单向CF/PA6复合材料性能劣化及老化寿命预测

利用热压成型工艺制备出碳纤维(CF)/尼龙6(PA6)复合材料单向板,并将复合材料试样置于蒸馏水热水浴中,分别在25,60,80℃下进行不同时间的湿热老化,再对湿热老化后复合材料试样进行吸湿测试、三点弯曲测试、微观形貌表征,探究复合材料的吸湿规律、力学性能劣化规律和微观形貌变化,并对复合材料的长期寿命进行预测。结果发现,复合材料在25,60℃下的吸湿行为基本符合Fick扩散定律,而80℃下在老化最后阶段出现了背离Fick扩散定律现象。复合材料的弯曲强度随老化温度、老化时间的增加呈下降趋势,分别在25,60,80℃下老化120 d后,试样弯曲强度分别下降了22.78%,25.0%,26.25%。但是老化温度、老化时间对弯曲弹性模量无显著影响,且CF与PA6树脂之间的界面黏合性能随着温度、时间的增加逐渐变差。以绍兴2021年平均温度作为服役温度,基于加速老化测试模型和阿伦尼乌斯理论建立了CF/PA6复合材料在服役环境下剩余弯曲强度的预测模型,可预测到1 400 d后,CF/PA6复合材料的弯曲强度保留率在64.8%左右。

自成核行为在CF/PPS复合材料中的差异性与普适性

以半结晶高分子为基体的碳纤维增强热塑性复合材料,其基体的结晶行为将受到碳纤维表面性质与热历史的共同影响。当这种材料在经历反复加热冷却的热循环过程时,基体会产生自成核行为,其结晶过程变得更加复杂。通过三种T300级碳纤维分别制备了碳纤维增强聚苯硫醚(CF/PPS)复合材料,并探究了二次熔融温度与CF/PPS复合材料结晶行为的关系,揭示了不同碳纤维表面性质对CF/PPS复合材料自成核行为的影响。在此基础上阐明了CF/PPS复合材料基体自成核与CF异相成核的竞争关系。最后,通过冲压成型制得具有自成核行为的CF/PPS复合材料角片,验证了自成核对于CF/PPS复合材料二次热成型技术的有效性与重要性。

一种基于Filter Faster R-CNN的数字PCR液滴检测技术

目的研究液滴数字聚合酶链式反应(ddPCR)液滴检测技术,去除图像中灰尘、气泡、芯片表面的划痕以及微小凹陷等因素产生的异常点对结果的影响,实现高通量、稳定和准确的ddPCR液滴的自动检测。方法提出Filter Faster R-CNN ddPCR液滴检测模型。使用Faster R-CNN生成液滴预测框,之后使用异常点过滤模块(Filter)去除阳性液滴预测框中的异常点。以诺如病毒片段的质粒为模板进行ddPCR实验,建立一个ddPCR数据集,用于模型的训练(2462例,约占78.56%)和测试(672例,约占21.44%)。对异常点过滤模块的3个过滤支路在验证集上进行消融实验,通过与其他ddPCR液滴检测模型进行比较的对比实验以及进行ddPCR的绝对定量实验。结果在少尘和多尘的环境中,Filter Faster R-CNN阳性液滴准确率为98.23%和88.35%,综合指标F1分数分别达到了99.15%和99.14%,高于其他相比较的模型。独立样本T检验的结果证明,相比未添加过滤模块的网络,添加过滤模块后能够显著提示模型在多尘环境中的阳性准确率。在ddPCR绝对定量实验中,将商业化流式检测设备的结果作为标准浓度,绘制了回归线。结果显示,回归线斜率为1.0005,截距为-0.025,决定系数达到了0.9997,二者结果高度一致。结论本文提出了一种基于Filter Faster R-CNN的ddPCR液滴检测技术,为在多种环境条件下的ddPCR实验提供了鲁棒的液滴检测方法。

非贵金属催化电极Ni/C@CF的制备及绿色类Fenton性能

传统芬顿(Fenton)法利用Fe2+催化H_(2)O_(2)产生具有强氧化性的羟基自由基(•OH),可以高效氧化降解水中有机污染物,但其操作pH范围窄(pH≈3)和易产生铁絮凝沉淀的缺点限制了其应用发展.原子氢H*作为一种单电子供体,可以将电子快速转移到H_(2)O_(2)中,生成•OH,适用于广泛的pH值,没有铁污泥产生,是一种新型高效绿色芬顿法.然而,原子H*更易相互结合形成H_(2),极不稳定,因此,探索合适的电催化剂对H*绿色Fenton的应用起着至关重要的作用.本文以炭黑作为载体,通过液相还原法制备了具有催化活性高、性能稳定的Ni/C@碳毡(Ni/C@CF)非贵金属电催化材料,制备的Ni纳米粒子均匀分散在炭黑上.以此电极材料为阴极,构建绿色Fenton催化体系,能够催化H_(2)O和H+生成H*,进而催化H_(2)O_(2)产生•OH,高效降解去除水中抗生素污染物.通过调节制备方法、电压、溶液pH值及外加氧化剂量,确定了该体系下的最佳反应条件.

Estimating the subsolar magnetopause position from soft X-ray images using a low-pass image filter

The Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)missions will image the Earth’s dayside magneto pause and cusps in soft X-rays after their respective launches in the near future,to specify glo bal magnetic reconnection modes for varying solar wind conditions.To suppo rt the success of these scientific missions,it is critical to develop techniques that extract the magnetopause locations from the observed soft X-ray images.In this research,we introduce a new geometric equation that calculates the subsolar magnetopause position(RS)from a satellite position,the look direction of the instrument,and the angle at which the X-ray emission is maximized.Two assumptions are used in this method:(1)The look direction where soft X-ray emissions are maximized lies tangent to the magnetopause,and(2)the magnetopause surface near the subsolar point is almost spherical and thus RSis nea rly equal to the radius of the magneto pause curvature.We create synthetic soft X-ray images by using the Open Geospace General Circulation Model(OpenGGCM)global magnetohydrodynamic model,the galactic background,the instrument point spread function,and Poisson noise.We then apply the fast Fourier transform and Gaussian low-pass filte rs to the synthetic images to re move noise and obtain accurate look angles for the soft X-ray pea ks.From the filte red images,we calculate RS and its accuracy for different LEXI locations,look directions,and solar wind densities by using the OpenGGCM subsolar magnetopause location as ground truth.Our method estimates RS with an accuracy of<0.3 RE when the solar wind density exceeds>10 cm-3.The accuracy improves for greater solar wind densities and during southward interplanetary magnetic fields.The method ca ptures the magnetopause motion during southwa rd interplaneta ry magnetic field turnings.Consequently,the technique will enable quantitative analysis of the magnetopause motion and help reveal the dayside reconnection modes for dynamic solar wind conditions.This technique will suppo rt the LEXI and SMILE missions in achieving their scientific o bjectives.

中子符合计数研究中^(252)Cf源强的影响分析

具有中子多重性的^(252)Cf源是中子符合计数实验测量和装置标定的首选。本文利用数值模拟手段研究^(252)Cf源强对中子符合计数的影响。根据中子符合计数实验的数据处理方法,通过生成带自发裂变事件标签的中子脉冲序列,按照移位寄存器的数据处理逻辑对中子脉冲序列进行处理,给出中子符合测量模拟结果。基于对不同源强下前景(R+A)和背景(A)的中子多重性分布的模拟结果,定量分析源强对真符合计数在前景测量结果中占比的影响。结果表明,在^(252)Cf源强为每秒几百或几千个中子时,真符合计数在R+A的中子符合计数中占绝对或明显优势(超过50%),推荐作为实验的中子源;源强为每秒几万个中子时,真符合计数在R+A的中子符合计数中占比约10%;源强达到每秒几十万个中子时,真符合计数淹没在偶然符合计数中(占比约1%),不建议用作实验的中子源。本文的研究可以为实验的源强选择、实验数据的有效性检查提供一个参考。

N610CF钢双丝埋弧焊接头CTOD试验研究

依据GB/T 21143—2014,对不同焊接速度的双丝埋弧焊(v_(1)=60 cm/min,v_(2)=70 cm/min,v_(3)=80 cm/min)下的N610CF钢焊接接头焊缝区和热影响区(HAZ)进行了CTOD试验研究,并对断口的微观形貌和金相组织进行了分析。结果表明,采用单丝埋弧焊打底+焊接速度为60 cm/min的串联式双丝埋弧焊填充和盖面所得到的焊接接头(HAZ和焊缝区)的CTOD平均值为0.683 mm,离散系数为9.6%,综合的断裂韧性最好。

不同埋弧焊工艺N610CF钢焊接接头的微型剪切试验

目前,国内水电站N610CF钢常采用单丝埋弧焊进行焊接。相比于双丝埋弧焊,单丝埋弧焊的焊接生产效率较低,焊剂消耗较多。文中采用微型剪切试验对N610CF钢分别对单丝埋弧焊和不同焊接速度(ν_(1)=60 cm/min,ν_(2)=70 cm/min,ν_(3)=80 cm/min)的双丝埋弧焊下的焊接接头进行了接头力学不均匀性研究。结果表明:双丝埋弧焊接头与单丝埋弧焊接头一样,在具有良好塑性的同时又能兼具较高的抗剪强度。

Recursive Filtering for Stochastic Systems With Filter-and-Forward Successive Relays

In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the measurement.In the successive relay,two cooperative relay nodes are adopted to forward the signals alternatively,thereby existing switching characteristics and inter-relay interferences(IRI).Since the filter-and-forward scheme is employed,the signal received by the relay is retransmitted after it passes through a linear filter.The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays.First,a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR.Then,novel filter structures with switching parameters are constructed for both FFSR and stochastic systems.With the help of the inductive method,filtering error covariances are presented in the form of coupled difference equations.Next,the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances.Moreover,the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance.Finally,the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.

Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems:Tackling Multiplicative Noises and Missing Measurements

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable.Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense.To this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented system.Subsequently,we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters.With the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain parameters.Finally,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

A multi-scale second-order autoregressive recursive filter approach for the sea ice concentration analysis

To effectively extract multi-scale information from observation data and improve computational efficiency,a multi-scale second-order autoregressive recursive filter(MSRF)method is designed.The second-order autoregressive filter used in this study has been attempted to replace the traditional first-order recursive filter used in spatial multi-scale recursive filter(SMRF)method.The experimental results indicate that the MSRF scheme successfully extracts various scale information resolved by observations.Moreover,compared with the SMRF scheme,the MSRF scheme improves computational accuracy and efficiency to some extent.The MSRF scheme can not only propagate to a longer distance without the attenuation of innovation,but also reduce the mean absolute deviation between the reconstructed sea ice concentration results and observations reduced by about 3.2%compared to the SMRF scheme.On the other hand,compared with traditional first-order recursive filters using in the SMRF scheme that multiple filters are executed,the MSRF scheme only needs to perform two filter processes in one iteration,greatly improving filtering efficiency.In the two-dimensional experiment of sea ice concentration,the calculation time of the MSRF scheme is only 1/7 of that of SMRF scheme.This means that the MSRF scheme can achieve better performance with less computational cost,which is of great significance for further application in real-time ocean or sea ice data assimilation systems in the future.

3D robust anisotropic diffusion filtering algorithm for sparse view neutron computed tomography 3D image reconstruction

The most critical part of a neutron computed tomography(NCT) system is the image processing algorithm,which directly affects the quality and speed of the reconstructed images.Various types of noise in the system can degrade the quality of the reconstructed images.Therefore,to improve the quality of the reconstructed images of NCT systems,efficient image processing algorithms must be used.The anisotropic diffusion filtering(ADF) algorithm can not only effectively suppress the noise in the projection data,but also preserve the image edge structure information by reducing the diffusion at the image edges.Therefore,we propose the application of the ADF algorithm for NCT image reconstruction.To compare the performance of different algorithms in NCT systems,we reconstructed images using the ordered subset simultaneous algebraic reconstruction technique(OS-SART) algorithm with different regular terms as image processing algorithms.In the iterative reconstruction,we selected two image processing algorithms,the Total Variation and split Bregman solved total variation algorithms,for comparison with the performance of the ADF algorithm.Additionally,the filtered back-projection algorithm was used for comparison with an iterative algorithm.By reconstructing the projection data of the numerical and clock models,we compared and analyzed the effects of each algorithm applied in the NCT system.Based on the reconstruction results,OS-SART-ADF outperformed the other algorithms in terms of denoising,preserving the edge structure,and suppressing artifacts.For example,when the 3D Shepp–Logan was reconstructed at 25 views,the root mean square error of OS-SART-ADF was the smallest among the four iterative algorithms,at only 0.0292.The universal quality index,mean structural similarity,and correlation coefficient of the reconstructed image were the largest among all algorithms,with values of 0.9877,0.9878,and 0.9887,respectively.

Optimize Purcell filter design for reducing influence of fabrication variation

To protect superconducting qubits and enable rapid readout, optimally designed Purcell filters are essential. To suppress the off-resonant driving of untargeted readout resonators, individual Purcell filters are used for each readout resonator.However, achieving consistent frequency between a readout resonator and a Purcell filter is a significant challenge. A systematic computational analysis is conducted to investigate how fabrication variation affects filter performance, through focusing on the coupling capacitor structure and coplanar waveguide(CPW) transmission line specifications. The results indicate that the T-type enclosing capacitor(EC), which exhibits lower structural sensitivity, is more advantageous for achieving target capacitance than the C-type EC and the interdigital capacitor(IDC). By utilizing a large-sized CPW with the T-type EC structure, fluctuations in the effective coupling strength can be reduced to 10%, given typical micro-nanofabrication variances. The numerical simulations presented in this work minimize the influence of fabrication deviations, thereby significantly improving the reliability of Purcell filter designs.

Electrically controllable spin filtering in zigzag phosphorene nanoribbon based normal–antiferromagnet–normal junctions

We investigated the electric controllable spin-filtering effect in a zigzag phosphorene nanoribbon(ZPNR) based normal–antiferromagnet–normal junction. Two ferromagnets are closely coupled to the edges of the nanoribbon and form the edge-to-edge antiferromagnetism. Under an in-plane electric field, the two degenerate edge bands of the edge-to-edge antiferromagnet split into four spin-polarized sub-bands and a 100% spin-polarized current can be easily induced with the maximal conductance 2e~2/h. The spin polarization changes with the strength of the electric field and the exchange field,and changes sign at opposite electric fields. The spin-polarized current switches from one edge to the other by reversing the direction of the electric field. The edge current can also be controlled spatially by changing the electric potential of the scattering region. The manipulation of edge current is useful in spin-transfer-torque magnetic random-access memory and provides a practical way to develop controllable spintronic devices.

A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

An improved particle filter indoor fusion positioning approach based on Wi-Fi/PDR/geomagnetic field

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning(PDR)and geomagnetic technology have the problems of large initial position error,low sensor accuracy,and geomagnetic mismatch.In this study,a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed,where Wi-Fi,PDR,and geomagnetic signals are integrated to improve indoor positioning performances.One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm.During the positioning process,an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively.The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected,which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm.In addition,this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving,which satisfies the real-time requirement of our fusion positioning approach.Through experimental verification,the average positioning accuracy of the proposed approach reaches 1.59 m,which improves 33.2%compared with the existing particle filter fusion positioning algorithms.

Adaptive H_(∞)Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.

An anti-aliasing filtering of quantum images in spatial domain using a pyramid structure

As a part of quantum image processing,quantum image filtering is a crucial technology in the development of quantum computing.Low-pass filtering can effectively achieve anti-aliasing effects on images.Currently,most quantum image filterings are based on classical domains and grayscale images,and there are relatively fewer studies on anti-aliasing in the quantum domain.This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain.It achieves the effect of anti-aliasing filtering on quantum images during the scaling process.First,we use the novel enhanced quantum representation(NEQR)and the improved quantum representation of color images(INCQI)to represent classical images.Since aliasing phenomena are more pronounced when images are scaled down,this paper focuses only on the anti-aliasing effects in the case of reduction.Subsequently,we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image,achieving the anti-aliasing effect.The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size.Finally,the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling,applying anti-aliasing filtering to the images results in smoother and clearer outputs.Additionally,the anti-aliasing filtering allows for manual intervention to select the desired level of image smoothness.

AAK Theorem and a Design of Multidimensional BIBO Stable Filters

Rational approximation theory occupies a significant place in signal processing and systems theory. This research paper proposes an optimal design of BIBO stable multidimensional Infinite Impulse Response filters with a realizable (rational) transfer function thanks to the Adamjan, Arov and Krein (AAK) theorem. It is well known that the one dimensional AAK results give the best approximation of a polynomial as a rational function in the Hankel semi norm. We suppose that the Hankel matrix associated to the transfer function has a finite rank.