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Hierarchical clustering-Based Algorithm for Deployment Planning of LoRa Gateway

IEEE ACCESS 2025年 13卷 25837-25857页

作者： Lutfie, Kalam Adhiansyah Purnamasari, Prima Dewi Gunawan, Dadang Enriko, I. Ketut Agung Juwono, Filbert H. Univ Indonesia Dept Elect Engn Depok City 16424 Indonesia Inst Teknol Telkom Purwokerto Dept Telecommun & Elect Engn Purwokerto Indonesia Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Peoples R China

Long-Range Wide Area Network (LoRaWAN) has become a promising communication method for the Internet of Things (IoT) system since it is capable of long-range communication with low power usage. Presently, LoRa gateway (GW) deployment in Indonesia relies on conventional methods, i.e., predicting coverage without incorporating formulas specific to LoRa GW performance or accounting for the area type. This research aims to deploy the LoRa GW using a machine-learning algorithm to cover every sensor demand. In this paper, we simulate, deploy, and measure the signal strength transmission between recommended LoRa GWs and end device (ED) demands to ensure the algorithm works and is usable on each different environment characteristic. The results of these experiments show that the algorithm can generate coordinate point recommendations for the LoRa GW to cover all ED demands in three different characteristic areas.

关键词： LoRaWAN Logic gates Internet of Things clustering algorithms Couplings Wireless communication Actuators Wireless sensor networks Sensitivity Propagation losses LoRa machine learning IoT

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Workflow for mitigating shape diversity in free-form surface panels through clustering and evaluation

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2025年

作者： Liu, Hequn Liu, Yan Southeast Univ Sch Architecture 2 Sipailou Nanjing 210018 Peoples R China Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

With the advancement of digital technologies, free-form surfaces are increasingly utilized in architectural design. Constructing such surfaces typically involves dividing them into numerous panels, which are often uniquely shaped, thereby significantly increasing fabrication and construction complexity, as well as adding to the economic burden. This paper introduces a workflow for optimizing free-form surface facades by reducing panel shape diversity. The proposed workflow consists of four key components: similarity measures (Euclidean and Manhattan distances), clustering algorithms (hierarchical clustering, K-means, and HDBSCAN), evaluation metrics (SC, DB, and CH), and validation through three case studies. The study confirms the correlation between clustering algorithm performance and surface type, highlighting its dependence on input data characteristics. The workflow is applied to the panel optimization of the Hongdao Railway Station project, validating its practical applicability by focusing on the selection of optimal algorithms and the configuration of clustering parameters. As a modular approach, it establishes a foundation for further refinement and extension of methods in future applications.

关键词： clustering algorithms validation metrics shape similarity assessment free-form surface

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A Radar Signal Deinterleaving Method Based on Enhanced Sparse Subspace clustering

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 2956-2972页

作者： Guo, Qiang Huang, Shuai Liu, Li-Chao Kaliuzhnyi, Mykola Douplii, Stepan Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Kharkiv Natl Univ Radio Elect Adv Res Lab UA-61166 Kharkov Ukraine Univ Munster Ctr Informat Technol D-48149 Munster Germany

In the modern information battlefield, radar signal deinterleaving plays a pivotal role in perceiving the electromagnetic spatial situation and grasping the initiative of the battle. The increasingly complex electromagnetic environment of modern battlefields leads to serious feature distribution space overlap of radar signal parameters, resulting in limited intraclass aggregation and interclass separability. Therefore, this article introduces the concept of a sparse subspace and proposes a radar signal deinterleaving method based on enhanced sparse subspace clustering. The generalized orthogonal matching pursuit algorithm is used in this study to construct a complex network on the basis of the self-expressiveness property among radar pulses in the same subspace, reducing the connection of pulses from different radars and thus decreasing computational complexity. The graph segmentation concept is incorporated into this algorithm, and Laplacian spectrum decomposition is employed to mine the geometric correlation from radar pulse signals. This approach enables efficient radar signal deinterleaving under nonideal conditions. Simultaneously, an algorithm based on an energy-corrected splitting threshold is proposed to calculate the number of cluster centers accurately. The proposed method effectively addresses the deinterleaving error issue caused by overlapping or missing working state parameters in intercepted complex radar pulse signals, achieving a deinterleaving accuracy of 94.74% for ten interleaved radar pulse signals with severe parameter feature overlap.

关键词： Radar clustering algorithms Matching pursuit algorithms Complex networks Approximation algorithms Electromagnetics Radar countermeasures Computational complexity Accuracy Spaceborne radar Complex network graph segmentation parameter feature overlap radar signal deinterleaving sparse subspace clustering

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clustering Algorithm by Boundary Detection Base on Entropy of KNN

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IEEE ACCESS 2025年 13卷 62353-62366页

作者： Ding, Jiaman Yin, Jinyuan Jia, Lianyin Fu, Xiaodong Wang, Hongbin Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Peoples R China Kunming Univ Sci & Technol Artificial Intelligence Key Lab Yunnan Prov Kunming 650500 Peoples R China

clustering analysis has been widely applied in various fields, and boundary detection based clustering algorithms have shown effective performance. In this work, we propose a clustering algorithm by boundary detection based on entropy of KNN (CBDEK). A border point contains only the nearest neighbors within a specific directional range. Thus, we define entropy of KNN (EK) to accurately identify the boundary of the cluster. Since entropy has the property of measuring uncertainty, it can be used to quantify the possibility that a point is a border point. A lower EK indicates an uneven neighbor distribution, increasing the possibility of the point being a border point. Then, the border points are clustered based on the directional similarity of their nearest neighbors. Specifically, if a border point is a neighbor of another border point and most of their nearest neighbors show directional similarity, they are considered to belong to the same cluster. Furthermore, we assign the label of a border point to the interior points located within the maximum nearest neighbor sub-block of this border point to facilitate an efficient allocation for the remaining points (interior points). In addition, our algorithm incorporates noise mitigation techniques using average distance and box plot analysis. The effectiveness of CBDEK is proven by a comparative evaluation of nine algorithms on 24 datasets.

关键词： clustering algorithms Nearest neighbor methods Entropy Noise Object recognition Shape Vectors Prevention and mitigation Noise measurement Uncertainty Boundary detection box plot clustering directional similarity entropy of KNN

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Between/Within View Information Completing for Tensorial Incomplete Multi-View clustering

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 1538-1550页

作者： Yao, Mingze Wang, Huibing Chen, Yawei Fu, Xianping Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Liaoning Peoples R China

Incomplete Multi-view clustering (IMvC) receives increasing attention due to its effectiveness in solving data-missing problems. With the information loss in incomplete situations, the core of IMvC needs to consider effectively overcoming the challenge of missing views, that is, exploring the underlying correlations from available data and recovering the missing information. However, most existing IMvC methods overemphasize the recovery-first principle with integrating the existing data from different views while neglecting the influence of view consistency in IMvC task together with valuable within view information. In this paper, a novel Between/Within View Information Completing for Tensorial Incomplete Multi-view clustering (BWIC-TIMC) has been proposed, in which between/within view information is jointly exploited for effectively completing the missing views. Specifically, the proposed method designs a dual tensor constraint module, which focuses on simultaneously exploring the view-specific correlations of incomplete views and enforcing the between view consistency across different views. With the dual tensor constraint, between/within view information can be effectively integrated for completing missing views for IMvC task. Furthermore, in order to balance different contributions of multiple views and alleviate the problem of feature degeneration, BWIC-TIMC implements an adaptive fusion graph learning strategy for consensus representation learning. Extensive comparative experiments with the-state-of-art baselines can demonstrate the effectiveness of BWIC-TIMC.

关键词： Tensors Correlation Bipartite graph Perturbation methods Kernel Fast Fourier transforms clustering methods clustering algorithms Web pages Vectors Between/within view information incomplete multi-view clustering (IMvC) dual tensor constraint

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Time-Domain-clustering Based Dynamic Channel Emulation for Millimeter-Wave MIMO Over-the-Air Testing

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第4期73卷 2086-2096页

作者： Jiang, Zhengbo Zhao, Wentao Guo, Chong Yan, Mengjie Hao, Zhang-Cheng Hong, Wei Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Nanjing Meatron Elect Co Ltd Nanjing 210096 Peoples R China

This article presents a novel methodology to emulate dynamic channels within the multi-probe anechoic chamber (MPAC) framework for over-the-air (OTA) testing. The approach integrates time-domain clustering and multi-objective optimization algorithms to emulate dynamic channels precisely. Simulations and experimental validation have been provided to demonstrate the effectiveness of the proposed method, where power-angular-spectrum (PAS) similarity percentage (PSP) values surpassing 87% can be consistently achieved. The time-domain clustering and probe selection technique significantly contribute to the reduction of emulation complexity by compressing both temporal and spatial domains of the channel. Consequently, this strategy enhances the feasibility of system performance testing in dynamic propagation scenarios within the MPAC and offers a novel framework for the emulation of dynamic channels in MPAC OTA testing.

关键词： Probes Testing Emulation Millimeter wave communication clustering algorithms Time-domain analysis Heuristic algorithms Channel emulation clustering dynamic channel multi-probe anechoic chamber (MPAC) over-the-air (OTA) testing

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Characterization and clustering of rock discontinuity sets:A review

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Journal of Rock Mechanics and Geotechnical Engineering 2025年第2期17卷 1240-1262页

作者： Changle Pu Jiewei Zhan Wen Zhang Jianbing Peng College of Geological Engineering and Geomatics Chang'an UniversityXi'an710054China Key Laboratory of Western China's Mineral Resources and Geological Engineering Ministry of EducationChang'an UniversityXi'an710054China College of Construction Engineering Jilin UniversityChangchun130026China

The characterization and clustering of rock discontinuity sets are a crucial and challenging task in rock mechanics and geotechnical *** the past few decades,the clustering of discontinuity sets has undergone rapid and remarkable ***,there is no relevant literature summarizing these achievements,and this paper attempts to elaborate on the current status and prospects in this ***,this review aims to discuss the development process of clustering methods for discontinuity sets and the state-of-the-art relevant ***,we introduce the importance of discontinuity clustering analysis and follow the comprehensive characterization approaches of discontinuity data.A bibliometric analysis is subsequently conducted to clarify the current status and development characteristics of the clustering of discontinuity *** methods for the clustering analysis of rock discontinuities are reviewed in terms of single-and multi-parameter clustering ***-parameter methods can be classified into empirical judgment methods,dynamic clustering methods,relative static clustering methods,and static clustering methods,reflecting the continuous optimization and improvement of clustering ***,this paper compares the current mainstream of single-parameter clustering methods with multi-parameter clustering *** is emphasized that the current single-parameter clustering methods have reached their performance limits,with little room for improvement,and that there is a need to extend the study of multi-parameter clustering ***,several suggestions are offered for future research on the clustering of discontinuity sets.

关键词： Discontinuity clustering clustering algorithms Discontinuity characterization Orientation analysis Rock mass

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CECEHO-GCS: A New Green Energy-Efficient clustering Protocol Based on Intelligent Optimization Theory in Industrial IoT

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IEEE INTERNET OF THINGS JOURNAL 2025年第8期12卷 10907-10919页

作者： Zhou, Peng Cao, Qike Cao, Bingyu Chen, Wei Jin, Bo Zhao, Fengda Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Korla 841000 Xinjiang Peoples R China Henan Inst Sci & Technol Xinxiang 453003 Henan Peoples R China Xinjiang Coll Sci & Technol Sch Informat Sci & Engn Korla 841000 Xinjiang Peoples R China Univ Coimbra Inst Syst & Robot Dept Elect & Comp Engn P-3030290 Coimbra Portugal Portuguese Natl Acad Sci P-1600477 Lisbon Portugal

To address the issues of battery dependence, energy consumption, and energy management imbalance in industrial wireless sensor networks (IWSNs), this study proposes a green and energy-saving multiobjective clustering scheme to improve network efficiency and reduce environmental pollution. Given that conventional methods struggle to effectively optimize IWSN clustering, this article specifically designs a novel multiobjective clustering model. During the optimization process, this model comprehensively considers four key performance indicators: 1) total remaining energy, 2) average network delay;3) average network packet loss rate;and 4) average distance from cluster heads (CHs) to the base station (BS), achieving holistic optimization of network performance. To further enhance clustering efficiency and network stability, this article also introduces a green energy-saving scheme based on the chaotic elite clone elephant herding optimization algorithm (i.e., CECEHO-GCS). This scheme ingeniously incorporates chaos operators in the initialization stage to enrich solution diversity and introduces clone and elite operators in the evolution stage, aiming to retain superior solutions and significantly enhance the algorithm's search capabilities. Through comparative experiments with four existing advanced clustering schemes: 1) LEACH-C;2) LEACH-R;3) ESCVAD;and 4) ARSH-FATI-CHS, the model and the algorithm proposed in this article, CECEHO-GCS, demonstrate significant advantages in improving network energy efficiency and service quality. Specifically, CECEHO-GCS has achieved an improvement of at least 19.27% in network lifespan and at least 16.89% in data throughput, opening up new avenues for green energy conservation and sustainable development in IWSNs.

关键词： clustering algorithms Optimization Wireless sensor networks Energy efficiency Energy consumption Green products Protocols Production Cloning Delays clustering protocol disposable battery elephant herding optimization green energy-saving industrial wireless sensor network (IWSN)

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Local High-Order Graph Learning for Multi-View clustering

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IEEE TRANSACTIONS ON BIG DATA 2025年第2期11卷 761-773页

作者： Wang, Zhi Lin, Qiang Ma, Yaxiong Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Northwest Minzu Univ Sch Math & Informat Lanzhou 730030 Peoples R China

As the accumulation of multi-view data continues to grow, multi-view clustering has become increasingly important in research fields like data mining. However, current methods have been criticized for their unsatisfactory performance, such as insufficient exploration of intra-view high-order relationships and poor characterization of inter-view diverse features. To overcome these challenges, we propose a novel approach called Local High-order Graph Learning for Multi-View clustering (LHGL_MVC). Our method aims to explore high-order relationships within a view while also considering diverse information between views. In LHGL_MVC, we learn the initial graphs of each view through self-representation, which are decomposed into consistent and diverse parts to better capture the diversity of different views. Based on consistent parts, we propose a novel local high-order graph learning approach to more effectively explore high-order relationships between samples within each view. At the same time, we leverage high-order relationships between views using the rotated tensor nuclear norm. Finally, we obtain a unified graph for clustering by fusing all consistent affinity graphs and their high-order graphs with adaptive weights. All procedures are integrated into an overall objective function, which mutually promotes during the optimization process. The comprehensive experiments conducted on eleven real-world datasets demonstrate that LHGL_MVC significantly outperforms existing algorithms in various measurements, highlighting the superiority of the proposed method.

关键词： Matrix decomposition clustering algorithms Tensors Feature extraction Correlation Vectors Data structures Multi-view clustering local high-order graph diversity tensor nuclear norm

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Image clustering With Transition Probabilities Learning

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2025年 34卷 1441-1453页

作者： Xue, Xingyu Zhao, Wenhui Gao, Quanxue Yang, Ming Deng, Cheng Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China Harbin Engn Univ Coll Math Sci Harbin 150001 Heilongjiang Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

Large-scale multi-view clustering for image data has achieved impressive clustering performance and efficiency. However, most methods lack interpretability in clustering and do not fully consider the complementarity of distributions between different views. To address these problems, we introduce Multi-View clustering with Transition Probabilities Learning (MVC-TPL). Specifically, we construct an anchor graph factorization model from the perspective of transition probabilities, while simultaneously learning transition probability matrices from samples to clusters and from anchor points to clusters, serving as soft label matrices for samples and anchor points, respectively. This model enables one-step label acquisition and provides the model with a sound probability interpretation. Moreover, since the clusters of samples and anchor points should be consistent across all views, we employ Schatten p-norm regularization on the two matrices, effectively mining the complementary information distributed among the views, thereby aligning the labels across views more consistently. Comprehensive testing on four small-scale datasets and three large-scale datasets confirms the effectiveness of this model.

关键词： Tensors Bipartite graph Data models clustering algorithms Probabilistic logic Computational modeling Data mining clustering methods Training Matrix decomposition Multi-view clustering anchor graph transition probability

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