The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for materials.We propose an iterative method for obtaining more...The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for materials.We propose an iterative method for obtaining more precise lattice parameters using the interaction points for the pseudo-Kossel pattern obtained from laser-induced X-ray diffraction(XRD).This method has been validated by the analysis of an XRD experiment conducted on iron single crystals.Furthermore,the method was used to calculate the compression ratio and rotated angle of an LiF sample under high pressure loading.This technique provides a robust tool for in-situ characterization of structural changes in single crystals under extreme conditions.It has significant implications for studying the equation of state and phase transitions.展开更多
The CNC machine tool is the fundamental equipment of the manufacturing industry,particularly in sectors where achieving high levels of accuracy is crucial.Geometric accuracy design is an important step in machine tool...The CNC machine tool is the fundamental equipment of the manufacturing industry,particularly in sectors where achieving high levels of accuracy is crucial.Geometric accuracy design is an important step in machine tool design and plays an essential role in determining the machining accuracy of the workpiece.Researchers have extensively studied methods to model,extract,optimize,and measure the geometric errors that affect the geometric accuracy of machine tools.This paper provides a comprehensive review of the state-of-the-art approaches and an overview of the latest research progress associated with geometric accuracy design in CNC machine tools.This paper explores the interrelated aspects of CNC machine tool accuracy design:modeling,analysis and optimization.Accuracy analysis,which includes geometric error modeling and sensitivity analysis,determines a machine tool’s output accuracy through its volumetric error model,given the known accuracy of its individual components.Conversely,accuracy allocation designs the accuracy of the machine tool components according to given output accuracy requirements to achieve optimization between the objectives of manufacturing cost,quality,reliability,and environmental impact.In addition to discussing design factors and evaluation methods,this paper outlines methods for verifying the accuracy of design results,aiming to provide a practical basis for ensuring that the designed accuracy is achieved.Finally,the challenges and future research directions in geometric accuracy design are highlighted.展开更多
BACKGROUND The accuracy of blind intra-articular injections in the shoulder is rather low.Inaccurate injections tend to lead to poorer treatment outcomes.The“Delaware posterior bone touch technique”has shown higher ...BACKGROUND The accuracy of blind intra-articular injections in the shoulder is rather low.Inaccurate injections tend to lead to poorer treatment outcomes.The“Delaware posterior bone touch technique”has shown higher accuracy in young,slender,healthy volunteers than the classical“Cyriax technique”.AIM To investigate whether the Delaware technique would also be more accurate in older patients with capsulitis.METHODS We analyzed the files of 100 consecutive patients with capsulitis who were treated with an intra-articular injection containing a mixture of triamcinolone,lidocaine,and air.After the injection,the shoulder was moved to determine whether a squishing sound could be produced.The squishing sound was interpreted as an accurate injection.The scores with the new Delaware technique were compared against those with the Cyriax technique in a previous study.RESULTS Squishing was heard after 87%of the injections.This was 13%(10%points)more than the 77%in the previous study(P=0.004).CONCLUSION The Delaware technique was significantly more accurate than the Cyriax technique also in middle aged patients with capsulitis.We hypothesize that the difference is caused by a lower risk that a part of the opening of the needle is still outside the capsule.展开更多
The travel-time corrections for the primary seismic phases of 72 stations in the Guangdong seismic network,relative to the 1D South China travel-time model,were determined using joint hypocentral determination(JHD)and...The travel-time corrections for the primary seismic phases of 72 stations in the Guangdong seismic network,relative to the 1D South China travel-time model,were determined using joint hypocentral determination(JHD)and statistical analysis methods.The travel-time corrections for the Pg phase of 72 stations range between-0.25 s and 0.14 s,while the corrections for the Sg phase range between 0.27 s and 0.35 s,and those for the Pn phase are between-0.86 s and 0.07 s.The spatial distribution of travel-time corrections for Pg,Sg,and Pn phases of 72 stations correlates well with the geological structure in this region.This indicates that the travel-time corrections for Pg and Sg phases are mainly caused by the discrepancy between the actual crustal velocity structure beneath the stations and the 1D South China travel-time model.These corrections empirically compensate for systematic travel-time errors arising from such discrepancies.The primary factor contributing to the travel-time corrections for the Pn phase is the Moho undulations or tilt.These corrections are intended to compensate for systematic errors in travel time caused by variations in the actual Moho.By integrating the obtained travel-time corrections into the HYPO-SAT location algorithm,test results showed an obvious improvement in location accuracy and origin time precision for explosion events.The variation of horizontal distance between repeating earthquake pairs has also improved,with 86%of the repeating earthquake pair spacing being more accurately estimated after correction.This suggests the crucial significance of travel-time correction in earthquake location,and the consideration of travel-time correction exerts a notable impact on enhancing earthquake location accuracy.展开更多
Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between Octob...Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between October 2022 and October 2024 were selected for high-frequency ultrasound diagnosis.The diagnostic efficacy of high-frequency ultrasound was evaluated by comparing it with the results of surgical pathology.Results:High-frequency ultrasound detected 50 benign nodules,primarily breast fibroadenomas,and 35 malignant nodules,mainly breast ductal carcinoma in situ.Based on surgical pathology results,the diagnostic accuracy of high-frequency ultrasound was 96.47%,specificity was 97.96%,and sensitivity was 94.44%.In high-frequency ultrasound diagnosis,the proportion of grade III and IV blood flow in malignant nodules was higher than that in benign nodules,while the proportion of regular shape and clear margins was lower.The proportion of microcalcifications and posterior echo attenuation was higher in malignant nodules,and the resistance index(RI)and peak blood flow velocity were lower than those in benign nodules(P<0.05).Conclusion:High-frequency ultrasound can effectively differentiate benign and malignant breast micronodules,determine specific nodule types,and exhibits high diagnostic accuracy and sensitivity.Additionally,benign and malignant nodules can be differentiated based on the grading of blood flow signals,sonographic features,and blood flow velocity,providing reasonable guidance for subsequent treatment plans.展开更多
Objective:To explore nursing measures for elderly patients with chronic obstructive pulmonary disease(COPD)and analyze the effect of continuous nursing pathways on improving the accuracy of aerosol use.Methods:From Ap...Objective:To explore nursing measures for elderly patients with chronic obstructive pulmonary disease(COPD)and analyze the effect of continuous nursing pathways on improving the accuracy of aerosol use.Methods:From April 2023 to April 2024,76 elderly COPD patients admitted to our hospital were randomly selected for nursing research.They were divided into two groups using a computer double-blind method,with 38 patients in each group.The control group received routine nursing,while the observation group applied the continuous nursing pathway.The nursing effects of the two groups were investigated and compared,including(1)aerosol accuracy;(2)cardiopulmonary function;(3)subjective well-being and self-care ability;(4)quality of life;and(5)nursing satisfaction.Results:Compared with the control group,the observation group had a significantly higher accuracy rate of aerosol use(P<0.05).Before nursing,there were no significant differences in cardiopulmonary function indicators,MUNSH scores,and ESCA scores between the two groups(P>0.05).After nursing,the patient's cardiopulmonary function improved significantly,and their subjective well-being and self-care ability increased.The observation group was significantly better than the control group in all the above indicators(P<0.05).The quality of life scores of the observation group were significantly higher than those of the control group(P<0.05).Conclusion:In the nursing of elderly patients with chronic obstructive pulmonary disease,the application of the continuous nursing pathway can effectively improve the accuracy of aerosol use and improve patients'cardiopulmonary function.展开更多
This brief presents a cryogenic voltage reference circuit designed to operate effectively across a wide temperature range from 30 to 300 K.A key feature of the proposed design is utilizing a current subtraction techni...This brief presents a cryogenic voltage reference circuit designed to operate effectively across a wide temperature range from 30 to 300 K.A key feature of the proposed design is utilizing a current subtraction technique for temperature compensation of the reference current,avoiding the deployment of bipolar transistors to reduce area and power consumption.Implemented with a 0.18-μm CMOS process,the circuit achieves a temperature coefficient(TC)of 67.5 ppm/K,which was not achieved in previous works.The design can also attain a power supply rejection(PSR)of 58 d B at 10 k Hz.Meanwhile,the average reference voltage is 1.2 V within a 1.6%3σ-accuracy spread.Additionally,the design is characterized by a minimal power dissipation of 1μW at 30 K and a compact chip area of 0.0035 mm~2.展开更多
Changes in lake levels, as an indicator of climate change, are crucial for understanding water resources.Satellite altimetry has proven to be an effective technique for monitoring water level changes in inland lakes. ...Changes in lake levels, as an indicator of climate change, are crucial for understanding water resources.Satellite altimetry has proven to be an effective technique for monitoring water level changes in inland lakes. However, high-altitude and high-latitude lakes undergo seasonal freezing and melting, affecting satellite altimetry accuracy. This paper evaluates the accuracy of lake level height observations by the CryoSat-2, which uses synthetic aperture radar(SAR) across seasons. First, we used lake boundary based on optical remote sensing data to extract the footprints of CryoSat-2 that fall on Namco and Zhari Namco.After elevation conversion and anomaly identification, we obtained the time series of lake levels. These data were compared and verified against lake levels from in-situ measurements to assess the accuracy of CryoSat-2. The results show that CryoSat-2 can monitor lake level height with an accuracy of about 10-13 cm. The correlation coefficient between CryoSat-2 observations and in-situ measurements over Namco is 0.80(p < 0.01), with a Root Mean Square Error(RMSE) of 13 cm. For Zhari Namco, the correlation coefficient is 0.91, with an RMSE of 10 cm, indicating a better match. At the seasonal scale, the seasonal correlation coefficients between CryoSat-2 and in-situ measurement in Namco are 0.47(spring),0.79(summer), and 0.91(fall) with no observations available for winter. The lower correlation in spring may be due to incomplete ice melting. For Zhari Namco, the seasonal correlation coefficients are 0.89(spring), 0.93(summer), 0.89(fall), and 0.87(winter). The results show that CryoSat-2 accuracy is higher in summer and fall, while slightly lower in spring and winter, indicating that ice formation affects accuracy. Even during winter, the altimetry results do not significantly exceed the in-situ lake water level observations.展开更多
Position-sensitive detector(PSD)is widely used in precision measurement fields such as flatness detection,auto-collimator systems,and degrees of freedom testing.However,due to factors such as uneven surface resistance...Position-sensitive detector(PSD)is widely used in precision measurement fields such as flatness detection,auto-collimator systems,and degrees of freedom testing.However,due to factors such as uneven surface resistance and differences in electrode structures,the nonlinearity of PSD becomes increasingly severe as the photosensitive surface moves from the center toward the edges of the four electrodes.To address this issue,a PSD nonlinearity correction algorithm is proposed.The algorithm utilizes the particle swarm optimization(PSO)algorithm to determine the optimal weights and thresholds,providing better initial parameters for the back propagation(BP)neural network.The BP neural network then iterates continuously until the error conditions are met,completing the correction process.Furthermore,a PSD nonlinearity correction system was developed,and the influence of different spot sizes on PSD positioning accuracy was simulated based on the current equation under the Gaussian spot model.This validated the robustness of the correction algorithm under varying spot sizes.The results demonstrate that the overall optimized error is reduced by 84.51%,and for spot sizes smaller than 1 mm,the error reduction exceeds 93.89%.This method not only meets the measurement accuracy requirements but also extends the measurement range of PSD.展开更多
This review comprehensively analyzes advancements in artificial intelligence,particularly machine learning and deep learning,in medical imaging,focusing on their transformative role in enhancing diagnostic accuracy.Ou...This review comprehensively analyzes advancements in artificial intelligence,particularly machine learning and deep learning,in medical imaging,focusing on their transformative role in enhancing diagnostic accuracy.Our in-depth analysis of 138 selected studies reveals that artificial intelligence(AI)algorithms frequently achieve diagnostic performance comparable to,and often surpassing,that of human experts,excelling in complex pattern recognition.Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy,alongside radiologist-level performance for pneumonia detection on chest X-rays.These technologies profoundly transform imaging by significantly improving processes in classification,segmentation,and sequential analysis across diversemodalities such as X-rays,Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound.Specific advancements with Convolutional Neural Networks,Recurrent Neural Networks,and ensemble learning techniques have facilitated more precise diagnosis,prediction,and therapy planning.Notably,Generative Adversarial Networks address limited data through augmentation,while transfer learning efficiently adapts models for scarce labeled datasets,and Reinforcement Learning shows promise in optimizing treatment protocols,collectively advancing patient care.Methodologically,a systematic review(2015-2024)used Scopus and Web of Science databases,yielding 7982 initial records.Of these,1189 underwent bibliometric analysis using the R package‘Bibliometrix’,and 138 were comprehensively reviewed for specific findings.Research output surged over the decade,led by Institute of Electrical and Electronics Engineers(IEEE)Access(19.1%).China dominates publication volume(36.1%),while the United States of America(USA)leads total citations(5605),and Hong Kong exhibits the highest average(55.60).Challenges include rigorous validation,regulatory clarity,and fostering clinician trust.This study highlights significant emerging trends and crucial future research directions for successful AI implementation in healthcare.展开更多
The machining process remains relevant for manufacturing high-quality and high-precision parts,which can be found in industries such as aerospace and aeronautical,with many produced by turning,drilling,and milling pro...The machining process remains relevant for manufacturing high-quality and high-precision parts,which can be found in industries such as aerospace and aeronautical,with many produced by turning,drilling,and milling processes.Monitoring and analyzing tool wear during these processes is crucial to assess the tool’s life and optimize the tool’s performance under study;as such,standards detail procedures to measure and assess tool wear for various tools.Measuring wear in machining tools can be time-consuming,as the process is usually manual,requiring human interaction and judgment.In the present work,an automated offline flank wear measurement algorithm was developed in Python.The algorithm measures the flank wear of coated end-mills and slot drills from Scanning Electron Microscopy(SEM)images,according to the ISO 8688 standard,following the same wear measurement procedure.SEM images acquired with different magnifications and tools with varying machining parameters were analyzed using the developed algorithm.The flank wear measurements were then compared to the manually obtained,achieving relative errors for the most common magnifications of around 2.5%.Higher magnifications were also tested,yielding a maximum relative error of 13.4%.The algorithm can measure batches of images quickly on an ordinary personal computer,analyzing and measuring a 10-image batch in around 30 s,a process that would require around 30 min when performed manually by a skilled operator.Therefore,it can be a reliable alternative to measuring flank wear on many tools from SEM images,with the possibility of being adjusted for other wear measurements on different kinds of tools and different image types,for example,on images obtained by optical microscopy.展开更多
Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring...Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM.展开更多
Machine learning-based Debris Flow Susceptibility Mapping(DFSM)has emerged as an effective approach for assessing debris flow likelihood,yet its application faces three critical challenges:insufficient reliability of ...Machine learning-based Debris Flow Susceptibility Mapping(DFSM)has emerged as an effective approach for assessing debris flow likelihood,yet its application faces three critical challenges:insufficient reliability of training samples caused by biased negative sampling,opaque decision-making mechanisms in models,and subjective susceptibility mapping methods that lack quantitative evaluation criteria.This study focuses on the Yalong River basin.By integrating high-resolution remote sensing interpretation and field surveys,we established a refined sample database that includes 1,736 debris flow gullies.To address spatial bias in traditional random negative sampling,we developed a semi-supervised optimization strategy based on iterative confidence screening.Comparative experiments with four treebased models(XGBoost,CatBoost,LGBM,and Random Forest)reveal that the optimized sampling strategy improved overall model performance by 8%-12%,with XGBoost achieving the highest accuracy(AUC=0.882)and RF performing the lowest(AUC=0.820).SHAP-based global-local interpretability analysis(applicable to all tree models)identifies elevation and short-duration rainfall as dominant controlling factors.Furthermore,among the tested tree-based models,XGBoost optimized with semisupervised sampling demonstrates the highest reliability in debris flow susceptibility mapping(DFSM),achieving a comprehensive accuracy of 83.64%due to its optimal generalization-stability equilibrium.展开更多
Accuracy allocation is crucial in the accuracy design of machining tools.Current accuracy allocation methods primarily focus on positional deviation,with little consideration for tool direction deviation.To address th...Accuracy allocation is crucial in the accuracy design of machining tools.Current accuracy allocation methods primarily focus on positional deviation,with little consideration for tool direction deviation.To address this issue,we propose a geometric error cost sensitivity-based accuracy allocation method for five-axis machine tools.A geometric error model consisting of 4l error components is constructed based on homogeneous transformation matrices.Volumetric points with positional and tool direction deviations are randomly sampled to evaluate the accuracy of the machine tool.The sensitivity of each error component at these sampling points is analyzed using the Sobol method.To balance the needs of geometric precision and manufacturing cost,a geometric error cost sensitivity function is developed to estimate the required cost.By allocating error components affecting tool direction deviation first and the remaining components second,this allocation scheme ensures that both deviations meet the requirements.We also perform numerical simulation of a BC-type(B-axis and C-axis type)five-axis machine tool to validate the method.The results show that the new allocation scheme reduces the total geometric error cost by 27.8%compared to a uniform allocation scheme,and yields the same positional and tool direction machining accuracies.展开更多
Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagn...Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagnostic imaging,such as resolution,radiation efficiency,and real-time processing.Methods:This work used a mixed-methods strategy,including controlled phantom experiments,retrospective multi-center clinical data analysis,and quantum-classical hybrid processing to assess enhancements in resolution,dosage efficiency,and diagnostic confidence.Statistical validation included analysis of variance(ANOVA)and receiver-operating characteristic curve analysis,juxtaposing quantum-enhanced methodologies with conventional and deep learning approaches.Results:Quantum entanglement reconstruction enhanced magnetic resonance imaging spatial resolution by 33.2%(P<0.01),quantum noise suppression facilitated computed tomography scans with a 60%reduction in radiation,and quantum beamforming improved ultrasound contrast by 27%while preserving real-time processing(<2 ms delay).Inter-reader variability(12%in Diagnostic Confidence Scores)showed that systematic training is needed,even if the performance was better.The research presented(1)a reusable clinical quantum imaging framework,(2)enhanced hardware processes(field-programmable gate array/graphics processing unit acceleration),and(3)cost-benefit analyses demonstrating a 22-month return on investment breakeven point.Conclusion:Quantum-enhanced imaging has a lot of promise for use in medicine,especially in neurology and cancer.Future research should focus on multi-modal integration(e.g.,positron emission tomography–magnetic resonance imaging),cloud-based quantum simulations for enhanced accessibility,and extensive trials to confirm long-term diagnostic accuracy.This breakthrough gives healthcare systems a technology roadmap and a reason to spend money on quantum-enhanced diagnostics.展开更多
This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering a...This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering applications, such as infrastructure monitoring and heritage preservation. Using a high-resolution UAV with a 20 MP (MegaPixels) sensor, four images of a brick wall test field were captured and processed in Agisoft Metashape, with resolutions compared against Leica T2002 theodolite measurements (1.0 mm accuracy). Advanced statistical methods (ANOVA (analysis of variance), Tukey tests, Monte Carlo simulations) and ground control points validated the results. Accuracy improved from 25 mm at 50 PPI to 5 mm at 150 PPI (p < 0.01), plateauing at 4 mm beyond 200 PPI, while 150 PPI reduced processing time by 62% compared to 300 PPI. Unlike prior studies, this research uniquely isolates resolution effects in a controlled civil engineering context, offering a novel 150 PPI threshold that balances precision and efficiency. This threshold supports Saudi Vision 2030’s smart infrastructure goals for megaprojects like NEOM, providing a scalable framework for global applications. Future research should leverage deep learning to optimize resolutions in dynamic environments.展开更多
The presence of residual stresses in materials or engineering structures can significantly influence their mechanical per-formance.Accurate measurement of residual stresses is of great importance to ensure their in-se...The presence of residual stresses in materials or engineering structures can significantly influence their mechanical per-formance.Accurate measurement of residual stresses is of great importance to ensure their in-service reliability.Although numerous instrumented indentation methods have been proposed to evaluate residual stresses,the majority of them require a stress-free reference sample as a comparison benchmark,thereby limiting their applicability in scenarios where obtaining stress-free reference samples is challenging.In this work,through a number of finite element simulations,it was found that the loading exponent of the loading load-depth curve and the recovered depth during unloading are insensitive to residual stresses.The loading curve of the stress-free specimen was virtually reconstructed using such stress-insensitive parameters extracted from the load-depth curves of the stressed state,thus eliminating the requirement for stress-free reference samples.The residual stress was then correlated with the fractional change in loading work between stressed and stress-free loading curves through dimensional analysis and finite element simulations.Based on this correlation,an instrumented sharp indentation method for measuring equibiaxial residual stress without requiring a stress-free specimen was established.Both numerical and experimental verifications were carried out to demonstrate the accuracy and reliability of the newly proposed method.The maximum relative error and absolute error in measured residual stresses are typically within±20%and±20 MPa,respectively.展开更多
Traditional automated guided vehicle(AGV)primarily relies on scheduling systems to manage warehouse locations and execute picking or placing tasks on fixedheight pallets.However,these conventional systems are illsuite...Traditional automated guided vehicle(AGV)primarily relies on scheduling systems to manage warehouse locations and execute picking or placing tasks on fixedheight pallets.However,these conventional systems are illsuited for scenarios involving variable heights,such as vehicle loading and unloading or the complex stacking of soft packages.To address the challenges of AGV endeffector operations in nonfixed height scenarios,this paper proposes an innovative solution leveraging lowcost depth camera sensors.By capturing image and depth data,and integrating deep learning,image processing,and spatial attitude calculation techniques,the method accurately determines the position of the endeffector center point relative to the upper plane of the fork.The approach effectively resolves a key issue in AGV operations within intelligent logistics scenarios that lack fixed heights.The proposed algorithm is deployed on a domestic embedded,lowcost ARM chip controller,and extensive experiments are conducted on a real AGV equipped with multiple stacked vehicles and nonstandard vehicles.The experimental results demonstrate that for diverse vehicles with different heights,the measurement error can be maintained within±10 mm,satisfying the requirements for highprecision measurement.The height measurement method developed in the paper not only enhances the AGV’s adaptability in nonfixed height scenarios but also significantly broadens its application potential across various industries.展开更多
Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for th...Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for thermal imaging and transport research.Here,we introduce an approach to measure nanoscale thermal resistance using in situ inelastic scanning transmission electron microscopy.By constructing unidirectional heating flux with controlled temperature gradients and analyzing electron energy-loss/gain signals under optimized acquisition conditions,nanometer-resolution in mapping phonon apparent temperature is achieved.Thus,interfacial thermal resistance is determined by calculating the ratio of interfacial temperature difference to bulk temperature gradient.This methodology enables direct measurement of thermal transport properties for atomic-scale structural features(e.g.,defects and heterointerfaces),resolving critical structure-performance relationships,providing a useful tool for investigating thermal phenomena at the(sub-)nanoscale.展开更多
基金National Natural Science Foundation of China(12102410)Fund of National Key Laboratory of Shock Wave and Detonation Physics(JCKYS2022212005)。
文摘The lattice parameter,measured with sufficient accuracy,can be utilized to evaluate the quality of single crystals and to determine the equation of state for materials.We propose an iterative method for obtaining more precise lattice parameters using the interaction points for the pseudo-Kossel pattern obtained from laser-induced X-ray diffraction(XRD).This method has been validated by the analysis of an XRD experiment conducted on iron single crystals.Furthermore,the method was used to calculate the compression ratio and rotated angle of an LiF sample under high pressure loading.This technique provides a robust tool for in-situ characterization of structural changes in single crystals under extreme conditions.It has significant implications for studying the equation of state and phase transitions.
基金Supported by the National Natural Science Foundation of China(Grant Nos.52375448,52275440).
文摘The CNC machine tool is the fundamental equipment of the manufacturing industry,particularly in sectors where achieving high levels of accuracy is crucial.Geometric accuracy design is an important step in machine tool design and plays an essential role in determining the machining accuracy of the workpiece.Researchers have extensively studied methods to model,extract,optimize,and measure the geometric errors that affect the geometric accuracy of machine tools.This paper provides a comprehensive review of the state-of-the-art approaches and an overview of the latest research progress associated with geometric accuracy design in CNC machine tools.This paper explores the interrelated aspects of CNC machine tool accuracy design:modeling,analysis and optimization.Accuracy analysis,which includes geometric error modeling and sensitivity analysis,determines a machine tool’s output accuracy through its volumetric error model,given the known accuracy of its individual components.Conversely,accuracy allocation designs the accuracy of the machine tool components according to given output accuracy requirements to achieve optimization between the objectives of manufacturing cost,quality,reliability,and environmental impact.In addition to discussing design factors and evaluation methods,this paper outlines methods for verifying the accuracy of design results,aiming to provide a practical basis for ensuring that the designed accuracy is achieved.Finally,the challenges and future research directions in geometric accuracy design are highlighted.
文摘BACKGROUND The accuracy of blind intra-articular injections in the shoulder is rather low.Inaccurate injections tend to lead to poorer treatment outcomes.The“Delaware posterior bone touch technique”has shown higher accuracy in young,slender,healthy volunteers than the classical“Cyriax technique”.AIM To investigate whether the Delaware technique would also be more accurate in older patients with capsulitis.METHODS We analyzed the files of 100 consecutive patients with capsulitis who were treated with an intra-articular injection containing a mixture of triamcinolone,lidocaine,and air.After the injection,the shoulder was moved to determine whether a squishing sound could be produced.The squishing sound was interpreted as an accurate injection.The scores with the new Delaware technique were compared against those with the Cyriax technique in a previous study.RESULTS Squishing was heard after 87%of the injections.This was 13%(10%points)more than the 77%in the previous study(P=0.004).CONCLUSION The Delaware technique was significantly more accurate than the Cyriax technique also in middle aged patients with capsulitis.We hypothesize that the difference is caused by a lower risk that a part of the opening of the needle is still outside the capsule.
基金supported by the National Key Research and Development Program of China(2023YFC3008605)the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(311021002)the Seismological Research Foundation for Youths of Guangdong Earthquake Agency(Open Funding Project of Key Laboratory of Earthquake Monitoring and Disaster Mitigation Technology,China Earthquake Administration)(GDDZY202309)。
文摘The travel-time corrections for the primary seismic phases of 72 stations in the Guangdong seismic network,relative to the 1D South China travel-time model,were determined using joint hypocentral determination(JHD)and statistical analysis methods.The travel-time corrections for the Pg phase of 72 stations range between-0.25 s and 0.14 s,while the corrections for the Sg phase range between 0.27 s and 0.35 s,and those for the Pn phase are between-0.86 s and 0.07 s.The spatial distribution of travel-time corrections for Pg,Sg,and Pn phases of 72 stations correlates well with the geological structure in this region.This indicates that the travel-time corrections for Pg and Sg phases are mainly caused by the discrepancy between the actual crustal velocity structure beneath the stations and the 1D South China travel-time model.These corrections empirically compensate for systematic travel-time errors arising from such discrepancies.The primary factor contributing to the travel-time corrections for the Pn phase is the Moho undulations or tilt.These corrections are intended to compensate for systematic errors in travel time caused by variations in the actual Moho.By integrating the obtained travel-time corrections into the HYPO-SAT location algorithm,test results showed an obvious improvement in location accuracy and origin time precision for explosion events.The variation of horizontal distance between repeating earthquake pairs has also improved,with 86%of the repeating earthquake pair spacing being more accurately estimated after correction.This suggests the crucial significance of travel-time correction in earthquake location,and the consideration of travel-time correction exerts a notable impact on enhancing earthquake location accuracy.
文摘Objective:To analyze the significance of high-frequency ultrasound in differentiating benign and malignant breast micronodules.Methods:Eighty-five patients with breast micronodules admitted for diagnosis between October 2022 and October 2024 were selected for high-frequency ultrasound diagnosis.The diagnostic efficacy of high-frequency ultrasound was evaluated by comparing it with the results of surgical pathology.Results:High-frequency ultrasound detected 50 benign nodules,primarily breast fibroadenomas,and 35 malignant nodules,mainly breast ductal carcinoma in situ.Based on surgical pathology results,the diagnostic accuracy of high-frequency ultrasound was 96.47%,specificity was 97.96%,and sensitivity was 94.44%.In high-frequency ultrasound diagnosis,the proportion of grade III and IV blood flow in malignant nodules was higher than that in benign nodules,while the proportion of regular shape and clear margins was lower.The proportion of microcalcifications and posterior echo attenuation was higher in malignant nodules,and the resistance index(RI)and peak blood flow velocity were lower than those in benign nodules(P<0.05).Conclusion:High-frequency ultrasound can effectively differentiate benign and malignant breast micronodules,determine specific nodule types,and exhibits high diagnostic accuracy and sensitivity.Additionally,benign and malignant nodules can be differentiated based on the grading of blood flow signals,sonographic features,and blood flow velocity,providing reasonable guidance for subsequent treatment plans.
文摘Objective:To explore nursing measures for elderly patients with chronic obstructive pulmonary disease(COPD)and analyze the effect of continuous nursing pathways on improving the accuracy of aerosol use.Methods:From April 2023 to April 2024,76 elderly COPD patients admitted to our hospital were randomly selected for nursing research.They were divided into two groups using a computer double-blind method,with 38 patients in each group.The control group received routine nursing,while the observation group applied the continuous nursing pathway.The nursing effects of the two groups were investigated and compared,including(1)aerosol accuracy;(2)cardiopulmonary function;(3)subjective well-being and self-care ability;(4)quality of life;and(5)nursing satisfaction.Results:Compared with the control group,the observation group had a significantly higher accuracy rate of aerosol use(P<0.05).Before nursing,there were no significant differences in cardiopulmonary function indicators,MUNSH scores,and ESCA scores between the two groups(P>0.05).After nursing,the patient's cardiopulmonary function improved significantly,and their subjective well-being and self-care ability increased.The observation group was significantly better than the control group in all the above indicators(P<0.05).The quality of life scores of the observation group were significantly higher than those of the control group(P<0.05).Conclusion:In the nursing of elderly patients with chronic obstructive pulmonary disease,the application of the continuous nursing pathway can effectively improve the accuracy of aerosol use and improve patients'cardiopulmonary function.
基金supported in part by the National Key Research and Development Program of China(2021YFA0715503)。
文摘This brief presents a cryogenic voltage reference circuit designed to operate effectively across a wide temperature range from 30 to 300 K.A key feature of the proposed design is utilizing a current subtraction technique for temperature compensation of the reference current,avoiding the deployment of bipolar transistors to reduce area and power consumption.Implemented with a 0.18-μm CMOS process,the circuit achieves a temperature coefficient(TC)of 67.5 ppm/K,which was not achieved in previous works.The design can also attain a power supply rejection(PSR)of 58 d B at 10 k Hz.Meanwhile,the average reference voltage is 1.2 V within a 1.6%3σ-accuracy spread.Additionally,the design is characterized by a minimal power dissipation of 1μW at 30 K and a compact chip area of 0.0035 mm~2.
基金financial supported by National Natural Science Foundation of China (42104010, 42174097, 41974093, and 41774088)the Fundamental Research Funds for the Central Universities
文摘Changes in lake levels, as an indicator of climate change, are crucial for understanding water resources.Satellite altimetry has proven to be an effective technique for monitoring water level changes in inland lakes. However, high-altitude and high-latitude lakes undergo seasonal freezing and melting, affecting satellite altimetry accuracy. This paper evaluates the accuracy of lake level height observations by the CryoSat-2, which uses synthetic aperture radar(SAR) across seasons. First, we used lake boundary based on optical remote sensing data to extract the footprints of CryoSat-2 that fall on Namco and Zhari Namco.After elevation conversion and anomaly identification, we obtained the time series of lake levels. These data were compared and verified against lake levels from in-situ measurements to assess the accuracy of CryoSat-2. The results show that CryoSat-2 can monitor lake level height with an accuracy of about 10-13 cm. The correlation coefficient between CryoSat-2 observations and in-situ measurements over Namco is 0.80(p < 0.01), with a Root Mean Square Error(RMSE) of 13 cm. For Zhari Namco, the correlation coefficient is 0.91, with an RMSE of 10 cm, indicating a better match. At the seasonal scale, the seasonal correlation coefficients between CryoSat-2 and in-situ measurement in Namco are 0.47(spring),0.79(summer), and 0.91(fall) with no observations available for winter. The lower correlation in spring may be due to incomplete ice melting. For Zhari Namco, the seasonal correlation coefficients are 0.89(spring), 0.93(summer), 0.89(fall), and 0.87(winter). The results show that CryoSat-2 accuracy is higher in summer and fall, while slightly lower in spring and winter, indicating that ice formation affects accuracy. Even during winter, the altimetry results do not significantly exceed the in-situ lake water level observations.
基金Supported by the National Natural Science Foundation of China(U1831133)Open Fund of Key Laboratory of Space Active Optoelectronics Technology,Chinese Academy of Sciences(2021ZDKF4)。
文摘Position-sensitive detector(PSD)is widely used in precision measurement fields such as flatness detection,auto-collimator systems,and degrees of freedom testing.However,due to factors such as uneven surface resistance and differences in electrode structures,the nonlinearity of PSD becomes increasingly severe as the photosensitive surface moves from the center toward the edges of the four electrodes.To address this issue,a PSD nonlinearity correction algorithm is proposed.The algorithm utilizes the particle swarm optimization(PSO)algorithm to determine the optimal weights and thresholds,providing better initial parameters for the back propagation(BP)neural network.The BP neural network then iterates continuously until the error conditions are met,completing the correction process.Furthermore,a PSD nonlinearity correction system was developed,and the influence of different spot sizes on PSD positioning accuracy was simulated based on the current equation under the Gaussian spot model.This validated the robustness of the correction algorithm under varying spot sizes.The results demonstrate that the overall optimized error is reduced by 84.51%,and for spot sizes smaller than 1 mm,the error reduction exceeds 93.89%.This method not only meets the measurement accuracy requirements but also extends the measurement range of PSD.
文摘This review comprehensively analyzes advancements in artificial intelligence,particularly machine learning and deep learning,in medical imaging,focusing on their transformative role in enhancing diagnostic accuracy.Our in-depth analysis of 138 selected studies reveals that artificial intelligence(AI)algorithms frequently achieve diagnostic performance comparable to,and often surpassing,that of human experts,excelling in complex pattern recognition.Key findings include earlier detection of conditions like skin cancer and diabetic retinopathy,alongside radiologist-level performance for pneumonia detection on chest X-rays.These technologies profoundly transform imaging by significantly improving processes in classification,segmentation,and sequential analysis across diversemodalities such as X-rays,Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and ultrasound.Specific advancements with Convolutional Neural Networks,Recurrent Neural Networks,and ensemble learning techniques have facilitated more precise diagnosis,prediction,and therapy planning.Notably,Generative Adversarial Networks address limited data through augmentation,while transfer learning efficiently adapts models for scarce labeled datasets,and Reinforcement Learning shows promise in optimizing treatment protocols,collectively advancing patient care.Methodologically,a systematic review(2015-2024)used Scopus and Web of Science databases,yielding 7982 initial records.Of these,1189 underwent bibliometric analysis using the R package‘Bibliometrix’,and 138 were comprehensively reviewed for specific findings.Research output surged over the decade,led by Institute of Electrical and Electronics Engineers(IEEE)Access(19.1%).China dominates publication volume(36.1%),while the United States of America(USA)leads total citations(5605),and Hong Kong exhibits the highest average(55.60).Challenges include rigorous validation,regulatory clarity,and fostering clinician trust.This study highlights significant emerging trends and crucial future research directions for successful AI implementation in healthcare.
文摘The machining process remains relevant for manufacturing high-quality and high-precision parts,which can be found in industries such as aerospace and aeronautical,with many produced by turning,drilling,and milling processes.Monitoring and analyzing tool wear during these processes is crucial to assess the tool’s life and optimize the tool’s performance under study;as such,standards detail procedures to measure and assess tool wear for various tools.Measuring wear in machining tools can be time-consuming,as the process is usually manual,requiring human interaction and judgment.In the present work,an automated offline flank wear measurement algorithm was developed in Python.The algorithm measures the flank wear of coated end-mills and slot drills from Scanning Electron Microscopy(SEM)images,according to the ISO 8688 standard,following the same wear measurement procedure.SEM images acquired with different magnifications and tools with varying machining parameters were analyzed using the developed algorithm.The flank wear measurements were then compared to the manually obtained,achieving relative errors for the most common magnifications of around 2.5%.Higher magnifications were also tested,yielding a maximum relative error of 13.4%.The algorithm can measure batches of images quickly on an ordinary personal computer,analyzing and measuring a 10-image batch in around 30 s,a process that would require around 30 min when performed manually by a skilled operator.Therefore,it can be a reliable alternative to measuring flank wear on many tools from SEM images,with the possibility of being adjusted for other wear measurements on different kinds of tools and different image types,for example,on images obtained by optical microscopy.
基金supporteded by Natural Science Foundation of Shanghai(Grant No.22ZR1463900)State Key Laboratory of Mechanical System and Vibration(Grant No.MSV202318)the Fundamental Research Funds for the Central Universities(Grant No.22120220649).
文摘Additive manufacturing(AM),particularly fused deposition modeling(FDM),has emerged as a transformative technology in modern manufacturing processes.The dimensional accuracy of FDM-printed parts is crucial for ensuring their functional integrity and performance.To achieve sustainable manufacturing in FDM,it is necessary to optimize the print quality and time efficiency concurrently.However,owing to the complex interactions of printing parameters,achieving a balanced optimization of both remains challenging.This study examines four key factors affecting dimensional accuracy and print time:printing speed,layer thickness,nozzle temperature,and bed temperature.Fifty parameter sets were generated using enhanced Latin hypercube sampling.A whale optimization algorithm(WOA)-enhanced support vector regression(SVR)model was developed to predict dimen-sional errors and print time effectively,with non-dominated sorting genetic algorithm Ⅲ(NSGA-Ⅲ)utilized for multi-objective optimization.The technique for Order Preference by Similarity to Ideal Solution(TOPSIS)was applied to select a balanced solution from the Pareto front.In experimental validation,the parts printed using the optimized parameters exhibited excellent dimensional accuracy and printing efficiency.This study comprehensively considered optimizing the printing time and size to meet quality requirements while achieving higher printing efficiency and aiding in the realization of sustainable manufacturing in the field of AM.In addition,the printing of a specific prosthetic component was used as a case study,highlighting the high demands on both dimensional precision and printing efficiency.The optimized process parameters required significantly less printing time,while satisfying the dimensional accuracy requirements.This study provides valuable insights for achieving sustainable AM using FDM.
基金funded by the Second Tibetan Plateau Scientific Expedition and Research,Ministry of Science and Technology(Project No.2019QZKK0902)the West Light Foundation of the Chinese Academy of Sciences(Project No.E3R2120)the Research Programme of Institute of Mountain Hazards and Environment,Chinese Academy of Sciences(Project No.IMHE-ZDRW-01).
文摘Machine learning-based Debris Flow Susceptibility Mapping(DFSM)has emerged as an effective approach for assessing debris flow likelihood,yet its application faces three critical challenges:insufficient reliability of training samples caused by biased negative sampling,opaque decision-making mechanisms in models,and subjective susceptibility mapping methods that lack quantitative evaluation criteria.This study focuses on the Yalong River basin.By integrating high-resolution remote sensing interpretation and field surveys,we established a refined sample database that includes 1,736 debris flow gullies.To address spatial bias in traditional random negative sampling,we developed a semi-supervised optimization strategy based on iterative confidence screening.Comparative experiments with four treebased models(XGBoost,CatBoost,LGBM,and Random Forest)reveal that the optimized sampling strategy improved overall model performance by 8%-12%,with XGBoost achieving the highest accuracy(AUC=0.882)and RF performing the lowest(AUC=0.820).SHAP-based global-local interpretability analysis(applicable to all tree models)identifies elevation and short-duration rainfall as dominant controlling factors.Furthermore,among the tested tree-based models,XGBoost optimized with semisupervised sampling demonstrates the highest reliability in debris flow susceptibility mapping(DFSM),achieving a comprehensive accuracy of 83.64%due to its optimal generalization-stability equilibrium.
基金supported by the Key R&D Program of Zhejiang Province(Nos.2023C01166 and 2024SJCZX0046)the Zhejiang Provincial Natural Science Foundation of China(Nos.LDT23E05013E05 and LD24E050009)the Natural Science Foundation of Ningbo(No.2021J150),China.
文摘Accuracy allocation is crucial in the accuracy design of machining tools.Current accuracy allocation methods primarily focus on positional deviation,with little consideration for tool direction deviation.To address this issue,we propose a geometric error cost sensitivity-based accuracy allocation method for five-axis machine tools.A geometric error model consisting of 4l error components is constructed based on homogeneous transformation matrices.Volumetric points with positional and tool direction deviations are randomly sampled to evaluate the accuracy of the machine tool.The sensitivity of each error component at these sampling points is analyzed using the Sobol method.To balance the needs of geometric precision and manufacturing cost,a geometric error cost sensitivity function is developed to estimate the required cost.By allocating error components affecting tool direction deviation first and the remaining components second,this allocation scheme ensures that both deviations meet the requirements.We also perform numerical simulation of a BC-type(B-axis and C-axis type)five-axis machine tool to validate the method.The results show that the new allocation scheme reduces the total geometric error cost by 27.8%compared to a uniform allocation scheme,and yields the same positional and tool direction machining accuracies.
文摘Background:Quantum-enhanced medical imaging algorithms–quantum entanglement reconstruction,quantum noise suppression,and quantum beamforming–propose possible remedies for significant constraints in traditional diagnostic imaging,such as resolution,radiation efficiency,and real-time processing.Methods:This work used a mixed-methods strategy,including controlled phantom experiments,retrospective multi-center clinical data analysis,and quantum-classical hybrid processing to assess enhancements in resolution,dosage efficiency,and diagnostic confidence.Statistical validation included analysis of variance(ANOVA)and receiver-operating characteristic curve analysis,juxtaposing quantum-enhanced methodologies with conventional and deep learning approaches.Results:Quantum entanglement reconstruction enhanced magnetic resonance imaging spatial resolution by 33.2%(P<0.01),quantum noise suppression facilitated computed tomography scans with a 60%reduction in radiation,and quantum beamforming improved ultrasound contrast by 27%while preserving real-time processing(<2 ms delay).Inter-reader variability(12%in Diagnostic Confidence Scores)showed that systematic training is needed,even if the performance was better.The research presented(1)a reusable clinical quantum imaging framework,(2)enhanced hardware processes(field-programmable gate array/graphics processing unit acceleration),and(3)cost-benefit analyses demonstrating a 22-month return on investment breakeven point.Conclusion:Quantum-enhanced imaging has a lot of promise for use in medicine,especially in neurology and cancer.Future research should focus on multi-modal integration(e.g.,positron emission tomography–magnetic resonance imaging),cloud-based quantum simulations for enhanced accessibility,and extensive trials to confirm long-term diagnostic accuracy.This breakthrough gives healthcare systems a technology roadmap and a reason to spend money on quantum-enhanced diagnostics.
文摘This study provides the first systematic evaluation of image resolution’s effect (50-300 PPI, pixels per inch) on UAV (unmanned aerial vehicle)-based digital close-range photogrammetry accuracy in civil engineering applications, such as infrastructure monitoring and heritage preservation. Using a high-resolution UAV with a 20 MP (MegaPixels) sensor, four images of a brick wall test field were captured and processed in Agisoft Metashape, with resolutions compared against Leica T2002 theodolite measurements (1.0 mm accuracy). Advanced statistical methods (ANOVA (analysis of variance), Tukey tests, Monte Carlo simulations) and ground control points validated the results. Accuracy improved from 25 mm at 50 PPI to 5 mm at 150 PPI (p < 0.01), plateauing at 4 mm beyond 200 PPI, while 150 PPI reduced processing time by 62% compared to 300 PPI. Unlike prior studies, this research uniquely isolates resolution effects in a controlled civil engineering context, offering a novel 150 PPI threshold that balances precision and efficiency. This threshold supports Saudi Vision 2030’s smart infrastructure goals for megaprojects like NEOM, providing a scalable framework for global applications. Future research should leverage deep learning to optimize resolutions in dynamic environments.
基金support from the National Natural Science Foundation of China(Grant Nos.12172332,11727803 and 12072009)the Zhejiang Provincial Natural Science Foundation of China(Grant No.LZ23A020007)the Fundamental Research Funds for the Provincial Universities of Zhejiang(Grant No.RF-C2022003).
文摘The presence of residual stresses in materials or engineering structures can significantly influence their mechanical per-formance.Accurate measurement of residual stresses is of great importance to ensure their in-service reliability.Although numerous instrumented indentation methods have been proposed to evaluate residual stresses,the majority of them require a stress-free reference sample as a comparison benchmark,thereby limiting their applicability in scenarios where obtaining stress-free reference samples is challenging.In this work,through a number of finite element simulations,it was found that the loading exponent of the loading load-depth curve and the recovered depth during unloading are insensitive to residual stresses.The loading curve of the stress-free specimen was virtually reconstructed using such stress-insensitive parameters extracted from the load-depth curves of the stressed state,thus eliminating the requirement for stress-free reference samples.The residual stress was then correlated with the fractional change in loading work between stressed and stress-free loading curves through dimensional analysis and finite element simulations.Based on this correlation,an instrumented sharp indentation method for measuring equibiaxial residual stress without requiring a stress-free specimen was established.Both numerical and experimental verifications were carried out to demonstrate the accuracy and reliability of the newly proposed method.The maximum relative error and absolute error in measured residual stresses are typically within±20%and±20 MPa,respectively.
基金Supported by the Key Research and Development Program of Anhui Province(No.201904a05020035)the Postdoctoral Research Initiative of Anhui Province(No.2024B804)the Hefei City Key Technology Research and Development‘Ranking’(No.2023SGJ017).
文摘Traditional automated guided vehicle(AGV)primarily relies on scheduling systems to manage warehouse locations and execute picking or placing tasks on fixedheight pallets.However,these conventional systems are illsuited for scenarios involving variable heights,such as vehicle loading and unloading or the complex stacking of soft packages.To address the challenges of AGV endeffector operations in nonfixed height scenarios,this paper proposes an innovative solution leveraging lowcost depth camera sensors.By capturing image and depth data,and integrating deep learning,image processing,and spatial attitude calculation techniques,the method accurately determines the position of the endeffector center point relative to the upper plane of the fork.The approach effectively resolves a key issue in AGV operations within intelligent logistics scenarios that lack fixed heights.The proposed algorithm is deployed on a domestic embedded,lowcost ARM chip controller,and extensive experiments are conducted on a real AGV equipped with multiple stacked vehicles and nonstandard vehicles.The experimental results demonstrate that for diverse vehicles with different heights,the measurement error can be maintained within±10 mm,satisfying the requirements for highprecision measurement.The height measurement method developed in the paper not only enhances the AGV’s adaptability in nonfixed height scenarios but also significantly broadens its application potential across various industries.
基金supported by the National Natural Science Foundation of China(Grant No.52125307)the National Key R&D Program of China(Grant No.2021YFB3501500)the support from the New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Rapid technological advancements drive miniaturization and high energy density in devices,thereby increasing nanoscale thermal management demands and urging development of higher spatial resolution technologies for thermal imaging and transport research.Here,we introduce an approach to measure nanoscale thermal resistance using in situ inelastic scanning transmission electron microscopy.By constructing unidirectional heating flux with controlled temperature gradients and analyzing electron energy-loss/gain signals under optimized acquisition conditions,nanometer-resolution in mapping phonon apparent temperature is achieved.Thus,interfacial thermal resistance is determined by calculating the ratio of interfacial temperature difference to bulk temperature gradient.This methodology enables direct measurement of thermal transport properties for atomic-scale structural features(e.g.,defects and heterointerfaces),resolving critical structure-performance relationships,providing a useful tool for investigating thermal phenomena at the(sub-)nanoscale.
