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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Hadi, Abdul Peng, Yan Qadir, Abdul Li, Yingsong Khan, Khushal College of Information and Communication Engineering Harbin Engineering University Harbin China Anhui Province Key Laboratory of Target Recognition and Feature Extraction LuAn230088 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China College of Intelligent System and Control Science Engineering Harbin Engineering University China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

Andrew's Sine Estimator (ASE) has recently been used to invent adaptive filtering, which can combat more kind of noises than conventional estimators. Inspired by the LMS and its sparse forms, normalization and proportionate ASE algorithms with Lawson constraints are constructed with the respective given names of Lawson normalization ASE (LNASE) and Lawson proportionate NASE (LPNASE). Simulations are performed to analyze the performance of these algorithms under impulsive noise. © 2022 IEEE.

关键词： Impulse noise

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DAFormer: Depth-aware 3D Object Detection Guided by Camera Model via Transformers

DAFormer: Depth-aware 3D Object Detection Guided by Camera M...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Junbin Gao Hao Ruan Bingrong Xu Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Automation Wuhan University of Technology Wuhan China

3D object detection plays a critical role in autonomous driving perception. Although multi-view-based perception solutions have made significant progress, their performance is still far from being ready for practical use. The estimation of pixel depth is dependent on camera intrinsic properties, which led us to explore a depth-aware model guided by camera parameters. Our contribution in this paper is the DAFormer, which incorporates camera parameters and position-aware image features to detect 3D objects. The depthaware module uses camera parameters to reweight image features and estimate depth, enhancing and offsetting the 3D position embedding. The object query uses depth information enhanced features for end-to-end 3D detection by Attention layers. DAFormer achieves impressive results on the standard nuScenes dataset without any additional embellishments.

关键词： Solid modeling Three-dimensional displays Estimation Object detection Detectors Cameras Feature extraction

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SDSFusion: A Semantic-Aware Infrared and Visible image Fusion Network for Degraded Scenes

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IEEE Transactions on image processing 2025年 34卷 3139-3153页

作者： Chen, Jun Yang, Liling Yu, Wei Gong, Wenping Cai, Zhanchuan Ma, Jiayi China University of Geosciences School of Automation Wuhan 430074 China Automation for Complex Systems Hubei Key Laboratory of Advanced Control and Intelligent Wuhan 430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan 430074 China China University of Geosciences Faculty of Engineering Wuhan 430074 China Macau University of Science and Technology School of Computer Science and Engineering Macau Macao Wuhan University Electronic Information School Wuhan 430072 China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. © 1992-2012 IEEE.

关键词： adversarial generative network image fusion Multi-modal fusion semantic awareness

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Research on Gas Turbine Rotor Fault Diagnosis Method Based on VIT Model and Transfer Learning

Research on Gas Turbine Rotor Fault Diagnosis Method Based o...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Qiao, Qi Wang, Hongjun School of Mechanical Electrical Engineering Beijing Information Science and Technology University Beijing100192 China Beijing International Science Cooperation Base of High-End Equipment Intelligent Perception Beijing100192 China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing100192 China

ISBN: (纸本)9783031494208

Aiming at the problems of difficulty in fault data collection and scarcity of fault samples in gas turbine rotor fault diagnosis, a gas turbine rotor fault diagnosis model based on VIT model and transfer learning is proposed. Firstly, the acquired one-dimensional vibration signal is converted into a two-dimensional time–frequency image by continuous wavelet transformation as the input of the VIT model, and the fault diagnosis is realized through its recognition of time–frequency image and feature extraction. Using CRWU bearing dataset to pre-train the model combined with transfer learning, only the weights in the MLP Head structure are migrated by freezing the part before the MLP Head structure and using the gas turbine rotor dataset fault classification, the results show that the fault recognition accuracy reaches 99.5% after transfer learning, and the superiority of the proposed model is proved by comparative experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Failure analysis

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Edge-Preserving Multi-Exposure Fusion with Guided Filtering for Structural Patch Decomposition 6

Edge-Preserving Multi-Exposure Fusion with Guided Filtering ...

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6th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2023

作者： Zhen, Yang Qi, Junwei Li, Yingsong College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China College of Computer Science and Technology Harbin Engineering University Harbin150001 China

ISBN: (纸本)9798350399059

Multi-exposure image fusion (MEF) is one of the effective ways to obtain high dynamic range (HDR) images. Multi-scale structural patch decomposition based MEF(MSPD) and edge-preserving structural patch decomposition (MESPD) have achieved state-of-the-art fusion quality. MESPD incorporates edge-preserving factors into MSPD to preserve details in a single-scale setting. However, we find that the edge-preserving factors put too much emphasis on the edge, resulting in artifacts on some edges and loss of details. To tackle this issue, guided filter is introduced into MSPD, which is directly used as an edge-preserving filter instead of mean filter to implicitly decompose images. In addition, we have designed a flexible adaptive weight, which can further maintain details of the light and dark areas. Likewise, we inherit MESPD and MSPD to extend our method to multi-scale framework. Extensive experimental results demonstrate our method can produce satisfactory results in both static and dynamic scenes with fewer artifacts and low computational cost. © 2023 IEEE.

关键词： image fusion

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Deep Learning-based AP Selection Method in Software-defined Networks 5

Deep Learning-based AP Selection Method in Software-defined ...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Duan, Miaomiao Zhi, Hui Yang, Lixia Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China School of Electronic and Information Engineering Anhui University Hefei230601 China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

In the Software Defined Network (SDN), the terminal has been connected to the network in the process when an access point failure or damage, if the access point is not properly selected, it will lead to a decline in the quality of service applications. To address this problem, most traditional AP selection methods use access point selection based on received signal strength without take into account the channel and channel capacity of the access point access, while this paper uses Deep Neural Networks (DNN)-based access point (AP) selection method and considers the channel and channel capacity as part of the parameters. In the SDN controller, the received signal strength, throughput, number of connected devices, channel, and the channel capacity and access point performance quality are used as the input and label of the DNN, respectively. The analysis and simulation results show that the algorithm can select AP with better performance quality compared with the traditional AP selection method based on received signal strength, and the correct rate is significantly improved by 5% when compared with the AP selection method based on feedforward neural network. Meanwhile, the DNN-based access point selection method in SDN achieves flexible control of network traffic and balances the load of access points in the network. © 2022 IEEE.

关键词： Deep neural networks

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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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An Infrared and Visible image Fusion Network for Dark Scenes

An Infrared and Visible Image Fusion Network for Dark Scenes

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Algorithm, image processing and Machine Vision (AIPMV), International Conference on

作者： Jiahao Zhang Zongshou Wei Gansu Key Laboratory of Plateau Traffic Information Engineering and Control Key Laboratory of Optoelectronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

The purpose of fusing infrared and visible photos is to provide high-quality combined images with clear objects and detailed textures, even in very dark environments. The visual quality and contrast of both infrared and visible images tend to suffer in low-light situations. Previous algorithms fused poor- quality source images directly, resulting in fused images that still had low contrast and lost detail information. Thus, in this paper, we first design a multi-level brightness adjustment unit to learn visible light illumination components layer by layer and perform luminance enhancement while fusing multi-source image information. Second, to enhance the network's capacity for feature extraction, we developed the parallel attention module. This module was inserted after each brightness adjustment module. Extensive trials show that our technique works better than nine common fusion methods when compared visually and using objective measures.

关键词： Training Learning systems Degradation Visualization Machine vision Brightness Lighting Feature extraction image fusion Testing

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Ensemble Learning Based Decision-Making Models on the Aero-Engine Bearing Fault Diagnosis

Ensemble Learning Based Decision-Making Models on the Aero-E...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Zhang, Shuo Li, Longqi Zhou, Heyang Liu, Haifeng Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China School of Control Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9789811581540

The aero-engine is an extremely complex and precise mechanical system. Due to the changing working conditions, there are many factors that cause structural vibrations. The normal operation of each component is related to the stability and safety of the entire system. Failure of components can cause huge disasters. This paper analyzes the aero-engine bearing vibration failure caused by the low-pressure rotor imbalance, and finds that the main difficulty in constructing a fault diagnosis model of aero-engine lies in the processing of imbalance data and the extraction of multidimensional feature data. To solve these problems, a fault diagnosis method is proposed. This method first uses the border-line smote method to balance the data, and then proposes a weight-based Gini coefficient feature extraction method for feature extraction. Finally, the extracted features are used to construct an aero-engine fault diagnosis model. The principle of the fault diagnosis model is to build classifiers to distinguish between normal classes and fault classes. This paper mainly studies the classification effect of the three algorithms xgboost, GDBT and adaboost, and discusses the superiority of different algorithms. And using the bench data of a research institute for testing, it is found that the fault diagnosis model constructed by using the xgboost algorithm has a better fault diagnosis accuracy. It shows that this method has a good application in aero-engine fault diagnosis. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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Dynamic Event-Triggered control for Multi-Agent Consensus with Relative Output Feedback

Dynamic Event-Triggered Control for Multi-Agent Consensus wi...

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American control Conference (ACC)

作者： Sikang Zhan Xianwei Li Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper studies the consensus problem of linear multi-agent systems (MASs) with relative output measurement on undirected graphs. Output-feedback protocols are designed for both leaderless and leader-follower consensus. Different from [1], dynamic event-triggered sampling strategies are integrated to reduce the communication burdens between agents. Compared with most of the existing results which are based absolute output, the proposed protocols are based on sampled relative output. Finally, a simulation example is provided to illustrate the theoretical results.

关键词： Protocols Decentralized control Numerical simulation Topology Closed loop systems Output feedback Multi-agent systems

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