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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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Joint extraction method for entity relations from mineral resources reports integrating dependency parsing and improved graph convolutional networks

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Ore Geology Reviews 2025年 182卷

作者： Qiu, Qinjun Jin, Xiangguo Tian, Miao Wu, Qirui Tao, Liufeng Chen, Jianguo Xie, Zhong Technology Innovation Center for Geological Data Intelligent Application Department of Natural Resources of Jiangsu Province 210012 China School of Computer and Science China University of Geosciences Hubei Wuhan430074 China National Engineering Research Center for Geographic Information System Hubei Wuhan430074 China Key Laboratory of Geological Survery and Evaluation of Ministry of Education Hubei Wuhan430074 China School of Future Technology China University of Geosciences Hubei Wuhan430074 China Wuhan University Hubei Wuhan430074 China

Geological reports, as crucial technical documents reflecting the outcomes of geological survey work, encapsulate extensive expert and domain knowledge. Geological knowledge graphs integrate vast amounts of data, facilitating efficient and rapid extraction of knowledge embedded within geoscientific data. The extraction of geological entity relations is a key method in creating these knowledge graphs. Existing techniques for extracting geological entities and their relations encounter difficulties such entity overlap, relation overlap, and the challenge of obtaining deep semantic information because of the vastness and complexity of geological data. Our study suggests a collaborative extraction model for entity relations that integrates dependency syntactic relations with a graph convolutional network (GCN) in order to address these problems. This model learns dependency syntactic structures and deep semantic information by building a GCN that includes dependency syntactic relations. A pointer network decoder is then added to increase entity relation extraction efficiency. Dependencies between words in a phrase, such as subject-verb and verb-object relations, are revealed via dependency syntactic analysis. By structuring these dependencies into a graph, the model captures syntactic structural information. Through operations involving adjacency matrices and feature matrices, the model effectively propagates and aggregates node information, thereby capturing the global dependency syntactic structure and deep semantic information of sentences. The integration of dependency syntactic relations with GCN processing enables the model to more accurately comprehend entity relations within sentences. Results from experiments show that this model successfully tackles problems like overlapping entity relations and the challenge of gleaning deep semantic information from geological texts. It achieves a 79.73% accuracy rate and a 77.98% F1 score on geological text datasets. © 2025

关键词： Geochemical surveys

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Multi-strategy optimization of HHO algorithm for path planning of warehouse robots

Multi-strategy optimization of HHO algorithm for path planni...

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2023 China Automation Congress, CAC 2023

作者： Cao, Yiyi Sun, Zhe Nanjing University of Posts and Telecommunications Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing China

ISBN: (纸本)9798350303759

Aimed at path planning of warehouse robots in a storage environment, this paper proposes a multi-strategy Harris Hawks Optimization algorithm (HHO). To better deal with the uneven population distribution caused by the high randomness of individual initialization, an escape energy updating mechanism was proposed to improve population information exchange. Considering that the global optimal individual was not chosen as the guide to update location in the searching process, a searching mechanism is designed based on the maximum and minimum of the individual location, which effectively improves the tightness in the searching process. The adaptive elastic factor is introduced to disturb the current local individual and realize the rapid escape from the local optimal. After the tests of 23 benchmark functions, the multi-strategy HHO algorithm is proved to be superior to HHO in convergence speed and accuracy. Lastly, the proposed HHO algorithm is applied to three different scale warehouse grid environments, which further verifies its applicability to the high-dimensional path planning problem, and effectively improves warehouse operation efficiency. © 2023 IEEE.

关键词： Motion planning

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Video Stabilization via Prediction with Time-Series Network and Image Inpainting with Pyramid Fusion

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Chinese Journal of Electronics 2021年第6期30卷 1103-1110页

作者： CHENG Keyang LI Shichao RONG Lan WANG Wenshan SHI Wenxi ZHAN Yongzhao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center Cyber Space Security Academy of Jiangsu University National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Xinjiang Lianhaichuangzhi Information Technology Co. Ltd.

Due to the poor filling effect of the video image defect commonly used in the video stabilization field, the video is seemed still unstable after the image stabilization process, which seriously affects the visual effect. To solve this problem, we improve a video stabilization method based on time-series network prediction and pyramid fusion restoration is proposed to optimize the visual effect after image *** flow of the proposed method is as follows: First,it is adaptive to determine whether the defect of the corresponding frame at the current time needs padding inpainting. Then, for the frame that needs to be inpainting,the frames generated before the current moment are sent to the model combining the convolutional neural networks and the gate recurrent unit to predict the part to be filled. Next the current defect image and the complete image to be filled are brought into the Laplacian pyramid reconstruction, and the improved weighted optimal suture is introduced for splicing during the fusion. Finally, the video frame is cut after reconstruction. The method is tested on a data set composed of videos commonly used in the field of video stabilization. The experimental results show that the average peak signal to noise ratio of the method is 2 to 5 d B higher than that of the comparison algorithm, and the average structural similarity index is improved by about 2% to 7% compared with the contrast algorithm.

关键词： image restoration image sequences video frame cutting improved weighted optimal suture gate recurrent unit video signal processing video stabilization method image fusion Laplacian pyramid reconstruction padding inpainting image resolution neural nets convolutional neural networks video image defect pyramid fusion restoration image inpainting visual effect optimization image reconstruction time-series network prediction image stabilization process

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application of an ADRC based on Wandering Lion Swarm Optimization Algorithm in Overhead Crane Systems Control 41

Application of an ADRC based on Wandering Lion Swarm Optimiz...

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第41届中国控制会议

作者： Zhe Sun Lei Yue Yijun Du Yufei Wang Xiangpeng Xie Zhixin Sun Post Industry Technology Research and Development Center of the State Posts Bureau(Internet of Things Technology) Post Big Data Technology and Application Engineering Research Center of Jiangsu ProvinceNanjing University of Posts and Telecommunications School of Automation Nanjing Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In order to improve the anti-interference and anti sway control performance of overhead crane system,an auto disturbance rej ection controller based on wandering strategy and improved LSO algorithm is designed in this *** at the parameter setting problem of automatic disturbance rejection controller(ADRC),the wandering lion group optimization algorithm(WLSO) is introduced,including Lion King wandering strategy and Lion Group wandering *** implementing numeric experiment test,the effectiveness of the WLSO is confirmed by benchmark ***,by implementing the simulation experiments under different conditions,the results show that the WLSO algorithm based ADRC controller has significantly modified the suppression of load swing and position control as well as the anti-interference.

关键词： Overhead crane Anti-swing control ADRC WLSO algorithm

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An Overhead Crane Anti-Swing Control Method Integrating Clustering Salp Swarm Algorithm and Fuzzy PID

An Overhead Crane Anti-Swing Control Method Integrating Clus...

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2023 China Automation Congress, CAC 2023

作者： Sun, Zhe Yue, Lei Sun, Zhixin Post Industry Technology Research and Development Center of State Posts Bureau Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798350303759

To address the anti-swing problem in the overhead crane system, fuzzy PID is a common approach. However, the requirement for expert involvement in parameter configuration of fuzzy PID poses challenges for its widespread application. In order to address this issue, we introduce clustering operators into the enhancement of the Salp Swarm Algorithm. By integrating Clustering Salp Swarm Algorithm and fuzzy PID and conduct comparative experiments with other classical swarm intelligence algorithms, the proposed approach enables effective fuzzy control even in the absence of expert experience. Simulation validation of this method has been conducted in the overhead crane system. © 2023 IEEE.

关键词： Fuzzy control

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The Mass Assembly History for Galaxies with MaNGA

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research in Astronomy and Astrophysics 2024年第3期24卷 58-69页

作者： Xue Ge Hong-Tao Wang Cheng-Long Lei Yun-Jun Guo Yi-Long Jiang Xiao-Xiao Cao School of Physics and Information Engineering Jiangsu Second Normal University Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal University Key Laboratory of Modern Astronomy and Astrophysics Nanjing UniversityMinistry of Education School of Science Langfang Normal University

How galaxies assemble masses through their own star formation or interaction with the external environment is still an important topic in the field of galaxy formation and *** use Value Added Catalogs with galaxy features that are spatially and temporally resolved from Sloan Digital Sky Survey data Release 17 to investigate the mass growth histories of early-type galaxies(ETGs)and late-type galaxies(LTGs).We find that the mass growth of ETGs is earlier than that of LTGs for massive galaxies(M_(*)>10^(10)M_⊙),while low-mass(M_(*)≤10^(10)M_⊙)ETGs have statistically similar mass assembly histories as low-mass *** stellar metallicity of all massive galaxies shows a negative gradient and basically does not change with ***,in low-mass galaxies,the stellar metallicity gradient of elliptical galaxies is negative,and the stellar metallicity gradient of lenticular and spiral galaxies evolves from positive to *** are not all in a high-density environment,but exhibit mass *** the tidal strength increases,the star formation rate of low-mass ETGs rapidly *** results support a picture where massive galaxies exhibit inside-out quenching mode,while low-mass galaxies show outside-in quenching *** effects play an important role in regulating the mass assembly histories of low-mass ETGs.

关键词： Galaxy evolution-galaxies star formation-Galaxy formation

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application of U-Net and Optimized Clustering in Medical Image Segmentation:A Review

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Computer Modeling in engineering & Sciences 2023年第9期136卷 2173-2219页

作者： Jiaqi Shao Shuwen Chen Jin Zhou Huisheng Zhu Ziyi Wang Mackenzie Brown School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200China School of Data Science Perdana UniversitySerdangSelangor43400Malaysia

As a mainstream research direction in the field of image segmentation,medical image segmentation plays a key role in the quantification of lesions,three-dimensional reconstruction,region of interest extraction and so *** with natural images,medical images have a variety of ***,the emphasis of information which is conveyed by images of different modes is quite *** it is time-consuming and inefficient to manually segment medical images only by professional and experienced ***,large quantities of automated medical image segmentation methods have been ***,until now,researchers have not developed a universal method for all types of medical image *** paper reviews the literature on segmentation techniques that have produced major breakthroughs in recent *** the large quantities of medical image segmentation methods,this paper mainly discusses two categories of medical image segmentation *** is the improved strategies based on traditional clustering *** other is the research progress of the improved image segmentation network structure model based on *** power of technology proves that the performance of the deep learning-based method is significantly better than that of the traditional *** paper discussed both advantages and disadvantages of different algorithms and detailed how these methods can be used for the segmentation of lesions or other organs and tissues,as well as possible technical trends for future work.

关键词： Medical image segmentation clustering algorithm U-Net

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research on vehicle routing problem based on improved dragonfly algorithm

Research on vehicle routing problem based on improved dragon...

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2023 China Automation Congress, CAC 2023

作者： Yang, Fan Cao, Yiyi Zhang, Xu YTO Express Co. LTD Jiangsu Nanjing China Nanjing University of Posts and Telecommunications Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Jiangsu Nanjing China Anhui Post Valley Express Intelligent Technology Co. LTD Anhui Wuhu China

ISBN: (纸本)9798350303759

Aiming at the low carrying rate and high fuel consumption cost of vehicle routing optimization, a vehicle routing model with time window is constructed. An improved dragonfly algorithm integrated with random learning strategy is designed and applied to solve the problem. The real-value calculation form of the five behaviors of the original dragonfly algorithm is changed into random learning target position pairs and applied to vehicle routing problem with time windows. By comparing the results with those of other three algorithms, the effectiveness and feasibility of improved effective dragon algorithm are further verified, which can reasonably allocate logistics capacity and human resources, and realize the cost reduction and efficiency increase of enterprise business. © 2023 IEEE.

关键词： Vehicle routing

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Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

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Computer Systems Science & engineering 2021年第12期39卷 373-390页

作者： Jiaming Mao Mingming Zhang Mu Chen Lu Chen Fei Xia Lei Fan ZiXuan Wang Wenbing Zhao State Grid Jiangsu Electric Power Co. Ltd.Information and Telecommunication BranchNanJing210003China Institute of Information and Communication Global Energy Interconnection Research InstituteState Grid Key Laboratory of Information and Network SecurityNanjing210003China Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province Research and Development Center of Post Industry Technology of the State Posts Bureau(Internet of Things Technology)Broadband Wireless Communication Technology Engineering Research Center of the Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing210003China Department of Electrical Engineering and Computer Science Cleveland State UniversityCleveland44115USA

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic *** the past few decades,network traffic identification has been a research hotspot in the field of network management and security ***,as more network services use encryption technology,network traffic identification faces many *** classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and *** learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled ***,in real scenarios,labeled samples are often difficult to *** paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised *** results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

关键词： Encrypted traffic recognition deep learning generative adversarial network traffic classification semisupervised learning

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