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3rd International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Zhang, Xingqiang Li, Huihui Liu, Xue Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Ministry of Transport Beijing Jiaotong University Beijing China Rizhao Planning Design Research Institute Group Co. LTD. Rizhao China

ISBN: (纸本)9781510671423

Traffic accident not only causes casualties, but also generates traffic congestion, resulting in severe traffic delays and other indirect economic losses. Based on the accurate identification of accident road section attribute, considering the accidental traffic impact is very important to improve the comprehensiveness and accuracy of road location safety evaluation. In this paper, OBD data is used to map matching, the fuzzy clustering algorithm is used to discriminate and classify the road traffic operation status. A kind of identification algorithm of the accident road section traffic attribute impact is proposed, which is used as case analysis of Beijing Fourth Ring Road. The analysis results show that the accident occupying lane is the most important factor of traffic delay, the more lanes are occupied, the more vehicles of single lane are delayed and the delay time is longer. © 2023 SPIE.

关键词： Roads and streets

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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A novel feature alignment-based cell nucleus identification scheme for medical decision-making systems

A novel feature alignment-based cell nucleus identification ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Gou, Fangfang Dai, Tingyi Wu, Jia Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Hunan University of Medicine General Hospital Huaihua418000 China Monash University Research Center for Artificial Intelligence MelbourneVIC 3800 Australia

ISBN: (纸本)9798350386226

AI medical imaging diagnosis enables physicians to rapidly acquire and analyze image information using deep learning techniques. Consequently, it holds a significant position in medical decision-making assistance. Specifically, the complexity and large scale of digital pathology images necessitate accurate recognition of cell arrangement, size, and shape, which aids physicians in identifying cancerous regions. However, existing medical image segmentation techniques often fail to deliver sufficiently detailed results in unknown target domains, resulting in the blurring of cell nucleus boundaries. In order to introduce more display-like information to solve the problem of obscure cell nucleus boundaries, this study proposes an accurate recognition method (UNW) for cell nucleus recognition in medical decision-making systems. It solves the problem of the distribution difference between the source domain and the unknown target domain by inserting feature center alignment (FCA) and feature distribution alignment (FDA) modules into the encoder and decoder. UNW performs instance normalization (IN) and instance whitening (IW) on the feature images to better identify the feature detail information, which results in more distinct boundaries between the cell nucleus and other tissues. Experimental results on various backbone networks show that our method can obtain more accurate decision results. Among them, the IOU reaches 0.704, which outperforms existing models, while the FCA and FDA maintain the segmentation ability of the model in the face of test data that is inconsistent with the distribution of the training data. © 2024 IEEE.

关键词： Image segmentation

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Online Q&A for Machine Learning Courses Based on Formula Recognition 4

Online Q&A for Machine Learning Courses Based on Formula Rec...

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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Chen, Xiaopan Li, Minghao Zhang, Tianxiang School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Henan University Kaifeng Henan Key Laboratory of Big Data Analysis and Processing China

ISBN: (纸本)9798350394610

Mathematical formulas are ubiquitous in courses such as Machine Learning. Understanding these formulas is the primary challenge for studying of students. If a searchable knowledge base can be built for these formulas, it will help students learn. Accurate identification of these formulas is an important step in building and querying the knowledge base. However, mathematical formulas have complex two-dimensional and nested structures, which makes them inconvenient to search like ordinary text. This paper employs Faster R-CNN to detect regions containing mathematical formulas and improves its backbone network, anchor box dimensions, and pre-training network to make it more accurate and efficient in identifing these formulas. Furthermore, in order to enhance the features and meaningful representation of mathematical formulas in the input image, we introduce the distance transformation. Finally, comparative experiments were conducted on the publicly dataset GTDB. Our proposed approach achieved recognition rates of 91.22% and 85.74% for isolated and hybrid formulas detection. © 2023 IEEE.

关键词： Deep learning

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Enhancing Online Teaching Effectiveness Through Hand-Raising Detection 4

Enhancing Online Teaching Effectiveness Through Hand-Raising...

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4th International Conference on Information Science and Education, ICISE-IE 2023

作者： Zhu, Xiaoke Dong, Zhiwei Chen, Xiaopan Shi, Yufeng Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing Kaifeng China

ISBN: (纸本)9798350394610

After the outbreak of the COVID-19 pandemic, online teaching has gradually emerged as an indispensable component of education. Despite its convenience, online education lacks the immediate interactive experience inherent in traditional classrooms. This paper presents a meticulously designed DS-YOLOv8 network model, specifically tailored for real-time recognition of students raising their hands under cameras. On our proprietary dataset, this network model exhibits exceptional accuracy in hand-raising detection, showcasing a 1.3% improvement in the AP50 index compared to the baseline YOLOv8 model. Our focus lies on the application of hand-raising detection within online classrooms with an objective to provide enhanced support for teachers by promptly identifying students raising their hands and thereby improving teaching efficiency while enriching the interactive experience of students in virtual learning environments. © 2023 IEEE.

关键词： Students

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Regularized Total Least Squares Broad Learning System for Regression

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Journal of Computers (Taiwan) 2025年第1期36卷 89-112页

作者： Xiong, Ke-Jia Yang, Guan-Ci Zhou, Tao Xie, Zhen-Qiang State Key Laboratory of Public Big Data Guizhou University Guizhou Province Guiyang City550025 China Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guizhou University Guizhou Province Guiyang City550025 China National Engineering Research Center of Big Data Application to The Improvement of Governance Capacity Guizhou Province Guiyang City550000 China

The Broad Learning System (BLS) has been extensively developed and applied across various fields due to its significant advantages, including high efficiency, strong generality, and scalability. However, in practical applications where both inputs and outputs are contaminated by noise, traditional BLS demonstrates suboptimal performance in handling sample data. This study introduces a novel regularized total least squares broad learning system (RTLS-BLS) designed to enhance the robustness of BLS when noisy values are present in both the input and the output of the training set. Initially, the k-support norm is integrated into the BLS-based autoencoder (BLS-AE), embedded within BLS, to extract robust features from the original input data. The BLS-AE equipped with the k-support norm effectively addresses or mitigates issues related to overly sparse (based on L1-norm) or overly dense (based on L2-norm) regularization of extracted features. Subsequently, regularized total least squares (RTLS) are employed to assess the output weights of BLS, further enhancing its robustness. Moreover, simultaneous perturbation measures for the coefficient matrix and the output are provided. Experimental results from two function approximation tasks, eight benchmark regression tasks, and two network interface flow datasets demonstrate that the proposed RTLS-BLS achieves significantly robust performance under noisy conditions compared to other methods. © 2025 Codon Publications. All rights reserved.

关键词： Adversarial machine learning

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A Study on application of Artificial Intelligence in College English Teaching Management and Evaluation 3

A Study on Application of Artificial Intelligence in College...

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3rd International Conference on Electronic InformationTechnology and Smart Agriculture, ICEITSA 2023

作者： Feng, Tao Deng, Jianjie Liu, Yiwen Gao, Yanxia Peng, Ling Fu, Jinrong School of Foreign Languages Huaihua University Hunan Huaihua418000 China School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China School of Computing Science University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (纸本)9798400716775

With the rapid development of artificial intelligence, AI technology has been widely applied in various industries, including education. In English teaching, AI technology provides teachers with objective and instructive feedback, offers students a more personalized learning experience, helping them improve their English language skills, etc., and brings new development opportunities and challenges to English teaching as well. However, there are still some problems that need to be solved in college English teaching in our university. For this reason, based on the application of AI technology in college English teaching, this paper mainly explores the impact and application countermeasures of AI on college English teaching from the following four aspects: individuals with differences, teaching management, classroom teaching and teaching evaluation. In addition, the profound integration of college English education and AI will foster the advancement and transformation of college English instruction, ultimately serving as a catalyst for its reform. © 2023 ACM.

关键词： Artificial intelligence

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MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model

arXiv

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arXiv 2025年

作者： Yang, Nai Wang, Yijie Wu, Fan Wei, Zhiwei School of Geography and Information Engineering China University of Geosciences Hubei Wuhan China School of Geographic Sciences Hunan Normal University Hunan Changsha China Hunan Key Laboratory of geospatial big data mining and application Hunan Normal University Hunan Changsha China

Choropleth maps, which utilize color schemes to visualize spatial patterns and trends, are simple yet effective tools for geographic data analysis. As such, color scheme design is a critical aspect of choropleth map creation. The traditional coloring methods offered by GIS tools such as ArcGIS and QGIS are not user-friendly for nonprofessionals. On the one hand, these tools provide numerous color schemes, making it hard to decide which one best matches the theme. On the other hand, it is difficult to fulfill some ambiguous and personalized coloring needs of users, such as requests for ‘summer-like’ map colors. To address these shortcomings, we develop a novel system that leverages a large language model and map color design principles to generate contextually relevant and user-aligned choropleth map color schemes. The system follows a three-stage process: data processing, which provides an overview of the data and classifies the data into meaningful classes;Color Concept Design, where the color theme and color mode are conceptualized based on data characteristics and user intentions;and Color Scheme Design, where specific colors are assigned to classes based on generated color theme, color mode, and user requirements. Our system incorporates an interactive interface, providing necessary visualization for choropleth map color design and allowing users to customize and refine color choices flexibly. Through user studies and evaluations, the system demonstrates acceptable usability, accuracy, and flexibility, with users highlighting the tool’s efficiency and ease of use. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

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Identifying and Analyzing Heavy Truck Stops from GPS data by Grid Processing 8

Identifying and Analyzing Heavy Truck Stops from GPS Data by...

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8th International Conference on Electromechanical Control Technology and Transportation, ICECTT 2023

作者： Xinwu, Yuan Yang, Yang Yuanyuan, Song Enjian, Yao Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing Haidian District Beijing100044 China Transportation Planning Research Institute Ministry of Transport Beijing Chaoyang District Beijing100028 China

ISBN: (纸本)9781510668331

Truck transportation occupies a dominant position in road transportation, with the increase of freight demand and the number of trucks, the traffic congestion on the road is also become an urgent problem to be solved. Different to the family cars, the trucks in the process of driving the parking need and occupy a larger space, and loading and unloading time is longer, easy to have an impact on the surrounding traffic. Therefore, based on GPS track data, this paper adopts the grid processing method to identify the truck stopping points and compare it with its nearby POI data to know the POI information near the truck's stopping points. And then the parking time of the trucks is analyzed and it is found that most of the parking time of the trucks is within 100 minutes. Finally, the DBSCAN density clustering algorithm for truck hotspot area identification model is built for this paper, and the parking hotspots of trucks are analyzed and the results of clustering are derived. It can provide a basis for the road pipeline department to control the traffic in the internal area of the hotspot as well as the surrounding area, reducing congestion and reducing the hidden danger, and improving the efficiency of truck transportation. © 2023 SPIE.

关键词： Trucks

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Deep Clustering Model for Time-Series data Based on Recurrence Plot and Variational Auto-Encoder 3

Deep Clustering Model for Time-Series Data Based on Recurren...

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3rd International Conference on Digital Signal and Computer Communications, DSCC 2023

作者： Xu, Cheng Chen, Junjie College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Huhhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Hohhot010018 China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

Time-series data are trendy and periodic, and have high data dimensionality, the current clustering methods cannot effectively target these characteristics. A recurrence plot variational auto-encoder deep clustering (RPVAEDC) model based on recurrence plot and variational auto-encoder is proposed to address the characteristics of time-series data. The time-series data are first transformed into recurrence plots to reveal their trends and periodicity;then the recurrence plots are fed into a deep clustering model for feature extraction and dimensionality reduction, and the distribution of the transformed data is normalized by variational auto-encoder;then the clustering results are obtained by adding a clustering layer to combine the auto-encoder reconstruction loss and clustering loss. It is experimentally demonstrated that the silhouette coefficient scores are achieved significantly better than other clustering algorithms on the public data sets. © 2023 SPIE.

关键词： Clustering algorithms

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