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Wireless Sensor Network Lifetime Enhancement Using Modified clustering and Scheduling algorithm

Circuits and Systems 2016年第8期7卷 1787-1793页

作者： K. Ramesh K. Somasundaram Department of IT Nandha Engineering College Erode India Department of CSE Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India

Random distribution of sensor nodes in large scale network leads redundant nodes in the application field. Sensor nodes are with irreplaceable battery in nature, which drains the energy due to repeated collection of data and decreases network lifetime. Scheduling algorithms are the one way of addressing this issue. In proposed method, an optimized sleep scheduling used to enhance the network lifetime. While using the scheduling algorithm, the target coverage and data collection must be maintained throughout the network. In-network, aggregation method also used to remove the unwanted information in the collected data in level. Modified clustering algorithm highlights three cluster heads in each cluster which are separated by minimum distance between them. The simulation results show the 20% improvement in network lifetime, 25% improvement in throughput and 30% improvement in end to end delay.

关键词： clustering algorithm Wireless Sensor Networks Scheduling algorithm Network Lifetime

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Time-series clustering of high-speed photography based on Multi-channel Variational Autoencoder in a scramjet combustor

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APPLIED THERMAL ENGINEERING 2025年 270卷

作者： Xu, Wanqian Liang, Chenqiang Qiu, Hongchao Zhang, Junlong Bao, Wen Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Heilongjiang Peoples R China

Aiming at the scenario of high-speed photographic data analysis and automatic diagnosis in ground combustion test benches, this study presents a neural network based on Multi-channel Variational Auto-Encoder (MVAE) for clustering and identifying temporal patterns in flame morphological properties. While variational autoencoder architectures have been previously developed for clustering tasks, their effectiveness in capturing the complex, nonlinear features of high-speed flame visualization data has not been thoroughly explored. The method was applied to several datasets obtained from high-speed photography of a scramjet combustor. The network extracts temporal sequence features through shared convolutional and hidden layers, utilizing reconstruction residuals as clustering criteria while optimizing feature extraction and clustering simultaneously. When applied to high-speed photography datasets from a strut-based scramjet combustor under varying equivalence ratios, the MVAE significantly outperforms traditional clustering methods, reducing clustering discrepancies by approximately 96% compared to K-means approaches. Application of this algorithm to two experimental cases with different equivalence ratios reveals distinct flame organization modes, with clear correlations to specific frequency characteristics in time-frequency spectrograms. The demonstrated capability of this method to automatically identify complex flame patterns without potential anthropogenic bias offers researchers a powerful tool for data-driven analysis of combustion dynamics and flame stabilization mechanisms in hypersonic propulsion systems.

关键词： clustering algorithm High-speed photography Multi-Variational Autoencoder Supersonic flame Scramjet combustor

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Spatial-temporal clustering enhanced multi-graph convolutional network for traffic flow prediction

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APPLIED INTELLIGENCE 2025年第6期55卷 1-19页

作者： Bao, Yinxin Shen, Qinqin Cao, Yang Shi, Quan Nantong Univ Sch Informat Sci & Technol Nantong Peoples R China Nantong Univ Sch Transportat & Civil Engn Nantong Peoples R China

Dynamics and uncertainty are the fundamental reasons for the difficulty in accurately predicting traffic flow. In recent years, graph convolutional networks have been widely used in traffic flow prediction because of their excellent dynamic feature mapping ability. However, the existing models usually overlook the correlations among the nodes and the complex impact of external factors on traffic flow, which make it challenging to explore the complex spatial-temporal features. To overcome these shortcomings, we propose a novel Spatial-temporal clustering enhanced Multi-Graph Convolutional Network (SCM-GCN) for traffic flow prediction. First, a Spatial-Temporal clustering (STS) module based on the improved adjacency matrix DBSCAN clustering algorithm is constructed, this module divides traffic nodes into multiple highly correlated clusters, each of which consists of multi-graph features and time-varying features. Then, a Multi-Graph Spatial Feature Extraction (MGSFE) module that integrates the graph convolution operation and attention mechanism is designed to extract dynamic spatial features of multi-graph and time-varying features. Next, the Time-Varying Feature Extraction (TVFE) module based on the dilated convolution and gated attention mechanism is constructed. It integrates the output of the MGSFE module to extract dynamic temporal features of time-varying features and output the predicted values. Finally, the comparison and ablation experiments on four datasets show that the proposed model performs better than state-of-the-art models. The key source code and data are available at https://***/Bounger2/SCMGCN.

关键词： Traffic prediction Spatial-temporal correlation Graph convolution External factors clustering algorithm

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KNEG-CL: Unveiling data patterns using a k-nearest neighbor evolutionary graph for efficient clustering

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INFORMATION SCIENCES 2025年 690卷

作者： Fei, Zexuan Ma, Yan Zhao, Jinfeng Wang, Bin Yang, Jie Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai Peoples R China Shanghai Normal Univ Coll Life Sci Shanghai Peoples R China Univ Technol Sydney Australian Artificial Intelligence Inst FEIT Sydney NSW Australia

Existing clustering algorithms often struggle to handle datasets that are diverse and complex, largely due to a dependency on Euclidean distance. Accurately quantifying distances between data points also poses challenges in practical scenarios. Additionally, the curse of dimensionality in high-dimensional datasets also impacts the performance of clustering algorithms. This paper introduces an innovative approach to overcome these challenges: the k-nearest neighbor evolution graph (kNEG), an unweighted directed graph that evolves by incrementally adding directed edges as the value of k increases. This design captures intricate details such as data point density and the direction of density variation. We present kNEG-CL, a clustering algorithm derived from kNEG, which leverages vertex degree and edge directionality to intuitively cluster data. kNEG-CL is guided by two principles: using vertex degrees to identify density peaks, and assessing a balance of outgoing and incoming edges for subcluster merging. By identifying density peaks and utilizing a density-boosting search for initial partitioning, followed by a two-stage merging process, our algorithm achieves high clustering accuracy. Extensive testing across varied datasets demonstrates the superior performance of kNEG-CL, particularly in handling large-scale and high-dimensional data, highlighting its effectiveness in clustering accuracy and computational efficiency.

关键词： K-nearest neighbor graph Euclidean distance Density peak Connected component clustering algorithm

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A novel coupled clustering FFT²multiscale method for modeling the nonlinear behavior and failure of composites

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 438卷

作者： Li, Menglei Magri, Marco Wang, Bing Harbin Inst Technol Natl Key Lab Sci & Technol Adv Composites Special Harbin 150001 Peoples R China Politecn Milan Dipartimento Matemat MOX Lab I-20133 Milan Italy

We propose a novel FFT2 parallel multiscale computational method to predict the nonlinear behavior and failure of composite materials. Unlike traditional multiscale methods, the proposed approach reformulates the mechanical boundary value problem into Lippmann-Schwinger integral equations at both the micro- and macro-scale, thereby leveraging the numerical ciency of the fast Fourier transform (FFT) method at both scales. The application of generic non-periodic) boundary conditions at the macro-scale is carried out by using the virtual boundary technique and buffer zones. In addition, the introduction of a clustering algorithm further proves the computational efficiency of the numerical method during the information transfer between scales. To ensure accurate damage prediction and mitigate spurious strain localization both scales, suitable regularization techniques are employed. The proposed multiscale method applied to investigate the transverse tension of unidirectional composite dog-bone specimens. After experimental verification, the method is applied to simulate 2D and 3D brittle fracture, elasto-plastic damage, and examples with non-uniform material orientation. The results demonstrate the robustness and adaptability of the clustering approach, which achieves up to 65.90 speedup and 81.62-fold reduction in memory usage compared to non-clustered multiscale methods, while maintaining a comparable level of accuracy.

关键词： Multiscale Composites Fast Fourier transform clustering algorithm Non-local damage model

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Automatic Point Cloud clustering for Surface Defect Diagnosis

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 12538-12547页

作者： Ye, Jidong Liu, Xingjian Madhusudanan, Harikrishnan Wang, Yue Zhu, Junhui Wang, Yong Ru, Changhai Liu, Xinyu Sun, Yu Dalian Univ Technol Inst Robot & Intelligent Syst IRIS Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol State Key Lab High Performance Precis Mfg Dalian 116024 Peoples R China Univ Toronto Dept Mech & Ind Engn Toronto ON M5S 3G8 Canada Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Peoples R China

Point cloud clustering is a promising method for 3D surface defect diagnosis in manufacturing but requires manual clustering parameter selection, reducing usability. This paper proposes an automatic point cloud clustering method to address this issue. It employs a strategy that progresses from coarse to fine. In the coarse searching stage, a K-Nearest Neighbor (KNN) graph analysis technique is developed to recognize potential defective regions in parallel. Moving on to the fine stage of extracting detailed defects, a modified DBSCAN algorithm is proposed, in which the clustering parameters are calculated automatically from the KNN graph analysis results. Experimental results showed that the proposed method achieved cloud clustering with automatically calculated clustering parameters for surface defect diagnosis. The proposed method outperformed the traditional region growing algorithm in accuracy (0.942 vs. 0.680) and processing speed (21500 points/sec vs. 8740 points/sec) without requiring manual intervention.

关键词： Point cloud compression Nearest neighbor methods clustering algorithms Three-dimensional displays Surface treatment Accuracy Robots Manuals Noise Manufacturing Defect diagnosis clustering algorithm automatic optical inspection point cloud

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Improvement of clustering algorithms Based on Energy Distance and Its Application in Cancer Genes

World Scientific Research Journal

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World Scientific Research Journal 2023年第11期9卷 19-27页

作者： Xiaoxia Deng Shanru Chen Shanshan Li

The energy distance, compared to traditional distance measures in clustering algorithms, has the ability to differentiate between classes with small mean differences. In this study, we improved the measurement method of traditional clustering algorithms using the energy distance and empirically demonstrated its effectiveness in cancer genomics data. The improved clustering algorithm outperformed traditional methods in both numerical cancer gene expression data and non‐numerical cancer gene survival data, showing superior classification performance and enhanced stability.

关键词： clustering algorithm Energy distance Cancer genes

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Blood glucose prediction for type 2 diabetes using clustering-based domain adaptation

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 105卷

作者： Yang, Tao Yu, Xia Tao, Rui Li, Hongru Zhou, Jian Northeastern Univ Coll Informat Sci & Engn 3-11 Wenhua Rd Shenyang 110819 Peoples R China Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6 Shanghai Diabet Inst Shanghai Clin Ctr Diabet Dept Endocrinol & Metab Shanghai 200233 Peoples R China

For patients with type 2 diabetes (T2D), accurate prediction of blood glucose variations is essential for maintaining glycemic control, decreasing the occurrence of hypoglycemic and hyperglycemic events, and preventing diabetes complications. However, this is difficult to achieve due to insufficient early glucose data and the complexity of glucose dynamics. Additionally, the high variability among individuals poses challenges for data transfer between patients. In this work, a clustering-based domain adaptation method is proposed for personalized glucose prediction of T2D with insufficient data. Firstly, the multi-level clustering method is used to subtype the heterogeneous group of patients with T2D into multiple homogenous subgroups to deal with the high inter-individual variability. Then, a domain adaptation prediction network is designed to overcome the challenges caused by insufficient historical data of the target patient through cross-patient knowledge transfer and obtain a personalized deep prediction model suitable for the target patient. The effectiveness of the proposed method was evaluated in a clinical dataset containing continuous glucose monitoring (CGM) measurement records from 908 patients with T2D, each with only a small amount of data. The 30-minute prediction horizon achieved an average root mean square error of 14.96 mg/dL, with over 94 % of predictions clinically accurate. In addition, we evaluated the long-term transferability of the proposed method on the publicly available ShanghaiT2DM Dataset and compared it with the state-of-the-art (SOTA) methods. The results demonstrate that the proposed personalized method can achieve accurate glucose prediction for patients with T2D, even with only one day of historical CGM records available.

关键词： Deep learning Transfer learning Domain adaptation clustering algorithm Type 2 diabetes Glucose prediction

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clustering of Passenger Flow and Land-Use of Beijing Urban Rail Transit Stations Based on Multi-Source Data

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2024年第1期31卷 131-144页

作者： Xia, Xuanxuan Li, Hongchang Lin, Kexin Ling, Kun Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Shandong Normal Univ Sch Business Jinan 250358 Peoples R China

With the deepening of transit-oriented development (TOD), the construction of high-density, multi-functional urban spatial structures around public transportation stations has become a significant focus in urban development planning. This study is aimed to explore the spatial and temporal cluster patterns and the relationship between taxi passenger flow and land use types around urban rail transit stations. Taking Beijing as an example, this study extracts the time series of pick-up and drop-off points the taxi GPS track dataset and employs the affinity propagation (AP) method to conduct the spatial and temporal cluster analysis on these taxi pick-up and drop-off points. Then, based on the urban POI dataset, this study classifies these stations into four functional types by adopting the K-means method. Finally, the partial least squares method has been used to explore the relationship between the taxi passenger flow and the land use types under different time intervals. The result indicates that: first, is a regional aggregation effect for the taxi passenger source in the rail transit station area, and the cluster areas are mainly located in Chaoyang and Haidian districts, well as along the circular metro line. Besides, there is a significant rush hour in the morning and evening for this passenger flow. Second, the commercial, residential, comprehensive transit stations are mainly located in the central urban districts of Beijing with balanced spatial distribution, while the industrial transit stations are dispersed to the outer suburbs of the city, which conform to Alonso's spatial distribution of land. Third, there is a significant positive correlation between taxi passenger sources rail transit station area and land use types containing transportation service, public facility and daily life service. This study analyses the population mobility and land cluster form around the urban rail transit from the micro-perspective, which verifies and supplements Alonso's tr

关键词： clustering algorithm land-use passenger flow rail transit station

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The application of integrating comprehensive evaluation and clustering algorithms weighted by maximal information coefficient for urban flood susceptibility

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年第1期344卷 118846页

作者： Wang, Hongfa Meng, Yu Wang, Huiliang Wu, Zening Guan, Xinjian Zhengzhou Univ Sch Water Conservancy & Transportat Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Henan Peoples R China

Different sub-regions of Zhengzhou city have various levels of sensitivity to flood due to the impact of urbani-zation. Thus, an accurate flood sensitivities assessment is a key tool for flood prevention and urban planning and development. To successfully link the urban flood sensitivity assessment with the real flood situation, a method combining clustering algorithm with comprehensive evaluation is presented. The proposed method is not affected by the classification standard of sensitivities levels and has a small and undemanding demand for flood data. First, Maximal Information Coefficient between conditional factors and flood is employed to determine the weight. Then, the different results are obtained by three clustering algorithms. Finally, a four-layer evaluation structure weighted by analytic hierarchy process is established to select the best flood susceptibility map. A case study in the Zhengzhou city, China shows that the positive scale amplification strategy is relatively best and the flood sensitivity of sub-regions in Zhengzhou city should be divided into four levels obtained by K-Means clustering. Hence, it supplies the valuable insights for the urban planning and flood mitigation.

关键词： Flood susceptibility Maximal information coefficient Weight assignment clustering algorithm Comprehensive evaluation Analytic hierarchy process

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