On the derivative-free quasi-Newton-type algorithm for separable systems of nonlinear equations

A derivative-free quasi-Newton-type algorithm in which its search direction is a product of a positive definite diagonal matrix and a residual vector is presented. The algorithm is simple to implement and has the ability to solve large-scale nonlinear systems of equations with separable functions. The diagonal matrix is simply obtained in a quasi-Newton manner at each iteration. Under some suitable conditions, the global and R-linear convergence result of the algorithm are presented. Numerical test on some benchmark separable nonlinear equations problems reveal the robustness and efficiency of the algorithm.

Ultra-sensitive AAV capsid detection by immunocapture-based qPCR following factor VIII gene transfer

Adeno-associated virus (AAV)-based gene therapy vectors are replication-incompetent and thus pose minimal risk for horizontal transmission or release into the environment. In studies with AAV5-FVIII-SQ (valoctocogene roxaparvovec), an investigational gene therapy for hemophilia A, residual vector DNA was detectable in blood, secreta, and excreta, but it remained unclear how long structurally intact AAV5 vector capsids were present. Since a comprehensive assessment of vector shedding is required by regulatory agencies, we developed a new method (termed iqPCR) that utilizes capsid-directed immunocapture followed by qPCR amplification of encapsidated DNA. The limit of detection for AAV5 vector capsids was 1.17E+04 and 2.33E+04 vg/mL in plasma and semen, respectively. Acceptable precision, accuracy, selectivity, and specificity were verified; up to 1.00E+09 vg/mL non-encapsidated vector DNA showed no interference. Anti-AAV5 antibody plasma concentrations above 141 ng/mL decreased AAV5 capsid quantification, suggesting that iqPCR mainly detects free capsids and not those complexed with antibodies. In a clinical study, AAV5-FVIII-SQ capsids were found in plasma and semen but became undetectable within nine weeks after dose administration. Hence, iqPCR monitors the presence and shedding kinetics of intact vector capsids following AAV gene therapy and informs the potential risk for horizontal transmission.

Diagnosis of Fault in Doubly-Fed Three-Phase Induction Generator in Wind Power Applications

Inter-turn short-circuits faults are one of the most common problems in Induction machines, especially in the Doubly-Fed Induction Generator Based-Wind Energy Conversion system. Accurate and fast fault diagnosis and detection minimize the down-time of the units as further damages can be prevented. In this paper, a robust fault detection approach for Doubly-Fed Induction Generator (DFIG) based on wind generators is proposed. The faults’ smart diagnosis and detection are based on the Sliding Mode Observer and Second Order Sliding Mode Control. The observer generates a residual for detection of stator Inter-Turn Short Circuit faults which can affect a system model. A decision system is used to process the residual vector to detect faults. In order to shed more light on the designed approach performances regarding the inter-turn short-circuit fault occurrence, simulations were implemented in MATLAB/Simulink environment.

An advanced shell model for the analysis of geometrical and material nonlinear shells

In this paper layered shells subjected to static loading are considered. The displacements of the Reissner–Mindlin theory are enriched by a an additional part. These so-called fluctuation displacements include warping displacements and thickness changes. They lead to additional terms for the material deformation gradient and the Green–Lagrangian strain tensor. Within a nonlinear multi-field variational formulation the weak form of the boundary value problem accounts for the equilibrium of stress resultants and couple resultants, the local equilibrium of stresses, the geometrical field equations and the constitutive equations. For the independent shell strains an ansatz with quadratic shape functions is chosen. This leads to a significant improved convergence behaviour especially for distorted meshes. Elimination of a set of parameters on element level by static condensation yields an element stiffness matrix and residual vector of a quadrilateral shell element with the usual 5 or 6 nodal degrees of freedom. The developed model yields the complicated three-dimensional stress state in layered shells for elasticity and elasto-plasticity considering geometrical nonlinearity. In comparison with fully 3D solutions present 2D shell model requires only a fractional amount of computing time.

WAVELETS FOR SELF-CALIBRATED BUNDLE BLOCK ADJUSTMENT

This paper entails a methodological novelty and builds upon prior research on a wavelets-based model for digital camera self-calibration. We introduce a new kernel function based on the compactly supported orthogonal third-order asymmetric Daubechies wavelet to correct systematic image distortion errors. Tests are done by using aerial images taken with a high-resolution metric digital aerial mapping camera. The quality of experimental results is evaluated by using reliable and high precision ground check points in the calibration field. For example, a four-fold block with this wavelet self-calibration model has the external accuracy of about 0.28 GSD (=ground sampling distance) in the horizontal direction, and about 0.43 GSD in the vertical direction, respectively, where 1GSD ≈ 4.6cm. The posterior standard deviations σ̂0 of unit weight are reduced from 0.37 pixel to 0.27 pixel. The residual vector lengths are also significantly reduced after our wavelet additional parameters are used. Experimental results support the proposal and demonstrate the applicability of this new model.

A rolling bearing fault diagnosis method based on fastDTW and an AGBDBN

In order to improve fault feature extraction and diagnosis for rolling bearings, a fault diagnosis method based on fast dynamic time warping (fastDTW) and an adaptive Gaussian-Bernoulli deep belief network (AGBDBN) is proposed in this paper. Firstly, for the non-stationary vibration signal characteristics of the bearing, the fastDTW algorithm is used to calculate the residual vector of the fault signal, thereby enhancing the fault characteristic information. Then, according to the continuous vibration value of the bearing vibration signal, a standard deep belief network (DBN) is improved to deal with the problem that the optimal setting for the learning rate is difficult to achieve in the deep neural network training process and the AGBDBN model is used for fault diagnosis. Finally, the proposed method is compared with a variety of model diagnosis methods. The experimental results show that the proposed method achieved good diagnostic results.