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arXiv 2024年

作者： Li, Jiang Wang, Xiaoping Zeng, Zhigang The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China The Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Multimodal emotion recognition in conversation (MERC) has garnered substantial research attention recently. Existing MERC methods face several challenges: (1) they fail to fully harness direct inter-modal cues, possibly leading to less-than-thorough cross-modal modeling;(2) they concurrently extract information from the same and different modalities at each network layer, potentially triggering conflicts from the fusion of multi-source data;(3) they lack the agility required to detect dynamic sentimental changes, perhaps resulting in inaccurate classification of utterances with abrupt sentiment shifts. To address these issues, a novel approach named GraphSmile is proposed for tracking intricate emotional cues in multimodal dialogues. GraphSmile comprises two key components, i.e., GSF and SDP modules. GSF ingeniously leverages graph structures to alternately assimilate inter-modal and intra-modal emotional dependencies layer by layer, adequately capturing cross-modal cues while effectively circumventing fusion conflicts. SDP is an auxiliary task to explicitly delineate the sentiment dynamics between utterances, promoting the model’s ability to distinguish sentimental discrepancies. Furthermore, GraphSmile is effortlessly applied to multimodal sentiment analysis in conversation (MSAC), forging a unified multimodal affective model capable of executing MERC and MSAC tasks. Empirical results on multiple benchmarks demonstrate that GraphSmile can handle complex emotional and sentimental patterns, significantly outperforming baseline models. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Recognizing Human-object Interactions for a Home Assistive Robot 8

Recognizing Human-object Interactions for a Home Assistive R...

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8th International Conference on Automation, control and Robotics Engineering, CACRE 2023

作者： Wang, Liangliang Huo, Guanglei Chen, Xinwei Huang, Chengxi Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

To enable robots to understand a specific assistive task during human-robot interactions under complex home scenes, at the center is the problem of human-object interaction (HOI) recognition. In particular, aiming at identifying efficiently, this paper proposes a graph convolutional network (GCN) by considering the similarity and proximity of human and object nodes, based on which the geometric positions and morphological relationships are explored. Also, a supplementary convolutional neural network (CNN) is fused to GCN to extract the global semantic features, which also ensures the recognition applicability for the cases where detectors fall into failure. Experiments on V-COCO demonstrate our approach, and results in real-world scenes on our assistive robot - Xiaozhi II verify its robustness. © 2023 IEEE.

关键词： Human robot interaction

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A Mode-Suppression Circuit for the Ka-Band $\text{TE}_{01}$ Gyrotron Travelling Wave Tube: Design and Measurement

A Mode-Suppression Circuit for the Ka-Band $\text{TE}_{01}$ ...

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International Conference on Vacuum Electronics

作者： Yuan Ma Guo Liu Yingjian Cao Weijie Wang Yu Wang Wei Jiang Yelei Yao Jianxuan Wang Yong Luo University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

In this paper, the design and measurement of a mode-suppression circuit (MSC) applied in the nonlinear interaction region of a Ka-band gyrotron travelling wave tube (gyro-TWT) are presented. The proposed MSC is capable to suppress the spurious $\text{TE}_{11}$ mode with no influence on the operating $\text{TE}_{01}$ mode which helps to improve the tube stability for the high-power operation. Simulation result shows MSC can provide a loss of more than -5 dB around 23.1 GHz for the spurious $\text{TE}_{11}$ mode which agrees well with the measured results.

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Design and Cold Test of a Mode-Suppression Circuit for the G-Band Gyro-TWT

Design and Cold Test of a Mode-Suppression Circuit for the G...

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International Conference on Vacuum Electronics

作者： Yingjian Cao Guo Liu Yuan Ma Yu Wang Weijie Wang Wei Jiang Yelei Yau Jianxun Wang Yong Luo University of Electronic Science and Technology of China Chengdu China Laboratory of Electromagnetic Space Cognition and Intelligent Control Beijing China

In this paper, the design and measurement of a mode-suppression circuit used in the nonlinear interaction region for a G-band gyrotron travelling wave tube (gyro-TWT) are presented. By achieving sufficient coupling losses, this circuit can effectively suppress the parasitic oscillations without affecting the operating mode, thus improving the tube stability during high power operation. The measured results agree well with the simulated results, which can provide a loss of 5.6 dB/cm to suppress the backward-wave oscillation (BWO) of the TE21 mode. This proves that the circuit can be used as a new scheme for gyro- TWTs to achieve high stability and high power.

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Dual-Camera All-in-Focus Neural Radiance Fields

arXiv

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arXiv 2025年

作者： Luo, Xianrui Wu, Zijin Peng, Juewen Sun, Huiqiang Cao, Zhiguo Lin, Guosheng Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Singapore639798 Singapore

We present the first framework capable of synthesizing the all-in-focus neural radiance field (NeRF) from inputs without manual refocusing. Without refocusing, the camera will automatically focus on the fixed object for all views, and current NeRF methods typically using one camera fail due to the consistent defocus blur and a lack of sharp reference. To restore the all-in-focus NeRF, we introduce the dual-camera from smartphones, where the ultra-wide camera has a wider depth-of-field (DoF) and the main camera possesses a higher resolution. The dual camera pair saves the high-fidelity details from the main camera and uses the ultra-wide camera’s deep DoF as reference for all-in-focus restoration. To this end, we first implement spatial warping and color matching to align the dual camera, followed by a defocus-aware fusion module with learnable defocus parameters to predict a defocus map and fuse the aligned camera pair. We also build a multi-view dataset that includes image pairs of the main and ultra-wide cameras in a smartphone. Extensive experiments on this dataset verify that our solution, termed DC-NeRF, can produce high-quality all-in-focus novel views and compares favorably against strong baselines quantitatively and qualitatively. We further show DoF applications of DC-NeRF with adjustable blur intensity and focal plane, including refocusing and split diopter. Copyright © 2025, The Authors. All rights reserved.

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Heterogeneous Graph Convolutional Network for Visual Reinforcement Learning of Action Detection 8

Heterogeneous Graph Convolutional Network for Visual Reinfor...

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8th International Conference on Automation, control and Robotics Engineering, CACRE 2023

作者： Wang, Liangliang Huang, Chengxi Chen, Xinwei Wuhan University of Technology School of Computer Science and Artificial Intelligence Luoshi Road 122 Wuhan430070 China -HIT Research Institute of Engineering and Technology Building 9 Software Park Quanzhou362008 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798350302776

Existing action detection approaches do not take spatio-temporal structural relationships of action clips into account, which leads to a low applicability in real-world scenarios and can benefit detecting if exploited. To this end, this paper proposes to formulate the action detection problem as a reinforcement learning process which is rewarded by observing both the clip sampling and classification results via adjusting the detection schemes. In particular, our framework consists of a heterogeneous graph convolutional network to represent the spatio-temporal features capturing the inherent relation, a policy network which determines the probabilities of a predefined action sampling spaces, and a classification network for action clip recognition. We accomplish the network joint learning by considering the temporal intersection over union and Euclidean distance between detected clips and ground-truth. Experiments on ActivityNet v1.3 and THUMOS14 demonstrate our method. © 2023 IEEE.

关键词： Reinforcement learning

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Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification

arXiv

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arXiv 2025年

作者： Liu, Dingkun Li, Siyang Wang, Ziwei Li, Wei Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding EEG signals across different headsets remains a significant challenge due to differences in the number and locations of the electrodes. To address this challenge, we propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains. To our knowledge, this is the first work to use knowledge distillation in cross-headset transfers. Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Transfer learning

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UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer

arXiv

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arXiv 2025年

作者： Wang, Xiang Zhang, Shiwei Tang, Longxiang Zhang, Yingya Gao, Changxin Wang, Yuehuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology United States Alibaba Group China Tsinghua University China

This report presents UniAnimate-DiT, an advanced project that leverages the cutting-edge and powerful capabilities of the open-source Wan2.1 model for consistent human image animation. Specifically, to preserve the robust generative capabilities of the original Wan2.1 model, we implement Low-Rank Adaptation (LoRA) technique to fine-tune a minimal set of parameters, significantly reducing training memory overhead. A lightweight pose encoder consisting of multiple stacked 3D convolutional layers is designed to encode motion information of driving poses. Furthermore, we adopt a simple concatenation operation to integrate the reference appearance into the model and incorporate the pose information of the reference image for enhanced pose alignment. Experimental results show that our approach achieves visually appearing and temporally consistent high-fidelity animations. Trained on 480p (832x480) videos, UniAnimate-DiT demonstrates strong generalization capabilities to seamlessly upscale to 720P (1280x720) during inference. The training and inference code is publicly available at https://github. com/ali-vilab/UniAnimate-DiT. Copyright © 2025, The Authors. All rights reserved.

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A Method for Evading Penetration and Guidance of the Hypersonic Missile

A Method for Evading Penetration and Guidance of the Hyperso...

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第43届中国控制会议

作者： Zhi Lin Jianglong Yu Hong Jiang Xiwang Dong Zhang Ren Qingke Tan Rui Lv Zhuo Liang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control LaboratoryBeihang University Institute of Unmanned System Beihang University Institute of Artificial Intelligence Beihang University China Academy of Launch Vehicle Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the problem of evading penetration of the hypersonic missile,this paper proposes a method to control the overload and maneuver time of the hypersonic missile for evasive ***,the kinematic model,the kinetic model,and the attack-defense confrontation model of the hypersonic missile and interceptor are ***,the evaluation function model of the hypersonic missile penetration effect is established,and a method to determine the penetration parameters with the maximum value of evaluation function is ***,the critical parameters of penetration,including the optimal overload and maneuver time,are obtained through online ***,the error conditions of environmental noise and time delay are introduced into the critical parameters,and the Monte Carlo simulation verifies that the evasive penetration method can improve the success rate of penetration of a single hypersonic missile against two interceptors.

关键词： Hypersonic missile attack-defense confrontation overload maneuver time Monte Carlo Simulation

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From DAO to TAO: Finding the Essence of Decentralization

From DAO to TAO: Finding the Essence of Decentralization

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Juanjuan Liang, Xiaolong Qin, Rui Wang, Fei-Yue Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Macau999078 China Institute of Automation State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9798350337020

Decentralized Autonomous Organizations (DAOs) have been gaining popularity in recent years due to their promise of realizing the decentralized Web 3.0. However, most DAOs rely heavily on token-centric value systems as well as allocate decision-making authority and yield-sharing rights according to the held tokens, which often lead to monopolization of power and rights. To address this issue, this paper contributes to propose a truly democratic organization model, named True Autonomous Organizations and Operations (TAOs), that does not count upon tokens and is guided by principles of contribution-based and on-demand allocation. We first discuss the design of TAOs, including their infrastructures, power structures, and value systems, and then provide a technical roadmap for implementing TAOs in the DeSci context. This research can provide a valuable guidance for the construction and application of TAOs. © 2023 IEEE.

关键词： Decentralized Autonomous Organizations and Operations (DAOs) governance systems open consortium blockchain True Autonomous Organizations and Operations (TAOs) value systems

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