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2024 China Automation Congress, CAC 2024

作者： Gu, Kangle Chen, Yiyi Wu, Jiayi Li, Chengdong Kong, Chunli Xin, Jingmin National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China First Affiliated Hospital College of Clinical Medicine Medical College of Henan University of Science and Technology Luoyang China Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology Key Laboratory of Precision Medicine of Lishui City Fifth Affiliated Hospital of Wenzhou Medical University Lishui China

ISBN: (纸本)9798350368604

Breast cancer represents the most prevalent malignancy and the leading cause of cancer-related mortality among women globally. Current grading systems rely on conventional pathological criteria necessitating invasive procedures for tissue or cellular acquisition, which are labor-intensive, subjective, and invasive. This paper introduces a non-invasive end-to-end deep learning framework for breast cancer grading. It marks the inaugural attempt to deploy deep learning methodologies on breast cancer magnetic resonance imaging (MRI), providing evidence for imaging biomarkers of breast cancer. Addressing the fine-grained and multi-scale lesions inherent in datasets, we propose a feature fusion approach based on self-attention mechanisms. Experimental results validate the feasibility of exploring breast cancer biomarkers on MRI through deep learning methodologies, with the novel feature fusion strategy enhancing the extraction of salient pixel information and augmenting classification accuracy. © 2024 IEEE.

关键词： Magnetic resonance imaging

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EFormer-VPR: Fusing Events and Frames with Transformer for Visual Place Recognition

EFormer-VPR: Fusing Events and Frames with Transformer for V...

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International Conference on Intelligent Transportation

作者： Yangjing Zhang Yanqing Shen Ziyu Zhu Renwei Hai Shitao Chen Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi P.R. China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Visual place recognition (VPR) is a challenging task faced by mobile robots and autonomous driving systems. In scenarios with glare or high-speed motion, image blurring makes it difficult for traditional cameras to perform reliable and accurate place recognition. In contrast, event cameras can capture target motion information without blur in high-speed motion scenes, but lack texture information in low-speed motion scenes. To leverage the complementary characteristics of these two sensors and improve the performance and robustness of VPR algorithms, we propose EFormer- VPR, which fuses target motion events and frames with transformer. This method firstly preprocesses the events stream within an adaptive time window using a clustering method, then uses transformer-based networks to extract features from motion frames and events separately and fuse them through a scoring module. Finally, features are aggregated using a VLAD layer and the whole pipeline is supervised by a triplet ranking loss. To verify the effectiveness of the proposed algorithm, we compare it with other VPR methods on the event-based driving datasets (Brisbane-Event-VPR, NeuroGPR) with challenging scenarios. Experimental results show that on the Brisbane- Event- Vprand NeuroGPR datasets, our method achieves state-of-the-art performance.

关键词： Image recognition Fuses Clustering methods Robot vision systems Clustering algorithms Feature extraction Transformers Cameras Data mining Visual place recognition

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Non-Thermal Plasma Applications in Maize Seed Remediation, Disinfection, and Improvement for Growth: A Review

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IEEE Access 2025年 13卷 108679-108709页

作者： Diana Guadalupe Gutiérrez-León Jorge Jurado-Páramo Benjamín Gonzalo Rodríguez-Méndez Diego Medina-Castro Régulo López-Callejas Department of Robotics Mechatronics and Data Engineering Polytechnic University of Guanajuato Guanajuato Mexico Department of Automotive Engineering Polytechnic University of Guanajuato Guanajuato Mexico Physics Department National Institute of Nuclear Research Ocoyoacac Estado de México Mexico

Maize is a crucial cereal with significant global importance due to its nutritional value and adaptability. However, its production is increasingly threatened by contamination—both chemical and microbiological—as well as climate change, which negatively impacts supply. Current alternatives aimed at ensuring production often come with considerable adverse effects. In this context, non-thermal plasma (NTP) emerges as a promising technology. It generates reactive chemical species and can also produce associated phenomena such as ultraviolet radiation emission and shock wave formation, which may help protect agricultural yields. Research over the past decade has consistently highlighted the potential of NTP to enhance the germination and development of maize seeds. This review is particularly relevant because it synthesizes current information and provides a structured perspective on the application of NTP in agriculture. It analyzes the experimental conditions—such as voltage, power, working gas, and treatment capacity—as well as the materials and typical discharge configurations in detail. This analysis is crucial for informing future research and refining these treatments. Furthermore, a significant contribution of this work is the categorization of scientific literature based on observed agronomic benefits into four key areas: chemical decontamination, inactivation of pathogenic microorganisms, improvement of seed physical properties, and optimization of seed storage conditions. This framework not only enhances understanding of the advances and multiple positive impacts of NTP but also emphasizes the importance of this technology for sustainable agriculture and food security. Additionally, it identifies knowledge gaps and promising areas for innovation.

关键词： Agricultural engineering Contamination Food products Plasma applications Seeds (agriculture) Pesticides Low-temperature plasmas Crops yield Climate change

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Data-Driven System Identification of Quadrotors Subject to Motor Delays

Data-Driven System Identification of Quadrotors Subject to M...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jonas Eschmann Dario Albani Giuseppe Loianno Tandon School of Engineering New York University Brooklyn NY USA Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi UAE

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Recently non-linear control methods like Model Predictive Control (MPC) and Reinforcement Learning (RL) have attracted increased interest in the quadrotor control community. In contrast to classic control methods like cascaded PID controllers, MPC and RL heavily rely on an accurate model of the system dynamics. The process of quadrotor system identification is notoriously tedious and is often pursued with additional equipment like a thrust stand. Furthermore, low-level details like motor delays which are crucial for accurate end-to-end control are often neglected. In this work, we introduce a data-driven method to identify a quadrotor’s inertia parameters, thrust curves, torque coefficients, and first-order motor delay purely based on proprioceptive data. The estimation of the motor delay is particularly challenging as usually, the RPMs can not be measured. We derive a Maximum A Posteriori (MAP)-based method to estimate the latent time constant. Our approach only requires about a minute of flying data that can be collected without any additional equipment and usually consists of three simple maneuvers. Experimental results demonstrate the ability of our method to accurately recover the parameters of multiple quadrotors. It also facilitates the deployment of RL-based, end-to-end quadrotor control of a large quadrotor under harsh, outdoor conditions.

关键词： Torque Accuracy Trajectory tracking Propioception Motors System identification Delays Torque measurement Tuning Quadrotors

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Advances in human organs-on-chips and applications for drug screening and personalized medicine

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Fundamental Research 2025年第3期5卷 1258-1272页

作者： Zhou, Chenyang Li, Zhangjie Lu, Kangyi Liu, Yijun Xuan, Lian Mao, Hongju Wang, Xiaolin Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai 200240 China National Center for Translational Medicine (Shanghai) SHU Branch Shanghai University Shanghai 200444 China

The limitations of conventional animal tests and two-dimensional cell culture hinder their advancement in fundamental research and clinical/translational applications. As an emerging alternative technology, organ-on-a-chip serves as a platform that faithfully simulates the key phenotypical, physiological, and functional features of human tissues/organs through the accurate regulation of the parameters such as physical and biochemical microenvironment, as well as cellular patterns. In this review, we mainly introduce the recent progress in the organ-on-a-chip field, including lung, gut, heart, liver, vasculature and multiorgan studies. Furthermore, we highlight the potential applications in drug screening and personalized medicine. Finally, we conclude the review by addressing the current challenges and future perspective in the technology and commercialization of organ-on-chips. We anticipate that the development of organ-on-a-chip technology will revolutionize the studies on biology and medicine by providing new understanding of mechanisms of diseases and insights into clinical therapeutics. © 2024

关键词： Drug screening Microfluidics Organ-on-a-chip Organoid Personalized medicine

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Socially-Compatible Motion Planning of Autonomous Vehicles with Verification on Real Human Data

Socially-Compatible Motion Planning of Autonomous Vehicles w...

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International Conference on Intelligent Transportation

作者： Chenxi Zhang Shitao Chen Jiamin Shi Jingmin Xin Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi P.R. China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

One of the fundamental challenges for autonomous vehicles in complex dynamic scenarios is planning human-like trajectories in a socially acceptable manner. Existing methods, focused solely on self-interest and implicitly modeling a singular driving style, prove inadequate for the deployment of autonomous vehicles in mixed traffic environments while maintaining social acceptability. This paper presents the Socially-Compatible Motion Planning (SCMP) framework, which dynamically captures the spatiotemporal interaction characteristics of traffic participants for various driving tasks and adaptively adjusts the driving style of autonomous vehicles. Guided by driving style, the motion planning module considers vehicle physical constraints, safety constraints and comfort constraints, thereby generating human-like trajectories that incorporate multiple driving styles. The proposed method was rigorously validated and evaluated using the real-world dataset nuPlan, where it demonstrated exceptional performance in various driving scenarios and tasks.

关键词： Dynamics Reinforcement learning Planning Trajectory Spatiotemporal phenomena Safety Vehicle dynamics Autonomous vehicles Intelligent transportation systems Predictive control

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BCLH2Pro:A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes

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Energy and AI 2024年第4期18卷 13-26页

作者： Thanadol Tuntiwongwat Sippawit Thammawiset Thongchai Rohitatisha Srinophakun Chawalit Ngamcharussrivichai Somboon Sukpancharoen Department of Mechanical Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Automation Robotics and Intelligent System Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Chemical Engineering Faculty of EngineeringKasetsart UniversityBangkok 10900Thailand Center of Excellence in Catalysis for Bioenergy and Renewable Chemicals(CBRC) Faculty of ScienceChulalongkorn UniversityPathumwanBangkok 10330Thailand Institute of Nanoscience and Nanotechnology(ION2) Universiti Putra MalaysiaSerdang 43400SelangorMalaysia Department of Agricultural Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Center for Alternative Energy Research and Development Khon Kaen UniversityKhon Kaen 40002Thailand

This study optimizes biomass chemical looping processes(BCLpro),a technique for converting biomass to energy,through machine learning(ML)for sustainable energy *** study proposes an integrated Fe_(2)O_(3)-based,BCLpro combining steam gasification for H2 *** Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model.A methodology involving K-Nearest Neighbors(KNN),Extreme Gradient Boosting(XGB),Light Gradient Boosting Machine(LGBM),Support Vector Machine(SVM),Random Forest(RF),and CatBoost(CB)algorithms was employed to predict H_(2)yields in the BCLpro,utilizing 10-fold cross-validation for robust model *** highlight the CB algorithm’s superior performance,achieving up to 98%predictive accuracy,with carbon content,reducer temperature,and Fe_(2)O_(3)/Al_(2)O_(3)mass ratio identified as crucial *** algorithm has been developed into a user-friendly tool,BCLH2Pro,accessible via a web *** tool is designed to assist in reducing costs,optimizing biomass selection,and planning operational conditions to maximize H2 yield in BCLpro *** to the tool can be obtained through the following link:http://***/.

关键词： Artificial intelligence Supervised learning Process simulation Chemical looping Agricultural waste

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Two-Stage Grasping: A New Bin Picking Framework for Small Objects

Two-Stage Grasping: A New Bin Picking Framework for Small Ob...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Hanwen Cao Jianshu Zhou Junda Huang Yichuan Li Ng Cheng Meng Rui Cao Qi Dou Yunhui Liu Department of Mechanical and Automation Engineering T Stone Robotics Institute The Chinese University of Hong Kong. Hong Kong Center for Logistics Robotics (HKCLR) Department of Compute Science and Engineering The Chinese University of Hong Kong.

This paper proposes a novel bin picking framework, two-stage grasping, aiming at precise grasping of cluttered small objects. Object density estimation and rough grasping are conducted in the first stage. Fine segmentation, detection, grasping, and pushing are performed in the second stage. A small object bin picking system has been realized to exhibit the concept of two-stage grasping. Experiments have shown the effectiveness of the proposed framework. Unlike traditional bin picking methods focusing on vision-based grasping planning using classic frameworks, the challenges of picking cluttered small objects can be solved by the proposed new framework with simple vision detection and planning.

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Robots with Attitude: Singularity-Free Quaternion-Based Model-Predictive Control for Agile Legged Robots

arXiv

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arXiv 2024年

作者： Zhang, Zixin Zhang, John Z. Yang, Shuo Manchester, Zachary The Center for Robotics and Biosystems Northwestern University EvanstonIL60208 United States The Robotics Institute Carnegie Mellon University PittsburghPA15213 United States The Department of Mechanical Engineering Carnegie Mellon University PittsburghPA15213 United States

We present a model-predictive control (MPC) framework for legged robots that avoids the singularities associated with common three-parameter attitude representations like Euler angles during large-angle rotations. Our method parameterizes the robot's attitude with singularity-free unit quaternions and makes modifications to the iterative linear-quadratic regulator (iLQR) algorithm to deal with the resulting geometry. The derivation of our algorithm requires only elementary calculus and linear algebra, deliberately avoiding the abstraction and notation of Lie groups. We demonstrate the performance and computational efficiency of quaternion MPC in several experiments on quadruped and humanoid robots. © 2024, CC BY.

关键词： Predictive control systems

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Efficient LiDAR Bundle Adjustment for Multi-Scan Alignment Utilizing Continuous-Time Trajectories

arXiv

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arXiv 2024年

作者： Wiesmann, Louis Marks, Elias Gupta, Saurabh Guadagnino, Tiziano Behley, Jens Stachniss, Cyrill The Center for Robotics University of Bonn Germany The Department of Engineering Science The University of Oxford United Kingdom The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Constructing precise global maps is a key task in robotics and is required for localization, surveying, monitoring, or constructing digital twins. To build accurate maps, data from mobile 3D LiDAR sensors is often used. Mapping requires correctly aligning the individual point clouds to each other to obtain a globally consistent map. In this paper, we investigate the problem of multi-scan alignment to obtain globally consistent point cloud maps. We propose a 3D LiDAR bundle adjustment approach to solve the global alignment problem and jointly optimize the available data. Utilizing a continuous-time trajectory allows us to consider the ego-motion of the LiDAR scanner while recording a single scan directly in the least squares adjustment. Furthermore, pruning the search space of correspondences and utilizing out-of-core circular buffer enables our approach to align thousands of point clouds efficiently. We successfully align point clouds recorded with a handheld LiDAR, as well as ones mounted on a vehicle, and are able to perform multi-session alignment. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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