Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion,the increasing attention on power electronic equipment is fault detection and diagnosis.A power electronic circuit is an essential part of a power electronic system.The state of its internal components affects the performance of the system.The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits.Therefore,an algorithm based on adaptive simulated annealing particle swarm optimization(ASAPSO)was used in the present study to optimize a backpropagation(BP)neural network employed for the online fault diagnosis of a power electronic circuit.We built a circuit simulation model in MATLAB to obtain its DC output voltage.Using Fourier analysis,we extracted fault features.These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization(PSO)and the ASAPSO algorithm.The accuracy of fault diagnosis was compared for the three networks.The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy,better reliability,and adaptability and can more effectively diagnose and locate faults in power electronic circuits.

Community structure and species diversity dynamics of a subtropical evergreen broad-leaved forest in China:2005 to 2020

Here,we characterize the temporal and spatial dynamics of forest community structure and species diversity in a subtropical evergreen broad-leaved forest in China.We found that community structure in this forest changed over a 15-year period.Specifically,renewal and death of common species was large,with the renewal of individuals mainly concentrated within a few populations,especially those of Aidia canthioides and Cryptocarya concinna.The numbers of individual deaths for common species were concentrated in the small and mid-diameter level.The spatial distribution of community species diversity fluctuated in each monitoring period,showing a more dispersed diversity after the 15-year study period,and the coefficient of variation on quadrats increased.In 2010,the death and renewal of the community and the spatial variation of species diversity were different compared to other survey years.Extreme weather may have affected species regeneration and community stability in our subtropical monsoon evergreen broad-leaved forests.Our findings suggest that strengthening the monitoring and management of the forest community will help better understand the long-and short-term causes of dynamic fluctuations of community structure and species diversity,and reveal the factors that drive changes in community structure.

A Blockchain-Based Efficient Cross-Domain Authentication Scheme for Internet of Vehicles

The Internet of Vehicles(IoV)is extensively deployed in outdoor and open environments to effectively address traffic efficiency and safety issues by connecting vehicles to the network.However,due to the open and variable nature of its network topology,vehicles frequently engage in cross-domain interactions.During such processes,directly uploading sensitive information to roadside units for interaction may expose it to malicious tampering or interception by attackers,thus compromising the security of the cross-domain authentication process.Additionally,IoV imposes high real-time requirements,and existing cross-domain authentication schemes for IoV often encounter efficiency issues.To mitigate these challenges,we propose CAIoV,a blockchain-based efficient cross-domain authentication scheme for IoV.This scheme comprehensively integrates technologies such as zero-knowledge proofs,smart contracts,and Merkle hash tree structures.It divides the cross-domain process into anonymous cross-domain authentication and safe cross-domain authentication phases to ensure efficiency while maintaining a balance between efficiency and security.Finally,we evaluate the performance of CAIoV.Experimental results demonstrate that our proposed scheme reduces computational overhead by approximately 20%,communication overhead by around 10%,and storage overhead by nearly 30%.

Modified multiple-component scattering power decomposition for PolSAR data based on eigenspace of coherency matrix

A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.

Regulating Effect of Substrate Temperature on Sputteringgrown Ge/Si QDs under Low Ge Deposition

The effect of deposition temperature on the morphology and optoelectronic performance of Ge/Si QDs grown by magnetron sputtering under low Ge deposition(~4 nm)was investigated by atomic force microscopy,Raman spectroscopy,and photoluminescence(PL)tests.The experimental results indicate that temperatures higher than 750℃effectively increase the crystallization rate and surface smoothness of the Si buffer layer,and temperatures higher than 600℃significantly enhance the migration ability of Ge atoms,thus increasing the probability of Ge atoms meeting and nucleating to form QDs on Si buffer layer,but an excessively high temperature will cause the QDs to undergo an Ostwald ripening process and thus develop into super large islands.In addition,some PL peaks were observed in samples containing small-sized,high-density Ge QDs,the photoelectric properties reflected by these peaks were in good agreement with the corresponding structural characteristics of the grown QDs.Our results demonstrate the viability of preparing high-quality QDs by magnetron sputtering at high deposition rate,and the temperature effect is expected to work in conjunction with other controllable factors to further regulate QD growth,which paves an effective way for the industrial production of QDs that can be used in future devices.

Experiment on inducing apoptosis of melanoma cells by micro-plasma jet

As a promising cancer treatment method,cold atmospheric plasma has received widespread attention in recent years.However,previous research has focused more on how to realize and expand the anti-cancer scope of plasma jet.There are also studies on the killing of small-scale cancer cells,but the effects of plasma jet on normal cells and normal cell clusters have been ignored.Therefore,we proposed a 50μm sized micro-plasma jet device,and used the device to treat melanoma cells(A-375)and human glial cells(HA1800)to evaluate their anti-cancer effects and effects on normal cells.The experimental results show that this kind of micro-plasma jet device can effectively inactivate cancer cells in a short period of time,while having little effect on normal cells.This work provides a certain experimental basis for the application offine plasma jet to clinically inactivate cancer cells.

Achieving dynamic privacy measurement and protection based on reinforcement learning for mobile edge crowdsensing of IoT

With the maturity and development of 5G field,Mobile Edge CrowdSensing(MECS),as an intelligent data collection paradigm,provides a broad prospect for various applications in IoT.However,sensing users as data uploaders lack a balance between data benefits and privacy threats,leading to conservative data uploads and low revenue or excessive uploads and privacy breaches.To solve this problem,a Dynamic Privacy Measurement and Protection(DPMP)framework is proposed based on differential privacy and reinforcement learning.Firstly,a DPM model is designed to quantify the amount of data privacy,and a calculation method for personalized privacy threshold of different users is also designed.Furthermore,a Dynamic Private sensing data Selection(DPS)algorithm is proposed to help sensing users maximize data benefits within their privacy thresholds.Finally,theoretical analysis and ample experiment results show that DPMP framework is effective and efficient to achieve a balance between data benefits and sensing user privacy protection,in particular,the proposed DPMP framework has 63%and 23%higher training efficiency and data benefits,respectively,compared to the Monte Carlo algorithm.

Understanding the dehydrogenation properties of Mg(0001)/MgH_(2)(110)interface from first principles

Magnesium hydride is one of the most promising solid-state hydrogen storage materials for on-board application.Hydrogen desorption from MgH_(2) is accompanied by the formation of the Mg/MgH_(2) interfaces,which may play a key role in the further dehydrogenation process.In this work,first-principles calculations have been used to understand the dehydrogenation properties of the Mg(0001)/MgH_(2)(110) interface.It is found that the Mg(0001)/MgH_(2)(110) interface can weaken the Mg-H bond.The removal energies for hydrogen atoms in the interface zone are significantly lower compared to those of bulk MgH_(2).In terms of H mobility,hydrogen diffusion within the interface as well as into the Mg matrix is considered.The calculated energy barriers reveal that the migration of hydrogen atoms in the interface zone is easier than that in the bulk MgH_(2).Based on the hydrogen removal energies and diffusion barriers,we conclude that the formation of the Mg(0001)/MgH_(2)(110) interface facilitates the dehydrogenation process of magnesium hydride.

Scheduling on tractor and trailer transportation considering the influence of disrupted events based on the contract net and simulated annealing algorithm

To provide a much more resilient transport scheme for tractor and trailer transportation systems,this paper explores the generation method of tractor and trailer transport schemes considering the influence of disrupted events.Three states of tractors including towing loaded trailers,towing empty trailers,and idle driving are taken into account.Based on the disruption management theory,a scheduling model is constructed to minimize the total deviation cost including transportation time,transportation path,and number of used vehicles under the three states of tractors.A heuristics based on the contract net and simulated annealing algorithm is designed to solve the proposed model.Through comparative analysis of examples with different numbers of newly added transportation tasks and different types of road networks,the performance of the contract net algorithm in terms of deviations in idle driving paths,empty trailer paths,loaded trailer paths,time,number of used vehicles,and total deviation cost are analyzed.The results demonstrate the effectiveness of the model and algorithm,highlighting the superiority of the disruption management model and the contract net annealing algorithm.The study provides a reference for handling unexpected events in the tractor and trailer transportation industry.

Research Status,Practical Dilemmas,and Optimization Strategies of Blended Teaching in Universities

Blended teaching has emerged as a prominent subject in the recent reform and innovation of higher education.It has become imperative and guiding for colleges and universities to embrace a mixed teaching approach that aligns with the evolving needs of education and teaching in the new era.This paper aims to provide a comprehensive overview of the research status surrounding blended teaching,encompassing fundamental issues,teaching design,practical guidance,teaching effectiveness,and evaluation.By critically examining the current challenges associated with blended teaching,this study proposes optimization strategies including enhancing student participation and interaction,promoting deep learning,improving teachers’preparedness,teaching technologies,and curriculum design capabilities,strengthening top-level design,and perfecting evaluation and incentive mechanisms.These strategies provide new directions for the reform of blended teaching.

Exploration and Practice of Big Data Introductory Courses for Big Data Management and Application Majors

As an introductory course for the emerging major of big data management and application,“Introduction to Big Data”has not yet formed a curriculum standard and implementation plan that is widely accepted and used by everyone.To this end,we discuss some of our explorations and attempts in the construction and teaching process of big data courses for the major of big data management and application from the perspective of course planning,course implementation,and course summary.After interviews with students and feedback from questionnaires,students are highly satisfied with some of the teaching measures and programs currently adopted.

Heterogeneous integration of InP HEMTs on quartz wafer using BCB bonding technology

Heterogeneous integrated InP high electron mobility transistors(HEMTs)on quartz wafers are fabricated successfully by using a reverse-grown InP epitaxial structure and benzocyclobutene(BCB)bonding technology.The channel of the new device is In_(0.7)Ga_(0.3)As,and the gate length is 100 nm.A maximum extrinsic transconductance gm,max of 855.5 mS/mm and a maximum drain current of 536.5 mA/mm are obtained.The current gain cutoff frequency is as high as 262 GHz and the maximum oscillation frequency reaches 288 GHz.In addition,a small signal equivalent circuit model of heterogeneous integration of InP HEMTs on quartz wafer is built to characterize device performance.

First-principles studies of electronic,optical,and mechanical properties of γ-Bi2Sn2O7

The detailed theoretical studies of electronic,optical,and mechanical properties of γ-Bi2Sn2O7 are carried out by using first-principle density functional theory calculations.Our calculated results indicate that γ-Bi2Sn2O7 is the p-type semiconductor with an indirect band gap of about 2.72 e V.The flat electronic bands close to the valence band maximum are mainly composed of Bi-6s and O-2p states and play a key role in determining the electrical properties of γ-Bi2Sn2O7.The calculated complex dielectric function and macroscopic optical constants including refractive index,extinction coefficient,absorption coefficients,reflectivity,and electron energy-loss function show that γ-Bi2Sn2O7 is an excellent light absorbing material.The analysis on mechanical properties shows that γ-Bi2Sn2O7 is mechanically stable and highly isotropic.

Lightweight Design of Commercial Vehicle Cab Based on Fatigue Durability

To better improve the lightweight and fatigue durability performance of the tractor cab,a multi-objective lightweight design of the cab was carried out in this study.First,the finite element model of the cab with counterweight loading was established and then confirmed by the physical testing,and use the inertial reliefmethod to obtain stress distribution under unit load.The cab-frame rigid-flexible couplingmulti-body dynamicsmodelwas built by Adams/car software.Taking the cab airbag mount displacement and acceleration signals acquired on the proving ground as the desired signals and obtaining the fatigue analysis load spectrum through Femfat-Lab virtual iteration.The fatigue simulation analysis is performed in nCode based on the Miner linear fatigue cumulative damage theory.Then,with themass and fatigue damage values as the optimization objectives,the bending-torsional stiffness and first-order bending-torsional mode as constraints,the thickness variables are screed based on the sensitivity analysis.The experimental design was carried out using the Optimal Latin hypercube method,and the multi-objective optimal design of the cab was carried out using theKriging approximationmodel fitting and particle swarmalgorithm.The weight of the optimized cab is reduced by 7.8%on the basis of meeting the fatigue durability performance.Finally,a seven-axis road simulation test rig was designed to verify its fatigue durability.The results show the optimized cab can consider both lightweight and durability.

Tailoring spatiotemporal dynamics of plasmonic vortices

Plasmonic vortices confining orbital angular momentums to surface have aroused wide research interest in the last decade.Recent advances of near-field microscopes have enabled the study on the spatiotemporal dynamics of plasmonic vortices,providing a better understanding of optical orbital angular momentums in the evanescent wave regime.However,these works only focused on the objective characterization of plasmonic vortex and have not achieved subjectively tailoring of its spatiotemporal dynamics for specific applications.Herein,it is demonstrated that the plasmonic vortices with the same topological charge can be endowed with distinct spatiotemporal dynamics by simply changing the coupler design.Based on a near-field scanning terahertz microscopy,the surface plasmon fields are directly obtained with ultrahigh spatiotemporal resolution,experimentally exhibiting the generation and evolution divergences during the whole lifetime of plasmonic vortices.The proposed strategy is straightforward and universal,which can be readily applied into visible or infrared frequencies,facilitating the development of plasmonic vortex related researches and applications.

Numerical optimisation of a classical stochastic system for targeted energy transfer

The paper studies stochastic dynamics of a two-degree-of-freedom system,where a primary linear system is connected to a nonlinear energy sink with cubic stiffness nonlinearity and viscous damping.While the primary mass is subjected to a zero-mean Gaussian white noise excitation,the main objective of this study is to maximise the efficiency of the targeted energy transfer in the system.A surrogate optimisation algorithm is proposed for this purpose and adopted for the stochastic framework.The optimisations are conducted separately for the nonlinear stiffness coefficient alone as well as for both the nonlinear stiffness and damping coefficients together.Three different optimisation cost functions,based on either energy of the system’s components or the dissipated energy,are considered.The results demonstrate some clear trends in values of the nonlinear energy sink coefficients and show the effect of different cost functions on the optimal values of the nonlinear system’s coefficients.

Structural evolutions and electronic properties of Au_nGd(n=6–15)small clusters:A first principles study

Structural, electronic, and magnetic properties of AunGd （n = 6-15） small clusters are investigated by using first principles spin polarized calculations and combining with the ab-initio evolutionary structure simulations. The calculated binding energies indicate that after doping a Gd atom AunGd cluster is obviously more stable than a pure AUn＋l cluster. Au6Gd with the quasiplanar structure has a largest magnetic moment of 7.421 gB. The Gd-4f electrons play an important role in determining the high magnetic moments of AunGd clusters, but in Au6Gd and Aul2Gd clusters the unignorable spin polarized effects from the Au-6s and Au-5d electrons further enhance their magnetism. The HOMO-LUMO （here, HOMO and LUMO stand for the highest occupied molecular orbital, and the lowest unoccupied molecular orbital, respectively） energy gaps of munGd clusters are smaller than those of pure AUn＋l clusters, indicating that AunGd clusters have potential as new catalysts with enhanced reactivity.

Technology Importation and Domestic Technology Acquisition in China from the Perspective of Innovation

Foreign and domestic technologies play different roles in a country's innovation. In recent years, Chinese firms have spent less on importing foreign technology and more on acquiring domestic technology. Based on the panel data model, state space model(SSM) and the Bayesian vector autoregressive model, this paper finds that the purchase of domestic technology plays a critical role in the innovation of high-tech firms with an elasticity coefficient higher than that of technology importation before 2010. China's high-tech firms can effectively absorb purchased domestic technology, which also brings about an increase in their independent R&D input and contributes more to innovation output than imported technology. Performance of technology importation is lackluster but shows an improving trend. Technology importation is not correlated with innovation output and has a substitutive effect with independent R&D input. China is yet to enhance its absorption of imported technology. Elasticity coefficient of technology importation turned positive in 2007 and increased year by year. China should promote synergy between technology importation and domestic technology acquisition.

Low Temperature Heat Capacity of Zn Substituted Cobalt Ferrite Nanosphere:The Relation between Magnetic Properties and Microstructure

Co_((1-x))ZnxFe_(2)O_(4)nanospheres(x=0,0.5,0.8)with a unidirectional cubic spinel structure were prepared by a solvothermal method.By using a range of theoretical and empirical models,the experimental heat capacity values were fitted as a function of temperature over a suitable temperature range to explain the possible relationship between the magnetic properties and microstructure of the nanospheres.As a result,at a low temperature(T<10 K),the parameter Bfswdecreases with increasing Zn concentration,implying that the exchange interaction between A and B sites decreases.At a relatively high temperature(T>50 K),the Debye temperature decreases with increasing Zn concentration,which is due to the weakening of the interatomic bonding force after the addition of non-magnetic materials to the Co Fe_(2)O_(4)spinel ferrite.

The advance of magnetic diagnostics system in support of EAST long-pulsed operation

In EAST long-pulsed discharge(hundreds of seconds),electric magnetic diagnosis(EMD)is very important,since EMD not only monitors tokamak security status but also provides accurate measurement accuracy for reconstruction of the plasma boundary.To avoid current measurement drift,a fiber optic current sensor,based on the Faraday effect,is developed and used for poloidal and plasma current feedback control for the first time,relative current measurement accuracy is within 0.5%.To ensure plasma boundary control accuracy,a detailed set of magnetic measurement calibration methods is developed before the plasma discharge.The maximum relative error is less than 1%,the corresponding control accuracy is within 1 cm.To minimize integrator drift error,a long-pulse integrator test is essential,the corresponding drift error needs to be subtracted in plasma control system.Besides,the saddle coil and Mirnov coil not only help to detect MHD issues,but are also utilized for plasma disruption prediction during the long-pulse discharge.