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affinity propagation clustering-aided two-label hierarchical extreme learning machine for Wi-Fi fingerprinting-based indoor positioning

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第6期13卷 3303-3317页

作者： Alitaleshi, Atefe Jazayeriy, Hamid Kazemitabar, Javad Babol Noshirvani Univ Technol Fac Elect & Comp Engn Babol Iran

With the rapid growth of indoor location-based services (LBS) along with the internet of things (IoT) applications in daily life, the development of indoor positioning systems has attracted significant attention. Since the global positioning system (GPS) struggles to perform indoors, cost-effective Wi-Fi fingerprinting has become a popular alternative for indoor positioning over the past decade. Accurate identification of the floor level in complex multi-floor buildings is a prerequisite for the success of the positioning systems. Nevertheless, Wi-Fi positioning accuracy is not satisfactory, mainly due to the signal overlap in the adjacent floors with hollow areas, device heterogeneity, and signal attenuation. In this paper, to overcome these issues, we propose a two-label hierarchical extreme learning machine. The affinity propagation clustering is applied to place overlapping signal points in the same cluster and improve deep learning-based ELM performance reducing the learning parameters of the classification layer. Then, two clusters and a floor label are assigned to each sample. In the training phase, the proposed model is trained based on the new labeled data. The final location is calculated based on the determined cluster and floor, in the positioning phase. The experimental result shows that the proposed method increases the average positioning accuracy by up to 40% compared to state-of-the-art methods. Also, in terms of floor identification, it outperforms compared methods.

关键词： affinity propagation clustering Indoor positioning Multi-floor building Two-label hierarchical extreme learning machine Wi-Fi fingerprinting

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affinity propagation clustering of Measurements for Multiple Extended Target Tracking

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SENSORS 2015年第9期15卷 22646-22659页

作者： Zhang, Tao Wu, Renbiao Tianjin Univ Sch Elect Informat Engn Tianjin 300072 Peoples R China Civil Aviat Univ China Tianjin Key Lab Adv Signal Proc Tianjin 300300 Peoples R China

More measurements are generated by the target per observation interval, when the target is detected by a high resolution sensor, or there are more measurement sources on the target surface. Such a target is referred to as an extended target. The probability hypothesis density filter is considered an efficient method for tracking multiple extended targets. However, the crucial problem of how to accurately and effectively partition the measurements of multiple extended targets remains unsolved. In this paper, affinity propagation clustering is introduced into measurement partitioning for extended target tracking, and the elliptical gating technique is used to remove the clutter measurements, which makes the affinity propagation clustering capable of partitioning the measurement in a densely cluttered environment with high accuracy. The Gaussian mixture probability hypothesis density filter is implemented for multiple extended target tracking. Numerical results are presented to demonstrate the performance of the proposed algorithm, which provides improved performance, while obviously reducing the computational complexity.

关键词： multiple extended target tracking measurement partitioning affinity propagation clustering probability hypothesis density filter elliptical gating

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affinity propagation clustering for Older Adults Daily Routine Estimation

Affinity Propagation Clustering for Older Adults Daily Routi...

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11th International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Jimenez Martin, Ana Miranda Gordo, Ismael Garcia Dominguez, Juan Jesus Torres-Sospedra, Joaquin Plaza, Sergio Lluva Gualda Gomez, David Univ Alcala Dept Elect Alcala De Henares Spain UBIK Geospatial Solut SL Castellon de La Plana Spain King Juan Carlos Univ Signal Theory & Commun Dept Madrid Spain

ISBN: (纸本)9781665404020

This work proposes a system that allows estimating and monitoring daily routine changes in a sensorized home through Machine Learning and affinity propagation clustering techniques. Older adults often have low-activity and rather routine lives, which means that these routines can be an indicator of their physical and cognitive state in order to lead an independent life and healthy ageing. Therefore, it is important to be able to generate precise routines, as well as to monitor them, to trigger alarms in case of significant variations. This proposal defines routines based on the time spent in each of the monitored rooms. The daily time in each room is estimated trough a Bluetooth Low Energy-based indoor localization system. The localization is obtained through the Bluetooth received signal strength, which is processed with different supervised algorithms and fused with the acceleration measured by the mobile receiver, obtaining an accuracy above 96 %. From these data, the sample has been synthetically expanded to generate four different routines, on which the proposed algorithm based on Principal Component Analysis and affinity propagation clustering has been tested, obtaining very promising results.

关键词： affinity propagation clustering routine generation indoor localization systems Bluetooth low energy (BLE) localization

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Automatic mode tracing of dispersion relations for guided waves in elastic waveguides via physics-driven affinity propagation (AP) clustering

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 222卷

作者： Yu, Xudong Liu, Bohan Shen, Hai Zuo, Peng Fan, Zheng Beihang Univ Sch Astronaut Beijing 100191 Peoples R China ASTAR Adv Remfg & Technol Ctr ARTC 3 Cleantech Loop01-01 CleanTech Two Singapore 637143 Singapore Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Ave Singapore 639798 Singapore

Guided ultrasonic waves are attractive screening tools for elongated engineering structures due to their ability to propagate over long distances and flexibility in selecting mode-frequency combinations. Both computing dispersion solutions and accurately tracing them into dispersion curves are essential for guided waves' non-destructive evaluation (NDE) and structural health monitoring (SHM) applications. Complex waveguide problems often require numerical methods to compute the eigen-solutions at discrete frequencies, and manual routines are usually adopted to trace different guided wave modes by directly comparing their mode shapes. However, challenges arise in intricate dispersion relations involving mode coupling, mode veering, and mode splitting. To address this, we propose an automated mode-tracing technique for guided waves via affinity propagation (AP) clustering. Upon solving the associated eigenvalue waveguide problem, physical field quantities are extracted to capture the alignment or dissimilarity features of different eigen-solutions. The physics-driven similarity matrix construction is performed by employing the weighted cosine distance and the similarity propagation in a high-dimensional feature space. High-quality set of exemplars and corresponding clusters (i.e. well traced guided wave modes) can be iteratively obtained using an optimized AP clustering algorithm. This paper presents the principles of the proposed technique, and then validates and illustrates its use through typical numerical examples. Accurate tracing of the dispersion curves has been achieved, independent of the eigenvalue computation procedures, and its robustness has also been manifested.

关键词： Guided waves Automatic mode tracing Dispersion curves affinity propagation clustering Complex waveguide problems

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A density-adaptive affinity propagation clustering algorithm based on spectral dimension reduction

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NEURAL COMPUTING & APPLICATIONS 2014年第7-8期25卷 1557-1567页

作者： Jia, Hongjie Ding, Shifei Meng, Lingheng Fan, Shuyan China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China

As a novel clustering method, affinity propagation (AP) clustering can identify high-quality cluster centers by passing messages between data points. But its ultimate cluster number is affected by a user-defined parameter called self-confidence. When aiming at a given number of clusters due to prior knowledge, AP has to be launched many times until an appropriate setting of self-confidence is found. K-AP algorithm overcomes this disadvantage by introducing a constraint in the process of message passing to exploit the immediate results of K clusters. The key to K-AP clustering is constructing a suitable similarity matrix, which can truly reflect the intrinsic structure of the dataset. In this paper, a density-adaptive similarity measure is designed to describe the relations between data points more reasonably. Meanwhile, in order to solve the difficulties faced by K-AP algorithm in high-dimensional data sets, we use the dimension reduction method based on spectral graph theory to map the original data points to a low-dimensional eigenspace and propose a density-adaptive AP clustering algorithm based on spectral dimension reduction. Experiments show that the proposed algorithm can effectively deal with the clustering problem of datasets with complex structure and multiple scales, avoiding the singularity problem caused by the high-dimensional eigenvectors. Its clustering performance is better than AP clustering algorithm and K-AP algorithm.

关键词： Spectral dimension reduction Distance measure Similarity matrix affinity propagation clustering

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A Novel Method of Recognizing Disturbance Events in Φ-OTDR Based on affinity propagation clustering and Perturbation Signal Selection

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IEEE SENSORS JOURNAL 2021年第12期21卷 13272-13282页

作者： Xu, Shaohua Qin, Zujun Zhang, Wentao Xiong, Xianming Li, Heng Wei, Zexian Postolache, Octavian Adrian Mi, Chao Guilin Univ Elect Technol Guangxi Key Lab Optoelect Informat Proc Guilin 541004 Peoples R China Guangxi Transportat Sci & Technol Grp Co Ltd Nanning 530007 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Wireless Wideband Commun & Signal Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Automat Detecting Technol & Instr Guilin 541004 Peoples R China Inst Telecomunicacoes P-1049001 Lisbon Portugal Iscte IUL Av Rovisco Pais P-1049001 Lisbon Portugal Shanghai Maritime Univ Container Supply Chain Technol Engn Res Ctr Minist Educ Shanghai 201306 Peoples R China

In order to reduce computational redundancy, and to recognize the perturbation signals quickly and efficiently, this paper proposed a novel recognition method for Phi-OTDR system based on affinity propagation clustering. Variational mode decomposition (VMD) are utilized to accurately extract the perturbation signal characteristic information buried in the vibration signals. Moreover, in order to improve the efficiency of clustering algorithm, a novel perturbation signal selection technique was developed in this work, whose effectiveness was experimentally verified. The proposed novel recognition method is then applied to the perturbation signal detection in Phi-OTDR system. The obtained results demonstrated that the proposed method is reliable and accurately identify different perturbation categories of signals.

关键词： Phi-OTDR variational mode decomposition affinity propagation clustering perturbation signal selection signal recognition

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Lung Cancer Classification and Gene Selection by Combining affinity propagation clustering and Sparse Group Lasso

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CURRENT BIOINFORMATICS 2020年第7期15卷 703-712页

作者： Li, Juntao Chang, Mingming Gao, Qinghui Song, Xuekun Gao, Zhiyu Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Henan Peoples R China Henan Univ Chinese Med Sch Informat Technol Zhengzhou 450046 Peoples R China

Background: Cancer threatens human health seriously. Diagnosing cancer via gene expression analysis is a hot topic in cancer research. Objective: The study aimed to diagnose the accurate type of lung cancer and discover the pathogenic genes. Methods: In this study, affinity propagation (AP) clustering with similarity score was employed to each type of lung cancer and normal lung. After grouping genes, sparse group lasso was adopted to construct four binary classifiers and the voting strategy was used to integrate them. Results: This study screened six gene groups that may associate with different lung cancer sub-types among 73 genes groups, and identified three possible key pathogenic genes, KRAS, BRAF and VDR. Furthermore, this study achieved improved classification accuracies at minority classes SQ and COID in comparison with other four methods. Conclusion: We propose the AP clustering based sparse group lasso (AP-SGL), which provides an alternative for simultaneous diagnosis and gene selection for lung cancer.

关键词： Lung cancer gene selection affinity propagation clustering sparse group lasso multi-classification miRNA

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Data-Driven Resource Management for Ultra-Dense Small Cells: An affinity propagation clustering Approach

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2019年第3期6卷 267-279页

作者： Wang, Li-Chun Cheng, Shao-Hung Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 300 Taiwan

Deploying dense small cells is the key to providing high capacity, but raise the serious issue of energy consumption and inter-cell interference. To understand the behaviors of ultra-dense small cells (UDSC) with dynamic interference and traffic patterns, this paper presents a data-driven resource management (DDRM) framework to implement power control and channel rearrangement in UDSC. We find that the inter-cell interference can be used to describe the affinity of cells. Thus, we propose an unsupervised learning algorithm for UDSC, called affinity propagation power control (APPC) mechanism. In principle, APPC first groups small cells into different clusters and identifies cluster centers. Next, the transmission power of a cluster center is decreased to reduce the interference to the neighboring cells' users in this cluster. Since lowering transmission power of a cluster center cell may cause the performance degradation to the users at the cell edge, a victim-aware channel rearrangement (VACR) mechanism is further designed to adjust the channel usage bandwidth of the neighboring cells in order to guarantee the quality of service of these victimized users. Our simulation results show that the DDRM framework can significantly improve energy efficiency and throughput in UDSC compared to the existing approaches.

关键词： affinity propagation clustering data-driven ultra-dense small cells energy efficiency quality of service

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A Hybrid Algorithm Based on S Transform and affinity propagation clustering for Separation of Two Simultaneously Artificial Partial Discharge Sources

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2015年第2期22卷 1042-1060页

作者： Wang, Ke Li, Jinzhong Zhang, Shuqi Liao, Ruijin Wu, Feifei Yang, Lijun Li, Jian Grzybowski, Stanislaw Yan, Jiaming China Elect Power Res Inst Beijing 100192 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Mississippi State Univ High Voltage Lab Dept Elect & Comp Engn Mississippi State MS 39762 USA China Univ Min & Technol Sch Elect Power Engn Xuzhou 221116 Peoples R China

This paper presents a hybrid algorithm for separation of two simultaneous partial discharge (PD) sources of oil-paper insulation based on S transform (ST) and affinity propagation clustering (APC). Similarities between PD pulses are acquired by comparisons of the corresponding ST-amplitude (STA) matrices, which are input of APC to realize the PD pulses separation and obtain two sub-groups of PD pulses having similar time-frequency characteristics. A classification-based model for separation results validation are developed using a support vector machine with particle swarm optimization (PSO-SVM) classifier and 27 phase-resolved partial discharge (PRPD) statistical features. Artificial defect models are made to simulate two PD sources simultaneously active. Several PD data of different two simultaneous PD sources are acquired in laboratory and adopted for algorithm testing. It is shown that ST computes very fast and is suitable for online PD applications. The separation results of PD data produced in laboratory are verified by the developed validation model, which demonstrate that ST combined with APC can effectively eliminate pulse-shaped noises (PSN) and separate pulses of two simultaneous PD sources. Comparisons with typical separation methods from the state of the art provide better separation performance of the proposed ST combined with APC algorithm for two simultaneous PD sources. The obtained results in this work provide a solid basis for the data mining technique that can be used to facilitate PD diagnosis of transformers.

关键词： Partial discharges pulses separation S transform time-frequency similarity affinity propagation clustering diagnostics oil-paper insulation

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Hierarchical Prediction Based on Two-Level affinity propagation clustering for Bike-Sharing System

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IEEE ACCESS 2018年 6卷 45875-45885页

作者： Jia, Wenzhen Tan, Yanyan Li, Jing Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Shandong Peoples R China Shandong Normal Univ Inst Data Sci & Technol Jinan 250358 Shandong Peoples R China Shandong Management Univ Sch Mech & Elect Engn Jinan 250358 Shandong Peoples R China

Bike-sharing system is a new transportation that has emerged in recent years. More and more people will choose to ride bicycle sharing at home and abroad. While we use shared bicycles conveniently, there are also unfavorable factors that affect the customer's riding experience in the bicycle-sharing system. Due to the rents or returns of bikes at different stations in different periods are imbalanced, the bikes in the system need to be rebalanced frequently. Therefore, there is an urgent need to predict and reallocate the bikes in advance. In this paper, we propose a hierarchical forecasting model that predicts the number of rents or returns to each station cluster in a future period to achieve redistribution. First, we propose a two-level affinity propagation clustering algorithm to divide bike stations into groups where migration trends of bikes among stations as well as geographical locations information are considered. Based on the two-level hierarchy of stations, the total rents of bikes are predicted. Then, we use a multi-similarity-based inference model to forecast the migration proportion of inter-cluster and across cluster, based on which the rents or returns of bikes at each station can be deduced. In order to verify the effectiveness of our two-level hierarchical prediction model, we validate it on the bike-sharing system of New York City and compare the results with those of other popular methods obtained. Experimental results demonstrate the superiority over other methods.

关键词： Bike-sharing system prediction affinity propagation clustering migration trend

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