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Dynamic event-triggered formation control of second-order nonholonomic systems

Journal of systems Engineering and Electronics 2023年第2期34卷 501-514页

作者： WANG Xiaoyu SUN Sijia XIAO Feng YU Mei School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

In this paper,the formation control problem of secondorder nonholonomic mobile robot systems is investigated in a dynamic event-triggered ***-triggered control protocols combined with persistent excitation(PE)conditions are *** event-detecting processes,an inactive time is introduced after each sampling instant,which can ensure a positive minimum sampling *** increase the flexibility of the event-triggered scheme,internal dynamic variables are included in event-triggering ***,the dynamic event-triggered scheme plays an important role in increasing the lengths of time intervals between any two consecutive *** addition,event-triggered control protocols without forward and angular velocities are also presented based on approximate-differentiation(low-pass)*** asymptotic convergence results are given based on a nested Matrosov theorem and artificial sampling methods.

关键词： nonholonomic system dynamic event-triggered control consensus-based formation

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Functionalised B-Cu@F-Ag@Au trimetallic nanomaterials with long term stability for rapid and highly sensitive in situ SERS detection of furfural in transformer oil

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High Voltage 2023年第2期8卷 293-304页

作者： Yu Lei Changding Wang Weiping Kong Mingliang Wang Xinyuan Zhang Abdolmaleki Nima Caisheng Wang Weigen Chen Fu Wan School of Electrical Engineering Chongqing UniversityChongqingChina State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina Electrical and Computer Engineering Wayne State UniversityWayneNebraskaUSA

The surface-enhanced Raman scattering(SERS)substrates enable a highly sensitive detection of furfural in the transformer ***,detection substrates with long-term stability are still extremely *** this work,we anchored the thiol-containing coupling agents 2,5-dimercapto-1,3,4-thiadiazole(DMTD)and 1,4-benzenedithiol(BDT)on the surface of bubble copper(B-Cu)and flower-like silver nanoparticles(FAg),*** three-dimensional SERS detection substrates with long-term stability by using a combination of chemical reduction and self-assembly methods were *** substrate has a minimum detection limit of 10^(−9) M for rhodamine B in oil with an enhancement factor of up to 2.23×10^(7).Importantly,the three-crystal BCu@F-Ag_(1)@Au_(5) substrate was used for the detection of furfural in the transformer oil with a detection limit of 2 mg/L and a relative standard deviation value of 2.46%.After 60 days of a simulated operation,the detection signal of furfural in the transformer oil samples at 75℃ and still reached the initial value of 77.53%,indicating that the substrate has a good long-term *** triple frame structured SERS detection platform shows great potential in tracking furfural in the aging transformer oil mixing systems.

关键词： stability. stability SERS

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Design and Verification of Parallel Hip Exoskeleton Considering Output Torque Anisotropy

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Journal of Bionic Engineering 2024年第3期21卷 1305-1320页

作者： Jilong Xu Yunzhan Niu Fucai Liu Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan UniversityQinhuangdao066004China Key Laboratory of Industrial Computer Control Engineering of Heibei Province Yanshan UniversityQinhuangdao066004China College of Mechanical Engineering Yanshan UniversityQinhuangdao066004China

In rehabilitation training,it is crucial to consider the compatibility between exoskeletons and human legs in ***,most exoskeletons today adopt an anthropomorphic serial structure,which results in rotational centers that are not precisely aligned with the center of the hip *** address this issue,we introduce a novel exoskeleton called the Parallel Hip Exoskeleton(PH-Exo)in this ***-Exo is meticulously designed based on the anisotropic law of output *** the friction of the drive components,a dynamic model of the human-machine complex is *** analysis demonstrates that PH-Exo not only exhibits outstanding torque performance but also achieves high controllability in both flexion/extension and adduction/abduction ***,a robust controller is designed to address model uncertainty,friction,and external *** experiments indicate that under the control of the robust controller,each motor achieves excellent tracking performance.

关键词： Hip exoskeleton Parallel mechanism Misalignment Anisotropy Output torque

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SGNet: A Multi-Scale Method for Steel Surface Defect Detection 10

SGNet: A Multi-Scale Method for Steel Surface Defect Detecti...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Shanshan Liu, Xiangzhi Man, Jiazheng Cao, Bingyang Dong, Yunfeng Wu, Xiaoming Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The large variation in target scales within steel surface defect images often results in low detection accuracy. To address this issue, we have developed a multi-scale defect detection method named SGNet. Initially, we introduced a Self-Guided Bi-Level Routing Transformer (SG-BiFormer) as the backbone network of our model. SG-BiFormer utilizes a self-guided strategy to model global information and dynamically reallocates tokens based on the importance of image regions, increasing token quantity in significant areas to preserve fine-grained details and reducing it in less critical areas. This strategy significantly enhances the accuracy and efficiency of the model in handling multi-scale targets. Subsequently, we incorporated Grouped Separable Convolutions (GSConv) to manage local information of multi-scale features. GSConv automatically adjusts the size and shape of the convolution kernels according to different input features. This design not only improves global information processing capabilities but also ensures sensitive and precise detail capture. Lastly, we employed MPDIoU as the loss function for our model, which optimizes corner positioning to tackle the challenges of regression tasks, substantially improving the accuracy of the model in handling complex defects. Experimental evidence confirms the effectiveness of this method. Our SGNet achieved a 77.1% mAP on the NEU-DET dataset, demonstrating outstanding performance. © 2024 IEEE.

关键词： Surface defects

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Deep Learning-based Mortality Prediction of COVID-19 Patients with Cardiovascular Disease 9

Deep Learning-based Mortality Prediction of COVID-19 Patient...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zheng, Huilin Liang, Yingyi Zhang, Daming Shi, Kai Dong, Luxi Xie, Xiaolan Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology College of Computer Science and Engineering Guilin China

ISBN: (纸本)9798350391367

Coronavirus disease 2019 (COVID-19) is one of the most dangerous diseases in recorded human history due to its high contagiousness and rapid dissemination. Moreover, people who already have medical issues like cardiovascular disease (CVD) have a higher risk of getting seriously ill. Therefore, this study proposes a deep learning-based prediction model for the mortality of COVID-19 patients with CVD. The research substances are introduced as follows. First, we obtain the original dataset from the Kaggle website provided by the Mexican government, and extract our experimental data. After that, we develop a deep learning-based prediction model for the mortality of COVID-19 patients with CVD while applying a 5-fold cross-validation approach for the model training and validation. Finally, we evaluate the performance of the proposed model and also compare it with many common machine learning models. In our experimental results, the proposed model achieved higher performance than all of the other models. It facilitates decision-making and provides an accurate mortality prediction for COVID-19 patients with CVD. © 2024 IEEE.

关键词： COVID-19

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ID-YOLOv7: A Method for Steel Surface Defect Detection Combining Residual Attention and Dynamic Upsampling 10

ID-YOLOv7: A Method for Steel Surface Defect Detection Combi...

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10th IEEE Smart World Congress, SWC 2024

作者： Liu, Shanshan Liu, Xiangzhi Man, Jiazheng Cao, Bingyang Dong, Yunfeng Wu, Xiaoming Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798331520861

Industrial defect detection is a crucial challenge in computer vision. The accuracy of detecting defects on steel surfaces is hindered by spatially varying noise. To address this challenge, we propose an efficient method for steel surface defect detection. Firstly, considering the impact of the shape and proportions of bounding boxes on localization accuracy, we developed an Adaptive Shape-Inner Intersection over Union (ASIIoU) loss function. This function dynamically adjusts auxiliary bounding boxes and takes into account their shape and scale to optimize the loss, thereby improving the accuracy of bounding box regression loss. Secondly, to enhance the ability of the model to learn local features, we designed a Content-Guided Residual Attention Block (CGRAB). It captures rich local features across different convolutional layers via skip connections while discarding irrelevant information. Lastly, to avoid the potential issue of detail blurring caused by traditional upsampling methods, we optimized the upsampling process to preserve key details and structural information in the image, reducing unnecessary noise amplification. Experimental validation demonstrates the effectiveness of our method, which achieved a 76.7 % mAP on the NEU-DET dataset. © 2024 IEEE.

关键词： Surface defects

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ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection

ResPocket: A Multi-Scale Feature Fusion Method for Improving...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Yang, Wenli Chen, Yaoyu Liao, Xiongwei Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology China

ISBN: (纸本)9798350386226

Accurate detection of protein binding sites is critical for facilitating drug design. Most existing models rely on features at a single scale, leading to the loss of crucial global or local structural information. Therefore, this paper proposes a novel 3D model (ResPocket), to fully capture the multi-scale features of proteins. First, Residual Block (ResB) based on U-Net is used to capture features from different scales, including global and local structural information. Then, an Enhanced Attention Module (EAM) is designed to encode semantic dependencies by effectively capturing global context information. In addition, ResPocket focuses on learning local information about protein pockets with Self-Guided Mask Block (SGMB), to increase the robustness of the overall model and reduce its dependence on external mask guidance. Experimental results show that ResPocket improves by an average of 4.89% on DCA Top-n, by an average of 1.48% and 2.25% on DCC and DVO metrics, respectively. © 2024 IEEE.

关键词： Binding sites

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A Blackbox Fuzzing Based on Automated State Machine Extraction 8

A Blackbox Fuzzing Based on Automated State Machine Extracti...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Zhang, Zhaowei Yu, Wenjing Wang, Zibin Xiang, Youlin National Key Laboratory of Science and Technology on Information System Security Beijing China School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Shanghai China

ISBN: (纸本)9798350331035

Currently, protocol fuzzing techniques mainly employ two approaches: greybox fuzzing based on mutation and blackbox fuzzing based on generation. Greybox fuzzing techniques use message exchanges between the protocol server and actual clients as seeds, and generate test cases through mutation. Although this approach can provide coverage information of the SUT's code and state space through instrumentation and feedback, its drawback lies in the relatively random mutation strategy, which makes it challenging to validate the SUT's message verification process. This paper addresses this limitation by utilizing artificial intelligence techniques to extract protocol state machines. It aims to overcome the reliance on manual work in blackbox fuzzing based on generation and leverage its advantages to generate more effective fuzzing test cases. The study utilizes Prompt-Learning technology to analyze the semantic information in protocol RFC documents, obtain corresponding intermediate representations, and extract protocol state machines from these representations. Taking the BGP protocol as an experimental subject, the experimental results demonstrate a certain level of accuracy in the obtained protocol state machines and the ability to generate test cases from these state machines, thereby enhancing the automation level of protocol fuzzing. © 2023 IEEE.

关键词： fuzzing network protocols network security protocol testing state machine extraction

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A Blockchain-Based Hierarchical Dynamic Collaborative Storage Model 10

A Blockchain-Based Hierarchical Dynamic Collaborative Storag...

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10th IEEE Smart World Congress, SWC 2024

作者： Yuan, Qile Liu, Xiangzhi Wang, Yu Dong, Yunfeng Jia, Haoyu Wu, Xiaoming Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The pervasive application of blockchain technology has led to a significant increase in data volume, presenting significant challenges to existing storage architectures in environments characterized by large volumes of data and highfrequency transactions. To enhance the scalability of blockchain, this study introduces an innovative framework, Hierarchical Dynamic Storage Management Chain Model, named HDSMC. The model consists of two stages. In the first phase of the model, when multimodal data files are uploaded, LBA techniques are employed to securely eliminate unnecessary redundancy, effectively preventing the storage of duplicate data. In the second phase, further processing is conducted on the generated blocks. This involves analyzing the frequency of block access to compute the variation in block 'heat'. Based on the Block Heat (BH), the number of replicas for each block across participating nodes is then determined. Furthermore, the framework implements an adaptive tiering strategy based on data access frequency and its importance, which facilitates the timely migration of data to an appropriate storage level. A simulated Hyperledger Fabric environment was employed to deploy the HDSMC framework and to assess its performance against current storage methodologies. The empirical outcomes indicated that this approach could achieve lower storage expenditures while ensuring high reliability. This research not only presents a novel strategy for blockchain storage management but also suggests potential applications for storage optimization in other big data scenarios. © 2024 IEEE.

关键词： Blockchain Data Sharding Hierarchical Storage Performance Optimization Scalability

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SAM-COD+: SAM-Guided Unified Framework for Weakly-Supervised Camouflaged Object Detection

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IEEE Transactions on Circuits and systems for Video Technology 2025年第5期35卷 4635-4647页

作者： Chen, Huafeng Wei, Pengxu Guo, Guangqian Gao, Shan Northwestern Polytechnical University Unmanned System Research Institute Xi’an710072 China National Key Laboratory of Unmanned Aerial Vehicle Technology Xi’an710072 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Peng Cheng Laboratory Shenzhen518066 China

Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods and struggle to simultaneously support all the existing types of camouflaged object labels, including scribbles, bounding boxes, and points. Even for Segment Anything Model (SAM), it is still problematic to handle the weakly-supervised COD and it typically encounters challenges of prompt compatibility of the scribble labels, extreme response, semantically erroneous response, and unstable feature representations, producing unsatisfactory results in camouflaged scenes. To mitigate these issues, we propose a unified COD framework in this paper, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels. Our SAM-COD employs a prompt adapter to handle scribbles as prompts based on SAM. Meanwhile, we introduce response filter and semantic matcher modules to improve the quality of the masks obtained by SAM under COD prompts. To alleviate the negative impacts of inaccurate mask predictions, a new strategy of prompt-adaptive knowledge distillation is utilized to ensure a reliable feature representation. To validate the effectiveness of our approach, we have conducted extensive empirical experiments on three mainstream COD benchmarks. The results demonstrate the superiority of our method against state-of-the-art weakly-supervised and even fully-supervised methods. Our source codes and trained models will be publicly released. © 1991-2012 IEEE.

关键词： Object recognition

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