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A Novel Multidimensional Statistics Denoising Algorithm Based on Gaussian Mixture Model for Photon-Counting LiDAR Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 13308-13323页

作者： Chen, Sitong Nie, Sheng Xi, Xiaohuan Xia, Shaobo Zhu, Feng Wang, Cheng Zhu, Xiaoxiao Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Changsha Univ Sci & Technol Dept Geomat Engn Changsha 410114 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Hubei Luojia Lab Wuhan 430079 Peoples R China China Earthquake Adm Inst Geol State Key Lab Earthquake Dynam Beijing 100029 Peoples R China

The micropulse multibeam photon-counting laser altimeter significantly improves the sampling density along the orbit while introducing abundant noise. Therefore, noise removal is crucial for the subsequent applications of the photon-counting laser altimeter. This study proposes a new multidimensional statistics algorithm based on the Gaussian mixture model for photon noise removal. The method can simultaneously utilize multiple point cloud statistics and avoid manually selecting denoising thresholds, dividing signals and noise adaptively. To evaluate the performance of the algorithm, it is applied to both simulated ICESat-2 data and real ICESat-2 data, and evaluation indicators including recall, precision, and the harmonic means of recall and precision are used for analysis. Experimental results indicate that compared to denoising using a single statistic, denoising with multidimensional statistics (specifically, the sum of the distance of k-nearest neighbors, the photon density, and the standard deviation of the height difference within an elliptic neighborhood of the target photon) generally yields better results. Multidimensional statistics can influence and constrain each other, leading to improved decision-making outcomes. After comparing the algorithm with mainstream DBSCAN and KNN methods, the results indicate that the GMM method achieves the best denoising results, with average F-values 5.22% and 7.26% higher than those of DBSCAN and KNN methods, respectively, across all datasets. In conclusion, this algorithm can comprehensively assess and make decisions based on different statistical measures, demonstrating robustness and adaptability in extracting signal photons, and is worth promoting in the photon denoising applications.

关键词： Photonics Noise Noise reduction Classification algorithms Point cloud compression clustering algorithms Training And land elevation satellite-2 (ICESat-2) cloud gaussian mixture model (GMM) ice multidimensional statistics photon denoising photon-counting lidar

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Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface clustering Algorithm

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 4680-4689页

作者： Guo, Zhizhou Liu, Baojian Wan, Wei Lu, Feng Niu, Xinliang Ji, Rui Jing, Cheng Li, Weiqiang Chen, Xiuwan Yang, Jun Bai, Zhaoguang Peking Univ Sch Earth & Space Sci Inst Remote Sensing & GIS Beijing 100871 Peoples R China China Meteorol Adm Key Lab Radiometr Calibrat & Validat Environm Sat Natl Satellite Meteorol Ctr Natl Ctr Space Weather Beijing Peoples R China Innovat Ctr FengYun Meteorol Satellite FYSIC Beijing 100081 Peoples R China China Acad Space Technol Xian Branch CAST XIAN Xian 710100 Peoples R China CSIC Inst Space Sci ICE Earth Observat Res Grp Barcelona 08193 Spain DFH Satellite Co Ltd Beijing 100094 Peoples R China

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm(3)center dot cm(-3). The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm(3)center dot cm(-3). This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.

关键词： Land surface Sea surface Surface roughness Rough surfaces Satellite broadcasting clustering algorithms Vegetation mapping Bufeng-1 (BF-1) global navigation satellite system reflectometry (GNSS-R) land surface clustering soil moisture (SM)

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Analysis of Chinese generic drug research hotspots and evolutionary trends based on clustering algorithm 24

Analysis of Chinese generic drug research hotspots and evolu...

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5th International Conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Zhang, Ludan Sheng, Xinmeng Zhao, Ziwei Cao, Shikui School of Health Management Changchun University of Chinese Medicine Chang Chun China Graduate School Changchun University of Chinese Medicine Chang Chun China

ISBN: (纸本)9798400718106

China’s generic drug industry has made great progress in recent years and become an important player in the global generic drug market. In this study, CNKI was used as the data source, and clustering and Cosine similarity algorithms were invoked to explore the development trend and research hotspots in the field of generic drugs in China, and the authors, issuing organizations, keywords, etc. of the screened eligible literature were analyzed for co-occurrence, and the knowledge map was drawn, and a total of 1,749 articles were included in the screened literature. The results show that the annual publication number of literature overall fluctuating growth trend. The authors of the publications mainly include Xu Mingyi, Shao Rong, and Niu Jianzhao, and the institutions are mainly China Academy of Food and Drug Control and China Pharmaceutical University. The research hotspots mainly focus on generic drugs, original drugs, drug registration, etc., of which band purchasing is the focus of generic drug research in recent years. The overall development of domestic generic drug research is good, but the influence of the published papers is insufficient. The cooperation between authors is relatively close, but the cross-institutional research cooperation needs to be strengthened. © 2024 ACM.

关键词： clustering algorithms

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Towards Dynamic Request Updating With Elastic Scheduling for Multi-Tenant Cloud-Based Data Center Network

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第2期11卷 2223-2237页

作者： Lu, Shuaibing Wu, Jie Shi, Jiamei Fang, Juan Zhang, Jiayue Liu, Haiming Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA Beijing Jiaotong Univ Sch Software Engn Beijing 100091 Peoples R China

Serving the ever-growing demand for computation, storage, and networking resources for multi-tenant in cloud computing is an important mission of Data Center Networks (DCNs). In this paper, we study the dynamic request updating problem, and our objective is to maximize the elasticity of cloud-based DCNs while achieving rapid response to multi-tenants. We use virtual clusters under the hose communication model to denote requests. Instead of using heuristic algorithms as the existing work does, this paper introduces a novel two-stage dynamic request updating framework with elastic resource scheduling strategy. In the first stage, we propose a multi-tenant fast initial provisioning scheme to realize the real-time response and analyze its optimality and complexity. Additionally, we provide a deep reinforcement learning-based dynamic updating strategy to enhance the elasticity of virtual clusters that are being used or scaling during the second stage. We train a fully connected neural network by creating a new feasible action set to realize the reduction, and it approximates the policy based on a proposed aggressive objective selection method to improve training speed while avoiding high dimensions caused by large scales of tenants and DCNs. Extensive evaluations demonstrate that our scheme outperforms baselines in terms of both elasticity and efficiency.

关键词： Dynamic scheduling Cloud computing Data centers Elasticity Neural networks clustering algorithms Processor scheduling Data center network dynamic request updating elastic scheduling multi-tenant resource provisioning

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Power Frequency Estimation Based on a Bayesian Online Changepoint Detector

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IEEE TRANSACTIONS ON POWER DELIVERY 2024年第4期39卷 2541-2551页

作者： Farshad, Mohammad Gonbad Kavous Univ Fac Basic Sci & Engn Dept Elect Engn Gonbad Kavous *** Iran

When the voltage and current signals measured in ac power systems are highly distorted due to high penetration of nonlinear loads and power electronics converters (e.g., in microgrids) and measurement noise, the traditional methods of frequency calculation will be associated with a significant error. This article introduces a novel power frequency estimation method based on a Bayesian online changepoint detector. In each time step of this method, the most efficient changepoints are detected in a window containing the latest measured voltage samples. The frequency is estimated and updated after applying some refining procedures on these changepoints. The results of numerical studies on various simulated and laboratory-generated signals under standard and severe conditions confirm that the suggested method can estimate the frequency accurately and robustly in dealing with frequency deviations, harmonics, interharmonics, dc components, noise, nonideal sampling, amplitude and phase angle modulations, and step variations. For instance, the proposed method can tolerate high noise levels and remain robust even at a signal-to-noise ratio of 20 dB, achieving mean absolute errors of 0.036 Hz without prefiltering and 0.004 Hz with prefiltering. It can also exhibit an invariant response to significant step variations in the voltage amplitude and phase angle.

关键词： Frequency estimation Detectors Vectors clustering algorithms Heuristic algorithms Bayes methods Noise Bayesian approach frequency measurement harmonic distortion noise

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Hierarchical Topology-Based Cluster Representation for Scalable Evolutionary Multiobjective clustering

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9846-9860页

作者： Zhu, Shuwei Xu, Lihong Goodman, Erik D. Tongji Univ Dept Elect & Informat Engn Shanghai 201804 Peoples R China Michigan State Univ BEACON Ctr Study Evolut Act E Lansing MI 48824 USA

Evolutionary multiobjective clustering (MOC) algorithms have shown promising potential to outperform conventional single-objective clustering algorithms, especially when the number of clusters k is not set before clustering. However, the computational burden becomes a tricky problem due to the extensive search space and fitness computational time of the evolving population, especially when the data size is large. This article proposes a new, hierarchical, topology-based cluster representation for scalable MOC, which can simplify the search procedure and decrease computational overhead. A coarse-to-fine-trained topological structure that fits the spatial distribution of the data is utilized to identify a set of seed points/nodes, then a tree-based graph is built to represent clusters. During optimization, a bipartite graph partitioning strategy incorporated with the graph nodes helps in performing a cluster ensemble operation to generate offspring solutions more effectively. For the determination of the final result, which is underexplored in the existing methods, the usage of a cluster ensemble strategy is also presented, whether k is provided or not. Comparison experiments are conducted on a series of different data distributions, revealing the superiority of the proposed algorithm in terms of both clustering performance and computing efficiency.

关键词： clustering algorithms Optimization Shape Diversity methods clustering methods Bipartite graph Task analysis clustering ensemble strategy multiobjective optimization number of clusters representation learning

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An Optimized Fuzzy clustering Algorithm for Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期126卷 2731-2751页

作者： Giri, Arindam Dutta, Subrata Neogy, Sarmistha Haldia Inst Technol Dept Comp Sci & Engn Haldia India Natl Inst Technol Dept Comp Sci & Engn Jamshedpur Bihar India Jadavpur Univ Dept Comp Sci & Engn Kolkata India

clustering is a promising solution to enhance lifetime of wireless sensor networks. Fuzzy logic is being used to address uncertainties in cluster head selection. In a multi-hop routing, cluster heads are overburdened with inter-cluster traffic in addition to intra-cluster traffic. In this paper, we propose an optimized fuzzy clustering algorithm for cluster head selection and a routing protocol to forward data to base station. In optimized fuzzy clustering algorithm, cluster heads are selected based on residual energy, distance from base station, and concentration of nodes using type-1 fuzzy logic. In order to route data to base station an energy efficient routing path is determined utilizing other cluster heads by particle swarm optimization. The fitness function of particle swarm optimization is defined so as to prolong the network lifetime keeping in mind wide application of WSN. Simulation results reveal that proposed algorithm attains longer lifetime and is able to forward more messages to sink.

关键词： Wireless sensor networks clustering algorithms Fuzzy logic Linear programming Particle swarm optimization

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MementoHash: A Stateful, Minimal Memory, Best Performing Consistent Hash Algorithm

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第4期32卷 3528-3543页

作者： Coluzzi, Massimo Brocco, Amos Antonucci, Alessandro Leidi, Tiziano Univ Appl Sci & Arts Southern Switzerland Dept Innovat Technol CH-6962 Lugano Viganello Switzerland

Consistent hashing is used in distributed systems and networking applications to spread data evenly and efficiently across a cluster of nodes. In this paper, we present MementoHash, a novel consistent hashing algorithm that eliminates known limitations of state-of-the-art algorithms while keeping optimal performance and minimal memory usage. We describe the algorithm in detail, provide a pseudo-code implementation, and formally establish its solid theoretical guarantees. To measure the efficacy of MementoHash, we compare its performance, in terms of memory usage and lookup time, to that of state-of-the-art algorithms, namely, AnchorHash, DxHash, and JumpHash. Unlike JumpHash, MementoHash can handle random failures. Moreover, MementoHash does not require fixing the overall capacity of the cluster (as AnchorHash and DxHash do), allowing it to scale indefinitely. The number of removed nodes affects the performance of all the considered algorithms. Therefore, we conduct experiments considering three different scenarios: stable (no removed nodes), one-shot removals (90% of the nodes removed at once), and incremental removals. We report experimental results that averaged a varying number of nodes from ten to one million. Results indicate that our algorithm shows optimal lookup performance and minimal memory usage in its best-case scenario. It behaves better than AnchorHash and DxHash in its average-case scenario and at least as well as those two algorithms in its worst-case scenario.

关键词： clustering algorithms Peer-to-peer computing Memory management Distributed databases Measurement Arrays Hash functions Consistent hashing load balancing scalability

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CouldPin-Fast: Effient and Effective Root Cause Localization for Shared Bandwidth Package Traffic Anomalies in Public Cloud Networks

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第3期17卷 850-864页

作者： Zhang, Shize Zhao, Yunfeng Lu, Jianyuan Yang, Shuai Lyu, Biao Zhu, Shunmin Dong, Enhuan Wang, Zhiliang Yang, Jiahai Alibaba Grp Hangzhou 310052 Peoples R China Tsinghua Univ BNRist Inst Network Sci & Cyberspace Beijing 100084 Peoples R China Zhejiang Univ Hangzhou 310027 Peoples R China Quan Cheng Lab Jinan 250103 Peoples R China

As cloud services become increasingly widespread, many public cloud tenants opt for Shared Bandwidth Package (sBwp) services for inbound/outbound communication. The sBwp service allows tenants to purchase shared bandwidth for multiple virtual machines (VMs) instead of buying it individually, which is a convenient and cost-effective traffic management mode. However, the sBwp service presents new challenges for operators to identify the root cause of abnormal sBwp traffic, especially in large-scale, globally distributed public clouds with millions of users. Developing a localization system in public cloud faces several challenges, including dynamic scalability, hyper-scale data efficiently obtaining, and complex application scenarios. To address these challenges, we propose a two-stage localization method called CloudPin-Fast. First, CloudPin-Fast employs a cold-start mode to meet dynamic requirements. Second, CloudPin-Fast implements a pre-filter to reduce the transmission and processing of hyper-scale data. Finally, CloudPin-Fast uses an anomaly localization algorithm based on multi-dimensional statistics fusion in the second stage to cover complex scenarios. The evaluation results on four production datasets have shown superior efficiency and effectiveness. We also share lessons learned from deploying CloudPin-Fast for over a year in a world-renowned public cloud vendor.

关键词： Cloud computing Location awareness Bandwidth Time series analysis Heuristic algorithms clustering algorithms Virtual machining Public cloud root cause localization shared bandwidth package traffic anomaly

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Fast Self-Supervised clustering With Anchor Graph

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第9期33卷 4199-4212页

作者： Wang, Jingyu Ma, Zhenyu Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Astronaut Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China

Benefit from avoiding the utilization of labeled samples, which are usually insufficient in the real world, unsupervised learning has been regarded as a speedy and powerful strategy on clustering tasks. However, clustering directly from primal data sets leads to high computational cost, which limits its application on large-scale and high-dimensional problems. Recently, anchor-based theories are proposed to partly mitigate this problem and field naturally sparse affinity matrix, while it is still a challenge to get excellent performance along with high efficiency. To dispose of this issue, we first presented a fast semisupervised framework (FSSF) combined with a balanced K-means-based hierarchical K-means (BKHK) method and the bipartite graph theory. Thereafter, we proposed a fast self-supervised clustering method involved in this crucial semisupervised framework, in which all labels are inferred from a constructed bipartite graph with exactly k connected components. The proposed method remarkably accelerates the general semisupervised learning through the anchor and consists of four significant parts: 1) obtaining the anchor set as interim through BKHK algorithm;2) constructing the bipartite graph;3) solving the self-supervised problem to construct a typical probability model with FSSF;and 4) selecting the most representative points regarding anchors from BKHK as an interim and conducting label propagation. The experimental results on toy examples and benchmark data sets have demonstrated that the proposed method outperforms other approaches.

关键词： Sparse matrices Bipartite graph clustering algorithms Task analysis Unsupervised learning Data models Supervised learning Bipartite graph label propagation self-supervised learning semisupervised framework special selection

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