High-precision lane keeping is essential for the future autonomous driving.However,due to the imbalanced and inaccurate datasets collected by human drivers,current end-to-end driving models have poor lane keeping the ...High-precision lane keeping is essential for the future autonomous driving.However,due to the imbalanced and inaccurate datasets collected by human drivers,current end-to-end driving models have poor lane keeping the effect.To improve the precision of lane keeping,this study presents a novel multi-state model-based end-to-end lane keeping method.First,three driving states will be defined:going straight,turning right and turning left.Second,the finite-state machine(FSM)table as well as three kinds of training datasets will be generated based on the three driving states.Instead of collecting the dataset by human drivers,the accurate dataset will be collected by the high-performance path following controller.Third,three sets of parameters based on 3DCNN-LSTM model will be trained for going straight,turning left and turning right,which will be combined with FSM table to form a multi-state model.This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error.The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.展开更多
Surface-enhanced Raman spectroscopy(SERS)has evolved from a laboratory technique to a practical tool for ultra-sensitive detection,particularly in the biomedical field,where precise molecular identification is crucial...Surface-enhanced Raman spectroscopy(SERS)has evolved from a laboratory technique to a practical tool for ultra-sensitive detection,particularly in the biomedical field,where precise molecular identification is crucial.Despite significant advancements,a gap remains in the literature,as no comprehensive review systematically addresses the high-precision construction of SERS substrates for ultrasensitive biomedical detection.This review fills that gap by exploring recent progress in fabricating high-precision SERS substrates,emphasizing their role in enabling ultrasensitive bio-medical sensors.We carefully examine the key to these advancements is the precision engineering of substrates,including noble metals,semiconductors,carbon-based materials,and two-dimensional materials,which is essential for achieving the high sensitivity required for ultrasensitive detection.Applications in biomedical diagnostics and molecular analysis are highlighted.Finally,we address the challenges in SERS substrate preparation and outline future directions,focusing on improvement strategies,design concepts,and expanding applications for these advanced materials.展开更多
Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predomina...Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predominantly on the phase-shifting approach,which involves collecting multiple interferograms and imposes stringent demands on environmental stability.These issues significantly hinder its ability to achieve real-time and dynamic high-precision measurements.Therefore,this study proposes a high-precision large-aperture single-frame interferometric surface profile measurement(LA-SFISPM)method based on deep learning and explores its capability to realize dynamic measurements with high accuracy.The interferogram is matched to the phase by training the data measured using the small aperture.The consistency of the surface features of the small and large apertures is enhanced via contrast learning and feature-distribution alignment.Hence,high-precision phase reconstruction of large-aperture optical components can be achieved without using a phase shifter.The experimental results show that for the tested mirror withΦ=820 mm,the surface profile obtained from LA-SFISPM is subtracted point-by-point from the ground truth,resulting in a maximum single-point error of 4.56 nm.Meanwhile,the peak-to-valley(PV)value is 0.0758λ,and the simple repeatability of root mean square(SR-RMS)value is 0.00025λ,which aligns well with the measured results obtained by ZYGO.In particular,a significant reduction in the measurement time(reduced by a factor of 48)is achieved compared with that of the traditional phase-shifting method.Our proposed method provides an efficient,rapid,and accurate method for obtaining the surface profiles of optical components with different diameters without employing a phase-shifting approach,which is highly desired in large-aperture interferometric measurement systems.展开更多
The elliptic integral method(EIM) is an efficient analytical approach for analyzing large deformations of elastic beams. However, it faces the following challenges.First, the existing EIM can only handle cases with kn...The elliptic integral method(EIM) is an efficient analytical approach for analyzing large deformations of elastic beams. However, it faces the following challenges.First, the existing EIM can only handle cases with known deformation modes. Second,the existing EIM is only applicable to Euler beams, and there is no EIM available for higher-precision Timoshenko and Reissner beams in cases where both force and moment are applied at the end. This paper proposes a general EIM for Reissner beams under arbitrary boundary conditions. On this basis, an analytical equation for determining the sign of the elliptic integral is provided. Based on the equation, we discover a class of elliptic integral piecewise points that are distinct from inflection points. More importantly, we propose an algorithm that automatically calculates the number of inflection points and other piecewise points during the nonlinear solution process, which is crucial for beams with unknown or changing deformation modes.展开更多
With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,spec...With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,specialized,and innovative enterprises.As a representative of such enterprises,JL Technology has faced challenges to its R&D efficiency due to talent loss in recent years.This study takes this enterprise as a case to explore feasible paths to reduce turnover rates through optimizing training and career development systems.The research designs a method combining learning maps and talent maps,utilizes a competency model to clarify the direction for engineers’skill improvement,implements talent classification management using a nine-grid model,and achieves personalized training through Individual Development Plans(IDPs).Analysis of the enterprise’s historical data reveals that the main reasons for turnover are unclear career development paths and insufficient resources for skill improvement.After pilot implementation,the turnover rate in core departments decreased by 12%,and employee satisfaction with training increased by 24%.The results indicate that matching systematic talent reviews with dynamic learning resources can effectively enhance engineers’sense of belonging.This study provides a set of highly operational management tools for small and medium-sized high-precision,specialized,and innovative technology enterprises,verifies their applicability in such enterprises,and offers replicable experiences for similar enterprises to optimize their talent strategies[1].展开更多
Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and v...Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.展开更多
In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete bounda...In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete boundary unit cells(i.e.,boundary defects)even though the bulk polarization is zero,which challenges the conventional understanding of HOTIs.Here,based on a Kekul´e-distorted honeycomb lattice with incomplete unit cells,we reveal that incomplete unit cells exhibit fractional charges through the analysis of Wannier centers by developing a compensation method and creating the concept of Wannier center domain(WCD)which is the smallest region that one Wannier center occupies.This method compensates for the missing parts of these boundary incomplete unit cells with additional WCDs to make them complete.The compensated WCDs automatically carry the corresponding charge,and this charge together with that of the incomplete unit cell constitutes the total charge of the complete unit cell after compensation.We conclude that the emergence of corner states is attributed to the filling anomaly,which is a fundamental mechanism.Our results refresh the understanding of HOTIs,especially those with structural discontinuities,and provide a novel design for topological states which have application value in producing optical functional devices.展开更多
Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate ...Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.展开更多
The development of collinear resonance ionization spectroscopy for studying the nuclear structure of nickel isotopes far from the stability line relies on high-efficiency two-color two-step photoionization pathways.We...The development of collinear resonance ionization spectroscopy for studying the nuclear structure of nickel isotopes far from the stability line relies on high-efficiency two-color two-step photoionization pathways.We systematically investigated the even-parity autoionization spectrum of atomic nickel through resonance ionization mass spectrometry(RIMS).Fifteen intense single-color photoionization lines and corresponding transitions in the 300-325 nm range were identified and excluded as potential interference peaks for subsequent two-color studies.Fifty-one even-parity autoionization states in the 64000-66800 cm^(-1)range were identified for the first time by scanning from five intermediate excited states of the3d^(8)(^(3)F)4s4p(^(3)P^(o))configuration.Forty-eight of these states were assigned unique total angular momentum quantum numbers(J)based on electric dipole transition selection rules.The autoionization state at 64437.77 cm^(-1)was identified as an optimal final state for enhancing photoionization efficiency in two-color two-step pathways.This study provides comprehensive datasets of even-parity autoionization states of nickel,supporting both the advancement of collinear resonance ionization spectroscopy for exotic nickel isotopes and theoretical modeling of autoionization states.The datasets are openly available at https://doi.org/10.57760/sciencedb.j00113.00280.展开更多
Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prereq...Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.展开更多
BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this ...BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this relationship remain unclear.AIM To investigate emotion regulation habits impact on students negative emotions during lockdown,using the coronavirus disease 2019 pandemic as a case example.METHODS During the coronavirus disease 2019 lockdown,an online cross-sectional survey was conducted at a Chinese university.Emotional states were assessed using the Depression,Anxiety,and Stress Scale-21(DASS-21),while demographic data and emotion regulation habits were collected concurrently.Data analysis was performed using SPSS version 27.0 and includedχ^(2)-tests for intergroup comparisons,Spearman’s rank-order correlation coefficient analysis to examine associations,and stepwise linear regression modeling to explore the relationships between emotion regulation habits and emotional states.Statistical significance was set atα=0.05.RESULTS Among the 494 valid questionnaires analyzed,the prevalence rates of negative emotional states were as follows:Depression(65.0%),anxiety(69.4%),and stress(50.8%).DASS-21 scores(mean±SD)demonstrated significant symptomatology:Total(48.77±34.88),depression(16.21±12.18),anxiety(14.90±11.91),and stress(17.64±12.07).Significant positive intercorrelations were observed among all DASS-21 subscales(P<0.01).Regression analysis identified key predictors of negative emotions(P<0.05):Risk factors included late-night frequency and academic pressure,while protective factors were the frequency of parental contact and the number of same-gender friends.Additionally,compensatory spending and binge eating positively predicted all negative emotion scores(β>0,P<0.01),whereas appropriate recreational activities negatively predicted these scores(β<0,P<0.01).CONCLUSION High negative emotion prevalence occurred among confined students.Recreational activities were protective,while compensatory spending and binge eating were risk factors,necessitating guided emotion regulation.展开更多
The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonal...The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations,particularly at finite temperatures.In this work,we propose a new algorithm for obtaining thermal pure quantum states,which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures.We implement this algorithm in our open-source C++template library,Physica.Combining the improved algorithm with state-of-the-art software engineering,our implementation achieves high performance and numerical stability.As an example,we demonstrate that for the 4×4 Hubbard model,our method runs approximately 10~3times faster than HΦ3.5.2.Moreover,the accessible temperature range is extended down toβ=32 across arbitrary doping levels.These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.展开更多
With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State I...With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.展开更多
Superconducting elect rides have attracted growing attention for their potential to achieve high superconducting transition temperatures(T_(C))under pressure.However,many known elect rides are chemically reactive and ...Superconducting elect rides have attracted growing attention for their potential to achieve high superconducting transition temperatures(T_(C))under pressure.However,many known elect rides are chemically reactive and unstable,making high-quality single-crystal growth,characterization,and measurements difficult,and most do not exhibit superconductivity at ambient pressure.In contrast,La_(3) In stands out for its ambient-pressure superconductivity(T_(C)∼9.4 K)and the availability of high-quality single crystals.Here,we investigate its low-energy electronic structure using angle-resolved photoemission spectroscopy and first-principles calculations.The bands near the Fermi energy(E_(F))are mainly derived from La 5d and In 5p orbitals.A saddle point is directly observed at the Brillouin zone(BZ)boundary,while a three-dimensional Van Hove singularity crosses E_(F) at the BZ corner.First-principles calculations further reveal topological Dirac surface states within the bulk energy gap above E_(F).The coexistence of a high density of states and in-gap topological surface states near𝐸F suggests that La3In offers a promising platform for tuning superconductivity and exploring possible topological superconducting phases through doping or external pressure.展开更多
The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges be...The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges between 0.2 and 0.4.This enhancement prompts a critical question:to what extent can quantum wells(QWs)be strained while still preserving the fundamental QSHI phase?In this study,we demonstrate the controlled molecular beam epitaxial growth of highly strained-layer QWs with an indium composition of x=0.5.These structures possess a substantial compressive strain within the In_(0.5)Ga_(0.5)Sb QW.Detailed crystal structure analyses confirm the exceptional quality of the resulting epitaxial films,indicating coherent lattice structures and the absence of visible dislocations.Transport measurements further reveal that the QSHI phase in InAs/In_(0.5)Ga_(0.5)Sb QWs is robust and protected by time-reversal symmetry.Notably,the edge states in these systems exhibit giant magnetoresistance when subjected to a modest perpendicular magnetic field.This behavior is in agreement with the𝑍2 topological property predicted by the Bernevig–Hughes–Zhang model,confirming the preservation of topologically protected edge transport in the presence of enhanced bulk strain.展开更多
To realize high-precision automatic measurement of two-dimensional geometric features on parts, a cooperative measurement system based on machine vision is constructed. Its hardware structure, functional composition a...To realize high-precision automatic measurement of two-dimensional geometric features on parts, a cooperative measurement system based on machine vision is constructed. Its hardware structure, functional composition and working principle are introduced. The mapping relationship between the feature image coordinates and the measuring space coordinates is established. The method of measuring path planning of small field of view （FOV） images is proposed. With the cooperation of the panoramic image of the object to be measured, the small FOV images with high object plane resolution are acquired automatically. Then, the auxiliary measuring characteristics are constructed and the parameters of the features to be measured are automatically extracted. Experimental results show that the absolute value of relative error is less than 0. 03% when applying the cooperative measurement system to gauge the hole distance of 100 mm nominal size. When the object plane resolving power of the small FOV images is 16 times that of the large FOV image, the measurement accuracy of small FOV images is improved by 14 times compared with the large FOV image. It is suitable for high-precision automatic measurement of two-dimensional complex geometric features distributed on large scale parts.展开更多
Multi-sensor coordinate unification in dimensional metrology is used in order to get holistic, more accurate and reliable information about a workpiece based on several or multiple measurement values from ...Multi-sensor coordinate unification in dimensional metrology is used in order to get holistic, more accurate and reliable information about a workpiece based on several or multiple measurement values from one or more sensors. Because of the problem that standard ball is deficient as a standard artifact in the coordinate unification of high-precision composite measurement in two dimensions （2D） , a new method is proposed in this paper which uses angle gauge blocks as standard artifacts to achieve coordinate unification between the image sensor and the tactile probe. By comparing the standard ball with the angle gauge block as a standard artifact, theoretical analysis and experimental results are given to prove that it is more precise and more convenient to use angle gauge blocks as standard artifacts to achieve coordinate unification of high-precision composite measurement in two dimensions.展开更多
A reliable multiphase flow simulator is an important tool to improve wellbore integrity and production decision-making.To develop a multiphase flow model with high adaptability and high accuracy,we first build a multi...A reliable multiphase flow simulator is an important tool to improve wellbore integrity and production decision-making.To develop a multiphase flow model with high adaptability and high accuracy,we first build a multiphase flow database with 3561 groups of data and developed a drift closure relationship with stable continuity and high adaptability.Second,a high-order numerical scheme with strong fault capture ability is constructed by effectively combining MUSCL technology,van Albada slope limiter and AUSMV numerical scheme.Finally,the energy equation is coupled into the AUSMV numerical scheme of the drift flow model in the form of finite difference.A transient non-isothermal wellbore multiphase flow model with wide applicability is formed by integrating the three technologies,and the effects of various factors on the calculation accuracy are studied.The accuracy of the simulator is verified by comparing the measurement results with the blowout experiment of a full-scale experimental well.展开更多
The Tibetan Plateau(TP)is the youngest orogenic belt resulting from a continental collision on the Earth.It is a natural laboratory for studying continental dynamics,such as continental convergence,plate subduction,an...The Tibetan Plateau(TP)is the youngest orogenic belt resulting from a continental collision on the Earth.It is a natural laboratory for studying continental dynamics,such as continental convergence,plate subduction,and plateau uplift.Investigating the deep structure of the TP has always been a popular issue in geological research.The Moho is the boundary between the crust and the mantle and therefore plays a crucial role in the Earth’s structure.Parameters such as depth and lateral variation,as well as the fine structure of the crust-mantle interface,reveal the lithospheric dynamics in the TP.Two methods are generally employed to study the Moho surface:seismic detection and gravity inversion.Seismic detection has the characteristic of high precision,but it is limited to a few cross-sectional lines and is quite costly.It is not suitable for and cannot be carried out over a large area of the TP.The Moho depth over a large area can be obtained through gravity inversion,but this method is affected by the nature of gravity data,and the accuracy of the inversion method is lower than that of seismic detection.In this work,a high-precision gravity field model was selected.The Parker-Oldenburg interface inversion method was used,within the constraints of seismic observations,and the Bott iteration method was introduced to enhance the inversion efficiency.The Moho depth in the TP was obtained with high precision,consistent with the seismic detection results.The research results showed that the shape of the Moho in the TP is complex and the variation range is large,reaching 60−80 km.In contrast with the adjacent area,a clear zone of sharp variation appears at the edge of the plateau.In the interior of the TP,the buried depth of the Moho is characterized by two depressions and two uplifts.To the south of the Yarlung Zangbo River,the Moho inclines to the north,and to the north,the Moho depresses downward,which was interpreted as the Indian plate subducting to the north below Xizang.The Moho depression on the north side of the Qiangtang block,reaching 72 km deep,may be a result of the southward subduction of the lithosphere.The Moho uplift of the Qiangtang block has the same strike as the Bangong−Nujiang suture zone,which may indicate that the area is compensated by a low-density and low-velocity mantle.展开更多
The magnetic field is one of the most important parameters in solar physics,and a polarimeter is the key device to measure the solar magnetic field.Liquid crystals based Stokes polarimeter is a novel technology,and wi...The magnetic field is one of the most important parameters in solar physics,and a polarimeter is the key device to measure the solar magnetic field.Liquid crystals based Stokes polarimeter is a novel technology,and will be applied for magnetic field measurement in the first space-based solar observatory satellite developed by China,Advanced Space-based Solar Observatory.However,the liquid crystals based Stokes polarimeter in space is not a mature technology.Therefore,it is of great scientific significance to study the control method and characteristics of the device.The retardation produced by a liquid crystal variable retarder is sensitive to the temperature,and the retardation changes 0.09°per 0.10℃.The error in polarization measurement caused by this change is 0.016,which affects the accuracy of magnetic field measurement.In order to ensure the stability of its performance,this paper proposes a high-precision temperature control system for liquid crystals based Stokes polarimeter in space.In order to optimize the structure design and temperature control system,the temperature field of liquid crystals based Stokes polarimeter is analyzed by the finite element method,and the influence of light on the temperature field of the liquid crystal variable retarder is analyzed theoretically.By analyzing the principle of highprecision temperature measurement in space,a high-precision temperature measurement circuit based on integrated operational amplifier,programmable amplifier and 12 bit A/D is designed,and a high-precision space temperature control system is developed by applying the integral separation PI temperature control algorithm and PWM driving heating films.The experimental results show that the effect of temperature control is accurate and stable,whenever the liquid crystals based Stokes polarimeter is either in the air or vacuum.The temperature stability is within±0.0150℃,which demonstrates greatly improved stability for the liquid crystals based Stokes polarimeter.展开更多
基金National Natural Science Foundation of China(U1764264/61873165).
文摘High-precision lane keeping is essential for the future autonomous driving.However,due to the imbalanced and inaccurate datasets collected by human drivers,current end-to-end driving models have poor lane keeping the effect.To improve the precision of lane keeping,this study presents a novel multi-state model-based end-to-end lane keeping method.First,three driving states will be defined:going straight,turning right and turning left.Second,the finite-state machine(FSM)table as well as three kinds of training datasets will be generated based on the three driving states.Instead of collecting the dataset by human drivers,the accurate dataset will be collected by the high-performance path following controller.Third,three sets of parameters based on 3DCNN-LSTM model will be trained for going straight,turning left and turning right,which will be combined with FSM table to form a multi-state model.This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error.The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.
基金supported by the projects funded by the Education Department of Shaanxi Provincial Government(NO.23JP116)the Natural Science Fund of Shaanxi Province(NO.2024JC-YBMS-396)+3 种基金the National Natural Science Foundation of China(NO.52171191,52371198,U22A20137)the Constructing National Independent Innovation Demonstration Zones(XM2024XTGXQ05)Shenzhen Science and Technology Innovation Program(JCYJ20220818102215033,GJHZ20210705142542015,JCYJ20220530160811027)Guangdong HUST Industrial Technology Research Institute,Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization(2023B1212060012).
文摘Surface-enhanced Raman spectroscopy(SERS)has evolved from a laboratory technique to a practical tool for ultra-sensitive detection,particularly in the biomedical field,where precise molecular identification is crucial.Despite significant advancements,a gap remains in the literature,as no comprehensive review systematically addresses the high-precision construction of SERS substrates for ultrasensitive biomedical detection.This review fills that gap by exploring recent progress in fabricating high-precision SERS substrates,emphasizing their role in enabling ultrasensitive bio-medical sensors.We carefully examine the key to these advancements is the precision engineering of substrates,including noble metals,semiconductors,carbon-based materials,and two-dimensional materials,which is essential for achieving the high sensitivity required for ultrasensitive detection.Applications in biomedical diagnostics and molecular analysis are highlighted.Finally,we address the challenges in SERS substrate preparation and outline future directions,focusing on improvement strategies,design concepts,and expanding applications for these advanced materials.
基金funded by the National Natural Science Foundation of China Instrumentation Program(52327806)Youth Fund of the National Nature Foundation of China(62405020)China Postdoctoral Science Foundation(2024M764131).
文摘Large-aperture optical components are of paramount importance in domains such as integrated circuits,photolithography,aerospace,and inertial confinement fusion.However,measuring their surface profiles relies predominantly on the phase-shifting approach,which involves collecting multiple interferograms and imposes stringent demands on environmental stability.These issues significantly hinder its ability to achieve real-time and dynamic high-precision measurements.Therefore,this study proposes a high-precision large-aperture single-frame interferometric surface profile measurement(LA-SFISPM)method based on deep learning and explores its capability to realize dynamic measurements with high accuracy.The interferogram is matched to the phase by training the data measured using the small aperture.The consistency of the surface features of the small and large apertures is enhanced via contrast learning and feature-distribution alignment.Hence,high-precision phase reconstruction of large-aperture optical components can be achieved without using a phase shifter.The experimental results show that for the tested mirror withΦ=820 mm,the surface profile obtained from LA-SFISPM is subtracted point-by-point from the ground truth,resulting in a maximum single-point error of 4.56 nm.Meanwhile,the peak-to-valley(PV)value is 0.0758λ,and the simple repeatability of root mean square(SR-RMS)value is 0.00025λ,which aligns well with the measured results obtained by ZYGO.In particular,a significant reduction in the measurement time(reduced by a factor of 48)is achieved compared with that of the traditional phase-shifting method.Our proposed method provides an efficient,rapid,and accurate method for obtaining the surface profiles of optical components with different diameters without employing a phase-shifting approach,which is highly desired in large-aperture interferometric measurement systems.
基金supported by the National Natural Science Foundation of China (Nos. 12172388 and 12472400)the Guangdong Basic and Applied Basic Research Foundation of China(No. 2025A1515011975)the Scientific Research Project of Guangdong Polytechnic Normal University of China (No. 2023SDKYA010)
文摘The elliptic integral method(EIM) is an efficient analytical approach for analyzing large deformations of elastic beams. However, it faces the following challenges.First, the existing EIM can only handle cases with known deformation modes. Second,the existing EIM is only applicable to Euler beams, and there is no EIM available for higher-precision Timoshenko and Reissner beams in cases where both force and moment are applied at the end. This paper proposes a general EIM for Reissner beams under arbitrary boundary conditions. On this basis, an analytical equation for determining the sign of the elliptic integral is provided. Based on the equation, we discover a class of elliptic integral piecewise points that are distinct from inflection points. More importantly, we propose an algorithm that automatically calculates the number of inflection points and other piecewise points during the nonlinear solution process, which is crucial for beams with unknown or changing deformation modes.
文摘With the intensifying competition in the integrated circuit(IC)industry,the high turnover rate of integrated circuit engineers has become a prominent issue affecting the technological continuity of high-precision,specialized,and innovative enterprises.As a representative of such enterprises,JL Technology has faced challenges to its R&D efficiency due to talent loss in recent years.This study takes this enterprise as a case to explore feasible paths to reduce turnover rates through optimizing training and career development systems.The research designs a method combining learning maps and talent maps,utilizes a competency model to clarify the direction for engineers’skill improvement,implements talent classification management using a nine-grid model,and achieves personalized training through Individual Development Plans(IDPs).Analysis of the enterprise’s historical data reveals that the main reasons for turnover are unclear career development paths and insufficient resources for skill improvement.After pilot implementation,the turnover rate in core departments decreased by 12%,and employee satisfaction with training increased by 24%.The results indicate that matching systematic talent reviews with dynamic learning resources can effectively enhance engineers’sense of belonging.This study provides a set of highly operational management tools for small and medium-sized high-precision,specialized,and innovative technology enterprises,verifies their applicability in such enterprises,and offers replicable experiences for similar enterprises to optimize their talent strategies[1].
基金supported by the Major Project for the Integration of ScienceEducation and Industry (Grant No.2025ZDZX02)。
文摘Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.
基金supported by the Natural Science Basic Research Program of Shaanxi Province (Grant Nos.2024JC-JCQN-06 and2025JC-QYCX-006)the National Natural Science Foundation of China (Grant No.12474337)Chinese Academy of Sciences Project (Grant Nos.E4BA270100,E4Z127010F,E4Z6270100,and E53327020D)。
文摘In conventional higher-order topological insulators(HOTIs),the emergence of topological states can be explained by using the nonzero bulk polarization index.However,corner states emerge in HOTIs with incomplete boundary unit cells(i.e.,boundary defects)even though the bulk polarization is zero,which challenges the conventional understanding of HOTIs.Here,based on a Kekul´e-distorted honeycomb lattice with incomplete unit cells,we reveal that incomplete unit cells exhibit fractional charges through the analysis of Wannier centers by developing a compensation method and creating the concept of Wannier center domain(WCD)which is the smallest region that one Wannier center occupies.This method compensates for the missing parts of these boundary incomplete unit cells with additional WCDs to make them complete.The compensated WCDs automatically carry the corresponding charge,and this charge together with that of the incomplete unit cell constitutes the total charge of the complete unit cell after compensation.We conclude that the emergence of corner states is attributed to the filling anomaly,which is a fundamental mechanism.Our results refresh the understanding of HOTIs,especially those with structural discontinuities,and provide a novel design for topological states which have application value in producing optical functional devices.
基金financial support provided by the Natural Science Foundation of Hebei Province,China(No.E2024105036)the Tangshan Talent Funding Project,China(Nos.B202302007 and A2021110015)+1 种基金the National Natural Science Foundation of China(No.52264042)the Australian Research Council(No.IH230100010)。
文摘Automated classification of gas flow states in blast furnaces using top-camera imagery typically demands a large volume of labeled data,whose manual annotation is both labor-intensive and cost-prohibitive.To mitigate this challenge,we present an enhanced semi-supervised learning approach based on the Mean Teacher framework,incorporating a novel feature loss module to maximize classification performance with limited labeled samples.The model studies show that the proposed model surpasses both the baseline Mean Teacher model and fully supervised method in accuracy.Specifically,for datasets with 20%,30%,and 40%label ratios,using a single training iteration,the model yields accuracies of 78.61%,82.21%,and 85.2%,respectively,while multiple-cycle training iterations achieves 82.09%,81.97%,and 81.59%,respectively.Furthermore,scenario-specific training schemes are introduced to support diverse deployment need.These findings highlight the potential of the proposed technique in minimizing labeling requirements and advancing intelligent blast furnace diagnostics.
基金supported by the China National Nuclear Corporation Basic Research Project(Grant No.CNNC-JCYJ-202327)。
文摘The development of collinear resonance ionization spectroscopy for studying the nuclear structure of nickel isotopes far from the stability line relies on high-efficiency two-color two-step photoionization pathways.We systematically investigated the even-parity autoionization spectrum of atomic nickel through resonance ionization mass spectrometry(RIMS).Fifteen intense single-color photoionization lines and corresponding transitions in the 300-325 nm range were identified and excluded as potential interference peaks for subsequent two-color studies.Fifty-one even-parity autoionization states in the 64000-66800 cm^(-1)range were identified for the first time by scanning from five intermediate excited states of the3d^(8)(^(3)F)4s4p(^(3)P^(o))configuration.Forty-eight of these states were assigned unique total angular momentum quantum numbers(J)based on electric dipole transition selection rules.The autoionization state at 64437.77 cm^(-1)was identified as an optimal final state for enhancing photoionization efficiency in two-color two-step pathways.This study provides comprehensive datasets of even-parity autoionization states of nickel,supporting both the advancement of collinear resonance ionization spectroscopy for exotic nickel isotopes and theoretical modeling of autoionization states.The datasets are openly available at https://doi.org/10.57760/sciencedb.j00113.00280.
基金Project supported by the National Natural Science Foundation of China(Grant No.61573266)the Natural Science Basic Research Program of Shaanxi(Grant No.2021JM-133)the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University(Grant No.YJSJ25009)。
文摘Efficient implementation of fundamental matrix operations on quantum computers,such as matrix products and Hadamard operations,holds significant potential for accelerating machine learning algorithms.A critical prerequisite for quantum implementations is the effective encoding of classical data into quantum states.We propose two quantum computing frameworks for preparing the distinct encoded states corresponding to matrix operations,including the matrix product,matrix sum,matrix Hadamard product and division.Quantum algorithms based on the digital encoding computing framework are capable of implementing the matrix Hadamard operation with a time complexity of O(poly log(mn/ε))and the matrix product with a time complexity of O(poly log(mnl/ε)),achieving an exponential speedup in contrast to the classical methods of O(mn)and O(mnl).Quantum algorithms based on the analog-encoding framework are capable of implementing the matrix Hadamard operation with a time complexity of O(k_(1)√mn·poly log(mn/ε))and the matrix product with a time complexity of O(k_(2)√1·poly log(mnl/ε)),where k_(1)and k_(2)are coefficients correlated with the elements of the matrix,achieving a square speedup in contrast to the classical counterparts.As applications,we construct an oracle that can access the trace of a matrix within logarithmic time,and propose several algorithms to respectively estimate the trace of a matrix,the trace of the product of two matrices,and the trace inner product of two matrices within logarithmic time.
文摘BACKGROUND The prevalence of negative emotional states,such as anxiety and depression,has increased annually.Although personal habits are known to influence emotional regulation,the precise mechanisms underlying this relationship remain unclear.AIM To investigate emotion regulation habits impact on students negative emotions during lockdown,using the coronavirus disease 2019 pandemic as a case example.METHODS During the coronavirus disease 2019 lockdown,an online cross-sectional survey was conducted at a Chinese university.Emotional states were assessed using the Depression,Anxiety,and Stress Scale-21(DASS-21),while demographic data and emotion regulation habits were collected concurrently.Data analysis was performed using SPSS version 27.0 and includedχ^(2)-tests for intergroup comparisons,Spearman’s rank-order correlation coefficient analysis to examine associations,and stepwise linear regression modeling to explore the relationships between emotion regulation habits and emotional states.Statistical significance was set atα=0.05.RESULTS Among the 494 valid questionnaires analyzed,the prevalence rates of negative emotional states were as follows:Depression(65.0%),anxiety(69.4%),and stress(50.8%).DASS-21 scores(mean±SD)demonstrated significant symptomatology:Total(48.77±34.88),depression(16.21±12.18),anxiety(14.90±11.91),and stress(17.64±12.07).Significant positive intercorrelations were observed among all DASS-21 subscales(P<0.01).Regression analysis identified key predictors of negative emotions(P<0.05):Risk factors included late-night frequency and academic pressure,while protective factors were the frequency of parental contact and the number of same-gender friends.Additionally,compensatory spending and binge eating positively predicted all negative emotion scores(β>0,P<0.01),whereas appropriate recreational activities negatively predicted these scores(β<0,P<0.01).CONCLUSION High negative emotion prevalence occurred among confined students.Recreational activities were protective,while compensatory spending and binge eating were risk factors,necessitating guided emotion regulation.
基金Fu-Zhou Chen for helpful discussions.The work is partly supported by the National Key Research and Development Program of China(Grant No.2022YFA1402704)the National Natural Science Foundation of China(Grant No.12247101)。
文摘The development of novel quantum many-body computational algorithms relies on robust benchmarking.However,generating such benchmarks is often hindered by the massive computational resources required for exact diagonalization or quantum Monte Carlo simulations,particularly at finite temperatures.In this work,we propose a new algorithm for obtaining thermal pure quantum states,which allows efficient computation of both mechanical and thermodynamic properties at finite temperatures.We implement this algorithm in our open-source C++template library,Physica.Combining the improved algorithm with state-of-the-art software engineering,our implementation achieves high performance and numerical stability.As an example,we demonstrate that for the 4×4 Hubbard model,our method runs approximately 10~3times faster than HΦ3.5.2.Moreover,the accessible temperature range is extended down toβ=32 across arbitrary doping levels.These advances significantly push forward the frontiers of benchmarking for quantum many-body systems.
基金supported by National Natural Science Foundation of China(NSFC)under grant U23A20310.
文摘With the growing advancement of wireless communication technologies,WiFi-based human sensing has gained increasing attention as a non-intrusive and device-free solution.Among the available signal types,Channel State Information(CSI)offers fine-grained temporal,frequency,and spatial insights into multipath propagation,making it a crucial data source for human-centric sensing.Recently,the integration of deep learning has significantly improved the robustness and automation of feature extraction from CSI in complex environments.This paper provides a comprehensive review of deep learning-enhanced human sensing based on CSI.We first outline mainstream CSI acquisition tools and their hardware specifications,then provide a detailed discussion of preprocessing methods such as denoising,time–frequency transformation,data segmentation,and augmentation.Subsequently,we categorize deep learning approaches according to sensing tasks—namely detection,localization,and recognition—and highlight representative models across application scenarios.Finally,we examine key challenges including domain generalization,multi-user interference,and limited data availability,and we propose future research directions involving lightweight model deployment,multimodal data fusion,and semantic-level sensing.
基金supported by the National Natural Science Foundation of China(Grant Nos.12222413,12174443,12274459,and 12404266)the National Key R&D Program of China(Grant Nos.2023YFA1406500,2022YFA1403800,and 2022YFA1403103)+3 种基金the Natural Science Foundation of Shanghai (Grant No.23ZR1482200)the Natural Science Foundation of Ningbo (Grant No.2024J019)the Science Research Project of Hebei Education Department (Grant No.BJ2025060)the funding of Ningbo Yongjiang Talent Program。
文摘Superconducting elect rides have attracted growing attention for their potential to achieve high superconducting transition temperatures(T_(C))under pressure.However,many known elect rides are chemically reactive and unstable,making high-quality single-crystal growth,characterization,and measurements difficult,and most do not exhibit superconductivity at ambient pressure.In contrast,La_(3) In stands out for its ambient-pressure superconductivity(T_(C)∼9.4 K)and the availability of high-quality single crystals.Here,we investigate its low-energy electronic structure using angle-resolved photoemission spectroscopy and first-principles calculations.The bands near the Fermi energy(E_(F))are mainly derived from La 5d and In 5p orbitals.A saddle point is directly observed at the Brillouin zone(BZ)boundary,while a three-dimensional Van Hove singularity crosses E_(F) at the BZ corner.First-principles calculations further reveal topological Dirac surface states within the bulk energy gap above E_(F).The coexistence of a high density of states and in-gap topological surface states near𝐸F suggests that La3In offers a promising platform for tuning superconductivity and exploring possible topological superconducting phases through doping or external pressure.
基金supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant Nos.XDB28000000 and XDB0460000)the Quantum Science and Technology-National Science and Technology Major Project (Grant No.2021ZD0302600)the National Key Research and Development Program of China(Grant No.2024YFA1409002)。
文摘The hybridization gap in strained-layer InAs/In_(x)Ga_(1−x) Sb quantum spin Hall insulators(QSHIs)is significantly enhanced compared to binary InAs/GaSb QSHI structures,where the typical indium composition,x,ranges between 0.2 and 0.4.This enhancement prompts a critical question:to what extent can quantum wells(QWs)be strained while still preserving the fundamental QSHI phase?In this study,we demonstrate the controlled molecular beam epitaxial growth of highly strained-layer QWs with an indium composition of x=0.5.These structures possess a substantial compressive strain within the In_(0.5)Ga_(0.5)Sb QW.Detailed crystal structure analyses confirm the exceptional quality of the resulting epitaxial films,indicating coherent lattice structures and the absence of visible dislocations.Transport measurements further reveal that the QSHI phase in InAs/In_(0.5)Ga_(0.5)Sb QWs is robust and protected by time-reversal symmetry.Notably,the edge states in these systems exhibit giant magnetoresistance when subjected to a modest perpendicular magnetic field.This behavior is in agreement with the𝑍2 topological property predicted by the Bernevig–Hughes–Zhang model,confirming the preservation of topologically protected edge transport in the presence of enhanced bulk strain.
基金The National Natural Science Foundation of China(No.51175267)the Natural Science Foundation of Jiangsu Province(No.BK2010481)+2 种基金the Ph.D.Programs Foundation of Ministry of Education of China(No.20113219120004)China Postdoctoral Science Foundation(No.20100481148)the Postdoctoral Science Foundation of Jiangsu Province(No.1001004B)
文摘To realize high-precision automatic measurement of two-dimensional geometric features on parts, a cooperative measurement system based on machine vision is constructed. Its hardware structure, functional composition and working principle are introduced. The mapping relationship between the feature image coordinates and the measuring space coordinates is established. The method of measuring path planning of small field of view （FOV） images is proposed. With the cooperation of the panoramic image of the object to be measured, the small FOV images with high object plane resolution are acquired automatically. Then, the auxiliary measuring characteristics are constructed and the parameters of the features to be measured are automatically extracted. Experimental results show that the absolute value of relative error is less than 0. 03% when applying the cooperative measurement system to gauge the hole distance of 100 mm nominal size. When the object plane resolving power of the small FOV images is 16 times that of the large FOV image, the measurement accuracy of small FOV images is improved by 14 times compared with the large FOV image. It is suitable for high-precision automatic measurement of two-dimensional complex geometric features distributed on large scale parts.
基金National Key Scientific Instrument and Equipment Development Project(No.2013YQ170539)
文摘Multi-sensor coordinate unification in dimensional metrology is used in order to get holistic, more accurate and reliable information about a workpiece based on several or multiple measurement values from one or more sensors. Because of the problem that standard ball is deficient as a standard artifact in the coordinate unification of high-precision composite measurement in two dimensions （2D） , a new method is proposed in this paper which uses angle gauge blocks as standard artifacts to achieve coordinate unification between the image sensor and the tactile probe. By comparing the standard ball with the angle gauge block as a standard artifact, theoretical analysis and experimental results are given to prove that it is more precise and more convenient to use angle gauge blocks as standard artifacts to achieve coordinate unification of high-precision composite measurement in two dimensions.
基金The work was supported by the National Natural Science Foundation of China(No.51874045)National Natural Science Foundation-Youth Foundation(52104056)+2 种基金Department of Natural Resources of Guangdong Province(GDNRC[2021]56)Postdoctoral innovative talents support program in China(BX2021374)Scientific Research Program of Hubei Provincial Department of Education(T2021004).
文摘A reliable multiphase flow simulator is an important tool to improve wellbore integrity and production decision-making.To develop a multiphase flow model with high adaptability and high accuracy,we first build a multiphase flow database with 3561 groups of data and developed a drift closure relationship with stable continuity and high adaptability.Second,a high-order numerical scheme with strong fault capture ability is constructed by effectively combining MUSCL technology,van Albada slope limiter and AUSMV numerical scheme.Finally,the energy equation is coupled into the AUSMV numerical scheme of the drift flow model in the form of finite difference.A transient non-isothermal wellbore multiphase flow model with wide applicability is formed by integrating the three technologies,and the effects of various factors on the calculation accuracy are studied.The accuracy of the simulator is verified by comparing the measurement results with the blowout experiment of a full-scale experimental well.
基金the National Natural Science Foundation of China(Grant No.42192535)the Open Fund of Wuhan,Gravitation and Solid Earth Tides,National Observation and Research Station(No.WHYWZ202204)+1 种基金the Strategic Pioneer Science and Technology Special Project of the Chinese Academy of Sciences(Grant No.XDB18010304)the National Natural Science Foundation of China(Grant No.41874096).
文摘The Tibetan Plateau(TP)is the youngest orogenic belt resulting from a continental collision on the Earth.It is a natural laboratory for studying continental dynamics,such as continental convergence,plate subduction,and plateau uplift.Investigating the deep structure of the TP has always been a popular issue in geological research.The Moho is the boundary between the crust and the mantle and therefore plays a crucial role in the Earth’s structure.Parameters such as depth and lateral variation,as well as the fine structure of the crust-mantle interface,reveal the lithospheric dynamics in the TP.Two methods are generally employed to study the Moho surface:seismic detection and gravity inversion.Seismic detection has the characteristic of high precision,but it is limited to a few cross-sectional lines and is quite costly.It is not suitable for and cannot be carried out over a large area of the TP.The Moho depth over a large area can be obtained through gravity inversion,but this method is affected by the nature of gravity data,and the accuracy of the inversion method is lower than that of seismic detection.In this work,a high-precision gravity field model was selected.The Parker-Oldenburg interface inversion method was used,within the constraints of seismic observations,and the Bott iteration method was introduced to enhance the inversion efficiency.The Moho depth in the TP was obtained with high precision,consistent with the seismic detection results.The research results showed that the shape of the Moho in the TP is complex and the variation range is large,reaching 60−80 km.In contrast with the adjacent area,a clear zone of sharp variation appears at the edge of the plateau.In the interior of the TP,the buried depth of the Moho is characterized by two depressions and two uplifts.To the south of the Yarlung Zangbo River,the Moho inclines to the north,and to the north,the Moho depresses downward,which was interpreted as the Indian plate subducting to the north below Xizang.The Moho depression on the north side of the Qiangtang block,reaching 72 km deep,may be a result of the southward subduction of the lithosphere.The Moho uplift of the Qiangtang block has the same strike as the Bangong−Nujiang suture zone,which may indicate that the area is compensated by a low-density and low-velocity mantle.
基金the National Natural Science Foundation of China(Grant Nos.11427803,11427901 and 11773040)the Strategic Pioneer Program on Space Science,Chinese Academy of Sciences(CAS)(XDA04061002 and XDA15010800)the Public Technology Service Center,National Astronomical Observatories of CAS(829011V01)。
文摘The magnetic field is one of the most important parameters in solar physics,and a polarimeter is the key device to measure the solar magnetic field.Liquid crystals based Stokes polarimeter is a novel technology,and will be applied for magnetic field measurement in the first space-based solar observatory satellite developed by China,Advanced Space-based Solar Observatory.However,the liquid crystals based Stokes polarimeter in space is not a mature technology.Therefore,it is of great scientific significance to study the control method and characteristics of the device.The retardation produced by a liquid crystal variable retarder is sensitive to the temperature,and the retardation changes 0.09°per 0.10℃.The error in polarization measurement caused by this change is 0.016,which affects the accuracy of magnetic field measurement.In order to ensure the stability of its performance,this paper proposes a high-precision temperature control system for liquid crystals based Stokes polarimeter in space.In order to optimize the structure design and temperature control system,the temperature field of liquid crystals based Stokes polarimeter is analyzed by the finite element method,and the influence of light on the temperature field of the liquid crystal variable retarder is analyzed theoretically.By analyzing the principle of highprecision temperature measurement in space,a high-precision temperature measurement circuit based on integrated operational amplifier,programmable amplifier and 12 bit A/D is designed,and a high-precision space temperature control system is developed by applying the integral separation PI temperature control algorithm and PWM driving heating films.The experimental results show that the effect of temperature control is accurate and stable,whenever the liquid crystals based Stokes polarimeter is either in the air or vacuum.The temperature stability is within±0.0150℃,which demonstrates greatly improved stability for the liquid crystals based Stokes polarimeter.
