工作中电子天平的检定

本文对电子天平这一精密仪器作了基本介绍,以图文结合的方式阐述了天平的检定方法及计算过程。     

Robust low bit‐rate transmission using multiple‐PBDBS (MPBDBS) approach

Abstract

Recently, Gao and Tu presented an efficient algorithm for robust low bit‐rate video transmission by using a partial backward decodable bit stream (PBDBS) approach. In this paper, we first present a multiple‐PBDBS (MPBDBS) approach to improve on the previous PBDBS approach. Next a mathematical theory is provided to minimize the error propagation length in each group of blocks (GOB). Further, a novel MPBDBS‐based algorithm is presented for robust video transmission. Experimental results demonstrate that our proposed MPBDBS‐based algorithm has better image quality when compared to the previous PBDBS‐based algorithm, but has some bit‐rate and execution‐time degradation. In our experiments, both single and two‐bit error models are investigated.     

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.     

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.     

The dynamic characteristics of taper seals

Abstract

The dynamic characteristics of taper seal has been investigated. The approach is similar to Black's analysis; the stiffness and damping coefficients are derived. The results showed that the taper seals presented more stable characteristics comparing with plain seals. The practical application of this theory is increasingly used in industry.     

Equation error versus output error methods

Abstract

For the identification of linear processes on the basis of ARX-models, equation error least squares (EELS) (often indicated as the one step ahead prediction error method) is frequently used rather than output error least squares (OELS). This is mainly because the minimum of the convex EE-criterion can easily be found, in contrast to the OE-criterion, which often displays multiple local minima. Both methods lead to the correct parameter values when the system is in the model set chosen. But in many practical situations, such as human behaviour, the real process under study will be of infinite order causing essentially different models to be found from either EE or OE criteria. Various aspects of these differences are analysed in this study. Much attention has been paid to the performance of a simulation based on a model estimated with an EELS. This simulation performance can be predicted and bounds can be given without executing the simulation itself. Furthermore the simulation performance is very poor for systems where the energy in the initial impulse response samples is very small compared with the energy in the remainder of the response. For these systems an equation error estimate cannot even provide a proper initial guess for an OELS minimization algorithm. Examples are presented that illustrate this effect.