Multi-Scale Analysis of Fretting Fatigue in Heterogeneous Materials Using Computational Homogenization

This paper deals with modeling of the phenomenon of fretting fatigue in heterogeneous materials using the multi-scale computational homogenization technique and finite element analysis (FEA). The heterogeneous material for the specimens consists of a single hole model (25% void/cell, 16% void/cell and 10% void/cell) and a four-hole model (25% void/cell). Using a representative volume element (RVE), we try to produce the equivalent homogenized properties and work on a homogeneous specimen for the study of fretting fatigue. Next, the fretting fatigue contact problem is performed for 3 new cases of models that consist of a homogeneous and a heterogeneous part (single hole cell) in the contact area. The aim is to analyze the normal and shear stresses of these models and compare them with the results of the corresponding heterogeneous models based on the Direct Numerical Simulation (DNS) method. Finally, by comparing the computational time and % deviations, we draw conclusions about the reliability and effectiveness of the proposed method.     

Bearing Failure Optimization of Composite Double-Lap Bolted Joints Based on a Three-Step Strategy Marked By Feasible Region Reduction and Model Decoupling

To minimize the mass and increase the bearing failure load of composite doublelap bolted joints, a three-step optimization strategy including feasible region reduction, optimization model decoupling and optimization was presented. In feasible region reduction, the dimensions of the feasible design region were reduced by selecting dominant design variables from numerous multilevel parameters by sensitivity analyses, and the feasible regions of variables were reduced by influence mechanism analyses. In model decoupling, the optimization model with a large number of variables was divided into various sub-models with fewer variables by variance analysis. In the third step, the optimization sub-models were solved one by one using a genetic algorithm, and the modified characteristic curve method was adopted as the failure prediction method. Based on the proposed optimization method, optimization of a double-lap single-bolt joint was performed using the ANSYS^® code. The results show that the bearing failure load increased by 13.5% and that the mass decreased by 8.7% compared with those of the initial design of the joint, which validated the effectiveness of the three-step optimization strategy.     

Phase Field Modelling Allotropic Transformation of Solid Solution

Based on multiphase field conception and integrated with the idea of vectorvalued phase field, a phase field model for typical allotropic transformation of solid solution is proposed. The model takes the non-uniform distribution of grain boundaries of parent phase and crystal orientation into account in proper way, as being illustrated by the simulation of austenite to ferrite transformation in low carbon steel. It is found that the misorientation dependent grain boundary mobility shows strong influence on the formation of ferrite morphology comparing with the weak effect exerted by misorientation dependent grain boundary energy. The evolution of various types of grain boundaries are quantitatively characterized in terms of its respective grain boundary energy dissipation. The simulated ferrite fraction agrees well with the expectation from phase diagram, which verifies this model.     

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.     

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size (the number of workers). Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform. In the stage of data aggregation, DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise (including both added noise of sensing data and the system noise in the sensing process). Through using optimal estimation of noisy aggregated sensing data, the platform can finally gain better utility of aggregated data while preserving workers’ privacy. Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.     

Functional Causality between Oil Prices and GDP Based on Big Data

This paper examines the causal relationship between oil prices and the Gross Domestic Product (GDP) in the Kingdom of Saudi Arabia. The study is carried out by a data set collected quarterly, by Saudi Arabian Monetary Authority, over a period from 1974 to 2016. We seek how a change in real crude oil price affects the GDP of KSA. Based on a new technique, we treat this data in its continuous path. Precisely, we analyze the causality between these two variables, i.e., oil prices and GDP, by using their yearly curves observed in the four quarters of each year. We discuss the causality in the sense of Granger, which requires the stationarity of the data. Thus, in the first Step, we test the stationarity by using the Monte Carlo test of a functional time series stationarity. Our main goal is treated in the second step, where we use the functional causality idea to model the co-variability between these variables. We show that the two series are not integrated; there is one causality between these two variables. All the statistical analyzes were performed using R software.     

Improvement of Stochastic Competitive Learning for Social Network

As an unsupervised learning method, stochastic competitive learning is commonly used for community detection in social network analysis. Compared with the traditional community detection algorithms, it has the advantage of realizing the timeseries community detection by simulating the community formation process. In order to improve the accuracy and solve the problem that several parameters in stochastic competitive learning need to be pre-set, the author improves the algorithms and realizes improved stochastic competitive learning by particle position initialization, parameter optimization and particle domination ability self-adaptive. The experiment result shows that each improved method improves the accuracy of the algorithm, and the F1 score of the improved algorithm is 9.07% higher than that of original algorithm.     

A Strategy of Signal Detection for Performance Improvement in Clipping Based OFDM System

In this paper, the supervised Deep Neural Network (DNN) based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Division Multiplexing (OFDM) ssystem. One of the main disadvantages for the OFDM is the high Peak to Average Power Ratio (PAPR). The clipping is a simple method for the PAPR reduction. However, an effect of the clipping is nonlinear distortion, and estimations for transmitting symbols are difficult despite a Maximum Likelihood (ML) detection at the receiver. The DNN based online signal detection uses the offline learning model where all weights and biases at fullyconnected layers are set to overcome nonlinear distortions by using training data sets. Thus, this paper introduces the required processes for the online signal detection and offline learning, and compares error performances with the ML detection in the clipping-based OFDM systems. In simulation results, the DNN based signal detection has better error performance than the conventional ML detection in multi-path fading wireless channel. The performance improvement is large as the complexity of system is increased such as huge Multiple Input Multiple Output (MIMO) system and high clipping rate.     

Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.