Failure strength of thin-walled cylindrical GFRP composite shell against static internal and external pressure for various volumetric fiber fraction

A study on a Circular cylindrical thin-walled shell failure made of GRP composite subjected to static internal and external pressure was carried out. The results were acquired using analytical and FEM simulation approaches for various volumetric fiber fractions. Fiber breakage, matrix breakage, interlaminate shear deformation, delamination shear deformation and micro buckling failure were investigated employing maximum failure criteria against internal and external pressure. One-ply cylindrical shell with fiber angle orientation of 0 degree was modeled in ABAQUS finite element simulation and the result was varied using analytical approaches. Moreover, the pressure fluctuations for various volumetric fiber fraction were quadratic according to plotted graphs. Meanwhile, MATLAB software was used for theoretical analysis. The comparison of two approaches was proved to be accurate. Subsequently, failure strength of various laminated GFRP cylindrical shell with different fiber angle orientations at each ply was studied for diverse volumetric fiber fraction factors. Stacking sequence, fiber angle orientations were mainly effective on failure strength

Deepfogsim: A toolbox for execution and performance evaluation of the inference phase of conditional deep neural networks with early exits atop distributed fog platforms

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox

An accuracy vs. complexity comparison of deep learning architectures for the detection of covid-19 disease

In parallel with the vast medical research on clinical treatment of COVID-19, an important action to have the disease completely under control is to carefully monitor the patients. What the detection of COVID-19 relies on most is the viral tests, however, the study of X-rays is helpful due to the ease of availability. There are various studies that employ Deep Learning (DL) paradigms, aiming at reinforcing the radiography-based recognition of lung infection by COVID-19. In this regard, we make a comparison of the noteworthy approaches devoted to the binary classification of infected images by using DL techniques, then we also propose a variant of a convolutional neural network (CNN) with optimized parameters, which performs very well on a recent dataset of COVID-19. The proposed model’s effectiveness is demonstrated to be of considerable importance due to its uncomplicated design, in contrast to other presented models. In our approach, we randomly put several images of the utilized dataset aside as a hold out set; the model detects most of the COVID-19 X-rays correctly, with an excellent overall accuracy of 99.8%. In addition, the significance of the results obtained by testing different datasets of diverse characteristics (which, more specifically, are not used in the training process) demonstrates the effectiveness of the proposed approach in terms of an accuracy up to 93%

Learning-in-the-Fog (LiFo): Deep learning meets Fog Computing for the minimum-energy distributed early-exit of inference in delay-critical IoT realms

Fog Computing (FC) and Conditional Deep Neural Networks (CDDNs) with early exits are two emerging paradigms which, up to now, are evolving in a standing-Alone fashion. However, their integration is expected to be valuable in IoT applications in which resource-poor devices must mine large volume of sensed data in real-Time. Motivated by this consideration, this article focuses on the optimized design and performance validation of {L} earning-{i} ext{n}-The-Fo g (LiFo), a novel virtualized technological platform for the minimum-energy and delay-constrained execution of the inference-phase of CDDNs with early exits atop multi-Tier networked computing infrastructures composed by multiple hierarchically-organized wireless Fog nodes. The main research contributions of this article are threefold, namely: (i) we design the main building blocks and supporting services of the LiFo architecture by explicitly accounting for the multiple constraints on the per-exit maximum inference delays of the supported CDNN; (ii) we develop an adaptive algorithm for the minimum-energy distributed joint allocation and reconfiguration of the available computing-plus-networking resources of the LiFo platform. Interestingly enough, the designed algorithm is capable to self-detect (typically, unpredictable) environmental changes and quickly self-react them by properly re-configuring the available computing and networking resources; and, (iii) we design the main building blocks and related virtualized functionalities of an Information Centric-based networking architecture, which enables the LiFo platform to perform the aggregation of spatially-distributed IoT sensed data. The energy-vs.-inference delay LiFo performance is numerically tested under a number of IoT scenarios and compared against the corresponding ones of some state-of-The-Art benchmark solutions that do not rely on the Fog support