Lumped-equivalent circuit model for multi-stage cascaded magnetoelectric dual-tunable bandpass filter

A lumped-equivalent circuit model of a novel magnetoelectric tunable bandpass filter, which is realized in the form of multi-stage cascading between a plurality of magnetoelectric laminates, is established in this paper for convenient analysis.The multi-stage cascaded filter is degraded to the coupling microstrip filter with only one magnetoelectric laminate and then compared with the existing experiment results. The comparison reveals that the insertion loss curves predicted by the degraded circuit model are in good agreement with the experiment results and the predicted results of the electromagnetic field simulation, thus the validity of the model is verified. The model is then degraded to the two-stage cascaded magnetoelectric filter with two magnetoelectric laminates. It is revealed that if the applied external bias magnetic or electric fields on the two magnetoelectric laminates are identical, then the passband of the filter will drift under the changed external field; that is to say, the filter has the characteristics of external magnetic field tunability and electric field tunability. If the applied external bias magnetic or electric fields on two magnetoelectric laminates are different, then the passband will disappear so that the switching characteristic is achieved. When the same magnetic fields are applied to the laminates, the passband bandwidth of the two-stage cascaded magnetoelectric filter with two magnetoelectric laminates becomes nearly doubled in comparison with the passband filter which contains only one magnetoelectric laminate. The bandpass effect is also improved obviously. This research will provide a theoretical basis for the design, preparation, and application of a new high performance magnetoelectric tunable microwave device.

基于改进Cascade R-CNN算法的船舶目标检测方法

为了解决实际场景下船舶目标检测精度低的问题,本文在Cascade R-CNN算法的基础上,提出一种船舶目标检测方法 Boat R-CNN。Boat R-CNN使用带自注意力机制的Swin-Transformer Tiny网络提取图像特征,使用Soft-NMS非极大值抑制方法提升候选框过滤精度,使用Smooth_L1损失函数加速模型收敛并减少梯度爆炸情况,使用CIOU边界框回归损失提高候选框回归质量,并针对船舶目标的形状特征优化锚框的长宽比,提高锚框的生成质量。实验结果表明,Boat R-CNN算法的精度相比原版Cascade R-CNN算法提高了21.8%,相比主流Faster R-CNN算法提高了30.3%,有效提升了实际场景下的船舶目标检测精度。

基于改进Cascade R-CNN的安全帽检测算法

针对安全帽检测中,目标形状、尺度变化大,易出现漏检、误检等问题,提出了一种基于改进级联基于区域的卷积神经网络(Cascade R-CNN)的安全帽检测算法。首先,对ResNet50进行改进形成D-ResNet50,利用可变形卷积仅增加少量参数就可增大感受野的特性,对特征提取网络的C2~C5卷积层进行重塑,提高网络对目标几何变换的适应能力和特征提取能力。其次,将D-ResNet50作为主干网络引入Cascade R-CNN,形成级联目标检测器,在每个阶段对正负样本重采样,抑制误检问题。再次,对递归特征金字塔进行改进,更高效地进行多尺度特征融合,并且基于反馈信息对特征进行二次处理,增强特征表达,提高网络的分类和定位能力。最后,使用Soft-非极大值抑制(Soft-NMS)进行后处理,进一步解决漏检问题。提出的方法在Hard hat workers数据集上的AP值相比检测基线提高了3.5%,与Sparse R-CNN、TridentNet、VFnet等先进算法相比分别提升了4.7%、5.9%、2.3%等。

Pluggable multitask diffractive neural networks based on cascaded metasurfaces

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering communities.It has been considered as one of the powerful tools in promoting the fields of imaging processing and object recognition.However,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems simultaneously.To push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal plug-ins.As the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared regimes.Encouragingly,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

基于Cascade R-CNN的乳腺钼靶肿块检测算法研究

乳腺癌生物学特性复杂,恶性程度极高,位于女性恶性肿瘤发病率首位。乳腺钼靶肿块的X射线检查是早期确诊乳腺癌的重要方式。但乳腺钼靶肿块的检测尚处于早期阶段,现有的计算机辅助检测检测精度较低。针对这一问题,论文提出了一种基于Cascade R-CNN的乳腺钼靶肿块检测方法。实验使用南佛罗里达大学的乳房X光检查数据集,将乳腺钼靶肿块分为良性和恶性两类。通过在特征网络中加入注意力模块,提取了较为丰富的乳腺钼靶肿块特征。此外,论文提出了一种新的FPN网络FA-FPN,进一步提高了乳腺钼靶肿块病灶特征的提取,解决了深层网络在下采样中特征出现稀释的问题,提高了乳腺钼靶肿块的检测准确率。经实验验证,该模型在南佛罗里达大学的乳房X光检查数据集上的mAP值达到82.9%,在AP75下表现尤为突出。该方法在乳腺钼靶肿块的检测中具有良好的性能,可以提高乳腺钼靶肿块的检测精度,并在一定程度上避免了误检和漏检。

Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares.However,the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.Herein,we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal(LC)holograms.The LC holograms are used as spatially separated shares to carry secret images.The polarization of the incident light and the distance between different shares are served as secret keys,which can significantly improve the information security and capacity.Besides,the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency,which further increases the information security.In implementation,an artificial neural network(ANN)model is developed to carefully design the phase distribution of each LC hologram.With the advantage of high security,high capacity and simple configuration,our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.

Efficient Design of Multi-stage Cascade Waveband Separator

We propose a cascade system of filters for realizing a non-uniform waveband separation for optical networks. The use of such separation is required at the DEMUX stage in a optical OXC switching wavebands. The design of the system is based on optimized balanced tree, which minimizes the overall optical loss.

基于CSS-Cascade Mask R-CNN的有遮挡多片烟叶部位识别

烟叶部位信息是进行烟叶分级的重要参考信息,准确识别烟叶部位对实现烟叶智能分级具有重要意义。在实际的烟叶智能分级应用中,为了提高分级效率,需要对多片烟叶等级进行同步识别。受现行上料方式的限制,同步识别的多片烟叶间往往存在局部遮挡的问题,给烟叶的目标检测和部位识别带来挑战。该研究提出一种基于改进Cascade Mask R-CNN,融合通道、非局部和空间注意力机制,并引入柔性极大值抑制检测框交并操作与斯库拉交并比损失函数(SIoU)的目标检测与识别模型(CSS-Cascade Mask R-CNN)。该模型对Cascade Mask R-CNN进行了三方面的改进:一是在其骨干网络Resent101上同时引入通道、非局部、空间3种注意力机制,使网络更加关注未被遮挡且部位特征明显区域的显著度;二是将Cascade Mask R-CNN中的损失函数SmoothL1Loss替换为SIoU损失函数,将预测框与真实框之间的方向差异引入到模型训练中提高模型检测精度;三是在筛选候选框时将常规的非极大抑制(non-max-suppression)替换为柔性非极大抑制,以避免删除候选框造成信息丢失。试验结果表明,利用提出的模型对有遮挡多片烟叶进行检测和部位识别,检测框平均准确率均值(bbox_mAP50)达到了80.2%,与改进前的Cascade Mask R-CNN模型相比提高了7.5个百分点。提出的模型与多个主流的目标检测模型(YOLOvX、YOLOv3、YOLOv5、Mask R-CNN、Cascade R-CNN)相比,分别高7.1、10.2、5.8、9.2、8.4个百分点,尤其是对较难区分的下部烟叶优势明显,因此研究结果可以为有遮挡多片烟叶部位的检测识别提供参考。

A Multi-Stage Differential-Multifactorial Evolutionary Algorithm for Ingredient Optimization in the Copper Industry

Ingredient optimization plays a pivotal role in the copper industry,for which it is closely related to the concentrate utilization rate,stability of furnace conditions,and the quality of copper production.To acquire a practical ingredient plan,which should exhibit long duration time with sufficient utilization and feeding stability for real applications,an ingredient plan optimization model is proposed in this study to effectively guarantee continuous production and stable furnace conditions.To address the complex challenges posed by this integer programming model,including multiple coupling feeding stages,intricate constraints,and significant non-linearity,a multi-stage differential-multifactorial evolution algorithm is developed.In the proposed algorithm,the differential evolutionary(DE)algorithm is improved in three aspects to efficiently tackle challenges when optimizing the proposed model.First,unlike traditional time-consuming serial approaches,the multifactorial evolutionary algorithm is utilized to optimize multiple complex models contained in the population of evolutionary algorithm caused by the feeding stability in a parallel manner.Second,a repair algorithm is employed to adjust infeasible ingredient lists in a timely manner.In addition,a local search strategy taking feedback from the current optima and considering the different positions of global optimum is developed to avoiding premature convergence of the differential evolutionary algorithm.Finally,the simulation experiments considering different planning horizons using real data from the copper industry in China are conducted,which demonstrates the superiority of the proposed method on feeding duration and stability compared with other commonly deployed approaches.It is practically helpful for reducing material cost as well as increasing production profit for the copper industry.

Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.

基于Local Cascade Ensemble方法的胎儿健康自动分类

为更好地自动评估胎儿宫内状态,提出一种基于local cascade ensemble(LCE)方法的胎儿健康状态分类模型。选用UCI数据集,使用ADASYN方法对不平衡数据集进行填充平衡,接着结合随机森林算法对数据特征进行选择,最后使用LCE方法对胎儿状态进行自动分类。实验结果表明,所提出模型使用的方法平均准确率、精确率、召回率和F1分数分别达到了0.9554、0.9054、0.9557和0.9290,对比传统的机器学习算法能得到更好的分类效果,有效降低了误判率。

Phase-locked single-mode terahertz quantum cascade lasers array

We demonstrated a scheme of phase-locked terahertz quantum cascade lasers(THz QCLs)array,with a single-mode pulse power of 108 mW at 13 K.The device utilizes a Talbot cavity to achieve phase locking among five ridge lasers with first-order buried distributed feedback(DFB)grating,resulting in nearly five times amplification of the single-mode power.Due to the optimum length of Talbot cavity depends on wavelength,the combination of Talbot cavity with the DFB grating leads to better power amplification than the combination with multimode Fabry-Perot(F-P)cavities.The Talbot cavity facet reflects light back to the ridge array direction and achieves self-imaging in the array,enabling phase-locked operation of ridges.We set the spacing between adjacent elements to be 220μm,much larger than the free-space wavelength,ensuring the operation of the fundamental supermode throughout the laser's dynamic range and obtaining a high-brightness far-field distribution.This scheme provides a new approach for enhancing the single-mode power of THz QCLs.

Enhanced picosecond terahertz wave generation based on cascade effects in a terahertz parametric generator

Enhanced terahertz wave generation via a Stokes cascade process has been demonstrated using picosecond pulse pumped terahertz parametric generation at 1 kHz repetition rate.Clear cascade saturation of terahertz output was observed,and the corresponding cascade-Stokes spectra were analyzed.The maximum terahertz wave average power was 22μW under a pump power of 30 W,whereas the maximum power conversion efficiency was 8×10^(-7)under a pump power of 21 W.The THz power fluctuation was measured to be about 1%in 20 min.This THz parametric source with a relatively stable output is suitable for a variety of practical applications.

Conjugated microporous polymers-scaffolded enzyme cascade systems with enhanced catalytic activity

Enhancing catalytic activity of multi-enzyme in vitro through substrate channeling effect is promis-ing yet challenging.Herein,conjugated microporous polymers(CMPs)-scaffolded integrated en-zyme cascade systems(I-ECSs)are constructed through co-entrapping glucose oxidase(GOx)and horseradish peroxidase(HRP),in which hydrogen peroxide(H_(2)O_(2))is the intermediate product.The interplay of low-resistance mass transfer pathway and appropriate pore wall-H_(2)O_(2)interactions facilitates the directed transfer of H_(2)O_(2),resulting in 2.4-fold and 5.0-fold elevation in catalytic activ-ity compared to free ECSs and separated ECSs,respectively.The substrate channeling effect could be regulated by altering the mass ratio of GOx to HRP.Besides,I-ECSs demonstrate excellent stabili-ties in harsh environments and multiple recycling.

Research on Regulation Method of Energy Storage System Based on Multi-Stage Robust Optimization

To address the scheduling problem involving energy storage systems and uncertain energy,we propose a method based on multi-stage robust optimization.This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method,which helps overcome the limitations of traditional methods in terms of time scale.The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day.To achieve this,a mathematical model is constructed to represent uncertain energy sources such as photovoltaic and wind power.The generalized Benders Decomposition method is then employed to solve the multi-stage objective optimization problem.By decomposing the problem into a series of sub-objectives,the system scale is effectively reduced,and the algorithm’s convergence ability is improved.Compared with other algorithms,the multi-stage robust optimization model has better economy and convergence ability and can be used to guide the power dispatching of uncertain energy and energy storage systems.

Enhancing the Electrocatalytic Oxidation of 5-Hydroxymethylfurfural Through Cascade Structure Tuning for Highly Stable Biomass Upgrading

Electrocatalytic 5-hydroxymethylfurfural oxidation reaction(HMFOR)provides a promising strategy to convert biomass derivative to highvalue-added chemicals.Herein,a cascade strategy is proposed to construct Pd-NiCo_(2)O_(4)electrocatalyst by Pd loading on Ni-doped Co3O4 and for highly active and stable synergistic HMF oxidation.An elevated current density of 800 mA cm^(-2)can be achieved at 1.5 V,and both Faradaic efficiency and yield of 2,5-furandicarboxylic acid remained close to 100%over 10 consecutive electrolysis.Experimental and theoretical results unveil that the introduction of Pd atoms can modulate the local electronic structure of Ni/Co,which not only balances the competitive adsorption of HMF and OH-species,but also promote the active Ni^(3+)species formation,inducing high indirect oxidation activity.We have also discovered that Ni incorporation facilitates the Co2+pre-oxidation and electrophilic OH*generation to contribute direct oxidation process.This work provides a new approach to design advanced electrocatalyst for biomass upgrading.

基于Open CASCADE的拱桥建模研究

几何引擎是三维BIM建模软件中的核心组件,对软件的性能、准确性、互操作性和用户体验等方面有着重要影响。商业软件的几何引擎虽然经过了多年的发展和优化具有较好的性能和稳定性,但常受限于软件厂商提供的API接口和商业软件所需要的系统环境,无法对底层数据进行访问或修改,灵活性较差,且不具备完全自主商业版权。基于开源几何引擎的应用在很多领域已有相关的研究成果,但在桥梁工程领域较少,基于开源几何引擎Open CASCADE对桥梁工程领域的模型构建技术进行探索,实现自主可控的桥梁工程三维建模平台,以拱桥为研究对象,结合几何引擎的模型构建技术,实现拱桥模型的创建。探索开源几何引擎在桥梁工程领域的建模实现能力,为实现更多桥型的三维建模以及基于模型更进一步的衍生应用积累经验,并为打造自主可控的桥梁工程建模软件提供参考。

Human immunodeficiency virus cascade–continuum of care stages and outcomes in a hospital in southern Brazil

BACKGROUND The human immunodeficiency virus(HIV)continuum of care cascade illustrates the 90-90-90 goals defined by the Joint United Nations Program on HIV/acquired immunodeficiency syndrome(UNAIDS).The care cascade includes the following five steps:Diagnosis,linkage to care,retention in care,adherence to antiretroviral therapy(ART),and viral suppression.AIM To elaborate the HIV cascade of patients diagnosed with HIV at the Nossa Senhora da Conceição Hospital(HNSC)and to determine possible local causes for the loss of patients between each step of the cascade.METHODS This retrospective cohort study included patients diagnosed with HIV infection from January 1,2015 to December 31,2016 and followed up until July 31,2019.The data were analyzed by IBM SPSS software version 25,and Poisson regression with simple robust variance was used to analyze variables in relation to each step of the cascade.Variables with P<0.20 were included in multivariable analysis,and P<0.05 was considered significant.Pearson’sχ^(2) test was used to compare the groups of patients followed up at the HNSC and those followed up at other sites.RESULTS The results were lower than those expected by the UNAIDS,with 94%of patients linked,91%retained,81%adhering to ART,and 84%in viral suppression.Age and site of follow-up were the variables with the highest statistical significance.A comparison showed that the cascade of patients from the HNSC had superior results than outpatients,with a significant difference in the last step of the cascade.CONCLUSION The specialized and continued care provided at the HNSC was associated with better results and was closer to the goals set by the UNAIDS.The development of the HIV cascade using local data allowed for the stratification and evaluation of risk factors associated with the losses occurring between each step of the cascade.

Coordinated Capacitor Voltage Balancing Method for Cascaded H-bridge Inverter with Supercapacitor and DC-DC Stage

Cascaded H-bridge inverter(CHBI) with supercapacitors(SCs) and dc-dc stage shows significant promise for medium to high voltage energy storage applications. This paper investigates the voltage balance of capacitors within the CHBI, including both the dc-link capacitors and SCs. Balance control over the dc-link capacitor voltages is realized by the dcdc stage in each submodule(SM), while a hybrid modulation strategy(HMS) is implemented in the H-bridge to balance the SC voltages among the SMs. Meanwhile, the dc-link voltage fluctuations are analyzed under the HMS. A virtual voltage variable is introduced to coordinate the balancing of dc-link capacitor voltages and SC voltages. Compared to the balancing method that solely considers the SC voltages, the presented method reduces the dc-link voltage fluctuations without affecting the voltage balance of SCs. Finally, both simulation and experimental results verify the effectiveness of the presented method.