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A Helmet Detection Algorithm Based on Transformers with Deformable Attention Module

Chinese Journal of Electronics 2025年第1期34卷 229-241页

作者： Songle Chen Hongbo Sun Yuxin Wu Lei Shang Xiukai Ruan Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Nanjing University of Posts and Telecommunications State Key Lab for Novel Software Technology Nanjing University Institute of Intelligent Locks Wenzhou University

Wearing a helmet is one of the effective measures to protect workers' safety. To address the challenges of severe occlusion, multi-scale, and small target issues in helmet detection, this paper proposes a helmet detection algorithm based on deformable attention transformers. The main contributions of this paper are as follows. A compact end-to-end network architecture for safety helmet detection based on transformers is proposed. It cancels the computationally intensive transformer encoder module in the existing detection transformer(DETR) and uses the transformer decoder module directly on the output of feature extraction for query decoding, which effectively improves the efficiency of helmet detection. A novel feature extraction network named Swin transformer with deformable attention module(DSwin transformer) is proposed. By sparse cross-window attention, it enhances the contextual awareness of multi-scale features extracted by Swin transformer, and keeps high computational efficiency simultaneously. The proposed method generates the query reference points and query embeddings based on the joint prediction probabilities, and selects an appropriate number of decoding feature maps and sparse sampling points for query decoding, which further enhance the inference capability and processing speed. On the benchmark safety-helmet-wearing-dataset(SHWD), the proposed method achieves the average detection accuracy mAP@0.5 of 95.4% with 133.35G floating-point operations per second(FLOPs) and 20 frames per second(FPS), the state-of-the-art method for safety helmet detection.

关键词： Head Accuracy Network architecture Benchmark testing Transformers Feature extraction Safety Decoding Computational efficiency Detection algorithms

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Investigation of the Architecture for A application Creation Platform Leveraging Modern technology on Smart Campus 24

Investigation of the Architecture for A Application Creation...

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5th International Conference on Computer Science and Management technology, ICCSMT 2024

作者： Huang, Jie Ma, Kai He, Xiaosong He, Hongjing Institute of Big Data Technology and Application Chongqing Institute of Engineering Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Chongqing China

ISBN: (纸本)9798400709999

Intelligent technologies are driving the development of smart campuses, fostering a dynamic and diverse intelligent ***, the current trend of customizing smart campus solutions often positions campus citizens as mere bystanders or end *** enhance participatory engagement in campus development and increase the autonomy of platform construction, this paper leverages the strengths of big data warehouses, data analytics, the Internet of Things (IoT), and cloud computing-specifically their security, concurrency, reliability, redundancy, and scalability-to propose a campus application creation *** platform aims to streamline the software development process, facilitate the publication of software products, and promote functionality sharing and collaborative *** introduce an architectural model comprising four components: the data lake and warehouse layer, the data ferry layer, the resource service layer, and the platform *** internal operational mechanisms of this architecture are discussed from the perspectives of"applications as data, " self-feedback analysis, application evaluation systems, and system *** advantages of the platform are analyzed, along with application cases from both teacher and student *** also explore the platform's significance within the campus context, including its potential to adjust campus functionality, enhance overall innovation capabilities in education, and increase the autonomy of software *** findings suggest that a modern application creation platform can significantly facilitate the self-improvement of smart campus functionalities, thereby further enhancing the intelligence of the campus. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Cloud platforms

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Liquid metal assistant self-propagating high-temperature synthesis of S-containing high-entropy MAX-phase materials

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材料科学技术（英文版） 2025年第6期209卷 1-8页

作者： Donglong Bai Qiang Wang Bin Deng Yang Li Ao Huang Zitong Cheng Yun Zhao Jing Li Wei Yao Jianguang Xu School of Materials and Energy Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai 201209China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional MaterialsShanghai 201209China School of Materials Science and Engineering Yancheng Institute of TechnologyYancheng 224000China School of Materials and Energy Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai 201209China

Due to their high-entropy effects,the high-entropy(HE)MAX-phase materials improve the comprehen-sive performance of MAX phases,opening up more possibilities for practical engineering ***,it is still challenging to obtain S-containing high-entropy MAX phases because of the high volatilization behavior of sulfur,suffering from issues such as high reaction temperature and long re-action time of traditional synthesis *** paper proposes a novel process named as liquid metal assistant self-propagating high-temperature synthesis(LMA-SHS)for efficient synthesis of high-purity S-containing high-entropy MAX-phase ***-melting-point metal(Sn or In)has been introduced into the raw mixture and melted into a liquid phase during the early stage of the SHS *** serv-ing as a"binder"between transition metal atoms of the M-site due to the negative mixing enthalpy,this liquid phase can accelerate mass and heat transfer during the SHS process,ensuring a uniform solid solution of each element and realizing the synthesis of high-purity(TiNbVZr)2SC in an extremely short *** synthesis method for high-entropy MAX-phase materials developed in this study,i.e.,LMA-SHS,showing very short reaction time,low energy consumption,high yield,and low cost,has the promise to be a general energy-and resource-efficient route towards high-purity HE materials.

关键词： High-entropy S-containing MAX phases Liquid metal LMA-SHS

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Efficient energy-preserving methods for the Schrödinger-Boussinesq equation

Efficient energy-preserving methods for the Schrödinger-Bou...

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作者： Yan, Jinliang Zheng, Lianghong Lu, Fuqiang Zhang, Qiongyue Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry College of Mathematics and Computer Fujian354300 China Department of Information and Computer Technology No.1 Middle School of Nanping Fujian353000 China Changzhou Institute of Technology Changzhou213032 China

In this paper, we present two novel energy-preserving schemes for the Schrödinger-Boussinesq system based on the second-order Adams prediction-correction method. We first introduce supplementary variables to the initial model simultaneous its invariants to obtain a consistent and structural stable system. Then, we discretize the extended system by directly using the Fourier pseudo-spectral method in space and second-order Adams prediction-correction method in time to construct conservative schemes. Numerical results show that the proposed schemes can reach second-order accuracy and can conserve the discrete invariants very well. © 2022 John Wiley & Sons, Ltd.

关键词： Forecasting

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Research of photon counting imaging detector based on delay line anode 10

Research of photon counting imaging detector based on delay ...

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10th Symposium on Novel Optoelectronic Detection technology and applications

作者： Liu, Yong'an Zhang, Yalong Yang, Xianghui Zhang, Ruili Liu, Zhe Sheng, Lizhi State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781510688155

ISBN: (纸本)9781510688148

Photon counting imaging detector based on microchannel plate (MCP) and position-sensitive anode readout has the ability to detect single photon, and has been widely used in space detection, high-energy physics and bioluminescence imaging. Traditionally, the detector consists of photocathode, electron multiplication part and position decoding part, in which position decoding is accomplished by position sensitive anode. The delay line anode not only has high spatial resolution and large detection area, but also has high count rate. Based on the principle of delay line decoding, an induction readout cross delay line anode (XDL) is designed and fabricated. And the performance of the detector is tested by using the experimental system. The results show that the width of XDL anode output pulse (FWHM) is about 4ns, the spatial resolution of the detector is better than 100μm, and the dark count rate is 2s). © 2025 SPIE.

关键词： Photocathodes

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A Revisit of Landsat-Observed Macroalgal Blooms in the Yellow Sea and East China Sea Before 2008

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Journal of Ocean University of China 2025年第2期24卷 525-531页

作者： YUAN Chao HUANG Juan WU Lingjuan XIAO Jie ZHANG Xuelei GAO Song LI Yifei XU Jiangling WANG Zongling Shandong Key Laboratory of Marine Ecological Environment and Disaster Prevention and Mitigation North China Sea Marine Forecasting and Hazard Mitigation CenterMinistry of Natural ResourcesQingdao 266061China Laboratory for Marine Ecology and Environmental Science Qingdao Marine Science and Technology CenterQingdao 266061China First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an 710129China

Harmful macroalgal blooms caused by Ulva prolifera and Sargassum horneri are increasing in the Yellow Sea and East China ***,our understanding of macroalgal blooms before 2008 is fragmented and *** time-series Landsat imagery from 1984 to 2008,we examined macroalgal bloom events and their evolutionary *** results suggest that no macroalgal blooms were observed before *** blooms could be traced back to 1999 and occurred on a small scale in 2000,2004 and 2005,before escalating into large green tides in 2007 and ***,these Ulva blooms were confined to the southern Yellow Sea from May to *** comparison,Sargassum blooms were first detected in the East China Sea in March 2000 and occurred almost every year thereafter,although the size of the blooms showed significant interannual *** distribution areas generally moved northwards from March until the bloom dissipated in May or June,suggesting the influence of the monsoon and *** investigation provided some insight into the bloom history of these two harmful macroalgal blooms in the Yellow Sea and East China Sea.

关键词： macroalgal bloom green tide golden tide remote sensing Landsat Yellow Sea East China Sea

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TAPFixer: automatic detection and repair of home automation vulnerabilities based on negated-property reasoning 24

TAPFixer: automatic detection and repair of home automation ...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Yinbo Yu Yuanqi Xu Kepu Huang Jiajia Liu National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Northwestern Polytechnical University China and Research & Development Institute of Northwestern Polytechnical University in Shenzhen China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Northwestern Polytechnical University China

ISBN: (纸本)9781939133441

Trigger-Action Programming (TAP) is a popular end-user programming framework in the home automation (HA) system, which eases users to customize home automation and control devices as expected. However, its simplified syntax also introduces new safety threats to HA systems through vulnerable rule interactions. Accurately fixing these vulnerabilities by logically and physically eliminating their root causes is essential before rules are deployed. However, it has not been well studied. In this paper, we present TAPFixer, a novel framework to automatically detect and repair rule interaction vulnerabilities in HA systems. It extracts TAP rules from HA profiles, translates them into an automaton model with physical and latency features, and performs model checking with various correctness properties. It then uses a novel negated-property reasoning algorithm to automatically infer a patch via model abstraction and refinement and model checking based on negated-properties. We evaluate TAPFixer on market HA apps (1177 TAP rules and 53 properties) and find that it can achieve an 86.65% success rate in repairing rule interaction vulnerabilities. We additionally recruit 23 HA users to conduct a user study that demonstrates the usefulness of TAPFixer for vulnerability repair in practical HA scenarios.

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SPDGrNet: A Lightweight and Efficient Image Classification Network for Zea mays Diseases

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Journal of Crop Health 2025年第3期77卷 1-12页

作者： Xu, Laixiang Cao, Bingxu Su, Jing feng Zhang, Fengyue Song, Hongyun Zhao, Junmin School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Henan University of Urban Construction Pingdingshan China School of Information Engineering Luohe Vocational Technology College Luohe China Institute of Technology University of Technology Luohe Luohe China School of Computer and Data Science Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan China School of Computer and Data Science Henan University of Urban Construction Pingdingshan China School of Computer and Data Science Research Center of Smart City and Big Data Engineering of Henan Province Research Center of Intelligent Campus Application Engineering of Henan Province Innovation Laboratory of Smart Traffic and Big Data Development of Hainan Province Henan University of Urban Construction Pingdingshan China

Controlling diseases on the foliage of cultivated plants has a major impact on crop yield and quality. Leaf symptoms and environmental information are the basis of crop disease and pest identification. Due to the complexity and diversity of disease leaf symptoms and environmental information, the accuracy of crop disease detection methods has decreased. Aiming at low disease identification efficiency and difficult feature modeling, we propose a new deep learning algorithm and obtain ideal results. We used improved convolutional neural networks (CNNs) SPDNet and GrNet (SPDGrNet) to perform Zea mays disease identification. The proposed research and analysis focus on four categories of Zea mays symptoms: healthy, leaf blight, southern leaf blight and grey spot. The experimental results and analysis confirm that the proposed approach outperforms other excellent plant disease recognition algorithms and achieves an overall classification accuracy of 98.96%. When compared to AlexNet, VGG-16, Inception-V4, ShuffleNet-V3, and Efficient-B7, the proposed model achieves accuracy improvements of 27.78%, 19.05%, 14.59%, 10.28%, and 7.06%, respectively, with corresponding accuracies of 71.18%, 79.91%, 84.37%, 88.68%, and 91.9%. The proposed model has outstanding generalization ability in open-source databases, such as the PlantifyDr, Plant Pathology, and New Plant Diseases dataset. The suggested strategy can provide ideas for transplanting models into mobile disease detection equipment. This is very helpful in the intelligent development of agricultural systems and crop leaf disease analysis.

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DESIGN AND ANALYSIS OF WIND TURBINE FAULT DIAGNOSIS SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

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Diagnostyka 2025年第1期26卷

作者： Luo, Xiaoli Ning, Jinye Wu, Min Scientific Research Department Hunan Electrical College of Technology Xiangtan411101 China Institute of Big Data and Artificial Intelligence Application Technology Hunan Electrical College of Technology Xiangtan411101 China School of Mechanical Engineering Hunan University of Science and Technology Xiangtan City411201 China

Wind turbines are apt to diverse faults during long-term operation in natural environments, which affect their power generation efficiency and lifespan. Therefore, based on convolutional neural networks, gradient descent method was introduced to optimize their parameter training. Meanwhile, synchronous compressed wavelet transform was utilized to enhance the fault signal's time-frequency information. The fault detection correlation operation was optimized through Pearson correlation coefficient. Finally, a new type of fan fault detection model was proposed. The average fault detecting accuracy of this model was the highest at 98.98%, the average loss value was the lowest at 0.08%, and the average time consumption was the shortest at 16.52s. The minimum mean square error for detecting inner and outer ring pitting of fan bearings was 0.016 and 0.018, respectively. As a result, the proposed new model performs excellently in terms of accuracy and reliability in fault detection, with detection accuracy generally superior to other existing models. This model can significantly improve wind turbine fault detection, reduce false alarm and false alarm rates, and provide effective guarantees for wind turbines' stable operation. © 202x by the Authors.

关键词： Convolutional neural networks

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