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In this paper, a novel high dynamic range (HDR) image zero-watermarking algorithm against the tone mapping attack is proposed. In order to extract stable and invariant features for robust zero-watermarking, the shift-invariant shearlet transform (SIST) is used to transform the HDR image. Firstly, the HDR image is converted to CIELAB color space, and the L component is selected to perform SIST for obtaining the low-frequency subband containing the robust structure information of the image. Secondly, the low-frequency subband is divided into nonoverlapping blocks, which are transformed by using discrete cosine transform (DCT) and singular value decomposition (SVD) to obtain the maximum singular values for constructing a binary feature image. To increase the watermarking security, a hybrid chaotic mapping (HCM) is employed to get the scrambled watermark. Finally, an exclusive-or operation is performed between the binary feature image and the scrambled watermark to compute robust zero-watermark. Experimental results show that the proposed algorithm has a good capability of resisting tone mapping and other image processing attacks.

A radar cross section (RCS) gradient calculation approach based on the discrete adjoint equation of Maxwell integral equation is proposed. The adjoint method can obtain the gradients of all design ...

This paper presents a high dynamic range (HDR) image zero watermarking method based on dual tree complex wavelet transform (DT-CWT) and quaternion. In order to be against tone mapping (TM), DT-CWT is used to transform the three RGB color channels of the HDR image for obtaining the low-pass sub-bands, respectively, since DT-CWT can extract the contour of the HDR image and the contour change of the HDR image is small after TM. The HDR image provides a wide dynamic range, and thus, three-color channel correlations are higher than inner-relationships and the quaternion is used to consider three color channels as a whole to be transformed. Quaternion fast Fourier transform (QFFT) and quaternion singular value decomposition (QSVD) are utilized to decompose the HDR image for obtaining robust features, which is fused with a binary watermark to generate a zero watermark for copyright protection. Furthermore, the binary watermark is scrambled for the security by using the Arnold transform. Experimental results denote that the proposed zero-watermarking method is robust to TM and other image processing attacks, and can protect the HDR image efficiently.

Plant leaf recognition is a computer vision task used to identify plant species. To address the problem that current plant leaf recognition algorithms have difficulty in recognizing fine-grained leaf classification between classes, this paper proposes a DMSNet (Deep Multi-Scale Network) model, a plant leaf classification algorithm based on multi-scale feature extraction. In order to improve the extraction ability of different fine-grained features of the model, the model is improved on the basis of Multi-scale Backbone Architecture model. In order to achieve better plant leaf classification, a visual attention mechanism module to DMSNet is added and ADMSNet (Attention-based Deep Multi-Scale Network), which makes the model focus more on the plant leaf itself, is proposed, essential features are enhanced, and useless features are suppressed. Experiments on real datasets show that the classification accuracy of the DMSNet model reaches 96.43%. In comparison, the accuracy of ADMSNet with the addition of the attention module reaches 97.39%, and the comparison experiments with ResNet-50, ResNext, Res2Net-50 and Res2Net-101 models on the same dataset show that DMSNet improved the accuracy by 4.6%, 18.57%, 3.72% and 3.84%, respectively. The experimental results confirm that the DMSNet and ADMSNet plant leaf recognition models constructed in this paper can accurately recognize plant leaves and have better performance than the traditional models.

GPS data often have position deviations in precision and are apt to be affected by noise; hence, it is essential to extract features from trajectories before performing large-scale data mining. A GeoHash-based spatial coding technique called GeoHashTree was used to index spatiotemporal trajectory points in order to improve the efficiency of nearest-neighbor search. The GeoHashTree was applied in trajectory clustering and an improved density-based clustering algorithm was proposed to reduce the time complexity of nearest-neighbor search from $O(n^2)$ to $O(n\\log{n})$. After extracting trajectory points with changing angles, the proposed clustering approach was employed to achieve deep-level feature extraction on trajectory points with changing angles, which aims to accurately identify feature points. Extensive experiments are conducted on real GPS data and the results demonstrate that the proposed trajectory-clustering algorithm based on the GeoHashTree spatial index structure can improve time performance by an average of 90.89% as well as guarantee the accuracy of clustering compared with the traditional clustering method. The visualization results show that the trajectory feature extraction approach can effectively find trajectory points with changing angles and discover a varying types of feature points from large-scale data sets. In addition, the proposed approach does not depend on road network data and can dynamically update with new incoming trajectory data as road networks change in real time.

Based on improved multi-objective particle swarm optimization (MOPSO) algorithm with principal component analysis (PCA) methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign) platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

Rotor airfoil design is investigated in this paper. There are many difficulties for this high-dimensional multi-objective problem when traditional multi-objective optimization methods are used. Therefore, a multi-layer hierarchical constraint method is proposed by coupling principal component analysis (PCA) dimensionality reduction and e-constraint method to translate the original high-dimensional problem into a bi-objective problem. This paper selects the main design objectives by conducting PCA to the preliminary solution of original problem with consideration of the priority of design objectives. According to the e-constraint method, the design model is established by treating the two top-ranking design goals as objective and others as variable constraints. A series of bi-objective Pareto curves will be obtained by changing the variable constraints, and the favorable solution can be obtained by analyzing Pareto curve spectrum. This method is applied to the rotor airfoil design and makes great improvement in aerodynamic performance. It is shown that the method is convenient and efficient, beyond which, it facilitates decision-making of the high-dimensional multi-objective engineering problem.

Based on the structural overlapped grid technology, the aerodynamic characteristics of DLR-F11 model, which is issued by the Second High Lift Prediction Workshop (HiLiftPW), are predicted by self-developed PMB3D solver. PMB3D is a RANS-equation solver, employing finite-volume discrete and implicit method. The computational accuracy of PMB3D solver with structural overlapped grid is verified by comparing the simulation results with some other CFD solvers, such as FUN3D, CFL3D and OverFlow et al. The simulation results demonstrate that (1) PMB3D could rival the other CFD solvers, since it can precisely predict the aerodynamic characteristics, the pressure distribution and the flow characteristics of the model; (2) the use of the structural overlapped grid technology could largely reduce the difficulty of grid generation for complex geometry, without affecting the accuracy of the flow simulation; (3) PMB3D with structural overlapped grid could quickly and accurately predict the aerodynamic characteristics, and it is a reliable analysis tool for aerodynamic design.

Experiment design method is a key to construct a highly reliable surrogate model for numerical optimization in large-scale project. Within the method, the experimental design criterion directly affects the accuracy of the surrogate model and the optimization efficient. According to the shortcomings of the traditional experimental design, an improved adaptive sampling method is proposed in this paper. The surrogate model is firstly constructed by basic sparse samples. Then the supplementary sampling position is detected according to the specified criteria, which introduces the energy function and curvature sampling criteria based on radial basis function (RBF) network. Sampling detection criteria considers both the uniformity of sample distribution and the description of hypersurface curvature so as to significantly improve the prediction accuracy of the surrogate model with much less samples. For the surrogate model constructed with sparse samples, the sample uniformity is an important factor to the interpolation accuracy in the initial stage of adaptive sampling and surrogate model training. Along with the improvement of uniformity, the curvature description of objective function surface gradually becomes more important. In consideration of these issues, crowdness enhance function and root mean square error (RMSE) feedback function are introduced in C criterion expression. Thus, a new sampling method called RMSE and crowdness enhance (RCE) adaptive sampling is established. The validity of RCE adaptive sampling method is studied through typical test function firstly and then the airfoil/wing aerodynamic optimization design problem, which has high-dimensional design space. The results show that RCE adaptive sampling method not only reduces the requirement for the number of samples, but also effectively improves the prediction accuracy of the surrogate model, which has a broad prospects for applications.

Gene Expression Programming (GEP) is a powerful evolutionary method for knowledge discovery and model learning. Based on the basic GEP algorithm, this paper proposes an improved algorithm named S_GEP, which is especially suitable for dealing with symbolic regression problems. The major advantages for this S_GEP method include: (1) A new method for evaluating individual without expression tree; (2) a corresponding expression tree construction schema for the new evaluating individual method if required by some special complex problems; and (3) a new approach for manipulating numeric constants so as to improve the convergence. A thorough comparative study between our proposed S_GEP method with the primitive GEP, as well as other methods are included in this paper. The comparative results show that the proposed S_GEP method can significantly improve the GEP performance. Several well-studied benchmark test cases and real-world test cases demonstrate the efficiency and capability of our proposed S_GEP for symbolic regression problems.

The paper introduces the FFD (Free Form Deformation) method based on the elastomeric theory into the aircraft aerodynamic configuration design, which provides a quick and efficient way to parameterization geometry and generate the calculation grids automatically. With analysis of the theory, it concludes characteristics of FFD. According to use the FFD method to the parameterization and the grid generation, airfoil optimization design system based on genetic algorithm and wing cowling optimization design system based on genetic algorithm and Kriging surrogate model are established in this paper, respectively. The optimization design for drag reduction on airfoil based on NACA0012 and the wing-cowling based on wing body of DLRF4 are used to give examples there. The results of the optimization design are remarkable, which obviously proves the FFD method is useful in the aerodynamic optimization design.

Unsteady effect of seriously separated flow is the main factor of modern aircraft buffeting. So accurate simulation of this complex flow becomes the basis associated with the research of aircraft buffeting. This paper constructs an unsteady numerical simulation method for separation flow based on modified delayed detached eddy simulation (MDDES) method by considering both modern computer resources and the credibility of simulating separation flow. The proposed method is also verified through the simulation of the separated flow by a typical fighter at high angle of attack. And then a robust and efficient technology for deforming mesh is established using radial basis function (RBF) and infinite interpolation method. Moreover, the platform for numerical simulation of buffeting is set up in combination with the structural dynamics equations in the modal space, by which the research of vertical tail buffeting caused by edge vortex is carried out on a fighter at large angle of attack. Through spectrum analysis of time-domain response of pressure pulsation on the location of vortex rupture, the results show that the pulsation frequency of vortex structure with different scales covers the inherent modal frequency of vertical tail structure. Compared to the Reynolds-averaged Navier-Stokes equations, the MDDES method can distinguish the more detailed and higher frequency small-scale vortex structure. Unlike flutter, displacement acceleration response of each mode in buffeting is dominated by its own mode. There exists strong coupling between the first bending mode and first torsion mode, and it leads to acceleration and large inertia impact of structure, which is the main factor causing structural fatigue. In sum, the obtained results verify the validity of the numerical means and the corresponding methods in the paper.

The mission planning of Geostationary Earth Orbit (GEO) on-orbit refuelling with uncertain object satellites is studied in this paper. Three key problems should be resolved here: Object Satellite (OSc) selection, mission sequence planning and transfer trajectory optimization. The optimization goal is to select the OSc and find the optimal servicing sequence with the best optimization objective value. Three optimization objectives, i.e. fuel cost, the total number of the OScs and the sum of the OSc priority value are taken into account. Considering this mission as a hybrid optimal control problem, a mathematical model is proposed. Multi-objective particle swarm optimization is employed to address the model. Several mission scenarios are compared and discussed. Numerical experiments indicate that 1) the method developed in this paper could address the problem efficiently; and 2) OSc distribution and varieties in optimization objective emphasis have considerable influences on the optimization results.

Magnetic resonance imaging (MRI) is by nature a multi-modality technique that provides complementary information about different aspects of diseases. So far no attempts have been reported to assess the potential of multi-modal MRI in discriminating individuals with and without migraine, so in this study, we proposed a classification approach to examine whether or not the integration of multiple MRI features could improve the classification performance between migraine patients without aura (MWoA) and healthy controls. Twenty-one MWoA patients and 28 healthy controls participated in this study. Resting-state functional MRI data was acquired to derive three functional measures: the amplitude of low-frequency fluctuations, regional homogeneity and regional functional correlation strength; and structural MRI data was obtained to measure the regional gray matter volume. For each measure, the values of 116 pre-defined regions of interest were extracted as classification features. Features were first selected and combined by a multi-kernel strategy; then a support vector machine classifier was trained to distinguish the subjects at individual level. The performance of the classifier was evaluated using a leave-one-out cross-validation method, and the final classification accuracy obtained was 83.67% (with a sensitivity of 92.86% and a specificity of 71.43%). The anterior cingulate cortex, prefrontal cortex, orbitofrontal cortex and the insula contributed the most discriminative features. In general, our proposed framework shows a promising classification capability for MWoA by integrating information from multiple MRI features.

In order to improve the reliability of fault detection and diagnosis for dynamic system, it is important to make full use of the information from different component of system. Multiple classifiers fusion is a technique that combines the decisions of different classifiers as to reduce the variance of estimation errors and improve the overall classification accuracy. This paper proposes a novel multiple classifiers fusion using within-class decision support for fault diagnosis. The new approach considers the fault diagnosis problem in time series. Then, one-step time series within-class decision support value and synchronization within-class decision support value are calculated to get association probability of each classifier in the same class recognition. Finally, calculate the fusion posterior probability outputs and normalize them for final decision. Experimental results demonstrate that the method is able to achieve a preferable solution, which has a better classification performance compared to single classifier.

This paper presents a novel fusion method for final decision making in multiple classifier system. The novel approach proposed fuses multiple individual classifiers through normalizing and re-subdividing classification output space. Firstly, each class decision output of each classifier in multiple classifier system is divided into four spaces by reliability of corresponding classifier. And then, reject degree is used to indicate classification to substitute for original output. Secondly, dynamic probability of each reject degree which used to combine multiple classifiers is calculated by using synchronization decision support value defined in this paper. Finally, the final decision can be made by integrated support degree based on support space of all classes. Experimental results demonstrate that the method is able to achieve a preferable solution, which has a better classification performance compared to single classifier and other combination methods.

The arbitrary space-shape free form deformation (FFD) method developed in this paper is based on non-uniform rational B-splines (NURBS) basis function and used for the integral parameterization of nacelle-pylon geometry. The multi-block structured grid deformation technique is established by Delaunay graph mapping method. The optimization objects of aerodynamic characteristics are evaluated by solving Navier–Stokes equations on the basis of multi-block structured grid. The advanced particle swarm optimization (PSO) is utilized as search algorithm, which combines the Kriging model as surrogate model during optimization. The optimization system is used for optimizing the nacelle location of DLR-F6 wing-body-pylon-nacelle. The results indicate that the aerodynamic interference between the parts is significantly reduced. The optimization design system established in this paper has extensive applications and engineering value.