Stability of Cracked Plates with Nonlinearly Variable Thickness Resting on Elastic Foundations

In this paper, the stability of rectangular cracked plates with nonlinearly variable thickness resting on the elastic foundations is studied. The thickness of the plate varies exponentially along the x-axis. Meanwhile, the elastic foundation is modeled by a two-parameter Pasternak elastic foundation type. The crack is assumed at the center of the plate with variable length and angle of inclination. The establishment of the stability equations of the cracked plate is based on the Higher Order Shear Deformation Theory (HSDT) combined with the phase field theory. Next, using the finite element method to solve the equations to find the minimum force that causes plate instability. To test the reliability of the computational theory, the results are compared with several reputable published papers. Then, the article will investigate the influence of elastic foundation, crack location, crack length and crack inclination on the stability of plate. The results show that the elastic foundation has a great influence on the plate stability, while the crack inclination angle has less influence. Finally, there are some images of the destabilization patterns of cracked plates placed on an elastic foundation

Stability of Cracked Plates with Nonlinearly Variable Thickness Resting on Elastic Foundations

In this paper, the stability of rectangular cracked plates with nonlinearly variable thickness resting on the elastic foundations is studied. The thickness of the plate varies exponentially along the x-axis. Meanwhile, the elastic foundation is modeled by a two-parameter Pasternak elastic foundation type. The crack is assumed at the center of the plate with variable length and angle of inclination. The establishment of the stability equations of the cracked plate is based on the Higher Order Shear Deformation Theory (HSDT) combined with the phase field theory. Next, using the finite element method to solve the equations to find the minimum force that causes plate instability. To test the reliability of the computational theory, the results are compared with several reputable published papers. Then, the article will investigate the influence of elastic foundation, crack location, crack length and crack inclination on the stability of plate. The results show that the elastic foundation has a great influence on the plate stability, while the crack inclination angle has less influence. Finally, there are some images of the destabilization patterns of cracked plates placed on an elastic foundation

SYNTHESIS OF COPPER-BASED NANOPARTICLE CATALYSTS BY DIFFERENT METHODS FOR TOTAL OXIDATION OF VOC

In this paper, the process of preparing 10 wt.% Cu/g-Al2O3 catalysts was studied by different methods. The changes in structure and texture of the catalysts were examined by X-ray diffraction (XRD), transmission electron microscopy (TEM) and Fourier-transform infrared spectroscopy (FT-IR). The activities of catalyst were investigated completely oxidized VOC (toluene and n-butanol) on gas-phase reactions over the Cu/g-Al2O3 catalyst. The results were found that influence of the size of copper nanoparticles enhancing copper dispersion and selectivity of the catalyst prepared by non-thermal plasma (NTP) was superior to those obtained from the impregnation (WI) and deposition-precipitation (DP). The total oxidation of VOC to CO2 and H2O was achieved above 275oC. Compared to the WI and DP, the NTP method increased the oxidation efficiency by 15-30%

Benchmarking Jetson Edge Devices with an End-to-end Video-based Anomaly Detection System

Innovative enhancement in embedded system platforms, specifically hardware accelerations, significantly influence the application of deep learning in real-world scenarios. These innovations translate human labor efforts into automated intelligent systems employed in various areas such as autonomous driving, robotics, Internet-of-Things (IoT), and numerous other impactful applications. NVIDIA's Jetson platform is one of the pioneers in offering optimal performance regarding energy efficiency and throughput in the execution of deep learning algorithms. Previously, most benchmarking analysis was based on 2D images with a single deep learning model for each comparison result. In this paper, we implement an end-to-end video-based crime-scene anomaly detection system inputting from surveillance videos and the system is deployed and completely operates on multiple Jetson edge devices (Nano, AGX Xavier, Orin Nano). The comparison analysis includes the integration of Torch-TensorRT as a software developer kit from NVIDIA for the model performance optimisation. The system is built based on the PySlowfast open-source project from Facebook as the coding template. The end-to-end system process comprises the videos from camera, data preprocessing pipeline, feature extractor and the anomaly detection. We provide the experience of an AI-based system deployment on various Jetson Edge devices with Docker technology. Regarding anomaly detectors, a weakly supervised video-based deep learning model called Robust Temporal Feature Magnitude Learning (RTFM) is applied in the system. The approach system reaches 47.56 frames per second (FPS) inference speed on a Jetson edge device with only 3.11 GB RAM usage total. We also discover the promising Jetson device that the AI system achieves 15% better performance than the previous version of Jetson devices while consuming 50% less energy power.Comment: 18 pages, 7 figures, 5 table

Programmation DC et DCA pour l'optimisation non convexe/optimisation globale en variables mixtes entières (Codes et Applications)

Basés sur les outils théoriques et algorithmiques de la programmation DC et DCA, les travaux de recherche dans cette thèse portent sur les approches locales et globales pour l'optimisation non convexe et l'optimisation globale en variables mixtes entières. La thèse comporte 5 chapitres. Le premier chapitre présente les fondements de la programmation DC et DCA, et techniques de Séparation et Evaluation (B&B) (utilisant la technique de relaxation DC pour le calcul des bornes inférieures de la valeur optimale) pour l'optimisation globale. Y figure aussi des résultats concernant la pénalisation exacte pour la programmation en variables mixtes entières. Le deuxième chapitre est consacré au développement d'une méthode DCA pour la résolution d'une classe NP-difficile des programmes non convexes non linéaires en variables mixtes entières. Ces problèmes d'optimisation non convexe sont tout d'abord reformulées comme des programmes DC via les techniques de pénalisation en programmation DC de manière que les programmes DC résultants soient efficacement résolus par DCA et B&B bien adaptés. Comme première application en optimisation financière, nous avons modélisé le problème de gestion de portefeuille sous le coût de transaction concave et appliqué DCA et B&B à sa résolution. Dans le chapitre suivant nous étudions la modélisation du problème de minimisation du coût de transaction non convexe discontinu en gestion de portefeuille sous deux formes : la première est un programme DC obtenu en approximant la fonction objectif du problème original par une fonction DC polyèdrale et la deuxième est un programme DC mixte 0-1 équivalent. Et nous présentons DCA, B&B, et l'algorithme combiné DCA-B&B pour leur résolution. Le chapitre 4 étudie la résolution exacte du problème multi-objectif en variables mixtes binaires et présente deux applications concrètes de la méthode proposée. Nous nous intéressons dans le dernier chapitre à ces deux problématiques challenging : le problème de moindres carrés linéaires en variables entières bornées et celui de factorisation en matrices non négatives (Nonnegative Matrix Factorization (NMF)). La méthode NMF est particulièrement importante de par ses nombreuses et diverses applications tandis que les applications importantes du premier se trouvent en télécommunication. Les simulations numériques montrent la robustesse, rapidité (donc scalabilité), performance et la globalité de DCA par rapport aux méthodes existantes.Based on theoretical and algorithmic tools of DC programming and DCA, the research in this thesis focus on the local and global approaches for non convex optimization and global mixed integer optimization. The thesis consists of 5 chapters. The first chapter presents fundamentals of DC programming and DCA, and techniques of Branch and Bound method (B&B) for global optimization (using the DC relaxation technique for calculating lower bounds of the optimal value). It shall include results concerning the exact penalty technique in mixed integer programming. The second chapter is devoted of a DCA method for solving a class of NP-hard nonconvex nonlinear mixed integer programs. These nonconvex problems are firstly reformulated as DC programs via penalty techniques in DC programming so that the resulting DC programs are effectively solved by DCA and B&B well adapted. As a first application in financial optimization, we modeled the problem pf portfolio selection under concave transaction costs and applied DCA and B&B to its solutions. In the next chapter we study the modeling of the problem of minimization of nonconvex discontinuous transaction costs in portfolio selection in two forms: the first is a DC program obtained by approximating the objective function of the original problem by a DC polyhedral function and the second is an equivalent mixed 0-1 DC program. And we present DCA, B&B algorithm, and a combined DCA-B&B algorithm for their solutions. Chapter 4 studied the exact solution for the multi-objective mixed zero-one linear programming problem and presents two practical applications of proposed method. We are interested int the last chapter two challenging problems: the linear integer least squares problem and the Nonnegative Mattrix Factorization problem (NMF). The NMF method is particularly important because of its many various applications of the first are in telecommunications. The numerical simulations show the robustness, speed (thus scalability), performance, and the globality of DCA in comparison to existent methods.ROUEN-INSA Madrillet (765752301) / SudocSudocFranceF

Machine Learning Models for Inferring the Axial Strength in Short Concrete-Filled Steel Tube Columns Infilled with Various Strength Concrete

Concrete-filled steel tube (CFST) columns are used in the construction industry because of their high strength, ductility, stiffness, and fire resistance. This paper developed machine learning techniques for inferring the axial strength in short CFST columns infilled with various strength concrete. Additive Random Forests (ARF) and Artificial Neural Networks (ANNs) models were developed and tested using large experimental data. These data-driven models enable us to infer the axial strength in CFST columns based on the diameter, the tube thickness, the steel yield stress, concrete strength, column length, and diameter/tube thickness. The analytical results showed that the ARF obtained high accuracy with the 6.39% in mean absolute percentage error (MAPE) and 211.31 kN in mean absolute error (MAE). The ARF outperformed significantly the ANNs with an improvement rate at 84.1% in MAPE and 65.4% in MAE. In comparison with the design codes such as EC4 and AISC, the ARF improved the predictive accuracy with 36.9% in MAPE and 22.3% in MAE. The comparison results confirmed that the ARF was the most effective machine learning model among the investigated approaches. As a contribution, this study proposed a machine learning model for accurately inferring the axial strength in short CFST columns