Repair of Second-Generation Recycled Fine Aggregate of Waste Concrete from Freeze-Thaw Environment by Carbonation Treatment

The reuse of waste recycled concrete from harsh environments has become a research hotspot in the field of construction.This study investigated the repair effect of carbonation treatment on second-generation recycled fine aggregate(SRFA)obtained from recycled fine aggregate concrete(RFAC)subjected to freeze-thaw(FT)cycles.Before and after carbonation,the properties of SRFA were evaluated.Carbonated second-generation recycled fine aggregate(CSRFA)at five substitution rates(0%,25%,50%,75%,100%)to replace SRFA was used to prepare carbonated second-generation recycled fine aggregate concrete(CSRFAC).The water absorption,porosity and mechanical properties of CSRFAC were tested,and its frost-resisting durability was evaluated.The results showed after carbonation treatment,the physical properties of SRFA was improved and met the requirements of II aggregate.The micro-hardness of the interfacial transition zone and attached mortar in CSRFA was 50.5%and 31.2%higher than that in SRFA,respectively.With the increase of CSRFA replacement rate,the water absorption and porosity of CSRFAC gradually decreased,and the mechanical properties and frost resistance of CSRFAC were gradually improved.Carbonation treatment effectively repairs the damage of SRFA caused by FT cycles and improves its application potential.

基于广义Curvelet变换和相空间特征聚类的VSP波场分离方法

垂直地震剖面(Vertical Seismic Profiling,VSP)蕴含大量的地层地质信息,是连接地震反射数据与测井资料之间的桥梁.VSP数据分辨率高,资料丰富,包括上行波、下行波、转换波等多种类型的波场,常被用于地层反射界面标定、介质衰减参数反演等.其中,如何有效地分离波场是将VSP资料应用于油气勘探的关键之一.广义Curvelet变换是一种具有多尺度、多角度的相空间变换,能够进行相空间域波场特性的提取.本文聚焦于VSP波场的上下行波分离问题,提出一种基于广义Curvelet变换和相空间特征聚类方法.首先,采用广义Curvelet变换对VSP波场进行特征提取,构造融入相空间角度信息的特征数据集;然后,利用K-means聚类算法在相空间对波场特征进行分类;最后,对分类结果进行反变换,完成VSP上下行波波场的自适应分离.为了验证方法的有效性,本文将所提出的方法用于合成数据和实际VSP数据的波场分离,并与常用的基于F-K变换的波场分离方法进行对比.处理结果表明,本文方法角度分辨率高、抗噪能力强,波场分离结果的保真保幅性好,这为后续的成像与地层的特征分析提供了重要基础资料.

基于Curvelet域的注意力机制卷积网络地震数据去噪

【目的】地震资料中的噪声严重影响着对地下地层信息的准确解释。基于地震资料中横向相关性较强的有效信号在Curvelet域分布在特定系数上,而随机噪声在Curvelet域通常会均匀分布于所有系数,可对信号进行更有效的分离。【方法】基于注意力机制卷积神经网络能够聚焦图像的重要特征,自适应提取关键信息的特点,提出一种基于Curvelet变换和注意力机制卷积神经网络(Curvelet-AU-Net)的地震数据噪声衰减方法。首先,将含噪声的地震数据通过Curvelet变换得到Curvelet变换系数,分析有效信号和噪声在Curvelet域的分布情况。其次,使用加入CBAM(Convolutional block attention module)注意力机制的U-Net网络,以含噪地震数据的Curvelet变换系数制作训练集作为输入数据,用无噪地震数据的Curvelet变换系数作为标签,通过比较实际输出与标签的损失函数值,并逐层反向传播梯度来更新网络参数,当损失函数值达到最小时,网络训练完成。最后,将测试数据输入训练好的网络模型中,再对网络输出数据进行Curvelet反变换即可得到地震数据去噪结果。【结果和结论】模拟数据与实际数据处理结果表明,与传统方法和普通卷积网络相比,该方法在不同噪声水平和尺度条件下对常见噪声(如随机噪声等)的衰减效果更优,获得的地震信号信噪比和保真度更高。由于该方法融合了Curvelet变换的稀疏表示优势和深度学习模型的自适应性,将为地震数据噪声衰减提供一种新的解决途径。

Effect of High Temperature Curing on the Frost Resistance of Recycled Aggregate Concrete and the Physical Properties of Second-Generation Recycled Coarse Aggregate under Freeze-Thaw Cycles

With the emphasis on environmental issues,the recycling of waste concrete,even recycled concrete,has become a hot spot in the field of architecture.But the repeated recycling of waste concrete used in harsh environments is still a complex problem.This paper discusses the durability and recyclability of recycled aggregate concrete(RAC)as a prefabricated material in the harsh environment,the effect of high-temperature curing(60℃,80℃,and 100℃)on the frost resistance of RAC and physical properties of the second generation recycled coarse aggregate(RCA_(2))of RAC after 300 freeze-thaw cycles were studied.The frost resistance of RAC was characterized by compressive strength,relative dynamic elastic modulus,and mass loss.As the physical properties of RCA_(2),the apparent density,water absorption,and crushing value were measured.And the SEM images of RAC after 300 freeze-thaw cycles were shown.The results indicated that the frost resistance of RAC cured at 80℃ for 7 days was comparable to that cured in the standard condition(cured for 28 days at 20℃±2℃ and 95%humidity),and the RAC cured at 100℃ was slightly worse.However,the frost resistance of RAC cured at 60℃ deteriorated seriously.The RAC cured at 80℃ for 7 days is the best.Whether after the freeze-thaw cycle or not,the RCA that curd at 60℃,80℃,and 100℃ for 7 days can also meet the requirements of Grade III RCA and be used as the aggregate of non-bearing part of prefabricated concrete components.RCA_(2) which is cured at 80℃ for 7 days had the best physical properties.

一种基于Curvelet变换的地震数据去噪方法和应用

如何去除噪声且不损失有效信号是地震数据处理中的一个重难点。实验将一种Curvelet自适应阈值去噪法应用到地震数据处理中,Curvelet变换系数由自适应阈值约束,经阈值函数映射得到估计系数,最后对所得的估计系数进行逆变换,实现地震数据去噪,有效提高地震数据信噪比。模型和实际数据处理结果表明,Curvelet自适应阈值去噪法较好地压制了噪声的同时保护有效信号,克服了F-X域滤波产生新噪声的缺陷,取得了较好去噪效果。

Multiple Matching Attenuation Based on Curvelet Domain Extended Filtering

The paper develops a multiple matching attenuation method based on extended filtering in the curvelet domain,which combines the traditional Wiener filtering method with the matching attenuation method in curvelet domain.Firstly,the method uses the predicted multiple data to generate the Hilbert transform records,time derivative records and time derivative records of Hilbert transform.Then,the above records are transformed into the curvelet domain and multiple matching attenuation based on least squares extended filtering is performed.Finally,the attenuation results are transformed back into the time-space domain.Tests on the model data and field data show that the method proposed in the paper effectively suppress the multiples while preserving the primaries well.Furthermore,it has higher accuracy in eliminating multiple reflections,which is more suitable for the multiple attenuation tasks in the areas with complex structures compared to the time-space domain extended filtering method and the conventional curvelet transform method.

Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

基于先验信息约束的Curvelet域地震数据POCS插值方法

由于野外采集环境的限制,常常无法采集得到完整规则的野外地震数据,为后续地震处理、解释工作的顺利进行,需要进行地震数据重构。凸集投影(POCS)方法利用地震波形在Curvelet域的稀疏特性,可以重构出高信噪比地震数据,该迭代算法稳定,其收敛速度较快。但在地震数据恢复的时候,由于直达波和炮集上部空白区域的影响,随着迭代的进行,重构数据中噪声干扰越来越严重,导致最终恢复的地震数据信噪比较低。本文在实现POCS迭代阈值算法基础上,引入先验信息约束的思想对算法进行优化。通过先进行坐标映射的方法进行炮集插值,然后将其作为先验信息约束进行插值,可以有效地压制迭代噪音对重构地震波形数据的影响。通过合成地震炮记录与实际炮集进行测试,结果表明本文提出的改进方法可以明显改善重构地震数据的信噪比,并提高地震波场同相轴的连续性。

The Second-generation Wavelet Transform and its Application in Denoising of Seismic Data

This paper discusses the principle and procedures of the second-generation wavelet transform and its application to the denoising of seismic data. Based on lifting steps, it is a flexible wavelet construction method using linear and nonlinear spatial prediction and operators to implement the wavelet transform and to make it reversible. The lifting scheme transform -includes three steps： split, predict, and update. Deslauriers-Dubuc （4, 2） wavelet transforms are used to process both synthetic and real data in our second-generation wavelet transform. The processing results show that random noise is effectively suppressed and the signal to noise ratio improves remarkably. The lifting wavelet transform is an efficient algorithm.

基于Fhyp-curvelet钢筋抑制的灾害目标成像方法

为了提高路面的强度和耐用性,一般会在路面下进行钢筋加固,钢筋回波与待检测的灾害目标回波在时空域上可能会产生严重的混叠。为了对灾害目标进行有效的检测与成像,文中提出一种钢筋回波干扰下的灾害目标成像方法。首先,利用快速双曲线曲波变换将雷达接收信号变换到多个不同的尺度空间,在该空间中所有目标的能量是聚集的且相互分离;然后,进行钢筋回波滤波,将灾害目标的回波数据重构到时空域后再变换到波数域,在该域下考虑多层地下介质中电磁波波速变化的特点;最后,再反变换到时空域进行灾害目标成像。文中所提成像方法均采用快速处理算法,提高了运算速度,且适用于多层地下介质中钢筋回波干扰下的灾害目标成像。仿真结果证明了本文所提算法的有效性。

Second-generation wavelet finite element based on the lifting scheme for GPR simulation

Ground-penetrating radar(GPR)is a highly efficient,fast and non-destructive exploration method for shallow surfaces.High-precision numerical simulation method is employed to improve the interpretation precision of detection.Second-generation wavelet finite element is introduced into the forward modeling of the GPR.As the finite element basis function,the second-generation wavelet scaling function constructed by the scheme is characterized as having multiple scales and resolutions.The function can change the analytical scale arbitrarily according to actual needs.We can adopt a small analysis scale at a large gradient to improve the precision of analysis while adopting a large analytical scale at a small gradient to improve the efficiency of analysis.This approach is beneficial to capture the local mutation characteristics of the solution and improve the resolution without changing mesh subdivision to realize the efficient solution of the forward GPR problem.The algorithm is applied to the numerical simulation of line current radiation source and tunnel non-dense lining model with analytical solutions.Result show that the solution results of the secondgeneration wavelet finite element are in agreement with the analytical solutions and the conventional finite element solutions,thereby verifying the accuracy of the second-generation wavelet finite element algorithm.Furthermore,the second-generation wavelet finite element algorithm can change the analysis scale arbitrarily according to the actual problem without subdividing grids again.The adaptive algorithm is superior to traditional scheme in grid refinement and basis function order increase,which makes this algorithm suitable for solving complex GPR forward-modeling problems with large gradient and singularity.

Alternating-current losses in two-layer superconducting cables consisting of second-generation superconductors coated by U-shaped ferromagnetic materials

Alternating-current losses in a two-layer superconducting cable, each layer being composed of 15 closely-spaced rectangular wires made up of second-generation superconductors when the ends of wires are coated by either a non-magnetic or strong ferromagnetic material having a U profile is numerically investigated. Computations are carried out through the finite-element method. The alternating-current losses do not increase significantly if the relative permeability of the coating is increased three orders of magnitude, provided that the current amplitude is less than half of the critical current in a superconducting wire. However, the losses are much higher for ferromagnetic coating if the amplitude of the applied current oscillating at 50 Hz is close to the critical current. The ferromagnetic coating is seen to accumulate the magnetic field lines normally on its surfaces, while the field lines are parallel to the long axes of the wires, leading to more significant flux penetration in the coated regions. This facilitates a uniform low-loss current flow in the uncoated regions of the wires. In contrast, coating with a non-magnetic material gives rise to a considerably smaller current flow in the uncoated regions, whereas the low-loss flow is maintained in the coated regions. Moreover, the current flows in opposite directions in the coated and uncoated regions, where the direction in each region is converse for the two materials.

Mutagenic Effects of Spaceflight Treatment on the Second-Generation Seeds of Alfalfa

[ Objective] To explore the effects of spaceflight on the second-generation seeds of alfalfa and provide a theoretical basis for mutation breeding. [Method] The seeds of Medicago stavia L. lines no. 1, no. 2 and no. 4 were carried into space by the Shijian-8 seed breeding satellite for a 15-d spaceflight treatment. After returning to the ground, seedlings were transplanted to field. Traits of the second-generation seeds of alfalfa were evaluated. [Result] The 1 000-grain weight of the second-generation seeds were 5% -9% significantly higher than that the control （P 〈 0.05）. The germination rate, seedling weight, shoot length and root length were significantly increased （P 〈 0.05）. The hard seed rate and the rate of moldy seeds were significantly decreased （ P 〈 0.05）. However, the rate of dead seeds was increased. [ Conclusion] Spaceflight treatment has positive mutagenic effects on the second-generation seeds of alfalfa.

Role of Second-Generation Drug-Eluting Stents and Bypass Grafting in Coronary Artery Disease:A Systematic Review and Meta-analysis

Background:The safety and efficacy of coronary artery bypass grafting(CABG)and second-generation drug-eluting stents(DESs)in patients with coronary artery disease(CAD)remain controversial.Therefore we aimed to compare the outcomes of CAD patients treated with CABG and second-generation DESs.Methods:We systematically searched the PubMed,Cochrane Library,Ovid,and Elsevier databases.Studies comparing second-generation DESs with CABG in CAD patients were included.RevMan 5.3 was used to extract and pool the data from the applicable studies.Results:Six trials(N=6604 participants)were included in this meta-analysis.Among all of the CAD patients,second-generation DESs were associated with no differences in the risks of all-cause death[risk ratio(RR)1.18,95% confi dence interval(CI)0.98–1.43,P=0.09],cardiovascular death(RR 1.14,95% CI 0.81–1.59,P=0.45),myocardial infarction(RR 1.22,95% CI 0.98–1.54,P=0.08),and stroke(RR 0.83,95% CI 0.59–1.17,P=0.29),but increased the risks of revascularization(RR 1.95,95% CI 1.66–2.30,P<0.001)and major adverse cardiac and cerebrovascular events(RR 1.72,95% CI:1.31–2.26,P<0.001)when compared with CABG.Conclusions:In the treatment of CAD patients,second-generation DESs was not associated with increased risks of all-cause death,cardiovascular death,myocardial infarction,and stroke,but increased the risks of revascularization and major adverse cardiac and cerebrovascular events when compared with CABG.

基于curvelet变换的红外图像去噪方法研究

针对传统小波算法所生成的可分离小波只具有有限的方向,不利于图像去噪,本文采用curvelet算法对红外图像进行去噪处理.该方法以小波变换为基础对图像进行分解,在分解所得的一系列小波子带中,以定义的平滑窗函数对曲线边缘进行平滑分割,再对平滑分割处理的每个子块进行Ridgelet变换.最后将小波阈值范围外的系数置零,以curvelet逆变换对原始图像进行重构.红外图像的去噪实验表明,本文算法有效可行,相比传统的小波算法、SVD算法,本算法能获得更高的PSNR数值,去噪效果更佳.

Thirty years of scientific research on second-generation antipsychotic drugs in Japan: A bibliometric analysis

Aims:Research on second-generation antipsychotic drugs (SGAs) has experienced great development in last decades.We did a bibliometric study on the scientific publications on SGAs in Japan.Methods: With theEMBASEandMEDLINEdatabases, we chose papers published from Japan with SGA descriptors. Price’s law and Bradford’s law has been used as bibliometric indicators for quantitating production and dispersion, respectively, of published papers on SGAs. We also calculated the participation index of different countries, and correlated those bibliometric data with some social and health data from Japan (such as totalper capitaexpenditure on health and gross domestic expenditure on research and development). Results: A sum of 669 original documents were published from Japan from 1982 to 2011. Those results fulfilled Price’s law, with scientific production on SGAs showing exponential growth (correlation coefficientr= 0.9261, as against anr= 0.8709 after linear adjustment). The most studied SGAs in Japan wererisperidone (n= 192), aripiprazole (n= 109), and olanzapine (n= 106). Division of documents into Bradford zones yielded a nucleus occupied exclusively by theProgress in Neuro-Psychopharmacology and Biological Psychiatry(49 articles). Those publications were in 157 different journals. Seven of the first 10 frequently used journals had an impact factor of being greater than 3. Conclusions: The SGA publications in Japan have been through exponential growth over the studied period, without evidence of reaching a saturation point.

Super-Resolution Based on Curvelet Transform and Sparse Representation

Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution algorithm.It uses the nonlocal mean filter as a prior step to produce a denoised image.The proposed algorithm is based on curvelet transform.It converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands.In parallel,we applied sparse representation with over complete dictionary for the denoised image.The proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher resolution.The experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced edges.The comparison study shows that the proposed super-resolution algorithm outperforms the state-of-the-art.The mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.

An Optimised Defensive Technique to Recognize Adversarial Iris Images Using Curvelet Transform

Deep Learning is one of the most popular computer science techniques,with applications in natural language processing,image processing,pattern iden-tification,and various otherfields.Despite the success of these deep learning algorithms in multiple scenarios,such as spam detection,malware detection,object detection and tracking,face recognition,and automatic driving,these algo-rithms and their associated training data are rather vulnerable to numerous security threats.These threats ultimately result in significant performance degradation.Moreover,the supervised based learning models are affected by manipulated data known as adversarial examples,which are images with a particular level of noise that is invisible to humans.Adversarial inputs are introduced to purposefully confuse a neural network,restricting its use in sensitive application areas such as bio-metrics applications.In this paper,an optimized defending approach is proposed to recognize the adversarial iris examples efficiently.The Curvelet Transform Denoising method is used in this defense strategy,which examines every sub-band of the adversarial images and reproduces the image that has been changed by the attacker.The salient iris features are retrieved from the reconstructed iris image by using a pretrained Convolutional Neural Network model(VGG 16)followed by Multiclass classification.The classification is performed by using Support Vector Machine(SVM)which uses Particle Swarm Optimization method(PSO-SVM).The proposed system is tested when classifying the adversarial iris images affected by various adversarial attacks such as FGSM,iGSM,and Deep-fool methods.An experimental result on benchmark iris dataset,namely IITD,produces excellent outcomes with the highest accuracy of 95.8%on average.

基于曲波域模型优化的多次波压制方法在浅地层剖面的应用

浅地层剖面主要用来探查海底的浅部构造和浅部资源,但因地震数据采集时受水深和海底地质条件等的影响,多次波极其发育,并且多次波能量强、频带宽,常规的自由表面多次波压制方法很难取得较好的压制效果。基于此,将地震数据自身褶积产生的多次波模型分为低频段模型和高频段模型,同时,将原始数据分为低频段数据和高频段数据,利用多个自适应相减法优化低频段模型;而对于高频段数据和高频段模型,将其转换到曲波域,比较其不同尺度、不同角度的差异,然后,对高频段模型进行优化,最后,将优化后高频段模型和低频段模型相加,得到最终优化后的多次波模型,再将其从原始数据中直接减去,以达到压制浅地层剖面中多次波的目的。实际资料的应用结果表明,该技术对多阶的海底相关多次波和强界面产生的多次波的压制效果较好,剖面的信噪比得到明显提高,有效信号得以凸显,有利于后期资料的应用。

基于Curvelet变换的缺失地震数据插值方法

Curvelet变换具有局部性、多尺度性和多方向性,在处理非稳态地震信号中具有较大的优势,可以利用Curvelet变换的压缩特性重建缺失的地震数据。本文首先分析了基于稀疏变换的缺失地震数据插值的基本原理,在反问题正则化理论框架下,针对L2范数约束和L1范数约束条件,分析了两种约束的差异,着重阐述了基于Curvelet变换的L1范数约束的插值方法,其优点在于对非线性同相轴不需要分窗口处理,并将凸集投影算法(POCS)引入到Curvelet变换的插值方法中,通过采用按指数规律衰减的阈值参数加快了迭代收敛的速度。理论和实际算例验证了Curvelet变换插值方法的有效性。