New Approaches in Monitoring Multivariate Categorical Processes based on Contingency Tables in Phase II

In some statistical process control (SPC) applications, quality of a process or product is characterized by contingency table. Contingency tables describe the relation between two or more categorical quality characteristics. In this paper, two new control charts based on the WALD and Stuart score test statistics are designed for monitoring of contingency table‐based processes in Phase‐II. The performances of the proposed control charts are compared with the generalized linear test (GLT) control chart proposed in the literature. The results show the better performance of the proposed control charts under small and moderate shifts. Moreover, new schemes are proposed to diagnose which cell corresponding to different levels of categorical variables is responsible for out‐of‐control signal. In addition, we propose EWMA–WALD and EWMA–Stuart score test control charts to improve the performance of Shewhart‐based control charts in detecting small and moderate shifts in contingency table parameters. Meanwhile, we compare the performances of two proposed EWMA‐based control charts with the ones of three existing control charts called EWMA–GLT, EWMA–GLRT and an EWMA‐type control chart for multivariate binomial/multinomial processes along with the ones of the corresponding Shewhart‐based control charts. A numerical example is given to show the efficiency of the proposed methods. Finally, the effect of parameter estimation in Phase I based on m historical contingency table on the performance of the Shewhart‐based control charts is studied. Copyright © 2016 John Wiley & Sons, Ltd.     

Experimental investigation of the flow-induced motion of a square-section cylinder

The flow-induced motion(FIM)of an elastically mounted square-section cylinder is experimentally investigated over a wide range of Reynolds numbers(1.5×104＜Re＜7....     

Evaluation of driver drowsiness using respiration analysis by thermal imaging on a driving simulator

In this paper, a new non-intrusive driver drowsiness detection method is introduced based on respiration analysis using facial thermal imaging. Drowsiness is the cause of many driving accidents all over the world. Drivers’ respiration system undergoes significant changes from wakefulness to drowsiness and can be used to detect drowsiness. Current respiration measurement methods are intrusive and uncomfortable making respiration the least measured vital sign during driving. In this paper, a new method is presented based on facial thermal imaging to analyze drivers’ respiration signal non-intrusively. Thirty subjects are tested in a car simulator. They are fully awake at the beginning and experience drowsiness during the tests. The mean and the standard deviation of the respiration rate and the inspiration-to-expiration time ratio are extracted from the subjects’ respiration signal. To detect drowsiness, the Support Vector Machine (SVM) and the K-Nearest Neighbor (KNN) classifiers are used. The Observer Rating of Drowsiness method is used for scoring the drowsiness level and validating the proposed method. The performance and the results of both methods are presented and compared. The results indicate that drowsiness can be detected with the accuracy of 90%, sensitivity of 92%, specificity of 85%, and precision of 91%.

     

Foam Cells as Therapeutic Targets in Atherosclerosis with a Focus on the Regulatory Roles of Non-Coding RNAs

Atherosclerosis is a major cause of human cardiovascular disease, which is the leading cause of mortality around the world. Various physiological and pathological processes are involved, including chronic inflammation, dysregulation of lipid metabolism, development of an environment characterized by oxidative stress and improper immune responses. Accordingly, the expansion of novel targets for the treatment of atherosclerosis is necessary. In this study, we focus on the role of foam cells in the development of atherosclerosis. The specific therapeutic goals associated with each stage in the formation of foam cells and the development of atherosclerosis will be considered. Processing and metabolism of cholesterol in the macrophage is one of the main steps in foam cell formation. Cholesterol processing involves lipid uptake, cholesterol esterification and cholesterol efflux, which ultimately leads to cholesterol equilibrium in the macrophage. Recently, many preclinical studies have appeared concerning the role of non-encoding RNAs in the formation of atherosclerotic lesions. Non-encoding RNAs, especially microRNAs, are considered regulators of lipid metabolism by affecting the expression of genes involved in the uptake (e.g., CD36 and LOX1) esterification (ACAT1) and efflux (ABCA1, ABCG1) of cholesterol. They are also able to regulate inflammatory pathways, produce cytokines and mediate foam cell apoptosis. We have reviewed important preclinical evidence of their therapeutic targeting in atherosclerosis, with a special focus on foam cell formation.     

Training a Hidden Markov Model with a Bayesian Spiking Neural Network

It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments of sequential data, such as individual spoken works. The approach combines a hidden Markov model (HMM) with a spiking neural network (SNN). The HMM, consisting of states and transitions, forms a fixed backbone with nonadaptive transition probabilities. The SNN, however, implements a biologically based Bayesian computation that derives from the spike timing-dependent plasticity (STDP) learning rule. The emission (observation) probabilities of the HMM are represented in the SNN and trained with the STDP rule. A separate SNN, each with the same architecture, is associated with each of the states of the HMM. Because of the STDP training, each SNN implements an expectation maximization algorithm to learn the emission probabilities for one HMM state. The model was studied on synthesized spike-train data and also on spoken word data. Preliminary results suggest its performance compares favorably with other biologically motivated approaches. Because of the model’s uniqueness and initial promise, it warrants further study. It provides some new ideas on how the brain might implement the equivalent of an HMM in a neural circuit.     

High Performance Microstrip Low Pass Filter for Wireless Communications

A microstrip low-pass filter using T-shaped resonators is designed to achieve an ultra-sharp transition band and high suppression level. The performance of the resonators is investigated based on an LC equivalent circuit and a transfer function to compute the equations of the transmission zeros. This filter has an acceptable stopband with high insertion loss (28 dB) by adopting a rectangular suppressor. Also, the width of the transition band is 0.09 GHz (with – 3 and ? 40 dB attenuation levels), that exhibits a very high sharpness (ξ = 411 dB/GHz). The proposed filter with a 3 dB cut-off frequency (f_c) of 1.32 GHz presents a high return loss in the passband (17 dB) and high figure of merit of 57,073. The designed filter is fabricated and measured, demonstrating sufficient agreement between the simulation and experimental results.

     

Spectral analysis of GFDM modulated signal under nonlinear behavior of power amplifier

Wireless Networks - Generalized frequency division multiplexing (GFDM) is a flexible non-orthogonal waveform candidate for 5G which can offer some advantages such as low out-of-band emission and...     

Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

Resistance spot welding (RSW) is a highly used joining procedure in automotive industry. In RSW, after a number of welds the welding electrode starts to wear and its diameter changes. This causes the weld nugget diameter abnormal variations and consequently reduces the weld strength. Therefore the tip of the electrode should be dressed in RSW. Selecting the optimum time for the welding electrode tip dressing operations is very important. In this research three welding parameters including the welding time, the welding current, and the welding pressure were identified as the main effective parameters on the weld nugget dimensions including the weld nugget diameter and height using full factorial design of experiments. Then using hybrid combination of the artificial neural networks and multi-objective genetic algorithm, the optimized values of the aforementioned parameters were specified. Finally experiments were fulfilled to estimate the admissible number of the weld spots which should be done before the electrode tip dressing operation.     

Evaluating multimedia features and fusion for example-based event detection

Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; high-level semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME’s performance in the 2012 TRECVID MED evaluation was one of the best reported.     

Mechanistic evaluation of fatigue cracking in asphalt pavements

Over the last several decades, significant research has been conducted to predict the fatigue cracking performance of asphalt pavements. Recently, the simplified viscoelastic continuum damage (S-VECD) model was developed as an efficient method of characterising the fatigue performance of asphalt mixtures under a wide range of loading conditions. Two important material properties that can be determined from the S-VECD model are the damage characteristic curve that defines how damage evolves in a specimen and the energy-based failure criterion that defines when the specimen fails. These two material functions are unique for a given mixture regardless of temperature, mode of loading, stress/strain amplitude and loading history. This study presents the application of the Layered Viscoelastic Crirtical Distresses (LVECD) programme to predict the fatigue performance of 18 pavement sections from different locations in the United States and Canada. The capability of the LVECD programme to capture crack initiation, crack propagation and damage in the pavement sections is investigated by comparing the simulation results with field observations. This study found reasonable agreement in trends between the damage growth throughout the pavement cross sections as predicted by the LVECD programme and the surface crack growth as evidenced by field observations.