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BESTAnP: Bi-Step Efficient and Statistically Optimal Estimator for Acoustic-n-Point Problem

IEEE Robotics and automation Letters 2025年第6期10卷 5313-5320页

作者： Sheng, Wenliang Zhao, Hongxu Chen, Lingpeng Zeng, Guangyang Shao, Yunling Hong, Yuze Yang, Chao Hong, Ziyang Wu, Junfeng East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Department of Automation Shanghai200237 China Chinese University of Hong Kong School of Data Science Shenzhen518172 China

We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial spherical coordinates and reveal their inherent relationships to translation and orientation. Based on this, we propose a bi-step efficient and statistically optimal AnP (BESTAnP) algorithm that decouples the estimation of translation and orientation. Specifically, in the first step, the translation estimation is formulated as the range-based localization problem based on distance-only measurements. In the second step, the rotation is estimated via eigendecomposition based on azimuth-only measurements and the estimated translation. BESTAnP is the first AnP algorithm that gives a closed-form solution for the full 6 °-of-freedom (DoF) pose. In addition, we conduct bias elimination for BESTAnP such that it owns the statistical property of consistency. Through simulation and real-world experiments, we demonstrate that compared with the state-of-the-art (SOTA) methods, BESTAnP is over ten times faster and features real-time capacity in resource-constrained platforms while exhibiting comparable accuracy. Moreover, we embed BESTAnP into a single sonar-based odometry which shows its effectiveness for trajectory estimation. © 2016 IEEE.

关键词： Sonar

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InteBOMB: Integrating generic object tracking and segmentation with pose estimation for animal behavior analysis

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Zoological Research 2025年第2期46卷 355-369页

作者： Hao Zhai Hai-Yang Yan Jing-Yuan Zhou Jing Liu Qi-Wei Xie Li-Jun Shen Xi Chen Hua Han Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology Institute of AutomationChinese Academy of Sciences School of Future Technology School of Artificial IntelligenceUniversity of Chinese Academy of Sciences School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Research Base of Beijing Modern Manufacturing Development Beijing University of Technology

Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis. Existing multianimal pose estimation workflows rely on tracking-bydetection frameworks for either bottom-up or top-down approaches, requiring retraining to accommodate diverse animal appearances. This study introduces InteBOMB, an integrated workflow that enhances top-down approaches by incorporating generic object tracking, eliminating the need for prior knowledge of target animals while maintaining broad generalizability. InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings. The “background enhancement” strategy optimizesforeground-backgroundcontrastiveloss,generating more discriminative correlation maps. The“online proofreading” strategy stores human-in-the-loop long-term memory and dynamic short-term memory,enabling adaptive updates to object visual features. The“automated labeling suggestion” technique reuses the visual features saved during tracking to identify representative frames for training set labeling. Additionally,the “joint behavior analysis” technique integrates these features with multimodal data, expanding the latent space for behavior classification and clustering. To evaluate the framework, six datasets of mice and six datasets of nonhuman primates were compiled, covering laboratory and natural scenes. Benchmarking results demonstrated a24% improvement in zero-shot generic tracking and a 21% enhancement in joint latent space performance across datasets, highlighting the effectiveness of this approach in robust, generalizable behavior analysis.

关键词： Background subtraction Behavior analysis Generic object tracking Joint latent space Online learning Pose estimation Selective labeling

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Structure-phase transformation of bismuth oxide to BiOCl/Bi_(24)O_(31)Cl_(10)shoulder-by-shoulder heterojunctions for efficient photocatalytic removal of antibiotic

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Journal of Environmental Sciences 2025年第3期149卷 149-163页

作者： Man Yang Zhenzhen Shi Shaodong Sun Bian Yang Jie Cui Jianing Li Daqin Yun Nian Lei Engineering Research Center of Conducting Materials and Composite Technology Ministry of EducationShaanxi Engineering Research Center of Metal-Based Heterogeneous Materials and Advanced Manufacturing TechnologyShaanxi Province Key Laboratory for Electrical Materials and Infiltration TechnologySchool of Materials Science and EngineeringXi’an University of TechnologyXi’an 710048China College of Energy Xiamen UniversityXiamen 361005China Beijing Key Laboratory of Ionic Liquids Clean Process Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China

Developing heterojunction photocatalyst with well-matched interfaces andmultiple charge transfer paths is vital to boost carrier separation efficiency for photocatalytic antibiotics removal,but still remains a great *** present work,a new strategy of chloride anion intercalation in Bi_(2)O_(3)via one-pot hydrothermal process is *** as-prepared Ta-BiOCl/Bi_(24)O_(31)Cl_(10)(TBB)heterojunctions are featured with Ta-Bi_(24)O_(31)Cl_(10)and Ta-BiOCl lined shoulder-by-shouleder via semi-coherent *** this TBB heterojunctions,the well-matched semi-coherent interfaces and shoulder-by-shoulder structures provide fast electron transfer andmultiple transfer paths,respectively,leading to enhanced visible light response and improved photogenerated charge ***,a type-II heterojunction for photocharge separation has been obtained,in which photogenerated electrons are drove from the CB(conduction band)of Ta-Bi_(24)O_(31)Cl_(10)to the both of bilateral empty CB of Ta-BiOCl and gathered on the CB of Ta-BiOCl,while the photogenerated holes are left on the VB(valence band)of Ta-Bi_(24)O_(31)Cl_(10),effectively hindering the recombination of photogenerated electron-hole ***,the separated electrons can effectively activate dissolved oxygen for the generation of reactive oxygen species(·O_(2)^(−)).Such TBB heterojunctions exhibit remarkably superior photocatalytic degradation activity for tetracycline hydrochloride(TCH)solution to Bi_(2)O_(3),Ta-BiOCl and Ta-Bi_(24)O_(31)Cl_(10).This work not only proposes a Ta-BiOCl/Bi_(24)O_(31)Cl_(10)shoulder-by-shoulder micro-ribbon architectures with semi-coherent interfaces and successive type-Ⅱheterojunction for highly efficient photocatalytic activity,but offers a new insight into the design of highly efficient heterojunction through phasestructure synergistic transformation strategy.

关键词： Heterojunction Bismuth oxide Charge separation/transfer Photocatalytic degradation Catalytic mechanism

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Decoupling compensation accuracy from sensor misalignment: A generalized black-box model for gravity gradiometers

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ISA Transactions 2025年 161卷 228-242页

作者： Yu, Mingbiao Liang, Yu Yu, Xiaobing Hu, Chenyuan Hou, Zhenlong Yu, Li Zhao, Wei Liu, Huafeng Zhang, Liang Fan, Ji Sun, Yuanhang Jiang, Congmei Tu, Liangcheng Zhou, Zebing Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education School of Mechanical Engineering GuiZhou University Guiyang550025 China MOE Key Laboratory of Fundamental Physical Quantities Measurement & Hubei Key Laboratory of Gravitation and Quantum Physics PGMF and School of Physics Huazhong University of Science and Technology Wuhan430074 China Zhuhai519082 China School of Resources and Civil Engineering Northeastern University Shenyang Liaoning110819 China

Moving-base gravity gradiometers are widely used in applications such as mineral exploration and passive navigation;however, their high sensitivity to platform motion presents significant challenges in compensating for motion-induced errors. Existing methods rely on precise alignment between motion sensors and the gradiometer's coordinate system, making compensation accuracy heavily dependent on sensor installation precision. To address this issue, we propose a novel generalized black-box modeling method for motion error compensation in gravity gradiometers. By incorporating sensor misalignment directly into the model, our approach effectively decouples compensation accuracy from installation precision. Constructed based on the gradiometer's dynamic characteristics and the excitation and input–output behavior of its sensitive components, the black-box model inherently avoids redundant operational terms, reduces multicollinearity, and lowers computational resource requirements. Validation through numerical simulations in a dynamic 0.1 g environment demonstrates that the output consistency between the black-box and analytical models reaches 10−10, achieving an compensation accuracy of 0.1 E. Furthermore, experimental results using a gravity gradiometer prototype confirm the model's effectiveness in real-world conditions. The proposed method significantly reduces motion errors induced by 1 mg linear motion and 0.001 rad/s angular motion, lowering the noise level to 4 ng/Hz, approaching the static inherent noise level of 1 ng/Hz. These results verify the black-box model's effectiveness and robustness in dynamic environments, highlighting its potential to enhance the practical performance of gravity gradiometers by mitigating motion-induced errors without stringent requirements on sensor installation precision. © 2025

关键词： Mineral exploration

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Electrochemical dissolution, reduction, and nucleation mechanisms of molybdenum in NaCl-KCl molten salt systems

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Journal of Materials Science and technology 2025年 238卷 45-54页

作者： Lv, Hongzhan Zhang, Liwen Xi, Xiaoli Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing University of Technology Beijing100124 China

This study investigates the anodic dissolution and electrochemical behavior of molybdenum in a NaCl-KCl molten salt system at 1023 K. The anodic dissolution process was systematically analyzed, revealing a sequential oxidation pathway of molybdenum into high-valence ions (Mo6+, Mo5+, Mo4+) under varying electrolysis potentials. Electrochemical Impedance Spectroscopy (EIS) demonstrated that the dissolution is governed by both charge transfer and diffusion mechanisms, with reduced impedance at higher potentials facilitating molybdenum dissolution. The reduction behavior of dissolved molybdenum ions was further explored using cyclic voltammetry (CV) and square wave voltammetry (SWV), confirming a multi-step reduction process controlled by diffusion and high reversibility. Nucleation studies using chronoamperometry established that molybdenum deposition follows an instantaneous nucleation mechanism. Morphological analysis of cathodic deposits revealed that current density significantly influences particle size, transitioning from nano-sized spherical particles to larger equiaxed crystals with increasing current density. These findings provide a comprehensive understanding of molybdenum's electrochemical properties in molten salts, offering valuable insights for optimizing electrolysis processes and advancing molybdenum-based material production. © 2025

关键词： Cyclic voltammetry

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A partially degradable composite consisting of Ti-Zr-Cu-Pd-Sn metallic glass and Fe-Mg alloy for orthopedic applications

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Acta Biomaterialia 2025年 198卷 514-529页

作者： Du, Peng Zuo, Kun Yan, Rongqiang Li, Kun Chen, Shilu Yuan, Bo Zhang, Liang Xie, Guoqiang Shenzhen518055 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Shenzhen518055 China State Key Laboratory of Advanced Welding and Joining Harbin Institute of Technology Harbin150001 China School of Materials Science and Engineering Shandong University of Technology Zibo255000 China

Partially-degradable biomaterials refers to smart implants where biodegradable metals can gradually be replaced by newly growing bone or living tissues, and leave behind a porous inert metal skeleton that stably binds with the new bone tissue. In this research, a partially degradable composite was designed by integrating Ti-Zr-Cu-Pd-Sn metallic glass (MG) with designed Fe-Mg alloy using spark plasma sintering (SPS). The mechanical alloying technique successfully enabled the fusion of immiscible Fe and Mg, addressing the issues of Fe's slow degradation and Mg's rapid breakdown, while also minimizing potential fractures in the metal framework due to hydrogen gas evolution. The controlled degradation of Mg(Fe) promotes the formation of Ca-P compounds, enhancing the bioactivity of the Fe-Mg composite. This design endows the composite with plastic and ductile deformation under compression, providing a viable solution to the brittle fracture behaviour commonly associated with conventional bulk metallic glasses (BMGs). This advancement holds promise for aligning with the natural growth rate of human bone, further augmenting the bioactive properties and practical applications of the MG/Fe-Mg composite material. Statement of significance: In this research, a partially degradable composite was designed by integrating Ti-Zr-Cu-Pd-Sn metallic glass (MG) with designed Fe-Mg alloy using SPS. The Fe-Mg alloy act as temporary space holders can gradually being replaced by newly formed bone, thus establishing a dynamic equilibrium between the biodegradation of the bio-metals and the inward growth of new bone. The degradation of Mg(Fe) promotes the formation of Ca-P compounds, enhancing the bioactivity of the composite. This design endows the composite with plastic deformation under compression, providing a viable solution to the brittle fracture behavior of conventional MGs. This advancement holds promise for aligning with the natural growth rate of human bone, further augmenting the

关键词： Magnesium alloys

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Dual-Protection Method against Eavesdroppers for Distributed State Estimation

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IEEE Transactions on Signal and Information Processing over Networks 2025年 11卷 427-438页

作者： Yu, Yan Yang, Chao Ding, Wenjie Yang, Wen Wang, Xiaofan East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai201418 China

This paper examines a security issue for state estimation over a wireless sensor network. The state estimates are transmitted among neighboring nodes through wireless channels in a distributed network, wherein the transmission of the data are vulnerable to the intercept from eavesdroppers, leading to important data privacy leakage. To prevent eavesdroppers from obtaining state estimates, we propose a dual-protection method that combines dynamic transformation with lightweight encryption, which aims to protect the privacy without raising suspicion from eavesdroppers. Furthermore, we consider the scenarios where eavesdroppers utilize side-channel information to gather data and attempt to deduce the encryption mechanism, subsequently inferring the real state estimate. We also provide the analysis to show that the eavesdropper with inference capabilities could not influence the estimation performance of sensors. Finally, the numerical examples are provided to illustrate the effectiveness of the privacy-preserving method. © 2015 IEEE.

关键词： Differential privacy

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Recent advances in photopolymerization 3D printing of alumina-ceramic

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Progress in Natural Science:Materials International 2025年第1期35卷 1-30页

作者： M.Irfan Hussain Min Xia XiaoNa Ren Zhen Shen Muhammad Jamil Changchun Ge Institute of Powder Metallurgy and Advanced Ceramics School of Material Science and Engineering University of Science and Technology Beijing State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of AutomationChinese Academy of Sciences College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics

Digital light processing(DLP) 3D printing has a huge potential for manufacturing intricate and customized ceramic parts with high precision and cost-effectiveness. Research in this field contributes to material innovation,opening the avenues for new process designs in both scientific and industrial sectors. However, the implementation of DLP 3D printing technology in ceramic research has not yet reached the maturity level as that in polymer and tissue engineering. Necessarily, a holistic in-depth literature reporting the successful integration of alumina ceramics within DLP 3D printing technology is urgently needed. This review, systematic examines recent progress in DLP technology, focusing on photopolymer resins that incorporate UV-sensitive monomers, photoinitiators, and dispersants, as well as their synergistic effects on achieving high-quality printing, desirable material properties, and enhanced performance. Further, the review discusses key factors including post-processing characteristics such as debinding and sintering, which influence microstructure, and defect formation including microcracks, porosity and voids. Finally, the challenges associated with printing and sintering are highlighted,aiming to identify focused focused development pathways and potential solution to optimize outcomes. This analysis clarifies existing challenges and also propsoes future applications for DLP technology in the aluminaceramic field.

关键词： Al2O3 ceramics Applications Digital light processing (DLP) Post-processing Resin

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Few-Shot Object Detection via Dual-Domain Feature Fusion and Patch-Level Attention

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1237-1250页

作者： Guangli Ren Jierui Liu Mengyao Wang Peiyu Guan Zhiqiang Cao Junzhi Yu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics Peking UniversityBeijing 100871China

Few-shot object detection receives much attention with the ability to detect novel class objects using limited annotated *** transfer learning-based solution becomes popular due to its simple training with good accuracy,however,it is still challenging to enrich the feature diversity during the training *** fine-grained features are also insufficient for novel class *** deal with the problems,this paper proposes a novel few-shot object detection method based on dual-domain feature fusion and patch-level *** original base domain,an elementary domain with more category-agnostic features is superposed to construct a two-stream backbone,which benefits to enrich the feature *** better integrate various features,a dual-domain feature fusion is designed,where the feature pairs with the same size are complementarily fused to extract more discriminative ***,a patch-wise feature refinement termed as patch-level attention is presented to mine internal relations among the patches,which enhances the adaptability to novel *** addition,a weighted classification loss is given to assist the fine-tuning of the classifier by combining extra features from FPN of the base training *** this way,the few-shot detection quality to novel class objects is *** on PASCAL VOC and MS COCO datasets verify the effectiveness of the method.

关键词： few-shot object detection dual-domain feature fusion patch-level attention

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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong UniversityXi’an 710049ShaanxiChina Shaanxi Key Laboratory of Intelligent Robots School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049ShaanxiChina Department of Electrical Engineering and Automation Luoyang Institute of Science and TechnologyLuoyang 471023HenanChina School of Mechanical Engineering Hangzhou Dianzi UniversityHangzhou 310018ZhejiangChina

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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