Articles de revues : « Perturbation-Based methods »

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Hongxing, Li. « Fuzzy clustering methods based on perturbation ». Fuzzy Sets and Systems 33, n^o 3 (décembre 1989) : 291–302. http://dx.doi.org/10.1016/0165-0114(89)90119-x.

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Martinelli, M., A. Dervieux, L. Hascoet, V. Pascual et A. Belme. « AD-based perturbation methods for uncertainties and errors ». International Journal of Engineering Systems Modelling and Simulation 2, n^o 1/2 (2010) : 65. http://dx.doi.org/10.1504/ijesms.2010.031872.

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San-Juan, Juan Félix, Montserrat San-Martín, Iván Pérez et Rosario López. « Hybrid perturbation methods based on statistical time series models ». Advances in Space Research 57, n^o 8 (avril 2016) : 1641–51. http://dx.doi.org/10.1016/j.asr.2015.05.025.

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Chioua, Moncef, Bala Srinivasan, Michel Perrier et Martin Guay. « EFFECT OF EXCITATION FREQUENCY IN PERTURBATION-BASED EXTREMUM SEEKING METHODS ». IFAC Proceedings Volumes 40, n^o 5 (2007) : 123–28. http://dx.doi.org/10.3182/20070606-3-mx-2915.00019.

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Ivanovs, Maksims, Roberts Kadikis et Kaspars Ozols. « Perturbation-based methods for explaining deep neural networks : A survey ». Pattern Recognition Letters 150 (octobre 2021) : 228–34. http://dx.doi.org/10.1016/j.patrec.2021.06.030.

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Liu, Song-Tao, et Yuesheng Xu. « Galerkin Methods Based on Hermite Splines for Singular Perturbation Problems ». SIAM Journal on Numerical Analysis 43, n^o 6 (janvier 2006) : 2607–23. http://dx.doi.org/10.1137/040607411.

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El Mokhtari, Rachid, Jean-Marc Cadou et Michel Potier-Ferry. « A two grid algorithm based on perturbation and homotopy methods ». Comptes Rendus Mécanique 330, n^o 12 (décembre 2002) : 825–30. http://dx.doi.org/10.1016/s1631-0721(02)01548-6.

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Zhang, Yale, et J. Fraser Forbes. « Performance Analysis of Perturbation-Based Methods for Real-Time Optimization ». Canadian Journal of Chemical Engineering 84, n^o 2 (19 mai 2008) : 209–18. http://dx.doi.org/10.1002/cjce.5450840208.

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Li, Lan, Yong Hui He et Bo Wang. « Comparison of Methods of Maximum Power Point Tracking of Photovoltaic Cells ». Applied Mechanics and Materials 380-384 (août 2013) : 3362–65. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3362.

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According to engineering mathematics model of solar photovoltaic cells, a simulation model of photovoltaic cells was established in Matlab. In view of problem that it is difficult to get higher tracking accuracy and response speed by use of perturbation and observation method which applied fixed perturbation step, the paper proposed an improved perturbation and observation method based on variable step. Through simulating photovoltaic cells control system, simulation curves of two kinds of methods of maximum power point tracking were compared. The simulation results show that the photovoltaic cells control system can track maximum power point more quickly and has better stability at the maximum power point by use of the improved perturbation and observation method.

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Cha, Philip D., et Austin Shin. « Perturbation Methods for the Eigencharacteristics of Symmetric and Asymmetric Systems ». Shock and Vibration 2018 (17 octobre 2018) : 1–25. http://dx.doi.org/10.1155/2018/8609138.

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Dynamic analysis for a vibratory system typically begins with an evaluation of its eigencharacteristics. However, when design changes are introduced, the eigensolutions of the system change and thus must be recomputed. In this paper, three different methods based on the eigenvalue perturbation theory are introduced to analyze the effects of modifications without performing a potentially time-consuming and costly reanalysis. They will be referred to as the straightforward perturbation method, the incremental perturbation method, and the triple product method. In the straightforward perturbation method, the eigenvalue perturbation theory is used to formulate a first-order and a second-order approximation of the eigensolutions of symmetric and asymmetric systems. In the incremental perturbation method, the straightforward approach is extended to analyze systems with large perturbations using an iterative scheme. Finally, in the triple product method, the accuracy of the approximate eigenvalues is significantly improved by exploiting the orthogonality conditions of the perturbed eigenvectors. All three methods require only the eigensolutions of the nominal or unperturbed system, and in application, they involve simple matrix multiplications. Numerical experiments show that the proposed methods achieve accurate results for systems with and without damping and for systems with symmetric and asymmetric system matrices.

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Chen, Bilian, Yajun Xie et Changfeng Ma. « SOME HIGH ORDER ITERATIVE METHODS FOR NONLINEAR EQUATIONS BASED ON THE MODIFIED HOMOTOPY PERTURBATION METHODS ». Asian-European Journal of Mathematics 03, n^o 03 (septembre 2010) : 395–408. http://dx.doi.org/10.1142/s1793557110000349.

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In this paper, we present some efficient iterative methods for solving nonlinear equation (systems of nonlinear equations, respectively) by using modified homotopy perturbation methods. We also discuss the convergence criteria of the present methods. Some numerical examples are given to illustrate the performance and efficiency of the proposed methods.

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Lazovskaya, Tatiana, Galina Malykhina et Dmitry Tarkhov. « Physics-Based Neural Network Methods for Solving Parameterized Singular Perturbation Problem ». Computation 9, n^o 9 (6 septembre 2021) : 97. http://dx.doi.org/10.3390/computation9090097.

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This work is devoted to the description and comparative study of some methods of mathematical modeling. We consider methods that can be applied for building cyber-physical systems and digital twins. These application areas add to the usual accuracy requirements for a model the need to be adaptable to new data and the small computational complexity allows it to be used in embedded systems. First, we regard the finite element method as one of the “pure” physics-based modeling methods and the general neural network approach as a variant of machine learning modeling with physics-based regularization (or physics-informed neural networks) and their combination. A physics-based network architecture model class has been developed by us on the basis of a modification of classical numerical methods for solving ordinary differential equations. The model problem has a parameter at some values for which the phenomenon of stiffness is observed. We consider a fixed parameter value problem statement and a case when a parameter is one of the input variables. Thus, we obtain a solution for a set of parameter values. The resulting model allows predicting the behavior of an object when its parameters change and identifying its parameters based on observational data.

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Langlands, T. A. M., et D. L. S. McElwain. « A modified Hertzian foil rolling model : approximations based on perturbation methods ». International Journal of Mechanical Sciences 44, n^o 8 (août 2002) : 1715–30. http://dx.doi.org/10.1016/s0020-7403(02)00058-9.

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Yang, Xiao-Dong, Ming Liu, Wei Zhang, Ying-Jing Qian et Roderick V. N. Melnik. « On the Perturbation Methods for Vibration Analysis of Linear Time-Varying Systems ». International Journal of Applied Mechanics 08, n^o 03 (avril 2016) : 1650035. http://dx.doi.org/10.1142/s1758825116500356.

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Some perturbation methods in the studying vibrations of the linear time-varying (LTV) system are discussed. Three classical perturbation methods, namely, averaging method, harmonic balance method, and multiple scales method with linear scales, have been used from a new perspective based on analytical approximations to the corresponding LTV ordinary differential equations. The deploying beam model has been taken as an example to validate the explicit approximate solutions obtained by these perturbation methods. It is demonstrated that such approximate solutions have good agreement with numerical and exact solutions, excluding the vicinity of the turning point.

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Paralič, Ján, Michal Kolárik, Zuzana Paraličová, Oliver Lohaj et Adam Jozefík. « Perturbation-Based Explainable AI for ECG Sensor Data ». Applied Sciences 13, n^o 3 (31 janvier 2023) : 1805. http://dx.doi.org/10.3390/app13031805.

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Deep neural network models have produced significant results in solving various challenging tasks, including medical diagnostics. To increase the credibility of these black-box models in the eyes of doctors, it is necessary to focus on their explainability. Several papers have been published combining deep learning methods with selected types of explainability methods, usually aimed at analyzing medical image data, including ECG images. The ECG is specific because its image representation is only a secondary visualization of stream data from sensors. However, explainability methods for stream data are rarely investigated. Therefore, in this article we focus on the explainability of black-box models for stream data from 12-lead ECG. We designed and implemented a perturbation explainability method and verified it in a user study on a group of medical students with experience in ECG tagging in their final years of study. The results demonstrate the suitability of the proposed method, as well as the importance of including multiple data sources in the diagnostic process.

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Jang, M., J. Choe et S. J. Kim. « Streamline-based Inversion Model Using Simulated Annealing Coupled with Geostatistical Perturbation Methods ». Energy Sources, Part A : Recovery, Utilization, and Environmental Effects 29, n^o 14 (31 août 2007) : 1285–94. http://dx.doi.org/10.1080/15567030600820435.

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Djermoune, E. H., et M. Tomczak. « Perturbation Analysis of Subspace-Based Methods in Estimating a Damped Complex Exponential ». IEEE Transactions on Signal Processing 57, n^o 11 (novembre 2009) : 4558–63. http://dx.doi.org/10.1109/tsp.2009.2024030.

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Li, Mengyang, Fengguang Su, Ou Wu et Ji Zhang. « Logit Perturbation ». Proceedings of the AAAI Conference on Artificial Intelligence 36, n^o 2 (28 juin 2022) : 1359–66. http://dx.doi.org/10.1609/aaai.v36i2.20024.

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Features, logits, and labels are the three primary data when a sample passes through a deep neural network. Feature perturbation and label perturbation receive increasing attention in recent years. They have been proven to be useful in various deep learning approaches. For example, (adversarial) feature perturbation can improve the robustness or even generalization capability of learned models. However, limited studies have explicitly explored for the perturbation of logit vectors. This work discusses several existing methods related to logit perturbation. Based on a unified viewpoint between positive/negative data augmentation and loss variations incurred by logit perturbation, a new method is proposed to explicitly learn to perturb logits. A comparative analysis is conducted for the perturbations used in our and existing methods. Extensive experiments on benchmark image classification data sets and their long-tail versions indicated the competitive performance of our learning method. In addition, existing methods can be further improved by utilizing our method.

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Mo, Wen Hui. « Dynamic Reliability Based on Perturbation Stochastic Finite Element ». Applied Mechanics and Materials 155-156 (février 2012) : 47–50. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.47.

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This paper proposes a method of calculating dynamic reliability using perturbation stochastic finite element. Dynamic analysis of perturbation stochastic finite element is introduced and the mean and variance of the stress can be obtained. Samples of stress and strength are generated by computer program. The Monte Carlo simulation is proposed to compute dynamic reliability of structure. Dynamic reliability of structure is computed by the stress-strength interference model. The proposed methods are demonstrated by a numerical example of axle.

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Lakshmi, M. Naga, et Dr K. Sandhya Rani. « PRIVACY PRESERVING CLUSTERING BASED ON SINGULAR VALUE DECOMPOSITION AND GEOMETRIC DATA PERTURBATION ». INTERNATIONAL JOURNAL OF COMPUTERS & ; TECHNOLOGY 10, n^o 3 (5 août 2013) : 1427–33. http://dx.doi.org/10.24297/ijct.v10i3.3272.

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Privacy preservation is a major concern when the application of data mining techniques to large repositories of data consists of personal, sensitive and confidential information. Singular Value Decomposition (SVD) is a matrix factorization method, which can produces perturbed data by efficiently removing unnecessary information for data mining. In this paper two hybrid methods are proposed which takes the advantage of existing techniques SVD and geometric data transformations in order to provide better privacy preservation. Reflection data perturbation and scaling data perturbation are familiar geometric data transformation methods which retains the statistical properties in the dataset. In hybrid method one, SVD and scaling data perturbation are used as a combination to obtain the distorted dataset. In hybrid method two, SVD and reflection data perturbation methods are used as a combination to obtain the distorted dataset. The experimental results demonstrated that the proposed hybrid methods are providing higher utility without breaching privacy.

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Grebenikov, E. A. « Concerning New Perturbation Methods in Solar System Dynamics ». International Astronomical Union Colloquium 165 (1997) : 399–404. http://dx.doi.org/10.1017/s0252921100046868.

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In this paper a new method of construction of the perturbation motion theory of celestial bodies, based on the averaging principle in view of frequency resonances, is stated. The first approximation of the asymptotic theory is the exact solution of the dynamics averaging equations, in which are included “secular” and “long-periodic” terms. The high-degree approximations are the exact solution of a known Krylov-Bogoliubov generalized equation. It is shown that these iterations are expressed in the analytical form by multiple Fourier series.

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Ouakad, Hassen M., Abdulrahman M. Alofi et Ali H. Nayfeh. « Dynamic Analysis of Multilayers Based MEMS Resonators ». Mathematical Problems in Engineering 2017 (2017) : 1–14. http://dx.doi.org/10.1155/2017/1262650.

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The dynamic behavior of a microelectromechanical system (MEMS) parallel and electrically coupled double-layers (microbeams) based resonator is investigated. Two numerical methods were used to solve the dynamical problem: the reduced-order modeling (ROM) and the perturbation method. The ROM was derived using the so-called Galerkin expansion with considering the linear undamped mode shapes of straight beam as the basis functions. The perturbation method was generated using the method of multiple scales by direct attack of the equations of motion. Dynamic analyses, assuming the above two numerical methods were performed, and a comparison of the results showed good agreement. Finally, a parametric study was performed using the perturbation on different parameters and the results revealed different interesting features, which hopefully can be useful for some MEMS based applications.

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Wang, Zhengguang. « Validation, Robustness, and Accuracy of Perturbation-Based Sensitivity Analysis Methods for Time-Series Deep Learning Models ». Proceedings of the AAAI Conference on Artificial Intelligence 38, n^o 21 (24 mars 2024) : 23768–70. http://dx.doi.org/10.1609/aaai.v38i21.30559.

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This work undertakes studies to evaluate Interpretability Methods for Time Series Deep Learning. Sensitivity analysis assesses how input changes affect the output, constituting a key component of interpretation. Among the post-hoc interpretation methods such as back-propagation, perturbation, and approximation, my work will investigate perturbation-based sensitivity Analysis methods on modern Transformer models to benchmark their performances. Specifically, my work intends to answer three research questions: 1) Do different sensitivity analysis methods yield comparable outputs and attribute importance rankings? 2) Using the same sensitivity analysis method, do different Deep Learning models impact the output of the sensitivity analysis? 3) How well do the results from sensitivity analysis methods align with the ground truth?

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Ratra, Ritu, Preeti Gulia et Nasib Singh Gill. « Performance analysis of perturbation-based privacy preserving techniques : an experimental perspective ». International Journal of Electrical and Computer Engineering (IJECE) 13, n^o 5 (1 octobre 2023) : 5273. http://dx.doi.org/10.11591/ijece.v13i5.pp5273-5281.

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<span lang="EN-US">Nowadays, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several hybrid perturbation strategies that may be used to protect data privacy. For this, two perturbation-based techniques named improved random projection perturbation (IRPP) and enhanced principal component analysis-based technique (EPCAT) were used. These methods are employed to assess the precision, run time, and accuracy of the experimental results. This paper provides the impacts of perturbation-based privacy preserving techniques. It is observed that hybrid approaches are more efficient than the traditional approach.</span>

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Zhang, Zhiwei, Xunzhang Gao, Shuowei Liu, Bowen Peng et Yufei Wang. « Energy-Based Adversarial Example Detection for SAR Images ». Remote Sensing 14, n^o 20 (15 octobre 2022) : 5168. http://dx.doi.org/10.3390/rs14205168.

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Adversarial examples (AEs) bring increasing concern on the security of deep-learning-based synthetic aperture radar (SAR) target recognition systems. SAR AEs with perturbation constrained to the vicinity of the target have been recently in the spotlight due to the physical realization prospects. However, current adversarial detection methods generally suffer severe performance degradation against SAR AEs with region-constrained perturbation. To solve this problem, we treated SAR AEs as low-probability samples incompatible with the clean dataset. With the help of energy-based models, we captured an inherent energy gap between SAR AEs and clean samples that is robust to the changes of the perturbation region. Inspired by this discovery, we propose an energy-based adversarial detector, which requires no modification to a pretrained model. To better distinguish the clean samples and AEs, energy regularization was adopted to fine-tune the pretrained model. Experiments demonstrated that the proposed method significantly boosts the detection performance against SAR AEs with region-constrained perturbation.

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Taylor, Zoe, Gregory S. Walsh, Hannah Hawkins, Mario Inacio et Patrick Esser. « Perturbations during Gait : A Systematic Review of Methodologies and Outcomes ». Sensors 22, n^o 15 (8 août 2022) : 5927. http://dx.doi.org/10.3390/s22155927.

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Background: Despite extensive literature regarding laboratory-based balance perturbations, there is no up-to-date systematic review of methods. This systematic review aimed to assess current perturbation methods and outcome variables used to report participant biomechanical responses during walking. Methods: Web of Science, CINAHL, and PubMed online databases were searched, for records from 2015, the last search was on 30th of May 2022. Studies were included where participants were 18+ years, with or without clinical conditions, conducted in non-hospital settings. Reviews were excluded. Participant descriptive, perturbation method, outcome variables and results were extracted and summarised. Bias was assessed using the Appraisal tool for Cross-sectional Studies risk of bias assessment tool. Qualitative analysis was performed as the review aimed to investigate methods used to apply perturbations. Results: 644 records were identified and 33 studies were included, totaling 779 participants. The most frequent method of balance perturbation during gait was by means of a treadmill translation. The most frequent outcome variable collected was participant step width, closely followed by step length. Most studies reported at least one spatiotemporal outcome variable. All included studies showed some risk of bias, generally related to reporting of sampling approaches. Large variations in perturbation type, duration and intensity and outcome variables were reported. Conclusions: This review shows the wide variety of published laboratory perturbation methods. Moreover, it demonstrates the significant impact on outcome measures of a study based on the type of perturbation used. Registration: PROSPERO ID: CRD42020211876.

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Pan, Christopher S., Sharon Chiou et Scott Hendricks. « The effect of drywall lifting method on workers' balance in a laboratory-based simulation ». Occupational Ergonomics 3, n^o 4 (17 novembre 2003) : 235–49. http://dx.doi.org/10.3233/oer-2003-3405.

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Voluntary body movement can import a perturbation to the postural stability/balance of a human body. Heavy manual material handling such as drywall lifting may increase this perturbation. The objective of this laboratory-based study was to quantify workers' postural stability while lifting drywall sheets through kinetic and kinematic analyses, and to identify the drywall lifting methods that caused the least perturbation on workers' balance. Sixty male construction workers participated in this study. A simulated drywall-lifting workstation was built and all subjects performed one of the four randomly assigned lifting methods. Kinetic and kinematic measurements were synchronized and collected using a piezoelectric force platform and a five-camera motion analysis system. Both center-of-pressure (COP) and center-of-mass (COM) data were analyzed to assess workers' postural stability. Univariate analyses and principal component analyses (PCA) were used to analyze 13 COP-based and 21 COM-based variables. Results from the univariate analyses and PCA significantly indicated that the three horizontal lifting methods created less perturbation than the vertical lifting method. Based on the results of this study and prior studies, it is concluded that horizontal lifting with both hands on top of the drywall appears to be the best work practice to reduce manual drywall handling hazards associated with fall potential and overexertion injuries.

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DOUGLAS, JIM, et SEONGJAI KIM. « IMPROVED ACCURACY FOR LOCALLY ONE-DIMENSIONAL METHODS FOR PARABOLIC EQUATIONS ». Mathematical Models and Methods in Applied Sciences 11, n^o 09 (décembre 2001) : 1563–79. http://dx.doi.org/10.1142/s0218202501001471.

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Classical alternating direction (AD) and fractional step (FS) methods for parabolic equations, based on some standard implicit time-stepping procedure such as Crank–Nicolson, can have errors associated with the AD or FS perturbations that are much larger than the errors associated with the underlying time-stepping procedure. We show that minor modifications in the AD and FS procedures can virtually eliminate the perturbation errors at an additional computational cost that is less than 10% of the cost of the original AD or FS method. Moreover, after these modifications, the AD and FS procedures produce identical approximations of the solution of the differential problem. It is also shown that the same perturbation of the Crank–Nicolson procedure can be obtained with AD and FS methods associated with the backward Euler time-stepping scheme. An application of the same concept is presented for second-order wave equations.

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Ning, W., et L. He. « Computation of Unsteady Flows Around Oscillating Blades Using Linear and Nonlinear Harmonic Euler Methods ». Journal of Turbomachinery 120, n^o 3 (1 juillet 1998) : 508–14. http://dx.doi.org/10.1115/1.2841747.

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A quasi-three-dimensional time-linearized Euler method has been developed to compute unsteady flows around oscillating blades. In the baseline method, unsteady flow is decomposed into a steady flow plus a linear harmonically varying unsteady flow. Both the steady flow equations and the unsteady perturbation equations are solved using a pseudo-time-marching method. Based upon this method, a novel nonlinear harmonic Euler method has been developed. Due to the nonlinearity of the aerodynamic governing equations, time-averaging generates extra “unsteady stress” terms. These nonlinear effects are included by a strongly coupled approach between the perturbation equations and the time-averaged equations. Numerical results demonstrate that nonlinear effects are very effectively modeled by the nonlinear harmonic method.

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Krishnarayalu, M. S. « Singular perturbation methods for a class of initial and boundary value problems in multi-parameter classical digital control systems ». ANZIAM Journal 46, n^o 1 (juillet 2004) : 67–77. http://dx.doi.org/10.1017/s1446181100013675.

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AbstractA stable linear time-invariant classical digital control system with several widely different small coefficients multiplying the lowest functions is considered. It is formulated as a multi-parameter singularly perturbed system. Perturbation methods are developed for both initial and boundary value problems based on asymptotic expansions of the perturbation parameters. The approximate solution consists of an outer solution and a number of boundary layer correction solutions equal to the number of initial conditions lost in the process of degeneration. An example is provided for illustration.

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Montazeri-Gh, Morteza, et Ali Rasti. « Analyzing different numerical linearization methods for the dynamic model of a turbofan engine ». Mechanics & ; Industry 20, n^o 3 (2019) : 303. http://dx.doi.org/10.1051/meca/2019012.

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State equations of aircraft engine dynamics usually required for controller design, are not available in closed form, so the dynamic models are commonly linearized numerically. Development of model-based controllers for aeroengine in the recent years necessitates the use of accurate linear models. However, there is no comprehensive study about the accuracy of the linear models obtained from nonlinear engine models. In this paper, the accuracy of different numerical linearization methods for linearizing the dynamic model of a turbofan engine is investigated. For this objective, a thermodynamic model of a two-spool turbofan engine is considered and three various numerical linearization methods are defined. The first method is based on the perturbation technique, including ordinary and central difference perturbation. The second one is a system identification method and the third one is tuning the elements of the matrices of the linear state-space model using genetic algorithm. The accuracy analysis of the presented procedures is performed for both single-input and double-input cases. In the single-input case, the fuel mass flow rate and in the double-input, in addition to the fuel, the bleed air taken from between the two compressors are considered as control variables. Finally, by defining different error criterions, the accuracy of the linearization methods is evaluated. The results show that the linear model obtained from system identification and central difference perturbation methods have higher percentage of compliances compared to the others.

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Asmolovskiy, Nikolay, Anton Tkachuk et Manfred Bischoff. « Numerical approaches to stability analysis of cylindrical composite shells based on load imperfections ». Engineering Computations 32, n^o 2 (20 avril 2015) : 498–518. http://dx.doi.org/10.1108/ec-10-2013-0246.

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Purpose – Current procedures of buckling load estimation for thin-walled structures may provide very conservative estimates. Their refinement offers the potential to use structure and material properties more efficiently. Due to the large variety of design variables, for example laminate layup in composite structures, a prohibitively large number of tests would be required for experimental assessment, and thus reliable numerical techniques are of particular interest. The purpose of this paper is to analyze different methods of numerical buckling load estimation, formulate simulation procedures suitable for commercial software and give recommendations regarding their application. All investigations have been carried out for cylindrical composite shells; however similar approaches are feasible for other structures as well. Design/methodology/approach – The authors develop a concept to apply artificial load imperfections with the aim to estimate as good as possible lower bounds for the buckling loads of shells for which the actual physical imperfections are not known. Single and triple perturbation load approach, global and local dynamic perturbation approach and path following techniques are applied to the analysis of a cylindrical composite shell with known buckling characteristics. Results of simulations are compared with published experimental data. Findings – A single perturbation load approach is reproduced and modified. Buckling behavior for negative values of the perturbation load is examined and a pattern similar to a positive perturbation load is observed. Simulations with three perturbation forces show a decreased (i. e. more critical) value of the buckling load compared to the single perturbation load approach. Global and local dynamic perturbation approaches exhibit a behavior suitable for lower bound estimation for structures with arbitrary geometries. Originality/value – Various load imperfection approaches to buckling load estimation are validated and compared. All investigated methods do not require knowledge of the real geometrical imperfections of the structure. Simulations were performed using a commercial finite element code. Investigations of sensitivity with respect to a single perturbation load are extended to the negative range of the perturbation load amplitude. A specific pattern for a global perturbation approach was developed, and based on it a novel simulation procedure is proposed.

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Wang, Xiangyu, Song Cen et Chenfeng Li. « Generalized Neumann Expansion and Its Application in Stochastic Finite Element Methods ». Mathematical Problems in Engineering 2013 (2013) : 1–13. http://dx.doi.org/10.1155/2013/325025.

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An acceleration technique, termed generalized Neumann expansion (GNE), is presented for evaluating the responses of uncertain systems. The GNE method, which solves stochastic linear algebraic equations arising in stochastic finite element analysis, is easy to implement and is of high efficiency. The convergence condition of the new method is studied, and a rigorous error estimator is proposed to evaluate the upper bound of the relative error of a given GNE solution. It is found that the third-order GNE solution is sufficient to achieve a good accuracy even when the variation of the source stochastic field is relatively high. The relationship between the GNE method, the perturbation method, and the standard Neumann expansion method is also discussed. Based on the links between these three methods, quantitative error estimations for the perturbation method and the standard Neumann method are obtained for the first time in the probability context.

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Khidir, Ahmed A. « A New Spectral-Homotopy Perturbation Method and Its Application to Jeffery-Hamel Nanofluid Flow with High Magnetic Field ». Journal of Computational Methods in Physics 2013 (30 décembre 2013) : 1–10. http://dx.doi.org/10.1155/2013/939143.

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We present a new modification of the homotopy perturbation method (HPM) for solving nonlinear boundary value problems. The technique is based on the standard homotopy perturbation method, and blending of the Chebyshev pseudospectral methods. The implementation of the new approach is demonstrated by solving the Jeffery-Hamel flow considering the effects of magnetic field and nanoparticle. Comparisons are made between the proposed technique, the standard homotopy perturbation method, and the numerical solutions to demonstrate the applicability, validity, and high accuracy of the present approach. The results demonstrate that the new modification is more efficient and converges faster than the standard homotopy perturbation method.

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Ferreira, Rafael N., Nuno Ferrete Ribeiro, Joana Figueiredo et Cristina P. Santos. « Provoking Artificial Slips and Trips towards Perturbation-Based Balance Training : A Narrative Review ». Sensors 22, n^o 23 (28 novembre 2022) : 9254. http://dx.doi.org/10.3390/s22239254.

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Humans’ balance recovery responses to gait perturbations are negatively impacted with ageing. Slip and trip events, the main causes preceding falls during walking, are likely to produce severe injuries in older adults. While traditional exercise-based interventions produce inconsistent results in reducing patients’ fall rates, perturbation-based balance training (PBT) emerges as a promising task-specific solution towards fall prevention. PBT improves patients’ reactive stability and fall-resisting skills through the delivery of unexpected balance perturbations. The adopted perturbation conditions play an important role towards PBT’s effectiveness and the acquisition of meaningful sensor data for studying human biomechanical reactions to loss of balance (LOB) events. Hence, this narrative review aims to survey the different methods employed in the scientific literature to provoke artificial slips and trips in healthy adults during treadmill and overground walking. For each type of perturbation, a comprehensive analysis was conducted to identify trends regarding the most adopted perturbation methods, gait phase perturbed, gait speed, perturbed leg, and sensor systems used for data collection. The reliable application of artificial perturbations to mimic real-life LOB events may reduce the gap between laboratory and real-life falls and potentially lead to fall-rate reduction among the elderly community.

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Deng, Zhiyang, Shilin Wei, Tao Chen, Xiaochun Song et Yihua Kang. « A wall-thinning measuring method based on magnetic permeability perturbation ». International Journal of Applied Electromagnetics and Mechanics 64, n^o 1-4 (10 décembre 2020) : 921–30. http://dx.doi.org/10.3233/jae-209406.

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In the determination of metal thickness by traditional electromagnetic nondestructive testing methods, the material’s electrical conductivity and magnetic permeability are required but practically unavailable or inexact. In this paper, a wall-thinning measuring method based on magnetic permeability variation of steel pipes is proposed. The direct current magnetization is applied to the ferromagnetic material, the internal magnetic field perturbation caused by the wall-thinning brings the permeability perturbation in the surface layer and be sensed by eddy current probe. The simulation results show that the permeability perturbation is closely related to wall thickness loss. The experiment results show that the signal amplitude increases with the thickness of the wall-thinning and the new method can measure the 10% wall-thinning of pipe. Finally, theoretical range of magnetization current values are discussed. The new method takes advantage of the changes in permeability and is not limited by thickness of object to be detected.

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Gottu Mukkula, Anwesh Reddy, et Sebastian Engell. « Handling Measurement Delay in Iterative Real-Time Optimization Methods ». Processes 9, n^o 10 (11 octobre 2021) : 1800. http://dx.doi.org/10.3390/pr9101800.

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This paper is concerned with the real-time optimization (RTO) of chemical plants, i.e., the optimization of the steady-state operating points during operation, based on inaccurate models. Specifically, modifier adaptation is employed to cope with the plant-model mismatch, which corrects the plant model and the constraint functions by bias and gradient correction terms that are computed from measured variables at the steady-states of the plant. This implies that the sampling time of the iterative RTO scheme is lower-bounded by the time to reach a new steady-state after the previously computed inputs were applied. If analytical process measurements (PAT technology) are used to obtain the steady-state responses, time delays occur due to the measurement delay of the PAT device and due to the transportation delay if the samples are transported to the instrument via pipes. This situation is quite common because the PAT devices can often only be installed at a certain distance from the measurement location. The presence of these time delays slows down the iterative real-time optimization, as the time from the application of a new set of inputs to receiving the steady-state information increases further. In this paper, a proactive perturbation scheme is proposed to efficiently utilize the idle time by intelligently scheduling the process inputs taking into account the time delays to obtain the steady-state process measurements. The performance of the proposed proactive perturbation scheme is demonstrated for two examples, the Williams–Otto reactor benchmark and a lithiation process. The simulation results show that the proposed proactive perturbation scheme can speed up the convergence to the true plant optimum significantly.

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Jeong, Soyeong, Pilwon Kim et Chang Hyeong Lee. « Perturbation and Truncation of Probability Generating Function Methods for Stiff Chemical Reactions ». Journal of Applied Mathematics 2015 (2015) : 1–8. http://dx.doi.org/10.1155/2015/864238.

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One can reformulate chemical master equations of the stochastic reaction network into a partial differential equation (PDE) of a probability generating function (PGF). In this paper, we present two improvements in such PGF-PDE approach, based on perturbation and double-truncation, respectively. The stiff system that involves fast and slow reactions together often requires high computational cost. By applying the perturbation method to PGF-PDEs, we expand the equation in terms of a small reaction rate which is often responsible for such stiffness of the system. Also by doubly truncating, we dump relatively small terms and reduce the computational load significantly at each time step. The terms corresponding to rare events are sieved out through truncations of Taylor expansion. It is shown through numerical examples of enzyme kinetics, transition model, and Brusselator model that the suggested method is accurate and efficient for approximation of the state probabilities.

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Duan, Jiale, Linyao Qiu, Guangjun He, Ling Zhao, Zhenshi Zhang et Haifeng Li. « A Region-Adaptive Local Perturbation-Based Method for Generating Adversarial Examples in Synthetic Aperture Radar Object Detection ». Remote Sensing 16, n^o 6 (12 mars 2024) : 997. http://dx.doi.org/10.3390/rs16060997.

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In synthetic aperture radar (SAR) imaging, intelligent object detection methods are facing significant challenges in terms of model robustness and application security, which are posed by adversarial examples. The existing adversarial example generation methods for SAR object detection can be divided into two main types: global perturbation attacks and local perturbation attacks. Due to the dynamic changes and irregular spatial distribution of SAR coherent speckle backgrounds, the attack effectiveness of global perturbation attacks is significantly reduced by coherent speckle. In contrast, by focusing on the image objects, local perturbation attacks achieve targeted and effective advantages over global perturbations by minimizing interference from the SAR coherent speckle background. However, the adaptability of conventional local perturbations is limited because they employ a fixed size without considering the diverse sizes and shapes of SAR objects under various conditions. This paper presents a framework for region-adaptive local perturbations (RaLP) specifically designed for SAR object detection tasks. The framework consists of two modules. To address the issue of coherent speckle noise interference in SAR imagery, we develop a local perturbation generator (LPG) module. By filtering the original image, this module reduces the speckle features introduced during perturbation generation. It then superimposes adversarial perturbations in the form of local perturbations on areas of the object with weaker speckles, thereby reducing the mutual interference between coherent speckles and adversarial perturbation. To address the issue of insufficient adaptability in terms of the size variation in local adversarial perturbations, we propose an adaptive perturbation optimizer (APO) module. This optimizer adapts the size of the adversarial perturbations based on the size and shape of the object, effectively solving the problem of adaptive perturbation size and enhancing the universality of the attack. The experimental results show that RaLP reduces the detection accuracy of the YOLOv3 detector by 29.0%, 29.9%, and 32.3% on the SSDD, SAR-Ship, and AIR-SARShip datasets, respectively, and the model-to-model and dataset-to-dataset transferability of RaLP attacks are verified.

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Chioua, Moncef, Bala Srinivasan, Michel Perrier et Martin Guay. « IMPROVING CONVERGENCE OF PERTURBATION-BASED EXTREMUM SEEKING METHODS FOR A CLASS OF DIFFERENTIALLY FLAT SYSTEMS ». IFAC Proceedings Volumes 40, n^o 12 (2007) : 270–75. http://dx.doi.org/10.3182/20070822-3-za-2920.00045.

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Chiba, Hayato. « Extension and Unification of Singular Perturbation Methods for ODEs Based on the Renormalization Group Method ». SIAM Journal on Applied Dynamical Systems 8, n^o 3 (janvier 2009) : 1066–115. http://dx.doi.org/10.1137/090745957.

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Gauthier, Cindy, Katherine Chan, Kei Masani et Kristin E. Musselman. « Perturbation-Based Training in Combination with Functional Electrical Stimulation : A Promising Mixed-methods Case Study ». Archives of Physical Medicine and Rehabilitation 101, n^o 11 (novembre 2020) : e26. http://dx.doi.org/10.1016/j.apmr.2020.09.072.

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Zhang, Xinyan, Jinzhong Min et Tianjie Wu. « A study of ensemble-sensitivity-based initial condition perturbation methods for convection-permitting ensemble forecasts ». Atmospheric Research 234 (avril 2020) : 104741. http://dx.doi.org/10.1016/j.atmosres.2019.104741.

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Ishimaru, Akira, John D. Rockway et Yasuo Kuga. « Rough surface Green's function based on the first-order modified perturbation and smoothed diagram methods ». Waves in Random Media 10, n^o 1 (janvier 2000) : 17–31. http://dx.doi.org/10.1088/0959-7174/10/1/302.

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Zhang, Qinglong, Lu Rao et Yubin Yang. « A Novel Visual Interpretability for Deep Neural Networks by Optimizing Activation Maps with Perturbation ». Proceedings of the AAAI Conference on Artificial Intelligence 35, n^o 4 (18 mai 2021) : 3377–84. http://dx.doi.org/10.1609/aaai.v35i4.16450.

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Interpretability has been regarded as an essential component for deploying deep neural networks, in which the saliency-based method is one of the most prevailing interpretable approaches since it can generate individually intuitive heatmaps that highlight parts of the input image that are most important to the decision of the deep networks on a particular classification target. However, heatmaps generated by existing methods either contain little information to represent objects (perturbation-based methods) or cannot effectively locate multi-class objects (activation-based approaches). To address this issue, a two-stage framework for visualizing the interpretability of deep neural networks, called Activation Optimized with Perturbation (AOP), is designed to optimize activation maps generated by general activation-based methods with the help of perturbation-based methods. Finally, in order to obtain better explanations for different types of images, we further present an instance of the AOP framework, Smooth Integrated Gradient-based Class Activation Map (SIGCAM), which proposes a weighted GradCAM by applying the feature map as weight coefficients and employs I-GOS to optimize the base-mask generated by weighted GradCAM. Experimental results on common-used benchmarks, including deletion and insertion tests on ImageNet-1k, and pointing game tests on COCO2017, show that the proposed AOP and SIGCAM outperform the current state-of-the-art methods significantly by generating higher quality image-based saliency maps.

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Chai, Yu, et Dong Mei Shang. « The Improvement of Maximum Power Point Tracker Algorithm in Photovoltaic Power System ». Applied Mechanics and Materials 157-158 (février 2012) : 377–80. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.377.

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Maximum power tracking is one of most important methods to improve efficiency of Photovoltaic power system. By simulating and analyzing the photovoltaic cell model，using Boost circuit to achieve maximum power tracking. Based on the analysis of the constant-voltage method, perturbation and observation method, then combining these two methods，I Put forward the constant-voltage method combined with perturbation and observation method, and has carried on the simulation.

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Yin, Hui, Dejie Yu, Shengwen Yin et Baizhan Xia. « Efficient Midfrequency Analysis of Built-Up Structure Systems with Interval Parameters ». Shock and Vibration 2015 (2015) : 1–18. http://dx.doi.org/10.1155/2015/712428.

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To improve the efficiency of midfrequency analysis of built-up structure systems with interval parameters, the second-order interval and subinterval perturbation methods are introduced into the hybrid finite element/statistical energy analysis (FE/SEA) framework in this paper. Based on the FE/SEA for built-up structure systems and the second-order interval perturbation method, the response variables are expanded with the second-order Taylor series and nondiagonal elements of the Hessian matrices are neglected. Extreme values of the expanded variables are searched by using efficient search algorithm. For large parameter intervals, the subinterval perturbation method is introduced. Numerical results verify the effectiveness of the proposed methods.

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Mendonça, Antônio Marcos, et JoséPaulo Bonatti. « Experiments with EOF-Based Perturbation Methods and Their Impact on the CPTEC/INPE Ensemble Prediction System ». Monthly Weather Review 137, n^o 4 (1 avril 2009) : 1438–59. http://dx.doi.org/10.1175/2008mwr2581.1.

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Abstract The impact of modifications of the perturbation method based on empirical orthogonal functions (EOFs) used operationally upon the ensemble prediction system (EPS) at the Center for Weather Prediction and Climate Studies/National Institute for Space Research (CPTEC/INPE) is evaluated. The main changes proposed in this study are to apply the EOF method to perturb the midlatitudes, apply additional perturbations to the surface pressure (P) and specific humidity (Q) fields, and compute regional perturbations over South America. The impact of these modifications in the characteristics of the initial perturbations and in the quality of the EPS forecasts is investigated. The EPS forecasts are evaluated through average statistical scores over the period 15 December 2004–15 February 2005. The statistical scores used in the evaluation are pattern anomaly correlation, root-mean-square error, ensemble spread, Brier skill score, and perturbation versus error correlation analysis (PECA). Results indicate that with the inclusion of perturbations on P and Q, EOF-based perturbations acquire a more baroclinic structure. It is also observed that the simultaneous application of additional perturbations both in the extratropics and to the P and Q fields improves the performance of CPTEC EPS and enhances the quality of forecast perturbations. Moreover, regional EOF-based perturbations computed over South America have positive impact on the ensemble forecasts over the target region.

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Sanapala, Lavanya, et Lakshmeeswari Gondi. « Mitigating Gradient-Based Data Poisoning Attacks on Machine Learning Models : A Statistical Detection Method ». Indian Journal Of Science And Technology 17, n^o 21 (25 mai 2024) : 2218–31. http://dx.doi.org/10.17485/ijst/v17i21.1035.

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Objectives: This research paper aims to develop a novel method for identifying gradient-based data poisoning attacks on industrial applications like autonomous vehicles and intelligent healthcare systems relying on machine learning and deep learning techniques. These algorithms performs well only if they are trained on good quality dataset. However, the ML models are prone to data poisoning attacks, targeting the training dataset, manipulate its input samples such that the machine learning algorithm gets confused and produces wrong predictions. The current detection techniques are effective to detect known attacks and lack generalized detection to unknown attacks. To address this issue, this paper aims to integrate security elements within the machine learning framework, guaranteeing effective identification and mitigation of known and unknown threats and achieve generalized detection. Methods: ML Filter, a unique attack detection approach integrates ML-Filter Detection Algorithm and the Statistical Perturbation Bounds Identification Algorithm to determine the given dataset is poisoned or not. DBSCAN algorithm is used to divide the dataset into several smaller subsets and perform algorithmic analysis for detection. The performance of the proposed method is evaluated in terms of True positive rate and significance test accuracy. Findings: The probability distribution differences between original and poisoned datasets vary with change in perturbation size rather than the datasets and ML models use for application. This finding lead to determine the perturbation bounds using statistical pairwise distance metrics and corresponding significance tests computed on the results. ML Filter demonstrates a high detection rate of 99.63% for known attacks and achieves a generalized detection accuracy of 98% for unknown attacks. Novelty: A secured ML architecture and a unique statistical detection approach ML-Filter, effectively detect data poisoning attacks, demonstrating significant advancements in detecting both known and unknown threats in industrial applications utilizing machine learning and deep learning algorithms. Keywords: Privacy and security, Adversarial machine learning, Secured ML Architecture, ML-Filter, Statistical Perturbation Bounds Identification Algorithm

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ANDRIANOV, IGOR VASIL'EVICH, VICTOR ISAAKOVICH OLEVS'KYY et JAN AWREJCEWICZ. « ANALYTICAL PERTURBATION METHOD FOR CALCULATION OF SHELLS BASED ON 2D PADÉ APPROXIMANTS ». International Journal of Structural Stability and Dynamics 13, n^o 07 (23 août 2013) : 1340003. http://dx.doi.org/10.1142/s0219455413400038.

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Calculations of nonlinear displacements and vibrations of inhomogeneous loaded shells with developable principal surface by means of different analytical methods are represented. It is shown that solutions to these methods are the expansions of exact solution in the Taylor series for an independent variable, and in the particular case — for the powers of a natural parameter. A method that provides a polynomial asymptotic approximation of the exact solution of the general form and its meromorphic continuation based on 1D and 2D Padé approximations is proposed. Calculations of nonlinear deformation and stability of elastic flexible circular cylindrical shell under uniform external pressures and of free oscillations of simply supported stringer shell demonstrate the efficiency and accuracy of the proposed method.

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