Using Taguchi''s method of experimental design to control errors inlayered perceptrons

A significant problem in the design and construction of an artificial neural network for function approximation is limiting the magnitude and the variance of errors when the network is used in the field. Network errors can occur when the training data does not faithfully represent the required function due to noise or low sampling rates, when the network's flexibility does not match the variability of the data, or when the input data to the resultant network is noisy. This paper reports on several experiments whose purpose was to rank the relative significance of these error sources and thereby find neural network design principles for limiting the magnitude and variance of network errors.     

测力平台系统输出压力中心参数的校正

目前使用的应变式测力平台,其检测的压力中心都是以力平台初始标定数据作为依据。由于生产测力平台的工艺及技术问题,随着测力平台使用时间增加、工作环境变化,传感器参数随之改变,很大程度影响了测力平台测量精度。针对这种情况,本研究通过对测力平台静态标定,建立了输出特性校正模型,利用最小二乘法求解该模型参数,并应用软件补偿实现对测力平台的校正。基于实验结果,讨论了最优模型结构参数,表明了此方法能够补偿测力平台的非线性,可在一定的温度和压力内实现测量准确度优于1 mm,使测力平台满足人体COP测量评定要求。     

Using human error information for error prevention

Developing error-free software requirements is of critical importance to the success of a software project. Problems that occur during requirements collection and specification, if not fixed early, are costly to fix later. Therefore, it is important to develop techniques that help requirements engineers detect and prevent requirements problems. As a human-centric activity, requirements engineering can be influenced by psychological research about human errors, which are the failings of human cognition during the process of planning and executinge a task. We have employed human error research to describe the types of problems that occur during requirements engineering. The goals of this research are: (1) to evaluate whether understanding human errors contributes to the prevention of errors and concomitant faults during requirements engineering and (2) to identify error prevention techniques used in industrial practice. We conducted a controlled classroom experiment to evaluate the benefits that knowledge of errors has on error prevention. We then analyzed data from two industrial surveys to identify specific prevention and mitigation approaches employed in practice. The classroom study showed that the better a requirements engineer understands human errors, the fewer errors and concomitant faults that engineer makes when developing a new requirements document. Furthermore, different types of Human Errors have different impacts on fault prevention. The industry study results identified prevention and mitigation mechanisms for each error type. Human error information is useful for fault prevention during requirements engineering. There are practices that requirements engineers can employ to prevent or mitigate specific human errors.     

An error analysis approach for laminated composite plate finite element models

Errors in laminated composite plate finite element models occur at both the individual element level and at the discretization level. This paper shows that parasitic shear causes individual element errors and that its sources must be eliminated if numerically and physically correct results are to be provided by the finite element analysis. In addition, discretization errors occur when the behavior of the continuum is represented by a finite number of degrees of freedom. A procedure to estimate discretization errors in laminated composite plate finite element models and guide refinement, in order to achieve an acceptable level of accuracy, is developed. The error estimator built is based on the energy norm of the error in stress resultants.     

How does people's trust in automated vehicles change after automation errors occur? An empirical study on dynamic trust in automated driving

When errors of automated vehicles (AVs) occur, drivers' trust can easily be destroyed, resulting in the reduction of the use of AVs. This study aims to examine how error of AVs declines driver's trust by impacting their subjective perceptions. A driving simulator experiment is conducted, in which 104 participants (male = 58; female = 46) experienced automated driving with automation errors and rated their trust. The results indicate that automation error will affect the driver's perceived predictability, perceived reliability, and perceived safety, which will lead to the decline of trust and abandonment of automated driving. With the occurrence of automation error of AVs, perceived safety plays a more critical role in drivers' trust. In addition, when automation errors occur in specific tasks with low risk, the trust of drivers will drop faster than that in high-risk tasks. This paper has explored the internal effects of the decline of driver's trust after automation errors of AVs, and further considers the influence of different external risks on these perception factors and trust. This study can help AVs manufacturers to formulate different degrees of trust repair strategies according to different driving tasks and accident severity.     

Training neural networks for minimum average risk with a special application to context dependent learning

The least squares criterion, as used by the backpropagation learning rule in multi-layer feed forward neural networks, does not always yield a solution that is in accordance with the desired behaviour of the neural network. This is for example the case when differentiation between different types of errors is required and the costs of the error types must be taken into account. In this paper the application of other error measures, specifically matched to the application, is investigated. The error measures used are based on the average risk, a function that is a weighted combination of the probabilities on the different types of errors that may occur. Special attention is payed to applications where the input patterns are not independent, and the average risk does not depend on the output of a single input pattern, but on its neighbourhood, or context. The ideas are illustrated with pulse detection in a one dimensional signal.     

An improved error handling method in SNMPv2 protocol operations

SNMPv1 is very successful due to its simplicity,flexibility,and extensibility,However,simplicity can also be a drawback,In SNMPv1,a manager can only reteieve one piece of information at a time,and an agent merely reports one protocol operation.These two interactions enlarge the number of protocol exchanges between a manager and an agent.SNMPv2 permits the retrieval of large data blocks,but the errors can only be handled one by one.These errors continually occur in a multivendor network.This paper proposes an error handling method in SNMPv2 protocol operations.In this method,all errors within a received PDU(Protocol Data Unit) can be detected by the agent simultaneously.     

一种基于模糊聚类的频域视频差错掩盖方法

当视频传输中出现不能恢复的误码或丢包时,在接收端就要使用差错掩盖技术对损坏的视频进行掩藏。论文提出一种基于模糊聚类的频域多特征量软判决差错掩盖方法,提高了差错掩盖效果。论文提出的算法主要分两个步骤:首先通过选择特征量对丢失块的周围相邻的块进行聚类分析,然后根据相似块的分布情况对丢失块进行掩盖。     

干涉式被动微波成像仪二维频谱特征及误差分析

干涉式被动微波成像仪(干涉式综合孔径微波辐射计)利用不同距离的干涉天线对形成的基线对视场范围内亮温分布的空间频谱进行采样,进而反演得到亮温图像。首先介绍了干涉式被动微波成像仪的基本工作原理,在此基础上分析了空间图像的二维频谱特征,并能利用这些特征从采样频谱中初步分析出原始亮温中的某些特殊分布,证实了在反演亮温存在明显振荡的情况下,可以从振荡方向判断存在误差的基线的大致位置。还分析了成像仪的天线位置误差、信道的幅度和相位误差以及天线方向图误差对反演亮温的影响及特点。这些分析将为从反演图像判断干涉式被动微波成像仪误差类型和来源提供重要的判别依据,并为后续的反演图像增强算法的设计提供重要的参考。     

Exception handling in robotics

The construction of robust and reliable robot systems able to handle errors arising from abnormal operating conditions is addressed. It is assumed that the robot program is logically correct but fails due to hardware or external state errors. The error-handling technique called exception handling is treated. The type of errors that can occur in a robot system and their error-handling requirements are described, and approaches to error handling and recovery are reviewed. Exception-handling facilities in Ada and AML/X are mentioned, and Exceptional C, the language chosen for exposition, is described. The use of exception-handling facilities is illustrated with two examples     

Improved Modeling of Elevation Error with Geostatistics

The elevations recorded within digital models are known to be fraught with errors of sampling, measurement and interpolation. Reporting of these errors according to spatial data standards makes several implicit and unacceptable assumptions about the error: it has no spatial distribution, and it is statistically stationary across a region, or even a nation. The approach explored in this paper employs actual elevations measured in ground and aerial survey at higher precision than the elevations in the DEM and recorded on standard paper maps. These high precision elevations are digitized and used to establish the real statistical and spatial distribution of the error. Direct measurements could also have been taken in the field by GPS or any other means of high precision data collection. These high precision elevations are subtracted from values stored in the DEM for approximately the same locations. The distribution of errors specific to the DEM can then be explored, and can be used in the geostatistical method of conditional stochastic simulation to derive alternative realizations of the error modeled and so of the DEM. Multiple versions of the derived products can also be determined. This paper compares the results of using different methods of error modeling. The best method, which gives widely implementable and defensible results, is that based on conditional stochastic simulation.     

Online check and recovery techniques for dependable embeddedprocessors

Efficient online check and fast recovery techniques for embedded systems aim to detect single or multiple errors within the same clock cycle in which they occur. It is argued that such techniques can enable fast error correction; detection of illegal states, micro- rollback for transient and permanent faults; and prioritized, controlled recovery     

Accuracy statistics for judging soft classification

In the literature one can find different accuracy measures that are built from the error matrix. However, standard accuracy assessment, which is based on the error matrix, is incomplete when dealing with fuzzy sets or when errors do not have the same importance. In this paper, we propose an extension of the error concept for soft (or crisp) classification that will be able to extend standard accuracy measures (e.g., overall, producer's, user's or Kappa statistic) that can be used in any framework: errors with different importance, soft classifier and crisp reference data (expert) or with a fuzzy expert. In particular, a weighted measure is built that takes into account the preferences of the decision maker in order to differentiate some errors that must not be considered equal.     

Intemal correction of dead-reckoning errors with a dual-drive compliant linkage mobil robot

This paper presents internal position error correction (IPEC)—a new method for accurate and reliable dead-reckoning with mobile robots. The IPEC method has been implemented on our recently developed multi-degree-of-freedom (MDOF) mobile platform, a vehicle in which two differential-drive mobile robots (called trucks) are physically connected through a compliant linkage. In addition to its four wheel encoders, the MDOF platform has one linear and two rotary internal encoders, which allow measurement of the relative distance and bearing between the two trucks. During operation, both trucks perform conventional dead-reckoning with their wheel encoders. But, in addition, the IPEC method uses information from the internal encoders to detect and correct dead-reckoning errors as soon as they occur. Our system, called compliant linkage autonomous platform with position error recovery (CLAPPER), requires neither external references (such as navigation beacons, artificial landmarks, known floorplans, or satellite signals), nor inertial navigation aids (such as accelerometers or gyros). Nonetheless, the experimental results included in this article show one to two orders of magnitude better positioning accuracy than systems based on conventional dead-reckoning. The CLAPPER corrects not only systematic errors, such as different wheel diameters, but also non-systematic errors, such as those caused by floor roughness, bumps, or cracks in the floor. These features are made possible by exploiting the new growth-rate concept for dead-reckoning errors that is introduced in this article for the first time. The growth-rate concept distinguishes between certain dead-reckoning errors that develop slowly and other dead-reckoning errors that develop quickly. Based on this concept, truck A frequently measures a property with slow-growing error characteristics on reference truck B (thus admitting a small error) to detect a fast-growing error on truck A (thus correcting a large error), and vice versa. © 1995 John Wiley & Sons, Inc.     

Reducing Soft Errors through Operand Width Aware Policies

Soft errors are an important challenge in contemporary microprocessors. Particle hits on the components of a processor are expected to create an increasing number of transient errors with each new microprocessor generation. In this paper, we propose simple mechanisms that effectively reduce the vulnerability to soft errors in a processor. Our designs are generally motivated by the fact that many of the produced and consumed values in the processors are narrow and their upper order bits are meaningless. Soft errors caused by any particle strike to these higher order bits can be avoided by simply identifying these narrow values. Alternatively, soft errors can be detected or corrected on the narrow values by replicating the vulnerable portion of the value inside the storage space provided for the upper order bits of these operands. As a faster but less fault tolerant alternative to ECC and parity, we offer a variety of schemes that make use of narrow values and analyze their efficiency in reducing soft error vulnerability of different data-holding components of a processor. On average, techniques that make use of the narrowness of the values can provide 49 percent error detection, 45 percent error correction, or 27 percent error avoidance coverage for single bit upsets in the first level data cache across all Spec2K. In other structures such as the immediate field of the issue queue, an average error detection rate of 64 percent is achieved.     

A Lookup Table Decoding of systematic (47, 24, 11) quadratic residue code

A new decoding algorithm for the binary systematic (47, 24, 11) quadratic residue (QR) code, a code that allows error-correction of up to five errors, is presented in this paper. The key idea behind this decoding technique is based on the existence of a one-to-one mapping between the syndromes “S₁” and correctable error patterns. By looking up a pre-calculated table, this algorithm determines the locations of errors directly, thus requires no multiplication operations over a finite field. Moreover, the algorithm dramatically reduces the memory required by approximately 89%. A full search confirms that when five or less errors occur, this algorithm decodes these errors perfectly. Since the implementation is written in the C-language, it is readily adaptable for use in Digital Signal Processing (DSP) applications.     

面向方面程序动态横切实现的错误分析

面向方面程序的错误分析是建立有效的面向方面软件测试方法的基础.本文基于主流的面向方面编程语言AspeetJ,从动态横切实现角度,针对新引入的方面语言部件的使用,提出面向方面程序中可能出现的两大类共五种错误类型:连接点签名语法匹配错误,连接点控制流行为语义匹配错误,连接点横切功能代码对连接点接口契约的前置条件、后置条件以及不变量的影响造成的错误.通过结合典型实例给出这些错误类型的表现,分析错误的特征,并对各种错误类型进行了测试角度的思考.     

An analytical quadratic model for the geometric error of a machine tool

In the analysis of geometric error of a machine, errors (especially angular errors) of a joint (axis) are assumed to remain constant with movement along the joint. The assumption, as actual measurements indicate, is not justifiable. Substantial variations can be observed. In this paper an analytical model for the prediction of geometric error of a machine tool is presented. Unlike previously proposed models, this model allows for the variation of errors along the machine's joints (axes). The model, developed using rigid body kinematics, relates the error vector at a point in the machine tool work space to the coordinates of that point by the dimensional and form errors of the individual links and joints of the machine's kinematic scheme. Shape and joint transforms are developed for inaccurate machine elements. An expression is developed for the case where the individual joint errors vary linearly with movement along the joint (or axis). The expression is quadratic. A comparison, made between the errors predicted by the model which allows for variation of individual errors and one that does not, indicates that the higher order terms of the expression make significant contributions to the predicted error. Finally, a method for estimating the coefficients of the model from the error obtained on a workpiece is proposed.     

一种基于H.263的混合错误掩盖方法

由于通信系统信道不可避免地存在噪声，传输数据流必然存在误码，特别是在H．263的视频码流中，即使一个比特的错误，常会影响一个区域的图像不能正确解码，且错误还会扩散到后续数帧图像，从而导致解码图像质量的严重恶化。该文在对现有的错误掩盖方法分析的基础上，提出了一种混合错误掩盖方法，它能根据运动向量的大小，自适应地采用空域掩盖算法或时域掩盖算法。该方法既考虑了图像的空间相关性，又考虑了时间相关性。模拟实验结果表明，采用该方法既改善了解码图像的质量，又降低了实现的复杂性，能够满足一定的适时处理要求。