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Optimized parameters of downhole all-metal PDM based on genetic algorithm

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Petroleum Science 2024年第4期21卷 2663-2676页

作者： Jia-Xing Lu Ling-Rong Kong yu Wang Chao Feng yu-Lin Gao China University of Geosciences (Beijing) Key Laboratory on Deep Geo-Drilling Technology of Ministry of Natural Resources

Currently, deep drilling operates under extreme conditions of high temperature and high pressure,demanding more from subterranean power motors. The all-metal positive displacement motor, known for its robust performance, is a critical choice for such drilling. The dimensions of the PDM are crucial for its performance output. To enhance this, optimization of the motor's profile using a genetic algorithm has been undertaken. The design process begins with the computation of the initial stator and rotor curves based on the equations for a screw cycloid. These curves are then refined using the least squares method for a precise fit. Following this, the PDM's mathematical model is optimized, and motor friction is assessed. The genetic algorithm process involves encoding variations and managing crossovers to optimize objective functions, including the isometric radius coefficient, eccentricity distance parameter,overflow area, and maximum slip speed. This optimization yields the ideal profile parameters that enhance the motor's output. Comparative analyses of the initial and optimized output characteristics were conducted, focusing on the effects of the isometric radius coefficient and overflow area on the motor's performance. Results indicate that the optimized motor's overflow area increased by 6.9%, while its rotational speed reduced by 6.58%. The torque, as tested by Infocus, saw substantial improvements of38.8%. This optimization provides a theoretical foundation for improving the output characteristics of allmetal PDMs and supports the ongoing development and research of PDM technology.

关键词： Positive displacement motor Genetic algorithm Profile optimization Matlab programming Overflow area

Network Intrusion Traffic Detection Based on Feature Extraction

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Computers, Materials & Continua 2024年第1期78卷 473-492页

作者： Xuecheng yu Yan Huang yu Zhang Mingyang Song Zhenhong Jia College of Information Science and Engineering Xinjiang UniversityUrumqi830046China Network Department China Mobile Communications Group Xinjiang CoLtd.UrumqiUrumqi830011China Xinjiang Signal Detection and Processing Key Laboratory Xinjiang UniversityUrumqi830000China

With the increasing dimensionality of network traffic,extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems(IDS).However,both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features,resulting in an analysis that is not an optimal ***,in order to extract more representative traffic features as well as to improve the accuracy of traffic identification,this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T^(2) and a multilayer convolutional bidirectional long short-term memory(MSC_BiLSTM)classifier model for network traffic intrusion *** method reduces the parameters and redundancy of the model by feature extraction and extracts the dependent features between the data by a bidirectional long short-term memory(BiLSTM)network,which fully considers the influence between the before and after *** network traffic is first characteristically downscaled by principal component analysis(PCA),and then the downscaled principal components are used as input to Hotelling’s T^(2) to compare the differences between *** datasets with outliers,Hotelling’s T^(2) can help identify the groups where the outliers are located and quantitatively measure the extent of the ***,a multilayer convolutional neural network and a BiLSTM network are used to extract the spatial and temporal features of network traffic *** empirical consequences exhibit that the suggested approach in this manuscript attains superior outcomes in precision,recall and F1-score juxtaposed with the prevailing *** results show that the intrusion detection accuracy,precision,and F1-score of the proposed MSC_BiLSTM model for the CIC-IDS 2017 dataset are 98.71%,95.97%,and 90.22%.

关键词： Network intrusion traffic detection PCA Hotelling’s T^(2) BiLSTM

MUS Model:A Deep Learning-Based Architecture for IoT Intrusion Detection

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Computers, Materials & Continua 2024年第7期80卷 875-896页

作者： yu Yan yu Yang Shen Fang Minna Gao Yiding Chen College of Information Engineering University of Engineering of the Chinese People’s Armed Police Force(PAP)Xi’an710000China College of Missile Engineering Rocket Force Engineering UniversityXi’an710000China

In the face of the effective popularity of the Internet of Things(IoT),but the frequent occurrence of cybersecurity incidents,various cybersecurity protection means have been proposed and *** them,Intrusion Detection System(IDS)has been proven to be stable and ***,traditional intrusion detection methods have shortcomings such as lowdetection accuracy and inability to effectively identifymalicious *** address the above problems,this paper fully considers the superiority of deep learning models in processing highdimensional data,and reasonable data type conversion methods can extract deep features and detect classification using advanced computer vision techniques to improve classification *** TransformField(MTF)method is used to convert 1Dnetwork traffic data into 2D images,and then the converted 2D images are filtered by UnsharpMasking to enhance the image details by sharpening;to further improve the accuracy of data classification and detection,unlike using the existing high-performance baseline image classification models,a soft-voting integrated model,which integrates three deep learning models,MobileNet,VGGNet and ResNet,to finally obtain an effective IoT intrusion detection architecture:the MUS *** types of experiments are conducted on the publicly available intrusion detection dataset CICIDS2018 and the IoT network traffic dataset N_BaIoT,and the results demonstrate that the accuracy of attack traffic detection is greatly improved,which is not only applicable to the IoT intrusion detection environment,but also to different types of attacks and different network environments,which confirms the effectiveness of the work done.

关键词： Cyberspace security intrusion detection deep learning Markov Transition Fields(MTF) soft voting integration

State and parameter identification of linearized water wave equation via adjoint method

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Science China(Information Sciences) 2024年第10期67卷 283-297页

作者： Yang yu Cheng-Zhong XU Hai-Long PEI Jinpeng yu School of Automation Qingdao University LAGEP Batiment CPE University Claude Bernard-Lyon 1 School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked ControlMinistry of Education Guangdong Engineering Technology Research Center of Unmanned Aerial Vehicle SystemSouth China University of Technology

In this paper, we focus on the state and parameter identification problem of a hydrodynamical system. This system is modeled as a linearized water wave equation(LWWE), a hyperbolic state-space model coupled with a Laplace equation. We assume that the wave elevation at two distinct points is the only measurement of water waves. We show that the state and water depth can be reconstructed from this point measurement records. The identification problem is recast as an optimization problem over an infinite-dimensional space. We propose the adjoint method-based identification algorithm to generate an estimated state and water depth. We then performed a numerical simulation to show the effectiveness of our designed algorithm by comparing it with existing studies.

关键词： linearized water wave equation (LWWE) point observation state estimation parameter identification adjoint method numerical simulation

Research on Bast Fiber Extracted from the White Bark of Three Species in the Genus Broussonetia

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Chinese Journal of Polymer Science 2024年第6期42卷 729-738页

作者： Shan-Shan Jin Si-Nong Wang Yan-Yan Huang Jing-yu Zhang Peng Liu Hui yu Hong-Dong Zhang yu-Liang Yang Institute for Preservation and Conservation of Chinese Ancient Books Fudan University LibraryFudan UniversityShanghai200433China State key laboratory of Molecular Engineering of polymers Department of Macromolecular ScienceFudan UniversityShanghai200433China

Against the backdrop of a global paper resource shortage,there is a growing need to identify fast-growing tree species capable of producing long-lasting *** plant species namely Broussonetia kazinoki,Broussonetia papyrifera and hybrid paper mulberry,belong to the Broussonetia genus,were collected from China to study their white bark suitability for pulp and *** chemical composition revealed that the holocellulose content in Broussonetia kazinoki and Broussonetia papyrifera was more than 80%.The molecular weight distribution of several holocellulose/α-cellulose is observed by GPC,which allows us to better observe the changes of various components on the molecular *** yield,lignin,whiteness,and molecular weight of the pulps obtained by NaOH treatment were *** microscope was used to characterize the fiber length-width ratio and ***,the improvement of the fiber rigidity method based on the Kratky-Porod chain model makes it more theoretical and further reveals the influencing factors of fiber *** study demonstrates the high potentiality of these three species for papermaking applications.

关键词： Broussonetia genus Cellulose Alkaline pulping Kratky-Porod chain model Fiber rigidity

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The first 10 m resolution thermokarst lake and pond dataset for the Lena Basin in the 2020 thawing season

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Big Earth Data 2024年第2期8卷 302-332页

作者： Yining yu Fengming Hui yu Zhou Chong Liu Xiao Cheng School of Geospatial Engineering and Science Sun Yat-sen Universityand Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)ZhuhaiChina Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University)Ministry of EducationZhuhaiChina Guangdong Provincial Key Laboratory of Geodynamics and Geohazards School of Earth Sciences and EngineeringSun Yat-sen UniversityZhuhaiChina

Climate warming rates in the Arctic are far greater than the global average,exerting stronger impacts on permafrost degradation and thermokarst landform *** lakes and ponds(TLPs),which are widely distributed in the Lena Basin in the Russian Arctic,play a vital role in altering local ***,the detailed distribution of TLPs in the Lena Basin still remains poorly *** this study,we built the first 10 m resolution TLP dataset for the Lena Basin in the 2020 thawing season by utilizing 4902 Sentinel-2 images.A robust mapping workflow was developed and implemented in the Google Earth Engine(GEE)*** accu-racy assessment demonstrates a satisfactory accuracy(93.63%),and our results exhibit a better consistency with real TLPs than global water body products.A total of 380,477 TLPs(~0.53%of the total surface area of the Lena Basin)were identified,showing an uneven distribution in the five *** TLPs were found to be mainly located within plain areas,with an active layer thickness in the range of 80-100 *** higher ground ice content and mean annual ground temperature were favorable for TLP *** dataset will be valuable for investigating the complex interac-tion between TLPs and *** will also serve as a baseline product for better incorporating thermokarst processes into perma-frostclimate models.

关键词： Thermokarst lakes and ponds Lena Basin permafrost GEE Sentinel-2

A data and physical model dual-driven based trajectory estimator for long-term navigation

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Defence Technology（防务技术） 2024年第10期40卷 78-90页

作者： Tao Feng yu Liu yue yu Liang Chen Ruizhi Chen School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong Kong999077China Chongqing Key Laboratory of Autonomous Navigation and Microsystem Chongqing University of Post and TelecommunicationsChongqing400065China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing(LIESMARS)Wuhan UniversityWuhan430000China

Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields,for instance,smart healthcare,emergency rescue,soldier positioning et *** performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors,disturbed local magnetic field,and complex motion modes of the *** paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE)framework,which can be applied for long-term navigation tasks.A Bi-directional Long Short-Term Memory(Bi-LSTM)based quasi-static magnetic field(QSMF)detection algorithm is developed for extracting useful magnetic observation for heading calibration,and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time *** addition,a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks,and enhanced by the magnetic and trajectory features assisted loop detection ***-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms,and final estimated heading and positioning accuracy indexes reaches 5and less than 2 m under the time period of 30 min,respectively.

关键词： Long-term navigation Wearable inertial sensors Bi-LSTM QSMF Data and physical model dual-driven

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Wellbore-heat-transfer-model-based optimization and control for cooling downhole drilling fluid

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Petroleum Science 2024年第3期21卷 1955-1968页

作者： Chao Wang He Liu Guo-Wei yu Chen yu Xian-Ming Liu Peng Huang Yangtze University Jingzhou434023HubeiChina State Key Laboratory of Petroleum Resources and Engineering China University of Petroleum(Beijing)Beijing2249China CNPC Bohai Drilling Engineering Co. LtdTianjin300280China Exploration Development and Construction Engineering Division of PetroChina Jidong Oilfield Company Tangshan63200HebeiChin Henan University of Urban Construction Pingdingshan467036HenanChina

To address the two critical issues of evaluating the necessity of implementing cooling techniques and achieving real-time temperature control of drilling fluids underground in the current drilling fluid cooling technology,we first established a temperature and pressure coupled downhole heat transfer model,which can be used in both water-based and oil-based drilling ***,fourteen factors,which could affect wellbore temperature,were *** on the standard deviation of the downhole temperature corresponding to each influencing factor,the influence of each factor was *** influencing factors that can be used to guide the drilling fluid's cooling technology were drilling fluid thermal conductivity,drilling fluid heat capacity,drilling fluid density,drill strings rotation speed,pump rate,viscosity,ROP,and injection *** nondominated sorting genetic algorithm was used to optimize these six parameters,but the optimization process took 182 *** these eight parameters'influence rules with the nondominated sorting genetic algorithm can reduce the optimization time to 108 ***,the downhole temperature has been demonstrated to increase with the inlet temperature increasing linearly under quasi-steady *** this law and PID,the downhole temperature can be controlled,which can reduce the energy for cooling the surface drilling fluid and can ensure the downhole temperature reaches the set value as soon as possible.

关键词： Drilling Cooling Influencing factors Analysis Optimization Control

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Wireless powering solution for implantable electronics based on ultra-low frequency magnetic energy focusing

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National Science Review 2024年第6期11卷 92-93页

作者： Haoyu Wang Xinge yu Department of Biomedical Engineering City University of Hong Kong

Implantable electronic devices play a crucial role at the frontiers of human health monitoring, disease diagnosis and medical treatment. With the development of their functionality, powering strategies have become a critical issue. However, with existing powering solutions,batteries have a limited lifetime [1]; selfpowered devices exhibit unstable and constrained output [2];

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