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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Peng Luo Qingyan Wang Junping Zhang Shouqiang Kang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Hyperspectral image classification (HSIC) is a pivotal technology in hyperspectral remote sensing, playing a widespread role in remote sensing applications. However, the limited number of labeled samples has always made hyperspectral image classification difficult. In response to this issue, researchers have delved into cross-domain classification studies. Moreover, significant progress on cross-domain HSIC has been made in recent years. Nevertheless, existing methods exhibit shortcomings, including inadequate exploitation of spectral and spatial information and a slow training speed, rendering them unsuitable for downstream application tasks. To address these challenges, this paper introduces a model of cross-domain few-shot learning with spectral-spatial split attention(S 3 A-CFSL). Channel attention and split attention are presented to emphasize effective spectral and spatial information for HSIC adaptively. Additionally, the ResNet variant, called ResNeSt, is employed to expedite the training speed of the model. Experimental results demonstrate notable enhancements in the proposed method's classification accuracy and model training speed across two public datasets.

关键词： Training Adaptation models Accuracy Feature extraction Rendering (computer graphics) Data mining Few shot learning

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FFGAN: An Auto-supervised Approach for Frontal Face Generation Via Disentangled Contrastive Learning 28th

FFGAN: An Auto-supervised Approach for Frontal Face Generati...

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28th International Conference on Knowledge Based and Intelligent information and engineering Systems, KES 2024

作者： Wiem, Grina Ali, Douik National Engineering School of Monastir University of Monastir Rue Ibn Jazzar Monastir5035 Tunisia Networked Objects Control and Communication Systems Laboratory Sousse Technology Centre National School of Engineers of Sousse University of Sousse Sahloul Belt Road Sousse4054 Tunisia

This work proposes a novel generative model, FF-GAN (Frontal Face Generative Adversarial Network), for generating high-quality and diverse frontal faces. FF-GAN utilizes contrastive learning to effectively learn the underlying representation of frontal faces from unlabeled image datasets. This approach allows the model to capture the essential characteristics of frontal faces without the need for explicit pose annotations. We evaluate the performance of FF-GAN using established metrics like FID (Fréchet Inception Distance), IS (Inception Score), and SSIM (Structural Similarity Index measure). The results demonstrate that FF-GAN achieves superior performance compared to existing methods, generating highly realistic and visually appealing frontal faces with exceptional structural coherence. This research contributes to the field of facial image generation by introducing an effective unsupervised learning approach based on contrastive learning for generating high-quality frontal faces. © 2024 The Authors.

关键词： Contrastive Learning

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Hyperspectral Image Change Detection Based on Simam Multi-Scale Joint Features

Hyperspectral Image Change Detection Based on Simam Multi-Sc...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xinling Li Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The remote sensing satellite system has steadily developed, and the availability of massive high-quality satellite remote sensing data has rapidly improved. The dynamic monitoring of land cover changes using hyperspectral data has received great attention. The existing change detection methods usually use the spatial correlation or spatial-spectral correlation of hyperspectral images, lacking an overall consideration of the three-dimensional temporal-spatial-spectral joint features, resulting in suboptimal change detection results. Given the considerations above, this paper proposes a SimAM multi-scale joint feature extraction network for hyperspectral image change detection. The proposed method first adopts SimAM multi-scale joint features network to consider the hyperspectral image as a whole, and extracts multi-scale temporal-spatial-spectral joint features from the network. Then, uses multi-scale feature weighted fusion module to weight different scale features after simple fusion, highlighting the change regions. Finally, adopts the batch-balance measurement module to measure the similarity of the bi-temporal fusion features and output the change detection result map. Experiments show that on two public datasets, the proposed method can achieve a good change detection effect.

关键词： Satellites Correlation Fuses Land surface Euclidean distance Feature extraction Monitoring

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Fe_(2)O_(3)-decorated boron/nitrogen-co-doped carbon nanosheets as an electrochemical sensing platform for ultrasensitive determination of paraquat in natural water

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Chinese Chemical Letters 2023年第1期34卷 446-451页

作者： Meng Li Xinzhong Wang Yelin Zhu Xiuxiu Jia Shusheng Zhang Huaisheng Wang Yongtao Li Guangzhi Hu School of Electronic Communication Technology Shenzhen Institute of Information TechnologyShenzhen 518172China College of Chemistry Zhengzhou UniversityZhengzhou 450000China Institute for Ecological Research and Pollution Control of Plateau Lakes School of Ecology and Environmental ScienceYunnan UniversityKunming 650504China School of Materials Science and Engineering Liaocheng UniversityLiaocheng 252000China Joint Institute for Environmental Research and Education College of resources and environmentSouth China Agricultural UniversityGuangzhou 510642China

Boron/nitrogen-co-doped carbon(BCN)nanosheets decorated with Fe_(2)O_(3) nanocrystals(Fe_(2)O_(3)–BCN)were cast on a glassy carbon electrode(GCE)and applied as an electrochemical sensor to effectively detect paraquat(PQ),a toxic herbicide,in aqueous environments.A linear experiment performed using square wave voltammetry(SWV)under optimized experimental conditions produced a decent linear relationship and a low detection limit(LOD)of 2.74 nmol/L(S/N=3).Repeatability,reproducibility,stability,and interference experiments confirmed that the Fe_(2)O_(3)–BCN/GCE system exhibited decent electrochemical sensing performance for PQ ***,the designed sensor showed high selectivity and a decent linear relationship with PQ concentration in natural water *** the best of our knowledge,this is the first study on the preparation of Fe_(2)O_(3)–BCN nanosheets for PQ *** proposed sensor can be employed as an effective alternative tool for distinguishing and processing PQ.

关键词： Paraquat Fe_(2)O_(3)-BCN Electrochemical sensor Contaminant detection

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Joint Classification Of Hyperspectral And LiDAR Data Based On Heterogeneous Attention Feature Fusion Network

Joint Classification Of Hyperspectral And LiDAR Data Based O...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhaoyang Wang Qingyan Wang Junping Zhang Xintao Liang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The fusion of hyperspectral image (HSI) and LiDAR data for classification has gained widespread attention. However, the current fusion methods still have limitations on the use of heterogeneous data information and the interaction of heterogeneous data features. Therefore, we propose a classification method based on heterogeneous attention feature fusion network (HAFF-Net). Firstly, The convolutional neural network is employed to capture local spatial features from multi-scale inputs of heterogeneous data. Secondly, the heterogeneous feature attention module is designed to deeply fuse the extracted local spatial features and promote the interaction of heterogeneous data. Next, a transformer encoder is utilized to extract global spectral features. Finally, the two extracted features are classified by decision fusion using different classifiers. The proposed approach exhibits effectiveness through experimental results on two datasets.

关键词： Laser radar Fuses Convolution Soft sensors Geoscience and remote sensing Feature extraction Transformers

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Hyperspectral Image Classification Method Based On Node Similarity Feature Fusion

Hyperspectral Image Classification Method Based On Node Simi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiameng Wang Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

There is abundant spectral and spatial information in Hyperspectral images (HSI). However, there exists a limitation of not using spatial information sufficiently in HSI classification. Besides, there is the limitation of mononuclear in feature extraction, resulting in insufficient feature extraction and insufficient utilization of data information. In view of these problems, a node similarity semi-supervised classification method of multiscale feature is proposed to break the limitation of mononuclear and achieve full extraction of spatial information. First, to extract pixel-level features, a three-dimensional (3-D) multiscale convolutional neural network (CNN) is used. Second, based on node similarity superpixel graph U-Net (NSGUNet) is proposed to extract superpixel-level features. Finally, the above two features are weighted fusing, the fused features are classified by sparse graph regularization. Experiments on three datasets illustrate that the proposed method is effective.

关键词： Accuracy Geoscience and remote sensing Feature extraction Convolutional neural networks Data mining Hyperspectral imaging Image classification

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Robust Federated Learning Algorithm Based on Chaotic Encryption and Dual-Server Detection

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IEEE Internet of Things Journal 2025年

作者： Kang, Shouqiang Wu, Yuxuan Wang, Yujing Wang, Qingyan Liang, Xintao Harbin University of Science and Technology School of Measurement and Control Technology and Communication Engineering Harbin150080 China

Federated learning allows users to collaboratively train models, but studies have shown that local model parameters may leak users’ privacy. Furthermore, the global model in federated learning could be compromised by Byzantine attacks. To address these issues, a robust federated learning algorithm based on chaotic encryption and dual-server detection (CDRFL) is proposed. First, CDRFL introduces a lightweight chaotic encryption method, where ciphertexts have a unique property of mutual cancellation, allowing the plaintext global model parameters to be derived without decryption. Moreover, CDRFL allows users to encrypt with different keys, further reducing the risk of privacy leakage. Next, a dual-server detection scheme is designed to defend against Byzantine attacks while preserving user privacy. Lastly, a reputation score mechanism with a forgetting function is proposed to fairly distinguish Byzantine participants. Experimental results demonstrate that, compared with popular robust aggregation schemes, CDRFL improves testing accuracy by 3.26% to 9.06%. When compared to existing chaotic encryption federated learning schemes, CDRFL enhances the ability to defend against Byzantine attacks and reduces time and communication overhead by 73.63% and 95.40%, respectively. © 2014 IEEE.

关键词： Federated learning

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State-of-the-Art Development of Complex Systems and Their Simulation Methods

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Complex System Modeling and Simulation 2021年第4期1卷 271-290页

作者： Yiming Tang Lin Li Xiaoping Liu School of Computer and Information and Engineering Research Center of Safety Critical Industry Measure and Control TechnologyMinistry of EducationHefei University of TechnologyHefei 230601China

The research on complex systems is different from that on general systems because the former must consider self-organization,emergence,uncertainty,predetermination,and *** an important method to transform the world,a simulation is one of the most important skills to discover complex *** this study,we provide a survey on complex systems and their simulation ***,the development history of complex system research is summarized from two main ***,the eight common characteristics of the most complex systems are ***,the simulation methods of complex systems are introduced in detail from four aspects,namely,meta-synthesis methods,complex networks,intelligent technologies,and other *** the overall point of view,intelligent technologies are the driving force,and complex networks are the advanced ***-synthesis methods are the integration strategy,and other methods are the *** addition,we show three complex system simulation examples:digital reactor simulation,simulation of a logistics system in the industrial site,and crowd evacuation *** examples show that a simulation is a useful means and an important method in complex system ***,the future development prospects for complex systems and their simulation methods are suggested.

关键词： systems science complex systems complexity complex system simulation

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Pruning CNN based on Combinational Filter Deletion 50

Pruning CNN based on Combinational Filter Deletion

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Huang, Wenli Wang, Xiujie Zhao, Zhihong Su, Li Sui, Shuai Wang, Jinjun School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Science and Technology on Communication Networks Laboratory Shijiazhuang China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Liaoning University of Technology Jinzhou China Institute of Artificial Intelligence and Robotic Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781665464543

Network pruning is a figurative model compression technique designed to lighten and accelerate neural network models. Most existing pruning methods prioritize the selection of filters by their importance or apply regularization based on the properties of individual filters, neglecting the internal connections within combinations of multiple filters. This work introduces a pruning method termed Combinational Filter Deletion (CFD), which incorporates a straightforward yet effective evaluation metric based on the diversity of filter combination distributions to reveal the characteristics inherent to multiple filter interactions. CFD enables the exploration of an expanded search space, offering a greater array of choices and leveraging the intrinsic information of conventional layers. Moreover, this method is both general and non-exclusive, capable of enhancing the efficacy of other single-filter-based pruning techniques. © 2024 IEEE.

关键词： Wiener filtering

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FPGA-Oriented Target Detection Accelerator Design

FPGA-Oriented Target Detection Accelerator Design

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Weizhe Huang School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

Target detection is widely applied in fields such as face recognition, autonomous driving, and industrial automation. However, when deploying target detection models based on convolutional neural networks on resource-constrained embedded platforms, issues like high computational complexity, long execution times, and significant on-chip memory usage arise. To address these challenges, this paper presents a target detection accelerator design based on Field Programmable Gate Array (FPGA). Firstly, the computational workload of convolutional neural networks is reduced by merging convolutional layers with batch normalization layers. Secondly, to mitigate on-chip storage space concerns, on-chip data fixed-point quantization is employed to decrease on-chip memory consumption. Subsequently, to tackle the issue of long execution times, a convolution calculation module based on the Winograd algorithm is designed, which reduces computation latency. Finally, a dual-buffer pipeline architecture is devised to optimize memory bandwidth. Experimental results demonstrate that the proposed solution accelerates YOLOv4-tiny on the ZYNQ-7020, achieving a performance of 21.35 GOP/s. The energy efficiency is 32.56 times higher than CPU and 9.76 times higher than GPU. Additionally, the convolution calculation module in this paper reduces inference time by 15% compared to the sliding window convolution calculation method.

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