Undesired ice accumulation on infrastructure and transportation systems leads to catastrophic events and significant economic losses.Although various anti-icing surfaces with photothermal effects can initially prevent...Undesired ice accumulation on infrastructure and transportation systems leads to catastrophic events and significant economic losses.Although various anti-icing surfaces with photothermal effects can initially prevent icing,any thawy droplets remaining on the horizontal surface can quickly re-freezing once the light diminishes.To address these challenges,we have developed a self-draining slippery surface(SDSS)that enables the thawy droplets to self-remove on the horizontal surface,thereby facilitating real-time anti-icing with the aid of sunlight(100 m W cm^(-2)).This is achieved by sandwiching a thin pyroelectric layer between slippery surface and photothermal film.Due to the synergy between the photothermal and pyroelectric layers,the SDSS not only maintains a high surface temperature of 19.8±2.2℃at the low temperature(-20.0±1.0℃),but also generates amount of charge through thermoelectric coupling.Thus,as cold droplets dropped on the SDSS,electrostatic force pushes the droplets off the charged surface because of the charge transfer mechanism.Even if the surface freezes overnight,the ice can melt and drain off the SDSS within 10 min of exposure to sunlight at-20.0±1.0℃,leaving a clean surface.This work provides a new perspective on the anti-icing system in the real-world environments.展开更多
A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance m...A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance method.Firstly,an altitude-energy profile is designed,and the bank angle is derived analytically as the initial iteration value for the predictor-corrector method.The predictor-corrector guidance method has been improved by deriving an analytical form for predicting the range-to-go error,which greatly accelerates the iterative speed.Then,a segmented guidance algorithm is proposed.The above analytically predictor-corrector guidance method is adopted when the energy exceeds an energy threshold.When the energy is less than the threshold,the equidistant test method is used to calculate the bank angle command,which ensures guidance accuracy as well as computational efficiency.Additionally,an adaptive guidance cycle strategy is applied to reduce the computational time of the reentry guidance trajectory.Finally,the accuracy and robustness of the proposed method are verified through a series of simulations and Monte-Carlo experiments.Compared with the traditional integral method,the proposed method requires 75%less computation time on average and achieves a lower landing error.展开更多
When multiple LCC-HVDC transmission lines are densely fed into a receiving AC system,voltage dips can easily propagate in the power system,resulting in multiple LCC commutation failures simultaneously.The VSC-HVDC can...When multiple LCC-HVDC transmission lines are densely fed into a receiving AC system,voltage dips can easily propagate in the power system,resulting in multiple LCC commutation failures simultaneously.The VSC-HVDC can be used to divide the receiving sys-tem into several interconnected sub-partitions and improve the voltage support capability of the receiving system.Compared with asyn-chronous interconnection,which completely separates the receiving systems with VSC-HVDC,incomplete segmentation with an AC connection is a more pertinent segmenting method for multilayer complex regional power grids.To analyze the voltage support capability of the VSC in incomplete segmentation,a micro-incremental model of the VSC was established,the operating impedance of the VSC was calculated,and the voltage support function of the VSC was quantified.The effect of the fault on the system short-circuit capacity was analyzed,and a calculation method for the multi-infeed short-circuit ratio in an incompletely segmented scenario was obtained.A VSC-segmented model of a two-infeed DC system was built on the EMTDC/PSCAD simulation platform,and the validity of the micro-increment model and accuracy of the proposed conclusions were verified.展开更多
The Cambrian platform margin in the Tarim Basin boasts favorable source-reservoir-cap assemblages,making it a significant target for hydrocarbon exploration in ultra-to extra-deep facies-controlled for-mations.Of the ...The Cambrian platform margin in the Tarim Basin boasts favorable source-reservoir-cap assemblages,making it a significant target for hydrocarbon exploration in ultra-to extra-deep facies-controlled for-mations.Of the three major basins in western China,Tarim is the only basin with large-scale platform margin where no exploration breakthrough has been achieved yet.This study determines the vertical and lateral differential evolution of the platform margin(in the Manxi area hereafter referred to as the Cambrian Manxi platform margin)through fine-scale sequence stratigraphic division and a segmented analysis.The platform margin can be divided into the Yuqi,Tahe,Shunbei,and Gucheng segments,from north to south,based on the development of different ancient landforms and the evolutionary process of the platform.The Yuqi and Shunbei segments exhibit relatively low-elevation ancient landforms.Both segments were in a submarine buildup stage during the Early Cambrian,resulting in overall limited scales of their reservoirs.The Gucheng segment features the highest-elevation ancient landforms and accordingly limited accommodation spaces.As a result,the rapid lateral migration of high-energy facies zones leads to the development of large-scale reservoirs with only limited thicknesses.In contrast,the Tahe segment,exhibiting comparatively high-elevation ancient landforms,is identified as the most favorable segment for the formation of large-scale reservoirs.The cap rocks of the platform margin are dominated by back-reef dolomitic flats and tight carbonate rocks formed in transgressive periods.A comprehensive evaluation of source rocks,reservoirs,and cap rocks indicates that the Tahe segment boasts the optimal hydrocarbon accumulation conditions along the platform margin.In this segment,the Shayilike Formation transgressive deposits and the high-energy mound-shoal complexes along the platform margin of the Wusonggeer Formation constitute the optimal reservoir-cap rock assemblage,establishing this segment as the most promising target for hydrocarbon exploration in the platform margin.展开更多
This work describes the discharge characteristics and acetone degradation with plasma under different electric fields based on a coaxial cylindrical dielectric barrier discharge(DBD)device energized by pulsed power.It...This work describes the discharge characteristics and acetone degradation with plasma under different electric fields based on a coaxial cylindrical dielectric barrier discharge(DBD)device energized by pulsed power.It is found that the segmented electrodes with appropriate spacing in coaxial cylindrical DBD are beneficial to the plasma ionization.In this work,the plasma distribution,discharge thermal effect,ionization of reactive species,and acetone degradation performance in coaxial cylindrical DBD with different segmented electrodes are systematically investigated.The experimental results show that segmented electrodes with a certain distance can cause additional ionization in the non-electrode-covered region between adjacent electrodes,thus enlarging the plasma region compared with a single electrode with equivalent total electrode length.The additional ionization involved the inner volume discharge between the quartz tubes and the outer surface discharge along the surface of the external quartz tube.The spatial distributions of the inner volume discharge and external surface discharge were predominantly governed by the radial and axial components of the inter-electrode electric field,respectively.The external surface discharge exhibited significant suppression when the electrode spacing was<1.5 mm,and it reached its maximum length at 3 mm spacing.When the electrode distance increased to 7-9 mm,a weak ionizing region appeared in the middle of the adjacent electrodes,which could be attributed to the gradual attenuation of the radial component with the increasing electrode spacing.A higher thermal effect and better oxidation of acetone to CO_(x)(CO and CO_(2))were achieved with the segmented electrode;the dual-segment configuration(3 mm per electrode)achieved a reactor temperature of 63.4℃,representing a 10℃enhancement over comparable single-electrode systems.Similarly,the CO_(2)and CO concentration reached 328.8 mg/m3and 105.7 mg/m3,respectively,in two 3 mm long segmented electrodes,which was an increase of 12.2%and 25.6%,respectively,compared with the single electrode.Notably,considering the equivalent ionization of the inner discharge with different electrodes,the enhanced thermal effects and CO_(x)conversion efficiency directly correlate with the expanded plasma zone induced by electrode segmentation.This work provides critical insights into optimizing electrode configurations for efficient plasma-assisted volatile organic compound degradation systems.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and stru...Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.展开更多
This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 20...This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.展开更多
Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the b...Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
Pavement snow and icing are worldwide problems, but effective countermeasures are just beginning to be developed in China. The two most common snow and ice removal methods are mechanical clearance and chemical melting...Pavement snow and icing are worldwide problems, but effective countermeasures are just beginning to be developed in China. The two most common snow and ice removal methods are mechanical clearance and chemical melting, and the advantages and disadvantages of each approach are discussed here, including environmental and structural damage caused by corrosive snow melting agents. New developments in chemical melting agents and mechanical equipment are discussed, and an overview of alternative thermal melting systems is presented, including the use of geothermy and non-geothermal heating systems utilizing solar energy, electricity, conductive pavement materials, and infrared/microwave applications. Strategic recommendations are made for continued enhancement of public safety in snow and ice conditions.展开更多
De-icing technology has become an increasingly important subject in numerous applications in recent years.However,the direct numerical modeling and simulation the physical process of thermomechanical deicing is limite...De-icing technology has become an increasingly important subject in numerous applications in recent years.However,the direct numerical modeling and simulation the physical process of thermomechanical deicing is limited.This work is focusing on developing a numerical model and tool to direct simulate the de-icing process in the framework of the coupled thermo-mechanical peridynamics theory.Here,we adopted the fully coupled thermo-mechanical bond-based peridynamics(TM-BB-PD)method for modeling and simulation of de-icing.Within the framework of TM-BB-PD,the ice constitutive model is established by considering the influence of the temperature difference between two material points,and a modified failure criteria is proposed,which takes into account temperature effect to predict the damage of quasi-brittle ice material.Moreover,thermal boundary condition is used to simulate the thermal load in the de-icing process.By comparing with the experimental results and the previous reported finite element modeling,our numerical model shows good agreement with the previous predictions.Based on the numerical results,we find that the developed method can not only predict crack initiation and propagation in the ice,but also predict the temperature distribution and heat conduction during the de-icing process.Furthermore,the influence of the temperature for the ice crack growth pattern is discussed accordingly.In conclusion,the coupled thermal-mechanical peridynamics formulation with modified failure criterion is capable of providing a modeling tool for engineering applications of de-icing technology.展开更多
This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of li...This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.展开更多
Segmented block copolymer based on nylon6 （N6） and polyethylene oxide （PEO） with stochiometric ratio was synthesized via a two-step process. The first step represents end capping of N6 in the presence of adipic ac...Segmented block copolymer based on nylon6 （N6） and polyethylene oxide （PEO） with stochiometric ratio was synthesized via a two-step process. The first step represents end capping of N6 in the presence of adipic acid leading to carboxy terminated N6, and the second one is polycondensation of the latter product with PEO in the presence of catalyst and thermostabilizer to form a high molecular weight multi-block copolymer. Several methods were applied to characterize the synthesized copolyrner such as Fourier transform infrared spectroscopy, proton nuclear magnetic resonance spectroscopy, differential thermal analysis, differential scanning calorimetry, X-ray diffraction and atomic force microscopy. The obtained results confirmed the multi-block structure for copolymer with a very high degree of micro-phase separation. Atomic force microscopy micrographs indicated that the morphology was the dispersion of high stiffness nanostructured polyamide （PA） domains in the amorphous region of PEO matrix, which can be very important in their performance for membrane processes.展开更多
The influences of the low-emissive graphite segmented electrode t）laeed near the channel exit on the discharge characteristics of a Hall thruster are studied using the particle- in-cell method. A two-dimensional phys...The influences of the low-emissive graphite segmented electrode t）laeed near the channel exit on the discharge characteristics of a Hall thruster are studied using the particle- in-cell method. A two-dimensional physical model is established according to the Hall thruster discharge channel configuration. The effects of electrode length on the potential, ion density, electron temperature, ionization rate and discharge current are investigated. It is found that, with tile increasing of the segmented electrode length, the equipotential lines bend towards the channel exit. and approximately parallel to the wall at the channel surface, the radial velocity and radial flow of ions are increased, and the electron temperature is also enhanced. Due to the conductive characteristic of electrodes, the radial electric field and the axial electron conductivity near the wall are enhanced, and the probability of the electron-atom ionization is reduced, which leads to the degradation of the ionization rate in the discharge channel. However, the interaction between electrons and the wall enhances the near wall conductivity, therefore the discharge current grows along with the segmented electrode length, and the performance of the thruster is also affected.展开更多
In order to research segmented diverters for aircraft lightning protection, a transient 2 D multiphysics model based on magnetohydrodynamics theory is proposed to predict the location of the arc plasma discharge and l...In order to research segmented diverters for aircraft lightning protection, a transient 2 D multiphysics model based on magnetohydrodynamics theory is proposed to predict the location of the arc plasma discharge and lightning channel, and to simulate the electrothermal behavior.Based on numerical calculation and preliminary analysis, factors that affect the breakdown voltage of the segmented diverter are discussed. The results show that the voltage increase rate of the voltage source, the width of the air gap between metal segments and the geometry of these segments influence the breakdown voltage of the strip. High-voltage tests of the segmented diverter are performed to reveal air breakdown of the strip and redirect the lightning current.Experimental and numerical results are compared to verify the correctness of the numerical model. The ionization of the air gap between metal segments and the breakdown voltage of the strip calculated by the model are qualitatively consistent with experimental results. The breakdown voltage of the segmented diverter is far lower than the lightning voltage. When a lightning strike occurs, the segmented diverter can be quickly ionized to form a plasma channel which can guide the lightning current well.展开更多
This paper proposes a numerical method to analyze the ice protection capability and predict the power requirements of a piezoelectric resonant de-icing system.The method is based on a coupled electro-mechanical finite...This paper proposes a numerical method to analyze the ice protection capability and predict the power requirements of a piezoelectric resonant de-icing system.The method is based on a coupled electro-mechanical finite element analysis which enables the fast computation of the modes of resonance of interest to de-ice curved surfaces and the estimation of the input voltage and current required for a given configuration(defined by its mode,actuator location,ice deposit,etc.).Eventually,the electric power to be supplied can be also assessed.The method is applied to a NACA 0024 leading edge equipped with piezoelectric actuators.First,two extension modes are analyzed and compared with respect to their efficiency and power requirements.Then,tests are carried out in an icing tunnel to verify the effectiveness of the piezoelectric ice protection system and the predictions of the maximal required power.The system allows de-icing the leading edge in less than 2 s for a glaze ice deposit.展开更多
Two series of polyurethanes based on mixed polychloromethyl methyl siloxane and poly-tetramethylene oxide in different weight fractions were synthesized. The phase separation ofsamples was studied using DSC and dynami...Two series of polyurethanes based on mixed polychloromethyl methyl siloxane and poly-tetramethylene oxide in different weight fractions were synthesized. The phase separation ofsamples was studied using DSC and dynamic mechanical property analysis. The results showedthat the introduction of chloromethyl group into polysiloxane increased its polarity and henceimproved the miscibilities with polytetramethylene oxide and polyurethane hard segment.Particularly, in the case of N-methyldiethanolamine extended materials, the surface and tensileproperties of these samples can be adjusted by various ratios of two soft segments.展开更多
Direct current plasma torches have been applied to generate unique sources of thermal energy in many industrial applications. Nevertheless, the successful ignition of a plasma torch is the key process to generate the ...Direct current plasma torches have been applied to generate unique sources of thermal energy in many industrial applications. Nevertheless, the successful ignition of a plasma torch is the key process to generate the unique source （plasma jet）. However, there has been tittle study on the underlying mechanism of this key process. A thorough understanding of the ignition process of a plasma torch will be helpful for optimizing the design of the plasma torch structure and selection of the ignition parameters to prolong the service life of the ignition module. Thus, in this paper, the ignition process of a segmented plasma torch （SPT） is theoretically and experimentally modeled and analyzed. Corresponding electrical models of different stages of the ignition process axe set up and used to derive the electrical parameters, e.g. the variations of the arc voltage and arc current between the cathode and anode. In addition, the experiments with different ignition parameters on a home-made SPT have been conducted. At the same time, the variations of the arc voltage and arc current have been measured, and used to verify the ones derived in theory and to determine the optimal ignition parameters for a particular SPT.展开更多
In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating ...In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating the reliability of equipment with a single model may lead to severer errors.However, if lifetime is divided into several different intervals according to the characteristics of its failure rate, piecewise fitting can more accurately approximate the failure rate of equipment. Therefore, in this paper, failure rate is regarded as a piecewise function, and two kinds of segmented distribution are put forward to evaluate reliability. In order to estimate parameters in the segmented reliability function, Bayesian estimation and maximum likelihood estimation（MLE） of the segmented distribution are discussed in this paper. Since traditional information criterion is not suitable for the segmented distribution, an improved information criterion is proposed to test and evaluate the segmented reliability model in this paper. After a great deal of testing and verification,the segmented reliability model and its estimation methods presented in this paper are proven more efficient and accurate than the traditional non-segmented single model, especially when the change of the failure rate is time-phased or multimodal. The significant performance of the segmented reliability model in evaluating reliability of proximity sensors of leading-edge flap in civil aircraft indicates that the segmented distribution and its estimation method in this paper could be useful and accurate.展开更多
基金supported by the National Natural Science Foundation of China(52273101,51922018,and 21875011)the Fundamental Research Funds for the Central Universities(KG21015201 and KG21020801)China Postdoctoral Science Foundation(2025M77422)。
文摘Undesired ice accumulation on infrastructure and transportation systems leads to catastrophic events and significant economic losses.Although various anti-icing surfaces with photothermal effects can initially prevent icing,any thawy droplets remaining on the horizontal surface can quickly re-freezing once the light diminishes.To address these challenges,we have developed a self-draining slippery surface(SDSS)that enables the thawy droplets to self-remove on the horizontal surface,thereby facilitating real-time anti-icing with the aid of sunlight(100 m W cm^(-2)).This is achieved by sandwiching a thin pyroelectric layer between slippery surface and photothermal film.Due to the synergy between the photothermal and pyroelectric layers,the SDSS not only maintains a high surface temperature of 19.8±2.2℃at the low temperature(-20.0±1.0℃),but also generates amount of charge through thermoelectric coupling.Thus,as cold droplets dropped on the SDSS,electrostatic force pushes the droplets off the charged surface because of the charge transfer mechanism.Even if the surface freezes overnight,the ice can melt and drain off the SDSS within 10 min of exposure to sunlight at-20.0±1.0℃,leaving a clean surface.This work provides a new perspective on the anti-icing system in the real-world environments.
基金National Natural Science Foundation of China(Nos.61773387 and 62022061).
文摘A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance method.Firstly,an altitude-energy profile is designed,and the bank angle is derived analytically as the initial iteration value for the predictor-corrector method.The predictor-corrector guidance method has been improved by deriving an analytical form for predicting the range-to-go error,which greatly accelerates the iterative speed.Then,a segmented guidance algorithm is proposed.The above analytically predictor-corrector guidance method is adopted when the energy exceeds an energy threshold.When the energy is less than the threshold,the equidistant test method is used to calculate the bank angle command,which ensures guidance accuracy as well as computational efficiency.Additionally,an adaptive guidance cycle strategy is applied to reduce the computational time of the reentry guidance trajectory.Finally,the accuracy and robustness of the proposed method are verified through a series of simulations and Monte-Carlo experiments.Compared with the traditional integral method,the proposed method requires 75%less computation time on average and achieves a lower landing error.
基金supported by the State Grid Science and Technology Project 5108-202218280A-2-87-XG.
文摘When multiple LCC-HVDC transmission lines are densely fed into a receiving AC system,voltage dips can easily propagate in the power system,resulting in multiple LCC commutation failures simultaneously.The VSC-HVDC can be used to divide the receiving sys-tem into several interconnected sub-partitions and improve the voltage support capability of the receiving system.Compared with asyn-chronous interconnection,which completely separates the receiving systems with VSC-HVDC,incomplete segmentation with an AC connection is a more pertinent segmenting method for multilayer complex regional power grids.To analyze the voltage support capability of the VSC in incomplete segmentation,a micro-incremental model of the VSC was established,the operating impedance of the VSC was calculated,and the voltage support function of the VSC was quantified.The effect of the fault on the system short-circuit capacity was analyzed,and a calculation method for the multi-infeed short-circuit ratio in an incompletely segmented scenario was obtained.A VSC-segmented model of a two-infeed DC system was built on the EMTDC/PSCAD simulation platform,and the validity of the micro-increment model and accuracy of the proposed conclusions were verified.
基金funded by SINOPEC Science and Technology Research Program (project Nos:P24226, P24077)Northwest Oil Field Company,SINOPEC.
文摘The Cambrian platform margin in the Tarim Basin boasts favorable source-reservoir-cap assemblages,making it a significant target for hydrocarbon exploration in ultra-to extra-deep facies-controlled for-mations.Of the three major basins in western China,Tarim is the only basin with large-scale platform margin where no exploration breakthrough has been achieved yet.This study determines the vertical and lateral differential evolution of the platform margin(in the Manxi area hereafter referred to as the Cambrian Manxi platform margin)through fine-scale sequence stratigraphic division and a segmented analysis.The platform margin can be divided into the Yuqi,Tahe,Shunbei,and Gucheng segments,from north to south,based on the development of different ancient landforms and the evolutionary process of the platform.The Yuqi and Shunbei segments exhibit relatively low-elevation ancient landforms.Both segments were in a submarine buildup stage during the Early Cambrian,resulting in overall limited scales of their reservoirs.The Gucheng segment features the highest-elevation ancient landforms and accordingly limited accommodation spaces.As a result,the rapid lateral migration of high-energy facies zones leads to the development of large-scale reservoirs with only limited thicknesses.In contrast,the Tahe segment,exhibiting comparatively high-elevation ancient landforms,is identified as the most favorable segment for the formation of large-scale reservoirs.The cap rocks of the platform margin are dominated by back-reef dolomitic flats and tight carbonate rocks formed in transgressive periods.A comprehensive evaluation of source rocks,reservoirs,and cap rocks indicates that the Tahe segment boasts the optimal hydrocarbon accumulation conditions along the platform margin.In this segment,the Shayilike Formation transgressive deposits and the high-energy mound-shoal complexes along the platform margin of the Wusonggeer Formation constitute the optimal reservoir-cap rock assemblage,establishing this segment as the most promising target for hydrocarbon exploration in the platform margin.
文摘This work describes the discharge characteristics and acetone degradation with plasma under different electric fields based on a coaxial cylindrical dielectric barrier discharge(DBD)device energized by pulsed power.It is found that the segmented electrodes with appropriate spacing in coaxial cylindrical DBD are beneficial to the plasma ionization.In this work,the plasma distribution,discharge thermal effect,ionization of reactive species,and acetone degradation performance in coaxial cylindrical DBD with different segmented electrodes are systematically investigated.The experimental results show that segmented electrodes with a certain distance can cause additional ionization in the non-electrode-covered region between adjacent electrodes,thus enlarging the plasma region compared with a single electrode with equivalent total electrode length.The additional ionization involved the inner volume discharge between the quartz tubes and the outer surface discharge along the surface of the external quartz tube.The spatial distributions of the inner volume discharge and external surface discharge were predominantly governed by the radial and axial components of the inter-electrode electric field,respectively.The external surface discharge exhibited significant suppression when the electrode spacing was<1.5 mm,and it reached its maximum length at 3 mm spacing.When the electrode distance increased to 7-9 mm,a weak ionizing region appeared in the middle of the adjacent electrodes,which could be attributed to the gradual attenuation of the radial component with the increasing electrode spacing.A higher thermal effect and better oxidation of acetone to CO_(x)(CO and CO_(2))were achieved with the segmented electrode;the dual-segment configuration(3 mm per electrode)achieved a reactor temperature of 63.4℃,representing a 10℃enhancement over comparable single-electrode systems.Similarly,the CO_(2)and CO concentration reached 328.8 mg/m3and 105.7 mg/m3,respectively,in two 3 mm long segmented electrodes,which was an increase of 12.2%and 25.6%,respectively,compared with the single electrode.Notably,considering the equivalent ionization of the inner discharge with different electrodes,the enhanced thermal effects and CO_(x)conversion efficiency directly correlate with the expanded plasma zone induced by electrode segmentation.This work provides critical insights into optimizing electrode configurations for efficient plasma-assisted volatile organic compound degradation systems.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
文摘Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods,which often demand extensive computational resources and struggle with diverse data acquisition techniques.This research presents a novel approach for vehicle classification and recognition in aerial image sequences,integrating multiple advanced techniques to enhance detection accuracy.The proposed model begins with preprocessing using Multiscale Retinex(MSR)to enhance image quality,followed by Expectation-Maximization(EM)Segmentation for precise foreground object identification.Vehicle detection is performed using the state-of-the-art YOLOv10 framework,while feature extraction incorporates Maximally Stable Extremal Regions(MSER),Dense Scale-Invariant Feature Transform(Dense SIFT),and Zernike Moments Features to capture distinct object characteristics.Feature optimization is further refined through a Hybrid Swarm-based Optimization algorithm,ensuring optimal feature selection for improved classification performance.The final classification is conducted using a Vision Transformer,leveraging its robust learning capabilities for enhanced accuracy.Experimental evaluations on benchmark datasets,including UAVDT and the Unmanned Aerial Vehicle Intruder Dataset(UAVID),demonstrate the superiority of the proposed approach,achieving an accuracy of 94.40%on UAVDT and 93.57%on UAVID.The results highlight the efficacy of the model in significantly enhancing vehicle detection and classification in aerial imagery,outperforming existing methodologies and offering a statistically validated improvement for intelligent traffic monitoring systems compared to existing approaches.
文摘This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities,focusing on recent trends from 2022 to 2025.The primary objective is to evaluate methodological advancements,model performance,dataset usage,and existing challenges in developing clinically robust AI systems.We included peer-reviewed journal articles and highimpact conference papers published between 2022 and 2025,written in English,that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification.Excluded were non-open-access publications,books,and non-English articles.A structured search was conducted across Scopus,Google Scholar,Wiley,and Taylor&Francis,with the last search performed in August 2025.Risk of bias was not formally quantified but considered during full-text screening based on dataset diversity,validation methods,and availability of performance metrics.We used narrative synthesis and tabular benchmarking to compare performance metrics(e.g.,accuracy,Dice score)across model types(CNN,Transformer,Hybrid),imaging modalities,and datasets.A total of 49 studies were included(43 journal articles and 6 conference papers).These studies spanned over 9 public datasets(e.g.,BraTS,Figshare,REMBRANDT,MOLAB)and utilized a range of imaging modalities,predominantly MRI.Hybrid models,especially ResViT and UNetFormer,consistently achieved high performance,with classification accuracy exceeding 98%and segmentation Dice scores above 0.90 across multiple studies.Transformers and hybrid architectures showed increasing adoption post2023.Many studies lacked external validation and were evaluated only on a few benchmark datasets,raising concerns about generalizability and dataset bias.Few studies addressed clinical interpretability or uncertainty quantification.Despite promising results,particularly for hybrid deep learning models,widespread clinical adoption remains limited due to lack of validation,interpretability concerns,and real-world deployment barriers.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Metaverse Support Program to Nurture the Best Talents(IITP-2024-RS-2023-00254529)grant funded by the Korea government(MSIT).
文摘Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics.While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information,existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors.In order to address these challenges and maximize the performance of brain tumor segmentation,this research introduces a novel SwinUNETR-based model by integrating a new decoder block,the Hierarchical Channel-wise Attention Decoder(HCAD),into a powerful SwinUNETR encoder.The HCAD decoder block utilizes hierarchical features and channelspecific attention mechanisms to further fuse information at different scales transmitted from the encoder and preserve spatial details throughout the reconstruction phase.Rigorous evaluations on the recent BraTS GLI datasets demonstrate that the proposed SwinHCAD model achieved superior and improved segmentation accuracy on both the Dice score and HD95 metrics across all tumor subregions(WT,TC,and ET)compared to baseline models.In particular,the rationale and contribution of the model design were clarified through ablation studies to verify the effectiveness of the proposed HCAD decoder block.The results of this study are expected to greatly contribute to enhancing the efficiency of clinical diagnosis and treatment planning by increasing the precision of automated brain tumor segmentation.
基金National Science and Technology Council,the Republic of China,under grants NSTC 113-2221-E-194-011-MY3 and Research Center on Artificial Intelligence and Sustainability,National Chung Cheng University under the research project grant titled“Generative Digital Twin System Design for Sustainable Smart City Development in Taiwan.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金supported by the National Natural Science Fund of China(No.41121061)the National Key Basic Research and Development Program(No.2012CB026102)the Fund of the "Hundred People Plan" of CAS(to WenBing Yu)
文摘Pavement snow and icing are worldwide problems, but effective countermeasures are just beginning to be developed in China. The two most common snow and ice removal methods are mechanical clearance and chemical melting, and the advantages and disadvantages of each approach are discussed here, including environmental and structural damage caused by corrosive snow melting agents. New developments in chemical melting agents and mechanical equipment are discussed, and an overview of alternative thermal melting systems is presented, including the use of geothermy and non-geothermal heating systems utilizing solar energy, electricity, conductive pavement materials, and infrared/microwave applications. Strategic recommendations are made for continued enhancement of public safety in snow and ice conditions.
基金the University of California at Berkeley.Ms.Y.Song gratefully acknowledges the financial support from the Chinese Scholar Council(CSC Grant No.201706680094).
文摘De-icing technology has become an increasingly important subject in numerous applications in recent years.However,the direct numerical modeling and simulation the physical process of thermomechanical deicing is limited.This work is focusing on developing a numerical model and tool to direct simulate the de-icing process in the framework of the coupled thermo-mechanical peridynamics theory.Here,we adopted the fully coupled thermo-mechanical bond-based peridynamics(TM-BB-PD)method for modeling and simulation of de-icing.Within the framework of TM-BB-PD,the ice constitutive model is established by considering the influence of the temperature difference between two material points,and a modified failure criteria is proposed,which takes into account temperature effect to predict the damage of quasi-brittle ice material.Moreover,thermal boundary condition is used to simulate the thermal load in the de-icing process.By comparing with the experimental results and the previous reported finite element modeling,our numerical model shows good agreement with the previous predictions.Based on the numerical results,we find that the developed method can not only predict crack initiation and propagation in the ice,but also predict the temperature distribution and heat conduction during the de-icing process.Furthermore,the influence of the temperature for the ice crack growth pattern is discussed accordingly.In conclusion,the coupled thermal-mechanical peridynamics formulation with modified failure criterion is capable of providing a modeling tool for engineering applications of de-icing technology.
基金supported by the National Natural Science Foundation of China(61571462)Weapons and Equipment Exploration Research Project(7131464)
文摘This paper proposes a desirable method to detect different kinds of low probability of intercept (LPI) radar signals, targeted at the main intra-pulse modulation method of LPI radar signals including the signals of linear frequency modulation, phase code, and frequency code. Firstly, it improves the coherent integration of LPI radar signals by adding the periodicity of the ambiguity function. Then, it develops a frequency domain detection method based on fast Fourier transform (FFT) and segmented autocorrelation function to detect signals without features of linear frequency modulation by virtue of the distribution characteristics of noise signals in the frequency domain. Finally, this paper gives a verification of the performance of the method for different signal-to-noise ratios by conducting simulation experiments, and compares the method with existing ones. Additionally, this method is characterized by the straightforward calculation and high real-time performance, which is conducive to better detecting all kinds of LPI radar signals.
文摘Segmented block copolymer based on nylon6 （N6） and polyethylene oxide （PEO） with stochiometric ratio was synthesized via a two-step process. The first step represents end capping of N6 in the presence of adipic acid leading to carboxy terminated N6, and the second one is polycondensation of the latter product with PEO in the presence of catalyst and thermostabilizer to form a high molecular weight multi-block copolymer. Several methods were applied to characterize the synthesized copolyrner such as Fourier transform infrared spectroscopy, proton nuclear magnetic resonance spectroscopy, differential thermal analysis, differential scanning calorimetry, X-ray diffraction and atomic force microscopy. The obtained results confirmed the multi-block structure for copolymer with a very high degree of micro-phase separation. Atomic force microscopy micrographs indicated that the morphology was the dispersion of high stiffness nanostructured polyamide （PA） domains in the amorphous region of PEO matrix, which can be very important in their performance for membrane processes.
基金supported by National Natural Science Foundation of China(Nos.11375039 and 11275034)the Key Project of Science and Technology of Liaoning Province,China(No.2011224007)the Fundamental Research Funds for the Central Universities,China(No.3132014328)
文摘The influences of the low-emissive graphite segmented electrode t）laeed near the channel exit on the discharge characteristics of a Hall thruster are studied using the particle- in-cell method. A two-dimensional physical model is established according to the Hall thruster discharge channel configuration. The effects of electrode length on the potential, ion density, electron temperature, ionization rate and discharge current are investigated. It is found that, with tile increasing of the segmented electrode length, the equipotential lines bend towards the channel exit. and approximately parallel to the wall at the channel surface, the radial velocity and radial flow of ions are increased, and the electron temperature is also enhanced. Due to the conductive characteristic of electrodes, the radial electric field and the axial electron conductivity near the wall are enhanced, and the probability of the electron-atom ionization is reduced, which leads to the degradation of the ionization rate in the discharge channel. However, the interaction between electrons and the wall enhances the near wall conductivity, therefore the discharge current grows along with the segmented electrode length, and the performance of the thruster is also affected.
基金supported by National Natural Science Foundation of China (No. 51475369)the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2018JM1001)
文摘In order to research segmented diverters for aircraft lightning protection, a transient 2 D multiphysics model based on magnetohydrodynamics theory is proposed to predict the location of the arc plasma discharge and lightning channel, and to simulate the electrothermal behavior.Based on numerical calculation and preliminary analysis, factors that affect the breakdown voltage of the segmented diverter are discussed. The results show that the voltage increase rate of the voltage source, the width of the air gap between metal segments and the geometry of these segments influence the breakdown voltage of the strip. High-voltage tests of the segmented diverter are performed to reveal air breakdown of the strip and redirect the lightning current.Experimental and numerical results are compared to verify the correctness of the numerical model. The ionization of the air gap between metal segments and the breakdown voltage of the strip calculated by the model are qualitatively consistent with experimental results. The breakdown voltage of the segmented diverter is far lower than the lightning voltage. When a lightning strike occurs, the segmented diverter can be quickly ionized to form a plasma channel which can guide the lightning current well.
文摘This paper proposes a numerical method to analyze the ice protection capability and predict the power requirements of a piezoelectric resonant de-icing system.The method is based on a coupled electro-mechanical finite element analysis which enables the fast computation of the modes of resonance of interest to de-ice curved surfaces and the estimation of the input voltage and current required for a given configuration(defined by its mode,actuator location,ice deposit,etc.).Eventually,the electric power to be supplied can be also assessed.The method is applied to a NACA 0024 leading edge equipped with piezoelectric actuators.First,two extension modes are analyzed and compared with respect to their efficiency and power requirements.Then,tests are carried out in an icing tunnel to verify the effectiveness of the piezoelectric ice protection system and the predictions of the maximal required power.The system allows de-icing the leading edge in less than 2 s for a glaze ice deposit.
文摘Two series of polyurethanes based on mixed polychloromethyl methyl siloxane and poly-tetramethylene oxide in different weight fractions were synthesized. The phase separation ofsamples was studied using DSC and dynamic mechanical property analysis. The results showedthat the introduction of chloromethyl group into polysiloxane increased its polarity and henceimproved the miscibilities with polytetramethylene oxide and polyurethane hard segment.Particularly, in the case of N-methyldiethanolamine extended materials, the surface and tensileproperties of these samples can be adjusted by various ratios of two soft segments.
基金the support of National Natural Science Foundation of China (No. 51405315)the Talents Introduction Project of Sichuan University (No. yj2012043)
文摘Direct current plasma torches have been applied to generate unique sources of thermal energy in many industrial applications. Nevertheless, the successful ignition of a plasma torch is the key process to generate the unique source （plasma jet）. However, there has been tittle study on the underlying mechanism of this key process. A thorough understanding of the ignition process of a plasma torch will be helpful for optimizing the design of the plasma torch structure and selection of the ignition parameters to prolong the service life of the ignition module. Thus, in this paper, the ignition process of a segmented plasma torch （SPT） is theoretically and experimentally modeled and analyzed. Corresponding electrical models of different stages of the ignition process axe set up and used to derive the electrical parameters, e.g. the variations of the arc voltage and arc current between the cathode and anode. In addition, the experiments with different ignition parameters on a home-made SPT have been conducted. At the same time, the variations of the arc voltage and arc current have been measured, and used to verify the ones derived in theory and to determine the optimal ignition parameters for a particular SPT.
基金supported by the National Natural Science Foundation of China (Nos. 60672164, 60939003, 61079013, 60879001, 90000871)the Special Project about Humanities and Social Sciences in Ministry of Education of China (No. 16JDGC008)+2 种基金National Natural Science Funds and Civil Aviation Mutual Funds (Nos. U1533128 and U1233114)Study On Reusing Sketch User Interface Oriented Design Knowledge (No. 16KJA520003)Six Talent Peaks Project In Jiangsu Province (No. 2016-XYDXXJS-088)
文摘In practice, the failure rate of most equipment exhibits different tendencies at different stages and even its failure rate curve behaves a multimodal trace during its life cycle. As a result,traditionally evaluating the reliability of equipment with a single model may lead to severer errors.However, if lifetime is divided into several different intervals according to the characteristics of its failure rate, piecewise fitting can more accurately approximate the failure rate of equipment. Therefore, in this paper, failure rate is regarded as a piecewise function, and two kinds of segmented distribution are put forward to evaluate reliability. In order to estimate parameters in the segmented reliability function, Bayesian estimation and maximum likelihood estimation（MLE） of the segmented distribution are discussed in this paper. Since traditional information criterion is not suitable for the segmented distribution, an improved information criterion is proposed to test and evaluate the segmented reliability model in this paper. After a great deal of testing and verification,the segmented reliability model and its estimation methods presented in this paper are proven more efficient and accurate than the traditional non-segmented single model, especially when the change of the failure rate is time-phased or multimodal. The significant performance of the segmented reliability model in evaluating reliability of proximity sensors of leading-edge flap in civil aircraft indicates that the segmented distribution and its estimation method in this paper could be useful and accurate.
