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Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 163卷

作者： Liu, Hongjie Shi, Haotian Yuan, Tengfei Fu, Sicheng Ran, Bin Xi'an Jiaotong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Univ Wisconsin Madison Civil & Environm Engn Madison WI 53706 USA Shanghai Univ SILC Business Sch Shanghai 201800 Peoples R China

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the Mk-LDA (meanshift kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-k-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

关键词： Internet of Things Point of interest k-means clustering algorithm Latent dirichlet allocation Feature inference Intelligent transportation

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Abnormal Electricity Consumption Detection Method for Smart Grid Using Fusion Matrix Completion and Improved clustering algorithm

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IEEE ACCESS 2024年 12卷 76634-76647页

作者： Zhang, Ning Zhu, Liang Heilongjiang Univ Technol Sch Elect & Informat Engn Jixi 158100 Peoples R China

With the application and development of new technologies such as the Internet of Things and big data in the energy field, smart grids have gradually become the main trend for the future development of the power system. However, frequent communication between various participants in the smart grid can lead to higher privacy leakage risks, and malicious attackers can cause economic losses and security accidents to the smart grid by injecting abnormal power data. To address the issues of data privacy protection and anomaly detection in smart grids, this study proposed a matrix completion based smart grid data privacy protection scheme and an unsupervised learning based smart grid anomaly data detection scheme. A matrix completion based smart grid data privacy protection scheme was proposed to address the issue of data privacy protection between different trust domains in the smart grid. This scheme repaired missing elements in the relevant matrix through matrix completion, and increased random perturbations by adding noise with the same statistical characteristics as the original data to achieve data privacy protection. To address the issue of missing power data labels and inaccurate principal component analysis caused by abnormal data, an improved k-means clustering algorithm was used to remove outliers and optimize the results, to achieve accurate detection of abnormal data. The experimental results showed that the proposed algorithm has an accuracy of 0.9, an F1 value of 0.7, and a Bayesian detection rate of 0.61, all of which are higher than other algorithms. The AUC value of the proposed method was 0.79, indicating that the scheme established in this chapter has certain advantages in detecting abnormal electricity consumption.

关键词： Smart grids Electricity Data privacy Protection clustering algorithms Security Quantum computing Internet of Things Matrix completion k-means clustering algorithm smart grid data privacy monitor

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A new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions

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JOURNAL OF GEODESY 2022年第11期96卷 91-91页

作者： Ulug, Rasit karslioglu, Mahmut Onur Middle East Tech Univ Dept Geodet & Geog Informat Technol TR-068000 Ankara Turkey Middle East Tech Univ Dept Civil Engn Geomat Engn Div Ankara Turkey

In this study, a new data-adaptive network design methodology called k-SRBF is presented for the spherical radial basis functions (SRBFs) in regional gravity field modeling. In this methodology, the cluster centers (centroids) obtained by the k-means clustering algorithm are post-processed to construct a network of SRBFs by replacing the centroids with the SRBFs. The post-processing procedure is inspired by the heuristic method, Iterative Self-Organizing Data Analysis Technique (ISODATA), which splits clusters within the user-defined criteria to avoid over- and under-parameterization. These criteria are the minimum spherical distance between the centroids and the minimum number of samples for each cluster. The bandwidth (depth) of each SRBF is determined using the generalized cross-validation (GCV) technique in which only the observations within the radius of impact area (RIA) are used. The numerical tests are carried out with real and simulated data sets to investigate the effect of the user-defined criteria on the network design. Different bandwidth limits are also examined, and the appropriate lower and upper bandwidth limits are chosen based on the empirical signal covariance function and user-defined criteria. Also, additional tests are performed to verify the performance of the proposed methodology in combining different types of observations, such as terrestrial and airborne data available in Colorado. The results reveal that k-SRBF is an effective methodology to establish a data-adaptive network for SRBFs. Moreover, the proposed methodology improves the condition number of normal equation matrix so that the least-squares procedure can be applied without regularization considering the user-defined criteria and bandwidth limits.

关键词： Regional gravity field modeling Spherical radial basis functions Data-adaptive network design Poisson kernel k-means clustering algorithm ISODATA

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Flexible resource allocation optimization model considering global k-means load clustering and renewable-energy consumption

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Clean Energy 2024年第5期8卷 129-143页

作者： Jie Jiao Puyu He Yuhong Zhang Jiyuan Zhang Zhuhan Long Hanjing Liu State Grid Sichuan Electric Power Company Economic Research Institute ChengduChina

Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy *** investment construction cost and operation cost of various flexible resources are different,and the adjustment ability is different in different ***,the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different *** this paper,the global k-means load clustering model is pro-posed and the 365-day net load is reduced to eight typical daily net loads by ***,a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is *** flexible resources involved include the flexible transformation of thermal power,hydropower,pumped storage,energy storage,and demand *** upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the *** results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable *** the abandonment rate of renewable energy decreases,the optimal capacity of pumped storage,electrochemical energy storage,and hydropower units *** the power-abandonment rate of renewable energy is 5%,the optimal allocation capacity of thermal power flexibility transformation,pumped storage,electrochemical energy storage,hydropower unit,and adjustable load in Province A is 5313,17090,5830,72113,and 4250 MW,*** the condition that the renewable-energy abandonment rate is 0,5%,and 10%respectively,the configured capacity of pumped storage is 20000,17090,and 14847 MW,respectively.

关键词： wind power photovoltaic power k-means clustering algorithm flexible resources complementary configuration operation optimization

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A Lightweight Mutual Privacy Preserving k-means clustering in Industrial IoT

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IEEE TRANSACTIONS ON NETWORk SCIENCE AND ENGINEERING 2024年第2期11卷 2138-2152页

作者： Hu, Chunqiang Liu, Jianshuo Xia, Hui Deng, Shaojiang Yu, Jiguo Chongqing Univ Sch Big Data & Software Engn Chongqing 400030 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China Ocean Univ China Coll Comp Sci & Technol Qingdao 266100 Peoples R China Qilu Univ Technol Big Data Inst Jinan 250316 Peoples R China

In many industrial Internet of Things application schemes, participants, such as IoT devices, often share information with each others for analysis and categorization purpose. Through the analysis and statistical processing of the information, some necessary information is exchanged, e.g., the attributes in manufacturing IoT devices' gathered data. In this context, many devices are necessarily trusted when sharing information, which raises concerns about data privacy leakage. As the number of IoT devices in the network increases, the scale of pairwise keys increases rapidly. Furthermore, the limitation of device's computing ability makes it hard to perform centralized computing. To address these issues, we propose a light weight k-means clustering scheme that performs clustering with high accuracy, maintaining at a low key management cost, and securing the private attributes of each participant or the intermediate variables. In our proposed scheme, we use aprivate optimal initial cluster center generation algorithm based on attribute weights, in order to achieve the better clustering quality. Secondly, we securely find the nearest clustering center for each participant. We dynamically split and merge clustering centers respectively, in order to meet the optimal clustering result. Last but not least, we calculate the clustering centers without revealing any private attributes about the participants' information during the process. The analysis indicates that our proposed scheme canresist collusion attacks and ensures that the cost of overall pair wisekey management and computation cost performed in data center are relatively low.

关键词： k-means clustering algorithm privacy preservation lightweight clustering algorithm homomorphic encryption

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Residential Building Duration Prediction Based on Mean clustering and Neural Network

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ADVANCES IN CIVIL ENGINEERING 2024年第1期2024卷

作者： Ji, Fanrong Nan, Yunquan Wei, Aifang Fan, Peiyan Luo, Zhaoyuan Song, Xiaoqing Shandong Jianzhu Univ Sch Management Engn Jinan 250101 Peoples R China Shandong Jianzhu Univ Coll Surveying & Geoinformat Jinan 250101 Peoples R China Univ Canberra Sch Design & Built Environm Canberra 2601 Australia

The duration of a residential building project will directly influence its successful implementation;hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA-BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA-BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA-BP neural network model, this research utilized an artificial bee colony (ABC)-improved k-means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC-k-means-GA-BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC-k-means-GA-BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency.

关键词： artificial bee colony algorithm BP neural network duration prediction genetic algorithm k-means clustering algorithm

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Construction of aesthetic education evaluation model based on k-means clustering analysis in the context of artificial intelligence

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 728-743页

作者： Li, Mo Guo, Shasha Chen, Jie Hebei Univ Engn Student Affairs Off Handan Peoples R China Hebei Univ Engn Sch Earth Sci & Engn 19Taiji RdEcon Devel Zone Handan 056038 Hebei Province Peoples R China

Enhancing aesthetic education can boost individuals' skills in recognizing, understanding, appreciating, and creating beauty. Nevertheless, traditional methods for evaluating aesthetic education are often subjective and have limited capacity to handle complex features effectively. This article was based on artificial intelligence to develop an aesthetic education evaluation model, and used an improved k-means clustering algorithm and a pre-trained ResNet-101 model for feature extraction. This article used WikiArt as an experimental data set, which contains 77,229 artworks from 15 art styles. This study used silhouette coefficient, Davies-Bouldin index, Calinski-Harabasz index, and Dunn index to evaluate the model clustering effect. Experimental results indicated that with a clustering \(k\) value of 10, the enhanced algorithm significantly advanced clustering quality, computational efficiency, and memory usage. This model has enhanced the objectivity and efficiency of aesthetic education evaluation and introduced new approaches for classifying and assessing artistic works.

关键词： k-means clustering algorithm aesthetic education evaluation artificial intelligence feature extraction ResNet-101 model

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Factor Modeling for clustering High-Dimensional Time Series

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2024年第546期119卷 1252-1263页

作者： Zhang, Bo Pan, Guangming Yao, Qiwei Zhou, Wang Univ Sci & Technol China Int Inst Finance Sch Management Dept Stat & Finance Hefei Peoples R China Nanyang Technol Univ Sch Phys & Math Sci Singapore Singapore London Sch Econ & Polit Sci Dept Stat London England Natl Univ Singapore Dept Stat & Data Sci Singapore Singapore

We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific factors, and the latent clusters. Numerical illustration with both simulated data as well as a real data example is also reported. As a spin-off, the proposed new approach also advances significantly the statistical inference for the factor model of Lam and Yao. for this article are available online.

关键词： Eigenanalysis Idiosyncratic components k-means clustering algorithm Strong and weak factors

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Topology-distance-based clustering method for water distribution network partitioning

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AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY 2023年第11期72卷 2186-2198页

作者： Du, kun Li, Jiangyun Xu, Wei Liu, Zilian Zheng, Feifei Kunming Univ Sci & Technol Fac Civil Engn & Mech Kunming 650500 Peoples R China Kunming Univ Sci & Technol Intelligent Infrastructure Operat & Maintenance Te Kunming 650500 Peoples R China Zhejiang Univ Coll Civil Engn & Architecture Hangzhou 310058 Peoples R China

Partitioning water distribution networks (WDNs) into district metered areas offers benefits including reduced nonrevenue water and simplified pressure management. However, current research in this field tends to narrowly focus on the topological relationships among nodes, often overlooking the influence of pressure reducing valves (PRVs) and pump stations on clustering results. To address this limitation, this study introduces a topology-distance-based clustering (TDBC) method that enhances the accuracy of partitioning by explicitly considering the impact of PRVs and pump stations. In the TDBC method, the Floyd algorithm is initially employed to construct a topological distance matrix that quantifies the degree of node connectivity. By amplifying topological distances for links including PRVs and pump stations, their effect on clustering results is incorporated. Subsequently, nodes are clustered using the k-means algorithm based on the resulting topology-distance matrix. The proposed TDBC approach is applied to four network cases, and its outcomes are compared with those of two traditional methods. The comparative analysis indicates that the TDBC algorithm achieves precise partitioning results for networks incorporating PRVs or pump stations, while ensuring a harmonious balance between modularity and the uniformity of the partitioning results, even in networks with complex structures and highly interconnected loops.

关键词： community structure detection k-means clustering algorithm partitioning water distribution network

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Customer segmentation using k-means clustering and the adaptive particle swarm optimization algorithm

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 107924-107924页

作者： Li, Yue Chu, Xiaoquan Tian, Dong Feng, Jianying Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved k-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum;thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the k-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the k-means cluster centres (kM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare kM-ALPSO on five UCI datasets. Finally, the improved kM-ALPSO (IkM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IkM-ALPSO for customer segmentation. (C) 2021 Elsevier B.V. All rights reserved.

关键词： k-means clustering algorithm Particle swarm optimization algorithm Adaptive parameter learning Mixed data Customer segmentation

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