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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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LGBM-improved-MLSE equalizer for 50Gb/s PAM4 IM-DD PON with 10G class optical transceivers

LGBM-improved-MLSE equalizer for 50Gb/s PAM4 IM-DD PON with ...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Qianwu Zhang Yuntong Jiang Shuaihang Duan Zicong Wang Pu Li Bingyao Cao Yating Wu Key Laboratory of Specialty Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Taiyuan China Key Laboratory of Advanced Transducers and Intelligent Control System Taiyuan University of Technology Ministry of Education Taiyuan China

A light gradient boosting machine improved-MLSE equalizer for 50Gb/s PAM4 IM-DD PON with 10G class optical transceivers is proposed and experimental results show that 21 dB power budget is achieved over 15km SSMF. ... 详细信息

ISBN: (数字)9781943580828

ISBN: (纸本)9781728176567

关键词： Maximum likelihood estimation Equalizers Boosting Transceivers Optical fiber communication Passive optical networks Photonics

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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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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Research on KDT-DBSCAN-based Personal Semantic Location Acquisition

Research on KDT-DBSCAN-based Personal Semantic Location Acqu...

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第32届中国控制与决策会议

作者： Rong Nie Zixuan Yao Dong Wei Kaizhang Hou Jishuai Lin Beijing Engineering Research Center of Security Alarm and Security Check School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing Municipal Science and Technology Commission Haidian Branch of Beijing Public Security Bureau

With the development of science and technology in recent years,the planning and construction of smart cities have entered a new *** them,smart city safety management is the foundation that supports the stable development of the entire city,and location-based services are one of its important technical *** popularity of GPS-equipped devices provides a large amount of data for trajectory *** personal semantic locations by mining these data is an application based on location *** semantic locations are frequently visited by individual users and have significant semantic meaning to users（such as home,work place,etc.）.The discovery of the user’s personal semantic location involves obtaining the physical location and *** present,related research mostly uses clustering algorithms to obtain the physical location,of which the DBSCAN algorithm is the most commonly *** the traditional DBSCAN algorithm determines whether a sample belongs to a certain cluster,it will generate a large number of repeated calculations,which will reduce the program operation *** order to solve this problem,this paper proposes an improved DBSCAN algorithm *** algorithm uses a k-d tree to screen samples that need to be *** excluding samples that do not need to be calculated,the purpose of improving the calculation efficiency can be *** the problem of semantic location recognition,this paper pre-defines the daily behavior patterns of people,and identifies the location semantics based on the temporal characteristics of the samples in each *** calculating the distance between samples,the algorithm proposed here is 8.8 times more efficient than the traditional algorithm.

关键词： Personal Semantic Location DBSCAN K-D Tree

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China's greenhouse gas budget during 2000-2023

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National Science Review 2025年第04期 24-36页

作者： Wenping Yuan Minqi Liang Yuanyi Gao Ling Huang Li Dan Hongtao Duan Songbai Hong Fei Jiang Weimin Ju Tingting Li Ziyang Lou Shilong Luan Xiao Lu Zhangcai Qin Lishan Ran Lulu Shen Fei Teng Xiangjun Tian Yilong Wang Jing Wei Jiangzhou Xia Xiaosheng Xia Lijun Yu Xu Yue Haicheng Zhang Wen Zhang Yuzhong Zhang Xu Zhao Qiuan Zhu Shilong Piao Xuhui Wang Institute of Carbon Neutrality Sino-French Institute for Earth System Science College of Urban and Environmental Sciences Peking University School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric PhysicsChinese Academy of Sciences Key Laboratory of Lake and Watershed Science for Water Security Nanjing Institute of Geography and LimnologyChinese Academy of Sciences State Key Laboratory of Lake Science and Environment Nanjing Institute of Geography and LimnologyChinese Academy of Sciences School of Urban Planning and Design Shenzhen Graduate School Peking University Frontiers Science Center for Critical Earth Material Cycling Nanjing University Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System Science Nanjing University Key Laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric PhysicsChinese Academy of Sciences Shanghai Engineering Research Center of Solid Waste Treatment and Resource Recovery School of Environmental Science & Engineering Shanghai Jiao Tong University Department of Geography The University of Hong Kong Department of Atmospheric and Oceanic Sciences School of PhysicsPeking University Institute of Energy Environment and Economy Tsinghua University State Key Laboratory of Tibetan Plateau Earth System Resources and Environment (TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of Sciences Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC) Institute of Atmospheric PhysicsChinese Academy of Sciences Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of

National greenhouse gas（GHG） budget,including CO2,CH4and N2O has increasingly become a topic of concern in international climate *** is paying increasing attention to reducing GHG emissions and increasing land sinks to effectively mitigate climate *** estimates of GHG fluxes are crucial for monitoring progress toward mitigating GHG emissions in *** study used comprehensive methods,including emission factor methods,process-based models,atmospheric inversions,and data-driven models,to estimate the long-term trends of GHG sources and sinks from all anthropogenic and natural sectors in China's mainland during 2000-2023,and produced an up-to-date China GHG Budget dataset（CNGHG）.The total gross emissions of the three GHGs show a 3-fold increase from 5.0（95% CI:4.9-5.1） Gt CO2-eq yr-1（in 2000） to 14.3（95% CI:13.8-14.8） Gt CO2-eqyr-1（in2023）.CO2emissions represented 81.8% of the GHG emissions in 2023,while 12.7% and 5.5% were for CH4and N2O,*** the largest CO2source,the energy sector contributed 87.4% *** contrast,the agriculture,forestry and other land use sector was the largest sector of CH4and N2O,representing 50.1% and 6 6.3% emissions,***,China's terrestrial ecosystems serve as a net CO2sink(1.0 Gt CO2yr-1,9596 CI:0.2-1.9 Gt CO2yr-1) during 2012 to 2021,equivalent to an average of 14.3% of fossil *** GHG emission estimates showed a general consistency with national GHG inventories,with gridded and sector-specific estimates of GHG fluxes over China,providing the basis for curtailing GHG emissions for each region and sector.

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Unconventional Spin Currents in Noncollinear Antiferromagnet Mn3Ge

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Nano letters 2025年第23期25卷 2025 Jun 2页

作者： Cuimei Cao Shiwei Chen Nian Xie Jinsen Chen Zhe Guo Hui Zhang Zheng Li Yihan Wang Xuepeng Qiu Shiheng Liang Qingfeng Zhan Long You School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China. School of Physics Hubei University Wuhan 430062 China. Key Laboratory of Intelligent Sensing System and Security Ministry of Education Hubei University Wuhan 430062 China. Shanghai Key Laboratory of Special Artificial Microstructure Materials School of Physics Science and Engineering Tongji University Shanghai 200092 China. School of Microelectronics Hubei University Wuhan 430062 China. Key Laboratory of Polar Materials and Devices (MOE) School of Physics and Electronic Science East China Normal University Shanghai 200241 China. Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan 430074 China. Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518000 China.

Unconventional spin-orbit torque (SOT) has garnered significant attention due to its potential for enabling energy-efficient and deterministic control of magnetization states. Noncollinear antiferromagnets (NCAFs) are considered ideal systems for generating such unconventional SOTs, making them highly promising for the development of next-generation spintronic devices. In this study, we report the generation of spin torques in noncollinear antiferromagnetic MnGe, revealing the presence of unconventional spin polarizations (specifically, - and -polarized spin currents) by employing the spin-torque ferromagnetic resonance (ST-FMR) technique. All-electric SOT switching was successfully demonstrated in a MnGe-based device with perpendicular magnetic anisotropy (PMA), with a critical current density of 4.2 × 10 A/cm. Furthermore, the device exhibits memristive behavior that effectively emulates the functionality of artificial synapses in convolutional neural networks (CNNs), achieving an accuracy of 92.5% in digital recognition tasks. These results are expected to pave the way toward next-generation fast and energy-efficient memory and neuromorphic computing.

关键词： all-electric SOT switching noncollinear antiferromagnet spin−orbit torque synaptic plasticity unconventional spin torque

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Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

arXiv

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tuğfe Ai, Bo The School of Electronic and Information Engineering The Frontiers Science Center for Smart Highspeed Railway System Beijing Jiaotong University Beijing100044 China Purple Mountain Laboratories Nanjing211111 China The Department of Computer Science KTH Royal Institute of Technology Kista164 40 Sweden The Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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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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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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