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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

共 15865 条记录，以下是71-80 订阅

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Automated Sperm Morphology analysis Based on Instance-Aware Part Segmentation

Automated Sperm Morphology Analysis Based on Instance-Aware ...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Chen, Wenyuan Song, Haocong Dai, Changsheng Jiang, Aojun Shan, Guanqiao Liu, Hang Zhou, Yanlong Abdalla, Khaled Dhanani, Shivani N. Moosavi, Katy Fatemeh Pathak, Shruti Librach, Clifford Zhang, Zhuoran Sun, Yu Univ Toronto Robot Inst Toronto ON Canada Dalian Univ Technol Sch Mech Engn Dalian Peoples R China Henan Univ Sch Artificial Intelligence Kaifeng Henan Peoples R China CReATe Fertil Ctr Toronto ON Canada Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Traditional sperm morphology analysis is based on tedious manual annotation. Automated morphology analysis of a high number of sperm requires accurate segmentation of each sperm part and quantitative morphology evaluation. State-of-the-art instance-aware part segmentation networks follow a "detect-then-segment" paradigm. However, due to sperm's slim shape, their segmentation suffers from large context loss and feature distortion due to bounding box cropping and resizing during ROI Align. Moreover, morphology measurement of sperm tail is demanding because of the long and curved shape and its uneven width. This paper presents automated techniques to measure sperm morphology parameters automatically and quantitatively. A novel attention-based instance-aware part segmentation network is designed to reconstruct lost contexts outside bounding boxes and to fix distorted features, by refining preliminary segmented masks through merging features extracted by feature pyramid network. An automated centerline-based tail morphology measurement method is also proposed, in which an outlier filtering method and endpoint detection algorithm are designed to accurately reconstruct tail endpoints. Experimental results demonstrate that the proposed network outperformed the state-of-the-art top-down RP-R-CNN by 9:2% APp vol, and the proposed automated tail morphology measurement method achieved high measurement accuracies of 95:34%;96:39%;91:20% for length, width and curvature, respectively.

关键词： Mathematical morphology

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Programmable inflatable origami

Programmable inflatable origami

引用

IEEE international conference on Soft robotics (RoboSoft)

作者： Babu, Saravana Prashanth Murali Das, Riddhi Mazzolai, Barbara Rafsanjani, Ahmad Univ Southern Denmark SDU Maersk Mc Kinney Moller Inst SDU Biorobot SDU Soft Robot DK-5230 Odense M Denmark Italian Inst Technol Bioinspired Soft Robot Lab Genoa Italy

ISBN: (纸本)9798350332223

Origami is a practical approach for developing soft robots and deployable structures. If the folding and stiffness are actively adjustable, we can program the motion of the resulting origami structure. Here, we propose an entirely soft inflatable origami actuator with variable stiffness and multimodal deformation. The programmable inflatable origami consists of a prismatic chamber based on the Kresling pattern with miniature fluidic channels at the mountain folds. Applying a vacuum to the central chamber provides the main actuation force, while the selective inflation of the fluidic channels controls the motion and changes the stiffness. We formulated a geometric description for the origami module to optimize the design parameters. Then, we fabricated the origami actuators from elastomeric rubber using a multistep single-material fabrication technique. Finally, we characterized the axial contraction and rotation angle and demonstrated variable stiffness and omnidirectional bending. Our work imbues origami actuators with embodied behavior presenting an integrated versatile soft robotic building block applicable to manipulation and locomotion scenarios.

关键词： soft robotics origami inflatables embodied intelligence

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Robust Co-Design of Canonical Underactuated Systems for Increased Certifiable Stability

Robust Co-Design of Canonical Underactuated Systems for Incr...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Girlanda, Federico Shala, Lasse Kumar, Shivesh Kirchner, Frank German Res Ctr Artificial Intelligence DFKI GmbH Robot Innovat Ctr Bremen Germany Univ Padua Dept Informat Engn Padua Italy Chalmers Univ Technol Dept Mech & Maritime Sci Dynam Div Gotheburg Sweden Univ Bremen Fac Math & Comp Sci Bremen Germany

ISBN: (纸本)9798350384581;9798350384574

Optimal behaviours of a system to perform a specific task can be achieved by leveraging the coupling between trajectory optimization, stabilization, and design optimization. This approach is particularly advantageous for underactuated systems, which are systems that have fewer actuators than degrees of freedom and thus require for more elaborate control systems. This paper proposes a novel co-design algorithm, namely Robust Trajectory Control with Design optimization (RTC-D). An inner optimization layer (RTC) simultaneously performs direct transcription (DIRTRAN) to find a nominal trajectory while computing optimal hyperparameters for a stabilizing time-varying linear quadratic regulator (TVLQR). RTC-D augments RTC with a design optimization layer, maximizing the system's robustness through a time-varying Lyapunov-based region of attraction (ROA) analysis. This analysis provides a formal guarantee of stability for a set of off-nominal states. The proposed algorithm has been tested on two different underactuated systems: the torque-limited simple pendulum and the cart-pole. Extensive simulations of off-nominal initial conditions demonstrate improved robustness, while real-system experiments show increased insensitivity to torque disturbances.

关键词： Robustness (control systems)

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Fine-Tuning Point Cloud Transformers with Dynamic Aggregation

Fine-Tuning Point Cloud Transformers with Dynamic Aggregatio...

引用

IEEE international conference on robotics and Automation (ICRA)

作者： Fei, Jiajun Deng, Zhidong Tsinghua Univ Inst Artificial Intelligence Tsinghua Univ THUAI Beijing Natl Res Ctr Informat Sci & Technol BNRis State Key Lab Intelligent Technol & SystDept Com Beijing 100084 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Point clouds play an important role in 3D analysis, which has broad applications in robotics and autonomous driving. The pre-training fine-tuning paradigm has shown great potential in the point cloud domain. Full fine-tuning is generally effective but leads to a heavy storage and computational burden, which becomes inefficient and unacceptable as the size of pre-trained models scales. Although efficient fine-tuning approaches have significant progress in other domains, they generally perform worse for point clouds. To overcome this dilemma, we revisit the official Point-MAE implementation and find the critical role of aggregation in fine-tuning performances. Inspired by such discoveries, we propose a novel dynamic aggregation (DA) method to replace previous static aggregation like mean or max pooling for pre-trained point cloud Transformers. Besides standard metrics such as accuracy or mIoU, we evaluate the number of tunable parameters and additional FLOPs for a fair comparison of our method to different fine-tuning approaches. We construct several DA variants and validate them through extensive experiments. Experimental results demonstrate that DA has competitive performances against full fine-tuning and other efficient fine-tuning approaches. The code is publicly available at https://***/JaronTHU/DynamicAggregation.

关键词： Distribution transformers

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Dynamic analysis and Testing of a Double-layer Isolator with an Electromagnetic Negative Stiffness Mechanism 9

Dynamic Analysis and Testing of a Double-layer Isolator with...

引用

9th IEEE international conference on Advanced robotics and Mechatronics (ICARM)

作者： Li, Qimin Sun, Anpeng Pu, Huayan Luo, Jun Mou, Lisheng Zhu, Jiahao Zhao, Jinglei Li, Xueping Bai, Ruqing Yi, Jin Li, Zhe Yuan, Shujin Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing 400044 Peoples R China CSSC Syst Engn Res Inst Beijing 100094 Peoples R China

ISBN: (纸本)9798350385731;9798350385724

Electromagnetic negative stiffness mechanism (NSM) has attracted considerable attention because it can expand the vibration isolation frequency band. However, existing electromagnetic NSMs are limited by a small adjustable range. To improve the generation efficiency and adjustable range of negative stiffness, the influence of the magnet size and the position relationship with the wire turns on the stiffness characteristics was analyzed, and an electromagnetic negative stiffness mechanism (ENSM) with an optimized configuration of the coils and magnets was proposed. The stiffness adjustable range of the ENSM is +/- 6350 Nm(-1), and the stiffness generation efficiency of the ENSM reaches 4.65 Nm(-1)A(-1). A double-layer vibration isolator with the ENSM connected in parallel is designed and manufactured, and its nonlinear dynamics are analyzed. The vibration isolation performance was experimentally tested and proved that the ENSM effectively expanded the vibration isolation frequency bandwidth.

关键词： Vibration analysis

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Joint Response and Background Learning for UAV Visual Tracking

Joint Response and Background Learning for UAV Visual Tracki...

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IEEE international conference on robotics and Automation (ICRA)

作者： Wang, Biao Li, Wenling Zhang, Bin Liu, Yang Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Correlation filter (CF)-based approaches have gained widespread attention in the field of unmanned aerial vehicle (UAV) visual tracking due to their light-weight characteristics. However, CFs are prone to generating low-quality response in challenging UAV scenarios, e.g., fast motion and background clutter. In this paper, in order to model the tracker more robustly, we first conduct an effective regularization analysis from the perspectives of response- and background-learning. Specifically, to address response degradation, we propose a module for learning temporal consistency and reversibility of response, supplemented by a novel background-aware module to enhance the ability to learn from negative samples. In addition, we propose a fast coarse-to-fine scale search strategy, which alleviates the challenges in estimating bounding boxes under non-uniform aspect ratios. We have developed two tracker versions, namely RBLT and DeepRBLT, based on the depth of the features. Comprehensive experiments on four UAV benchmarks and one generic benchmark have indicated the superiority of our trackers compared to other state-of-the-art trackers, with enough speed for real-time applications.

关键词： Unmanned aerial vehicles (UAV)

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Dynamical Feature Extraction and pattern Recognition for Mental Workload Level with FNIRS 17th

Dynamical Feature Extraction and Pattern Recognition for Men...

引用

17th international conference on Intelligent robotics and Applications

作者： Zhang, Lianchi Zhang, Jingting Mu, Fengjun Huang, Rui Univ Elect Sci & Technol China Chengdu 611731 Sichuan Peoples R China

ISBN: (纸本)9789819607822;9789819607839

Mental Workload Level (MWL) is an important indicator reflecting the human's cognitive state in human-computer interaction, such as the applications of vehicle driving and mental state assessment. A useful method of MWL assessment is by utilizing functional near-infrared spectroscopy (fNIRS) to monitor the brain tissue blood oxygen concentration during human's cognitive process. However, existing fNIRS-based MWL classification methods still have limited performance in terms of classification accuracy and interpretability, since they have not precisely extracted the feature describing human's cognitive behavior, e.g., cerebral hemodynamics. To address this issue, this paper proposes a new dynamical feature extraction and pattern recognition method for MWL classification by using Dynamic System Theory (DST) and Deterministic Learning (DL) technique. A so-called dynamical feature is extracted by modeling the cerebral hemodynamics using fNIRS in human's cognitive process, which is mathematically interpretable and specific to the human'sMWLaccording to DST. Dynamical pattern recognition scheme is then proposed by combining DL and Support Vector Machine, aiming to identify high and low levels of MWL with the dynamical feature of fNIRS. Compared to methods such as EEGNet, DeepConvNet, DCNN, SVM, fNIRS-PreT, and Logistic Regression, our method achieved a performance improvement of 2% to 6% for classifying mental workload levels.

关键词： Functional Near-infrared Spectroscopy Mental Workload Level Dynamical pattern Recognition Deterministic Learning Dynamic System Theory

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Towards reliable medical image analysis based on deep learning with XAI

Towards reliable medical image analysis based on deep learni...

引用

2023 international conference on Image, Signal Processing, and pattern Recognition, ISPP 2023

作者： Hao, Jingbo School of Information and Artificial Intelligence Nanchang Institute of Science & Technology 998 Gezaoshan Ave. Honggutan District Nanchang China

ISBN: (纸本)9781510666351

Deep learning-based medical image analysis (DLB-MIA) has achieved great success hitherto, but is in urgent need of model interpretability. Explainable artificial intelligence (XAI) can be used for improving model interpretability and is expected to help in providing crucial trustworthiness in DLB-MIA. To expedite the utilization of XAI technology for reliable DLB-MIA, the current status of XAI methods and toolkits is introduced firstly. The application of XAI technology in DLB-MIA tasks is then discussed with close attention in respect of method taxonomies and adversarial robustness respectively. © 2023 SPIE.

关键词： Deep learning

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Influence of Technology Skills, Digital Infrastructure, and Data Integration on Artificial intelligence in Product Development 10

Influence of Technology Skills, Digital Infrastructure, and ...

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10th international conference on Automation, robotics, and Applications, ICARA 2024

作者： Bhowmick, Anuj Seetharaman, A. S.P. Jain School of Global Management Dubai United Arab Emirates S.P. Jain School of Global Management Singapore Singapore

ISBN: (纸本)9798350394245

This study's conceptual model explains several aspects of how artificial intelligence (AI) affects product development. It also demonstrates how this knowledge can be leveraged to an organization's advantage for expansion and sustainability. Product development is identified as the study's dependent variable after a thorough analysis of online literature sources. Three independent variables - technology skills, digital infrastructure, and data integration - were identified. The study objectives and research questions were created to evaluate the impact of the independent variables on product development. It has been determined that a robust product management process backed by an intelligent data-based decision-making system influences business resilience and sustainability. © 2024 IEEE.

关键词： Data integration

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Psychological Conditions analysis Based on Text Detection and Facial Expression Recognition 17th

Psychological Conditions Analysis Based on Text Detection an...

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17th international conference on Intelligent robotics and Applications

作者： Yang, Yujie Southwest Jiaotong Univ Leeds Joint Sch Chengdu Sichuan Peoples R China

ISBN: (纸本)9789819607884;9789819607891

As awareness of the importance of mental health continues to grow, there is an increasing demand for innovative diagnostic solutions that prioritize both accuracy and accessibility. This paper explores the development and validation of an AI-driven diagnostic tool that integrates facial expression analysis with textual data analytics to enhance the precision of mental health diagnoses. Utilizing the "Synthetic Therapy Conversations" dataset alongside over 10,000 facial expression datasets, we have developed a multimodal AI tool that synergistically combines textual content and facial expressions to assess emotional states, it simulates the human brain's comprehensive ability in processing emotional and linguistic information. According to accuracy, the accuracy rate is higher when version-RFB is used for facial detection, ResNet-18 is used for facial expression recognition, and Random Forest is used for text analysis.

关键词： Psychological conditions analysis Multimodal recognition Facial emotion recognition Facial Detection Text analysis Data enhancement

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