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22nd IEEE International Conference on Communication technology, ICCT 2022

作者： Qu, Zonghua Wei, Chunling Beijing Institute of Control Engineering Science and Technology on Space Intelligent Control Laboratory Beijing China

ISBN: (纸本)9781665470674

Faced with a large number of non-cooperative targets in space, more complete image data can be obtained at a closer distance. Image segmentation can be performed to obtain morphological information on the relevant targets, which can provide reliable data support for subsequent tasks such as situational awareness. In this paper, we improve the Deeplab V3+ network by adding Convolutional Block Attention Module (CBAM), which incorporates the channel attention mechanism and the spatial attention mechanism, and verify that the algorithm has good performance and can be used for practical applications by segmenting the spacecraft attitude estimation dataset from the European space Agency space Rendezvous laboratory. © 2022 IEEE.

关键词： Image segmentation

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Fusion-CAM: Fine-Grained and High-Faithfulness Explanations for Deep Convolutional Network via Hierarchical Fusion 7

Fusion-CAM: Fine-Grained and High-Faithfulness Explanations ...

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7th IEEE Advanced Information technology, Electronic and Automation control Conference, IAEAC 2024

作者： Zheng, Chen Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

The interpretability of the convolutional neural networks(CNNs) has become a research hotspot. A popular explanation method is based on Class Activation Mapping (CAM), which visualizes the salient regions most relevant to neural network decisions. However, many CAM methods use the feature maps produced by the final convolution layer to generate the class activation maps, which usually have a low spatial resolution and can only generate coarse-grained visual explanations that provide a rough spatial location of the target object. In this paper, we propose a novel CAM method named Fusion-CAM. It improves traditional CAM methods by combining final class activation map containing semantic information with intermediate layer class activation maps containing fine-grained details, to generate fine-grained visual explanations with high faithfulness. In order to obtain high-quality intermediate layer class activation maps, we utilize Layer-wise Relevance Propagation (LRP) to obtain the weighting components of each channel of the intermediate layer feature maps, and the intermediate layer class activation maps generated by weighted summation are less noisy and have clear fine-grained details, which help to improve the quality of the final class activation map. Qualitative and quantitative experiments show that Fusion-CAM can be easily attached to different CAM methods to improve their performance. © 2024 IEEE.

关键词： Convolution

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A Dual-branch Model for Early Detection of Alzheimer's Disease Using Resting-State fMRI 7

A Dual-branch Model for Early Detection of Alzheimer's Disea...

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7th IEEE Advanced Information technology, Electronic and Automation control Conference, IAEAC 2024

作者： Wang, Yixuan Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Alzheimer's disease (AD) is the most prevalent form of dementia, and early diagnosis is crucial for delaying and treating AD. Resting-state functional magnetic resonance imaging (rs-fMRI), a widely used medical imaging technique, offers rich temporal and spatial data, which has led researchers to explore various feature extraction methods based on rs-fMRI images for AD identification. However, the related work still suffers from insufficient utilization of temporal and spatial information which leads to unsatisfactory early diagnosis. In this study, we propose a dual-branch fusion model to extract spatial-temporal features from rs-fMRI images. Our proposed model can extract temporal features at different levels. We developed a Class Activation Sequence (CAS) branch, which is a structure that emphasizes the function of each temporal node throughout the whole time series. Additionally, we created a time-domain local branch for local feature extraction. Further, we designed a fusion module for the model to describe temporal contextual relationships and fuse features at various levels. We tested the performance of the model on the ADNI dataset, and the experimental results show that compared with other algorithms, the dual-branch fusion model achieves higher classification accuracy on several classification tasks including early diagnosis, which proves the advantage of the dual-branch fusion model in temporal and spatial feature extraction for rs-fMRI images, and our work also provides a foundation for the temporal domain characterization of rs-fMRI images. © 2024 IEEE.

关键词： Feature extraction

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Implementation and Application of Solvers for Simulation and Execution of control Algorithms in NCSLab 6

Implementation and Application of Solvers for Simulation and...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Su, Jiuzheng Lei, Zhongcheng Hu, Wenshan Liu, Guo-Ping Wuhan University Department of Artificial Intelligence and Automation Wuhan China Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen China

ISBN: (纸本)9798350353594

In order to solve the problems of high dependency and low flexibility caused by calling three-party services during simulation and experiments, this paper uses the NCSLab (Networked control System laboratory) as an application platform to implement the functions of multiple ordinary differential equation solvers, which are used for real-time simulation and experimentation of control algorithm. This paper mainly integrates the common ordinary differential equation solving methods and NCSLab platform, and verifies the support of NCSLab platform for different solving methods through the actual simulation process. © 2024 IEEE.

关键词： Networked control systems

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HTRNN: A New Protein Nuclear Localization Prediction Tool 7

HTRNN: A New Protein Nuclear Localization Prediction Tool

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7th IEEE Advanced Information technology, Electronic and Automation control Conference, IAEAC 2024

作者： Miao, Xinyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China Wuhan China

ISBN: (纸本)9798350339161

Current protein nuclear localization assays encounter multiple challenges that underscore the constraints of conventional biochemical assays and sequence-based procedures. This paper highlights the emerging interest in utilizing artificial intelligence to surmount these limitations. Specifically, a supervised deep learning algorithm, employing the HTRNN model, is presented to identify mechanisms responsible for the nuclear location of proteins. This methodology embraces an entire data-driven end-to-end structure, making expert experience or previous biological knowledge unnecessary. By utilizing amino acid sequence features from both the head and tail regions, the algorithm displays remarkable detection and generalization capabilities, which have been validated by experimental results on present tagged nuclear location protein datasets. Overall, the data-driven algorithm plays a crucial role in enhancing the identification of protein nuclear localization properties. © 2024 IEEE.

关键词： Proteins

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Lightweight Storage Framework for Blockchain-Enabled Internet of Things Under Cloud Computing

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Computers, Materials & Continua 2023年第5期75卷 3607-3624页

作者： Xinyi Qing Baopeng Ye Yuanquan Shi Tao Li Yuling Chen Lei Liu School of Computer and Artificial Intelligence Huaihua CollegeHuaihua418000China Guizhou Science and Technology Innovation Service Center Co Guiyang550000China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua418000China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550000China

Due to its decentralized,tamper-proof,and trust-free characteristics,blockchain is used in the Internet of Things(IoT)to guarantee the reliability of ***,some technical flaws in blockchain itself prevent the development of these applications,such as the issue with linearly growing storage capacity of blockchain *** the other hand,there is a lack of storage resources for sensor devices in IoT,and numerous sensor devices will generate massive data at ultra-high speed,which makes the storage problem of the IoT enabled by blockchain more *** are various solutions to reduce the storage burden by modifying the blockchain’s storage policy,but most of them do not consider the willingness of *** attempt to make the blockchain more compatible with the IoT,this paper proposes a storage optimization scheme that revisits the system data storage problem from amore practically oriented *** will only store transactional data that they are directly involved *** addition,a transaction verification model is developed to enable peers to undertake transaction verification with the aid of cloud computing,and an incentive mechanism is premised on the storage optimization scheme to assure data *** results of the simulation experiments demonstrate the proposed scheme’s advantage in terms of storage and throughput.

关键词： Blockchain internet of things storage optimization transaction verification cloud computing incentive mechanism

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Frequency Information Matters for Image Matting 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Luo, Rongsheng Wei, Rukai Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory For Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Image matting aims to estimate the opacity of foreground objects in order to accurately extract them from the background. Existing methods are only concerned with RGB features to obtain alpha mattes, limiting the perception of local tiny details. To address this issue, we introduce frequency information as an auxiliary clue to accurately distinguish foreground boundaries and propose the Frequency Matting Network (FMN). Specifically, we deploy a Frequency Boosting Module (FBM) in addition to the Discrete Cosine Transform (DCT) to extract frequency information from input images. The proposed FBM is a learnable component that empowers the model to adapt to complex scenarios. Furthermore, we design a Domain Aggregation Module (DAM) to effectively fuse frequency features with RGB features. With the assistance of frequency clues, our proposed FMN achieves significant improvements in matting accuracy and visual quality compared with state-of-the-art methods. Extensive experiments on Composition-1k and Distinctions-646 datasets demonstrate the superiority of introducing frequency information for image matting. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Discrete cosine transforms

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Distributed Dimensionality Reduction Filtering for CPSs Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1080-1082页

作者： Xiaoyuan Zheng Hao Zhang Xindi Yang Huaicheng Yan School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Department of Control Science and Engineering Tongji UniversityShanghai 200092China Key Laboratory of Smart Manufacturing in Energy Chemcical Process of Ministry of Education East China University of Science and TechnologyShanghai 200237 School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

Dear Editor,This letter concerns about the distributed dimensionally reduction filtering for a class of cyber physical systems with denial of service(DoS)*** each sensor exchanging measurements with neighbors subjecte... 详细信息

关键词： DoS service filtering

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Multi-Path Spatial Detail-Aware Network for Semantic Segmentation

Multi-Path Spatial Detail-Aware Network for Semantic Segment...

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2023 China Automation Congress, CAC 2023

作者： Wang, Quanyi Xie, Xinlin Si, Haoyue Wang, Jianan Fang, Liang Xie, Gang School of Electronic Information Engineering Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350303759

In the existing semantic segmentation, small objects and slender strip objects that are easy to blend into the surrounding background are generally difficult to be accurately identified and segmented. We propose a Multi-Path Spatial Detail-Aware Network (MSDNet) for semantic segmentation of traffic scenes. Specifically, an interactive Dual-branch Parallel Complementary module (DPC) based on the Transformer-CNN framework is firstly proposed to enhance the discriminative representation of similar feature encodings between different objects. Then, a spatial detail assistant module (SDA) with attention mechanism is designed to help the network obtain more detailed spatial detail information. Moreover, a Cross-layer Feature Aggregation module (CFA) is constructed for further aligning and aggregating feature maps of different resolutions in adjacent two layers, which can improve the segmentation performance of small objects. Extensive experimental results show that the proposed method achieves the accuracy of 81.16% mean Intersection over Union (mIoU) on the challenging Cityscapes dataset. © 2023 IEEE.

关键词： Semantic Segmentation

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An Efficient Bidirectional Weighted Global Feature Extraction Algorithm for Steel Surface Defects

An Efficient Bidirectional Weighted Global Feature Extractio...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Taoyuan Xie, Gang Xie, Xinlin Li, Linjuan Zhang, Haoxue Liang, Yu School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan China

ISBN: (纸本)9798350303759

Focus on the drawbacks of slow detection speed, and poor detection accuracy for small objects in existing strip steel surface defect detection methods, we propose an efficient bidirectional weighted global feature extraction algorithm for steel surface defects. Firstly, the backbone feature extraction network is consisted of BNCSP-TR(BottleNeckCSP with transformer) blocks to enhance global information and optimize the extraction ability of small object features. Secondly, the enhancement in the network's accuracy for detecting small-sized defects is achieved through the design of BiFPN-Light (lightweight bidirectional weighted feature pyramid network) for multi-scale feature fusion. Finally, a more efficient and concise decoupled detection head called Eff-DcHead is designed to solve the contradiction between classification tasks and regression tasks. Extensive experimental results illustrate that the mAP of our algorithm on NEU-DET reaches 82.4%, which is 4.1% higher than the original algorithm. Simultaneously, the algorithm achieves a detection speed of 92.1 FPS, catering to the demands of industrial environments. © 2023 IEEE.

关键词： Surface defects

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