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MASS FLOW DETECTION TECHNOLOGY FOR SEED AND FERTILISER PARTICLES AND ITS APPLICATION IN UAV-BASED SPREADING

INMATEH - Agricultural engineering 2025年第1期75卷 584-599页

作者： Wang, Yanshuang Song, Cancan Gao, Yongqi Lan, Yubin Wang, Guobin Ming, Rui College of Agricultural Engineering and Food Science Shandong University of Technology Zibo China Shandong University of Technology Sub-center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology Zibo China Research of Institute of Ecological Unmanned Farm Shandong University of Technology Zibo China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China

The processes of sowing and fertiliser application represent a significant aspect of agricultural production. In order to achieve efficient and precise seeding and fertiliser application, mass flow detection of seed and fertiliser particles can facilitate real-time monitoring and precise decision-making for intelligent seeding and fertiliser application. However, the diversity of seed and fertiliser particle types and particle flow modes presents a challenge for existing detection methods, particularly in meeting the varying operational requirements, especially for particle mass flow detection in low-altitude and high-speed sowing operations of agricultural drones. In such cases, the overlap and flow rate will have a significant impact on the detection results due to the large displacement and the continuous high-throughput dense phase of the particle flow. This paper provides a summary of the existing seed and fertiliser particle mass flow detection techniques and their underlying working principles. It compares the direct detection based on mass with the indirect detection methods based on velocity and concentration, and analyses their respective advantages, disadvantages and applicability. It also explores the possibility of optimising the existing detection methods for the specific needs of agricultural UAVs and considers the potential introduction of cutting-edge science and technology in order to develop an efficient, accurate and convenient detection system to meet the growing market demand. © (2025), (INMA Bucharest). All Rights Reserved.

关键词： Precision agriculture

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Safety Control of Switched Systems via Multiple Barrier Functions

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IEEE Transactions on Automatic Control 2025年

作者： Long, Lijun Huang, Chunxiao Sun, Zhendong Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University China

This paper is concerned with the problem of safety control for switched systems, where different safe sets are allowed for different subsystems and safety is not necessarily possessed for subsystems. A necessary and sufficient condition is presented for safety of switched systems by exploiting a multiple barrier functions (MBFs) method. To reveal the relationship about safety between subsystems and switched systems, an extended Nagumo's Theorem in the switched systems framework is derived. Also, several switched control design conditions are respectively provided to ensure safety of switched systems. Based on the union of MBFs and multiple Lyapunov functions (MLFs), a novel safety-critical control framework is established to simultaneously ensure safety and stability of switched systems. Also, an auxiliary cost function is introduced to transform the safety-critical optimal control (SCOC) problem of switched systems into the one of non-switched systems. Finally, three examples are illustrated to demonstrate the effectiveness of the proposed methods. © 1963-2012 IEEE.

关键词： Lyapunov functions

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Optimizing Ionospheric Semi-variograms with Variable Correlation Parameters and Modeling Spatial Threats with Multiple Metrics for China Region

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Hongwen Fang, Kun Wang, Zhipeng Lan, Xiaowei Hou, Xiaopeng Zhu, Yanbo Beihang University School of Electronics and Information Engineering Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China Aviation Data Communication Corporation Beijing100191 China

The ionosphere presents a significant challenge for satellite-based augmentation system (SBAS) due to its unpredictable ionospheric variations affecting satellite single-frequency (SF) signal propagation. The traditional grid model estimates vertical delays and uncertainties at ionospheric grid points (IGPs) but is limited in capturing spatial variations and undersampled threats when employing a constant semi-variogram. This article aims to enhance ionospheric integrity and system availability of BeiDou SBAS (BDSBAS) in China and surrounding areas by optimizing universal Kriging-based semi-variograms with spatio-temporally varying correlation parameters for both isotropic and anisotropic ionospheric models. Using observation data from 30 stations of Crustal Movement Observation Network of China (CMONOC) from January to December 2021, semi-variogram parameters, including decorrelation distance, nugget, and sill, were estimated every half hour. Results demonstrated that these parameters exhibit significant temporal dependence for both the region and each IGP, indicating that variable semi-variograms are more effective for Kriging fitting and SBAS ionospheric corrections. Additionally, the undersampled threat model was expanded by introducing a norm-based angular metric (NAM) alongside the fit radius and the relative centroid metric. data from periods of higher geomagnetic and solar activity in 2021 and 2022 were used to identify raw undersampled threat samples for two-metric and multi-metric threat models across typical, isotropic, and anisotropic semi-variograms. Evaluations showed that variable anisotropic semi-variograms reduced the representative ionospheric threat level to 1.75 m, compared to 1.92 m for typical semi-variograms and 1.82 m for isotropic ones. Further testing, using data from nine additional stations under different solar and geomagnetic conditions, was conducted to assess BDSBAS performances based on different combinations of semi-variograms and th

关键词： Ionosphere

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Synthesis and photoluminescence characterization of Sm3+/Eu3+ single-doped Gd2MgTiO6 phosphor for LEDs

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Journal of Molecular Structure 2025年 1339卷

作者： Huang, Xiaoju Zhang, Guihong Deng, Chaoyong Zhang, Min Key Laboratory of Electronic Functional Composite Materials of Guizhou Province College of Big Data and Information Engineering Guizhou University Guiyang550025 China Guizhou Academy of Sciences Guiyang550001 China

Trivalent rare earth ions samarium and europium ions were doped into the prepared Gd2MgTiO6 host crystal structure by high temperature solid phase method. XRD patterns show that the synthesized materials belong to the monoclinic structure with space group P21/n. Using UV-visible spectroscopy, the band gaps can be calculated to be 4.18, 4.15 and 3.84 eV for host, Sm3+ and Eu3+-doped samples, respectively. For Sm-doped sample excited with 407 nm, the PL spectrum shows three prominent peaks at 565 nm, 603 nm and 649 nm, which are attributed to the transitions of 4G→5/26H5/2, 4G→5/26H7/2 and 4G→5/26H9/2, respectively. The 591 nm and 615 nm emissions of Eu3+ doping correspond to the transitions from 5D0 to 7F1 and 7F2, respectively. The optimal concentrations are 0.03 mol and 0.09 mol for Sm3+ and Eu3+ doping, respectively. The prepared samples can be used in the field of LEDs thanks to lower color temperature CCT=1700∼2200 K and higher color purity >97%. © 2025 Elsevier B.V.

关键词： Metal ions

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Xin Xu, Feng Tao, Feifei Tong, Yao Lyu, Xin Zhong, Jianyi Kaup, André College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (纸本)9798350368741

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study. © 2025 IEEE.

关键词： frequency transformer remote sensing images Semantic segmentation spectrum enhancement

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AGCD: An Attention-Guided Graph Convolution Network for Change Detection of Remote Sensing Images

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Remote Sensing 2025年第8期17卷 1367-1367页

作者： Li, Heng Lyu, Xin Li, Xin Fang, Yiwei Xu, Zhennan Wang, Xinyuan Zhang, Chengming Xu, Chun Chen, Shaochuan Lu, Chengxin College of Computer Science and Software Engineering Hohai University Nanjing211100 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211100 China

Change detection is a crucial field in remote sensing image analysis for tracking environmental dynamics. Although convolutional neural networks (CNNs) have made impressive strides in this field, their grid-based processing structures struggle to capture abundant semantics and complex spatial-temporal correlations of bitemporal features, leading to high uncertainty in distinguishing true changes from pseudo changes. To overcome these limitations, we propose the Attention-guided Graph convolution network for Change Detection (AGCD), a novel framework that integrates a graph convolutional network (GCN) and an attention mechanism to enhance change-detection performance. AGCD introduces three novel modules, including Graph-level Feature Difference Module (GFDM) for enhanced feature interaction, Multi-scale Feature Fusion Module (MFFM) for detailed semantic representation and Spatial-Temporal Attention Module (STAM) for refined spatial-temporal dependency modeling. These modules enable AGCD to reduce pseudo changes triggered by seasonal variations and varying imaging conditions, thereby improving the accuracy and reliability of change-detection results. Extensive experiments on three benchmark datasets demonstrate that AGCD’s superior performance, achieving the best F1-score of 90.34% and IoU of 82.38% on the LEVIR-CD dataset and outperforming existing state-of-the-art methods by a notable margin. © 2025 by the authors.

关键词： Change detection

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Mamba in Speech: Towards an Alternative to Self-Attention

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1933-1948页

作者： Xiangyu Zhang Qiquan Zhang Hexin Liu Tianyi Xiao Xinyuan Qian Beena Ahmed Eliathamby Ambikairajah Haizhou Li Julien Epps School of Electrical Engineering and Telecommunications The University of New South Wales (UNSW) Sydney NSW Australia College of Computing and Data Science Nanyang Technological University Singapore School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong (Shenzhen) Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Transformer and its derivatives have achieved success in diverse tasks across computer vision, natural language processing, and speech processing. To reduce the complexity of computations within the multi-head self-attention mechanism in Transformer, Selective State Space Models (i.e., Mamba) were proposed as an alternative. Mamba exhibited its effectiveness in natural language processing and computer vision tasks, but its superiority has rarely been investigated in speech signal processing. This paper explores solutions for applying Mamba to speech processing by discussing two typical speech processing tasks: speech recognition, which requires semantic and sequential information, and speech enhancement, which focuses primarily on sequential patterns. The experimental results confirm that bidirectional Mamba (BiMamba) consistently outperforms vanilla Mamba, highlighting the advantages of a bidirectional design for speech processing. Moreover, experiments demonstrate the effectiveness of BiMamba as an alternative to the self-attention module in the Transformer model and its derivates, particularly for the semantic-aware task. The crucial technologies for transferring Mamba to speech are then summarized in ablation studies and the discussion section, offering insights for extending this research to a broader scope of tasks.

关键词： Transformers Speech enhancement Speech recognition Convolution Computational modeling Natural language processing Training Random access memory Kernel Semantics

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IEEE Transactions on Computational Biology and Bioinformatic...

引用

IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 203-215页

作者： Ruijie Li Qiao Ning Yaomiao Zhao Shikai Guo Hui Li Department of Information Science and Technology Dalian Maritime University Dalian China DUT Artificial Intelligence Institute Dalian China Dalian Key Laboratory of Artificial Intelligence Dalian China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Computer Science and Engineering Northeastern University Shenyang China

In recent years, microRNA (miRNA) has been recognized as crucial in the progression of human diseases. However, existing computational methods for identifying miRNA-disease associations often overlook the rich association information contained in specific long-distance pathways and lack effective exploration of potential associations. In this study, we propose a biologically interpretable “similarity-association-similarity” metapath and heterogeneous-hyper network (HeteroHyperNet) learning approach for miRNA-disease association prediction (MHMDA). In MHMDA, a “similarity-association-similarity” multi-hop metapaths learning method based on hierarchical attention perception is proposed to explore specific long-distance associated pathway information connecting potentially associated miRNAs and diseases. In addition, a HeteroHyperNet learning approach integrating heterogeneous network and hyper network is designed to progressively learn direct association information and potential association information between miRNA and disease. The “similarity-association-similarity” metapath with hierarchical attention significantly enhances the learning of long-distance biological associations, while the HeteroHyperNet comprehensively learns the known and potential associations of miRNA-disease, greatly improving the richness and accuracy of information. A large number of experimental results show that MHMDA has demonstrated excellent performance in the prediction of miRNA-disease association. In addition, cross independent dataset experiment and cold start experiment on miRNA and disease prove the effectiveness of MHMDA on sparse association points, and its stability and reliability in predicting potential miRNA-disease association are further confirmed.

关键词： Diseases Predictive models Autoencoders Feature extraction Biological system modeling Bioinformatics Kernel Computational biology Heterogeneous networks Accuracy

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AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes

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Remote Sensing 2025年第9期17卷 1556-1556页

作者： Li, Zhenteng Lian, Sheng Pan, Dengfeng Wang, Youlin Liu, Wei College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350002 China Key Laboratory of Intelligent Metro of Universities in Fujian Fuzhou350108 China School of Software and Information Engineering East China Jiaotong University Nanchang330013 China

Object detection in unmanned aerial vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: adaptive small object enhancement (ASOE) and dynamic class-balanced copy–paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, maintaining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a (Formula presented.) average precision (AP) on the VisDrone dataset, surpassing its counterparts by at least (Formula presented.). © 2025 by the authors.

关键词： Vehicle detection

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Improving Multi-Label Contrastive Learning by Leveraging Label Distribution

arXiv

引用

arXiv 2025年

作者： Chen, Ning Lyu, Shen-Huan Wu, Tian-Shuang Wang, Yanyan Tang, Bin Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples and effectively utilizing label information. Previous studies selected positive and negative samples based on the overlap between labels and used them for label-wise loss balancing. However, these methods suffer from a complex selection process and fail to account for the varying importance of different labels. To address these problems, we propose a novel method that improves multi-label contrastive learning through label distribution. Specifically, when selecting positive and negative samples, we only need to consider whether there is an intersection between labels. To model the relationships between labels, we introduce two methods to recover label distributions from logical labels, based on Radial Basis Function (RBF) and contrastive loss, respectively. We evaluate our method on nine widely used multi-label datasets, including image and vector datasets. The results demonstrate that our method outperforms state-of-the-art methods in six evaluation metrics. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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