This study introduces a new method for creating accurate microscale finite element (FE) models of 3D printed composites. The approach involves utilizing conventional micro-computed tomography (micro-CT) and neural net...
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This study introduces a new method for creating accurate microscale finite element (FE) models of 3D printed composites. The approach involves utilizing conventional micro-computed tomography (micro-CT) and neural network algorithms and is applied to single 3D printed composite filaments that are reinforced with Kevlar fibers. Initially, images from micro-CT scans are processed using the YOLOv7 (you only look once) algorithm to differentiate the fibers in the micro-CT images, resulting in an accurate representation of the fibers in the microstructure. The fibers are then integrated into representative volume elements (RVEs) that are simulated using the FE method to predict the effective elastic properties of the 3D printed composite. The results are compared with experiments and indicate that this approach leads to accurate predictions of the elastic properties. Additionally, it is demonstrated that the printed filaments display transversely isotropic behavior, with the axis of isotropy aligned with the length of the printed filament. These findings highlight the potential of this approach for ameliorating the design and production of 3D printed composites.
Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in business and industry. This is primarily...
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Time series classification using novel techniques has experienced a recent resurgence and growing interest from statisticians, subject-domain scientists, and decision makers in business and industry. This is primarily due to the ever increasing amount of big and complex data produced as a result of technological advances. A motivating example is that of Google trends data, which exhibit highly nonlinear behavior. Although a rich literature exists for addressing this problem, existing approaches mostly rely on first- and second-order properties of the time series, since they typically assume linearity of the underlying process. Often, these are inadequate for effective classification of nonlinear time series data such as Google Trends data. Given these methodological deficiencies and the abundance of nonlinear time series that persist among real-world phenomena, we introduce an approach that merges higher order spectral analysis with deep convolutional neuralnetworks for classifying time series. The effectiveness of our approach is illustrated using simulated data and two motivating industry examples that involve Google trends data and electronic device energy consumption data.
The paper is devoted to topical issues of monitoring infrastructure facilities. The aim of the work is to discuss and analyze the most promising and effective methods for monitoring transport infrastructure facilities...
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The paper is devoted to topical issues of monitoring infrastructure facilities. The aim of the work is to discuss and analyze the most promising and effective methods for monitoring transport infrastructure facilities, developed as a result of recent interdisciplinary studies. After analyzing and combining the results of previous studies, the team of authors presented a model of a complex for monitoring transport infrastructure facilities based on the joint use of video cameras and laser scanners as a permanent and periodic source of information about engineering structures, respectively. Also, the technology of computer vision, neural network algorithms and artificial intelligence methods in relation to the field of monitoring are discussed in the paper. As a result, the structure of an intelligent system for support and decision-making is presented in a graphical form, as well as a block diagram of a stationary monitoring complex based on video surveillance cameras. Conclusions are made about the feasibility and prospects of using such complexes for the needs of monitoring engineering infrastructure facilities, as well as the impact of the development of technologies used in them on world progress in general.
Softwares play an important role in controlling complex systems. Monitoring the proper functioning of the components of such systems is the principal role of softwares. Often, a petite fault in one of the subsystems m...
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Softwares play an important role in controlling complex systems. Monitoring the proper functioning of the components of such systems is the principal role of softwares. Often, a petite fault in one of the subsystems may cause irreparable damages;therefore, it is of great importance to be able to predict software faults and estimate the reliability of softwares. In this survey, we present a classification of various methods proposed in the literature to predict software reliability. This study summarizes the results of more than 200 research papers in the field. We also discuss the challenges involved in prediction methods along with proposed partial solutions (i.e., Bayesian methods) to improve the accuracy of such predictions. Moreover, we review numerous evaluation measures introduced so far to assess the performance of prediction models, the datasets they are based on, and also the results they yield.
The additional economic costs caused by environmental degradation are often neglected by land developers,which lead to the fact that the cost of some projects is much higher than the benefit and even cause irreparable...
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The additional economic costs caused by environmental degradation are often neglected by land developers,which lead to the fact that the cost of some projects is much higher than the benefit and even cause irreparable damage to the ***,our goal is to create a comprehensive evaluation model that incorporates the cost of environmental degradation into the cost-benefit assessment of land *** this basis,it provides optimal investment suggestions for land use project planners and managers,while maximizing benefits;it also pays attention to the important issue of environmental protection.
Herein, a high-performance single-crystal diamond (SCD) detector (4.5x4.5x0.3mm(3)) to achieve accurate pulse shape discrimination, which is critical for source tracking in harsh and complex radiation conditions, is d...
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Herein, a high-performance single-crystal diamond (SCD) detector (4.5x4.5x0.3mm(3)) to achieve accurate pulse shape discrimination, which is critical for source tracking in harsh and complex radiation conditions, is demonstrated. Enabled by a deep learning algorithm based on self-organizing map (SOM) neuralnetworks, and using the transient current technique (TCT) for sampling the detector's response to gamma, alpha, and neutron radiation fields, the SCD detector achieves high recognition accuracy of 97.51%. The SCD detector exhibits a low leakage current of 0.75pAmm(-2) under an electric field of 0.51V mu m(-1), and its response to Pu-238 alpha-rays shows that the charge collection efficiency for electrons and holes is as high as 99.2 and 98.8% respectively, with an energy resolution as low as 1.42%. The results indicate that the high-performance SCD detector assisted by the machine learning algorithm can effectively distinguish alpha-particles and gamma-rays with a potential application in separating the neutron and gamma events as well.
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