Applicability Of Proposed Method Research Articles

Adaptive First-Crossing Approach for Life-Cycle Reliability Analysis

Abstract Life-cycle reliability analysis can effectively estimate and present the changes in the state of safety for structures under dynamic uncertainties during their lifecycle. The first-crossing approach is an efficient way to evaluate time-variant reliability-based on the probabilistic characteristics of the first-crossing time point (FCTP). However, the FCTP model has a number of critical challenges, such as computational accuracy. This paper proposes an adaptive first-crossing approach for the time-varying reliability of structures over their whole lifecycle, which can provide a tool for cycle-life reliability analysis and design. The response surface of FCTP regarding input variables is first estimated by performing support vector regression. Furthermore, the adaptive learning algorithm for training support vector regression is developed by integrating the uniform design and the central moments of the surrogate model. Then, the convergence condition, which combines the raw moments and entropy of the first-crossing probability distribution function (PDF), is constructed to build the optimal first-crossing surrogate model. Finally, the first-crossing PDF is solved using the adaptive kernel density estimation to obtain the time-variant reliability trend during the whole lifecycle. Examples are demonstrated to specify the proposed method in applications.

Linear Convergence of a Proximal Alternating Minimization Method with Extrapolation for \(\boldsymbol{\ell_1}\) -Norm Principal Component Analysis

A popular robust alternative of the classic principal component analysis (PCA) is the -norm PCA (L1-PCA), which aims to find a subspace that captures the most variation in a dataset as measured by the -norm. L1-PCA has shown great promise in alleviating the effect of outliers in data analytic applications. However, it gives rise to a challenging nonsmooth, nonconvex optimization problem, for which existing algorithms are either not scalable or lack strong theoretical guarantees on their convergence behavior. In this paper, we propose a proximal alternating minimization method with extrapolation (PAMe) for solving a two-block reformulation of the L1-PCA problem. We then show that for both the L1-PCA problem and its two-block reformulation, the Kurdyka–Łojasiewicz exponent at any of the limiting critical points is . This allows us to establish the linear convergence of the sequence of iterates generated by PAMe and to determine the criticality of the limit of the sequence with respect to both the L1-PCA problem and its two-block reformulation. To complement our theoretical development, we show via numerical experiments on both synthetic and real-world datasets that PAMe is competitive with a host of existing methods. Our results not only significantly advance the convergence theory of iterative methods for L1-PCA but also demonstrate the potential of our proposed method in applications.

Boost power factor correction converter with adaptive harmonic compensation control

AbstractIn low‐voltage distribution systems, the increased penetration of power electronic devices leads to the decentralization of harmonic sources, which puts the sensitive loads or communication systems in the risk status. The conventional methods of point‐to‐point harmonic control are not applicable. In order to solve this problem without increasing additional investments, this paper focuses on mitigating the harmonic interference through the power factor correction (PFC) converter. Hence, a critical conduction mode (CRM) single‐phase boost PFC converter with adaptive harmonic compensation (AHC) control is proposed to reduce the harmonic interference at user side. Firstly, the feed‐forward control loop of a PFC converter is decoupled into the fundamental branch and harmonic branch to trace the harmonic interference and generate the compensation current. Then the compensation gain is designed by the compensation loop gain and compensation capacity of PFC converter. Meanwhile, the characteristics of the PFC converter are analyzed to study the effect of the AHC control on the active power transmission and output load, which can provide important support for the proposed method in application. Finally, a 160‐W experimental prototype is built to verify the effectiveness of the proposed AHC control.

Dual cascade DNA walking-induced “super on” photocurrent response for constructing a novel antibiotic biosensing method

The construction of effective methods for the convenient testing of antibiotic residues in real samples has attracted considerable interest. Herein, we designed a dual cascade DNA walking amplification strategy and combined it with the controllable photocurrent regulation of a photoelectrode to develop a novel photoelectrochemical (PEC) biosensing method for antibiotic detection. The photoelectrode was prepared through the surface modification of a glassy carbon electrode with the TiO2/CdS QDs nanocomposite synthesized by an in situ hydrothermal deposition method. The strong anodic PEC response of the nanocomposite could be well inhibited by the introduction of a silver nanoclusters (Ag NCs)-labeled DNA hairpin onto its surface. Upon the target biorecognition reaction, an Mg2+-dependent DNAzyme (MNAzyme)-driven DNA walking was triggered to release another MNAzyme strand-linked streptavidin (SA) complex. As this SA complex could serve as a four-legged DNA walker, its cascade walking on the electrode surface not only released Ag NCs but also caused the linking of Rhodamine 123 with the electrode to realize the “super on” photocurrent output. By using kanamycin as the model analyte, this method showed a very wide linear range from 10 fg mL−1 to 1 ng mL−1 and a very low detection limit of 0.53 fg mL−1. Meanwhile, the simple photoelectrode preparation and the aptamer recognition-based autonomous DNA walking resulted in the convenient manipulation and excellent repeatability. These unique performances determine the great potential of the proposed method for practical applications.

Application of the Threat Modeling Method In an Operating System

Due to the increase in professionals adopting the home office model due to the COVID-19 pandemic, the threat to company information and assets has become more evident. This work aims to identify, describe and evaluate the impacts of applying the threat modeling method, using risk management standards, on corporate computers with the aid of a monitoring system. The proposed method for application suggests the adoption of processes and a system for updating, controlling and managing the Windows Operating System to reduce the threats faced. The research identified security using the STRIDE and DREAD methods and the ISO and NIST security standards. It verified 14 types of threats found in an operating system that can be properly identified and mitigated with the threat exploitation method.

Multimodal Biometric Fusion Algorithm Based on Ranking Partition Collision Theory

Score-based multimodal biometric fusion has been shown to be successful in addressing the problem of unimodal techniques’ vulnerability to attack and poor performance in low-quality data. However, difficulties still exist in how to unify the meaning of heterogeneous scores more effectively. Aside from the matching scores themselves, the importance of the ranking information they include has been undervalued in previous studies. This study concentrates on matching scores and their ranking information and suggests the ranking partition collision (RPC) theory from the standpoint of the worth of scores. To meet both forensic and judicial needs, this paper proposes a method that employs a neural network to fuse biometrics at the score level. In addition, this paper constructs a virtual homologous dataset and conducts experiments on it. Experimental results demonstrate that the proposed method achieves an accuracy of 100% in both mAP and Rank1. To show the efficiency of the proposed method in practical applications, this work carries out more experiments utilizing real-world data. The results show that the proposed approach maintains a Rank1 accuracy of 99.2% on the million-scale database. It offers a novel approach to fusion at the score level.

A finite-difference method for stress modelling based on wave propagation

SUMMARY The determinations of detailed stress states are of great importance for various environmental and engineering investigations, which makes numerical stress modelling a key issue in many fields. We developed a new stress modelling method governed by elastic wave equations using finite-difference scheme. By introducing an artificial damping factor to the particle velocity in wave modelling, the proposed method is able to solve both the dynamic stress evolution and the static stress state of equilibrium. We validate the proposed method both in body force and surface force benchmarks in different scales. With the proposed method, we are able to substantially improve the modelling accuracy of models in unbounded domain by using the perfectly matched layer as the artificial boundary conditions. A 3-D concrete-faced rockfill dam model is further presented as a numerical example of practical investigation. The consistent results with the finite-element method further illustrate the proposed method's applicability. As a minor modification to wave modelling scheme, the proposed stress modelling method is not only accurate for geological models through different scales, but also physically reasonable and easy to implement for geophysicists.

Maximum Power per Ampere Modulation for Cascaded H-Bridge Converters

This article shows how to exploit the degree of freedom represented by the common-mode voltage, which is inherent in three-phase Cascaded H-bridge (CHB) converters, to minimize the total rms current value of the distributed dc sources. An optimal common-mode voltage injection law is derived, constituting a maximum power per ampere (MPPA) modulation strategy with respect to the currents of the dc sources. The potential benefits introduced by the proposed algorithm are analyzed in the context of battery-fed CHB converters and validated experimentally with a three-time-constant Randles model of a battery cell. The MPPA strategy is compared with traditional common-mode voltage injection methods, and battery loss reduction is demonstrated. The obtained ES is dependent on the converter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> and power factor. Experimental tests on a 36-cell full-bridge CHB converter validate the simulation and numerical derivation. To demonstrate the efficacy of the proposed method in practical applications, a 3 MW energy storage system (ESS) and a 110 kW battery electric vehicle (BEV) undergoing standard drive cycles are presented as case studies. Compared to traditional modulation strategies, the MPPA strategy reduces the battery losses by up to 10.9% in the ESS and 27.5% in the BEV application.

Forward prediction for tunnel geology and classification of surrounding rock based on seismic wave velocity layered tomography

Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surrounding rock in front of the tunnel face. In this work, a forward-prediction method for tunnel geology and classification of surrounding rock is developed based on seismic wave velocity layered tomography. In particular, for the problem of strong multi-solution of wave velocity inversion caused by few ray paths in the narrow space of the tunnel, a layered inversion based on regularization is proposed. By reducing the inversion area of each iteration step and applying straight-line interface assumption, the convergence and accuracy of wave velocity inversion are effectively improved. Furthermore, a surrounding rock classification network based on autoencoder is constructed. The mapping relationship between wave velocity and classification of surrounding rock is established with density, Poisson's ratio and elastic modulus as links. Two numerical examples with geological conditions similar to that in the field tunnel and a field case study in an urban subway tunnel verify the potential of the proposed method for practical application.

Adaptive Sliding Mode Control With Disturbance Observer for Speed Regulation System of Permanent Magnet Synchronous Motor

In order to improve the robustness and speed tracking performance of a permanent magnetic synchronous motor (PMSM) system, under the framework of PMSM vector control, a nonlinear speed control method based on improved sliding mode control (SMC) and disturbance observer is proposed in this paper. First, a novel fast reaching law is designed to make the system trajectory reach the sliding mode surface within a finite time and to alleviate the chattering phenomenon. Second, a nonlinear integral terminal sliding mode surface is proposed by introducing sign and fractional error integration, which achieves finite-time convergence of the speed tracking errors and integral errors without the singular problem. Then, based on the proposed reaching law and the proposed sliding mode surface, an adaptive sliding mode speed controller with disturbance observer which adaptively compensates the output of the improved sliding mode speed controller, is developed not only to guarantee the finite-time convergence performance of the state trajectories, but also to demonstrate stronger robustness, higher precision and lower chattering compared to conventional sliding mode controller. Finally, the simulation and experimental results confirm the validity of the proposed method for practical applications.

Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images

There are two types of methods for image segmentation. One is traditional image processing methods, which are sensitive to details and boundaries, yet fail to recognize semantic information. The other is deep learning methods, which can locate and identify different objects, but boundary identifications are not accurate enough. Both of them cannot generate entire segmentation information. In order to obtain accurate edge detection and semantic information, an Adaptive Boundary and Semantic Composite Segmentation method (ABSCS) is proposed. This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances. It includes adaptively dividing and modifying the aerial images with the proposed principles and methods, using the deep learning method to semantic segment and preprocess the small divided pieces, using three traditional methods to segment and preprocess original-size aerial images, adaptively selecting traditional results to modify the boundaries of individual objects in deep learning results, and combining the results of different objects. Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset, and their results are analyzed qualitatively and quantitatively. The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method. This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.

Consistent Approximation of Interpolating Splines in Image Metamorphosis

This paper investigates a variational model for splines in the image metamorphosis model for the smooth interpolation of key frames in the space of images. The Riemannian manifold of images based on the metamorphosis model defines shortest geodesic paths interpolating two images as minimizers of the path energy which measures the viscous dissipation caused by the motion field and dissipation caused by the material derivative of the image intensity along motion paths. In this paper, we aim at smooth interpolation of multiple key frame images picking up the general observation of cubic splines in Euclidean space which minimize the squared acceleration along the interpolation path. To this end, we propose the spline functional which combines quadratic functionals of the Eulerian motion acceleration and of the second material derivative of the image intensity as the proper notion of image intensity acceleration. We propose a variational time discretization of this model and study the convergence to a suitably relaxed time continuous model via varGamma -convergence methodology. As a byproduct, this also allows to establish the existence of metamorphosis splines for given key frame images as minimizers of the time continuous spline functional. The time discretization is complemented by effective spatial discretization based on finite differences and a stable B-spline interpolation of deformed quantities. A variety of numerical examples demonstrates the robustness and versatility of the proposed method in applications. For the minimization of the fully discrete energy functional, a variant of the iPALM algorithm is used.

Novel detection approach for thermal defects: Study on its feasibility and application to vehicle cables

AbstractIt is important to accurately assess the thermal defect state of equipment to ensure its safe operation. A novel thermal defect detection method called the square even‐order derivative method is proposed. First, the derivative functions of temperature were constructed to investigate the main‐side‐peak characteristics of the derivative curve, and the mathematical expressions for the position, depth, intensity of the defects, and parameters of the derivative curve were deduced, which can be used to quantitatively detect the defects. Then, numerical and experimental analyses of an example case of defected epoxy resin plates were carried out to verify that thermal defects can be effectively and accurately detected by the proposed method. Finally, the method was applied to a vehicle cable to detect its thermal defects in another example case of defected vehicle cable, which preliminarily validated the feasibility of the proposed method in practical applications.

Using Tacit Expert Knowledge to Support Shop-floor Operators Through a Knowledge-based Assistance System

The increasing complexity of industrial production systems is challenging employees on the shop-floor in their daily work. Specific knowledge about manufacturing processes is often not available in explicit form but mainly as tacit knowledge of experienced shop-floor workers. A systematic approach to knowledge externalization and reuse is required to make this operational knowledge available. This paper proposes a method to systematically capture and structure expert knowledge while incorporating knowledge management and social research methods. The proposed method's application and evaluation occur in a continuous manufacturing scenario, externalizing tacit knowledge about coping with manufacturing anomalies. A digital assistance system is designed and prototypically implemented to manage and reuse the externalized knowledge. The early involvement of shop-floor workers in the development phase of the prototype ensures usability and user acceptance of the assistance system. The assistance system is developed as a collaboration supporting artifact in the shop-floor's common information space. To observe the resulting productivity performance improvements in the manufacturing scenario, a KPI-based evaluation of the assistance system is presented. Finally, a discussion about the major contributions of this paper, namely the development of an approach for knowledge externalization and a human-centered design of an assistance system, takes place. To assess the novelty of these approaches, they are contrasted with the state of the art identified in the literature before a final summary of the results is presented.

An evaluation of aromatic hydrocarbons in recycled PET fibers by a reliable and effective static headspace GC-FID method

The volatile organic compounds (VOCs) released from recycled polyethylene terephthalate products has raised much concern in consideration of ecological safety. This work has established a reliable and effective headspace GC-flame ionization detection (HS-GC-FID) method for the determination of aromatic hydrocarbons (including benzene, toluene, ethylbenzene, xylene, styrene and cumene) in R-PET fibers. The optimum equilibration time and temperature, and sample weight were identified as 40 min, 110C and 1.0 g, respectively. By contrasting with 15 mL headspace bottle, the testing results of samples incubated in 120 mL headspace bottle provided a higher data repeatability. The linear equations to calculate VOCs content were obtained by linear regression analysis, and the linear regression coefficients were validated to be over 0.997. Limits of detection for benzene, toluene, ethylbenzene, p-xylene + m-xylene, o-xylene + styrene and cumene were calculated as 3.95 ng, 3.68 ng, 4.16 ng, 3.30 ng, 2.59 ng, 8.64 ng, respectively. Reproducibility tests of the method generated relative standard deviations of 6.0% or less, with recoveries in the range of 82%–109%. The availability of proposed method in application of VOCs detection in R-PET fibers was confirmed by detecting VOCs contents of virgin PET fibers, and R-PET fibers produced by physical, chemical and physical-co-chemical processes.。

SA-NET.V2: REAL-TIME VEHICLE DETECTION FROM OBLIQUE UAV IMAGES WITH USE OF UNCERTAINTY ESTIMATION IN DEEP META-LEARNING

Abstract. In recent years, unmanned aerial vehicle (UAV) imaging is a suitable solution for real-time monitoring different vehicles on the urban scale. Real-time vehicle detection with the use of uncertainty estimation in deep meta-learning for the portable platforms (e.g., UAV) potentially improves video understanding in real-world applications with a small training dataset, while many vehicle monitoring approaches appear to understand single-time detection with a big training dataset. The purpose of real-time vehicle detection from oblique UAV images is to locate the vehicle on the time series UAV images by using semantic segmentation. Real-time vehicle detection is more difficult due to the variety of depth and scale vehicles in oblique view UAV images. Motivated by these facts, in this manuscript, we consider the problem of real-time vehicle detection for oblique UAV images based on a small training dataset and deep meta-learning. The proposed architecture, called SA-Net.v2, is a developed method based on the SA-CNN for real-time vehicle detection by reformulating the squeeze-and-attention mechanism. The SA-Net.v2 is composed of two components, including the squeeze-and-attention function that extracts the high-level feature based on a small training dataset, and the gated CNN. For the real-time vehicle detection scenario, we test our model on the UAVid dataset. UAVid is a time series oblique UAV images dataset consisting of 30 video sequences. We examine the proposed method's applicability for stand real-time vehicle detection in urban environments using time series UAV images. The experiments show that the SA-Net.v2 achieves promising performance in time series oblique UAV images.

A reaction diffusion-based B-spline level set (RDBLS) method for structural topology optimization

This work uses the zero-level contour of a parameterized level set function, a linear combination of cubic B-spline basis functions, to express the structural profile in structural topology optimization. Together with mean compliance, diffusion energy is minimized under a volume constraint to control the structural complexity. The design variables, namely the coefficients of cubic B-spline basis functions, are updated by solving the reaction–diffusion equation within a finite element analysis framework. The bisectional algorithm accurately calculates the Lagrangian multiplier of the volume constraint in each iteration. In addition to expressing the optimized structure smoothly, the proposed method is highly efficient. For instance, it only takes 20 iterations to solve the cantilever and MBB beams in 2D. For 3D optimization, we obtain several elegant bridge designs using nearly one million elements, demonstrating the great potential of the proposed method for practical applications.

An Adaptive Sliding-Mode Controller With a Modified Reduced-Order Proportional Integral Observer for Speed Regulation of a Permanent Magnet Synchronous Motor

This article proposes an adaptive sliding-mode control (ASMC) with a modified reduced-order proportional integral observer (MROPIO) to provide speed control for a permanent magnet synchronous motor against unknown disturbances, such as system parameter uncertainties and external load torque variation. The ASMC method, based on a new sliding-mode reaching law, improves chattering in the control signal and reduces the time required for the system trajectory to reach the sliding-mode surface. The MROPIO estimates unknown disturbances and provides feed-forward compensation for the ASMC, thus overcoming incorrect estimations of unknown disturbances during speed jumps can cause overshoots in the speed response. All the proposed methods were implemented on an industrial motor drive. The experimental results confirm the validity of the proposed method for practical applications.

Erratic and random noise attenuation using adaptive local orthogonalization

The local orthogonalization (LO) approach has been broadly applied to attenuate random noise and deal with the signal-leakage problem induced using the traditional denoising schemes. First, this approach removes noise and applies the LO weight (LOW) operator to the originally estimated signal. Then, the signal leakage is predicted and subsequently recovered from the original noise component. Finally, the original denoised component and the recovered signal are mixed to output the denoised seismic signal. However, this approach has limits when the seismic data include random and erratic noise. We find that this shortcoming is mainly caused by the weakness of the traditional denoising operators (e.g., curvelet and f- x deconvolution methods), which are insufficient to adapt themselves to the originally denoised signal and noise sections for the LOW process. Therefore, we aim to attenuate random and erratic noise using an adaptive LO method. By mixing the hard thresholding (HT) operator and the exponential moving-average (EMA) filter, we have developed a novel operator, which we call the HTEMA operator. Then, by introducing this novel operator into the curvelet method framework, we adapt the initially denoised signal and noise sections for the LOW process. The proposed operator is applied to the frequency domain curvelet coefficients provided by the inverse transform. Then, we use the LOW operator to compensate for the damage of useful signals. The proposed approach is not limited to the curvelet method but can be applied to other traditional denoising methods. We estimate the noise attenuation performance by the visual inspections, local similarity maps, and the signal-to-noise ratio. Synthetic and field data examples indicate the effectiveness of the proposed method in applications to erratic and random noise attenuation compared with the curvelet, f- x deconvolution, and the LO methods.

DIC measurement for large-scale structures based on adaptive warping image stitching.

As a representative method of optical non-interference measurement, digital image correlation (DIC) technology is a non-contact optical mechanics method that can measure the displacement and deformation of the whole field. However, when the measurement range of the field is too large, the existing DIC method cannot measure the full-field strain accurately, which limits the application of the DIC measurement in the case of a large size and wide-field view. To address this issue, a DIC measurement method for large-scale structures based on adaptive warping image stitching is proposed in this paper. First, multiple adjacent high-resolution images are collected at different locations of large-scale structures. Secondly, the collected images are stitched by applying the adaptive warping image stitching algorithm to obtain a panoramic image. Finally, the DIC algorithm is applied to solve the whole deformation field. In the experiments, we first verify the feasibility of the proposed method for image matching and fusion through the numerical simulation of a rigid body translation experiment. Then the accuracy and robustness of the proposed method in practical application are verified by rigid body translation and a three-point bending experiment. The experimental results demonstrate that the measurement range of DIC is improved significantly with the adaptive warping image stitching algorithm.