Study of statistical damage constitutive model of layered composite rock under triaxial compression

The layered composite rock was subjected to triaxial compression tests under constant confining pressure and the stress–strain curves under different confining pressures were obtained. Based on the continuous damage theory and statistical strength theory, it is assumed that the strength of rock microelements obeys Weibull distribution by taking the defects such as random micro-cracks in the rock into account. The statistical constitutive model of layered composite rock with damage correction is established by taking the axial strain of rock as a random distribution variable of microelement strength. The model parameters were determined by the curve fitting method and referring to some test parameters. By comparing the experimental data and the constitutive model curve, the rationality and feasibility of the model are verified.

Cubic Curve Fitting Method in Reconstruction of Chinese Calligraphy Outline

Curve plays a significant role in CAGD and brings the good impact of computers to manufacturing industries in designing 2 and 3-dimensional shapes and objects. Reconstruction of Chinese calligraphy outline based on the actual character is presented in this paper. Chinese calligraphy is the stylized artistic writings of Chinese characters. It is believed that this writing may help to express the feelings and ideas of the writers, which are difficult to be described. The shapes, smooth lines, and perfect curves are among the important qualities which are particularly emphasized in selecting good Chinese calligraphy. The Cubic B-Spline, Cubic Trigonometric Spline, and Cubic Trigonometric Bezier were used to generate the curves. The factors that have influenced the effects of the curves modifications were examined based on the changes of control polygon and the values of shape parameter. The fastest approach was then chosen by measuring the processing time required to construct the complete design. Results show the Cubic Trigonometric Bezier curve produced the closest curves to the control polygon, accurate to the actual character with λ = 1 and CPU time taken is 2.032 seconds. This is followed by Cubic Trigonometric Spline and Cubic B-Spline.

Post Necking Behaviour and Hardening Characterization of Mild Steel

This paper investigates the post necking phenomenon in mild steel using six different hardening laws (Hollomon, Swift, Ludwik, Ghosh, Voce and Hockett-Sherby) by extrapolation method. This is carried out through the finite element simulation on tensile deformation of a mild steel specimen under quasi-static condition. Reference flow curves are obtained analytically and found helpful for the numerical simulation. The material parameters of the above hardening laws are evaluated by curve fitting method based on the pre necking experimental data and their suitability is examined before and after necking.

Social Video Advertisement Replacement and its Evaluation in Convolutional Neural Networks

This paper introduces a method to use deep convolutional neural networks (CNNs) to automatically replace advertisement (AD) photo on social (or self-media) videos and provides the suitable evaluation method to compare different CNNs. An AD photo can replace a picture inside a video. However, if a human being occludes the replaced picture in the original video, the newly pasted AD photo will block the human occluded part. The deep learning algorithm is implemented to segment the human being from the video. The segmented human pixels are then pasted back to the occluded area, so that the AD photo replacement becomes natural and perfect appearance in the video. This process requires the predicted occlusion edge to be closed to the ground truth occlusion edge, so that the AD photo can be occluded naturally. Therefore, this research introduces a curve fitting method to measure the predicted occlusion edge’s error. By using this method, three CNN methods are applied and compared for the AD replacement. They are mask of regions convolutional neural network (Mask RCNN), a recurrent network for video object segmentation (ROVS) and DeeplabV3. The experimental results show the comparative segmentation accuracy of the different models and DeeplabV3 shows the best performance.

Real time Adaptive Approach for Hidden Targets Shape Identification using through Wall Imaging System

In Through-wall Imaging (TWI) system, shape-based identification of the hidden target behind the wall made of any dielectric material like brick, cement, concrete, dry plywood, plastic and Teflon, etc. is one of the most challenging tasks. However, it is very important to understand that the performance of TWI systems is limited by the presence of clutter due to the wall and also transmitted frequency range. Therefore, the quality of obtained image is blurred and very difficult to identify the shape of targets. In the present paper, a shape-based image identification technique with the help of a neural network and curve-fitting approach is proposed to overcome the limitation of existing techniques. A real time experimental analysis of TWI has been carried out using the TWI radar system to collect and process the data, with and without targets. The collected data is trained by a neural network for shape identification of targets behind the wall in any orientation and then threshold by a curve-fitting method for smoothing the background. The neural network has been used to train the noisy data i.e. raw data and noise free data i.e. pre-processed data. The shape of hidden targets is identified by using the curve fitting method with the help of trained neural network data and real time data. The results obtained by the developed technique are promising for target identification at any orientation.

Experimental study on the coefficient of friction of the ball screw

The coefficient of friction (COF) is a key factor to estimate the performance of ball screws. Pieces of research focus on the experimental study of the COF, leading to the COF chosen empirically in many studies. To acquire the COF of the HJG-4010, a measuring system is conducted to detect the friction torque under different preloads and rotational speeds and the effects of the applied axial load and rotational speed on the COF are analyzed. By the curve fitting method, the Stribeck curve of the ball screw is obtained. The experimental results show that the lubricating state can be divided into two categories: the mixed lubrication state, and the hydrodynamic lubrication state. This study is beneficial to choose a suitable working condition for a different performance of the ball screw.

Data Clustering for Fuzzyfier Value Derivation

The fuzzifier value m is improving significant factor for achieving the accuracy of data. Therefore, in this chapter, various clustering method is introduced with the definition of important values for clustering. To adaptively calculate the appropriate purge value of the gap type −2 fuzzy c-means, two fuzzy values m1 and m2 are provided by extracting information from individual data points using a histogram scheme. Most of the clustering in this chapter automatically obtains determination of m1 and m2 values that depended on existent repeated experiments. Also, in order to increase efficiency on deriving valid fuzzifier value, we introduce the Interval type-2 possibilistic fuzzy C-means (IT2PFCM), as one of advanced fuzzy clustering method to classify a fixed pattern. In Efficient IT2PFCM method, proper fuzzifier values for each data is obtained from an algorithm including histogram analysis and Gaussian Curve Fitting method. Using the extracted information form fuzzifier values, two modified fuzzifier value m1 and m2 are determined. These updated fuzzifier values are used to calculated the new membership values. Determining these updated values improve not only the clustering accuracy rate of the measured sensor data, but also can be used without additional procedure such as data labeling. It is also efficient at monitoring numerous sensors, managing and verifying sensor data obtained in real time such as smart cities.

A Simulation Model for Forecasting COVID-19 Pandemic Spread: Analytical Results Based on the Current Saudi COVID-19 Data

The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.