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

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

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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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Mixture of Experts Based Scenario Prediction for Motion Forecasting

Mixture of Experts Based Scenario Prediction for Motion Fo...

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11th international symposium on computational intelligence and Industrial Applications, ISCIIA 2024

作者： Jia, Zongwei Ye, Peijun State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819647552

Vehicle motion prediction is of great significance in human-machine shared driving, as it is a basis for the virtual driver to make suitable driving strategies so as to better assist the human driver or even complete its own autonomous driving. While recent studies have achieved good results by applying an intention prediction module into the multi-modal trajectory generation strategy, they mostly give supervision between intention proposals and final predicted points with insufficient alignment method. This leads to a relatively low accuracy of the vehicle motion prediction. To tackle this challenge, we propose a graph-based model with mixture of experts (MOE) based intention prediction module, introducing an efficient scenario-based intention prediction mechanism to improve the performance of intention prediction task. Several testing was conducted on the Argoverse motion forecasting dataset, which showed that our model excels in predicting trajectories for multiple agents. And the ablation experiments have verified the efficency of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep Learning Graph Neural Networks Motion Forecasting

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Research on Sensor Fault Diagnosis Method Based on KPCA-AE Algorithm

Research on Sensor Fault Diagnosis Method Based on KPCA-AE A...

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11th international symposium on computational intelligence and Industrial Applications, ISCIIA 2024

作者： Fan, Jiongwei Zhang, Jia School of Automation Beijing Institute of Technology Beijing China National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9789819647552

Sensors occupy a pivotal role in indoor fire detection, and any malfunction within their operation can potentially escalate into severe accidents. Multi-sensor networks utilized in indoor fire early warning systems are inherently characterized by their high dimensionality and intricate linear interdependencies, presenting substantial operational complexities. To address these complexities, the paper introduces a fault diagnosis method based on Kernel Principal Component Analysis combined with Autoencoders (KPCA-AE). This methodology harmoniously integrates KPCA and AE, where KPCA is utilized to reduce the dimensionality of the data, thereby facilitating the training of the AE model. The proposed method adeptly and precisely identifies faults based on reconstruction errors, introducing a novel and effective paradigm for fault diagnosis in indoor fire sensors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autoencoder Fault diagnosis Kernel principal component analysis Sensors

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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...

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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...

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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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Solving the Stochastic Resource Allocation Problem Through an Adaptive Variable Neighborhood Search Algorithm

Solving the Stochastic Resource Allocation Problem Through ...

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11th international symposium on computational intelligence and Industrial Applications, ISCIIA 2024

作者： Ma, Weijie Wang, Heng Zhang, Jingyu Wang, Yujue Yu, Rui School of Automation Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9789819647552

This study investigates the stochastic resource allocation (SRA) problem, commonly found in complex systems, where the success rates of resources completing tasks are described probabilistically. An adaptive variable neighborhood search (AVNS) algorithm is proposed to address the SRA problem, featuring a permutation encoding method based on pseudo-resources and pseudo-tasks. The AVNS algorithm employs multiple permutation-based operators in its shaking and intensification phases, regulated by an adaptive mechanism to determine the sequence of operator selection. Additionally, a marginal benefit-based constructive algorithm is utilized for initializing the AVNS’s initial solution. To validate the effectiveness of the proposed AVNS, we designed 24 test cases with varying distributions of task values and resource success probabilities, and analyzed the optimality gaps of different algorithms. The computational experiments demonstrate that the AVNS algorithm provides superior allocation schemes compared to existing algorithms in most instances, highlighting its robustness and efficiency in solving complex SRA problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： permutation-based optimization Stochastic Resource Allocation variable neighborhood search

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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 ...

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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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Gaussian and Non-Gaussian Noise Effects on Data-Driven Modeling: A Comparative Investigation

Gaussian and Non-Gaussian Noise Effects on Data-Driven Mod...

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11th international symposium on computational intelligence and Industrial Applications, ISCIIA 2024

作者： Mersha, Bemnet Wondimagegnehu Dai, Yaping Hirota, Kaoru School of Automation Beijing Institute of Technology Beijing 100081 China Beijing Institute of Technology Zhuhai Tangjiawan Guangdong Zhuhai 519088 China

ISBN: (纸本)9789819647521

To ensure reliable predictions for various applications, it is essential to understand the impact of Gaussian and non-Gaussian noise on data-driven modeling. Gaussian noise has been a convenient assumption for model development, but real-world scenarios sometimes defy Gaussian assumptions. Our research compared the performances of the direct and parametric data-driven modeling methods with Gaussian and non-Gaussian sensor noise. The direct data-driven modeling methods that are evaluated are direct data-driven methods with pulse input-output dataset and direct data-driven methods with step input-output dataset. The parametric models that are evaluated are the Output Error (OE), Autoregressive with Moving Average and Exogenous Input (ARMAX), Autoregressive with Exogenous Input (ARX) and Box-Jenkins model (BJ). The result shows that the performance of the direct and the parametric data-driven modeling methods deteriorates under non-Gaussian noise. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data-driven modeling Gaussian Noise Model performance non-Gaussian Noise

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Multi-agent Reinforcement Learning for Sparse Reward Tasks Using Incremental Goal Enhanced Method

Multi-agent Reinforcement Learning for Sparse Reward Tasks ...

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11th international symposium on computational intelligence and Industrial Applications, ISCIIA 2024

作者： Han, Minglei Guo, Zhentao Sun, Licheng Ding, Ao Wang, Tianhao Zhao, Guiyu Ma, Hongbin School of Automation Beijing Institute of Technology Beijing 100081 China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing 100081 China

ISBN: (纸本)9789819647552

As the application of artificial intelligence continues to expand, complex decision-making problems such as multi-player gaming, multi-robot planning and multi-vehicle controlling have become new challenges for machine intelligence. MARL which concentrates on learning the optimal strategies of multiple agents that coexist in a shared environment, is a valid method to solve multi-agent decision-making challenges. Among MARL Algorithms, the MAPPO algorithm has won the favor of machine learning community due to its superb performance. However, the original MAPPO algorithm suffers from sparse reward issues. To overcome the sparse rewards problem and achieve sufficient learning in complex task, this paper proposes a IGE-MAPPO which uses a IGM that generates a variable-density and bi-domain reward signal, and conducts experiments on SMAC. The results show that the IGE-MAPPO algorithm can adapt to a variety of complex environment and has improved performance compared with other typical MARL algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Incremental Goals Multi-Agent Proximal Policy Optimization Sparse Rewards

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