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IEEE International Conference on Systems, Man and Cybernetics

作者： Ruimin Peng Jiayu An Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Electroencephalogram (EEG)-based seizure sub-type classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutuaISHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Diver-gence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification.

关键词： Training Adaptation models Data privacy Fuses Brain modeling Transformers Data models Electroencephalography Decision trees Cybernetics

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Hierarchical Spatio-temporal Decoupling for Text-to- Video Generation

Hierarchical Spatio-temporal Decoupling for Text-to- Video G...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhiwu Qing Shiwei Zhang Jiayu Wang Xiang Wang Yujie Wei Yingya Zhang Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Alibaba Group Fudan University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite diffusion models having shown powerful abilities to generate photorealistic images, generating videos that are realistic and diverse still remains in its infancy. One of the key reasons is that current methods intertwine spatial content and temporal dynamics together, leading to a notably increased complexity of text-to-video generation (T2V). In this work, we propose HiGen, a diffusion model-based method that improves performance by decoupling the spatial and temporal factors of videos from two perspectives, i.e., structure level and content level. At the structure level, we decompose the T2V task into two steps, including spatial reasoning and temporal reasoning, using a unified denoiser. Specifically, we generate spatially coherent priors using text during spatial reasoning and then generate temporally coherent motions from these priors during temporal reasoning. At the content level, we extract two subtle cues from the content of the input video that can express motion and appearance changes, respectively. These two cues then guide the model's training for generating videos, enabling flexible content variations and enhancing temporal stability. Through the decoupled paradigm, HiGen can effectively reduce the complexity of this task and generate realistic videos with semantics accuracy and motion stability. Extensive experiments demonstrate the superior performance of HiGen over the state-of-the-art T2V methods. We have released our source code and models.

关键词： Training Source coding Semantics Spatial coherence Cognition Stability analysis Complexity theory

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A Time-Independent control Method for Variable-Speed Load-Bearing Walking with Suspended Backpack

A Time-Independent Control Method for Variable-Speed Load-Be...

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第43届中国控制会议

作者： Zhiyuan He Yu Cao Yifei Guo Jian Huang Hubei Key Laboratory of Brain-inspired Intelligent Systems Key Laboratory for Image Processing and Intelligent ControlSchool of Artificial Intelligence and AutomationHuazhong University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Walking with a load brings a significant burden to human shoulders,resulting in increasing metabolic energy consumption and the risk of skeleton and muscle *** backpack has been widely conducted for load-bearing walking,but exiting time-dependent control methods and event-based control methods are unable to exploit the characteristics of human gait *** this case,a time-independent control(TIC) method capable of eliminating the time dependence and adapting to variable speed is *** on the dynamical model of human-backpack system,an assistance profile along with the parameter optimization method are introduced which allows the backpack to reduce the burden on human shoulders during load-bearing *** the simulation evaluation,the proposed TIC method is compared with an impedance controller based time-dependent method and a locked backpack,under both constant speed and variable speed *** results demonstrates that the TIC case achieves an 89.1% reduction in dynamic load on shoulder under the condition of constant speed and a 78.2% reduction under the condition of variable speed,compared with the LOCKED case.

关键词： suspended backpack load carriage time-independent control

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Transformer-Based Differential Superpixel Selection Network for Remote Sensing Images Change Detection

Transformer-Based Differential Superpixel Selection Network ...

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Artificial intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Tihao Cui Xinlin Xie Jingjing Liang Haoxue Zhang Shanxi Provincial Key Laboratory of Advanced Control and Industrial Intelligence School of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

In remote sensing images change detection, the results of Transformer often face the low precision and edge fitting problems, which are caused by the highly complex background of building categories and the imbalance of sample ratio. To address these challenges, we propose a Transformer-based differential superpixel selection network. First, a differential superpixel selection module is proposed which introduces difference selecting to refine the edge feature representation of the object region by using the boundary information of the selected superpixel. Second, we propose a multi-scale feature-aware enhancement module, which fuses multi-scale feature differences with the features extracted by the hierarchical Transformer encoder. This module enhances difference awareness while suppressing interference from complex backgrounds. Finally, we construct a composite loss function that combines the focal loss function with the binary cross-entropy loss function. The composite loss function can pay more attention to hardly classifying samples and suppress the influence of easily classifying samples. Our proposed algorithm is tested on the widely used LEVIR-CD and DSIFN-CD datasets. The results show that it effectively improved detection accuracy and edge fitting compared to existing methods.

关键词： Accuracy Sensitivity Image edge detection Fitting Buildings Interference Transformers Feature extraction Manufacturing Remote sensing

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Named Entity Recognition Based on Knowledge Fusion and Boundary Feature Sharing

Named Entity Recognition Based on Knowledge Fusion and Bound...

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Advanced Algorithms and control Engineering (ICAACE), International Conference on

作者： Anrui Li Housheng Su School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan P. R. China Key Laboratory of Image Processing and Intelligence Control of Ministry of Education Huazhong University of Science and Technology Wuhan P. R. China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Named entity recognition is a core task in the field of information extraction, which aims to recognize a relevant entity from unstructured text and classify that entity. Aiming at the problem that existing nested named entity recognition models cannot achieve better compatibility in recognizing long entities and the performance of the models, this study proposes a nested named entity recognition model based on knowledge fusion and boundary feature sharing. First, a joint training network based on the concepts of lexicon and knowledge graph is constructed to improve the text feature representation to obtain richer contextual information; second, the bimodal attention mechanism is used to capture the semantic correlation between lexical items and generate the spanning semantic representation matrix; furthermore, the boundary feature sharing strategy is designed to improve the generalization ability of the model for the rough transition problem of entity spanning boundaries. To verify the effectiveness of the model, it is tested on three benchmark datasets, including ACE2004, ACE2005, and SciERC datasets. The experimental results show that the proposed model outperforms the corresponding baseline model in terms of entity recognition F1 index.

关键词： Training Knowledge engineering Attention mechanisms Text recognition Semantics Named entity recognition Knowledge graphs Lexicon Information retrieval Context modeling

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TMMM: Transformer in Multimodal Sentiment Analysis under Missing Modalities

TMMM: Transformer in Multimodal Sentiment Analysis under Mis...

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International Joint Conference on Neural Networks (IJCNN)

作者： Huanyu Wu Siyang Li Dongrui Wu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The cross-task gap presents a significant challenge for multimodal models because of the differences in input-output workflows. For instance, multimodal pre-trained transformers may encounter uni-modal data during testing. To mitigate the gap, this paper introduces a Transformer in Multimodal Sentiment Analysis under Missing Modalities (TMMM) aims to perform well using missing-modal data during testing. TMMM uses a missing multimodal training approach to prevent accuracy degradation in testing. At the same time, a new network architecture allows the model to reconstruct missing modalities during testing. Classification token fusion and Mixture-of-Experts structures further enhance the model’s performance. A pre-training method utilizing contrastive learning, which can construct negative samples with positive samples, is proposed to overcome insufficient labeled data. Our experiments demonstrated the effectiveness of TMMM on two datasets with no modalities missing, i.e., it consistently achieved the highest classification accuracy and Macro-F1, which outperformed the best state-of-the-art baseline on each dataset by about 2% and 2.5%. Additionally, TMMM usually performs better than other baselines on datasets with missing modalities during testing.

关键词： Training Sentiment analysis Accuracy Contrastive learning Network architecture Transformers Brain modeling

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Active Disturbance Rejection Gain Scheduling control of Hypersonic Vehicle Based on Guardian Maps

Active Disturbance Rejection Gain Scheduling Control of Hype...

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Data Driven control and Learning Systems (DDCLS)

作者： Tingyu Zhang Yongshuai Wang Mingwei Sun Mingrui Hao Zengqiang Chen College of Artificial Intelligence Nankai University Tianjin China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

This paper presented an adaptive gain scheduling algorithm for hypersonic vehicles with wide envelopes. First, the linear parameter variable parameter (LPV) model of hypersonic vehicles is established in the flight envelope. Then, the active disturbance rejection controller is considered to cope with disturbances and uncertainties, and the control gain is scheduled using the guardian maps method to adapt to the wide envelope of velocity and height. Finally, numerical simulation results show good control performance and meet the required performance indexes in all operating conditions, demonstrating the effectiveness and robustness of the proposed method.

关键词： Uncertainty PI control Scheduling algorithms Atmospheric modeling Numerical simulation Scheduling Robustness

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BD-WNet: Boundary Decoupling based W-shape Network for Road Segmentation in Optical Remote Sensing Imagery

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Fan, Shilong Xie, Jianlin Zhuang, Yin Chen, He Su, Zhibao Li, Lianlin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing100081 China China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory Beijing100072 China Peking University Schools of Computer Science and State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China

Using very high-resolution optical remote sensing images for road segmentation is a challenging and important interpretation task. Different from other segmentation tasks, road segmentation typically faces unpredictable structure, irregular distribution, and complex background interference. Thus, establishing an effective and stable semantic description for road segmentation becomes a challenge. In this article, a novel architecture called Boundary Decoupling based W-shape Network (BD-WNet) is proposed for achieving road segmentation from optical remote sensing imagery. First, a novel W-shaped double encoder-decoder architecture network is designed to provide more stable semantic description, which can be used for road body extraction. Second, the unstable semantic features within the initial stage of double encoder-decoder architecture are considered for road boundary description. For decoupling unstable boundary information from the output feature of first encoder-decoder, a boundary separate model is designed, which is called Boundary-Body Decoupling (BBD) module. This module utilizes the flow field mechanism to compare image features before and after passing through the encoder-decoder. The stable features among the overall features are decoupled into the main body of the road, while the dynamic features are decoupled into the boundary of the road. Third, a boundary and body weighting fusion model is also designed to fuse stable road body and unstable road boundary information for supervised learning. Extensive experiments have been carried out on remote sensing road segmentation datasets, and our method achieves impressive performance. Specifically, the proposed BD-WNet achieves 82.5% F1 score and 69.8 IoU% on DeepGlobe dataset, 75.5% F1 score and 60.7% IoU on CHN6-CUG dataset and 93.8% F1 score and 88.3% IoU on Ottawa Road Dataset. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Distributed Formation Tracking control and Collision Avoidance with Formation Transformation for Fixed-Wing UAV Swarm 43

Distributed Formation Tracking Control and Collision Avoidan...

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43rd Chinese control Conference, CCC 2024

作者： Yao, Bingchang Liu, Yishi Liu, Yumeng Dong, Xiwang Tan, Qingke Lv, Rui Liang, Zhuo Beihang University Beijing100191 China Institute of Software Chinese Academy of Sciences China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Institute of Unmanned System Beihang University Beijing100191 China China Academy of Launch Vehicle Technology China

ISBN: (纸本)9789887581581

This article proposes a distributed consensus-based formation tracking and dynamic obstacle avoidance method for fixed wing unmanned aerial vehicles (UAVs). The tracking target moves along a pre-set geometric path, while other UAVs automatically track the target to form various distributed formation shapes and always perform dynamic collision avoidance. The fixed wing UAVs in this article form a distributed formation based on information exchange between neighbors, and further the formation process is improved by incorporating position allocation into the formation transformation. Firstly, the fixed wing UAV is modeled and simplified. Secondly, a basic formation control protocol is designed based on consensus strategy, and a tracking target feedback is added on it. Then, an anti-collision controller and a variable formation position allocation strategy are designed to avoid the collision. Finally, the effectiveness of the presented method is verified through numerical simulations. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Unmanned aerial vehicles (UAV)

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Lightweight Vision Transformer for Small Data Sets 38

Lightweight Vision Transformer for Small Data Sets

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Fang, Wenxing Su, Yanxu Institutes of Physical Science and Information Technology Anhui University Hefei China School of Artificial Intelligence Anhui University Hefei230601 China Southeast University Key Laboratory of Measurement and Control of Complex System of Engineering Ministry of Education Nanjing210096 China

ISBN: (纸本)9798350303636

With the rapid development of Transformers in the field of computer vision, models based on Transformers have become highly competitive architectures in the area. Although variants of Transformer models have achieved increasing accuracy on image classification tasks, the size of the training set and the number of parameters required by the models have increased dramatically. When dealing with small datasets, such models face problems such as overfitting and undergeneralization, leading to poor accuracy on the test set. We propose a new lightweight vision transformer (LVT) to address these issues. We reconstructed the backbone network, which learns the relationship between pixels through local window self-attention and global self-attention computation. We also use the attention pooling approach to fuse the token sequences generated by the backbone network more meticulously. We trained on the CIFAR-10 and CIFAR-100 datasets from scratch and compared them with a modern convolutional neural network. The experimental results show that LVT outperforms the modern convolutional neural network in terms of accuracy and efficiency. On the test set of CIFAR-10, we have obtained an accuracy of 96.83%, which indicates that our model can effectively solve the problems facing the training of small datasets and has a wide range of application prospects. © 2023 IEEE.

关键词： lightweight pooling self-attention windows

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