Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamformin...Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.展开更多
Rotor blade is one of the most significant components of helicopters. But due to its highspeed rotation characteristics, it is difficult to collect the vibration signals during the flight stage.Moreover, sensors are h...Rotor blade is one of the most significant components of helicopters. But due to its highspeed rotation characteristics, it is difficult to collect the vibration signals during the flight stage.Moreover, sensors are highly susceptible to damage resulting in the failure of the measurement.In order to make signal predictions for the damaged sensors, an operational modal analysis(OMA) together with the virtual sensing(VS) technology is proposed in this paper. This paper discusses two situations, i.e., mode shapes measured by all sensors(both normal and damaged) can be obtained using OMA, and mode shapes measured by some sensors(only including normal) can be obtained using OMA. For the second situation, it is necessary to use finite element(FE) analysis to supplement the missing mode shapes of damaged sensor. In order to improve the correlation between the FE model and the real structure, the FE mode shapes are corrected using the local correspondence(LC) principle and mode shapes measured by some sensors(only including normal).Then, based on the VS technology, the vibration signals of the damaged sensors during the flight stage can be accurately predicted using the identified mode shapes(obtained based on OMA and FE analysis) and the normal sensors signals. Given the high degrees of freedom(DOFs) in the FE mode shapes, this approach can also be used to predict vibration data at locations without sensors. The effectiveness and robustness of the proposed method is verified through finite element simulation, experiment as well as the actual flight test. The present work can be further used in the fault diagnosis and damage identification for rotor blade of helicopters.展开更多
目的采用文献计量学分析虚拟现实(virtual reality,VR)技术在医学教育领域的应用研究,探讨该领域的研究现况、热点和发展趋势等。方法检索2003年1月—2023年9月Web of Science核心合集数据库中VR技术应用于医学教育或教学中的相关文献,...目的采用文献计量学分析虚拟现实(virtual reality,VR)技术在医学教育领域的应用研究,探讨该领域的研究现况、热点和发展趋势等。方法检索2003年1月—2023年9月Web of Science核心合集数据库中VR技术应用于医学教育或教学中的相关文献,并运用CiteSpace 6.1.R6和VOSviewer 1.6.18等软件对发文国家/地区、机构、期刊、共同引用参考文献及关键词进行分析,并可视化科学知识图谱。结果共纳入文献1621篇,该领域自2018年后发文量迅速增长;美国在该领域的发展中起主导作用;中国的发文量居第五位,但影响力有待提高;研究热点主要包括手术模拟、技能训练、远程教学等。结论VR技术在医学教育领域的应用越来越受到重视,建议进一步加强对这一领域的探索,以期为未来的医学教育提供新的发展方向。展开更多
BACKGROUND Orthopaedic surgical education has traditionally depended on the apprenticeship model of“see one,do one,teach one”.However,reduced operative exposure,stricter work-hour regulations,medicolegal constraints...BACKGROUND Orthopaedic surgical education has traditionally depended on the apprenticeship model of“see one,do one,teach one”.However,reduced operative exposure,stricter work-hour regulations,medicolegal constraints,and patient safety concerns have constrained its practicality.Simulation-based training has become a reliable,safe,and cost-efficient alternative.Dry lab techniques,especially virtual and augmented reality,make up 78%of current dry lab research,whereas wet labs still set the standard for anatomical realism.AIM To evaluate the effectiveness,limitations,and future directions of wet and dry lab simulation in orthopaedic training.METHODS A scoping review was carried out across four databases-PubMed,Cochrane Library,Web of Science,and EBSCOhost-up to 2025.Medical Subject Headings included:"Orthopaedic Education","Wet Lab","Dry Lab","Simulation Training","Virtual Reality",and"Surgical Procedure".Eligible studies focused on orthopaedic or spinal surgical education,employed wet or dry lab techniques,and assessed training effectiveness.Exclusion criteria consisted of non-English publications,abstracts only,non-orthopaedic research,and studies unrelated to simulation.Two reviewers independently screened titles,abstracts,and full texts,resolving discrepancies with a third reviewer.RESULTS From 1851 records,101 studies met inclusion:78 on dry labs,7 on wet labs,4 on both.Virtual reality(VR)simulations were most common,with AI increasingly used for feedback and assessment.Cadaveric training remains the gold standard for accuracy and tactile feedback,while dry labs-especially VR-offer scalability,lower cost(40%-60%savings in five studies),and accessibility for novices.Senior residents prefer wet labs for complex tasks;juniors favour dry labs for basics.Challenges include limited transferability data,lack of standard outcome metrics,and ethical concerns about cadaver use and AI assessment.CONCLUSION Wet and dry labs each have unique strengths in orthopaedic training.A hybrid approach combining both,supported by standardised assessments and outcome studies,is most effective.Future efforts should aim for uniform reporting,integrating new technologies,and policy support for hybrid curricula to enhance skills and patient care.展开更多
目的:采用网状Meta分析系统评价不同非侵入性神经调控技术对孤独症谱系障碍儿童的康复疗效。方法:系统检索中国知网、维普网、万方数据知识服务平台、中国生物医学文献数据库、Pub Med、Web of Science、Cochrane Library、Embase数据库...目的:采用网状Meta分析系统评价不同非侵入性神经调控技术对孤独症谱系障碍儿童的康复疗效。方法:系统检索中国知网、维普网、万方数据知识服务平台、中国生物医学文献数据库、Pub Med、Web of Science、Cochrane Library、Embase数据库,选择不同非侵入性神经调控技术改善孤独症谱系障碍的随机对照试验,检索时限为各数据库建库至2025年1月,由2名研究者独立进行文献筛选、数据提取并对纳入的研究进行质量评价,应用Stata 18.0软件对数据进行网状Meta分析。结果:共纳入32个随机对照试验,涉及8种干预方式。网状Meta分析结果显示,在常规康复训练的基础上,在改善孤独症行为量表评分方面,虚拟现实技术效果最佳[SMD=-12.55,95%CI(-20.85,-4.25),P<0.05],其次为θ爆发式磁刺激[SMD=-11.34,95%CI(-20.94,-1.74),P<0.05]、重复经颅磁刺激[SMD=-9.28,95%CI(-12.80,-5.77),P<0.05]、神经反馈技术[SMD=-8.75,95%CI(-15.26,-2.23),P<0.05];在儿童孤独症评定量表评分方面,虚拟现实技术改善效果最为显著[SMD=-6.36,95%CI(-9.61,-3.11),P<0.05],其次为重复经颅磁刺激[SMD=-5.98,95%CI(-9.46,-2.51),P<0.05]、神经反馈技术[SMD=-4.63,95%CI(-9.14,-0.13),P<0.05]、经皮神经电刺激[SMD=-4.14,95%CI(-5.73,-2.55),P<0.05];在孤独症治疗评估量表评分方面,神经反馈技术改善效果最显著[SMD=-16.44,95%CI(-24.10,-8.78),P<0.05],其次为虚拟现实技术[SMD=-14.09,95%CI(-22.45,-5.73),P<0.05]、重复经颅磁刺激[SMD=-12.06,95%CI(-16.45,-7.68),P<0.05]、经皮神经电刺激[SMD=-10.58,95%CI(-20.44,-0.72),P<0.05]、经颅直流电刺激[SMD=-9.75,95%CI(-18.62,-0.88),P<0.05]。结论:当前证据表明,在常规康复训练基础上,不同非侵入性神经调控技术对孤独症谱系障碍的改善效果存在差异。虚拟现实技术在改善孤独症行为量表和儿童孤独症评定量表评分方面表现出最佳效果,而神经反馈技术在孤独症治疗评估量表评分方面的改善效果最为显著。受纳入研究数量和质量的限制,上述结论尚待更多高质量研究予以验证。展开更多
近年来,基于深度学习的低光图像增强方法受Retinex理论的启发,先估计照明图调整亮度,再恢复反射率以实现低光增强。因此,通过分析低光场景反射图与参考反射图的相似度,提出一种反射先验图引导的低光图像增强网络(RP-Net)。首先,在Lab色...近年来,基于深度学习的低光图像增强方法受Retinex理论的启发,先估计照明图调整亮度,再恢复反射率以实现低光增强。因此,通过分析低光场景反射图与参考反射图的相似度,提出一种反射先验图引导的低光图像增强网络(RP-Net)。首先,在Lab色彩空间分解出相似反射图,并设计反射先验特征自适应提取器(RPAE)在主干网络以不同尺度从相似反射图中重新编码和筛选引导特征;其次,通过设计的反射先验特征引导注意力块(RPGB)将引导信息注入主干网络。此外,针对传统逐像素L1损失的局限性,从频域分析的视角出发,设计一种频域调和损失函数,以从全局频谱分布优化增强效果。在LOLv1、LOLv2和LSRW数据集上的实验结果表明,所提方法在结构相似性(SSIM)上优于现有主流方法,在LOLv2-syn和LSRW数据集上得到的峰值信噪比(PSNR)相较于Retinexformer和SAFNet(Spatial And Frequency Network)分别提高了1.29 dB和2.08 dB,并且在色彩保真和增强效果的平衡上表现出色。展开更多
基金supported by the National Key Research and Development Plan of China(No.2023YFB3406500)the National Natural Science Foundation of China(No.52475132)+2 种基金the Aeronautical Science Foundation of China(No.20200015053001)the Shaanxi Key Research Program Project,China(No.2024GX-ZDCYL-01–16)the Xi’an Key Industrial Chain Technology Research Project,China(No.2023JH-RGZNGG-0033)。
文摘Traditional beamforming techniques may not accurately locate sources in scenarios with both stationary and rotating sound sources.The existence of rotating sound sources can cause blurring in the stationary beamforming map.Current algorithms for separating different moving sound sources have limited effectiveness,leading to significant residual noise,especially when the rotating source is strong enough to mask stationary sources completely.To overcome these challenges,a novel solution utilizing a virtual rotating array in the modal domain combined with robust principal component analysis is proposed to separate sound sources with different rotational speeds.This approach,named Robust Principal Component Analysis in the Modal domain(RPCA-M),investigates the performance of convex nuclear norm and non-convex Schatten-p norm to distinguish stationary and rotating sources.By comparing the errors in Cross-Spectral Matrix(CSM)recovery and acoustic imaging across different algorithms,the effectiveness of RPCA-M in separating stationary and moving sound sources is demonstrated.Importantly,this method effectively separates sound sources,even when there are significant variations in their amplitudes at different rotation speeds.
基金supported by grants from the High-Level Oversea Talent Introduction Plan,Chinathe Special Fund for Basic Scientific Research in Central Universities of China-Doctoral Research and Innovation Fund Project,China(No.3072023CFJ0206).
文摘Rotor blade is one of the most significant components of helicopters. But due to its highspeed rotation characteristics, it is difficult to collect the vibration signals during the flight stage.Moreover, sensors are highly susceptible to damage resulting in the failure of the measurement.In order to make signal predictions for the damaged sensors, an operational modal analysis(OMA) together with the virtual sensing(VS) technology is proposed in this paper. This paper discusses two situations, i.e., mode shapes measured by all sensors(both normal and damaged) can be obtained using OMA, and mode shapes measured by some sensors(only including normal) can be obtained using OMA. For the second situation, it is necessary to use finite element(FE) analysis to supplement the missing mode shapes of damaged sensor. In order to improve the correlation between the FE model and the real structure, the FE mode shapes are corrected using the local correspondence(LC) principle and mode shapes measured by some sensors(only including normal).Then, based on the VS technology, the vibration signals of the damaged sensors during the flight stage can be accurately predicted using the identified mode shapes(obtained based on OMA and FE analysis) and the normal sensors signals. Given the high degrees of freedom(DOFs) in the FE mode shapes, this approach can also be used to predict vibration data at locations without sensors. The effectiveness and robustness of the proposed method is verified through finite element simulation, experiment as well as the actual flight test. The present work can be further used in the fault diagnosis and damage identification for rotor blade of helicopters.
文摘BACKGROUND Orthopaedic surgical education has traditionally depended on the apprenticeship model of“see one,do one,teach one”.However,reduced operative exposure,stricter work-hour regulations,medicolegal constraints,and patient safety concerns have constrained its practicality.Simulation-based training has become a reliable,safe,and cost-efficient alternative.Dry lab techniques,especially virtual and augmented reality,make up 78%of current dry lab research,whereas wet labs still set the standard for anatomical realism.AIM To evaluate the effectiveness,limitations,and future directions of wet and dry lab simulation in orthopaedic training.METHODS A scoping review was carried out across four databases-PubMed,Cochrane Library,Web of Science,and EBSCOhost-up to 2025.Medical Subject Headings included:"Orthopaedic Education","Wet Lab","Dry Lab","Simulation Training","Virtual Reality",and"Surgical Procedure".Eligible studies focused on orthopaedic or spinal surgical education,employed wet or dry lab techniques,and assessed training effectiveness.Exclusion criteria consisted of non-English publications,abstracts only,non-orthopaedic research,and studies unrelated to simulation.Two reviewers independently screened titles,abstracts,and full texts,resolving discrepancies with a third reviewer.RESULTS From 1851 records,101 studies met inclusion:78 on dry labs,7 on wet labs,4 on both.Virtual reality(VR)simulations were most common,with AI increasingly used for feedback and assessment.Cadaveric training remains the gold standard for accuracy and tactile feedback,while dry labs-especially VR-offer scalability,lower cost(40%-60%savings in five studies),and accessibility for novices.Senior residents prefer wet labs for complex tasks;juniors favour dry labs for basics.Challenges include limited transferability data,lack of standard outcome metrics,and ethical concerns about cadaver use and AI assessment.CONCLUSION Wet and dry labs each have unique strengths in orthopaedic training.A hybrid approach combining both,supported by standardised assessments and outcome studies,is most effective.Future efforts should aim for uniform reporting,integrating new technologies,and policy support for hybrid curricula to enhance skills and patient care.
文摘近年来,基于深度学习的低光图像增强方法受Retinex理论的启发,先估计照明图调整亮度,再恢复反射率以实现低光增强。因此,通过分析低光场景反射图与参考反射图的相似度,提出一种反射先验图引导的低光图像增强网络(RP-Net)。首先,在Lab色彩空间分解出相似反射图,并设计反射先验特征自适应提取器(RPAE)在主干网络以不同尺度从相似反射图中重新编码和筛选引导特征;其次,通过设计的反射先验特征引导注意力块(RPGB)将引导信息注入主干网络。此外,针对传统逐像素L1损失的局限性,从频域分析的视角出发,设计一种频域调和损失函数,以从全局频谱分布优化增强效果。在LOLv1、LOLv2和LSRW数据集上的实验结果表明,所提方法在结构相似性(SSIM)上优于现有主流方法,在LOLv2-syn和LSRW数据集上得到的峰值信噪比(PSNR)相较于Retinexformer和SAFNet(Spatial And Frequency Network)分别提高了1.29 dB和2.08 dB,并且在色彩保真和增强效果的平衡上表现出色。
