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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Liu, Jiale Xie, Guoda Huang, Zhixiang Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9781733509657

This paper presents a novel approach for calculating graphene-based structures in the complying divergence implicit finite-difference time-domain (CDI-FDTD) method. To begin, the differential iteration formula of graphene structure in the CDI-FDTD method is derived utilizing the auxiliary-differential-equation (ADE) method. Next, the transmission characteristics of an infinite graphene sheet in an open space are investigated by conducting simulation analyses within the CDI-FDTD framework, considering various magnetostatic or electrostatic biases. Finally, the simulation results were compared with analytical solutions and the results obtained from traditional FDTD methods, confirming the unconditional stability and reliability of the proposed method. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Graphene

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SC-LSTM Based Human Action Recognition

SC-LSTM Based Human Action Recognition

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Naixin Kang Pengfei Yi Qingshu Geng Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Human Action Recognition is one of the most applied research directions in the field of Computer Vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human computer interaction Computer vision Accuracy Image recognition Feature extraction Complexity theory Human activity recognition

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An Active Visual Perception Methodology for the Safety of Human-Robot Collaborations

An Active Visual Perception Methodology for the Safety of Hu...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Siyuan Cheng Pengfei Yi Mengmeng Chen Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

In industrial settings with multiple robotic arms, ensuring human-robot interaction safety through visual feedback is essential. However, existing methods use multiple pre-set cameras to avoid occlusion, making implementation challenging in compact spaces. We propose an adaptive active visual perception method for safety in human-robot collaboration by equipping a single RGBD camera on an idle robotic arm. This method eliminates the need to add additional brackets, is low cost, and is suitable for compact environments compared to current methods. Firstly, to capture comprehensive and accurate global information as much as possible, an active visual perception strategy integrating static visual perception and proprioceptive pose optimization is designed; then, an optimization-based robot position and pose decision method is proposed by using the real-time robots state and obstacles state, which enables the camera at the end of the robot to acquire more effective views; finally, a dual-robot following strategy is proposed to ensure that it can work in tandem. The validity was proved by experimentation. The video are available at https://***/03lwb.

关键词： Visualization Service robots Robot vision systems Human-robot interaction Streaming media Cameras Manipulators

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iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detection 27th

iGrasp: An Interactive 2D-3D Framework for 6-DoF Grasp Detec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Jiang, Jian-Jian Wu, Xiao-Ming Chen, Zibo Wei, Yi-Lin Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783031781124

For 6-DoF grasp detection, we aim at introducing a new interactive 2D-3D framework which filters out irrelevant information and makes both modalities collaborate effectively to generate robust grasps and accelerate inference speed greatly. This cannot be accomplished by existing works on learning to grasp that merely utilize 3D point clouds or leverage both 2D textures and 3D point clouds. Our framework is called iGrasp, a novel three-step design between 2D textures and 3D point clouds, where the interaction modelling enhances both modalities. Concretely, we propose the 2D-to-3D interaction to leverage objectness masks generated from 2D textures to filter out target-irrelevant information in 3D point clouds. Then, we introduce the 3D-to-2D interaction to leverage structural priors from 3D point cloud features with cross-attention and cylinder grouping to refine 2D texture features. Finally, we combine the refined 2D texture features and 3D point cloud features for generating high-quality 6-DoF grasp poses. Our experiments on the large-scale real-world dataset, namely GraspNet-1Billion, demonstrate that iGrasp surpasses state-of-the-art methods by 4.66/3.53 mAP on RealSense/Kinect and reduces the inference time by 28%. Real-world experiments further verify the effectiveness of iGrasp. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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GDTNet: A Synergistic Dilated Transformer and CNN by Gate Attention for Abdominal Multi-organ Segmentation 30th

GDTNet: A Synergistic Dilated Transformer and CNN by Gate ...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Zhang, Can Wang, Zhiqiang Zhang, Yuan Li, Xuanya Hu, Kai Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan411105 China Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province Xiangnan University Chenzhou423000 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9783031533013

As one of the key problems in computer-aided medical image analysis, learning how to model global relationships and extract local details is crucial to improve the performance of abdominal multi-organ segmentation. While current techniques for Convolutional Neural Networks (CNNs) are quite mature, their limited receptive field makes it difficult to balance the ability to capture global relationships with local details, especially when stacked onto deeper networks. Thus, several recent works have proposed Vision Transformer based on a self-attentive mechanism and used it for abdominal multi-organ segmentation. However, Vision Transformer is computationally expensive by modeling long-range relationships on pairs of patches. To address these issues, we propose a novel multi-organ segmentation framework, named GDTNet, based on the synergy of CNN and Transformer for mining global relationships and local details. To achieve this goal, we innovatively design a Dilated Attention Module (DAM) that can efficiently capture global contextual features and construct global semantic information. Specifically, we employ a three-parallel branching structure to model the global semantic information of multiscale encoded features by Dilated Transformer, combined with global average pooling under the supervision of Gate Attention. In addition, we fuse each DAM with DAMs from all previous layers to further encode features between scales. Extensive experiments on the Synapse dataset show that our method outperforms ten other state-of-the-art segmentation methods, achieving accurate segmentation of multiple organs in the abdomen. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Computer aided analysis

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Broadband external rejection miniaturized SIW filter based on C-shaped symmetric structure

Broadband external rejection miniaturized SIW filter based o...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Xiao, Shuang Li, Minquan Qu, Xin Li, Chen Zhang, Zufeng Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Liaoyang Province Hefei230601 China

ISBN: (纸本)9781665452366

To effectively improve the frequency selectivity of the filter, a substrate integrated waveguide (SIW) filter with miniaturization and out-of-band rejection was designed. To achieve the desired filter response, symmetrical C-shaped resonators are etched on the top and bottom layers of the SIW filter. The C-shaped resonator etched on the top layer of SIW achieves miniaturization and filtering characteristics. In order to improve the frequency response, a two-stage SIW filter and a SIW filter with coupled gap-ring defected ground structure (CGRDGS) are proposed, and an equivalent circuit is given to verify the transmission response. The simulation results show that the filter achieves miniaturization, out-of-band rejection and low insertion loss, which can be used in 5G wireless communication The simulation results show that the filter achieves miniaturization, out-of-band rejection and low insertion loss, which can be used in 5G wireless communication. © 2022 IEEE.

关键词： Resonators

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Acoustic Word Embedding Model with Transformer Encoder and Multivariate Joint Loss

Acoustic Word Embedding Model with Transformer Encoder and M...

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intelligent computing and Wireless Optical Communications (ICWOC), International Conference on

作者： Yunyun Gao Qiang Zhang Lasheng Zhao Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China

As a representation of speech information, acoustic word embedding can enable query-by-example keyword search with low-resource speech data. An acoustic word embedding model with Transformer encoder and multivariate joint loss is presented in this article. First of all, on the basis of BLSTM, the model can extract richer information by adding a Transformer encoder, which improves representation ability of the embedding vector. Then, the contrast loss is improved according to the multiple negative samples, and combined with the hinge loss with distance characteristics, the model generates a more compact vector representation and improves the identification of features. Finally, considering the unbalanced characteristics of samples, the anti-focal loss is introduced to form a multivariate joint loss together with the above two losses, so as to make the model performance better. The model has achieved certain results in word recognition tasks and has certain competitiveness compared with other acoustic word models.

关键词：

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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Humanoid Robot Collaborative Lifting Integrating Executable Judgment

Humanoid Robot Collaborative Lifting Integrating Executable ...

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2022 IEEE International Symposium on Product Compliance Engineering - Asia, ISPCE-ASIA 2022

作者： Chang, Hua Yi, Pengfei Liu, Rui Dong, Jing Hou, Yaqing Zhou, Dongsheng The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Anthropomorphic robots

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Truthful Mechanism for Resource Allocation and Pricing in Vehicle-Assisted Mobile Edge computing

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8171-8186页

作者： Liu, Xi Liu, Jun Li, Weidong Qujing Normal University Key Laboratory of Intelligent Sensor and System Design School of Information Engineering Qujing655001 China Qujing Normal University Engineering Research Center of Intelligent System and Advanced Materials Yunnan Province Universities Qujing655001 China Yunnan College of Business Management School of Education Kunming650000 China Yunnan University School of Mathematics and Statistics Kunming650504 China

Mobile edge computing (MEC) provides stable and powerful computing services, and vehicle computing provides rich sensing services. Therefore, we develop vehicle-assisted mobile edge computing (VAMEC), integrating MEC and vehicle computing to maximize their advantages and broaden their applications. We address the resource allocation and pricing problem in VAMEC, in which the vehicle, as a resource provider, collaborates with the MEC servers to provide services to users. To enable MEC servers and vehicles to work in cooperation, we propose a mixed auction model that consists of two-step auctions: the first one-way auction and the second double auction. We consider three types of users based on their characteristics, where users participate in different auctions depending on the types. Users and vehicles are self-interested and may submit untrue values to harm the system's profits. Therefore, we propose the overall truthfulness for the mixed auction model, that is users or vehicles that cannot obtain higher utility by declaring untrue values regardless of which auction they participate in. To the best of our knowledge, this is the first study proposing the overall truthfulness and showing how to design the mechanism that achieves it. We then propose an overall truthfulness mechanism and show it achieves individual rationality, budget balance, and consumer sovereignty. Simulation results indicate that the proposed mechanism can increase social welfare by at least 24%, compared with other mechanisms. © 1967-2012 IEEE.

关键词： Mobile edge computing

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