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Individual Tree Segmentation Based on Region-Growing and Density-Guided Canopy 3-D Morphology Detection Using UAV LiDAR Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 8897-8909页

作者： Li, Shihua Zhao, Shunda Tian, Zhilin Tang, Hao Su, Zhonghua Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China Minist Nat Resources Technol Innovat Ctr Southwest Land Space Ecol Rest Chengdu 610045 Peoples R China Xihua Univ Sch Comp & Software Engn Chengdu 610039 Peoples R China

Forest tree information is crucial for monitoring forest resources and developing forestry management strategies. Airborne laser scanning is an efficient and rapid method for acquiring 3-D point cloud data of forests, making up for the shortcomings of optical remote sensing. Especially for unmanned aircraft vehicle laser scanning, the high-density point cloud provides the possibility of a detailed depiction of forest structures. Extracting individual trees from point cloud data is a prerequisite for fine forestry research. Currently, existing methods fail to fully utilize the height information, density information, and vertical structure details of tree crowns within point cloud data, resulting in complex algorithmic processes and reduced reliability. This study proposes a novel method that combines the canopy height model (CHM) and point cloud data for segmenting individual trees and employs height and density information for morphological detection of tree canopies. First, based on CHM-based segmentation, the point cloud density feature is introduced to identify the wrong segmented trees. Next, the height and density information are combined to guide the selection of vertical profiles. Finally, a 3-D morphological detection is conducted on the profiles to extract the potential trees. We validate the accuracy of the method on German forests. The results indicated that the average F1-scores of six study plots were 0.95, which improved by 5% after the introduction of density information. The primary source of errors was the irregularity of tree morphology. Our method produced reasonable results across different parameter settings, demonstrating its insensitivity to parameter configurations.

关键词： Vegetation Forestry Point cloud compression Accuracy Surveys Three-dimensional displays Morphology Laser radar Autonomous aerial vehicles clustering algorithms Density individual tree segmentation morphology region growing unmanned aerial vehicle (UAV) light detection and ranging (LiDAR)

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Visualizing Temporal Topic Embeddings with a Compass

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2025年第1期31卷 272-282页

作者： Palamarchuk, Daniel Williams, Lemara Mayer, Brian Danielson, Thomas Faust, Rebecca Deschaine, Larry North, Chris Virginia Polytech Inst & State Univ Blacksburg VA 24060 USA Savannah River Natl Lab Savannah GA USA Tulane Univ Tulane LA USA

Dynamic topic modeling is useful at discovering the development and change in latent topics over time. However, present methodology relies on algorithms that separate document and word representations. This prevents the creation of a meaningful embedding space where changes in word usage and documents can be directly analyzed in a temporal context. This paper proposes an expansion of the compass-aligned temporal Word2Vec methodology into dynamic topic modeling. Such a method allows for the direct comparison of word and document embeddings across time in dynamic topics. This enables the creation of visualizations that incorporate temporal word embeddings within the context of documents into topic visualizations. In experiments against the current state-of-the-art, our proposed method demonstrates overall competitive performance in topic relevancy and diversity across temporal datasets of varying size. Simultaneously, it provides insightful visualizations focused on temporal word embeddings while maintaining the insights provided by global topic evolution, advancing our understanding of how topics evolve over time.

关键词： Visualization Compass Vectors Streams Electronic mail clustering algorithms Training High dimensional data Dynamic topic modeling Cluster analysis

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Colorful Star Motif Counting: Concepts, algorithms and Applications

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第3期37卷 1105-1125页

作者： Qin, Hongchao Sen, Gao Li, Rong-Hua Chen, Hongzhi Yuan, Ye Wang, Guoren Beijing Inst Technol Dept Comp Sci Beijing 100811 Peoples R China Natl Univ Singapore Sch Comp Comp Sci Singapore 119077 Singapore ByteDanceInfrastruct Team Beijing 100098 Peoples R China

A colorful star motif is a star-shaped graph where any two nodes have different colors. Counting the colorful star motif can help to analyze the structural properties of real-life colorful graphs, model higher-order clustering, and accelerate the mining of the densest subgraph exhibiting h-clique characteristics in graphs. In this manuscript, we introduce the concept of colorful h-star in a colored graph and proposes two higher-order cohesive subgraph models, namely colorful h-star core and colorful h-star truss. We show that the colorful h-stars can be counted and updated very efficiently using a novel dynamic programming (DP) algorithm. Based on the proposed DP algorithm, we develop a colorful h-star core decomposition algorithm which takes O(hm) time, O(hn+m) space;and a colorful h-star truss decomposition algorithm which takes O(hm(1.5)) time, O(hm) space, where mm and n denote the number of edges and nodes of the graph respectively. Moreover, we also propose a graph reduction technique based on our colorful h-star core model to accelerate the computation of the approximation algorithm for h-clique densest subgraph mining. The results of comprehensive experiments on 11 large real-world datasets demonstrate the efficiency, scalability and effectiveness of the proposed algorithms.

关键词： Stars Approximation algorithms Image color analysis Color Heuristic algorithms clustering algorithms Computational modeling Wireless communication Dynamic programming Analytical models Colorful star K-core K-truss H-clique densest subgraph

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Brain-inspired multimodal motion and fine-grained action recognition

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FRONTIERS IN NEUROROBOTICS 2025年 18卷 1502071页

作者： Li, Yuening Yang, Xiuhua Chen, Changkui Wuhan Sports Univ Wuhan Peoples R China Shandong Adm Inst Communist Party China Sch Phys Educ & Training Party Sch Shandong Prov Comm Jinan Shandong Peoples R China

Introduction Traditional action recognition methods predominantly rely on a single modality, such as vision or motion, which presents significant limitations when dealing with fine-grained action recognition. These methods struggle particularly with video data containing complex combinations of actions and subtle motion *** Typically, they depend on handcrafted feature extractors or simple convolutional neural network (CNN) architectures, which makes effective multimodal fusion challenging. This study introduces a novel architecture called FGM-CLIP (Fine-Grained Motion CLIP) to enhance fine-grained action recognition. FGM-CLIP leverages the powerful capabilities of Contrastive Language-Image Pretraining (CLIP), integrating a fine-grained motion encoder and a multimodal fusion layer to achieve precise end-to-end action recognition. By jointly optimizing visual and motion features, the model captures subtle action variations, resulting in higher classification accuracy in complex video *** and discussion Experimental results demonstrate that FGM-CLIP significantly outperforms existing methods on multiple fine-grained action recognition datasets. Its multimodal fusion strategy notably improves the model's robustness and accuracy, particularly for videos with intricate action patterns.

关键词： brain-inspired multimodal action recognition CLIP clustering algorithms

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4D-CS: Exploiting Cluster Prior for 4D Spatio-Temporal LiDAR Semantic Segmentation

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IEEE ROBOTICS AND AUTOMATION LETTERS 2025年第1期10卷 468-475页

作者： Zhong, Jiexi Li, Zhiheng Cui, Yubo Fang, Zheng Northeastern Univ Fac Robot Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Minist Educ Shenyang 110819 Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110819 Peoples R China

Semantic segmentation of LiDAR points has significant value for autonomous driving and mobile robot systems. Most approaches explore spatio-temporal information of multi-scan to identify the semantic classes and motion states for each point. However, these methods often overlook the segmentation consistency in space and time, which may result in point clouds within the same object being predicted as different categories. To handle this issue, our core idea is to generate cluster labels across multiple frames that can reflect the complete spatial structure and temporal information of objects. These labels serve as explicit guidance for our dual-branch network, 4D-CS, which integrates point-based and cluster-based branches to enable more consistent segmentation. Specifically, in the point-based branch, we leverage historical knowledge to enrich the current feature through temporal fusion on multiple views. In the cluster-based branch, we propose a new strategy to produce cluster labels of foreground objects and apply them to gather point-wise information to derive cluster features. We then merge neighboring clusters across multiple scans to restore missing features due to occlusion. Finally, in the point-cluster fusion stage, we adaptively fuse the information from the two branches to optimize segmentation results. Extensive experiments confirm the effectiveness of the proposed method, and we achieve state-of-the-art results on the multi-scan semantic and moving object segmentation on SemanticKITTI and nuScenes datasets.

关键词： Feature extraction Semantics Laser radar Semantic segmentation Point cloud compression Motion segmentation clustering algorithms Object segmentation Convolution Aggregates Semantic scene understanding deep learning methods clustering

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Optimizing Athlete Training and Injury Mitigation Using Fuzzy Information-Based Skeletal Motion Analysis

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IEEE ACCESS 2025年 13卷 460-470页

作者： Shou, Zaiyong Hou, Ying Chongqing Normal Univ Coll Phys Educ & Hlth Sci Chongqing 401331 Peoples R China Sichuan Int Studies Univ Dept Phys Educ Chongqing 400031 Peoples R China

In modern sports, enhancing athletic performance and minimizing the risk of injuries is crucial to optimize athlete training and injury mitigation. An athlete's efficiency, endurance, and recovery times can be improved using targeted training programs. This study aims to develop a new strategy to study different training programs for injury mitigation based on an intuitionistic fuzzy rough set (IFRS). An IFRS is vital to reduce uncertainty during data collection and analysis. IFRS bridges two significant frameworks used to reduce uncertainty in information, i.e., the intuitionistic fuzzy set (IFS) and the rough set (RS). First, we develop new similarity measures based on IFRS to enhance accuracy. Then, we use the developed similarity measures to choose the most suitable training program for injury mitigation of athletes according to the demands of modern sports. To do so, different training programs are assessed based on several crucial factors using the proposed measures. The developed measures enhance the accuracy of data collection concerning various uncertain factors crucial in injury mitigation. An example is solved to show the application of the developed measures in injury mitigation. The main contribution of the proposed study is to enhance accuracy when human opinion is modelled.

关键词： Injuries Training Prevention and mitigation Uncertainty clustering algorithms Fuzzy sets Decision making Sports Rough sets Data collection Injury mitigation fuzzy information aggregation fuzzy information handling multi-attribute group decision-making

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Resource Allocation for User-Centric Cell-Free Massive MIMO Downlink

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IEEE SENSORS JOURNAL 2025年第1期25卷 1682-1693页

作者： Wang, Xuan Liu, Yi Li, Cong Xidian Univ State Key Lab Integrated Serv Network Xian 710071 Peoples R China Acad Space Elect Informat Technol Xian 710100 Peoples R China

Given the strong macro-diversity gain, cell-free massive multiple-input-multiple-output (MIMO) with many access points (APs) can provide a more uniform service level for randomly distributed users or user sensors (USs). There has, however, been little implemented in practical applications, due to the challenges of substantial backhaul overhead and high signal processing demands in cell-free massive MIMO systems. In this article, we propose a resource allocation scheme that incorporates the effective algorithms for AP selection, pilot assignment, and power allocation, to improve the performance of user-centric cell-free massive MIMO systems. In particular, a grouping-based AP selection algorithm is investigated to select APs with significant gain contributions to USs. Based on this, we propose an improved K-means clustering (I-KMC)-based pilot assignment algorithm to reduce pilot contamination caused by pilot reuse. To improve the reliability of data transmission, we present two power allocation algorithms to maximize the achievable sum-rate, namely sequential convex approximation in combination with geometric programming (SCA-GP) approach, and nonmonotone accelerated proximal gradient (NAPG) approach with line search. Results demonstrate that the proposed scheme achieves similar sum-rate performance to the scenario where all USs are served by all APs, but with significantly lower power consumption.

关键词： Massive MIMO Resource management Downlink Backhaul networks Signal processing algorithms Sensors clustering algorithms Uplink Fading channels Array signal processing Cell-free massive multiple-input-multiple-output (MIMO) grouping-based access point (AP) selection improved K-means clustering (I-KMC) nonmonotone accelerated proximal gradient (NAPG) sequential convex approximation in combination with geometric programming (SCA-GP)

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Optimizing Clustered Cell-Free Networking for Sum Ergodic Capacity Maximization With Joint Processing Constraint

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第1期24卷 571-584页

作者： Xia, Funing Wang, Junyuan Dai, Lin Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Inst Adv Study Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai 201804 Peoples R China City Univ Hong Kong Dept Elect Engn SAR Hong Kong Peoples R China

Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. Different from the existing works that aim to maximize the number of subnetworks without considering the limited processing capability of base-stations (BSs), this paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of user equipments (UEs) in each subnetwork to constrain the joint processing complexity. By successfully transforming the combinatorial NP-hard clustered cell-free networking problem into an integer convex programming problem, the problem is solved by the branch-and-bound method. To further reduce the computational complexity, a bisection clustered cell-free networking ((BCF)-F-2-Net) algorithm is proposed to decompose the network hierarchically. Simulation results show that compared to the branch-and-bound based scheme, the proposed (BCF)-F-2-Net algorithm significantly reduces the computational complexity yet achieves nearly the same network decomposition result. Moreover, our (BCF)-F-2-Net algorithm achieves near-optimal performance and outperforms the state-of-the-art benchmarks with up to 25% capacity gain.

关键词： clustering algorithms Wireless networks Partitioning algorithms Interference Cellular networks Simulation Signal processing algorithms Uplink Optimization Computer architecture Clustered cell-free networking sum ergodic capacity maximization joint processing constraint bisection algorithm

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A Distributed Robust Voltage Control Framework for Active Distribution Systems

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 1896-1906页

作者： Xu, Junjun Mu, Yu Cheng, Wei Gong, Guangjing Huang, Jungao Zhang, Tengfei Hu, Qinran Chen, Kang Wu, Andrew Yang Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China State Grid Elect Power Res Inst NARI Grp Corp Nanjing 211106 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Hong Kong Polytech Univ Sch Profess Educ & Execut Dev Hong Kong 999077 Peoples R China

Voltage fluctuations in active distribution systems (ADS) have increased with the high penetration of uncertain energy resources, making unified governance more challenging. This paper proposes a novel distributed robust voltage control (DRVC) method for ADS based on coordinated clustering of photovoltaic and energy storage systems, aiming to aggregate decentralized resources into interrelated clusters to achieve distributed voltage regulation. Firstly, an improved Louvain algorithm based clustering method for ADS is introduced, further incorporating the voltage sensitivity and regulation capability of generators into the modularized function. After that, a local robust voltage regulation model for each cluster is constructed, and the global multi-objective optimization function is solved through a distributed robust solution method for integrating intelligent optimization to further enhance the robustness. Case studies and results demonstrate the performance of the proposed voltage control framework in resisting uncertainties by contrast with existing models.

关键词： Voltage control clustering algorithms Data models Power system stability Sensitivity Reactive power Partitioning algorithms Robust control Regulation Microgrids Distributed generator distribution system robust voltage control uncertainty

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Community-Based Memetic Algorithm for Influence Maximization in Large-Scale Networks

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IEEE ACCESS 2025年 13卷 72754-72768页

作者： Roy, Mithun Pan, Indrajit Siliguri Inst Technol Siliguri 734009 West Bengal India RCC Inst Informat Technol Kolkata 700015 West Bengal India

Effective information diffusion across large-scale network is key for influence maximization. Recent research has shown a significant surge in interest in modeling, performance estimation, and seed identification across various networked systems. Moreover, a simulation of useful interactions among many significant groups within networks was developed to simulate real-world marketing and spreading information more accurately. A good diffusion model identifies the minimum number of effective seeds capable of achieving maximum diffusion effects across the network. Limited focus has been placed on measuring the strength of seeds in competitive spreading situations. There is a research gap in determining effective strategy for this purpose. This study proposes a memetic algorithm based on a community for large-scale social networks. The proposed algorithm optimizes the influence spread by identifying the most influential nodes among the communities, depending on their inter- or intra-community propagation dynamics. This algorithm combines the concept of genetic algorithm with a reachability-based local search method to accelerate the convergence process. This approach offers a robust method for maximizing the influence of network structure and interactions. An experimental evaluation on real-world social network datasets shows the performance superiority of this community-based memetic algorithm (CBMA-IM) over existing algorithms.

关键词： Social networking (online) Memetics Heuristic algorithms Genetic algorithms clustering algorithms Optimization Greedy algorithms Evolutionary computation Integrated circuit modeling Diffusion models Community structure influence maximization local search method memetic algorithm social structural strength

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