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Application and visualization of typical clustering algorithms in seismic data analysis

Procedia Computer Science 2019年 151卷 171-178页

作者： Z. Fan X. Xu Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing 210023 China

Earthquake brings enormous loss of lives and properties to human beings due to its suddenness, destructiveness and inscrutability. The new techniques for analyzing seismic data can reveal the distribution of earthquakes, which helps us master the laws of earthquake disasters and reduce the risks brought by them. In this paper, we applied K-means and DBSCAN clustering algorithms to the analysis of seismic data. Their performances in fitting seismic belts with seismic datasets are compared. First, we map the positional parameters in the seismic data to coordinate points on a two-dimensional plane and then cluster them with the DBSCAN algorithm. In addition, we combine the magnitude and depth properties of seismic data, use the Elbow method to find the best K value, and then classifies the dataset by K-means algorithm. We visualize the results, and the distinction of each classification is clear. The experimental results show that the DBSCAN algorithm has a better effect on fitting the seismic belt, and the classification results of K-means algorithm for earthquakes are also in line with expectations.

关键词： Seismic Distribution Clustering K-means DBSCAN Visualization

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Investigating 16 Open Clusters in the Kepler/K2 - Gaia DR3 field. I. Membership, Binary, and Rotation

arXiv

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arXiv 2023年

作者： Long, Liu Bi, Shaolan Zhang, Jinghua Zhang, Xianfei Zhang, Liyun Ge, Zhishuai Li, Tanda Chen, Xunzhou Li, YaGuang Ye, LiFei Sun, TianCheng Zhou, JianZhao Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China South-Western Institute for Astronomy Research Yunnan University Chenggong District Kunming650500 China College of Physics Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Beijing Planetarium Beijing Academy of Science and Technology Beijing100044 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China School of Physics University of Sydney NSW2006 Australia Stellar Astrophysics Centre Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus CDK-8000 Denmark

Using data from the Gaia data Release 3 (Gaia DR3) and Kepler/K2, we present a catalog of 16 open clusters with ages ranging from 4 to 4000 Myr, which provides detailed information on membership, binary systems, and rotation. We assess the memberships in 5D phase space, and estimate the basic parameters of each cluster. Among the 20,160 members, there are 4,381 stars identified as binary candidates and 49 stars as blue straggler stars. The fraction of binaries vary in each cluster, and the range between 9% to 44%. We obtain the rotation periods of 5,467 members, of which 4,304 are determined in this work. To establish a benchmark for the rotation-age-color relation, we construct color-period diagrams. We find that the rotational features of binaries are similar to that of single stars, while features for binaries are more scattered in the rotation period. Moreover, the morphology of the color-period relationship is already established for Upper Scorpius at the age of 19 Myr, and some stars of varying spectral types (i.e. FG-, K-, and M-type) show different spin-down rates after the age of ∼ 110 Myr. By incorporating the effects of stalled spin-down into our analysis, we develop an empirical rotation-age-color relation, which is valid with ages between 700 - 4000 Myr and colors corresponding to a range of 0.5 BP − GRP)0 Copyright © 2023, The Authors. All rights reserved.

关键词： Color

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Server load prediction algorithm based on CM-MC for cloud systems

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Journal of Systems Engineering and Electronics 2018年第5期29卷 1069-1078页

作者： XU Xiaolong ZHANG Qitong MOU Yiqi LU Xinyuan Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications State Key Laboratory of Information Security Chinese Academy of Sciences Shanghai Stock Exchange Technology Co. LtdShanghai Stock Exchange Institute of Big Data Research at Yancheng Nanjing University of Posts and Telecommunications

Accurate prediction of server load is important to cloud systems for improving the resource utilization, reducing the energy consumption and guaranteeing the quality of service(QoS).This paper analyzes the features of cloud server load and the advantages and disadvantages of typical server load prediction algorithms, integrates the cloud model(CM) and the Markov chain(MC) together to realize a new CM-MC algorithm, and then proposes a new server load prediction algorithm based on CM-MC for cloud systems. The algorithm utilizes the historical data sample training method of the cloud model, and utilizes the Markov prediction theory to obtain the membership degree vector, based on which the weighted sum of the predicted values is used for the cloud model. The experiments show that the proposed prediction algorithm has higher prediction accuracy than other typical server load prediction algorithms, especially if the data has significant volatility. The proposed server load prediction algorithm based on CM-MC is suitable for cloud systems, and can help to reduce the energy consumption of cloud data centers.

关键词： cloud computing load prediction cloud model Markov chain energy saving

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Evolution of cooperation in spatial heterogeneous social dilemmas with dynamic self-adaptive matching

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Applied Mathematics and Computation 2026年 508卷

作者： Xiaopeng Li Xuexue Yang Xiuli Zhang Weiwei Han Yan Xu College of Big Data Statistics Guizhou University of Finance and Economics Guiyang Guizhou 550025 PR China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology Taiyuan Normal University Taiyuan Shanxi 030619 PR China School of Information Engineering Tianjin University of Commerce Tianjin 300134 PR China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 PR China

From the perspective of real-world interaction scenarios, the assumption of monotypic social dilemma engagement or fixed role allocations within heterogeneous social dilemmas contexts fundamentally misrepresents the dynamics of human social cognition. Empirical evidence consistently suggests that behavioral plasticity is achieved through adaptive identity switching across heterogeneous social dilemma environments. Herein, we develop an evolutionary game model simultaneously encompassing multiple social dilemmas (prisoner's dilemma game and snowdrift game), wherein agents engage in polymorphic game-environment governed by their own adaptive characteristic. Specifically, during the strategy update phase, if the agent x ′ s current strategy is retained, its strategy stickiness θ x increases one unit; otherwise, the metric reverts to the initial value of 0. Additionally, if θ x reaches the maximum value of 100, it signifies the termination of the old agent and the initialization of a new one, with the metric also reverting to 0. Moreover, agent whose strategy adherence is beyond predefined threshold θ t h is classified as possessing strong social dilemma resolution capacity, while that is below the threshold is allocated to mild-conflict scenarios. Subsequently, through sufficient Monte Carlo simulation, we systematically investigate the evolutionary trajectories and stable-state characteristics of cooperation in dynamically adaptive multi-social dilemma systems, yielding several insightful findings.

关键词： Evolution of cooperation Evolutionary dynamics Dynamic self-adaptive Heterogeneous social dilemmas

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Nonlinear variational inequality problems involving uncertain variable

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2019年第4期81卷 65-74页

作者： Li, Cunlin Jia, Zhifu Ningxia Key Laboratory of intelligent information and Big Data Processing Governance and social management research center of Northwest Ethnic regions North Minzu University Yinchuan China State Key Laboratory of Mechanics Control of Mechanical Structures Institute of Nano Science and Department of Mathematics Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Mathematics and Information Science North Minzu University Yinchuan China

Variational inequality problems have been extensively studied and applied in the area of optimization theory, but much of it has been developed in a deterministic domain. In this paper, the uncertain variational inequality is studied. Under the compact uncertain event space, we establish an uncertain nonlinear expected residual minimization (UNERM) model. Considering that uncertain variable usually has no density function, we propose integration by parts method to solve UNERM model. Under reasonable assumptions and Slater’s constraint qualification, two kinds of convergence, such as convergence of global optimal solutions and convergence of stationary points, are investigated. © 2019, Politechnica University of Bucharest. All rights reserved.

关键词： Uncertainty analysis

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A Privacy-Preserving and Verifiable Federated Learning Scheme

A Privacy-Preserving and Verifiable Federated Learning Schem...

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IEEE International Conference on Communications (ICC)

作者： Xianglong Zhang Anmin Fu Huaqun Wang Chunyi Zhou Zhenzhu Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing PR China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Due to the complexity of the data environment, many organizations prefer to train deep learning models together by sharing training sets. However, this process is always accompanied by the restriction of distributed storage and privacy. Federated learning addresses this challenge by only sharing gradients with the server without revealing training sets. Unfortunately, existing research has shown that the server could extract information of the training sets from shared gradients. Besides, the server may falsify the calculated result to affect the accuracy of the trained model. To solve the above problems, we propose a privacy-preserving and verifiable federated learning scheme. Our scheme focuses on processing shared gradients by combining the Chinese Remainder Theorem and the Paillier homomorphic encryption, which can realize privacy-preserving federated learning with low computation and communication costs. In addition, we introduce the bilinear aggregate signature technology into federated learning, which effectively verifies the correctness of aggregated gradient. Moreover, the experiment shows that even with the added verification function, our scheme still has high accuracy and efficiency.

关键词： Servers Machine learning Training Encryption Aggregates Computational modeling Privacy

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Urban subsidence zones prone to flooding: mitigated deformation trends post-2024 Guilin megaflood

npj Natural Hazards

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npj Natural Hazards 2025年第1期2卷 1-9页

作者： Pinglang Kou Xu Dong Yuxiang Tao Ruikun Chen Zhengwu Yuan Ying Xia Qiang Xu Ali P. Yunus Rui Liu Qin Ji Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism (TMDPD MCT) Chongqing University of Posts and Telecommunications Chongqing 400065 China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu Sichuan 610059 China Department of Earth and Environmental Sciences Indian Institute of Science Education and Research Mohali Sahibzada Ajit Singh Nagar Punjab 140 306 India Chongqing Key Laboratory of GIS Application School of Geography and Tourism Chongqing Normal University Chongqing 401331 China

Climate change intensifies urban flood hazards, yet existing research often overlooks the complex dynamic relationships between surface deformation, soil properties, and flooding. This study uses the 2024 Guilin flood event as a case study, integrating SBAS-InSAR, DInSAR techniques, and various machine learning methods to explore the complex interactions between surface deformation, soil characteristics, and flooding. The results show that the flood caused significant water expansion, with ground subsidence mainly concentrated in the southern and eastern parts of Guilin, highly coinciding with the severely flooded areas. The flood-inundated areas exhibited opposite deformation trends before and after the flood, shifting from subsidence to uplift, while road subsidence also showed a dynamic process. Different machine learning methods showed varying performance in predicting surface deformation, with the ERT model performing relatively well. Soil thickness was positively correlated with surface subsidence within a certain range, and this relationship exhibited noticeable nonlinear characteristics post-flood. The findings of this study have important practical implications for urban flood mitigation, aiding urban planners in more accurately identifying flood-prone areas, especially those experiencing subsidence.

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Controlled-source electromagnetic signal detection using a hybrid deep learning model: Convolutional and long short-term memory neural networks

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Computers and Geosciences 2025年 204卷

作者： Liu, Yecheng Li, Diquan Li, Jin Hu, Yanfang Liu, Zijie Zhang, Xian School of Geosciences and Info-Physics Central South University Changsha 410083 China Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor Ministry of Education Central South University Changsha 410083 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha 410081 China Hunan Provincial Key Laboratory of Finance & Economics Big Data Science and Technology School of Information Technology and Management Hunan University of Finance and Economics Changsha 410205 China

Controlled-source electromagnetic (CSEM) method using a periodic transmitted signal source suppresses random noise by superimposing and averaging the recorded signal over multiple periods. However, it still faces great challenges in strong interference environments. Here we present a CSEM signal detection method based on a hybrid architecture of convolutional neural network (CNN) and long short-term memory neural network (LSTM). Our method improves the quality of the recorded signal by filtering out all the noise periods and retaining the useful signal periods. The core lies in combining the spatial feature extraction capability of CNN with the time series modeling capability of LSTM, which can deeply excavate the feature differences between noise and CSEM useful signal. Meanwhile, a classification model of signal and noise is constructed using a large-scale training dataset. This hybrid model exhibits superior performance compared to other deep learning models such as CNN or LSTM. Also, we propose a novel signal detection mechanism that not only maintains the periodicity of CSEM signal, but also greatly enhances processing efficiency. The results of synthetic and measured data demonstrate that our method can obtain useful signals from CSEM data containing different noise types and significantly improve the quality of sounding curves. In particular, our method is useful for CSEM signal detection in strong interference. © 2025 Elsevier Ltd

关键词： Controlled-source electromagnetic method Deep learning Noise Signal detection

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Proxy Re-Encryption Scheme For Complicated Access Control Factors Description in Hybrid Cloud

Proxy Re-Encryption Scheme For Complicated Access Control Fa...

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IEEE International Conference on Communications (ICC)

作者： Zheng Lian Mang Su Anmin Fu Huaqun Wang Chunyi Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing PR China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Hybrid cloud has both the strong computing power of public cloud and easy control of private cloud. It provides users with robust services and convenience, meanwhile faces numerous security challenges. How to implement the effective access control is one of them, the purpose of which is deploying policy in private cloud to protect the ciphertext in public cloud. Furthermore, it becomes more and more difficult to describe the access control policy, which is suitable for multi-factor and dynamic updating. Considering the issues above, we propose a proxy re-encryption (PRE) scheme for complicated access control factors description in hybrid cloud. Firstly, we build the system model combining PRE with access control in hybrid cloud. Secondly, we design the algorithm for our scheme including the key construction with multi-factor and its weight, which achieve the target of dynamic updating. Finally, we analyze the security of this scheme by the mathematical method and performance by theory, experiment and comparisons with some other works. Our scheme has made the deployment of access control in hybrid cloud more reliable and scalable.

关键词： Cloud computing Access control Cryptography XML Reliability Privacy

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A Predictive-Trend-Aware and Critical-Path-Estimation-Based Method for Workflow Scheduling Upon Cloud Services

A Predictive-Trend-Aware and Critical-Path-Estimation-Based ...

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IEEE International Conference on Services Computing (SCC)

作者： Yi Pan Xiaoning Sun Yunni Xia Wanbo Zheng Xin Luo College of Computer Science Chongqing University Chongqing China Data Science Research Center Faculty of Science Kunming University of Science and Technology Kunming China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

The cloud computing paradigm is featured by its ability to offer elastic computational resource provisioning patterns and deliver on-demand and versatile services. It's thus getting increasingly popular to build business process and workflow-based applications upon cloud computing platforms. However, it remains a difficulty to guarantee cost-effectiveness and quality of service of cloud-based workflows because real-world cloud services are usually subject to real-time performance variations or fluctuations. Existing researches mainly consider that cloud are with constant performance and formulate the scheduling decision-making as a static optimization problem. In this work, instead, we consider that scientific computing processes to be supported by decentralized cloud infrastructures are with fluctuating QoS and aim at managing the monetary cost of workflows with the completion-time constraint to be satisfied. We address the performance-trend-aware workflow scheduling problem by leveraging a time-series-based prediction model and a Critical-Path-Duration-Estimation-based (CPDE for short) scheduling strategy. The proposed method is capable of exploiting real-time trends of performance changes of cloud infrastructures and generating dynamic workflow scheduling plans. To prove the effectiveness of our proposed method, we build a large-prime-number-generation workflow supported by real-world third-party commercial clouds and show that our method clearly beats existing approaches in terms of cost, workflow completion time, and Service-Level-Agreement (SLA) violation rate.

关键词： Cloud computing Processor scheduling Computational modeling Quality of service Predictive models Dynamic scheduling Real-time systems

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