Accelerated Primal-Dual Projection Neurodynamic Approach With Time Scaling for Linear and Set Constrained Convex Optimization Problems

The Nesterov accelerated dynamical approach serves as an essential tool for addressing convex optimization problems with accelerated convergence rates.Most previous studies in this field have primarily concentrated on unconstrained smooth con-vex optimization problems.In this paper,on the basis of primal-dual dynamical approach,Nesterov accelerated dynamical approach,projection operator and directional gradient,we present two accelerated primal-dual projection neurodynamic approaches with time scaling to address convex optimization problems with smooth and nonsmooth objective functions subject to linear and set constraints,which consist of a second-order ODE(ordinary differential equation)or differential conclusion system for the primal variables and a first-order ODE for the dual vari-ables.By satisfying specific conditions for time scaling,we demonstrate that the proposed approaches have a faster conver-gence rate.This only requires assuming convexity of the objective function.We validate the effectiveness of our proposed two accel-erated primal-dual projection neurodynamic approaches through numerical experiments.

Integer multiple quantum image scaling based on NEQR and bicubic interpolation

As a branch of quantum image processing,quantum image scaling has been widely studied.However,most of the existing quantum image scaling algorithms are based on nearest-neighbor interpolation and bilinear interpolation,the quantum version of bicubic interpolation has not yet been studied.In this work,we present the first quantum image scaling scheme for bicubic interpolation based on the novel enhanced quantum representation(NEQR).Our scheme can realize synchronous enlargement and reduction of the image with the size of 2^(n)×2^(n) by integral multiple.Firstly,the image is represented by NEQR and the original image coordinates are obtained through multiple CNOT modules.Then,16 neighborhood pixels are obtained by quantum operation circuits,and the corresponding weights of these pixels are calculated by quantum arithmetic modules.Finally,a quantum matrix operation,instead of a classical convolution operation,is used to realize the sum of convolution of these pixels.Through simulation experiments and complexity analysis,we demonstrate that our scheme achieves exponential speedup over the classical bicubic interpolation algorithm,and has better effect than the quantum version of bilinear interpolation.

Direct scaling of residual displacements for bilinear and pinching oscillators

The estimation of residual displacements in a structure due to an anticipated earthquake event has increasingly become an important component of performance-based earthquake engineering because controlling these displacements plays an important role in ensuring cost-feasible or cost-effective repairs in a damaged structure after the event.An attempt is made in this study to obtain statistical estimates of constant-ductility residual displacement spectra for bilinear and pinching oscillators with 5%initial damping,directly in terms of easily available seismological,site,and model parameters.None of the available models for the bilinear and pinching oscillators are useful when design spectra for a seismic hazard at a site are not available.The statistical estimates of a residual displacement spectrum are proposed in terms of earthquake magnitude,epicentral distance,site geology parameter,and three model parameters for a given set of ductility demand and a hysteretic energy capacity coefficient in the case of bilinear and pinching models,as well as for a given set of pinching parameters for displacement and strength at the breakpoint in the case of pinching model alone.The proposed scaling model is applicable to horizontal ground motions in the western U.S.for earthquake magnitudes less than 7 or epicentral distances greater than 20 km.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

The Impact of Model Based Offset Scaling Technique on the Amplitude Variation with Offset Responses from 3D Seismic Data Acquired from the Tano Basin, Offshore Ghana

Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.

基于逆chirp scaling的合成孔径雷达卷积欺骗干扰方法

针对现有合成孔径雷达(synthetic aperture radar,SAR)欺骗干扰方法生成虚假场景时难以兼顾逼真度和低计算量的问题,提出了一种基于逆chirp scaling(CS)成像方法的SAR卷积欺骗干扰方法.该方法利用相位相乘补偿虚假场景内各虚假点的距离徙动,避免了插值运算,在降低求解干扰机频率响应函数计算量的同时,提高了虚假场景的生成精度.并且通过对欺骗模板进行线性调频Z变换(chirp-Z transform,CZT),直接从模板图像变换到实际SAR信号对应的空间频域,再次避免了插值运算,进一步降低了求解干扰机频率响应函数的计算量,提高了干扰机实时性.理论分析和仿真实验验证了相比逆omega-K(ωk)近似方法和经典的两步生成方法,本文方法能够兼顾虚假场景的生成精度和计算效率.

Scalings for the Alfvén-cyclotron instability in a bi-kappa plasma

The particle velocity distribution in space plasma usually exhibits a non-Maxwellian high-energy tail that can be well modeled by kappa distributions.In this study,we focus on the growth rates of the Alfvén-cyclotron instability driven by ion temperature anisotropy in a kappa plasma.By solving the kinetic linear dispersion equation,we explore the sensitivity of growth rates to the spectral indexκof a bi-kappa distribution under different plasma conditions,including a variety of plasma beta β_(hp) and temperature anisotropy A_(hp) values of hot protons.Furthermore,a concise,analytic scaling formula is derived that relates the dimensionless maximum growth rate to three independent variables:the spectral index and the plasma beta and temperature anisotropy of hot protons.Our results show that as theκ-value increases,the instability bandwidth narrows and the maximum growth rate increases significantly.For higherβ_(hp)and A_(hp)′the maximum instability undergoes a sharp increase as well.When our fits of dimensionless maximum growth rates are compared with solutions to kinetic linear dispersion theory,the results generally exhibit good agreement between them.Especially under the circumstances of largeκ-values and highβ_(hp)and A_(hp)′the scalings of maximum growth rates primarily accurately model the numerical solutions.Our analytic expressions can readily be used in large-scale models of the Earth’s magnetosphere to understand wave generation due to the Alfvén-cyclotron instability.

Scaling and clogging treatment of aging tunnel drainage pipes in karst areas using eco-friendly acid agent

In karst areas,the drainage pipes of aging tunnels are prone to be clogged by precipitated carbonates,resulting in lining cracking and tunnel leaking.As a result,not only the driving safety will be deteriorated,but also the water pressure on the lining might also be elevated significantly.For the structural stability and service lifespan of old tunnels,it is of great importance to remove these precipitated carbonates in time.Traditional treatment methods are often destructive to some extent or not efficient enough.This study aims to experimentally develop an eco-friendly acid-based chemical cleaning method to remove carbonate precipitations efficiently.The proposed chemical cleaning agent is an aqueous solution with strong acidity,consisting of sulfamic acid,water,and additives.The factors affecting the cleaning efficiency include the acid solubility,temperature and flow rate of the cleaning agent,as well as additives.Elevating the solution temperature to 50
C or a flow rate of no less than 0.2 m/s can improve cleaning efficiency.Although the salt effect cannot work,1 wt%of polymaleic acid as a surfactant could further promote the cleaning rate.The cleaning efficiency will increase with the flow rate in a power function.The relatively low flow rate that improves the cleaning rate considerably can avoid highpressure-induced mechanical damage to tunnel drainpipes.The waste could be easily treated to acceptable levels using commercial sewage treatment products and can also be recycled in agriculture.With the chemical cleaning,the water pressure at the arch springing of the lining will reduce with the increased radius of transverse drainpipes in a power function.The proposed acid-based cleaning method,which is highly efficient,non-or low-destructive to aging tunnels,sufficiently safe for humans,and friendly enough to the environment,will offer a promising alternative to remove the precipitated carbonates in tunnel drainpipes efficiently.

Prediction of the viscosity of natural gas at high temperature and high pressure using free-volume theory and entropy scaling

Eighteen models based on two equations of state(EoS),three viscosity models,and four mixing rules were constructed to predict the viscosities of natural gases at high temperature and high pressure(HTHP)conditions.For pure substances,the parameters of free volume(FV)and entropy scaling(ES)models were found to scale with molecular weight,which indicates that the ordered behavior of parameters of Peng-Robinson(PR)and Perturbed-Chain Statistical Associating Fluid Theory(PC-SAFT)propagates to the behavior of parameters of viscosity model.Predicting the viscosities of natural gases showed that the FV and ES models respectively combined with MIX4 and MIX2 mixing rules produced the best accuracy.Moreover,the FV models were more accurate for predicting the viscosities of natural gases than ES models at HTHP conditions,while the ES models were superior to PRFT models.The average absolute relative deviations of the best accurate three models,i.e.,PC-SAFT-FV-MIX4,tPR-FVMIX4,and PC-SAFT-ES-MIX2,were 5.66%,6.27%,and 6.50%,respectively,which was available for industrial production.Compared with the existing industrial models(corresponding states theory and LBC),the proposed three models were more accurate for modeling the viscosity of natural gas,including gas condensate.

Nonlinear perturbation of a high-order exceptional point:Skin discrete breathers and the hierarchical power-law scaling

We study the nonlinear perturbation of a high-order exceptional point(EP)of the order equal to the system site number L in a Hatano-Nelson model with unidirectional hopping and Kerr nonlinearity.Notably,we find a class of discrete breathers that aggregate to one boundary,here named as skin discrete breathers(SDBs).The nonlinear spectrum of these SDBs shows a hierarchical power-law scaling near the EP.Specifically,the response of nonlinear energy to the perturbation is given by E_(m)∝Γ~(α_(m)),whereα_(m)=3^(m-1)is the power with m=1,...,L labeling the nonlinear energy bands.This is in sharp contrast to the L-th root of a linear perturbation in general.These SDBs decay in a double-exponential manner,unlike the edge states or skin modes in linear systems,which decay exponentially.Furthermore,these SDBs can survive over the full range of nonlinearity strength and are continuously connected to the self-trapped states in the limit of large nonlinearity.They are also stable,as confirmed by a defined nonlinear fidelity of an adiabatic evolution from the stability analysis.As nonreciprocal nonlinear models may be experimentally realized in various platforms,such as the classical platform of optical waveguides,where Kerr nonlinearity is naturally present,and the quantum platform of optical lattices with Bose-Einstein condensates,our analytical results may inspire further exploration of the interplay between nonlinearity and non-Hermiticity,particularly on high-order EPs,and benchmark the relevant simulations.

Aerodynamic shape and drag scaling law of a flexible fibre in a flowing medium

The study of a flexible body immersed in a flowing medium is one of the best way to find its aerodynamic shape.This Letter revisited the problem that was first studied by Alben et al.(Nature 420,479–481,2002).To determine the aerodynamic shape of the fibre,a simpler approach is proposed.A universal drag scaling law is obtained and the universality of the Alben-Shelley-Zhang scaling law is confirmed by using dimensional analysis.A complete Maple code is provided for finding aerodynamic shape of the fibre in the flowing medium.

Quantum color image scaling based on bilinear interpolation

As a part of quantum image processing, quantum image scaling is a significant technology for the development of quantum computation. At present, most of the quantum image scaling schemes are based on grayscale images, with relatively little processing for color images. This paper proposes a quantum color image scaling scheme based on bilinear interpolation, which realizes the 2^(n_(1)) × 2^(n_(2)) quantum color image scaling. Firstly, the improved novel quantum representation of color digital images(INCQI) is employed to represent a 2^(n_(1)) × 2^(n_(2)) quantum color image, and the bilinear interpolation method for calculating pixel values of the interpolated image is presented. Then the quantum color image scaling-up and scaling-down circuits are designed by utilizing a series of quantum modules, and the complexity of the circuits is analyzed.Finally, the experimental simulation results of MATLAB based on the classical computer are given. The ultimate results demonstrate that the complexities of the scaling-up and scaling-down schemes are quadratic and linear, respectively, which are much lower than the cubic function and exponential function of other bilinear interpolation schemes.

A modified generalized scaling law for the similitude of dynamic strain in centrifuge modeling

Soil strain is the key parameter to control the elasto-plastic deformation and even the failure processes.To overcome the defect that the strain of the model soil is always smaller than that of the prototype in Iai′s generalized scaling law(GSL),a modified scaling law was proposed based on Iai′s GSL to secure the same dynamic shear strain between the centrifuge model and the prototype by modulating the amplitude and frequency of the input motion at the base.A suite of dynamic centrifuge model tests of dry sand level ground was conducted with the same overall scaling factor(λ=200)under different centrifugal accelerations by using the technique of“modeling of models”to validate the modified GSL.The test results show that the modified GSL could achieve the same dynamic strain in model as that of the prototype,leading to better modeling for geotechnical problems where dynamic strain dominates the response or failure of soils.Finally,the applicability of the proposed scaling law and possible constraints on geometry scaling due to the capability limits of existing centrifuge shaking tables are discussed.

Dynamic scaling characteristics of single-phase natural circulation based on different strain transformations

To understand the dynamical system scaling(DSS)analysis theory,the applicability of DSSβ-andω-strain transformation methods for the scaling analysis of complex loops was explored.A simplified model consisting of two loops was established based on the primary and secondary sides of a nuclear reactor,andβ-andω-strain transformation methods were used to ana-lyze the single-phase natural circulation in the primary circuit.For comparison with the traditional method,simplified DSSβ-andω-strain methods were developed based on the standard scaling criterion.The strain parameters in these four methods were modified to form multiple groups of scaled-down cases.The transient process of the natural circulation was simulated using the Relap5 code,and the variation in the dynamic flow characteristics with the strain numbers was obtained using different scaling methods.The results show that both the simplified and standard DSS methods can simulate the dynamic characteristics of natural circulation in the primary circuit.The scaled-down cases in the simplified method exhibit the same geometric scaling and correspond to small core power ratios.By contrast,different scaled-down cases in the standard DSS method correspond to different geometric scaling criteria and require more power.The dynamic process of natural circula-tion can be simulated more accurately using the standard DSS method.

Ground-state phase diagram,symmetries,excitation spectra and finite-frequency scaling of the two-mode quantum Rabi model

We investigate the two-mode quantum Rabi model(QRM)describing the interaction between a two-level atom and a two-mode cavity field.The quantum phase transitions are found when the ratioηof transition frequency of atom to frequency of cavity field approaches infinity.We apply the Schrieffer–Wolff(SW)transformation to derive the low-energy effective Hamiltonian of the two-mode QRM,thus yielding the critical point and rich phase diagram of quantum phase transitions.The phase diagram consists of four regions:a normal phase,an electric superradiant phase,a magnetic superradiant phase and an electromagnetic superradiant phase.The quantum phase transition between the normal phase and the electric(magnetic)superradiant phase is of second order and associates with the breaking of the discrete Z_(2) symmetry.On the other hand,the phase transition between the electric superradiant phase and the magnetic superradiant phase is of first order and relates to the breaking of the continuous U(1)symmetry.Several important physical quantities,for example the excitation energy and average photon number in the four phases,are derived.We find that the excitation spectra exhibit the Nambu–Goldstone mode.We calculate analytically the higher-order correction and finite-frequency exponents of relevant quantities.To confirm the validity of the low-energy effective Hamiltonians analytically derived by us,the finite-frequency scaling relation of the averaged photon numbers is calculated by numerically diagonalizing the two-mode quantum Rabi Hamiltonian.

Underwater Sea Cucumber Target Detection Based on Edge-Enhanced Scaling YOLOv4

Sea cucumber detection is widely recognized as the key to automatic culture.The underwater light environment is complex and easily obscured by mud,sand,reefs,and other underwater organisms.To date,research on sea cucumber detection has mostly concentrated on the distinction between prospective objects and the background.However,the key to proper distinction is the effective extraction of sea cucumber feature information.In this study,the edge-enhanced scaling You Only Look Once-v4(YOLOv4)(ESYv4)was proposed for sea cucumber detection.By emphasizing the target features in a way that reduced the impact of different hues and brightness values underwater on the misjudgment of sea cucumbers,a bidirectional cascade network(BDCN)was used to extract the overall edge greyscale image in the image and add up the original RGB image as the detected input.Meanwhile,the YOLOv4 model for backbone detection is scaled,and the number of parameters is reduced to 48%of the original number of parameters.Validation results of 783images indicated that the detection precision of positive sea cucumber samples reached 0.941.This improvement reflects that the algorithm is more effective to improve the edge feature information of the target.It thus contributes to the automatic multi-objective detection of underwater sea cucumbers.

A Novel Color Image Watermarking Method with Adaptive Scaling Factor Using Similarity-Based Edge Region

This study aimed to deal with three challenges:robustness,imperceptibility,and capacity in the image watermarking field.To reach a high capacity,a novel similarity-based edge detection algorithm was developed that finds more edge points than traditional techniques.The colored watermark image was created by inserting a randomly generated message on the edge points detected by this algorithm.To ensure robustness and imperceptibility,watermark and cover images were combined in the high-frequency subbands using Discrete Wavelet Transform and Singular Value Decomposition.In the watermarking stage,the watermark image was weighted by the adaptive scaling factor calculated by the standard deviation of the similarity image.According to the results,the proposed edge-based color image watermarking technique has achieved high payload capacity,imperceptibility,and robustness to all attacks.In addition,the highest performance values were obtained against rotation attack,to which sufficient robustness has not been reached in the related studies.

Call for papers: Special Issue on “Novel technologies for sustainable monitoring of polar environment upscaling from in situ observations to aerial and space-borne remote sensing”

Polar regions have received increasing scientific research attentions,in great part,due to its dramatic changes of temperature in recent decades.Satellite remote sensing data provides consistent,regional and large scales patterns of polar oceans and sea ice that are essential for polar climate modelling and operational service.One the other hand,acquiring in situ observations data is hampered by harsh environmental conditions.These ground truths are critical for remote sensing algorithms and numerical models’validation,and therefore,play important roles to improve the quality of polar weather and climate forecast and enhance better understanding of advances in polar science.

RotatS:temporal knowledge graph completion based on rotation and scaling in 3D space

As the research of knowledge graph(KG)is deepened and widely used,knowledge graph com-pletion(KGC)has attracted more and more attentions from researchers,especially in scenarios of in-telligent search,social networks and deep question and answer(Q&A).Current research mainly fo-cuses on the completion of static knowledge graphs,and the temporal information in temporal knowl-edge graphs(TKGs)is ignored.However,the temporal information is definitely very helpful for the completion.Note that existing researches on temporal knowledge graph completion are difficult to process temporal information and to integrate entities,relations and time well.In this work,a rotation and scaling(RotatS)model is proposed,which learns rotation and scaling transformations from head entity embedding to tail entity embedding in 3D spaces to capture the information of time and rela-tions in the temporal knowledge graph.The performance of the proposed RotatS model have been evaluated by comparison with several baselines under similar experimental conditions and space com-plexity on four typical knowl good graph completion datasets publicly available online.The study shows that RotatS can achieve good results in terms of prediction accuracy.

Water scaling predication for typical sandstone geothermal reservoirs in the Xi'an Depression

The Xi'an Depression in the Guanzhong Basin of western China has been suggested to contain geothermal resources that could aid China in achieving carbon neutrality and optimizing energy structure.However,the high concentration of total dissolved solids(TDS)and scale-forming ions in geothermal water from the depression causes severe scaling problems in harvesting geothermal energy.To reduce scale-related problems,accurate identification of scale types and prediction of scaling during geothermal energy utilization are crucial.This study starts with identifying the types and trends of scaling in the study area,using index-based discriminant methods and hydrogeochemical simulation to calculate and analyze the mineral saturation index of water samples from some wellheads and of reconstructed fluid samples of geothermal reservoirs.The results indicate that the scales are mostly calcium carbonate scales rather than sulfate scales as a result of temperature changes.Several portions of the geothermal water systems are found to have distinct mineral scaling components.Quartz and chalcedony are formed in low temperature areas,while carbonate minerals are in high temperature areas.Despite the low iron content of geothermal water samples from the study area,scaling is very common due to scaling-prone iron minerals.The findings can be used to evaluate geothermal drainage systems and guide anti-scaling during geothermal energy utilization in similar settings.