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Smart-Meter Big Data for Load Forecasting: An Alternative Approach to clustering

IEEE ACCESS 2022年 10卷 8377-8387页

作者： Alemazkoor, Negin Tootkaboni, Mazdak Nateghi, Roshanak Louhghalam, Arghavan Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Univ Massachusetts Dartmouth Dept Civil & Environm Engn N Dartmouth MA 02747 USA Univ Virginia Dept Engn Syst & Environm Charlottesville VA 22904 USA

Accurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters and separate prediction models are developed for each cluster. The cluster-based predictions are then aggregated to compute the total demand. CBAs have provided promising results compared to conventional approaches that are generally not conducive to integrating smart-meter data. However, CBAs are computationally costly and suffer from the curse of dimensionality, especially under scenarios involving smart-meter data from millions of customers. In this work, we propose an efficient reduced model approach (RMA) that leverages a novel hierarchical dimension reduction algorithm to enable the integration of fine-resolution high-dimensional smart-meter data for millions of customers in load prediction. We demonstrate the applicability of our proposed approach by using data from a utility company, based in Illinois, United States, with more than 3.7 million customers and present model performance in-terms of forecast accuracy. The proposed hierarchical dimension reduction approach enables utilizing the high-resolution data from smart-meters in a scalable manner that is not exploitable otherwise. The results shows significant improvements in forecast accuracy compared to the available approaches that either do not harness fine-resolution data or are not scalable to large-scale smart-meter big data.

关键词： Predictive models Load forecasting Data models Load modeling Prediction algorithms clustering algorithms Big Data Short-term load forecasting smart-meter data big data hierarchical dimension reduction

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Enhancing IoT-Enabled Sustainable Smart Cities With Secure and Energy-Aware Data Collection Using Meta-Heuristic Technique

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IEEE SENSORS JOURNAL 2024年第14期24卷 22974-22991页

作者： Salim, Ahmed Khedr, Ahmed M. Osamy, Walid Qassim Univ Appl Coll Unit Sci Res Buraydah Saudi Arabia Zagazig Univ Math Dept Zagazig 44519 Egypt Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

Internet of Things (IoT)-enabled wireless sensor networks (WSNs) are integral to enhancing urban living, sustainability, and operational efficiency in smart cities. However, their wireless and distributed nature leaves them vulnerable to security threats. Existing security algorithms face challenges in IoT-based WSNs due to resource constraints, particularly regarding energy-efficient and trustworthy cluster head (CH) selection. Many current techniques assume complete trust among all nodes, including CHs, which does not align with real-world scenarios. Malicious nodes posing as CHs can disrupt routing decisions and communication, particularly critical in smart city contexts. To address this, our research incorporates trust-based security into CH selection processes for IoT-based WSNs in smart cities. By prioritizing trustworthy CHs and considering potential malicious nodes, we aim to enhance network reliability. Our method assesses each sensor node's (SN) trust and residual energy to optimize CH selection, mitigating the impact of malicious activities on WSN performance. By utilizing the Mountain Gazelle Optimizer (MGO), we combine trust concepts with meta-heuristic methods to enhance security, energy efficiency, and network lifetime. In terms of network lifetime, the proposed work exceeds the existing methods: 1.24 times better than LEACH-TM, 0.40 times better than DGTTSSA, 0.33 times better than ETCHS, 1.12 times better than eeTMFOGA, 6.70 times better than E-LEACH, 0.55 times better than Improved-LEACH, 0.61 times better than MG-LEACH, and 0.79 times better than RCH-LEACH in the scenario where the base station (BS) is positioned at the center.

关键词： Wireless sensor networks Security clustering algorithms Optimization Smart cities Energy consumption Routing clustering Internet of Things (IoT) modern cities smart cities sustainable smart city sustainable urbanization trust urban problems wireless sensor networks (WSNs)

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MITDBA: Mitigating Dynamic Backdoor Attacks in Federated Learning for IoT Applications

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 10115-10132页

作者： Wang, Yongkang Zhai, Di-Hua Han, Dongyu Guan, Yuyin Xia, Yuanqing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Yangtze Delta Reg Acad Beijing Inst Technol Jiaxing 314001 Peoples R China Beijing Bldg Mat Acad Sci Res Beijing 100041 Peoples R China

Federated learning (FL) is widely used in the Internet of Things (IoT) systems. However, FL is susceptible to backdoor attacks due to its inherently distributed and privacy-preserving nature. Existing studies assume that backdoor triggers on different malicious clients are universal, and most defense algorithms are designed to counter backdoor attacks based on this assumption. Recently, dynamic backdoor attacks have been proposed to undermine robust algorithms in centralized machine learning. We introduce dynamic backdoor attacks into the FL system and develop three types of dynamic backdoors named Aggregation, Single, and Continuous to target the FL system. To defend against such attacks, we propose a novel robust algorithm called MITDBA, which utilizes gramian information to capture high-order representations, then employs spectral signatures to detect and remove malicious clients, and finally utilizes clipping operations to filter the selected local models during the aggregation process. We conduct attack and defense experiments on MNIST, CIFAR-10, and GTSRB data sets. The experimental results demonstrate that our designed attack strategies can successfully insert dynamic backdoors into the global model, bypassing the existing state-of-the-art defenses, but these attacks can be effectively mitigated by MITDBA.

关键词： Heuristic algorithms Training Internet of Things Data models Automobiles Vehicle dynamics clustering algorithms Dynamic backdoor federated learning (FL) gram matrix robust spectral signature

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Adaptive Mixed-Attribute Data clustering Method Based on Density Peaks

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COMPLEXITY 2022年第0期2022卷

作者： Liu, Shihua Wenzhou Polytech Sch Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Network Secur Detect & Protect Engn Techn Wenzhou 325035 Peoples R China

The clustering of mixed-attribute data is a vital and challenging issue. The density peaks clustering algorithm brings us a simple and efficient solution, but it mainly focuses on numerical attribute data clustering and cannot be adaptive. In this paper, we studied the adaptive improvement method of such an algorithm and proposed an adaptive mixed-attribute data clustering method based on density peaks called AMDPC. In this algorithm, we used the unified distance metric of mixed-attribute data to construct the distance matrix, calculated the local density based on K-nearest neighbors, and proposed the automatic determination method of cluster centers based on three inflection points. Experimental results on real University of California-Irvine (UCI) datasets showed that the proposed AMDPC algorithm could realize adaptive clustering of mixed-attribute data, can automatically obtain the correct number of clusters, and improved the clustering accuracy of all datasets by more than 22.58%, by 24.25%, by 28.03%, by 22.5%, and by 10.12% for the Heart, Cleveland, Credit, Acute, and Adult datasets compared to that of the traditional K-prototype algorithm, respectively. It also outperformed a modified density peaks clustering algorithm for mixed-attribute data (DPC_M) algorithms.

关键词： clustering algorithms

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clustering Product Development Project Organization From the Perspective of Social Network Analysis

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2022年第6期69卷 2482-2496页

作者： Yang, Qing Yang, Na Browning, Tyson R. Jiang, Bin Yao, Tao Univ Sci & Technol Beijing Sch Econ & Management Beijing 100083 Peoples R China Texas Christian Univ Neeley Sch Business Ft Worth TX 76129 USA Depaul Univ Driehaus Coll Business Chicago IL 60604 USA Penn State Univ Harold & Inge Marcus Dept Ind & Mfg Engn University Pk PA 16802 USA

In product development (PD) organizations, coordinating technical dependencies among teams with different expertise in overlapping processes is a fundamental challenge. This article takes a more sophisticated approach than prior methodologies to improve coordination via organizational clustering, by accounting for both team structural and attribute similarity from the perspective of social network analysis. We built models to quantify the impact of the overlapping processes on the interaction strength among PD teams, which we then used to construct structural similarity by combining tie strength and social cohesion among teams via the design structure matrix. To evaluate the organization network, we propose social embeddedness-related centrality indices within (intracluster) and across (intercluster) team groupings. To facilitate knowledge sharing, we base team attribute similarity on product- and process-related expertise among teams. We integrate the modularity index and an improved silhouette index to find an optimal number of clusters, which we then incorporate with team similarity measures as inputs to a spectral clustering algorithm. An industrial example illustrates the proposed model. The clustering results reinforce several managerial practices but also yield new insights, such as how to measure similarity among teams based on organizational network characteristics and how structural and attribute similarities impact the optimal organizational structure.

关键词： Organizations clustering algorithms Social networking (online) Product development Indexes Complexity theory Analytical models clustering algorithm design structure matrix (DSM) organization design product development (PD) project management social network analysis (SNA)

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Multiview Spectral clustering With Bipartite Graph

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 3591-3605页

作者： Yang, Haizhou Gao, Quanxue Xia, Wei Yang, Ming Gao, Xinbo Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Westfield State Univ Dept Math Westfield MA 01086 USA Westfield State Univ Dept Comp & Informat Sci Westfield MA 01086 USA Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing 400065 Peoples R China

Multi-view spectral clustering has become appealing due to its good performance in capturing the correlations among all views. However, on one hand, many existing methods usually require a quadratic or cubic complexity for graph construction or eigenvalue decomposition of Laplacian matrix;on the other hand, they are inefficient and unbearable burden to be applied to large scale data sets, which can be easily obtained in the era of big data. Moreover, the existing methods cannot encode the complementary information between adjacency matrices, i.e., similarity graphs of views and the low-rank spatial structure of adjacency matrix of each view. To address these limitations, we develop a novel multi-view spectral clustering model. Our model well encodes the complementary information by Schatten p-norm regularization on the third tensor whose lateral slices are composed of the adjacency matrices of the corresponding views. To further improve the computational efficiency, we leverage anchor graphs of views instead of full adjacency matrices of the corresponding views, and then present a fast model that encodes the complementary information embedded in anchor graphs of views by Schatten p-norm regularization on the tensor bipartite graph. Finally, an efficient alternating algorithm is derived to optimize our model. The constructed sequence was proved to converge to the stationary KKT point. Extensive experimental results indicate that our method has good performance.

关键词： Tensors Matrix decomposition Laplace equations clustering algorithms Computational modeling Adaptation models Eigenvalues and eigenfunctions Multi-view clustering graph fusion large scale data

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Unsupervised Cross-Domain Person Re-Identification Method Based on Attention Block and Refined clustering

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IEEE ACCESS 2022年 10卷 105930-105941页

作者： Hui, Yan Wu, Xi Hu, Xiuhua Liu, Huan You, Shijie Xian Technol Univ Sch Comp Sci & Engn State & Prov Joint Engn Lab Adv Network Xian 710021 Shaanxi Peoples R China

Most unsupervised cross-domain person re-identification methods based on clustering suffer from a lack of feature discrimination and clustering generates pseudo-labels noise, leading to a decrease in accuracy. To solve these problems, this paper proposes an unsupervised cross-domain person re-identification method based on attention block and refined clustering. Firstly, ResNet50 is selected as the backbone network, coordinate attention and triple attention are concatenated and embedded in ResNet50 to extract fine-grained features, perform feature aggregation, and mine fine-grained information. Secondly, a refined clustering strategy is proposed to achieve a coarse-to-fine clustering process by designing the measurement standards for clustering, determining its reliability, and eliminating noisy samples. Finally, the hybrid memory bank dynamically stores cluster centers and continues to update them with iterations, adapting to changes in clusters and performing invariant learning. The experimental results show that the new method designed in the paper improves the accuracy of rank-1 and mAP by 0.4% and 2.4%, respectively, on the target domain Market-1501 dataset, and improves the accuracy of rank-1 and mAP by 0.4% and 1.1%, respectively, on the target domain DukeMTMC-ReID dataset, compared with other typical methods.

关键词： Feature extraction clustering algorithms Unsupervised learning Lighting Heuristic algorithms Convolutional neural networks Stacking Attention mechanism cross-domain person re-identification hybrid memory bank refined clustering strategy unsupervised methods

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Depth-Based Condition Monitoring and Contributing Factor Analysis for Anomalies in Combined Cycle Power Plant

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IEEE ACCESS 2024年 12卷 73400-73412页

作者： Lim, Munwon Kim, Yunhee Jin, Shengxin Ha, Sangwoo Yong Chang, Sung Su Kang, Hae Sang Park, Gyu Lee Joo, Mi Jung, Chang-Sik Cho, Youngjin Bae, Suk Joo Hanyang Univ Dept Ind Engn Seoul 04763 South Korea Korea Elect Power Res Inst Daejeon 34056 South Korea Rozetatech Seongnam 13440 South Korea

Unexpected fault or failure in the power plant have caused high maintenance costs, the loss of energy production, and even safety issues. Developments in sensor technologies and data analytics have aided proper preventive maintenance actions for the system to improve asset availability and reduce repair costs. Nevertheless, effective condition monitoring of a power plant experiences a considerable nuisance from challenging issues such as inherent data characteristics such as high correlations between process variables, irrelevant information from environmental noises, and system complexity. To resolve these problems, this paper proposes an integrated monitoring scheme for performing efficient corrective actions by identifying the variables related to anomalies in combined cycle power plants. The scheme includes a clustering-based linear discriminant analysis to extract key variables for reducing dimensionality to efficiently handle the data, followed by employing the Mahalanobis depth statistics for anomaly detection and causal analysis via contribution scores. The proposed monitoring scheme is applied to condition monitoring data of a combined cycle power plant in South Korea, which include two types of anomalous operations. The reliability and robustness of the proposed condition monitoring scheme are validated by comparing other state-of-the-art methods. The proposed method shows a potential in efficiently detecting anomalies during operation and even early detecting the precursors of anomalies. It is expected to prevent imminent faults or failures by taking proper actions to relevant key process parameters of combined cycle power plant in advance.

关键词： Power generation Condition monitoring clustering algorithms Monitoring Maintenance Analytical models Turbines Anomaly detection Medical services Biomedical monitoring causal inference condition-based maintenance contribution scores health monitoring

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Machine learning based tomographic image reconstruction technique to detect hollows in wood

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WOOD SCIENCE AND TECHNOLOGY 2024年第4期58卷 1491-1516页

作者： Yildizcan, Ecem Nur Ari, Mehmet Erdi Tunga, Burcu Gelir, Ali Kurul, Fatih As, Nusret Dundar, Turker Istanbul Tech Univ Fac Arts & Sci Math Engn Istanbul Turkiye Istanbul Univ Fac Arts & Sci Comp Sci Istanbul Turkiye Istanbul Tech Univ Fac Arts & Sci Math Engn Istanbul Turkiye Istanbul Univ Cerrahpasa Fac Forestry Istanbul Turkiye

A new technique based on machine learning algorithms was introduced to detect internal wood defects. This technique relies on analyzing segmented propagation rays of stress waves and successfully generates the tomographic images of the defects by using the stress wave velocity. Utilizing a dual-stage methodology, the initial phase involves ray segmentation for the precise delineation of stress wave propagation, while the subsequent stage integrates advanced classification and clustering algorithms to facilitate the generation of tomographic images. This approach effectively tackles the inherent challenges associated with accurate segmentation and classification of stress wave velocity rays. The effectiveness of the proposed method was evaluated using both synthetic and experimental data. The results showed that the proposed method, when compared with some state-of-the-art methods, has a superior ability to accurately detect defective regions in the wood. The success of the proposed method is evaluated with four different evaluation metrics. It determined that over 90% success is achieved for all metrics. In comparison with related studies, it determined that the results are improved by 7-22% compared to the literature.

关键词： clustering algorithms

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Structured Graph Learning for Scalable Subspace clustering: From Single View to Multiview

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 8976-8986页

作者： Kang, Zhao Lin, Zhiping Zhu, Xiaofeng Xu, Wenbo Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China

Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: they encounter the expensive time overhead, they fail to explore the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable graph learning framework, seeking to address the above three challenges simultaneously. Specifically, it is based on the ideas of anchor points and bipartite graph. Rather than building an n x n graph, where n is the number of samples, we construct a bipartite graph to depict the relationship between samples and anchor points. Meanwhile, a connectivity constraint is employed to ensure that the connected components indicate clusters directly. We further establish the connection between our method and the K-means clustering. Moreover, a model to process multiview data is also proposed, which is linearly scaled with respect to n. Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.

关键词： Bipartite graph clustering methods Periodic structures Laplace equations clustering algorithms Videos Training Anchor graph bipartite graph connectivity constraint large-scale data multiview learning out of sample subspace clustering

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