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Graph-geometric message passing via a graph convolution transformer for FKP regression

Science China(Information Sciences) 2024年第12期67卷 176-190页

作者： Huizhi ZHU Wenxia XU Jian HUANG Baocheng YU Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology

In this paper, the forward kinematics problem(FKP) of the Gough-Stewart platform(GSP) with six degrees of freedom(6 DoFs) is estimated via deep learning. We propose a graph convolution transformer model by systematically analyzing some challenges encountered with using deep learning regression on largescale data. We attempt to leverage the graph-geometric message as input and singular value decomposition(SVD) orthogonalization for SO(3) manifold learning. This study is the first in which a robot with a sophisticated closed-loop mechanism is described by a graph structure and a specific deep learning model is proposed to solve the FKP of the GSP. Qualitative and quantitative experiments on our dataset demonstrate that our model is feasible and superior to other methods. Our method can guarantee error percentages of translation and rotation less than 1 mm and 1° of 81.9% and 96.7%, respectively.

关键词： deep learning graph-structured learning graph convolution transformer forward kinematics problem Gough-Stewart platform

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Gradient-based adversarial example generation with root mean square propagation 1

Gradient-based adversarial example generation with root mean...

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1st International Conference on Artificial Intelligence and Human-Computer Interaction, ArtInHCI 2023

作者： Hong, Dian Chen, Deng Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781643685083

Nowadays, the security of neural networks has attracted more and more attention. Adversarial examples are one of the problems that affect the security of neural networks. The gradient-based attack method is a typical attack method, and the Momentum Iterative Fast Gradient Sign Method (MI-FGSM) is a typical attack algorithm among the gradient-based attack algorithms. However, this method may suffer from the problems of excessive gradient growth and low efficiency. In this paper, we propose a gradient-based attack algorithm RMS-FGSM based on Root Mean Square Propagation (RMSProp). RMS-FGSM algorithm avoids excessive gradient growth by Exponential Weighted Moving Average method and adaptive learning rate when gradient updates. Experiments on MNIST and CIFAR-100 and several models show that the attack success rate of our approach is higher than the baseline methods. Above all, our generated adversarial examples have a smaller perturbation under the same attack success rate. © 2024 The Authors.

关键词： Iterative methods

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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Long-term stock price forecast based on PSO-Informer model 5

Long-term stock price forecast based on PSO-Informer model

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5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Liu, Hailun Chen, Deng Wei, Wei Wei, Ziqiang Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781510662520

The long-term prediction of stock prices provides an important reference for quantitative investment decisions. Aiming at the problem of insufficient accuracy of long-term series prediction in existing stock forecasting models, this paper proposes a long-term stock price series forecasting method based on PSO-Informer. Methods First, 43 kinds of technical indicator factors and K-line data were selected to construct the input data, and then the PSO-Informer model was used to predict the future 60 time points of the stock closing price. In the model training process, the particle swarm algorithm is used to optimize the parameters of the Informer network. Based on the 5-minute K-line data of the SSE 50 stock index and the CSI 300 stock index, experimental research was conducted respectively. Taking the accuracy of the long-term stock price prediction overall trend as the evaluation index, and the prediction accuracy reaches 68.2% and 67.5% respectively. The comparison experiments with ARIMA, Prophet, PSO-LSTM and Informer prediction models show that the model has the best performance and is practical. © 2023 SPIE.

关键词： Investments

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An iterative attention context fusion network for semantic segmentation of remote sensing image 12

An iterative attention context fusion network for semantic s...

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SPIE 12th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2023

作者： Chen, Yanpu Wang, Haihui Zhang, Shuiping Hu, Po Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In recent years, high-resolution remote sensing image segmentation has become a key task involving technology in many fields, mainly used to accurately extract target information from remote sensing images, and widely used in land detection, coverage classification, etc. These images are characterized by high resolution and large scale, and the overall segmentation requires high performance of hardware devices, the downsampling method usually used is easy to affect the segmentation quality, cropping and slicing the image will lead to the lack of edge information, and the problems of category homogenization, changes in complex scenes, and noise and occlusion make the segmentation challenging. Therefore, in this paper, we propose An iterative attention context fusion network (IACFNet) based on the attention mechanism as well as information fusion at different scales, which iteratively calculates the attention module weights connecting low and high features and refines the problem of spatial information loss, utilizing multiscale segmentation for information complementation, and utilizing an improved boundary loss function to precisely define boundary instances. Our proposed method obtains a performance improvement of about 2.8% and 1.5% in the mean intersection over union (mIoU) metric compared to the current state-of-the-art methods, respectively. © 2024 SPIE.

关键词： Semantics

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The ringed residual u-net with non-natural regions feature for image splicing forgery detection and localization

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Journal of intelligent and Fuzzy Systems 2024年第4期46卷 7447-7459页

作者： Wang, Qi Lu, Tongwei School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China

Recently, with the emergence of many image editing tools (photoshop, Topaz studio, etc.), the authenticity of images has been severely challenged. However, the performance of some existing traditional feature extraction methods and detection methods based on convolutional neural network (CNN) is poor, and the information provided by the features extracted from the network is limited and single. In this paper, an end-To-end ringed residual U-Net is proposed to detect image splicing forgery by blending features of non-natural regions. Some regions with significant differences from the image background are defined as non-natural regions(such as the irregular border at the splicing of images). In this paper, a feature enhancement module for non-natural regions is constructed, which the image through the pooling of four different scales, and these features are then combined with the original image and input to the backbone network for processing, aiming to highlight regions of the image that differ significantly from the background. Therefore, after adding the feature enhancement module for non-natural regions to the end-To-end ring residual U-Net, more attention will be paid to the tampering regions in the feature extraction stage, image manipulation detection and localization will also become more accurate. Compared with some mainstream methods, this method achieves better performance on the three standard datasets(CASIA2.0, NIST2016, COLUMBIA). In addition, it has excellent robustness under JPEG compression attack and noise corruption attack. © 2024-IOS Press. All rights reserved.

关键词： Topaz

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SSD object detection algorithm based on knowledge map

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International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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DSSNet: a transformer-based network for dense scene text detection and recognition in complex environments 5

DSSNet: a transformer-based network for dense scene text det...

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5th International Conference on Signal Processing and Computer Science, SPCS 2024

作者： Li, Jing Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

This paper presents a novel approach for dense scene text detection called DSSNet (Dense Script Spotter Network). The network leverages ResNet and FPN for feature extraction, employing multi-scale feature fusion and Transformer-based feature processing to enhance text recognition across varying sizes. The method generates text instance shapes using Bézier central curves and performs text recognition by integrating positional query information. Experimental results on the DSTD1500 and ICDAR2015 datasets demonstrate that DSSNet outperforms existing methods in terms of text localization accuracy, recognition accuracy, and annotation flexibility. © 2025 SPIE.

关键词： Feature extraction

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A SAR Denoising Network Based on Generative Adversarial Learning 6

A SAR Denoising Network Based on Generative Adversarial Lear...

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6th International Conference on robotics, Control and Automation Engineering, RCAE 2023

作者： Yi, Niannian Pei, Shengchang Ai, Nianxin Zhang, Shuiping Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350315301

Synthetic Aperture Radar (SAR) images are critical in microwave remote sensing and are used for resource exploration and target location. However, due to the principle of coherent imaging, SAR images are often interfered by speckle noise, which reduces image quality. It is necessary to remove noise but retain details. We are committed to improving the network structure to increase the diversity and authenticity of generated images. Inspired by Se-Res-Unet, we fuse generative adversarial networks for SAR image denoising and named the model SAR-RsuGan. In it, the generator and the discriminator compete with each other to generate high-quality images. We use synthetic SAR images for training and compare with other methods to obtain the best performance. © 2023 IEEE.

关键词： Synthetic aperture radar

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Emotion Recognition Based on Wasserstein Distance Fusion of Audiovisual Features 6

Emotion Recognition Based on Wasserstein Distance Fusion of ...

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6th International Conference on robotics, Control and Automation Engineering, RCAE 2023

作者： Ai, Nianxin Zhang, Shuiping Yi, Niannian Ma, Zhiwei Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350315301

The task of multimodal emotion recognition aims to discern human emotions by utilizing both speech and facial expressions. Given the significance of complementarity between different modal sources, fusion has emerged as a prominent area of research in multimodal emotion recognition. However, when merging multiple modalities, two distinct challenges arise: the high dimensionality of feature vectors and redundancy of information. Furthermore, this complexity introduces intricacies into the data. To address these challenges, we introduce a model based on the Wasserstein distance. This model extracts meaningful features from each modality by minimizing the Wasserstein distance, reducing information redundancy through the selection of these meaningful features. Subsequently, the effective features from both modalities are integrated, partially mitigating the high dimensionality of the feature data and simplifying it. Finally, a classification task is applied to the integrated effective features to determine the ultimate emotional category. Experimental results conducted on the RA VDESS dataset demonstrate that our model outperforms post-fusion models by achieving an accuracy 3.7 % higher, reaching an impressive accuracy rate of 85.2%. © 2023 IEEE.

关键词： Emotion Recognition

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