Thickness distribution of multi-stage incremental forming with different forming stages and angle intervals

Although multi-stage incremental sheet forming has always been adopted instead of single-stage forming to form parts with a steep wall angle or to achieve a high forming performance, it is largely dependent on empirical designs. In order to research multi-stage forming further, the effect of forming stages(n) and angle interval between the two adjacent stages(Δα) on thickness distribution was investigated. Firstly, a finite element method(FEM) model of multi-stage incremental forming was established and experimentally verified. Then, based on the proposed simulation model, different strategies were adopted to form a frustum of cone with wall angle of 30° to research the thickness distribution of multi-pass forming. It is proved that the minimum thickness increases largely and the variance of sheet thickness decreases significantly as the value of n grows. Further, with the increase of Δα, the minimum thickness increases initially and then decreases, and the optimal thickness distribution is achieved with Δα of 10°.Additionally, a formula is deduced to estimate the sheet thickness after multi-stage forming and proved to be effective. And the simulation results fit well with the experimental results.

DISTRIBUTED MONITORING SYSTEM RELIABILITY ESTIMATION WITH CONSIDERATION OF STATISTICAL UNCERTAINTY

Taking into account the whole system structure and the component reliability estimation uncertainty, a system reliability estimation method based on probability and statistical theory for distributed monitoring systems is presented. The variance and confidence intervals of the system reliability estimation are obtained by expressing system reliability as a linear sum of products of higher order moments of component reliability estimates when the number of component or system survivals obeys binomial distribution. The eigenfunction of binomial distribution is used to determine the moments of component reliability estimates, and a symbolic matrix which can facilitate the search of explicit system reliability estimates is proposed. Furthermore, a case of application is used to illustrate the procedure, and with the help of this example, various issues such as the applicability of this estimation model, and measures to improve system reliability of monitoring systems are discussed.

Unseen head pose prediction using dense multivariate label distribution

Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.

High accuracy in automatic detection of atrial fibrillation for Holter monitoring

Atrial fibrillation(AF) has been considered as a growing epidemiological problem in the world,with a substantial impact on morbidity and mortality.Ambulatory electrocardiography(e.g.,Holter) monitoring is commonly used for AF diagnosis and therapy and the automated detection of AF is of great significance due to the vast amount of information provided.This study presents a combined method to achieve high accuracy in AF detection.Firstly,we detected the suspected transitions between AF and sinus rhythm using the delta RR interval distribution difference curve,which were then classified by a combination analysis of P wave and RR interval.The MIT-BIH AF database was used for algorithm validation and a high sensitivity and a high specificity(98.2% and 97.5%,respectively) were achieved.Further,we developed a dataset of 24-h paroxysmal AF Holter recordings(n=45) to evaluate the performance in clinical practice,which yielded satisfactory accuracy(sensitivity=96.3%,specificity=96.8%).

Atmospheric environmental capacity and urban atmospheric load in China's Mainland

Daily and annual average atmospheric environmental capacity coefficient(A-value) sequences for China's Mainland are calculated from hourly data recorded at 378 ground stations over 1975–2014. A-values at different recurrence intervals are calculated by fitting the sequences to Pearson type III distribution curves. Based on these A-values and source-sink balance(reference concentration 100 μg m^(-3)), atmospheric environmental capacities at the recurrence intervals are calculated for all of China's Mainland and each provincial administrative region. The climate average atmospheric environmental capacity reference value for the entire mainland is 2.169×10~7 t yr^(-1). An urban atmospheric load index is defined for analyses of the impact of population density on the urban atmospheric environment. Analyses suggest that this index is also useful for differentiating whether air quality changes are attributable to varying meteorological conditions or variations of artificial emission rate.Equations guiding the control of unorganized emission sources are derived for preventing air quality deterioration during urban expansion and population concentration.