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Intelligent recommender system based on quantum clustering and matrix completion

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第15期34卷 e6943-e6943页

作者： Ouedrhiri, Oumayma Banouar, Oumayma El Hadaj, Salah Raghay, Said Fac Sci & Tech Lab Comp Sci Engn & Syst Marrakech Morocco Sidi Mohamed Ben Abdellah Univ Lab Innovat Technol Fes Morocco Fac Sci & Tech Lab Appl Math Marrakech Morocco

Faster than classical algorithms, quantum algorithms benefit from the superposition property of quantum information to offer significant speedup to complex algorithms. Therefore, quantum computing can be used to help machine learning algorithms by boosting their performance and accelerate the processing of time-consuming ones. clustering algorithms are very complex unsupervised learning algorithms. Indeed, the similarity calculation (distance) between input vectors is a resource-consuming step, especially when working with large datasets. In this article, we propose a new better-performing recommender system that operates as a combination of an adapted quantum K-means algorithm and the singular value decomposition (SVT) algorithm. We integrate the developed quantum clustering algorithm to a prediction process of the proposed recommender system using matrix completion. To the best of our knowledge, no system with such details was proposed in the literature. The system was applied on the MovieLens dataset without a dimensionality reduction step and evaluated according to measures of information retrieval systems. The results of the quantum K-means algorithm show that the quantum version leads to a logarithmic reduction of the time complexity compared to the classical algorithm. The proposed system has proved to be better than the previous tested ones in terms of precision and recall.

关键词： clustering algorithms K-means algorithms matrix completion MovieLens quantum machine learning recommender systems

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A Novel Modeling Framework Based on Customized Kernel-Based Fuzzy C-Means clustering in Iron Ore Sintering Process

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第2期27卷 950-961页

作者： Hu, Jie Wu, Min Chen, Luefeng Pedrycz, Witold China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada

This article proposes a customized kernel-based fuzzy C-means (CKFCM) clustering, which provides an original framework for data interpretation and data analysis and delivers an effective solution for an accurate division of actual run data under multiple operating conditions in an iron ore sintering process (IOSP). The improvement of CKFCM clustering is achieved by including an adjustment factor introduced into the kernel-based fuzzy C-means clustering. The adjustment factor only needs to consider a small amount of labeled data that are determined based on expert experience. Subsequently, the CKFCM clustering is applied to the modeling of the IOSP, and the Spearman's rank correlation coefficient method is utilized to determine input variables of the model under different operating conditions. Furthermore, the broad learning model is employed to build the prediction model for each operating condition. Finally, we conducted an in-depth analysis of the presented clustering method. Its performance has been experimentally demonstrated by many publicly available datasets. Meanwhile, simulation results involving actual run data of the IOSP demonstrate the superiority and effectiveness of the developed model in carbon efficiency prediction. We show that the developed model outperforms the state-of-the-art prediction models in achieving a good balance between simplicity and accuracy.

关键词： Data models clustering algorithms Predictive models Mechatronics Iron IEEE transactions Input variables Actual run data broad learning model carbon efficiency prediction customized kernel-based fuzzy C-means (CKFCM) iron ore sintering process (IOSP) multiple operating conditions

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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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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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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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Maritime Traffic Route Detection Framework Based on Statistical Density Analysis From AIS Data Using a clustering Algorithm

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IEEE ACCESS 2022年 10卷 23355-23366页

作者： Lee, Jeong-Seok Lee, Hyeong-Tak Cho, Ik-Soon Korea Maritime & Ocean Univ Grad Sch Busan 49112 South Korea Korea Maritime & Ocean Univ Ocean Sci & Technol Sch Busan 49112 South Korea Korea Maritime & Ocean Univ Div Nav Convergence Studies Busan 49112 South Korea

Maritime traffic routes by ships navigation vary according to country and geographic characteristics, and they differ according to the characteristics of the ships. In ocean areas adjacent to coasts, regulated routes are present, e.g., traffic separation scheme for ships entering and leaving;however, most ocean areas do not have such routes. Maritime traffic route research has been conducted based on computer engineering to create routes;however, ship characteristics were not considered. Thus, this article proposes a framework to generate maritime traffic routes using statistical density analysis. Here, automatic identification system (AIS) data are used to derive quantitative traffic routes. Preprocessing is applied to the AIS data, and a similar ship trajectory pattern is decomposed into a matrix based on the Hausdorff-distance algorithm and then stored in a database. A similar pattern makes the AIS trajectory simple using the Douglas-Peucker algorithm. In addition, density-based spatial clustering of applications with noise (DBSCAN) is performed to identify the waypoints of vessels then create routes by connecting waypoints. The width of maritime routes created based on a similar ship trajectory is subjected to kernel density estimation analysis (KDE). Then, waypoints evaluation of the main route is performed from the results of KDE 75% and 90% considering the statistical in the total maritime traffic, and the results applied to the targeted ocean area are compared. Finally, the result of KDE 90% of maritime traffic with framework analyzed the safety route, which can be a basis for developing routes for maritime autonomous surface ships.

关键词： Marine vehicles Artificial intelligence Trajectory Oceans clustering algorithms Navigation Safety AIS data clustering algorithm DBSCAN kernel density estimation framework maritime traffic route

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Adaptive Contourlet Fusion clustering for SAR Image Change Detection

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 2295-2308页

作者： Zhang, Wenhua Jiao, Licheng Liu, Fang Yang, Shuyuan Liu, Jia Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Xian 710071 Shaanxi Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

In this paper, a novel unsupervised change detection method called adaptive Contourlet fusion clustering based on adaptive Contourlet fusion and fast non-local clustering is proposed for multi-temporal synthetic aperture radar (SAR) images. A binary image indicating changed regions is generated by a novel fuzzy clustering algorithm from a Contourlet fused difference image. Contourlet fusion uses complementary information from different types of difference images. For unchanged regions, the details should be restrained while highlighted for changed regions. Different fusion rules are designed for low frequency band and high frequency directional bands of Contourlet coefficients. Then a fast non-local clustering algorithm (FNLC) is proposed to classify the fused image to generate changed and unchanged regions. In order to reduce the impact of noise while preserve details of changed regions, not only local but also non-local information are incorporated into the FNLC in a fuzzy way. Experiments on both small and large scale datasets demonstrate the state-of-the-art performance of the proposed method in real applications.

关键词： Speckle Radar polarimetry Wavelet transforms Neural networks clustering algorithms Synthetic aperture radar Image segmentation Synthetic aperture radar unsupervised change detection Contourlet fusion non-local clustering

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Federated clustering for Electricity Consumption Pattern Extraction

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IEEE TRANSACTIONS ON SMART GRID 2022年第3期13卷 2425-2439页

作者： Wang, Yi Jia, Mengshuo Gao, Ning Von Krannichfeldt, Leandro Sun, Mingyang Hug, Gabriela Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Swiss Fed Inst Technol Power Syst Lab CH-8092 Zurich Switzerland Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

The wide popularity of smart meters enables the collection of massive amounts of fine-grained electricity consumption data. Extracting typical electricity consumption patterns from these data supports the retailers in their understanding of consumer behaviors. In this way, diversified services such as personalized price design and demand response targeting can be provided. Various clustering algorithms have been studied for electricity consumption pattern extraction. These methods have to be implemented in a centralized way, assuming that all smart meter data can be accessed. However, smart meter data may belong to different retailers or even consumers themselves who are not willing to share their data. In order to better protect the privacy of the smart meter data owners, this paper proposes two federated learning approaches for electricity consumption pattern extraction, where the k-means clustering algorithm can be trained in a distributed way based on two frequently used strategies, namely model-averaging and gradient-sharing. Numerical experiments on two real-world smart meter datasets are conducted to verify the effectiveness of the proposed method.

关键词： Smart meters clustering algorithms Collaborative work Load modeling Data privacy Indexes Servers Federated clustering k-means smart meter data analytics pattern extraction data privacy

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User clustering and Power Allocation for Massive MIMO With NOMA-Inspired Cognitive Radio

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第7期71卷 7656-7664页

作者： Shili, Maroua Hajjaj, Moufida Ammari, Mohamed Lassaad Univ Carthage Higher Sch Com municat Tunis InnovCOM Lab Ariana 2083 Tunisia

This paper proposes a massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system based on cognitive radio (CR) networks. To reduce the users interference, we propose an efficient user clustering algorithm. Within a cluster, we consider the user with the weak channel gain as a primary user (PU) and the user with the strong channel gain as a secondary user (SU). Given the quality of service for both PUs and SUs, we propose a power allocation (PA) method that maximizes the sum rate. The PA optimization problem is discussed into three scenarios which depend on the available transmit power. Thereafter, we derive a robust solution for the PA issue under imperfect channel state information (CSI) assumptions. Simulation results demonstrate that our proposed NOMA-CR scheme can significantly improve the spectral efficiency (SE). In addition, the proposed robust PA scheme for the imperfect CSI case reduces the performance loss due to the channel uncertainty.

关键词： NOMA Radio frequency Quality of service Resource management Massive MIMO clustering algorithms Precoding NOMA cognitive radio massive MIMO power allocation user clustering

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Optimized Cluster Routing Protocol With Energy-Sustainable Mechanisms for Wireless Sensor Networks

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IEEE ACCESS 2024年 12卷 99661-99671页

作者： Abose, Tadele A. Tekulapally, Venumadhav Kejela, Diriba C. Megersa, Ketema T. Daka, Samuel T. Jember, Kehali A. Mattu Univ Dept Elect & Comp Engn Mattu Ethiopia Adama Sci & Technol Univ Dept Elect & Commun Adama Ethiopia

clustering algorithms have a key role in decreasing energy consumption and increasing network longevity in wireless sensor networks. This work advances on previous homogeneous and heterogeneous algorithms, including low-energy adaptive clustering hierarchical routing protocol (LEACH), distributed residual energy LEACH (DIS-RES-EL), residual energy LEACH (RES-EL), energy efficient LEACH (EEL), and stable election protocol (SEP), by introducing novel clustering methodologies. It introduces novel improved residual energy LEACH (IMP-RES-EL) and energy efficient stable election protocol (EE-SEP) to improve the efficiency of clustering algorithms in energy savings for homogeneous and heterogeneous wireless sensor networks. The simulation result shows that, in addition to prolonging network lifetime and optimal routing, these methods transported more data packets from the cluster to sensor nodes and then to base stations than other techniques. When compared to the stable election protocol (SEP), the proposed energy-efficient stable election protocol (EE-SEP) influences the number of bunch heads formed over their lifetime, the organization's stability, the number of nodes shipped off the base station from each cluster head, and the organization's overall lifetime. When comparing the two current algorithms, EE-SEP and LEACH, for various topologies, the findings demonstrate that EE-SEP is the most energy efficient directing convention for extending the previously described qualities. This attribute has not been discussed thus far. The results also show that the IMP-RES-EL algorithm successfully increases network lifespan while minimizing energy dissipation and transmissions between sensor nodes and base stations or cluster heads (CHs). For all of the suggested homogeneous and heterogeneous algorithms, network lifetime in rounds rose by 36%, aggregated data packets from CHs to BS increased by 44%, and total data packets to BSs improved by 20%.

关键词： clustering algorithms Wireless sensor networks Network topology Topology Energy efficiency Voting Base stations Heterogeneous networks Energy consumption Base station clustering algorithms cluster heads heterogeneous network homogeneous network routing protocol wireless sensor networks

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