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VSLM: Virtual Signal Large Model for Few-Shot Wideband Signal Detection and Recognition

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 909-925页

作者： Hao, Xiaoyang Yang, Shuyuan Liu, Ruoyu Feng, Zhixi Peng, Tongqing Huang, Bincheng Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China AVIC Xian Flight Automat Control Res Inst AVIC FAC Software Engn Dept Xian 710076 Peoples R China MeiTuan Grocery Retail Dept Beijing 100029 Peoples R China China Elect Technol Grp Corp Inst Informat Sci & Technol Beijing 100036 Peoples R China

Most existing wideband signal detection and recognition (WSDR) methods rely on diverse, large-scale, and well-labeled training data, which are often difficult to obtain in practical application scenarios such as non-cooperative environments and novel signaling regimes. In this article, we propose a method for constructing a virtual signal large model (VSLM) and applying it to tackle the WSDR challenge under few-shot or even cross-domain few-shot scenarios. Firstly, we design two plug-and-play modules, virtual sample generation (VSG) and virtual category generation (VCG), for VSLM, respectively. VSG simulates the local and overall relationship between the burst signal and the constant signal, which is mainly completed by extracting time-frequency meta-block and data enhancement. Based on VSG and the multi-label concept, we further create virtual novel categories by injecting customizable semantic information into meta-blocks. Then, we further propose a dual decoupled network (DDN) to train the VSLM. DDN enhances signal details by decoupling low gray values (DLGV) in time-frequency representation, and alleviates conflicts during multi-task joint optimization by decoupling spectrum localization and signal classification. Finally, based on the wideband spectrogram dataset, extensive experiments have validated that our proposed methods can significantly improve the performance of WSDR under few-shot conditions.

关键词： Wideband Signal detection Time-frequency analysis Feature extraction Wireless communication Modulation Narrowband Computational modeling Accuracy Spectrogram Wideband signal detection and recognition (WSDR) few-shot dual decoupling network (DDN) virtual sample generation (VSG) virtual category generation (VCG)

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Dirt Detection and Segmentation Network for Autonomous Washing Robots 5th

Dirt Detection and Segmentation Network for Autonomous Washi...

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5th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Gu, Shangbin Peng, Gang Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China

ISBN: (纸本)9783031189128;9783031189135

In the field of industrial, robots are becoming a modern method to perform automatic washing on large industrial components while detecting dirt. Therefore, the detection and segmentation of dirt have a great impact on optimizing the effects and improving quality of washing by modern washing robots. We propose DDSN (Dirt Detection and Segmentation Network) in this paper which improves the SVDD (Support Vector Data Description), using neural networks to obtain the optimal kernel function of SVDD, so that training images with no dirt can be mapped to the smallest hypersphere in the feature space. In dirt detection and segmentation, the distance between the test images and the centers of the corresponding hypersphere is defined as the feature value, and the dirt scores of the pixels can be obtained. Before training of dirt dataset, we use a larger anomaly detection dataset named MVTecAD which is also the one-class classification to pretrain the feature extraction network, which makes up for the lack of samples in the dirt dataset and speeds up the convergence of the model. Afterwards, we transfer the feature extraction network to training the dirt dataset of large industrial components. The results show that the methods proposed in this paper performs well in detection and segmentation of both MVTecAD dataset and dirt dataset of large industrial components.

关键词： Robot vision Dirt detection Dirt segmentation

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A Quality Prediction and Parameter Optimization Approach for Turbine Blade Multistage Manufacturing

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第1期29卷 3-15页

作者： Liu, Qianjun Vassiliadis, Vassilios S. Wu, Yudie Zhang, Jiajun Cheng, Cheng Yuan, Ye Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MOE Key Lab Intelligent Control & Image Proc Wuhan 430074 Peoples R China Univ Cambridge Cambridge CB2 1TN England Wuxi Turbine Blade Co Ltd Wuxi 214000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

As core components of an aircraft engine, turbine blades are featured by complex curved surface with strict precision requirements in multistage manufacturing processes (MMPs). Hence, reliable quality prediction and parameter optimization strategy on MMPs have long been in great demand. However, most techniques focus on the precision improving of single-stage single-product manufacturing systems;these improvements are difficult to implement in MMPs with coordinate transformations among multiple machines. In this article, we propose a noninvasive feature-based quality prediction and parameter optimization framework for high-precision MMPs. In particular, rather than retrofitting sensors onto machinery after installation, this study presents an executable and noninvasive solution by using coordinate measuring machine (CMM) data as input features for prediction. Based on features of different stages of machining, a sequential multistep deep learning architecture is developed to predict the final product quality. A preheating optimization algorithm, modified from a local gradient search method, is used for the optimization of key initial machining features, which results are further improved by the L-BFGS method, thus increasing the final production quality. To estimate the effectiveness of the proposed framework, experiments are carried out on two series of aviation turbine blades with complex curved surfaces from the Wuxi Turbine Blade Company Ltd. China. CMM data at key stages (i.e., blade root milling and comprehensive accurate milling) are used as inputs to predict the final geometrical errors. Comparison results verify that the proposed framework achieves the smallest root mean square error at 0.030 mm and 0.032 mm for two blades, respectively.

关键词： Blades Optimization Turbines Production Milling Prediction algorithms Manufacturing Deep learning intelligence optimization multistage manufacturing processes (MMPs) quality prediction turbine blade machining

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Fully Distributed Observer Design for Mobile Targets

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2023年第3期10卷 1696-1708页

作者： Wang, Xiaoling Fan, Zhen Wang, Lin Su, Housheng Lam, James Nanjing Univ Posts & Telecommun Coll Automat & Artificial Intelligence Nanjing 210023 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Ctr IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Wuhan 430074 Peoples R China Univ of Hong Kong Dept Mech Engn Hong Kong Peoples R China

In this paper, a sensor network is used to implement the distributed observer design problem for a linear time-invariant mobile target system with constant or time-varying velocity. Each sensor can only access a part of the output information of the target. Two types of interacting dynamics are endowed to each sensor, one is a consensus-based algorithm for the state estimation using the local measurements, while the other is a leader-following flocking-like algorithm such that the mobile sensors can avoid collision, maintain communication, and track the target. By adopting adaptive coupling gains on the consensus-based term in the state estimation algorithm, a fully distributed observer which is independent of the communication topology associated with the sensor network is constructed. Finally, numerical simulations are presented to illustrate the effectiveness of the proposed theoretical results.

关键词： Observers Heuristic algorithms Topology Target tracking Network topology Couplings Linear systems Consensus distributed observer flocking mobile sensor network multi-agent system

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MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects 17th

MPIB: An MPI-Based Bokeh Rendering Framework for Realistic P...

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17th European Conference on Computer Vision (ECCV)

作者： Peng, Juewen Zhang, Jianming Luo, Xianrui Lu, Hao Xian, Ke Cao, Zhiguo Huazhong Univ Sci & Technol Sch AIA Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Adobe Res San Jose CA USA

ISBN: (纸本)9783031200670;9783031200687

Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background. However, it is challenging for existing bokeh rendering methods to simulate realistic partial occlusion effects due to the missing information of the occluded area in an all-in-focus image. Inspired by the learnable 3D scene representation, Multiplane image (MPI), we attempt to address the partial occlusion by introducing a novel MPI-based high-resolution bokeh rendering framework, termed MPIB. To this end, we first present an analysis on how to apply the MPI representation to bokeh rendering. Based on this analysis, we propose an MPI representation module combined with a background inpainting module to implement high-resolution scene representation. This representation can then be reused to render various bokeh effects according to the controlling parameters. To train and test our model, we also design a ray-tracing-based bokeh generator for data generation. Extensive experiments on synthesized and real-world images validate the effectiveness and flexibility of this framework.

关键词： Bokeh rendering Multiplane image Partial occlusion

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Juewen Peng Zhiyu Pan Chengxin Liu Xianrui Luo Huiqiang Sun Liao Shen Ke Xian Zhiguo Cao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology S-Lab Nanyang Technological University

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge.

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Facial Action Unit Detection by Adaptively Constraining Self-Attention and Causally Deconfounding Sample

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2025年第4期133卷 1711-1726页

作者： Shao, Zhiwen Zhu, Hancheng Zhou, Yong Xiang, Xiang Liu, Bing Yao, Rui Ma, Lizhuang China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat MoE Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Xuzhou 221116 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

Facial action unit (AU) detection remains a challenging task, due to the subtlety, dynamics, and diversity of AUs. Recently, the prevailing techniques of self-attention and causal inference have been introduced to AU detection. However, most existing methods directly learn self-attention guided by AU detection, or employ common patterns for all AUs during causal intervention. The former often captures irrelevant information in a global range, and the latter ignores the specific causal characteristic of each AU. In this paper, we propose a novel AU detection framework called AC2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{AC}<^>{2}$$\end{document}D by adaptively constraining self-attention weight distribution and causally deconfounding the sample confounder. Specifically, we explore the mechanism of self-attention weight distribution, in which the self-attention weight distribution of each AU is regarded as spatial distribution and is adaptively learned under the constraint of location-predefined attention and the guidance of AU detection. Moreover, we propose a causal intervention module for each AU, in which the bias caused by training samples and the interference from irrelevant AUs are both suppressed. Extensive experiments show that our method achieves competitive performance compared to state-of-the-art AU detection approaches on challenging benchmarks, including BP4D, DISFA, GFT, and BP4D+ in constrained scenarios and Aff-Wild2 in unconstrained scenarios.

关键词： Adaptively constraining self-attention Causal intervention Sample confounder Facial AU detection

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Hierarchical Feature Embedding for Visual Tracking 17th

Hierarchical Feature Embedding for Visual Tracking

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17th European Conference on Computer Vision (ECCV)

作者： Pi, Zhixiong Wan, Weitao Sun, Chong Gao, Changxin Sang, Nong Li, Chen Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan Peoples R China Tencent WeChat Shenzhen Peoples R China

ISBN: (纸本)9783031200465;9783031200472

Features extracted by existing tracking methods may contain instance- and category-level information. However, it usually occurs that either instance- or category-level information uncontrollably dominates the feature embeddings depending on the training data distribution, since the two types of information are not explicitly modeled. A more favorable way is to produce features that emphasize both types of information in visual tracking. To achieve this, we propose a hierarchical feature embedding model which separately learns the instance and category information, and progressively embeds them. We develop the instance-aware and category-aware modules that collaborate from different semantic levels to produce discriminative and robust feature embeddings. The instance-aware module concentrates on the instance level in which the inter-video contrastive learning mechanism is adopted to facilitate inter-instance separability and intra-instance compactness. However, it is challenging to force the intra-instance compactness by using instance-level information alone because of the prevailing appearance changes of the instance in visual tracking. To tackle this problem, the category-aware module is employed to summarize high-level category information which remains robust despite instance-level appearance changes. As such, intra-instance compactness can be effectively improved by jointly leveraging the instance- and category-aware modules. Experimental results on various benchmarks demonstrate the proposed method performs favorably against the state-of-the-arts. The code is available on https://***/zxgravity/CIA.

关键词： Instance-level Category-level Visual tracking

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Proxy-Based control of intelligent Assistive Walker for Intentional Sit-to-Stand Transfer

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第2期27卷 904-915页

作者： Huang, Jian Yan, Shihao Yang, Daqian Wu, Dongrui Wang, Lei Yang, Zhaohui Mohammed, Samer Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Tongji Med Coll Dept Rehabil Union Hosp Wuhan 430022 Peoples R China

Sit-to-stand (STS) transfer is an easy everyday activity for healthy people, but it is a challenging task for the elderly, and patients with lower-limb weaknesses. This article presents an intelligent assistive walker, which assists the user to accomplish safe STS transfers. An STS intention recognition method is proposed based on an optimized adaptive network-based fuzzy inference system (ANFIS), where the walker is able to accurately detect a voluntary STS and to apply an appropriate control accordingly. The walker is controlled by using a proxy-based controller with respect to the detected human intention, that is measured using thin-film force sensing resistors. During the STS transfer, the assistance force is generated on-line using a virtual spring-damper between the proxy and the linear actuator of the assistive walker. The stability of the proposed controller is theoretically analyzed, and verified by simulations. Experiments with five young healthy subjects and one elderly subject demonstrated that the proposed assistive walker is able to efficiently follow the user's intention to complete stable and compliant STS transfers.

关键词： Legged locomotion Force Senior citizens Actuators Sensors Mechatronics Force measurement Assistive walker optimized adaptive network-based fuzzy inference system (ANFIS) proxy-based controller sit-to-stand (STS) transfer

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Stabilization of switched systems with unstable modes via mode-partition-dependent ADT

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Journal of the Franklin Institute 2023年第2期360卷 1308-1326页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian116024 China Institute of Artificial Intelligence and Future Networks Beijing Normal University at ZhuhaiGuangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province and School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

This paper concerns the stability of switched systems orchestrating between unstable modes. First, a mode partition is applied to select stabilizing switching from the switching between modes from different subsets. Second, a piecewise Lyapunov-like function is applied to decline at stabilizing switching times. Then, the stability criteria build on mode-partition-dependent average dwell time by collecting enough stabilizing switching behavior to stabilize the destabilizing switching behavior and unstable modes. Finally, the simulation verification shows the feasibility and effectiveness of the method. © 2022 The Franklin Institute

关键词： Stability criteria

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