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hierarchical clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation

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REMOTE SENSING 2025年第1期17卷

作者： Hamzaoui, Yassir Civera, Marco Miano, Andrea Bonano, Manuela Fabbrocino, Francesco Prota, Andrea Chiaia, Bernardino Politecn Torino Dept Struct Bldg & Geotech Engn Corso Duca Abruzzi 24 I-10129 Turin Italy Telematic Univ Pegaso Ctr Direzionale Isola F2 Dept Engn I-80143 Naples Italy Consiglio Nazl Ric CNR Inst Electromagnet Sensing Environm Via Diocleziano 328 I-80124 Naples Italy Univ Naples Federico II Dept Struct Engn & Architecture Via Claudio 21 I-80125 Naples Italy

The conventional Structural Health Monitoring (SHM) framework focuses on individual structures. However, preliminary studies are required at a large territorial scale to effectively identify the most vulnerable elements. This becomes particularly challenging in urban settings, where numerous buildings of varied shapes, ages, and structural conditions are closely spaced from one another. A twofold task is therefore required: the automated identification and differentiation of various structures, coupled with a ranking system based on perceived structural risk, here assumed to be linked to their deformation patterns. It integrates displacement measurements acquired through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, specifically employing the full-resolution Small Baseline Subset (SBAS) approach coupled with hierarchical clustering. The effectiveness of this method is successfully demonstrated and validated in two selected areas of Rome, Italy, serving as case studies. The results of this vast-area scale monitoring can be used to select the constructions that need a more in-depth assessment.

关键词： machine learning hierarchical clustering Differential Interferometric Synthetic Aperture Radar (DInSAR) Small Baseline Subset (SBAS) COSMO-SkyMed QGIS built environment

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hierarchical clustering of groups' collaborative discourses during the computer-supported collaborative concept mapping process

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ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT 2025年第1期73卷 465-488页

作者： Ouyang, Fan Zheng, Luyi Dai, Xinyu Xu, Weiqi Jiao, Pengcheng Zhejiang Univ Coll Educ Hangzhou 310000 Zhejiang Peoples R China Hangzhou Cuiyuan No 1 Primary Sch Educ Grp Hangzhou 310000 Zhejiang Peoples R China Nashai Expt Sch Huiminqiao Primary Sch Campus Zhoushan 316000 Zhejiang Peoples R China Zhejiang Univ Ocean Coll Zhoushan 316021 Zhejiang Peoples R China

Computer-supported collaborative concept mapping (CSCCM), as one of the computer-mediated instruction and learning strategies, has been used to foster collaborative knowledge construction (CKC). Previous research has characterized groups based on final knowledge artifacts, products, or performances, rather than the temporal, process-oriented characteristics generated during the collaborative learning process. To fill this gap, this research clustered groups into distinct clusters based on the collaborative discourse data by using agglomerative hierarchical clustering approach, and examined the process characteristics of different clusters and associated performances. Four clusters were identified and labeled. Cluster 1, the high-performing cluster, was characterized as the actively-engaged, idea-centered, consensus-achieved, and socioemotional-engaged cluster. Cluster 2, the low-performing cluster, was characterized as the inactively-engaged, information-shared, goal-oriented, and reflection-revolved cluster. Cluster 3, the medium-performing cluster, was characterized as the inactively-engaged, problems-unsolved, and reflection-revolved cluster. Cluster 4, the medium-performing cluster, was characterized as the actively-engaged, idea-centered, and goal-oriented cluster. Based on the results, this research proposed instructional strategies and assessment implications for improving CSCCM research and practice.

关键词： Computer-supported collaborative learning Collaborative knowledge construction Computer-supported collaborative concept mapping Learning analytics hierarchical clustering

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Research on peanut pedigree analysis and variety identification methods based on deep features extraction and hierarchical clustering

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INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2025年第1期60卷

作者： Hua, Zhenlu Liu, Xiaoyang Zhang, Runzhe Jia, Xiaofei Zhao, Longgang Qiao, Lixian Deng, Limiao Qingdao Agr Univ Sch Sci & Informat Qingdao 266109 Peoples R China Northwestern Univ Sch Profess Studies Chicago IL USA Qingdao Agr Univ Coll Food Sci & Engn Qingdao Peoples R China Qingdao Agr Univ Coll Prataculture Qingdao Peoples R China Qingdao Agr Univ Coll Agron Qingdao Peoples R China

In order to screen high-quality peanut pod varieties on food processing production lines and promote the sustainable development of the peanut as well as the expansion of its consumer market, this study improved the recognition ability of peanut pod varieties and the efficiency of deep feature extraction by optimising the ResNet50 deep learning network model. Experimental results showed that the accuracy of the optimised network model reached 91.6%, which was 2.1% higher than the original ResNet50 model. Additionally, this study extracted the deep features of the appearance morphology of peanut pods, used the agglomerative clustering method to explore the genetic relationship of hybrid offspring varieties under the same line, and constructed a pedigree diagram containing 18 varieties. These research findings provide a crucial scientific foundation for the cultivation of high-quality peanut varieties. The selected high-quality peanuts not only enhance the added value of the peanut industry but also further expand the market potential of peanut by-products.

关键词： pedigree analysis hierarchical clustering variety identification ResNet50 peanut quality

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Module Partition of Mechatronic Products Based on Core Part hierarchical clustering and Non-Core Part Association Analysis

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APPLIED SCIENCES-BASEL 2025年第5期15卷 2322-2322页

作者： Wang, Shuai Song, Yi-Fei Zou, Guang-Yu Man, Jia-Xiang Xuzhou Univ Technol Sch Environm Engn Xuzhou 221018 Peoples R China China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Peoples R China Xuzhou Univ Technol Sch Mechatron Engn Xuzhou 221018 Peoples R China

Production using modular architecture can not only shorten the product development cycle and improve the efficiency of product development, but also facilitate the upgrading of a product's main functions and the recycling of materials. However, mechatronic products are plagued by various problems, such as greater difficulty in development and longer product development cycles due to their large numbers of parts with intricate internal relationships. However, the existing modular design method still faces problems when dealing with the modular design of mechatronic products. The structure of mechanical and electrical products is very complex, which is not conducive to the establishment of a model, and complex structural models lead to low efficiency and poor accuracy of module identification. Therefore, we propose an integrated module division method for mechatronic products based on core part hierarchical clustering and non-core part association analysis. Firstly, the core part screening method is used to simplify the structural model of mechatronic products and reduce the difficulty of modeling. Then, based on the core parts, the corresponding product design structural matrix (DSM) model is established. Secondly, the hierarchical clustering algorithm is used to obtain the module division scheme of different levels of mechatronic products, and the optimal modular scheme is obtained through an evaluation of modularity and a rationality analysis of module structure. Finally, based on the analysis of the association strength between the non-core parts and the existing modules, the non-core parts are classified into the corresponding product modules, and the final modularization scheme is obtained. A case study demonstrates the feasibility of the proposed method through the modular design of an electric bicycle.

关键词： module partition mechatronic products hierarchical clustering association analysis

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Optimizing capacity expansion modeling with a novel hierarchical clustering and systematic elbow method: A case study on power and storage units in Spain

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ENERGY 2025年 323卷

作者： Riyahi, Milad Martin, Alvaro Gutierrez Univ Politecn Madrid Higher Tech Sch Engineers Telecommun Madrid Spain

To reduce the computational complexity of Capacity Expansion Models, the planning horizon must be simplified into representative time-periods. Also, to accurately model the expansion of power and storage units, these representative time periods must reveal the mid-term dynamics of the planning horizon. In this paper, a novel hierarchical clustering algorithm is presented that retains the chronology of the original data in creating representative time periods. The proposed algorithm, first, determines the optimal number of clusters with a modified elbow method, enhanced with a stopping criterion to prevent it from running uselessly. The designed stopping criterion works based on percentage variance and runtime to determine the number of clusters systematically. Then, the proposed clustering algorithm employs a novel selection strategy based on the Euclidean distance, k-Medoid, and k-Means to determine the most proper representative vector in each cluster. In this way, it reduces the computational time of capacity expansion models while maintaining the accuracy of final answers. To evaluate its performance, the proposed algorithm is tested on energy data, including demand, photovoltaic, wind, and hydrogen generation, across hourly, daily, and weekly time periods. Also, the performance of the proposed clustering algorithm in selecting the number of clusters and clustering is compared with the results of some well-known methods on accuracy and runtime metrics. Numerical results show that the proposed clustering method selects a more appropriate number of clusters in less computational time than other systematic approaches. Moreover, findings on clustering show that the proposed algorithm achieves the highest accuracy on weekly and daily time periods compared to well-known clustering methods, with the error rate of 118 % and 52 %, respectively. Furthermore, implementation results show that the proposed clustering reduces the computational time of capacity expansion model

关键词： Capacity expansion model hierarchical clustering Euclidean distance Elbow method Stopping criterion K-medoids K -means

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Study on modular design methodology of marine SMR system based on fuzzy hierarchical clustering and improved genetic algorithm

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PROGRESS IN NUCLEAR ENERGY 2025年 185卷

作者： Tang, Zhihong Wang, Liyuan Guo, Shusheng Liang, Guopeng Zhang, Wenjun Zhang, Lide Rui, Min Guan, Guan Wang, Yunlong China Nucl Power Technol Res Inst Co Ltd Shenzhen 5128026 Peoples R China Dalian Univ Technol Sch Naval Architecture Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China

Marine Small Modular Reactors (MSMR) integrate SMR technology with ship technology, offering unique value in meeting the energy demands of the open ocean and remote islands. However, the design and construction of MSMR face challenges such as space constraints, complex system integration, and the need to adapt to advanced ship modular construction technologies. Therefore, efficient modular partitioning methods are required to enhance overall efficiency and reliability. The module partitioning of MSMR systems takes into account multiple factors and is a combinatorial optimization problem with performance constraints. This study aims to reflect the internal structure of the system hierarchical tree, provide clear guidance for module partitioning, and improve the computational efficiency of solving combinatorial problems. This paper propose a module division and optimization method for MSMR systems based on fuzzy hierarchical clustering and a genetic algorithm. Initially, the components of the small modular reactor power plant system are clustered into modules of different levels using fuzzy hierarchical clustering. Subsequently, a genetic algorithm is employed to solve the combinatorial optimization problem of the module division scheme, resulting in the optimal division scheme. The feasibility and effectiveness of the method are verified through the modular case of the Radioactive Waste Gas System (WGS). This method can provide guidance for the modularization design of the entire ocean modular reactor system. The method provided in this article can provide a research foundation for future modular design of MSMR and improve design efficiency.

关键词： MSMR Modular design hierarchical clustering Genetic algorithm

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A robust hierarchical clustering algorithm for automatic identification of clusters

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APPLIED INTELLIGENCE 2025年第6期55卷 1-20页

作者： Long, Jianwu Wang, Qiang Liu, Luping Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China

Aggregation-based hierarchical clustering algorithms are widely used in data analysis due to their robust clustering performance. Although some existing hierarchical clustering methods can identify the number of clusters in a dataset, most are only effective for well-separated clusters and struggle to identify the number of clusters in complex datasets, particularly non-convex noisy datasets. To address these shortcomings, this paper proposes a robust hierarchical clustering algorithm for automatic identification of clusters(RHCAIC), which can identify the optimal number of clusters while providing reliable clustering results. To reduce the impact of noise in clustering, the method first calculates reverse density and designs a dynamic noise discriminator to denoise the dataset. Based on the fact that more similar points have a higher probability of being clustered into the same cluster among multiple results of hierarchical clustering, a robust solution was designed. After constructing a directed graph using the kNN algorithm, the graph merging process is performed by iteratively traversing the directed edges. During this process, the number of clusters is identified, and the clustering results of the denoised dataset are obtained. Finally, by incorporating density information into the noise clustering, the final clustering results are obtained. A series of experiments conducted on 12 synthetic datasets and 8 real datasets demonstrate that, compared to seven other benchmark algorithms, the RHCAIC algorithm not only accurately identifies the number of clusters in the dataset but also produces better clustering results.

关键词： Natural neighbors Denoising kNN directed graph hierarchical clustering Identifying the number of clusters

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Parallelly Running and Privacy-Preserving Agglomerative hierarchical clustering in Outsourced Cloud Computing Environments

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IEEE TRANSACTIONS ON BIG DATA 2025年第1期11卷 174-189页

作者： Park, Jeongsu Lee, Dong Hoon Korea Univ Grad Sch Informat Secur Seoul 02841 South Korea

As a Big Data analysis technique, hierarchical clustering is helpful in summarizing data since it returns the clusters of the data and their clustering history. Cloud computing is the most suitable option to efficiently perform hierarchical clustering over numerous data. However, since compromised cloud service providers can cause serious privacy problems by revealing data, it is necessary to solve the problems prior to using the external cloud computing service. Privacy-preserving hierarchical clustering protocol in an outsourced computing environment has never been proposed in existing works. Existing protocols have several problems that limit the number of participating data owners or disclose the information of data. In this article, we propose a parallelly running and privacy-preserving agglomerative hierarchical clustering (ppAHC) over the union of datasets of multiple data owners in an outsourced computing environment, which is the first protocol to the best of our knowledge. The proposed ppAHC does not disclose any information about input and output, including the data access patterns. The proposed ppAHC is highly efficient and suitable for Big Data analysis to handle numerous data since its cost for one round is independent of the amount of data. It allows data owners without sufficient computing capability to participate in a collaborative hierarchical clustering.

关键词： Cloud computing Protocols Big Data Data privacy Servers Cryptography Computational modeling hierarchical clustering privacy-preserving computation cloud computing

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Seasonality of precipitation isotopes associated with water vapor budget: A hierarchical clustering-based zoning in China

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ATMOSPHERIC RESEARCH 2025年 317卷

作者： Jie, Haixiang Shi, Yudong Wang, Shengjie Lei, Shijun Qian, Yuqing Li, Hongyang Che, Cunwei Zhu, Xiaofan Zhang, Mingjun Northwest Normal Univ Coll Geog & Environm Sci Key Lab Resource Environm & Sustainable Dev Oasis Lanzhou 730070 Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Lanzhou 730000 Peoples R China

As a natural tracer of the water cycle, the stable water isotopes in precipitation usually show spatial dependency. The geographical zoning of precipitation isotopes provides a spatial perspective to understand the large-scale climatological background associated with isotopic fractionation. Here a Ward hierarchical clustering analysis was conducted on a monthly delta 18O product across China with a spatial resolution of 10 ' x 10 ' (latitude by longitude), and a new zoning scheme for precipitation isotopes in China was designed. According to the intraannual isotopic variations, the terrestrial area of China was divided into seven subregions in this new scheme, i.e., Northeast China (NE), North China (N), South China (S), arid Northwest China (NWa), extreme arid Northwest China (NWea), cold Southwest China (SWc), and humid Southwest China (SWh). We examined the monthly variations in precipitation isotopes and local meteoric water lines for each subregion. The subregional slopes of local meteoric water lines range between 6.52 (Northeast China) and 8.20 (South China). According to the climate reanalysis data, we quantified the input and output of atmospheric water vapor from the meridional and zonal boundaries for each subregion, and found that the regional isotopic characteristics are consistent with the water vapor budgets. The scheme reveals the large-scale moisture transport of monsoon systems and the topographic effect of plateaus and basins. The new zoning scheme in this study provides fundamental isotopic information for diverse climate backgrounds in China, and is useful for regional studies in the atmospheric, hydrological and ecological fields.

关键词： Precipitation isotopes hierarchical clustering Zoning Water vapor budget China

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A multi-radar emitter sorting and recognition method based on hierarchical clustering and TFCN

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DIGITAL SIGNAL PROCESSING 2025年 160卷

作者： Zhang, Jiaxiang Wang, Bo Han, Xinrui Zhao, Min Liang, Zhennan Chen, Xinliang Liu, Quanhua Beijing Inst Technol Sch Informat & Elect Radar Res Lab Beijing 100081 Peoples R China Yangtze Delta Reg Acad Beijing Inst Technol Jiaxing 314003 Peoples R China Beijing Inst Technol Chongqing Innovat Ctr Chongqing 401120 Peoples R China Beijing Inst Technol Key Lab Elect & Informat Technol Satellite Nav Beijing 100081 Peoples R China

Radar emitter recognition is an important part of the electronic attack and defense system, and its key task is to sort and identify various information of emitters from mixed pulse streams. Pulse descriptor word, as an easily obtainable feature, is often used for sorting and recognition. Aiming at the problems of high complexity and difficulty in handling intra-class clustering and inter-class aliasing of existing sorting methods, a hierarchical clustering method based on Kernel Density Estimation-Kullback-Leibler Divergence-Template Matching (KDEKLD-TM) is proposed. Firstly, the down-sampled data is used to construct central clusters, greatly improving the processing speed. Then, with probability theory as the theoretical support, inter-class clustering on all samples is performed based on maximum posterior probability. Finally, based on cluster distribution similarity and periodic template matching, intra-class merging and inter-class deinterleaving are completed. After sorting, considering the periodic differences in pulse repetition intervals among different types of emitter and the insufficient attention paid by existing recognition methods to this feature, a time-frequency convolution network (TFCN) based emitter recognition method is proposed for the first time in terms of pulse description word. Using time-frequency analysis (TFA) to extract periodic features and using convolutional neural networks (CNN) for classification, the onedimensional sequence classification problem is treated as a two-dimensional image classification problem. The proposed method is simulated in a typical scenario with intra-class clustering and inter-class aliasing. The results show that the proposed method can sort a total of 2.06 million aliased samples composed of 10 classes within 58.61 s, and the recognition accuracy reaches 96.33%. The comparison with the baseline method proves the effectiveness and progressiveness of the proposed method. Finally, the sorting and recognition

关键词： Radar emitter sorting and recognition hierarchical clustering Time-frequency convolution network Intra-class clustering Inter-class aliasing

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