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Indicators and Validation Methods for Swarm Situational Awareness Assessment of Uncrewed Aerial Vehicles 1

7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Zhang, Hang Song, Chuang Li, WeiXue Hao, Mingrui School of Software Northwestern Polytechnical University Shaanxi Xi’an710072 China Science and Technology On Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (数字)9789819733323

ISBN: (纸本)9789819733316

Swarm situational awareness technology for uncrewed aerial vehicles (UAVs) is a core technology for mastering environmental information and gaining decision-making advantages. However, evaluation indicators and validation methods for situational awareness technology have always been lacking. This article thoroughly investigates the evaluation methods for situational awareness technology indicators both domestically and internationally. By analyzing the application scenarios of UAV group situational awareness technology, this article proposes an evaluation indicator system and mathematical simulation validation method for situational awareness technology. Finally, the design process and actual verification results of each indicator in the evaluation indicator system are explained in detail. The effectiveness of the evaluation indicator is demonstrated through simulation and comparative research, which further proves the reliability of the validation method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision making

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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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Research on adaptive and differentiated control method of drive controller in Wentian and Mengtian experimental cabin of space station

Research on adaptive and differentiated control method of dr...

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2022 Chinese Automation Congress, CAC 2022

作者： Meng, Qichen Shen, Yiwei Liang, Juping Xiang, Kun Zhang, Guozhu Shanghai Institute of Aerospace Control Technology Shanghai201109 China Shanghai Key Laboratory of Space Intelligent Control Technology Shanghai201109 China

ISBN: (纸本)9781665465335

Focused on the key single machine of the Alpha sun orientation subsystem in the power sub-system of the Wentian and Mengtian experimental cabin of the space station - the in-cabin and extra-vehicle drive controller which needed to autonomously complete the cabin segment (Wentian, Mengtian) and cabin space (inside and outside the cabin) identification and independently fulfill the needs of the differentiated control of flexible sailboards in the Wentian and Mengtian experimental cabins by receiving the commands of the space station digital management sub-system and the GNC sub-system according to the requirements of the space station's energy assurance tasks, this paper proposed an in-cabin and extra-cabin drive controller autonomous identification and differentiated control method. At the same time, combined with the different working modes inside and outside the cabin, the drive controller executed the information collection and processing of other functional components in the Alpha sun orientation subsystem, and completed the closed-loop control and fault detection processing of the Alpha sun orientation device, and sent the different processing information to the GNC sub-system and digital tube sub-system. As a key single machine of the power supply subsystem which was a key sub-system of China's space station, an important national project, the in-cabin and out-cabin drive controller was very important to ensure the energy security of the entire space station. © 2022 IEEE.

关键词： controllers

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Application of NaCl in Cold Atmospheric Plasma Jet and Plasma-Activated Solution to Enhance Virus Inactivation

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Plasma Medicine 2023年第2期13卷 47-63页

作者： Wang, Han Liu, Chao Wu, Yahui Bashir, Mona Alrasheed Shao, Changsheng Huang, Qing CAS Key Laboratory of High Magnetic Field and Ion Beam Physical Biology Anhui Key Laboratory of Environmental Toxicology and Pollution Control Technology Hefei Institute of Intelligent Agriculture Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China Science Island Branch of Graduate School University of Science & Technology of China Hefei230026 China High Magnetic Field Laboratory Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China

Viruses are serious pathogenic contaminants that gravely threaten human health. There is an urgent need for environment-friendly and novel disinfection methods to inactivate various viruses. In this study, we applied the technique of cold atmospheric plasma (CAP) for virus disinfection. Particularly, we utilized the cold atmospheric plasma jet (CAPJ) and the plas-ma-activated solution (PAS) approaches for the treatment of bacteriophage MS2, and we added NaCl in the treatment system to enhance the disinfection efficiency. The inactivation effects under different CAPJ-PAS conditions using various plasma working gases were investigated. Also, the involved inactivation mechanisms due to respective active species were scrutinized. Our results showed that the addition of NaCl could considerably enhance the virus inactivation efficiency in the air/oxygen-CAPJ-PAS treatment, and during the direct CAPJ treatment, the primary inactivation factors were attributed to ·OH,1O2, ONOOH and ClO–, while for the indirect PAS treatment, the major inactivation contributors stemmed from1O2 and ClO–. This work therefore demonstrates the usefulness of NaCl in the CAPJ-PAS treatment in the virus inactivation and offers new perspectives on the application of CAP technology in disinfection of viruses. © 2023 by Begell House, Inc. ***.

关键词： Disinfection

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DFDNet: Disentangling and Filtering Dynamics for Enhanced Video Prediction 39

DFDNet: Disentangling and Filtering Dynamics for Enhanced Vi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gan, Lianqiang Lai, Junyu Ju, Jingze Gao, Lianli Bin, Yi School of Aeronautics and Astronautics University of Electronic Science and Technology of China China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province China School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Technology Tongji University China

ISBN: (纸本)157735897X

Videos inherently contain complex temporal dynamics across various spatial directions, often entangled in ways that obscure effective dynamic extraction. Previous studies typically process video spatiotemporal features without disentangling, which hampers their ability to extract dynamic information. Additionally, the extraction of dynamics is disrupted by transient high-dynamic information in video sequences, e.g., noise or flicker, which has received limited attention in the literature. To tackle those problems, this paper proposes the Disentangling and Filtering Dynamics Network (DFDNet). Firstly, to disentangle the interwoven dynamics, DFDNet decomposes the spatially encoded video sequences into lower dimensional sequences. Secondly, a learnable threshold filter is proposed to eliminate the transient high-dynamic information. Thirdly, the model incorporates an MLP to extract the temporal dependencies from the disentangled and filtered sequences. DFDNet demonstrates competitive performance across four chosen datasets, including both low and high-resolution videos. Specifically, on the low-resolution Moving MNIST dataset, DFDNet achieves a 19% improvement on MSE over the previous state-of-the-art model. On the high-resolution SJTU4K dataset, it outperforms the previous state-of-the-art model by 10% on the LPIPS metric under similar inference time. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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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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Accurate and rapid image segmentation method for bayberry automatic picking via machine learning

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International Journal of Agricultural and Biological Engineering 2023年第6期16卷 246-254页

作者： Huan Lei Chentong Li Yu Tang Zhenyu Zhong Zeyu Jiao Institute of Intelligent Manufacturing Guangdong Academy of SciencesGuangzhou 510070China Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent ManufacturingGuangdong Academy of ScienceGuangzhou 510070China School of Automation Guangdong Polytechnic Normal UniversityGuangzhou 510665China

Due to the short ripening period and complex picking environment,bayberry generally relies on mechanical equipment for picking,especially the automatic picking system guided by ***,it is crucial to locate the bayberry in the view accurately and *** efforts have been made,the existing methods are difficult to implement due to the limited amount of data and the processing *** this study,an accurate and rapid segmentation method based on machine learning was proposed to address this ***,the images collected by the visual guidance system were pre-processed by contrast-limited adaptive histogram equalization(CLAHE)based on the Y component of the YUV color *** advantage of the color difference map of RB and RG for the segmentation of different colors,an adaptive color difference map foreground segmentation method was then adopted for bayberry region foreground ***,distance transforms and marking control watershed methods were exploited to achieve single bayberry fruit ***,with the help of the convex hull theory and fruit shape characteristics,the irregular background interference areas were filtered out,which improved the accuracy of bayberry segmentation *** experimental results show that this method can achieve better segmentation of bayberry in complex orchard environment with an accuracy of 97.4%and only takes 0.136 s to calculate once.

关键词： bayberry image segmentation machine learning automatic picking computer vision

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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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Multi-Class Token Attention Learning for Weakly Supervised Semantic Segmentation 23

Multi-Class Token Attention Learning for Weakly Supervised S...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Zeng, Zhen Luo, Huilan School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Ganzhou341000 China

ISBN: (纸本)9798331528041

Weakly supervised semantic segmentation (WSSS) using only image-level class labels is a challenging task. Due to the local receptive fields of convolutional neural networks (CNNs), CAMs applied to CNNs often suffer from partial activation-highlighting the most discriminative parts of the image. To capture both local features and global representations, we propose an approach called MCTRM, which is a WSSS solution based on an improved Conformer as backbone. To ensure each class token's discriminative ability for the respective target class, we design a class-aware training strategy (CAT) to establish a one-to-one correspondence between the output class tokens and the ground-truth class labels. To enhance the class discrimination ability between each class token, we propose a class token regularization module (CTRM) to enhance the learning of discriminative class tokens so that the model can better capture the unique features and attributes of each class. To obtain a fine-grained CAM, we propose the refinement module (RM) to combine the class-specific attention and inherent patch-level pairwise affinity between image patches learned by the Transformer branch with the CAM of the CNN branch. Despite its simplicity, MCTRM achieves the state-of-the-art performance of 69.0% and 68.4% on the PASCAL VOC 2012 validation and test sets, respectively. © 2024 IEEE.

关键词： Semantic Segmentation

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