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An optimization method of fidelity parameters of formation fluid sampling cylinder while drilling

Petroleum Exploration and Development 2022年第2期49卷 458-467页

作者： JIANG Chuanlong YAN Tingjun ZHANG Yang SUN Tengfei CHEN Zhongshuai SUN Haoyu College of Mechanical and Electrical Engineering Beijing University of Chemical TechnologyBeijing 100029China Drilling Technology Research Institute of Shengli Oilfield Service Corporation SinopecDongying 257000China

A design idea of fidelity sampling cylinder while drilling based on surface nitrogen precharging and supplemented by downhole pressurization was proposed, and the working mode and optimization method of sampling parameters were discussed. The nitrogen chamber in the sampling cylinder functions as an energy storage air cushion, which can supplement the pressure loss caused by temperature change in the sampling process to some extent. The downhole pressurization is to press the sample into the sample chamber as soon as possible, and further increase the pressure of sample to make up for the pressure that the nitrogen chamber cannot provide. Through the analysis of working mode of the sampling fidelity cylinder, the non-ideal gas state equation was used to deduce and calculate the optimal values of fidelity parameters such as pre-charged nitrogen pressure, downhole pressurization amount and sampling volume according to whether the bubble point pressure of the sampling fluid was known and on-site emergency sampling situation. Besides, the influences of ground temperature on fidelity parameters were analyzed, and corresponding correction methods were put forward. The research shows that the fidelity sampling cylinder while drilling can effectively improve the fidelity of the sample. When the formation fluid sample reaches the surface, it can basically ensure that the sample does not change in physical phase state and keeps the same chemical components in the underground formation.

关键词： sampling while drilling formation fluid sample fidelity bubble point pressure nitrogen pre-charge downhole pressurization parameter optimization

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Deep Learning with Optimal Hierarchical Spiking Neural Network for Medical Image Classification

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Computer Systems Science & Engineering 2023年第2期44卷 1081-1097页

作者： P.Immaculate Rexi Jenifer S.Kannan Department of Computer Science and Engineering Anjalai Ammal Mahalingam Engineering CollegeKoilvenni614403Tamil NaduIndia Department of CSE E.G.S.Pillay Engineering CollegeNagapattinam611002Tamil NaduIndia

Medical image classification becomes a vital part of the design of computer aided diagnosis(CAD)*** conventional CAD models are majorly dependent upon the shapes,colors,and/or textures that are problem oriented and exhibited complementary in medical *** recently developed deep learning(DL)approaches pave an efficient method of constructing dedicated models for classification *** the maximum resolution of medical images and small datasets,DL models are facing the issues of increased computation *** this aspect,this paper presents a deep convolutional neural network with hierarchical spiking neural network(DCNN-HSNN)for medical image *** proposed DCNN-HSNN technique aims to detect and classify the existence of diseases using medical *** addition,region growing segmentation technique is involved to determine the infected regions in the medical ***,NADAM optimizer with DCNN based Capsule Network(CapsNet)approach is used for feature extraction and derived a collection of feature ***,the shark smell optimization algorithm(SSA)based HSNN approach is utilized for classification *** order to validate the better performance of the DCNN-HSNN technique,a wide range of simulations take place against HIS2828 and ISIC2017 *** experimental results highlighted the effectiveness of the DCNN-HSNN technique over the recent techniques interms of different *** type your abstract here.

关键词： Medical image classiﬁcation spiking neural networks computer aided diagnosis medical imaging parameter optimization deep learning

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Emitter layer optimization in heterojunction bifacial silicon solar cells

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Journal of Semiconductors 2022年第12期43卷 65-71页

作者： Adnan Shariah Feda Mahasneh Department of Physics Jordan University of Science and TechnologyIrbid 22110Jordan

Silicon solar cells continue to dominate the market,due to the abundance of silicon and their acceptable *** heterojunction with intrinsic thin layer(HIT)structure is now the dominant *** the efficiency of these cells could expand the development choices for HIT solar *** presented a detailed investigation of the emitter a-Si:H(n)lay-er of a p-type bifacial HIT solar cell in terms of characteristic parameters which include layer doping concentration,thickness,band gap width,electron affinity,hole mobility,and so *** cell composition:(ZnO/nc-Si:H(n)/a-Si:H(i)/c-Si(p)/a-Si:H(i)/nc-Si:H(p)/ZnO).The results reveal optimal values for the investigated parameters,for which the highest computed efficiency is 26.45%when lighted from the top only and 21.21%when illuminated from the back only.

关键词： HIT solar cells bifacial solar cells nano-crystalline silicon films gradient doping parameter optimization

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Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

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Computers, Materials & Continua 2022年第2期70卷 2639-2653页

作者： V.Praveena A.Vijayaraj P.Chinnasamy Ihsan Ali Roobaea Alroobaea Saleh Yahya Alyahyan Muhammad Ahsan Raza Department of Computer Science and Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Information Technology Vignan’s Foundation for ScienceTechnology&ResearchGuntur522213India Department of Information Technology Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Community College in DwadmiShaqra University11961Saudi Arabia Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan

In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously *** such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary *** network security poses a serious challenge to UAV networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the UAV *** IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data *** order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection *** this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow optimization(DRL-BWO)algorithm,for UAV *** addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion *** parameter optimization of DRL technique,BWO algorithm is *** helps in improving the intrusion detection performance of UAV *** extensive set of experimental analysis was performed to highlight the supremacy of the proposed *** the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.

关键词： Intrusion detection UAV networks reinforcement learning deep learning parameter optimization

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Leveraging Gradient-Based Optimizer and Deep Learning for Automated Soil Classification Model

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Computers, Materials & Continua 2023年第7期76卷 975-992页

作者： Hadeel Alsolai Mohammed Rizwanullah Mashael Maashi Mahmoud Othman Amani A.Alneil Amgad Atta Abdelmageed Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityP.O.box 103786Riyadh11543Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Soil classification is one of the emanating topics and major concerns in many *** the population has been increasing at a rapid pace,the demand for food also increases *** approaches used by agriculturalists are inadequate to satisfy the rising demand,and thus they have hindered soil *** comes a demand for computer-related soil classification methods to support *** study introduces a Gradient-Based Optimizer and Deep Learning(DL)for Automated Soil Clas-sification(GBODL-ASC)*** presented GBODL-ASC technique identifies various kinds of soil using DL and computer vision *** the presented GBODL-ASC technique,three major processes are *** the initial stage,the presented GBODL-ASC technique applies the GBO algorithm with the EfficientNet prototype to generate feature *** soil categorization,the GBODL-ASC procedure uses an arithmetic optimization algorithm(AOA)with a Back Propagation Neural Network(BPNN)*** design of GBO and AOA algorithms assist in the proper selection of parameter values for the EfficientNet and BPNN models,*** demonstrate the significant soil classification outcomes of the GBODL-ASC methodology,a wide-ranging simulation analysis is performed on a soil dataset comprising 156 images and five *** simulation values show the betterment of the GBODL-ASC model through other models with maximum precision of 95.64%.

关键词： Soil classification earth sciences machine learning parameter optimization metaheuristics

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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

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Computer Systems Science & Engineering 2023年第4期45卷 687-700页

作者： Fadwa Alrowais Sami Althahabi Saud S.Alotaibi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Recently,Internet of Things(IoT)devices produces massive quantity of data from distinct sources that get transmitted over public *** becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be *** development of automated tools for cyber threat detection and classification using machine learning(ML)and artificial intelligence(AI)tools become essential to accomplish security in the IoT *** is needed to minimize security issues related to IoT gadgets ***,this article introduces a new Mayfly optimization(MFO)with regularized extreme learning machine(RELM)model,named MFO-RELM for Cybersecurity Threat Detection and classification in IoT *** presented MFORELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT *** accomplishing this,the MFO-RELM model pre-processes the actual IoT data into a meaningful *** addition,the RELM model receives the pre-processed data and carries out the classification *** order to boost the performance of the RELM model,the MFO algorithm has been employed to *** performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.

关键词： Cybersecurity threats classification internet of things machine learning parameter optimization

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Mechanism Design and Motion Analysis of Heavy⁃Load Transfer Robot with Parallel Four⁃Bar Mechanism

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Transactions of Nanjing University of Aeronautics and Astronautics 2022年第5期39卷 606-618页

作者： ZHANG Jing WANG Dongbao WU Guangping GUO Hongwei LIU Rongqiang College of Mechanical and Vehicle Engineering Taiyuan University of TechnologyTaiyuan 030024P.R.China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240P.R.China State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001P.R.China

Heavy-load transfer robots are widely used in automobile production and machinery manufacturing to improve production *** order to meet the needs of large billet transfer,a 4-DOF transfer robot is designed in this paper,which consists of parallel four-bar *** Jacobian matrix referring to the mapping matrix from the joint velocity to the operating space velocity of the transfer robot can be solved by the differential-vector *** mean value of the Jacobian matrix condition number in the workspace is used as the global performance index of the robot velocity and the optimization *** constraint condition is established based on the actual working *** the linkage length optimization is carried out to decrease the length of the linkage and to increase the global performance index of *** total length of robot rods is reduced by 6.12%.The global performance index of velocity is improved by 45.15%.Taking the optimized rod length as the mechanism parameter,the distribution of the motion space of the transfer robot is ***,the results show that the proposed method for establishing the Jacobian matrix of the lower-mobility robot and for the optimization of the rods based on the velocity global performance index is accurate and *** workspace distribution of the robot meets the design requirements.

关键词： parameter optimization motion analysis mechanism design transfer robot heavy-load

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Evaluation of Performance of Polar WRF Model in Simulating Precipitation over Qinghai-Tibet Plateau

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Journal of Tropical Meteorology 2023年第4期29卷 410-430页

作者：薛建军肖子牛 China Meteorological Administration Training Centre Beijing 100081 China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029 China

Considering the complex topographic forcing and large cryosphere concentration,the present study utilized the polar-optimized WRF model(Polar WRF)to conduct downscaling simulations over the Qinghai-Tibet Plateau(TP)and its surrounding ***-group experiments with the 10 km horizontal resolution are used to evaluate the modeling of ***,on the basis of the model ground surface properties upgrade and the optimized Noah-MP,the“better-performing”configuration suite for modeling precipitation is comprehensively *** model parameters such as nudging options,five cumulus parameterization schemes,two planetary boundary layer schemes,and six microphysics schemes are investigated to further refine the Polar WRF ***,the precipitation simulation for a full calendar year is compared with multiple reanalyses and *** simulations demonstrate that the Polar WRF model successfully captures the general features of precipitation over this region and is sensitive to model *** on the results,it is recommended to use grid nudging with q intensity coefficient of 0.0002,the multi-scale kain-fritsch cumulus parameterization,the Yonsei University boundary layer scheme,and the Morrison 2-mom microphysics with reduced default droplet concentration value of 100 ***,the model performance is better than the ERA-interim and TRMM *** is comparable to,and in some cases slightly better than,the CRA-Land,especially in the prediction for the western part of the plateau where in situ observations are limited,and the cryosphere-atmosphere interaction is more pronounced.

关键词： Polar WRF precipitation Qinghai-Tibet Plateau downscaling simulations parameter optimization

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A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 2057-2070页

作者： Abdul Rahaman Wahab Sait Mohamad Khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl Hofuf31982Al-AhsaSaudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia

In healthcare sector,image classiﬁcation is one of the crucial problems that impact the quality output from image processing *** purpose of image classiﬁcation is to categorize different healthcare images under various class labels which in turn helps in the detection and management of *** Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging *** technique is commonly utilized by healthcare professionals for Brain Tumor(BT)*** recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided *** current study focuses on the design of automated Deep Learning-based BT Detection and Classiﬁcation model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of *** proposed DLBTDC-MRI technique involves medianﬁltering technique to remove the noise and enhance the quality of MRI ***,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI ***,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature ***,Artiﬁcial Fish Swarm optimization(AFSO)with Artiﬁcial Neural Network(ANN)model is utilized as a classiﬁer to decide the presence of *** order to assess the enhanced BT classiﬁcation performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.

关键词： Brain tumor medical imaging image classiﬁcation handcrafted features deep learning parameter optimization

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Longitudinal control for person-following robots

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Journal of Intelligent and Connected Vehicles 2022年第2期5卷 88-98页

作者： Liang Wang Jiaming Wu Xiaopeng Li Zhaohui Wu Lin Zhu China Academy of Transportation Sciences BeijingChina Department of Architecture and Civil Engineering Chalmers University of TechnologyGothenburgSweden University of South Florida TampaFloridaUSA

Purpose–This paper aims to address the longitudinal control problem for person-following robots(PFRs)for the implementation of this ***/methodology/approach–Nine representative car-following models are analyzed from PFRs application and the linear model and optimal velocity model/full velocity difference model are qualified and selected in the PFR ***–A lab PFR with the bar-laser-perception device is developed and tested in the field,and the results indicate that the proposed models perform well in normal person-following ***/value–This study fills a gap in the research on PRFs longitudinal control and provides a useful and practical reference on PFRs longitudinal control for the related research.

关键词： Person following robot Longitudinal control model parameter optimization

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