Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration

In this paper, we present an adaptive two-step contourlet-wavelet iterative shrinkage/thresholding （TcwlST） algorithm for remote sensing image restoration. This algorithm can be used to deal with various linear inverse problems （LIPs）, including image deconvolution and reconstruction. This algorithm is a new version of the famous two-step iterative shrinkage/thresholding （TWIST） algorithm. First, we use the split Bregrnan Rudin-Osher-Fatemi （ROF） model, based on a sparse dictionary, to decompose the image into cartoon and texture parts, which are represented by wavelet and contourlet, respectively. Second, we use an adaptive method to estimate the regularization parameter and the shrinkage threshold. Finally, we use a linear search method to find a step length and a fast method to accelerate convergence. Results show that our method can achieve a signal-to-noise ratio improvement （ISNR） for image restoration and high convergence speed.

Quantitatively characterizing sandy soil structure altered by MICP using multi-level thresholding segmentation algorithm

The influences of biological,chemical,and flow processes on soil structure through microbially induced carbonate precipitation(MICP)are not yet fully understood.In this study,we use a multi-level thresholding segmentation algorithm,genetic algorithm(GA)enhanced Kapur entropy(KE)(GAE-KE),to accomplish quantitative characterization of sandy soil structure altered by MICP cementation.A sandy soil sample was treated using MICP method and scanned by the synchrotron radiation(SR)micro-CT with a resolution of 6.5 mm.After validation,tri-level thresholding segmentation using GAE-KE successfully separated the precipitated calcium carbonate crystals from sand particles and pores.The spatial distributions of porosity,pore structure parameters,and flow characteristics were calculated for quantitative characterization.The results offer pore-scale insights into the MICP treatment effect,and the quantitative understanding confirms the feasibility of the GAE-KE multi-level thresholding segmentation algorithm.

Infuences of Different Admixtures on the Drying Shrinkage Characteristics of Metakaolin-based Geopolymer Mortar

To investigate the influences of different admixtures on the drying shrinkage of polymer mortar in a metakaolin base,the experiments of VAE(vinyl acetate ethylene copolymer),APAM(anionic polyacrylamide)and CPAM(cationic polyacrylamide)on the drying shrinkage properties of geopolymer mortar were designed under normal temperature curing conditions.An SP-175 mortar shrinkage dilatometer was introduced to measure the dry shrinkage of geopolymer mortar.Meanwhile,the drying shrinkage properties of geopolymer mortar are exhibited by the parameters of water loss rate,drying shrinkage rate,drying shrinkage strain and drying shrinkage coefficient.The experimental data are further fitted to obtain the prediction model of dry shrinkage of geopolymer mortar,which can better reflect the relationship between dry shrinkage rate and time.Finally,the experimental results demonstrate that the dry shrinkage of geopolymer mortar can be significantly increased by adding 4%VAE admixture,meanwhile under the condition that the polymer film formed by VAE reaction can strengthen and toughen the mortar.2.5%APAM admixture and 1.5%CPAM admixture can enhance the dry shrinkage performance of geopolymer mortar in a certain range.

Effect of Shrinkage Reducing Agent and Steel Fiber on the Fluidity and Cracking Performance of Ultra-High Performance Concrete

Due to the low water-cement ratio of ultra-high-performance concrete(UHPC),fluidity and shrinkage cracking are key aspects determining the performance and durability of this type of concrete.In this study,the effects of different types of cementitious materials,chemical shrinkage-reducing agents(SRA)and steel fiber(SF)were assessed.Compared with M2-UHPC and M3-UHPC,M1-UHPC was found to have better fluidity and shrinkage cracking performance.Moreover,different SRA incorporation methods,dosage and different SF types and aspect ratios were implemented.The incorporation of SRA and SF led to a decrease in the fluidity of UHPC.SRA internal content of 1%(NSRA-1%),SRA external content of 1%(WSRA-1%),STS-0.22 and STE-0.7 decreased the fluidity of UHPC by 3.3%,8.3%,9.2%and 25%,respectively.However,SRA and SF improved the UHPC shrinkage cracking performance.NSRA-1%and STE-0.7 reduced the shrinkage value of UHPC by 40%and 60%,respectively,and increased the crack resistance by 338%and 175%,respectively.In addition,the addition of SF was observed to make the microstructure of UHPC more compact,and the compressive strength and flexural strength of 28 d were increased by 26.9%and 19.9%,respectively.

Effect of Lightweight Aggregates Incorporation on the Mechanical Properties and Shrinkage Compensation of a Cement-Ground Granulated Blast Furnace Slag-Phosphogypsum Ternary System

Shrinkage-induced cracking is a common issue in concrete structures,where the formation of cracks not only affects the aesthetic appearance of concrete but also potentially reduces its durability and strength.In this study,the effect of ceramsite sand addition on the properties of a ternary system of cement-ground granulated blast furnace slag(GGBFS)-phosphogypsum(PG)is investigated.In particular,the fluidity,rheology,hydration heat,compressive strength,autogenous shrinkage,and drying shrinkage of the considered mortar specimens are analyzed.The results indicate that an increase in PG content leads to a decrease in fluidity,higher viscosity,lower exothermic peak,and lower compressive strength.However,the shrinkage of the mortar specimens is effectively compensated.The incorporation of internal curing water from ceramsite sand improves fluidity,decreases both yield stress and viscosity,enhances the degree of hydration,and induces mortar expansion.However,the inferior mechanical properties of the ceramsite sand generally produce a decrease in the compressive strength.

Isotropic sintering shrinkage of 3D glass-ceramic nanolattices:backbone preforming and mechanical enhancement

There is a perpetual pursuit for free-form glasses and ceramics featuring outstanding mechanical properties as well as chemical and thermal resistance.It is a promising idea to shape inorganic materials in three-dimensional(3D)forms to reduce their weight while maintaining high mechanical properties.A popular strategy for the preparation of 3D inorganic materials is to mold the organic–inorganic hybrid photoresists into 3D micro-and nano-structures and remove the organic components by subsequent sintering.However,due to the discrete arrangement of inorganic components in the organic-inorganic hybrid photoresists,it remains a huge challenge to attain isotropic shrinkage during sintering.Herein,we demonstrate the isotropic sintering shrinkage by forming the consecutive–Si–O–Si–O–Zr–O–inorganic backbone in photoresists and fabricating 3D glass–ceramic nanolattices with enhanced mechanical properties.The femtosecond(fs)laser is used in two-photon polymerization(TPP)to fabricate 3D green body structures.After subsequent sintering at 1000℃,high-quality 3D glass–ceramic microstructures can be obtained with perfectly intact and smooth morphology.In-suit compression experiments and finite-element simulations reveal that octahedral-truss(oct-truss)lattices possess remarkable adeptness in bearing stress concentration and maintain the structural integrity to resist rod bending,indicating that this structure is a candidate for preparing lightweight and high stiffness glass–ceramic nanolattices.3D printing of such glasses and ceramics has significant implications in a number of industrial applications,including metamaterials,microelectromechanical systems,photonic crystals,and damage-tolerant lightweight materials.

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

How Does Urban Shrinkage Affect Land Use Efficiency?A Case Study of Shrinking Cities in Northeast China

The effect of urban shrinkage has gradually become a new topic.Theoretically,urban shrinkage may exert great influence on land use efficiency(LUE)through various urban subsystems,but there is currently limited research examining these pathways.Using the Super-SBM-Undesirable model and the Structural Equation Model(SEM),this study calculates the LUE of shrinking cities in Northeast China and simulates the process of urban shrinkage affecting LUE.To quantify the process of urban shrinkage affecting LUE,three mediation variables,namely the economy,public services,and innovation,are used as latent variables to apply SEM.The results show that urban shrinkage will affect LUE through a direct path and indirect paths.In the direct path,urban shrinkage leads to an improvement in LUE.In the indirect paths,the economy and innovation will transmit the negative effect of urban shrinkage on LUE,while public services will reverse this effect.An important contribution of this study is that it quantifies the paths of urban shrinkage affecting LUE,thereby expanding the understanding of urban shrinkage effect and laying a foundation for the sustainable development of shrinking cities.

Vehicle Abnormal Behavior Detection Based on Dense Block and Soft Thresholding

With the rapid advancement of social economies,intelligent transportation systems are gaining increasing atten-tion.Central to these systems is the detection of abnormal vehicle behavior,which remains a critical challenge due to the complexity of urban roadways and the variability of external conditions.Current research on detecting abnormal traffic behaviors is still nascent,with significant room for improvement in recognition accuracy.To address this,this research has developed a new model for recognizing abnormal traffic behaviors.This model employs the R3D network as its core architecture,incorporating a dense block to facilitate feature reuse.This approach not only enhances performance with fewer parameters and reduced computational demands but also allows for the acquisition of new features while simplifying the overall network structure.Additionally,this research integrates a self-attentive method that dynamically adjusts to the prevailing traffic conditions,optimizing the relevance of features for the task at hand.For temporal analysis,a Bi-LSTM layer is utilized to extract and learn from time-based data nuances.This research conducted a series of comparative experiments using the UCF-Crime dataset,achieving a notable accuracy of 89.30%on our test set.Our results demonstrate that our model not only operates with fewer parameters but also achieves superior recognition accuracy compared to previous models.

Analysis and Prediction Model Reinforced UHPC Shrinkage Property

This paper explores the shrinkage of reinforced UHPC under high-temperature steam curing and natural curing conditions.The results are compared with the existing shrinkage prediction models.The results show that the maximum shrinkage strain of reinforced UHPC after steam curing is 164μεand gradually becomes zero.As for natural curing,the maximum shrinkage strain is 173μεand the value stabilizes on the 10th day after pouring.This indicated that steam curing can significantly reduce shrinkage time.Compared with the plain UHPC tested in the previous literature,the structural reinforcement can significantly inhibit the UHPC shrinkage and greatly reduce the risk of cracking due to shrinkage.By comparing the results in this paper with the existing models for predicting the shrinkage strain development,it is found that the formula recommended in the French UHPC structural and technical specification is suitable for the shrinkage curve in the present paper.

基于截断p-shrinkage范数的航空发动机数据重构

针对航空发动机传感器的数据缺失问题,提出基于张量奇异值阈值(TSVT)的张量重构模型LRTC-PTNN,对航空发动机的传感器数据进行重构。LRTC-PTNN模型运用截断pshrinkage范数的方式代替原始张量迹范数作为张量秩的凸包络,并根据TSVT的特性,计算了传感器之间的相关性,选取传感器截面作为重构精度最佳的数据输入方向,使用交替乘子法实现LRTCPTNN算法。选取NASA提供的PHM2008数据集进行实验,对数据集进行标准化,并在重构后进行恢复,将多个时间序列个数相近的发动机传感器数据构建为高维张量的形式,设置2种传感器的数据缺失场景进行实验,结果表明:重构后数据的均方根误差和平均绝对百分比误差范围分别为2.10~13.13和0.32~1.49,LRTC-PTNN模型优于现有的基线模型,且在极端情况下有较强的鲁棒性。

Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

Correlation analysis between the Aral Sea shrinkage and the Amu Darya River

The shrinkage of the Aral Sea,which is closely related to the Amu Darya River,strongly affects the sustainability of the local natural ecosystem,agricultural production,and human well-being.In this study,we used the Bayesian Estimator of Abrupt change,Seasonal change,and Trend(BEAST)model to detect the historical change points in the variation of the Aral Sea and the Amu Darya River and analyse the causes of the Aral Sea shrinkage during the 1950–2016 period.Further,we applied multifractal detrend cross-correlation analysis(MF-DCCA)and quantitative analysis to investigate the responses of the Aral Sea to the runoff in the Amu Darya River,which is the main source of recharge to the Aral Sea.Our results showed that two significant trend change points in the water volume change of the Aral Sea occurred,in 1961 and 1974.Before 1961,the water volume in the Aral Sea was stable,after which it began to shrink,with a shrinkage rate fluctuating around 15.21 km3/a.After 1974,the water volume of the Aral Sea decreased substantially at a rate of up to 48.97 km3/a,which was the highest value recorded in this study.In addition,although the response of the Aral Sea's water volume to its recharge runoff demonstrated a complex non-linear relationship,the replenishment of the Aral Sea by the runoff in the lower reaches of the Amu Darya River was identified as the dominant factor affecting the Aral Sea shrinkage.Based on the scenario analyses,we concluded that it is possible to slow down the retreat of the Aral Sea and restore its ecosystem by increasing the efficiency of agricultural water use,decreasing agricultural water use in the middle and lower reaches,reducing ineffective evaporation from reservoirs and wetlands,and increasing the water coming from the lower reaches of the Amu Darya River to the 1961–1973 level.These measures would maintain and stabilise the water area and water volume of the Aral Sea in a state of ecological restoration.Therefore,this study focuses on how human consumption of recharge runoff affects the Aral Sea and provides scientific perspective on its ecological conservation and sustainable development.

Spatial Distribution and Influencing Factors of Urban Shrinkage from the Perspective of Factor Flow - A Case Study of National Urban Agglomerations

This paper examines national urban agglomerations by taking factor flows as the focal point as the research subject.By dividing the stages of urban agglomeration development,a comprehensive framework of urban shrinkage is constructed,encompassing economic,population,and social shrinkage.The study explores the spatial distribution characteristics of urban shrinkage during different stages of urban agglomeration and investigates the influencing factors using a geographic detector model.The findings reveal that urban shrinkage within urban agglomerations is widely spread,predominantly in peripheral areas.During the diffusion stage,urban shrinkage is scattered,with population shrinkage concentrated in peripheral regions,economic shrinkage concentrated on old industrial cities,and social shrinkage concentrated on the northeast.The outcomes of the geographic detector model indicate that traffic flow,capital flow,information flow,node importance,network connectivity,government investment,openness,and environmental regulations all play significant roles in shaping the spatial distribution of urban shrinkage.

基于Simple Thresholding和CUSUM联合算法的L波段太阳射电流量可观测频段分析

L波段太阳射电爆发是导航系统不稳定的潜在影响因素,通过L波段内精密太阳射电流量的监测可以实时发现太阳射电爆发干扰导航事件,为此,云南天文台拟建立一个L波段多频点太阳射电监测系统。无线电环境的有效评估对于该监测系统观测数据的稳定获取至关重要。介绍了监测平台的无线电监测准备研究,通过对云南天文台L波段无线电环境进行100 h的测试,提出一种基于Simple Thresholding算法和CUSUM(Cumulative Sum)算法的改进阈值算法,遴选出介于北斗B1,B2和B3频点,全球定位系统L1和L2频点之间7个5 MHz无线电干扰较少的无线电通带,分别为1551~1555 MHz,1596~1600 MHz,1161~1165 MHz,1221~1225 MHz,1246~1250 MHz,1291~1295 MHz和1231~1235 MHz,其洁净率分别为98.329%,98.301%,98.315%,98.335%,98.224%,97.650%和98.260%,均符合太阳观测需求,为下一步接收机的设计和信号处理提供了依据。

Asymptotic Consistency of the James-Stein Shrinkage Estimator

The study explores the asymptotic consistency of the James-Stein shrinkage estimator obtained by shrinking a maximum likelihood estimator. We use Hansen’s approach to show that the James-Stein shrinkage estimator converges asymptotically to some multivariate normal distribution with shrinkage effect values. We establish that the rate of convergence is of order and rate , hence the James-Stein shrinkage estimator is -consistent. Then visualise its consistency by studying the asymptotic behaviour using simulating plots in R for the mean squared error of the maximum likelihood estimator and the shrinkage estimator. The latter graphically shows lower mean squared error as compared to that of the maximum likelihood estimator.

Early-Age Properties Development of Recycled Glass Powder Blended Cement Paste:Strengths,Shrinkage,Nanoscale Characteristics,and Environmental Analysis

Recycling solid waste in cement-based materials cannot only ease its load on the natural environment but also reduce the carbon emissions of building materials.This study aims to investigate the effect of recycled glass powder(RGP)on the early-age mechanical properties and autogenous shrinkage of cement pastes,where cement is replaced by 10%,20%and 30%of RGP.In addition,the microstructure and nano-mechanical properties of cement paste with different RGP content and water to binder(W/B)ratio were also evaluated using SEM,MIP and nanoindentation techniques.The results indicate that the early-age autogenous shrinkage decreases with the increase of RGP content and W/B ratio.While the mechanical strength deteriorates due to the addition of RGP,it can be compensated by reducing the W/B ratio.Although the addition of RGP increases the total porosity of the hardened paste,it reduces the small size porosity(<50 nm).In addition,the proportions of different types of C-S-H are changed,and the volume fraction of porosity is increased,but that of hydration products of cement paste is reduced due to the incorporation of RGP.Besides its pozzolanic activity,the mitigated shrinkage deformation that RGP is generating in cement pastes is encouraging for its use as a novel supplementary cementitious material that reduces the early-age cracking risk of cement-based materials.Meanwhile,the life cycle assessments indicate that the RGP-cement component is an economical and eco-friendly novel engineering material.

The Mechanism Behind Urban Population Growth and Shrinkage from the Perspective of Urban Network Externalities

Urban shrinkage is a global phenomenon,and it will coexist with urban growth for many years.At the same time,the network connection between cities continuously improved due to the construction of the transportation and information networks.However,the relationship between urban network externalities and urban population growth/shrinkage remains unclear.Therefore,based on high-speed railway(HSR)flow data,a spatial econometric model is used to explore the mechanism behind urban population growth and shrinkage from the perspective of network externalities in China.The results indicate that:1)the urban network experiences a certain clubbing effect.Growing cities that are strongly connected are concentrated along China’s main railway lines and the southeastern coastal areas,while shrinking cities that are weakly connected are distributed at the periphery of the network.2)Moreover,the network externality disregards spatial distance and together with the agglomeration externality influences the growth and shrinking of cities.3)Urban economic development still promotes the development of Chinese cities.However,the improvement of the urban economy has a negative cross-regional spillover effect on neighboring cities due to urban competition.4)Lastly,Local spillovers of urban network externalities are positive,while cross-regional ones are negative.Consequently,the government needs to promote the construction of multi-dimensional network connections between cities to promote cities’sustainable development.This study reveals the relationship between urban network externalities and urban development,enriches the theories of network externalities and urban growth/shrinkage,and provides a reference for regional coordinated development.

Exploring the Mechanical Properties,Shrinkage and Compensation Mechanism of Cement Stabilized Macadam-Steel Slag from Multiple Perspectives

Steel slag is characterized by high strength,good wear resistance and micro-expansion.This study aims at exploring the potential of steel slag in cement stabilized aggregates,mainly including mechanical properties,shrinkage and compensation mechanisms.For this purpose,the compressive strength and compressive resilient modulus of cement stabilized aggregates with different steel slag contents(CSMS)were initially investigated.Subsequently,the effects of steel slag and cement on dry shrinkage,temperature shrinkage,and total shrinkage were analyzed through a series of shrinkage test designs.Additionally,in combination with X-ray diffraction(XRD)and Scanning electron microscope(SEM),the characteristic peaks and microscopic images of cement,steel slag and cement-steel slag at different hydration ages were analyzed to identify the chemical substances causing the expansion volume of steel slag and reveal the compensation mechanism of CSMS.The results show that the introduction of 20%steel slag improved the mechanical properties of CSMS by 16.7%,reduced dry shrinkage by 21%,increased temperature shrinkage by 5.8%and reduced its total shrinkage by 19.2%.Compared with the hydration reaction of cement alone,the composite hydration reaction of steel slag with cement does not produce new hydrates.Furthermore,it is noteworthy that the volume expansion of the f-CaO hydration reaction in steel slag can compensate for the volume shrinkage of cement-stabilized macadam.This research can provide a solid theoretical basis for the application and promotion of steel slag in cement-stabilized macadam and reduce the possibility of shrinkage cracking.

End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels

With the rapid development of deep learning methods, the data-driven approach has shown powerful advantages over the model-driven one. In this paper, we propose an end-to-end autoencoder communication system based on Deep Residual Shrinkage Networks (DRSNs), where neural networks (DNNs) are used to implement the coding, decoding, modulation and demodulation functions of the communication system. Our proposed autoencoder communication system can better reduce the signal noise by adding an “attention mechanism” and “soft thresholding” modules and has better performance at various signal-to-noise ratios (SNR). Also, we have shown through comparative experiments that the system can operate at moderate block lengths and support different throughputs. It has been shown to work efficiently in the AWGN channel. Simulation results show that our model has a higher Bit-Error-Rate (BER) gain and greatly improved decoding performance compared to conventional modulation and classical autoencoder systems at various signal-to-noise ratios.