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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

共 3096 条记录，以下是41-50 订阅

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MamUNet: Mamba Enhances Vessel Segmentation in NIR-II Fluorescence Imaging

MamUNet: Mamba Enhances Vessel Segmentation in NIR-II Fluore...

引用

IEEE international symposium on Biomedical Imaging

作者： Keyi Han Anqi Xiao Jie Tian Zhenhua Hu CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The Transformer architecture achieves excellent global context understanding of sequential data through the self-attention mechanism, but it faces computational challenges when modeling long-range dependencies. Mamba, based on the state space model (SSM), has emerged as a notable approach for effectively addressing these long-range dependencies, significantly improving computational and storage efficiency. Therefore, we propose MamUNet, a new method that combines the powerful modeling capabilities of the Mamba model with the strengths of the UNet architecture for complex vessel segmentation tasks in NIR-II fluorescence images. The encoder employs the classic network ResNet to learn local low-level features, followed by Mamba for global feature learning to capture more contextual information. We conducted experiments on a dataset of human brain NIR-II fluorescence vessel images and three publicly available retinal vessel datasets. The results demonstrate that our method achieves more accurate vessel segmentation with lower computational cost, outperforming other comparison methods.

关键词： Representation learning Image segmentation Accuracy computational modeling Computer architecture Fluorescence Transformers Retinal vessels computational efficiency Faces

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Application of an Improved Residual Attention Neural Network in Mechanical Part Classification 9th

Application of an Improved Residual Attention Neural Network...

引用

9th international symposium on Artificial intelligence and robotics, ISAIR 2024

作者： Zhang, Tianrui Wang, Aizeng School of Mechanical Engineering and Automation Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9789819629138

This paper introduces an improved neural network that integrates residual network and attention mechanisms, achieving a significant improvement of 99.50% accuracy in mechanical part classification tasks. The proposed network enhance accuracy, precision, recall, and F1-score compared to traditional CNNs and other advanced networks in handling complex backgrounds and multi-angle part images. This advancement shows potential in smart manufacturing, offering greater automation, minimizing human intervention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart manufacturing

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Explaining Internal Representations in Deep Networks: Adversarial Vulnerability of Image Classifiers and Learning Sequential Tasks with Sparse Reward

Explaining Internal Representations in Deep Networks: Advers...

引用

international symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Igor Farkaš Department of Applied Informatics Physics and Informatics Centre for Cognitive Science Faculty of Mathematics Comenius University Bratislava Slovak Republic

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Modern artificial intelligence based on deep neural networks has demonstrated in the past decade great achievements in concrete tasks, sometimes even surpassing human performance. On the other hand, there exist fundamental problems in these models, be it image classification or natural language tasks. Since deep networks are inherently black boxes, it is important to design and apply techniques that help shed light on the functioning of the trained models. In the talk, we will discuss two domains. First, in the context of image classification we will illustrate the effect of adversarial examples that can easily fool trained models, hence revealing their lack of robustness. In the second part, we will deal with sequential tasks, such as computer games, with an extremely sparse reward. Introducing the concept of intrinsic motivation, we will describe the neural networks based model that can successfully use reinforcement learning and self-supervised knowledge distillation to solve these tasks thanks to optimized organization of its internal representations. Finally, we briefly mention our current work related to cognitive robotics.

关键词： Knowledge engineering Video games computational modeling Natural languages Reinforcement learning Organizations Robustness Informatics Machine intelligence Image classification

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2nd international Conference on Sustainable Development Using Machine Learning, Artificial intelligence, and IoT, ICSD 2024

2nd International Conference on Sustainable Development Usin...

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2nd international Conference on Sustainable Development Using Machine Learning, Artificial intelligence, and IoT, ICSD 2024

ISBN: (纸本)9783031717284

The proceedings contain 38 papers. The special focus in this conference is on Sustainable Development Using Machine Learning, Artificial intelligence, and IoT. The topics include: Advancing Sustainable Security: AI-Driven Embedded Hardware for Mobile robotics;Evaluating Cyber-Crime Using Machine Learning and AI Approach for Environmental Sustainability;Fostering Social Relationships in Higher Education Institutions Through AI-Powered Solutions for Sustainable Development;impact of Artificial intelligence and Machine Learning in the Sustainable Transformation of the Pharma Industry;Integrating Robot-Based Resources to Foster computational Thinking and Communication Skills in EFL Education: A Path Towards Sustainable Development;Sustainable Development in Cancer Diagnosis: Empowering Precision Medicine with Artificial intelligence and CRC Detection Tools;sustainable Development Through Digital Twin Technology: Optimizing the Supply Chain;fostering Sustainable Growth via the Fusion of Artificial intelligence and Computer Vision;Sustainable Digital and Enterprise Transformation for Omni-Channel Experiences Empowered by Data and AI/ML;sustainable Finance Beyond Banking Shaping the Future of Financial Technology;Sustainable Safeguarding: Implementing Fraud Defense Systems for Authorized Push Payments Through robotics Process automation and AI Integration;Advancing Sustainable Development: Harnessing AI for Efficient and Eco-Friendly Customer Support in IoT Solutions;enabling Sustainable Interaction - A Novel Approach to Hand Gesture-Based Action Recognition for Intelligent Systems;exploring Network Privacy Measures in Mobile Networks;exploring Sustainable Financial Network Analytics: Opportunities and Challenges in Systemic Risk Management and Investment Analysis;fostering Sustainable Circular Economy Practices in India: A Bibliometric Review of IoT Based Technologies;Leveraging Block-DAO Integration for Sustainable Smart Manufacturing: Operational Efficiency, Supply Chai

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Comparison of Manual and AI-assisted Labeling Techniques in Pixel-wise Instance Segmentation

Comparison of Manual and AI-assisted Labeling Techniques in ...

引用

international symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Sándor Tarsoly Péter Galambos Artur I. Károlv Antal Bejczy Center for Intelligent Robotics University Research and Innovation Center Obuda University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Instance segmentation in computer vision poses significant challenges in dataset preparation, especially for tasks with many instances per image. This is primarily due to the extensive labeling requirements. Recent advancements in deep learning have introduced AI tools, such as the Segment Anything Model (SAM), which can aid in generating accurate segmentation masks. This paper investigates the efficiency and quality of three annotation techniques: manual, interactive AI-assisted, and AI-assisted with pre-labeling, applied to complex instance segmentation of densely packed objects. Our experiments use mushroom segmentation as a representative many-instance problem. Evaluation metrics, including IoU, Precision, Recall, and F1 score, are used to assess annotation accuracy and speed. The results indicate that while manual labeling remains highly accurate for simple tasks, AI-assisted tools significantly improve labeling efficiency and maintain high accuracy for complex tasks with numerous objects. These findings highlight the potential of SAM-based AI-assisted labeling for practical applications in dataset preparation for dense instance segmentation tasks.

关键词： Instance segmentation Computer vision Accuracy Annotations computational modeling Manuals Object segmentation Streaming media Labeling Artificial intelligence

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Proceedings of i-SAIRAS 2005 - The 8th international symposium on Artificial intelligence, robotics and automation in Space

Proceedings of i-SAIRAS 2005 - The 8th International Symposi...

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i- SAIRAS 2005 - The 8th international symposium on Artificial intelligence, robotics and automation in Space

The proceedings contain 120 papers from the Proceedings of i-SAIRAS 2005 - The 8th international symposium on Artificial intelligence, robotics and automation in Space. The topics discussed include: development of the ESA ExoMars Rover;design and control of a passively steered, dual axle vehicle;orbital robotics spiral evolution for future exploration missions;situation awareness of onboard system autonomy;multiagent-based scheduling and execution of activities for space systems;an evolutionary algorithm for near-optimal autonomous resource management;drilling automation for subsurface planetary exploration;multiple-target single cycle instrument placement;and learning classifiers for science event detection in remote sensing imagery.

关键词： robotics

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Role of Artificial intelligence in Fostering Internal Stakeholders’ Performance in Academia

Role of Artificial Intelligence in Fostering Internal Stakeh...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), international Conference

作者： Batani Raghavendra Rao Vinoth S Anusha R Batni Raghavendra S Navaratna Faculty of Management JAIN Deemed-to-be University Bengaluru India Chartered Accountant Audit Senior Audit & Assurance Deloitte Dublin Ireland Robotics Automation Engineer Princeton Neuroscience Institute Princeton University USA

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The study aims to examine the role of Artificial intelligence (AI) in furthering the performance of internal stakeholders in the academia. It aims to explore the positive impact of leveraging AI in fostering the development of students, faculty members and admin or operational excellence. The study also exposes the challenges and the risks in AI adoption. AI’s role in the corporate world, especially in banking, insurance, health etc., is widely discussed and therefore, this study is a significant one as it is concerning the academia. Primary data was collected by conducting the survey. 205 respondents participated in the survey. The respondents were categorized into three clusters, namely, emerging, engaged and experienced based on the degree of adoption of AI. To examine the academic stakeholders’ behaviour, K-Means clustering method was employed. Multiple regression tool was used to study the perceived benefits of AI adoption in academia. Decision tree was used to develop challenges matrix and to study the interrelated associations. Sentiment analysis was leveraged to examine the AI adoption sentiments among stakeholders of the academia. The study revealed that AI has a positive influence on all the stakeholders of academia. AI assists teachers, helps students and admin team so that the learning is epibranchial and fruitful. AI should be adopted in a calibrated way after considering the AI challenges. The outcome is win-win for the academic world if AI is appropriately implemented.

关键词： Surveys Training Support vector machines Sentiment analysis Privacy computational modeling Real-time systems Stakeholders Decision trees Artificial intelligence

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A Learning-Based Monitoring System for Factory Assembly Behavior 9th

A Learning-Based Monitoring System for Factory Assembly Beh...

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9th international symposium on Artificial intelligence and robotics, ISAIR 2024

作者： Liu, Yi Wang, Hao Liu, Xiaodong Jiao, Man Li, Wenchang Wang, Xiaochuan Liu, Ruijun Beijing Research Institute of Automation for Machinery Industry Co. Ltd. Beijing China Beijing Technology and Business University Beijing China

ISBN: (纸本)9789819629107

With the advancement of deep learning, industries are now demanding higher standards for assembly product quality and efficiency. This study integrates temporal action localization, human pose estimation, and graph comparison algorithms, utilizing a multi-thread concurrency mechanism to facilitate real-time monitoring and assessment of assembly behavior. Such an approach enables monitoring personnel to focus solely on videos depicting abnormal behavior, thereby enhancing the overall quality and efficiency of the assembly process. In Sect. 2, we conduct an overview of factory assembly behavior techniques. Subsequently, we introduce the technology roadmap integrated into our system, present the architecture, demonstrate experimental results on our dataset, and draw conclusions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Efficiency

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Collaborative Optimization Algorithm of Deep Learning and Compliant Control in Online Trajectory Planning

Collaborative Optimization Algorithm of Deep Learning and Co...

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international Conference on Communication Systems and Network Technologies (CSNT)

作者： Junting Liao Chunjian Zhao Hui Xie School of Computer and Artificial Intelligence Xiangnan University Chenzhou China China Telecom Hunan Chenzhou Branch Chenzhou China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

With the rapid development of robotics and automation, online trajectory planning faces problems such as poor adaptability to dynamic environments and low computational efficiency. This study aims to propose a collaborative optimization algorithm that combines deep learning and compliant control to improve the efficiency and accuracy of online trajectory planning. First, CNN is used for feature extraction and a prediction model is established. Then a compliant control strategy is designed to deal with dynamic obstacles to ensure the flexibility and stability of the robot in trajectory planning. The trained CNN model is then used to generate real-time trajectory predictions. The robot motion is adjusted through a fuzzy logic controller to optimize the relative position of the trajectory and obstacles. The average obstacle avoidance success rate of the algorithm in dynamic environments reaches 97%. The collaborative optimization algorithm of deep learning and compliant control effectively improves the real-time and accuracy of online trajectory planning, providing a new solution for the application of intelligent robots in complex environments.

关键词： Deep learning Trajectory planning Heuristic algorithms Collaboration Predictive models Prediction algorithms Real-time systems Trajectory Convolutional neural networks Optimization

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A Multi-view Graph Neural Network Approach for Magnetic Resonance Imaging-Based Diagnosis of Knee Injuries 9th

A Multi-view Graph Neural Network Approach for Magnetic Reso...

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9th international symposium on Artificial intelligence and robotics, ISAIR 2024

作者： Deng, Biyong Pan, Jiashan Tang, Xiaoyu Fu, Haitao Hu, Shushan School of Artificial Intelligence Hubei University Wuhan430062 China Department of Orthopedic Surgery Beijing Jishuitan Hospital Guizhou Hospital Guiyang550014 China

ISBN: (纸本)9789819629138

The knee plays a pivotal role in the human anatomy, serving as a cornerstone for support, mobility, shock attenuation, and balance. Currently, magnetic resonance imaging (MRI) remains the preferred method for diagnosing knee injuries, including anterior cruciate ligament (ACL) tears and meniscal tears, due to its efficiency and accuracy in medical imaging. However, the interpretation and understanding of knee MRI images are time-consuming, laborious, require sufficient expertise, and are also prone to diagnostic errors. Thus, it is imperative to devise a computational method employing knee MRI for intelligent diagnosis of knee injuries, as this could expedite medical assessments by physicians, reduce costs, and substantially reduce the risk of misdiagnosis. Although several computational methods have been proposed to diagnose knee injuries, most rely heavily on local features in MRI images and exhibit low prediction accuracy. In this paper, we proposed a novel multi-view graph neural network, abbreviated as MVGNN, to identify knee injuries (specifically ACL tears and meniscal tears) by leveraging graph representations derived from multiple MRI views. Comprehensive experiments demonstrate that MVGNN achieves state-of-the-art results for diagnosing knee injuries, with a 5.1% improvement in accuracy on ACL data and a 4.8% improvement on Men data, compared to the second-best method, MVCNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graph neural networks

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