A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

Multi-blade rubbing characteristics of the shaft-disk-blade-casing system with large rotation

Blade rubbing faults cause detrimental impact on the operation of aeroengines. Most of the existing studies on blade rubbing in the shaft-disk-blade-casing(SDBC) system have overlooked the elastic deformation of the blade, while some only consider the whirl of the rotor, neglecting its spin. To address these limitations, this paper proposes a dynamic model with large rotation for the SDBC system. The model incorporates the spin and whirl of the rotor, enabling the realistic reproduction of multiblade rubbing faults. To verify the accuracy of the SDBC model with large rotation and demonstrate its capability to effectively consider the rotational effects such as the centrifugal stiffening and gyroscopic effects, the natural characteristics and dynamic responses of the proposed model are compared with those obtained from reported research and experimental results. Furthermore, the effects of the rotating speed, contact stiffness,and blade number on the dynamic characteristics of the SDBC system with multi-blade rubbing are investigated. The results indicate that the phase angle between the rotor deflection and the unbalance excitation force increases with the increasing rotating speed,which significantly influences the rubbing penetration of each blade. The natural frequency of the SDBC system with rubbing constrain can be observed in the acceleration response of the casing and the torsional response of the shaft, and the frequency is related to the contact stiffness. Moreover, the vibration amplitude increases significantly with the product of the blade number under rubbing, and the rotating frequency approaches the natural frequency of the SDBC system. The proposed model can provide valuable insight for the fault diagnosis of rubbing in bladed rotating machinery.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Integrated analysis of plasma rotation effect on HL-3 hybrid scenario

The hybrid scenario,which has good confinement and moderate MHD instabilities,is a proposed operation scenario for international thermonuclear experimental reactor(ITER).In this work,the effect of plasma rotation on the HL-3 hybrid scenario is analyzed with the integrated modeling framework OMFIT.The results show that toroidal rotation has no obvious effect on confinement with a high line averaged density of n_(bar)~(7)×10^(19)m^(-3).In this case,the ion temperature only changes from 4.7 keV to 4.4 keV with the rotation decreasing from 10^(5) rad/s to 10^(3) rad/s,which means that the turbulent heat transport is not dominant.While in the scenarios characterized by lower densities,such as n_(bar)~4×10^(19)m^(-3),turbulent transport becomes dominant in determining heat transport.The ion temperature rises from 3.8 keV to 6.1 keV in the core as the rotation velocity increases from 10^(3) rad/s to 10^(5) rad/s.Despite the ion temperature rising,the rotation velocity does not obviously affect electron temperature or density.Additionally,it is noteworthy that the variation in rotation velocity does not significantly affect the global confinement of plasma in scenarios with low density or with high density.

{1012}twin–twin intersection-induced lattice rotation and dynamic recrystallization in Mg–6Al–3Sn–2Zn alloy

This study investigated the formation mechanism of new grains due to twin–twin intersections in a coarse-grained Mg–6Al–3Sn–2Zn alloy during different strain rates of an isothermal compression.The results of electron backscattered diffraction investigations showed that the activated twins were primarily{1012}tension twins,and 60°<1010>boundaries formed due to twin–twin intersections under different strain rates.Isolated twin variants with 60°<1010>boundaries transformed into new grains through lattice rotations at a low strain rate(0.01 s^(−1)).At a high strain rate(10 s^(−1)),the regions surrounded by subgrain boundaries through high-density dislocation arrangement and the 60°<1010>boundaries transformed into new grains via dynamic recrystallization.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

A solution method for decomposing vector fields in Hamilton energy

Hamilton energy,which reflects the energy variation of systems,is one of the crucial instruments used to analyze the characteristics of dynamical systems.Here we propose a method to deduce Hamilton energy based on the existing systems.This derivation process consists of three steps:step 1,decomposing the vector field;step 2,solving the Hamilton energy function;and step 3,verifying uniqueness.In order to easily choose an appropriate decomposition method,we propose a classification criterion based on the form of system state variables,i.e.,type-I vector fields that can be directly decomposed and type-II vector fields decomposed via exterior differentiation.Moreover,exterior differentiation is used to represent the curl of low-high dimension vector fields in the process of decomposition.Finally,we exemplify the Hamilton energy function of six classical systems and analyze the relationship between Hamilton energy and dynamic behavior.This solution provides a new approach for deducing the Hamilton energy function,especially in high-dimensional systems.

Vibrational Suspension of Two Cylinders in a Rotating Liquid-Filled Cavity with a Time-Varying Rotation Rate

The dynamics of rotating hydrodynamic systems containing phase inclusions are interesting due to the related widespread occurrence in nature and technology.The influence of external force fields on rotating systems can be used to control the dynamics of inclusions of various types.Controlling inclusions is of current interest for space technologies.In low gravity,even a slight vibration effect can lead to the appearance of a force acting on phase inclusions near a solid boundary.When vibrations are applied to multiphase hydrodynamic systems,the oscillating body intensively interacts with the fluid and introduces changes in the related flow structure.Asymmetries in the fluid flow lead to the appearance of an averaged force.As a result,the body is repelled from the cavity boundary and takes a position at a certain distance from it.The vibrationally-induced movement of phase inclusions in liquids can be used to improve various technological processes(for example,when degassing and cleaning liquids from solid inclusions,mixing various components,etc.).This study presents a relevant methodology to study the averaged vibrational force acting on a pair of free cylindrical bodies near the oscillating wall of a cavity.Attention is paid to the region of moderate and low dimensionless frequencies when the size of the inclusion is consistent with the thickness of the Stokes boundary layer.The dynamics of these bodies is considered in a horizontal cylindrical cavity with a fluid undergoing modulated rotation.The average lift force of a vibrational nature is measured by the method of quasi-stationary suspension of bodies whose density differs from the density of the liquid in a static centrifugal force field.The developed technique makes it possible to determine the dependence of the lift force on vibration parameters and the distance from the oscillating boundary at which solid inclusions are located.It is shown that in the region of moderate dimensionless frequencies,the average lift force acting on an inclusion near the boundary undergoing modulated rotation almost linearly depends on the dimensionless frequency.

An Initial Perturbation Method for the Multiscale Singular Vector in Global Ensemble Prediction

Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.

Improved Twin Support Vector Machine Algorithm and Applications in Classification Problems

The distribution of data has a significant impact on the results of classification.When the distribution of one class is insignificant compared to the distribution of another class,data imbalance occurs.This will result in rising outlier values and noise.Therefore,the speed and performance of classification could be greatly affected.Given the above problems,this paper starts with the motivation and mathematical representing of classification,puts forward a new classification method based on the relationship between different classification formulations.Combined with the vector characteristics of the actual problem and the choice of matrix characteristics,we firstly analyze the orderly regression to introduce slack variables to solve the constraint problem of the lone point.Then we introduce the fuzzy factors to solve the problem of the gap between the isolated points on the basis of the support vector machine.We introduce the cost control to solve the problem of sample skew.Finally,based on the bi-boundary support vector machine,a twostep weight setting twin classifier is constructed.This can help to identify multitasks with feature-selected patterns without the need for additional optimizers,which solves the problem of large-scale classification that can’t deal effectively with the very low category distribution gap.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

Strategies for improving crop comprehensive benefits via a decision-making system based on machine learning in the rice‒rape,rice‒wheat and rice‒garlic rotation systems in Southwest China

Rice‒rape,rice‒wheat and rice‒garlic rotations are common cropping systems in Southwest China,and they have played a significant role in ensuring ecological and economic benefits(EB)and addressing the challenges of China’s food security in the region.However,the crop yields in these rotation systems are 1.25‒14.73%lower in this region than the national averages.Intelligent decision-making with machine learning can analyze the key factors for obtaining better benefits,but it has rarely been used to enhance the probability of obtaining such benefits from rotations in Southwest China.Thus,we used a data-intensive approach to construct an intelligent decision‒making system with machine learning to provide strategies for improving the benefits of rice-rape,rice-wheat,and rice-garlic rotations in Southwest China.The results show that raising the yield and partial fertilizer productivity(PFP)by increasing seed input under high fertilizer application provided the optimal benefits with a 10%probability in the rice-garlic system.Obtaining high yields and greenhouse gas(GHG)emissions by increasing the N application and reducing the K application provided suboptimal benefits with an 8%probability in the rice-rape system.Reducing N and P to enhance PFP and yield provided optimal benefits with the lowest probability(8%)in the rice‒wheat system.Based on the predictive analysis of a random forest model,the optimal benefits were obtained with fertilization regimes by reducing N by 25%and increasing P and K by 8 and 74%,respectively,in the rice-garlic system,reducing N and K by 54 and by 36%,respectively,and increasing P by 38%in rice-rape system,and reducing N by 4%and increasing P and K by 65 and 23%in rice-wheat system.These strategies could be further optimized by 17‒34%for different benefits,and all of these measures can improve the effectiveness of the crop rotation systems to varying degrees.Overall,these findings provide insights into optimal agricultural inputs for higher benefits through an intelligent decision-making system with machine learning analysis in the rice-rape,rice‒wheat,and rice-garlic systems.

Towards privacy-preserving and efficient word vector learning for lightweight IoT devices

Nowadays,Internet of Things(IoT)is widely deployed and brings great opportunities to change people's daily life.To realize more effective human-computer interaction in the IoT applications,the Question Answering(QA)systems implanted in the IoT services are supposed to improve the ability to understand natural language.Therefore,the distributed representation of words,which contains more semantic or syntactic information,has been playing a more and more important role in the QA systems.However,learning high-quality distributed word vectors requires lots of storage and computing resources,hence it cannot be deployed on the resource-constrained IoT devices.It is a good choice to outsource the data and computation to the cloud servers.Nevertheless,it could cause privacy risks to directly upload private data to the untrusted cloud.Therefore,realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging task.In this paper,we present a novel efficient word vector learning scheme over encrypted data.We first design a series of arithmetic computation protocols.Then we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted data.The proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting privacy.Security analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.

Preheating-assisted solid-state friction stir repair of Al-Mg-Si alloy plate at different rotational speeds

Additive friction stir deposition(AFSD)is a novel structural repair and manufacturing technology has become a research hotspot at home and abroad in the past five years.In this work,the microstructural evolution and mechanical performance of the Al-Mg-Si alloy plate repaired by the preheating-assisted AFSD process were investigated.To evaluate the tool rotation speed and substrate preheating for repair quality,the AFSD technique was used to additively repair 5 mm depth blind holes on 6061 aluminum alloy substrates.The results showed that preheat-assisted AFSD repair significantly improved joint bonding and joint strength compared to the control non-preheat substrate condition.Moreover,increasing rotation speed was also beneficial to improve the metallurgical bonding of the interface and avoid volume defects.Under preheating conditions,the UTS and elongation were positively correlated with rotation speed.Under the process parameters of preheated substrate and tool rotation speed of 1000 r/min,defect-free specimens could be obtained accompanied by tensile fracture occurring in the substrate rather than the repaired zone.The UTS and elongation reached the maximum values of 164.2MPa and 13.4%,which are equivalent to 99.4%and 140%of the heated substrate,respectively.

Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection

This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

Diffraction deep neural network-based classification for vector vortex beams

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum communications.However,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the receiver.In this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the DDNN.The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks.The energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of turbulence.It has a faster convergence and better accuracy than that based on a convolutional neural network.

Dual-Fields Rotational Total Skin Electron Therapy: Investigation and Implementation

Purpose: To present a protocol of a dual-field rotational (DFR) total skin electron therapy (TSET) and to provide an assessment of clinical implementation, dosimetry properties, and skin dose evaluation. Methods and Materials: The DFR-TSET combined the Stanford 6-field and McGill rotational methods. Dual 6 MeV electron beams in high dose total skin electron mode were used for DFR-TSET on a commercial linac. Beam profiles and dosimetric properties were measured using solid phantoms. The dose rate at expanded source-to-surface distance (SSD) was a combination of static rate and rotational rate. In vivo dosimetry of patient skin was performed on patients’ skin using film, metal oxide semiconductor field-effect transistors (MOSFET), and optically stimulated luminescent dosimeters (OSLD). Results: Dual field rotational total skin electron therapy exhibited good (≤±10%) uniformity in the beam profiles in the vertical direction at an extended SSD of 332 cm with a gantry angulation of ±20˚ deviated from the horizontal direction. In-vivo measurements confirmed acceptable uniformity of the patients’ total body surfaces and revealed anatomically self-blocked or shielded areas where underdosing occurred. Conclusions: The clinical implementation of DFR-TSET effectively utilizes the special mode on a linac. This technique provides short beam-on times, uniform dose distribution, large treatment field, and reduced dose of x-ray contamination to the patients. In-vivo measurements indicate satisfactory delivery and dose uniformity of the prescribed dose. Electron boost fields are recommended at normal SSDs to address underdosed areas.

Numerical Simulations of Snow Accumulation in the Bogie Region of a Train Considering Snow Particle Rotation

To investigate the influence of snow particle rotational motion on the accumulation of snow in the bogie region of high-speed trains,an Euler‒Lagrange numerical approach is adopted.The study examines the effects of snow particle diameter and train speed on the ensuing dynamics.It is shown that considering snow particle rotational motion causes significant deviation in the particle trajectories with respect to non-rotating particles.Such a deviation increases with larger snow particle diameters and higher train speeds.The snow accumulation on the overall surface of the bogie increases,and the amount of snow on the vibration reduction device varies greatly.In certain conditions,the amount of accumulated snow can increase by several orders of magnitudes.

Mechanism of principal stress rotation and deformation failure behavior induced by excavation in roadways

The failure modes of rock after roadway excavation are diverse and complex.A comprehensive investigation of the internal stress field and the rotation behavior of the stress axis in roadways is essential for elucidating the mechanism of roadway failure.This study aimed to examine the spatial relationship between roadways and stress fields.The law of stress axis rotation under three-dimensional(3D)stress has been extensively studied.A stress model of roadways in the spatial stress field was established,and the far-field stress state at different spatial positions of the roadways was analyzed.A mechanical model of roadways under a 3D stress state was established using far-field stress solutions as boundary conditions.The distribution of principal stressesσ1,σ2 andσ3 around the roadways and the variation of the stress principal axis were solved.It was found that the stability boundary of the stress principal axis exhibits hysteresis when compared with that of the principal stress magnitudes.A numerical analysis model for spatial roadways was established to validate the distribution of principal stress and the mechanism of principal axis rotation.Research has demonstrated that the stress axis undergoes varying degrees of spatial rotation in different orientations and radial depths.Based on the distribution of principal stress and the rotation law of the stress principal axis,the entire evolution mechanism of the two stress adjustments to form the final failure form after roadway excavation has been revealed.The on-site detection results also corroborate the findings presented in this paper.The results provide a basis for the analysis of the failure mechanism under a 3D stress state.