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Chinese Intelligent Automation Conference, CIAC 2023

作者： Yan, Qian Chen, Yifei Wu, Caicong China Agriculture University Beijing100080 China China Agriculture University College of Information and Electrical Engineering Key Laboratory of Agricultural Machinery Monitoring and Big Data Beijing100080 China

ISBN: (纸本)9789819961863

The nutrient element content of plants is one of the important factors affecting the growth and yield of crops. The use of the convolutional neural network (CNN) can quickly and accurately detect the degree of nutrient deficiency in plants, thereby liberating manpower and improving crop yields. This study uses MobileNetV3-Large as the backbone, combines a convolutional block attention module (CBAM) to obtain MobileNetV3-CBAM, and introduces gated linear units (GLU). During the training process, fine-tuning of transfer learning improves the speed and accuracy of model training. The identification of elements deficient in plants is studied using an open-source dataset, and compared with the cutting-edge CNN, the proposed lightweight MobileNetV3-CBAM with GLU has better comprehensive performance: I. For the imbalanced dataset, the proposed MobileNetV3-CBAM model achieves outstanding results in classifying plant nutrient deficiencies, with a remarkable test accuracy of 96.54%. II. The proposed model occupies a small memory (10.5M), and the training speed is remarkable, which can realize the identification of plant nutrient deficiency in a low-computing environment. III. After 10-fold cross-validation, the robustness of the model is good. This study can provide a theoretical basis and technical support for the real-time detection of nutrient deficiency in plant leaves. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Convolutional neural networks

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Extraction of straight field roads between farmlands based on agricultural vehicle-mounted LiDAR

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International Journal of Agricultural and Biological engineering 2022年第5期15卷 155-162页

作者： Lili Yang Yuanyuan Xu Yajie Liang Jia Qin Yuanbo Li Xinxin Wang Weixin Zhai Long Wen Zhibo Chen Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural AffairsBeijing 100083China

The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational *** achieve high performance,perception tasks(such as obstacle detection,road extraction,and drivable area extraction)are of great *** with structured roads,field roads between farmlands,including unstructured roads and semi-structured roads,are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured *** study proposed an extraction method for the straight field roads between *** proposed method was based on the point cloud data acquired by LiDAR(Velodyne VLP-16)mounted on a John Deere 12046B-1204 *** proposed method has three aspects:Euclidean Clustering-based extraction,boundary-based extraction,and road point cloud curve segment ***,Euclidean Clustering with K-Dimensional(KD)-Tree data structure was adopted to extract the road curve segments close to the LiDAR composed of road ***,the boundary lines constraint was constructed to extract the distant road curve ***,the local distance ratio was used to modify the extracted road curve *** average extraction accuracy for both semi-structured and unstructured roads exceeded 98%,and the false positive rate(FPR)was less than 0.5%.These experimental findings demonstrated that the proposed road extraction method was precise and *** proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving.

关键词： road extraction straight field road autonomous agricultural vehicle LiDAR farmland

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Semantic-Reconstructed Graph Transformer Network for Event Detection

Semantic-Reconstructed Graph Transformer Network for Event D...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhuochun Miao Xingrui Zhuo Gongqing Wu Chenyang Bu Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Anhui China

Event detection (ED) is a key subtask of information extraction to extract key events, such as stock rise and fall and social public opinion, from news or social media. Although current GCN-based event detection methods achieve remarkable success via building graphs with dependency trees, they typically suffer from two challenges: 1) They use sequence models to learn contextual information of sentences, ignoring the longterm dependencies problem of sequence models might learn ineffective information and make it propagate in GCN layers. 2) Most methods do not exploit global dependency label information and grammatical structure information that convey rich linguistic knowledge directly, and only consider local dependency label information. To cope with these challenges, we propose a novel event detection model via semantic-reconstructed graph transformer networks (SRGTNED), which incorporates semantic reconstruction and path information collection methods. Using the semantic reconstruction method, we assign a pruned sequence to each word based on the path information to capture contextual information consistent with sentence semantics. Moreover, to better utilize global dependency label information and grammatical structure information, a Graph Transformer Network (GTN)-based heterogeneous graph embedding framework is introduced to automatically learn path information between important words by converting sentences as heterogeneous graphs. We conduct experiments on the ACE2005 dataset and the Commodity News dataset, and the experimental results demonstrate that our method significantly outperforms 11 state-of-the-art baselines in terms of the F1-score.

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A novel single-phase Ca3Gd2Si3O12:Dy3+, Sm3+ phosphor exhibiting high thermal stability for solid-state lighting applications

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Ceramics International 2025年

作者： Zhang, Yanzhi Deng, Xiaoxu Cui, Ruirui Guo, Xiang Zhang, Jun Deng, Chaoyong Key Laboratory of Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyan550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China College of Computer and Information Engineering Guizhou University of Commerce Guiyang550014 China

In order to obtain efficient single-phase co-doped phosphors with excellent luminescence properties under near-ultraviolet excitation, Ca3Gd2Si3O12:xDy3+ (CGSO: xDy3+) and Ca3Gd2Si3O12:0.08Dy3+, ySm3+ (CGSO:0.08Dy3+, ySm3+) phosphors were effectively manufactured using the classical high-temperature solid-phase method. The phase structure, morphology and XPS test results of the phosphor all show that the Ca3Gd2Si3O12(CGSO) material has been successfully synthesized, and Dy3+ and Sm3+ ions have been successfully doped into the matrix lattice. Under 362 nm illumination, the PL spectra of CGSO:0.08Dy3+, ySm3+ phosphor revealed that the typical peak intensity of Dy3+ ions declined as the amount of Sm3+ ions increased, whereas the distinctive peak intensity of Sm3+ ions achieved its maximum at y = 0.07. As Sm3+ doping grows larger, the fluorescence lifetime shortens, signifying the occurrence of transferring energy among Dy3+ and Sm3+ ions, with a maximum efficiency of 81 % being attained. The Ca3Gd2Si3O12:0.08Dy3+, ySm3+ phosphor demonstrates a commendable thermal stability of 90.17 %, with adjustments to the Sm3+ concentration revealing the potential to yield warm white light, as evidenced by both CIE chromaticity coordinates and CCT measurements. In addition, compared with the phosphor containing only Dy3+, the LED assembled with CGSO:0.08Dy3+, ySm3+ phosphor containing Sm3+ ions and 365 nm LED chip emits warmer white light with lower color temperature, which confirms the application value of CGSO:0.08Dy3+, ySm3+ phosphor in the domain of solid-state lighting. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Semiconductor doping

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Study on Dynamic Evaluation of Limiting Factors of High-Speed Railway Station Operation Efficiency 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Jiahe, Liu Rui, Song Xiaopeng, Shen Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing China China Railway Engineering Design and Consulting Group Beijing China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

With the construction and development of China’s high-speed railway, China’s high-speed railway has entered a new development era. In order to adapt to the development trend of rapid passenger transport, public transport and high frequency of high-speed railway, master the operation of high-speed railway stations, make efficient use of station resources and optimize the technical operation process, this study builds a dynamic index evaluation system of high-speed railway stations by selecting dynamic indicators that limit the operation efficiency of stations, determines the index weight by entropy weight method, establishes a model by gray correlation analysis method and solves the calculation, so as to analyze the transportation efficiency of high-speed railway stations in different periods, determine the bottlenecks affecting the operation efficiency of high-speed railway stations and propose reasonable improvement measures to achieve the purpose of improving the operation efficiency of high-speed railway stations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Railroads

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Multivariate Prediction of Retail Sales by Multi-task Time Series Learning

Multivariate Prediction of Retail Sales by Multi-task Time S...

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2021 International Conference on Computer Graphics, Artificial Intelligence, and data Processing, ICCAID 2021

作者： Lin, Miaopei Yu, Jianxing Yin, Jian Sun Yat-sen University School of Computer Science and Engineering Guangdong Key Laboratory of Big Data Analysis and Processing China

ISBN: (数字)9781510652170

ISBN: (纸本)9781510652163

Predicting retail sales is a hot research topic that can help firms achieve on-demand procurement. That can reduce the extra costs caused by an inventory shortage or surpluses. Traditional methods usually regard the sales of a certain product as the independent time series and then solve the task by a curve fitting model. However, the sales volume is not a single value, but the one aggregated from many types of products. There are correlations between different types of product sales. The increase in one type of product sales may lead to changes in sales in another type. Without capturing the correlation between different product sales, it is difficult to obtain satisfactory performance. To solve this problem, we propose a new multi-task method based on multivariate time series learning. Considering there are multiple product types and each type has different trend characteristics, we first cluster the time series of all product types and then model each as a multivariate prediction task. We then design a new dilated convolution network to fusion the features of related products and the trend characteristic of each task. Moreover, we develop a trend structural entropy network to grasp the fluctuation features of the task. A new self-enhancement mechanism is proposed to finely capture the correlations among tasks. Through multi-task learning, the model can effectively improve prediction accuracy by using the complementary information of closely related time series. Experimental results on two real data sets show the effectiveness of our approach. © 2022 SPIE.

关键词： Sales

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Vision BEV-MAE: Masked Bird’s Eye View Autoencoders for Camera-based 3D Object Detection

Vision BEV-MAE: Masked Bird’s Eye View Autoencoders for Cam...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Zhenguo Cao, Yuanzhouhan Zhang, Hui Chen, Naiyue Ren, Chao Li, Yidong Key Laboratory of Big Data & Artificial Intelligence in Transportation Beijing Jiaotong University Ministry of Education China School of Computer Science and Technology Beijing Jiaotong University China College of Electronics and Information Engineering Sichuan University China

ISBN: (纸本)9798350368604

3D object detection is one of the fundamental perception tasks in autonomous driving. In recent years, perception in the Bird’s Eye View (BEV) has attracted tremendous attention due to its outstanding ability in preserving geometric cues. However, like other supervised learning tasks, BEV-based perception suffers from inadequate labeled training data. In this work, we propose the Vision BEV-MAE, a self-supervised learning approach for BEV perception from camera images. Specifically, we design a feature-level masking strategy to mask the mid-level representations, i.e., the encoded BEV features instead of directly masking input multi-view images, and reconstruct BEV features with a light-weight decoder. Our Vision BEV-MAE is independent of any geometric data such as point clouds, or any additional supervision, and can be easily implemented within any state-of-the-art BEV perception frameworks to improve the performance. We conduct experiments on benchmark dataset to validate our approach. © 2024 IEEE.

关键词： Self-supervised learning

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential *** of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ***,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene *** addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local *** tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent *** main highlights of this study include two *** the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene ***,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local *** the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during *** experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

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Each Fake News is Fake in its Own Way: An Attribution Multi-Granularity Benchmark for Multimodal Fake News Detection

arXiv

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arXiv 2024年

作者： Guo, Hao Ma, Zihan Zeng, Zhi Luo, Minnan Zeng, Weixin Tang, Jiuyang Zhao, Xiang Laboratory for Big Data and Decision Nation University of Defence Technology China School of Computer Science and Technology Xi’an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China

Social platforms, while facilitating access to information, have also become saturated with a plethora of fake news, resulting in negative consequences. Automatic multimodal fake news detection is a worthwhile pursuit. Existing multimodal fake news datasets only provide binary labels of real or fake. However, real news is alike, while each fake news is fake in its own way. These datasets fail to reflect the mixed nature of various types of multimodal fake news. To bridge the gap, we construct an attributing multi-granularity multimodal fake news detection dataset AMG, revealing the inherent fake pattern. Furthermore, we propose a multi-granularity clue alignment model MGCA to achieve multimodal fake news detection and attribution. Experimental results demonstrate that AMG is a challenging dataset, and its attribution setting opens up new avenues for future research. © 2024, CC BY-NC-ND.

关键词： Fake detection

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Photoluminescence properties and energy transfer of novel orange–red emitting phosphors:Ba_(3)Bi_(2)(PO_(4))_(4):Sm^(3+),Eu^(3+) for white light-emitting diodes

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Rare Metals 2021年第10期40卷 2882-2891页

作者： Rui-Rui Cui Xiang Guo Xin-Yong Gong Chao-Yong Deng Key Laboratory of Functional Composite Materials of Guizhou Province Department of Electronic ScienceCollege of Big Data and Information EngineeringGuizhou UniversityGuiyang 550025China

Sm^(3+), Eu^(3+)co-coped Ba_(3)Bi_(2)(PO_(4))_(4) phosphors,as potential phosphors for white light-emitting diode applications, were synthesized through the solid-state reaction method for the first time. The crystal structure,absorption spectra, photoluminescence properties, decay time, energy transfer mechanism, temperature-dependent properties, and Commission International De L’Eclairage(CIE) chromaticity coordinates were investigated systematically. The pure eulytite-type Ba_(3)Bi_(2)(PO_(4))_(4) phase was obtained after heating at 980 ℃ for 5 h. A notably enhanced absorption efficiency at 393 nm was observed when Sm^(3+), as a sensitizer, was doped into Ba_(3)Bi_(1.82)(PO_(4))_(4): 0.18 Eu^(3+)and the band gap of the Ba_(3)Bi_(2)(PO_(4))_(4) host was estimated to be 4.19 eV. The emission intensity of Ba_(3)Bi_(1.82)(PO_(4))_(4): 0.18 Eu^(3+)was significantly enhanced when Sm^(3+)was co-doped. The existence and mechanism of energy transfer from Sm^(3+) to Eu^(3+)were evaluated by photoluminescence spectra and decay time measurements. The CIE chromaticity coordinate of Ba3 Bi1.75(PO4)4: 0.07 Sm^(3+), 0.18 Eu^(3+) phosphor was calculated to be(0.5746, 0.4197), respectively.

关键词： Ba_(3)Bi_(2)(PO_(4))_(4):Sm^(3+) Eu^(3+) Phosphors Photoluminescence Energy transfer

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