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Online Computation Offloading and Trajectory Scheduling for UAV-Enabled Wireless Powered Mobile Edge Computing

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China Communications 2022年第4期19卷 257-273页

作者： Han Hu Xiang Zhou Qun Wang Rose Qingyang hu The Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China The Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China The Department of Electrical and Computer Engineering Utah State UniversityLoganUTUSA.

The unmanned aerial vehicle(UAV)-enabled mobile edge computing(MEC) architecture is expected to be a powerful technique to facilitate 5 G and beyond ubiquitous wireless connectivity and diverse vertical applications and services, anytime and anywhere. Wireless power transfer(WPT) is another promising technology to prolong the operation time of low-power wireless devices in the era of Internet of Things(IoT). However, the integration of WPT and UAV-enabled MEC systems is far from being well studied, especially in dynamic environments. In order to tackle this issue, this paper aims to investigate the stochastic computation offloading and trajectory scheduling for the UAV-enabled wireless powered MEC system. A UAV offers both RF wireless power transmission and computation services for IoT devices. Considering the stochastic task arrivals and random channel conditions, a long-term average energyefficiency(EE) minimization problem is formulated.Due to non-convexity and the time domain coupling of the variables in the formulated problem, a lowcomplexity online computation offloading and trajectory scheduling algorithm(OCOTSA) is proposed by exploiting Lyapunov optimization. Simulation results verify that there exists a balance between EE and the service delay, and demonstrate that the system EE performance obtained by the proposed scheme outperforms other benchmark schemes.

关键词： energy efficiency mobile edge computing UAV-enabled wireless power transfer trajectorys cheduling

Key nanomaterials for industrial chemical process

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Nano Research 2023年第5期16卷 6013-6014页

作者： Xiang Feng Han Hu De Chen State Key Laboratory of Heavy Oil Processing China University of Petroleum(East China)Qingdao 266580China Department of Chemical Engineering Norwegian University of Science and TechnologyTrondheim 7491Norway

Growing concerns about the increasing global energy consumption and CO_(2)emission have greatly accelerated the development of new nanotechnologies in industrial chemical processes.However,this poses huge scientific and technological challenges,and normally requires novel materials and also novel synthesis strategies for traditional nanomaterials.This topical collection includes some of the latest studies on nanoscience and nanotechnology in chemical processes,such as separation,energy storage,and catalytic reactions(e.g.,hydrogenation,dehydrogenation,hydrogen evolution,selective oxidation,C–C coupling,and C–O bond formation).It includes 4 topics:(i)nanoscience and nanotechnology towards industrial process;(ii)nanomaterials for novel green chemical processes;(iii)novel nanomaterials for traditional processes;and(iv)novel synthesis strategy for nanomaterials.

关键词： materials chemical process

Three-dimensional magnetotelluric modeling in the spherical and Cartesian coordinate systems:A comparative study

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Earth and Planetary Physics 2023年第4期7卷 499-512页

作者： Qi han XiangYun hu School of Geophysics and Geomatics China University of GeosciencesWuhan 430074China China University of Geosciences Press Wuhan 430074China

With the increase in the coverage area of magnetotelluric data,three-dimensional magnetotelluric modeling in spherical coordinates and its differences with respect to traditional Cartesian modeling have gradually attracted attention.To fully understand the influence of the Earth’s curvature and map projection deformations on Cartesian modeling,qualitative and quantitative analyses based on realistic three-dimensional models need to be examined.Combined with five representative map projections,a type of model conversion method that transforms the original spherical electrical conductivity model to Cartesian coordinates is described in this study.The apparent resistivity differences between the spherical western United States electrical conductivity model and the corresponding five Cartesian models are then compared.The results show that the cylindrical equal distance map projection has the smallest error.A meridian convergence correction resulting from the deformation of the map projection is introduced to rotate the Cartesian impedance tensor from grid north to geographic north,which reduces differences from the spherical results.On the basis of the magnetotelluric field data,the applicability of the Cartesian coordinate system to western and contiguous United States models is quantitatively evaluated.Precise interpretations of the contiguous United States model were found to require spherical coordinates.

关键词： spherical magnetotelluric modeling model conversion realistic electrical conductivity model quantitative evaluation

Visualization-based prediction of dendritic copper growth in electrochemical cells using convolutional long short-term memory

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能源与人工智能(英文)

能源与人工智能(英文) 2022年第4期10卷 149-160页

作者： Roshan Kumar Trina Dhara Han Hu Monojit Chakraborty Department of Chemical Engineering Indian Institute of Technology KharagpurKharagpur721302India Department of Mechanical Engineering University of ArkansasFayettevilleAR72701United States of America

Electrodeposition in electrochemical cells is one of the leading causes of its performance deterioration. The prediction of electrodeposition growth demands a good understanding of the complex physics involved, which can lead to the fabrication of a probabilistic mathematical model. As an alternative, a convolutional Long shortterm memory architecture-based image analysis approach is presented herein. This technique can predict the electrodeposition growth of the electrolytes, without prior detailed knowledge of the system. The captured images of the electrodeposition from the experiments are used to train and test the model. A comparison between the expected output image and predicted image on a pixel level, percentage mean squared error, absolute percentage error, and pattern density of the electrodeposit are investigated to assess the model accuracy. The randomness of the electrodeposition growth is outlined by investigating the fractal dimension and the interfacial length of the electrodeposits. The trained model predictions show a significant promise between all the experimentally obtained relevant parameters with the predicted one. It is expected that this deep learning-based approach for predicting random electrodeposition growth will be of immense help for designing and optimizing the relevant experimental scheme in near future without performing multiple experiments.

关键词： Electrodeposition Electrochemical cell Deep learning Data-driven modelling Convolutional long short-term memory

Cloud-Type-Dependent 1DVAR Algorithm for Retrieving Hydrometeors and Precipitation in Tropical Cyclone Nanmadol from GMI Data

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Advances in Atmospheric Sciences 2024年第3期41卷 407-419页

作者： Linjun han Fuzhong WENG Hao hu Xiuqing hu School of the Environment China University of Geosciences(Wuhan)Wuhan 430074China Chinese Academy of Meteorological Sciences Beijing 100081China CMA Earth System Modeling and Prediction Centre Beijing 100081China Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites National Satellite Meteorological Center(National Center for Space Weather)of CMABeijing 100081China

Understanding the structure of tropical cyclone(TC)hydrometeors is crucial for detecting the changes in the distribution and intensity of precipitation.In this study,the GMI brightness temperature and cloud-dependent 1DVAR algorithm were used to retrieve the hydrometeor profiles and surface rain rate of TC Nanmadol(2022).The Advanced Radiative Transfer Modeling System(ARMS)was used to calculate the Jacobian and degrees of freedom(△DOF)of cloud water,rainwater,and graupel for different channels of GMI in convective conditions.The retrieval results were compared with the Dual-frequency Precipitation Radar(DPR),GMI 2A,and IMERG products.It is shown that from all channels of GMI,rain water has the highest△DOF,at 1.72.According to the radiance Jacobian to atmospheric state variables,cloud water emission dominates its scattering.For rain water,the emission of channels 1–4 dominates scattering.Compared with the GMI 2A precipitation product,the 1DVAR precipitation rate has a higher correlation coefficient(0.713)with the IMERG product and can better reflect the location of TC precipitation.Near the TC eyewall,the highest radar echo top indicates strong convection.Near the melting layer where Ka-band attenuation is strong,the double frequency difference of DPR data reflects the location of the melting.The DPR drop size distribution(DSD)product shows that there is a significant increase in particle size below the melting layer in the spiral rain band.Thus,the particle size may be one of the main reasons for the smaller rain water below the melting layer retrieved from 1DVAR.

关键词： cloud-dependent 1DVAR hydrometeor precipitation GMI DPR

Region-adaptive Concept Aggregation for Few-shot Visual Recognition

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Machine Intelligence Research 2023年第4期20卷 554-568页

作者： Mengya han Yibing Zhan Baosheng Yu Yong Luo Han Hu Bo Du Yonggang Wen Dacheng Tao School of Computer Science National Engineering Research Center for Multimedia SoftwareWuhan UniversityWuhan430072China hubei Luojia Laboratory Wuhan430072China School of Computer Science The University of SydneySydney2006Australia JD Explore Academy Beijing101116China School of Information and Electronics Beijing Institute of TechnologyBeijing100081China School of Computer Science and Engineering Nanyang Technological UniversitySingapore639798Singapore

Few-shot learning (FSL) aims to learn novel concepts from very limited examples. However, most FSL methods suffer from the issue of lacking robustness in concept learning. Specifically, existing FSL methods usually ignore the diversity of region contents that may contain concept-irrelevant information such as the background, which would introduce bias/noise and degrade the performance of conceptual representation learning. To address the above-mentioned issue, we propose a novel metric-based FSL method termed region-adaptive concept aggregation network or RCA-Net. Specifically, we devise a region-adaptive concept aggregator (RCA) to model the relationships of different regions and capture the conceptual information in different regions, which are then integrated in a weighted average manner to obtain the conceptual representation. Consequently, robust concept learning can be achieved by focusing more on the concept-relevant information and less on the conceptual-irrelevant information. We perform extensive experiments on three popular visual recognition benchmarks to demonstrate the superiority of RCA-Net for robust few-shot learning. In particular, on the Caltech-UCSD Birds-200-2011 (CUB200) dataset, the proposed RCA-Net significantly improves 1-shot accuracy from 74.76% to 78.03% and 5-shot accuracy from 86.84% to 89.83% compared with the most competitive counterpart.

关键词： Few-shot learning metric-based meta learning concept learning region-adaptive concept-aggregation

Carbonate Ontology and Its Application for Integrating Microfacies Data

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地球科学学刊:英文版 2023年第5期34卷 1328-1338页

作者： Yiwei Xu Xiumian hu Zhong han State Key Laboratory of Paleobiology and Stratigraphy Nanjing Institute of Geology and PaleontologyChinese Academy of SciencesNanjing 210008China State Key Laboratory of Mineral Deposit Research School of Earth Sciences and Engineering.Nanjing UniversityNanjing 210023China State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Institute of Sedimentary GeologyChengdu University of TechnologyChengdu 610059China

Carbonate rocks record essential information on changes in paleoclimate and paleoceanography. Abundant geological and geochemical data of carbonate rocks have been accumulated over the past decades;however, most of the data are stored in the published literature with highly unstructured forms, and are thus difficult to reuse. The ontology is a standard knowledge model for data integration, which can promote data storage and reutilization. This study proposes a carbonate ontology that represents the concepts in carbonate microfacies. The carbonate ontology constructed by the top-down process contains 215 terms of classifications and petrographic descriptions of carbonate rocks. Furthermore, carbonate microfacies of the Cretaceous(Aptian) carbonate platform in the Betic Cordillera and Jurassic carbonate platform in Tibet provide the data from case studies for the testing and initial validation of the proposed ontology. The carbonate ontology is under continuous expansion following the bottom-up approach and open access on the website of the deep-time digital Earth(DDE) program.

关键词： ontology carbonate rock carbonate petrography microfacies heterogeneous termino logy DDE knowledge graph

Triboelectric nanogenerators:the beginning of blue dream

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Frontiers of Chemical Science and Engineering 2023年第6期17卷 635-678页

作者： Wanli Wang Dongfang Yang Xiaoran Yan Licheng Wang Han Hu Kai Wang College of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266071China State Key Laboratory of Heavy Oil Processing College of Chemistry and Chemical EngineeringChina University of Petroleum(East China)Qingdao 266580China Xi’an Traffic Engineering Institute Xi’an 710300China School of Information Engineering Zhejiang University of TechnologyHangzhou 310023China

Wave energy is inexhaustible renewable energy.Making full use of the huge ocean wave energy resources is the dream of mankind for hundreds of years.Nowadays,the utilization of water wave energy is mainly absorbed and transformed by electromagnetic generators(EMGs)in the form of mechanical energy.However,waves usually have low frequency and uncertainty,which means low power generation efficiency for EMGs.Fortunately,in this slow current and random direction wave case,the triboelectric nanogenerator(TENG)has a relatively stable output power,which is suitable for collecting blue energy.This article summarizes the main research results of TENG in harvesting blue energy.Firstly,based on Maxwell’s displacement current,the basic principle of the nanogenerator is expounded.Then,four working modes and three applications of TENG are introduced,especially the application of TENG in blue energy.TENG currently used in blue energy harvesting is divided into four categories and discussed in detail.After TENG harvests water wave energy,it is meaningless if it cannot be used.Therefore,the modular storage of TENG energy is discussed.The output power of a single TENG unit is relatively low,which cannot meet the demand for high power.Thus,the networking strategy of large-scale TENG is further introduced.TENG’s energy comes from water waves,and each TENG’s output has great randomness,which is very unfavorable for the energy storage after large-scale TENG integration.On this basis,this paper discusses the power management methods of TENG.In addition,in order to further prove its economic and environmental advantages,the economic benefits of TENG are also evaluated.Finally,the development potential of TENG in the field of blue energy and some problems that need to be solved urgently are briefly summarized.

关键词： blue energy triboelectric nanogenerator water wave energy networking strategy micro nano-energy self-powered devices

Risk Factors Asscociated with Hypokalemia during Postanesthesia Recovery and Its Impact on Outcomes in Gynecological Patients:A Propensity Score Matching Study

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当代医学科学（英文） 2024年第2期44卷 441-449页

作者： Bei-bei WANG Li hu Xin-yue hu Dong han Jing WU Department of Anesthesiology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Institute of Anesthesia and Critical Care Medicine Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Key Laboratory of Anesthesiology and Resuscitation(huazhong University of Science and Technology) Ministry of EducationWuhan 430022China

Objective:This study aimed to explore the risk factors and outcomes of hypokalemia during the recovery period from anesthesia in the gynecological population.Methods:This retrospective cohort study included 208 patients who underwent gynecological surgery at our institution between January 2021 and March 2022.Data were collected for each patient,including demographics,disease status,surgical data,and clinical information.Preoperative bowel preparation,postoperative gastrointestinal function,and electrolyte levels were compared between the two groups using propensity score matching(PSM).Results:The incidence of hypokalemia(serum potassium level＜3.5 mmol/L)during the recovery period from anesthesia was approximately 43.75％.After PSM,oral laxative use(96.4％vs.82.4％,P=0.005),the number of general enemas(P=0.014),and the rate of＞2 general enemas(92.9％vs.77.8％,P=0.004)were identified as risk factors for hypokalemia,which was accompanied by decreased PaCO2 and hypocalcemia.There were no significant differences in postoperative gastrointestinal outcomes,such as the time to first flatus or feces,the Ⅰ-FEED score(a scoring system was created to evaluate impaired postoperative gastrointestinal function),or postoperative recovery outcomes,between the hypokalemia group and the normal serum potassium group.Conclusion:Hypokalemia during postanesthesia recovery period occurred in 43.75％of gynecological patients,which resulted from preoperative mechanical bowel preparation;however,it did not directly affect clinical outcomes,including postoperative gastrointestinal function,postoperative complications,and length of hospital stay.

关键词： hypokalemia gynecological surgery postanesthesia recovery perioperative electrolyte disturbances postoperative recovery