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Riemannian Distance-Based Fast k-medoids clustering algorithm for Cooperative Spectrum Sensing

IEEE SYSTEMS JOURNAL 2022年第1期16卷 880-890页

作者： Zhang, Shunchao Wang, Yonghua Zhang, Yongwei Wan, Pin Zhuang, Jiawei Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

Spectrum sensing is a fundamental component in a cognitive radio network to utilize the frequency bands effectively. In this article a fast Riemannian distance-based k-medoids (FRDk)-based cooperative spectrum sensing (CSS) method is developed to identify the state of primary user (PU). In particular, two CSS scenarios are considered, one is secondary users (SUs) with a single antenna and the other is SUs with multiple antennas. In the multiantenna case, a Riemannian mean-based data fusion method is proposed to fuse sensing data from SUs with multiple antennas. To implement CSS, we propose a FRDk-based framework, where SUs collect sensing data, preprocess, and upload these data to an appointed fusion center (FC). Then, the FC transforms these data as samples on a manifold and uses the FRDk algorithm to train a classifier for identifying the state of PU. Furthermore, the convergence and the complexity analysis of training process are presented. Finally, the effectiveness of the proposed FRDk-based CSS method is verified under different conditions.

关键词： Cascading style sheets Sensors Manifolds Covariance matrices clustering algorithms Wireless communication Wireless sensor networks Cognitive radio (CR) cooperative spectrum sensing (CSS) k-medoids clustering algorithm Riemannian distance Riemannian mean

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A k-medoids clustering algorithm for Mixed Feature-type Symbolic Data

A <i>K</i>-Medoids Clustering Algorithm for Mixed Feature-ty...

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IEEE International Conference on Systems, Man and Cybernetics (SMC)

作者： de Assis, Elaine Cristina de Souza, Renata M. C. R. Univ Fed Pernambuco Ctr Comp Sci UFPE Recife PE Brazil

ISBN: (纸本)9781457706530

A k-medoids clustering algorithm for mixed feature-type symbolic data represented by categorical, interval-valued and histogram-valued is presented in this paper. The algorithm furnishes a partition and a prototype to each class by optimizing an adequacy criterion based on a suitable standardized Euclidean distance. To evaluate the proposed algorithm, several real symbolic data sets are considered and the results furnished by this algorithm are compared with the results furnished by a partitional algorithm for mixed feature-type symbolic data of the literature of symbolic data analysis in terms of the the correct Rand index.

关键词： symbolic data analysis k-medoids clustering algorithm mixed feature-type symbolic data standardized Euclidean distance

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User Interest Modeling for P2P Document Sharing Systems based on k-medoids clustering algorithm 7

User Interest Modeling for P2P Document Sharing Systems base...

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7th International Joint Conference on Computational Sciences and Optimization (CSO)

作者： Qin, Chunxiu Yang, Zhimo Liu, Huailiang Xidian Univ Sch Econ & Management Xian 710071 Peoples R China

ISBN: (纸本)9781479953721

User interest modeling is an important way for P2P document sharing systems to improve the level of information service such as personalized information retrieval and document recommendation. Based on k-medoids clustering, the paper presents a method of user interest modeling for P2P document sharing systems. Staring from the perspective of the shared document, the proposed approach creates the initial user interest model with k-mediods clustering algorithm. Then, combining with the related results of user's historical queries, the initial user interest model is improved and complete user interest model is obtained.

关键词： P2P document sharing system user interest model k-medoids clustering algorithm

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Hyperspectral imaging technique based on Geodesic k-medoids clustering and Gabor wavelets for pork quality evaluation

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2017年第6期15卷 1750066-1750066页

作者： Zeng, Shan Chen, Ling Jiang, Liang Gao, Chongjun Wuhan Polytech Univ Coll Math & Comp Sci Wuhan 430023 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China

This paper presents a pork quality evaluation method based on the hyperspectral image datasets of 96 pork samples in the range of 400-1000 nm. First, through the k-medoids clustering algorithm based on manifold distance, 30 important wavelengths are selected from 753 wavelengths, and final 8 optimum wavelengths are obtained based on the discriminant value and the spectral resolution. Then, the two-dimensional Gabor wavelet transform is used to extract the eight texture features of the image under the final eight wavelengths respectively, to form a 64-dimensional features of pork quality. Finally, using the fussy C-means (FCM) algorithm based on Isomap dimension reduction, the pork quality evaluation model is constructed. The result of wavelength extraction experiments show that although there is a strong linear correlation between adjacent bands in the hyperspectral image, there is an obvious nonlinear manifold relation in the whole band. Therefore, the k-medoids clustering algorithm based on manifold distance in this paper is more reasonable than the traditional principal component analysis (PCA) in characteristic wavelength selection. According to the experiment of pork quality evaluation, two-dimensional Gabor wavelet transform can extract the texture characteristics of pork better. Compared with the FCM algorithm based on PCA, the FCM algorithm based on Isomap can better solve the high-dimensional clustering problem, and can distinguish fresh chilled meat, frozen-thawed meat and spoiled meat accurately. The study shows that hyperspectral image technology can be used in pork quality evaluation.

关键词： Hyperspectral image technology Gabor wavelet k-medoids clustering algorithm pork quality evaluation

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clustering algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

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IEEE ACCESS 2020年 8卷 5777-5786页

作者： Zhang, Shunchao Wang, Yonghua Wan, Pin Zhuang, Jiawei Zhang, Yongwei Li, Yi Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China South Cent Univ Nationalities Hubei Key Lab Intelligent Wireless Commun Wuhan 430074 Peoples R China

In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on k-medoids and Mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast k-medoids or Mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme.

关键词： Cognitive radio robust cooperative spectrum sensing sensing data fusion k-medoids clustering algorithm Mean-shift clustering algorithm

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A dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR

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ADVANCES IN SPACE RESEARCH 2024年第7期74卷 2831-2845页

作者： Wang, Tianyu Zhang, Rui Yang, Yunjie Liu, Anmengyun Jiang, Yao Lv, Jichao Tu, Jinsheng Song, Yunfan Southwest Jiaotong Univ Fac Geosci & Engn Chengdu 610031 Peoples R China Hohai Univ Sch Earth Sci & Engn Nanjing 211100 Peoples R China China Meteorol Adm Inst Plateau Meteorol Chengdu Peoples R China Heavy Rain & Drought Flood Disasters Plateau & Bas Chengdu 610072 Peoples R China

Due to the influence of environmental factors (i.e., terrain and surface coverage) around the GPS receivers, the snow depth retrieval results obtained by the existing global positioning system interferometric reflection (GPS-IR) method show significant variability. The resulting loss of reliability and accuracy limits the broad application of this technology. Therefore, this paper proposes a dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR to fully leverage multi-source satellite observation data for automatic and high-precision snow depth retrieval. The model employs Dynamic Time Warping distance measurement combined with the k-medoids clustering algorithm to categorize frequency sequences obtained from various satellite trajectories, facilitating effective integration of multi-constellation data and acquisition of optimal datasets. Additionally, Long Short-Term Memory networks are integrated to capture and process the long-term dependencies in snow depth data, enhancing the model's adaptability in handling time-series data. Validated against SNOTEL measured data and standard machine learning algorithms (such as BP Neural Networks, RBF, and SVM), the model's retrieval capability is confirmed. For P351 and AB39 sites, the correlation coefficients for L1 band data retrieval were both 0.996, with RMSEs of 0.051 and 0.018 m, respectively. The experiment results show that the proposed model demonstrates superior precision and robustness in snow depth retrieval compared to the previous method. Then, we analyze the accuracy loss caused by sudden snowfall events. The proposed model and methodology offer new insights into the in-depth study of snow depth monitoring. (c) 2024 COSPAR. Published by Elsevier B.V.

关键词： Dynamic Time Warping (DTW) Global Positioning System Interferometric Reflectometry (GPS-IR) k-medoids clustering algorithm Long Short-Term Memory (LSTM) networks Snow depth retrieval

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clustering-based privacy preserving anonymity approach for table data sharing

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2020年第4期11卷 768-773页

作者： Piao, Chunhui Liu, Liping Shi, Yajuan Jiang, Xuehong Song, Ning Shijiazhuang Tiedao Univ Sch Informat Sci & Technol Shijiazhuang Hebei Peoples R China Construct Informat Ctr Hebei Prov Dept Housing & Urban Rural Dev Shijiazhuang Hebei Peoples R China Shijiazhuang Tiedao Univ Sci & Technol Dept Shijiazhuang Hebei Peoples R China

Government data sharing can effectively improve the efficiency and quality of government services and enhance the ability of providing government services. However, data sharing may bring the risk of citizen privacy leakage. It is a challenging problem on improving government governance and service levels when sharing government data while guaranteed citizens' privacy. For the diversity types and complex attributes of government data, this paper proposes a cluster-based anonymous table data sharing privacy protection method (CATDS). Firstly, preprocessing the data table. According to the correlation degree between attributes, the clustering algorithm is used to divide the data attribute column to generate multiple tables. That can reduce the data dimension and improve the algorithm execution speed. Then clustering the table data usingk-medoids clustering algorithm to generate a clustering result table that initially satisfies the k-anonymity requirement. That can reduce the next generalization degree and improve the data availability. Finally, anonymizing the resulting clusters through generalization technique to ensure the privacy of the shared data. By comparing the CATDS with the Incognito algorithm which is a classical k-anonymity algorithm, it is proved that the proposed algorithm can effectively reduce the amount of information loss and improve the availability of shared table data while protecting the private information of shared table data.

关键词： Data sharing Table data Privacy-preserving k-medoids clustering algorithm k-Anonymity

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A cooperative spectrum sensing method based on signal decomposition and k-medoids algorithm

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INTERNATIONAL JOURNAL OF SENSOR NETWORkS 2019年第3期29卷 171-180页

作者： Wang, Yonghua Zhang, Shunchao Zhang, Yongwei Wan, Pin Wang, Shikun Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Boston Coll Comp Sci Dept Chestnut Hill MA 02467 USA

To solve the problem of low sensing performance and low accuracy of threshold estimation in traditional spectrum sensing systems with low signal-to-noise ratio (SNR), we proposes a cooperative spectrum sensing (CSS) method based on signal decomposition and k-medoids clustering algorithm. Firstly, to improve the sensing performance of the system in the case of fewer cooperative secondary users, a feature extraction method based on empirical mode decomposition and matrix decomposition and recombination is proposed. The method can accurately acquire the characteristic information of the sampled signal and improve the feature accuracy. Finally, the features are classified using the k-medoids clustering algorithm. In the experimental part, the result shows that the method can effectively improve the sensing performance of the spectrum sensing system at low SNR.

关键词： spectrum sensing decomposition and recombination k-medoids clustering algorithm feature extraction

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Smart Wristwatches Employing Finger-Conducted Voice Transmission System

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2019年第2期15卷 965-975页

作者： Song, kwangsub Chang, Joon-Hyuk Hanyang Univ Dept Elect Engn Seoul 04763 South Korea

The principal aim of this paper is to present a novel speech transmission system that conveys speech between an actuator in a wearable wristwatch and the ear bone of a user through a finger. If an individual wears a smart watch equipped with an actuator that can play speech sent via communication lines, speech vibrations propagate from the actuator to fingertips through the human tissue and bone. When an individual places his or her finger into their ear, speech conducted through the finger can be registered and heard. While listening to finger-conducted speech, sounds are muffled, significantly degrading the intelligibility of speech. To mitigate this problem, a formant enhancement filter is applied to the speech prior to being fed into the actuator. With this method, the impulse response of human tissue and bones between the fingertips and wrist on which the watch is worn is first estimated to account for speaker-dependent distortion. Based on the estimated impulse response, a gain filter is used to boost the sound spectra, especially within the formant regions, to compensate for frequency distortion prior to speech transmission. On the other hand, since the impulse responses of humans are quite different for each individual, we propose the novel idea of a personalized algorithm that guides users to select an appropriate gain filter, using the k-medoids clustering algorithm. Also, when an individual uses the proposed system, speech quality is degraded due to the ambient noise and acoustic echo between the microphone and actuator in the watch. Thus, to reduce background noise and acoustic echo, an integrated acoustic echo and background noise suppression algorithm is employed. Extensive simulations of the proposed system were performed by creating a novel phantom, which mimics the human hand with an aid of an ear simulator. We demonstrate that the proposed system has improved speech quality, when transmitting speech from the wearable wristwatch to a human perceptua

关键词： Finger-conducted speech transmission formant enhancement formant linear predictive coding co-efficients k-medoids clustering algorithm wearable wrist-watch

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Delamination Assessment of Glass-Fiber Reinforced Polymer Using Microwave Technique

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IEEE SENSORS JOURNAL 2024年第12期24卷 19050-19060页

作者： Shin Yee, Tan Akbar, Muhammad Firdaus Shrifan, Nawaf H. M. M. Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Gelugor 14300 Pulau Pinang Malaysia Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Kampar Selangor 43000 Malaysia

Composite insulations are routinely deployed as protective measures against high-temperature environments and corrosive influences on metal substrates. However, aging and cyclic processes cause defects, including metal substrate-insulating layer delamination. Despite the growth of artificial intelligence-driven microwave approaches for defect detection, precise quantification of defect severity, particularly in terms of size, presents a low accuracy. These challenges are due to the intrinsic irregularity characterizing glass fiber reinforced polymers (GFRP), coupled with the presence of outliers within microwave measurement data. This study presents a novel microwave approach with a k-medoid clustering algorithm to improve the reliability of defect detection. This method measures and analyzes wave reflection from a waveguide probe operating at 18-26.5 GHz, scanning on a coated metal substrate. A well-calibrated vector network analyzer (VNA) evaluates the reflected wave responses, which are then analyzed using a Gaussian filter and the k-medoids clustering technique. The acquired microwave data are transformed to the time domain to reveal subsurface defects hidden behind the insulation. The k-medoid technique uses these properties to cluster data for accurate defect inspection and sizing. By eliminating dataset outliers, defect size assessment beneath insulating layers is improved. The proposed algorithm is tested using measured data for validation. The proposed algorithm achieves an accuracy of 81.21% in predicting defect size and can locate defects down to 1 mm depth. The proposed algorithm emerges as a robust and promising model for defect detection, providing substantial contributions across diverse industrial realms.

关键词： Defect under insulation delamination Gaussian filter glass fiber reinforced polymer (GFRP) k-medoids clustering algorithm

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