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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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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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Rethinking Distribution Alignment for Inter-class Fairness 1

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1st International Conference on Artificial Intelligence Security and Privacy, AIS and P 2023

作者： Ye, Jinhuang Wu, Jiawei Li, Zuoyong Zheng, Xianghan College of Computer and Big Data Fuzhou University Fuzhou350108 China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen518107 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789819997886

ISBN: (纸本)9789819997879

Semi-supervised learning (SSL) is a successful paradigm that can use unlabelled data to alleviate the labelling cost problem in supervised learning. However, the excellent performance brought by SSL does not transfer well to the task of class imbalance. The reason is that the class bias of pseudo-labelling further misleads the decision boundary. To solve this problem, we propose a new plug-and-play approach to handle the class imbalance problem based on a theoretical extension and analysis of distribution alignment. The method, called Basis Transformation Based Distribution Alignment (BTDA), efficiently aligns class distributions while taking into account inter-class *** implements the basis transformation through a learnable transfer matrix, thereby reducing the performance loss caused by pseudo-labelling biases. Extensive experiments show that our proposed BTDA approach can significantly improve performance in class imbalance tasks in terms of both accuracy and recall metrics when integrated with advanced SSL algorithms. Although the idea of BTDA is not complex, it can show advanced performance on datasets such as CIFAR and SVHN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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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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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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CLOE: Novelty Detection via Contrastive Learning with Outlier Exposure 1

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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Liu, Tianyang Liang, Quan Yang, Hua Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789819964987

ISBN: (纸本)9789819964970

Novelty detection (ND) methods seek to identify anomalies within a specific dataset. Although self-supervised representation learning is commonly used in such applications, inadequate training data may reduce the effectiveness of these methods. It is thus reasonable to use external data to improve these performances. Here, we propose a simple and effective network, CLOE, for image-based novelty detection. Our method includes a pretrained ViT model as a feature extractor and employs the contrastive learning technique to train the dataset with external data. We compare the performance of two types of extra training settings: (1) The augmented data of the original dataset. (2) The fake images obtained from generative models. The demonstrated approach achieves a new state-of-the-art performance in novelty detection, as evidenced by achieving an ROC-AUC of 99.72% on the CIFAR-10 dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Statistics

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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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Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding

Label-aware Multi-level Contrastive Learning for Cross-lingu...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Liang, Shining Shou, Linjun Pei, Jian Gong, Ming Zuo, Wanli Zuo, Xianglin Jiang, Daxin College of Computer Science and Technology Jilin University China Key laboratory of Symbolic Computation and Knowledge Engineering MOE STCA Microsoft Department of Electrical & Computer Engineering Duke University United States

Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data. The recent multilingual code-switching approach achieves better alignments of model representations across languages by constructing a mixed-language context in zero-shot cross-lingual SLU. However, current code-switching methods are limited to implicit alignment and disregard the inherent semantic structure in SLU, i.e., the hierarchical inclusion of utterances, slots, and words. In this paper, we propose to model the utterance-slot-word structure by a multi-level contrastive learning framework at the utterance, slot, and word levels to facilitate explicit alignment. Novel code-switching schemes are introduced to generate hard negative examples for our contrastive learning framework. Furthermore, we develop a label-aware joint model leveraging label semantics to enhance the implicit alignment and feed to contrastive learning. Our experimental results show that our proposed methods significantly improve the performance compared with the strong baselines on two zero-shot cross-lingual SLU benchmark datasets. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Nutrient Deficiency Diagnosis of Plants Based on Transfer Learning and Lightweight Convolutional Neural Networks MobileNetV3-Large 11

Nutrient Deficiency Diagnosis of Plants Based on Transfer Le...

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11th International Conference on Computing and Pattern Recognition, ICCPR 2022

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

ISBN: (纸本)9781450397056

Nutrient Deficiency Diagnosis of Plants is an important application in precision agriculture. At present, nutrient deficiency diagnosis of plants mainly depends on manual identification, which makes it difficult to ensure efficiency and accuracy. Therefore, based on deep learning and focusing on the problems of difficult convergence and poor real-time performance of the existing deep convolution neural network in the detection of plant nutrient deficiency, this study proposes a lightweight model - UMNet (Nutrient-MobileNetV3-Network) for plant nutrient deficiency detection. This model enhances the collected rice leaf images to expand the dataset, then migrates the knowledge learned by the MobilenetV3-Large network on the ImageNet dataset to UMNet, redesigns a new full connection layer, and uses a new activation function. The experimental results show that: (1) Transfer learning solves the problem of insufficient training data. Compared with learning without transfer learning, the accuracy increases by 7.22% ∼9.63%, which greatly improves the convergence speed and recognition accuracy of the model. (2) Compared with complex convolutional neural networks(CNN), such as InceptionV3, InceptionResnetV2 and VGG16, the lightweight network UMNet has lower storage requirements and shorter training time. At the same time, it can still ensure high accuracy, and the recognition accuracy is better than other lightweight networks with the same complexity: ShuffleNetV2, EfficientNetB0 and Xception. The identification accuracy of the plant nutrient deficiency detection model UMNet constructed in this paper can reach 97.80%, and the training time of a single epoch is about 46.4s. It only takes 1.45s to predict the nutrient deficiency of a single object, which realizes the intelligent detection in the field of plant nutrient deficiency, and it will promote academic exploration of deep learning in plant phenotype research. © 2022 ACM.

关键词： Image recognition

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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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