Graceful Degradation FEC Layer for Multimedia Broadcast/Multicast Service in LTE Mobile Systems

This paper proposes an additional forward error correction (FEC) layer to compensate for the defectiveness inherent in the conventional FEC layer in the Long Term Evolution specifications. The proposed additional layer is called a graceful degradation (GD)‐FEC layer and maintains desirable service quality even under burst data loss conditions of a few seconds. This paper also proposes a non‐delayed decoding (NDD)‐GD‐FEC layer that is inherent in the decoding process. Computer simulations and device‐based tests show a better loss recovery performance with a negligible increase in CPU utilization and occupied memory size.     

Moment adjusted imputation for multivariate measurement error data with applications to logistic regression

In clinical studies, covariates are often measured with error due to biological fluctuations, device error and other sources. Summary statistics and regression models that are based on mis-measured data will differ from the corresponding analysis based on the “true” covariate. Statistical analysis can be adjusted for measurement error, however various methods exhibit a tradeoff between convenience and performance. Moment Adjusted Imputation (MAI) is a measurement error in a scalar latent variable that is easy to implement and performs well in a variety of settings. In practice, multiple covariates may be similarly influenced by biological fluctuations, inducing correlated, multivariate measurement error. The extension of MAI to the setting of multivariate latent variables involves unique challenges. Alternative strategies are described, including a computationally feasible option that is shown to perform well.     

Human error probability estimation by coupling simulator data and deterministic analysis

Operator error in diagnosis and execution of task have significant impact on Nuclear Power Plant (NPP) safety. These human errors are classified as mistakes (rule base and knowledge based errors), slip (skill based) and lapses (skill based). Depending on the time of occurrence, human errors have been categorized as i) Category ‘A’ (Pre-Initiators): actions during routine maintenance and testing wherein errors can cause equipment malfunction ii) Category ‘B’ (Initiators): actions contributing to initiating events or plant transients iii) Category ‘C’ (Post-Initiators): actions involved in operator response to an accident. There have been accidents in NPPs because of human error in an operator's diagnosis and execution of an event. These underline the need to appropriately estimate HEP in risk analysis. There are several methods that are being practiced in Probabilistic Safety Assessment (PSA) studies for quantification of human error probability. However, there is no consensus on a single method that should be used. In this paper a method for estimating HEP is proposed which is based on simulator data for a particular accident scenario. For accident scenarios, the data from real NPP control room is very sparsely available. In the absence of real data, simulator based data can be used. Simulator data is expected to provide a glimpse of probable human behavior in real accident situation even though simulator data is not a substitute for real data. The proposed methodology considers the variation in crew performance time in simulator exercise and in available time from deterministic analysis, and couples them through their respective probability distributions to obtain HEP. The emphasis is on suitability of the methodology rather than particulars of the cited example.     

Equivalent a posteriori error estimates for elliptic optimal control problems with L1-control cost

An elliptic optimal control problem involving the $L^1$ norm of the control in the cost functional is considered in this paper. We use the full discretization and the variational discretization to approximate the control problem and the efficient and reliable a posteriori error estimates are obtained for the two cases. For the variational discretization, we also analyze the convergence of adaptive finite element methods. In the end, some examples are provided to validate our analysis.     

光学显微定位系统定位精度分析

为了提高光学显微定位系统对细胞微生物识别定位的精度：一方面，必须改进手眼标定方法；另一方面，需要提高全局图像识别的准确性，因此，提出一种两步法对系统进行手眼标定。首先，通过标定固定靶标来确定系统原点，并得到视觉模块相对于系统原点的转换关系；然后，根据每次拍照的起始点位置、拍照的数量和移动的步长求解出全局图像相对于系统原点的转换关系；最后，为了进一步提高全局转换关系的准确度，提出一种基于傅里叶变换的误差矫正方法，利用傅里叶变换求解出视觉模块在移动过程中的误差，并加入系统进行补偿。实验结果表明，误差补偿之后，系统X轴方向的误差均值从10.23 μm降为-0.002 μm，Y轴方向的误差均值从6.9 μm降为-0.50 μm，显微定位系统的平均定位精度达到了99%以上。结果表明，所提方法可很好地用于光学显微定位系统对细胞微生物进行高精度的自动化抓取。     

Using Neural Networks to predict HFACS unsafe acts from the pre-conditions of unsafe acts

Abstract

Human Factors Analysis and Classification System (HFACS) is based upon Reason’s organizational model of human error which suggests that there is a ‘one to many’ mapping of condition tokens (HFACS level 2 psychological precursors) to unsafe act tokens (HFACS level 1 error and violations). Using accident data derived from 523 military aircraft accidents, the relationship between HFACS level 2 preconditions and level 1 unsafe acts was modelled using an artificial neural network (NN). This allowed an empirical model to be developed congruent with the underlying theory of HFACS. The NN solution produced an average overall classification rate of ca. 74% for all unsafe acts from information derived from their level 2 preconditions. However, the correct classification rate was superior for decision- and skill-based errors, than for perceptual errors and violations.

Practitioner Summary: A model to predict unsafe acts (HFACS level 1) from their preconditions (HFACS level 2) was developed from the analysis of 523 military aircraft accidents using an artificial NN. The results could correctly predict approximately 74% of errors.     

Mechanism of mid-spatial-frequency waviness removal by viscoelastic polishing tool

Nanoscale roughness with ultra-precise form control can be readily achieved using compliant finishing methods such as bonnet polishing. However, their weak point lies in the difficulty of removing mid-spatial-frequency (MSF) waviness in the typical range from 0.1 to 5.0 mm wavelength. To overcome this shortcoming, a bonnet tool filled with viscoelastic fluid is developed and a comprehensive model is established to disclose its distinct removal behavior in the MSF range. The model considers tool viscoelasticity, stress distribution and workpiece topography. Experiments show high consistency with theoretical predictions, and show that MSF waviness can be effectively reduced using the proposed method.     

地应力套心解除法测量精度的校验方法研究

介绍了国内外空心包体应变计等套心解除地应力测试技术的基本情况,套心解除测试技术测量精度研究的现状,进行测量精度校验研究的依据和重要意义。提出了一种在岩石洞室进行校验试验的装置,论述了校验试验环境、物理模型组成、试验过程和试验真值获取注意事项。首次给出并重点论述了二维和三维地应力测量精度的定量表述方法,给出了量值综合误差、量值最大误差、角度综合误差和角度最大误差公式,以及多样本的平均误差公式。对上述测量精度误差公式的表述效果进行了讨论,对比了在“一般情况下”和“极端情况下”综合误差和最大误差表述方式的效果,明确了表述公式的适用性。