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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Liang Wang Dongxu Gao Hongwei Gao Guozhang Jiang Zhaojie Ju School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China School of Computing England University of Portsmouth Britain Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Optical coherence tomography angiography (OCTA) is an advanced, non-invasive imaging technique. Automatic segmentation of vascular networks in retinal OCTA images is essential for the early diagnosis and progression assessment of various vision-related diseases. However, most existing methods for OCTA image vessel segmentation rely on encoder-decoder architectures, which typically involve a high number of parameters and result in slower inference speeds. In this paper, we introduce FRNet V2, an accurate and efficient neural network specifically designed for retinal vessel segmentation in OCTA images. This is achieved by integrating a modified recursive ConvNeXt V2 block and a recursive HAAM attention mechanism into a full-resolution convolutional network framework. We evaluate our proposed method on two large public datasets: ROSSA and OCTA-500. Experimental results demonstrate that our network achieves performance on par with other methods, while boasting significantly fewer parameters and faster inference speeds. This makes it highly suitable for practical industrial applications.

关键词： Image segmentation Accuracy Three-dimensional displays Computational modeling Angiography Optical coherence tomography Neural networks Object segmentation Retinal vessels Convolutional neural networks

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Adaptive Mix for Semi-Supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Shen, Zhiqiang Cao, Peng Su, Junming Yang, Jinzhu Zaiane, Osmar R. School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

Mix-up is a key technique for consistency regularization-based semi-supervised learning methods, generating strong-perturbed samples for strong-weak pseudo-supervision. Existing mix-up operations are performed either randomly or with predefined rules, such as replacing low-confidence patches with high-confidence ones. The former lacks control over the perturbation degree, leading to overfitting on randomly perturbed samples, while the latter tends to generate images with trivial perturbations, both of which limit the effectiveness of consistency learning. This paper aims to answer the following question: How can image mix-up perturbation be adaptively performed during training? To this end, we propose an Adaptive Mix algorithm (AdaMix) for image mix-up in a self-paced learning manner. Given that, in general, a model's performance gradually improves during training, AdaMix is equipped with a self-paced curriculum that, in the initial training stage, provides relatively simple perturbed samples and then gradually increases the difficulty of perturbed images by adaptively controlling the perturbation degree based on the model's learning state estimated by a self-paced regularize. We develop three frameworks with our AdaMix, i.e., AdaMix-ST, AdaMix-MT, and AdaMix-CT, for semi-supervised medical image segmentation. Extensive experiments on three public datasets, including both 2D and 3D modalities, show that the proposed frameworks are capable of achieving superior performance. For example, compared with the state-of-the-art, AdaMix-CT achieves relative improvements of 2.62% in Dice similarity coefficient and 48.25% in average surface distance on the ACDC dataset with 10% labeled data. The results demonstrate that mix-up operations with dynamically adjusted perturbation strength based on the segmentation model's state can significantly enhance the effectiveness of consistency regularization. The code will be released at https://***/Senyh/AdaMix. Copyright © 2024,

关键词： Self-supervised learning

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Dual-layer Broadband High-gain Circularly Polarized Patch Antenna with L-shaped Stub

Dual-layer Broadband High-gain Circularly Polarized Patch An...

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IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Wen Li Wei Xue Yingsong Li Tian Hong Loh Kwok L. Chung College of Information and Communication Engineering Harbin Engineering University Harbin China The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Middlesex UK School of Computing Science and Engineering Huizhou University Huizhou China

This paper introduced a dual-layer broadband high-gain circularly polarized (CP) antenna. The proposed broadband CP antenna consists of the radiating patch and couping structure, which is made up of opening slot patch and L-shaped stub. An axial-ratio bandwidth of 26 % (4.36-5.66 GHz) is shown by this proposed dual-layer CP patch antenna with a simulated -10-dB reflection coefficient bandwidth of 37 % (4.23-6.15 GHz). The broadband characteristics offered by this CP antenna are promising for a wide range of future applications.

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A New Semantic Segmentation Diagram for Intelligent Transportation Based on Heterogeneous Knowledge Base

A New Semantic Segmentation Diagram for Intelligent Transpor...

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IEEE Conference on Wireless Communications and Networking

作者： Jingyuan Tang Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Semantic segmentation is regarded as an important technology for future communication and sensing networks due to its promising ability to extract features of transmit data. It integrates the functionality of computer vision and can realize high-fidelity transmission in the channel with lower bandwidth. Knowledge Base (KB) is a key component for the semantic segmentation framework. In this paper, we consider the scenario of intelligent transportation and propose a heterogeneous KB to extract the to-be-transmitted features and information at multiple levels, i.e., the raw level, symbol level, feature level and image level. The compressed features are obtained by a deep-learning-based framework. The proposed KB is shared with the transmitter and the receiver, by a service-oriented multi-stream transmission algorithm to meet various requirements of services. Experiments results demonstrate significant performance gain in terms of quality of service and encoding efficiency.

关键词： Semantic segmentation Semantics Knowledge based systems Transportation Symbols Object detection Streaming media

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Coordinate Attention-Based Convolution Neural Network for In-loop Filter of AVS3

Coordinate Attention-Based Convolution Neural Network for In...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Ruotong Wu Songlin Sun Jiaqi Zou Shaokang Wang Zhilei Ling School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Employing deep learning is a promising solution for reducing encoding bit rate in future video encoding systems. This paper proposes a neural network-based in-loop filter for the third generation of Audio Video Coding Standard (AVS3). The proposed network introduces a coordinate attention mechanism-based convolutional neural network in-loop filter (AMCNNLF) with a flexible attention module and a residual feature aggregation (RFA) module. Specifically, the attention module focuses on salient features to capture the visual structure, while the RFA module takes full advantage of the local refinement features. By leveraging the encoding parameters, we introduce the Quantizer Parameter (QP) values as auxiliary features to enable the proposed network suitable for processing encoded videos with multiple QPs. Experimental results indicate that the proposed network reduces the average Bjøntegaard-Delta rate (BD-Rate) of luma component about 0.4% under all intra configuration compared with the benchmark.

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Finite Element Analysis for the Effects of the Descending Aorta Tortuosity on Aortic Hemodynamics

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Procedia Computer Science 2022年 209卷 148-156页

作者： Jiapeng Li Xuehao Cao Lisheng Xu Lin Qi College of Medicine and Biological Information Engineering Northeastern University Shenyang 110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang 110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang 110169 China

Large arteries in the human body, such as the aorta, are susceptible to atherosclerosis, and aortic hemodynamic analysis has been widely used to predict cardiovascular disease. In this study, we aim to explore whether the tortuosity of the descending aorta (DA) is associated with the development of atherosclerosis and to investigate how vessel curvature affects blood flow characteristics. To investigate the relationship between tortuosity of DA and atherosclerosis, six models and different degrees of tortuosity were considered and combined for a total of 36 computational models. The models were numerically calculated by finite element analysis based on Fluid-Structure Interaction (FSI), and hemodynamic parameters including blood flow velocity, Wall Shear Stress (WSS), Oscillatory Shear Index (OSI), and Relative Residence Time (RRT), were calculated that are used to identify the locations of thrombosis and atherosclerosis. Our results showed that flow disturbances such as flow recirculation and secondary flow were more severe in the high-tortuosity vessels compared with the less-tortuosity vessels.

关键词： Computational Fluid Dynamics Fluid-Structure Interaction Descending Aorta Tortuosity

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CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior

arXiv

引用

arXiv 2023年

作者： Gao, Pan Han, Donghong Zhou, Rui Zhang, Xuejiao Wang, Zikun School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Swinburne University of Technology Australia

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors’ emotions. The reason is that considering both interlocutors’ emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models in both automatic and manual evaluation. © 2023, CC BY.

关键词： Knowledge management

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Towards Continual Knowledge Graph Embedding via Incremental Distillation

arXiv

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arXiv 2024年

作者： Liu, Jiajun Ke, Wenjun Wang, Peng Shang, Ziyu Gao, Jinhua Li, Guozheng Ji, Ke Liu, Yanhe School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications [Southeast University Ministry of Education China Institute of Computing Technology Chinese Academy of Sciences China

Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To address this issue, the continual knowledge graph embedding (CKGE) task has been proposed to train the KGE model by learning emerging knowledge efficiently while simultaneously preserving decent old knowledge. However, the explicit graph structure in KGs, which is critical for the above goal, has been heavily ignored by existing CKGE methods. On the one hand, existing methods usually learn new triples in a random order, destroying the inner structure of new KGs. On the other hand, old triples are preserved with equal priority, failing to alleviate catastrophic forgetting effectively. In this paper, we propose a competitive method for CKGE based on incremental distillation (IncDE), which considers the full use of the explicit graph structure in KGs. First, to optimize the learning order, we introduce a hierarchical strategy, ranking new triples for layer-by-layer learning. By employing the inter- and intra-hierarchical orders together, new triples are grouped into layers based on the graph structure features. Secondly, to preserve the old knowledge effectively, we devise a novel incremental distillation mechanism, which facilitates the seamless transfer of entity representations from the previous layer to the next one, promoting old knowledge preservation. Finally, we adopt a two-stage training paradigm to avoid the over-corruption of old knowledge influenced by under-trained new knowledge. Experimental results demonstrate the superiority of IncDE over state-of-the-art baselines. Notably, the incremental distillation mechanism contributes to improvements of 0.2%-6.5% in the mean reciprocal rank (MRR) score. More exploratory experiments validate the effectiveness of IncDE in proficiently learning new knowledge while preserving old knowledge across all time steps. Copyright © 2024, The Authors. A

关键词： Distillation

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MFANet: Mixed Feature Attention Network for Port Lane Detection

MFANet: Mixed Feature Attention Network for Port Lane Detect...

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Chinese Control Conference (CCC)

作者： Jinwei Zhang Jinya Su Dewei Yi Jun Yang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education and the School of Automation Southeast University Nanjing China Department of Computing Science University of Aberdeen Aberdeen U.K.

With the advancement of industrial automation and artificial intelligence technology, unmanned port autonomy has gained increasing attention. Port automatic driving technology is a critical component of unmanned autonomous systems, enabling rubber tire gantry cranes (RTGs) to autonomously navigate by detecting port lanes and implementing control commands. In this study, a deep learning-based method is proposed for detecting port lanes in complex port scenes. The method employs the Inception module and attention mechanism to enhance lane feature extraction, and a structural loss is introduced to explicitly constrain the detection results and incorporate the prior characteristics of port lanes. A novel Mixed Feature Attention Network (MFANet) is proposed to implement the method for port lane detection. Experimental results demonstrate that MFANet effectively improves the accuracy of port lane detection while maintaining high computational efficiency. Furthermore, the performance of MFANet is evaluated in a real-world application scenario.

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A New Neurodynamics-Based Model-Less Fault-Tolerant Scheme for Redundant Robot Motion Planning and Control

SSRN

引用

SSRN 2023年

作者： Wang, Xin Tan, Ning Zhong, Zhaohui Huang, Kai School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou China

For tasks utilizing redundant manipulators, there involves motion of a number of joints when performing tracking control. In some cases, one or a few of joints might fail and then result in task failure or even damage, which means that fault tolerance is an indispensable capability for robot control systems. In this paper, we propose a novel fault-tolerant scheme to endow the redundant robots with this capability in the condition that certain joints are not working. More importantly, our scheme can achieve the fault-tolerant tracking control in a data-driven manner based on zeroing neurodyanmics when the robot kinematic model is unavailable, which is much different from existing fault-tolerant control algorithms of redundant robots that require to the kinematic model in advance. The proposed model-less fault tolerant tracking control (MLFC) scheme considers the fault-tolerant control problem of redundant manipulator as a quadratic programming (QP) problem. Then, with the help of a discrete solver proposed in this paper, the fault-tolerant control problem is transformed into a unified computing problem in the condition that without knowing the redundant manipulator's model. Extensive simulations and experiments based on a six degrees of freedom (DOF) redundant manipulator are analyzed the verification of our scheme. © 2023, The Authors. All rights reserved.

关键词： Fault tolerance

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