Perceived vs. measured effects of advanced cockpit systems on pilot workload and error: Are pilots' beliefs misaligned with reality?

Four types of advanced cockpit systems were tested in an in-flight experiment for their effect on pilot workload and error. Twelve experienced pilots flew conventional cockpit and advanced cockpit versions of the same make and model airplane. In both airplanes, the experimenter dictated selected combinations of cockpit systems for each pilot to use while soliciting subjective workload measures and recording any errors that pilots made. The results indicate that the use of a GPS navigation computer helped reduce workload and errors during some phases of flight but raised them in others. Autopilots helped reduce some aspects of workload in the advanced cockpit airplane but did not appear to reduce workload in the conventional cockpit. Electronic flight and navigation instruments appeared to have no effect on workload or error. Despite this modest showing for advanced cockpit systems, pilots stated an overwhelming preference for using them during all phases of flight.     

Robust codes and robust,fault-tolerant architectures of the Advanced Encryption Standard

Hardware implementations of cryptographic algorithms are vulnerable to fault analysis attacks. Methods based on traditional fault-tolerant architectures are not suited for protection against these attacks. To detect these attacks we propose an architecture based on robust nonlinear systematic error-detecting codes. These nonlinear codes are capable of providing uniform error detecting coverage independently of the error distributions. They make no assumptions about what faults or errors will be injected by an attacker. Architectures based on these robust constructions have fewer undetectable errors than linear codes with the same n, k. We present the general properties and construction methods of these codes as well as their application for the protection of a cryptographic devices implementing the Advanced Encryption Standard.     

Clustering data with measurement errors

Traditional clustering methods assume that there is no measurement error, or uncertainty, associated with data. Often, however, real world applications require treatment of data that have such errors. In the presence of measurement errors, well-known clustering methods like k-means and hierarchical clustering may not produce satisfactory results.In this article, we develop a statistical model and algorithms for clustering data in the presence of errors. We assume that the errors associated with data follow a multivariate Gaussian distribution and are independent between data points. The model uses the maximum likelihood principle and provides us with a new metric for clustering. This metric is used to develop two algorithms for error-based clustering, hError and kError, that are generalizations of Ward's hierarchical and k-means clustering algorithms, respectively.We discuss types of clustering problems where error information associated with the data to be clustered is readily available and where error-based clustering is likely to be superior to clustering methods that ignore error. We focus on clustering derived data (typically parameter estimates) obtained by fitting statistical models to the observed data. We show that, for Gaussian distributed observed data, the optimal error-based clusters of derived data are the same as the maximum likelihood clusters of the observed data. We also report briefly on two applications with real-world data and a series of simulation studies using four statistical models: (1) sample averaging, (2) multiple linear regression, (3) ARIMA models for time-series, and (4) Markov chains, where error-based clustering performed significantly better than traditional clustering methods.     

Clustering data with measurement errors

Traditional clustering methods assume that there is no measurement error, or uncertainty, associated with data. Often, however, real world applications require treatment of data that have such errors. In the presence of measurement errors, well-known clustering methods like k-means and hierarchical clustering may not produce satisfactory results.In this article, we develop a statistical model and algorithms for clustering data in the presence of errors. We assume that the errors associated with data follow a multivariate Gaussian distribution and are independent between data points. The model uses the maximum likelihood principle and provides us with a new metric for clustering. This metric is used to develop two algorithms for error-based clustering, hError and kError, that are generalizations of Ward's hierarchical and k-means clustering algorithms, respectively.We discuss types of clustering problems where error information associated with the data to be clustered is readily available and where error-based clustering is likely to be superior to clustering methods that ignore error. We focus on clustering derived data (typically parameter estimates) obtained by fitting statistical models to the observed data. We show that, for Gaussian distributed observed data, the optimal error-based clusters of derived data are the same as the maximum likelihood clusters of the observed data. We also report briefly on two applications with real-world data and a series of simulation studies using four statistical models: (1) sample averaging, (2) multiple linear regression, (3) ARIMA models for time-series, and (4) Markov chains, where error-based clustering performed significantly better than traditional clustering methods.     

GPS导航系统中的地图匹配算法

传统算法对于复杂路况的匹配正确率较低,为此,提出一种适合于交通网络拓扑结构变化的动态平行四边形匹配算法。利用权值选出匹配路段,并将全球定位系统(GPS)轨迹点投影到匹配路段上,在交叉路口以平行四边形匹配准则消除车辆轨迹点沿道路方向的误差,通过动态偏差更新,解决卫星换星、大气云层遮挡、多路径效应等因素造成的偏差问题。引入该算法的GPS车载系统在合肥市实地跑车实验结果表明,其对于复杂路况仍能进行正确匹配,真实再现车辆行驶情况。     

Absolute calibration of an ABB IRB 1600 robot using a laser tracker

The absolute accuracy of an ABB IRB 1600 industrial robot is improved using a 29-parameter calibration model, developed after extensive experimentation. The error model takes into account all possible geometric errors (25 geometric error parameters to be identified through optimization, in addition to the pose parameters for the base and tool frames and four error parameters related to the compliance in joints 2, 3, 4 and 5). The least squares optimization technique is used to find the 29 error parameters that best fit the measures acquired with a laser tracker. Contrary to most other similar works, the validation of the robot's accuracy is performed with a very large number of measures (1,000) throughout the complete robot's joint space. After calibration, the mean/maximum position errors at any of eight different measurement points on the end-effector (all offset from axis 6 by approximately 120 mm) are reduced from 0.968 mm/2.158 mm respectively, to 0.364 mm/0.696 mm.     

基于场景图划分的无人机影像定位算法

针对大规模长序列无人机（UAV）影像定位中存在的速度慢、误差漂移等问题,结合UAV影像的特点,提出了一种基于场景图划分的UAV影像定位算法。首先,利用全球定位系统（GPS）辅助信息缩小特征匹配的空间搜索范围,从而加速同名点的提取;之后结合视觉一致性和空间一致性来构建场景图,并利用归一化割（Ncut）对其进行划分;接着,对各组场景图进行增量重建;最后,利用光束法平差（BA）融合场景图从而计算出场景的三维模型。此外,在BA阶段,所提算法对代价函数进行扩充,即加入了GPS空间约束信息。在四个UAV影像数据集上的实验结果表明,与COLMAP等多种运动恢复结构（SFM）算法相比,所提算法的定位速度提升了50%,重投影误差减小了41%,定位误差控制在0.5m之内。此外,通过有无GPS辅助下的算法的实验对比,可以得知引入相对和绝对GPS约束的BA有效解决了误差漂移问题,避免了出现歧义性结果,并且极大地减小了定位误差。     

Knowledge based sensor fault detection for gas turbines under consideration of model based methods

The gas path analysis is a useful tool for modeling jet engines and stationary gas turbines, respectively. Such models are used for diagnosis that means that measurement values as inputs deliver state variables as outputs from the model. The measurement values are not fault free. They are disturbed by noise and by systematic sensor errors. Therefore algorithms have to be implemented which filter and compensate these errors; furthermore the modeling is not exact. Thereby the algorithms must be extended and adapted to this case. In alternation to the model based procedures the diagnosis problem can also be solved by knowledge based methods using an expert system. Such an expert system is developed and presented in this paper. It is an efficient tool — also in connection with the model based procedures — for detecting faults in the states of the engine and faults in the sensors as well. The diagnosis software package is applied on military and civilian jet engines and it is used in power plants for stationary gas turbines.     

面向测量误差模型的外生变量估计方法

从观察数据中发现因果关系在近年来得到了越来越多学者的关注,其中外生变量在理解因果机制中扮演者重要的角色。然而,现有的因果发现方法大多假设观察变量就是真实发生的因(果)变量,忽略了测量误差带来的影响。为此,提出了一种解决测量模型下的外生变量估计方法。通过引入triad约束,根据此约束来找出与其余所有相关成对变量都满足triad约束的变量,即外生变量。该算法不仅能够解决含有测量误差数据的估计问题,而且对于没有测量误差的数据仍然适用。实验将该算法应用于真实网络产生的数据中,结果表明,无论变量是否含有测量误差,提出方法均优于现有的其他算法。同时,基于移动基站的真实数据实验也验证了算法的有效性。     

基于电信数据的室外定位技术研究

越来越多的移动计算依赖位置信息提供基于位置的服务,移动设备的室外定位技术至关重要。目前广为采用的方式是GPS,但移动设备端的GPS位置信息依赖移动设备如手机的GPS传感器获取,电信运营商虽然为用户提供通话和数据服务,却无法获得用户的精确GPS位置。针对这种情况,提出利用手机端和电信基站之间的连接信号数据(简称电信数据),实现移动设备的定位服务。考虑到电信运营商积累了海量的电信数据,因此通过研究基于电信数据的室外定位技术,使得运营商获取用户位置成为可能。提取电信特征数据、以手机所在GPS位置作为标签数据,研究了五种基于机器学习模型的室外定位算法,实现了从基站信号数据到GPS坐标点的预测,通过大量的实验对比了这些方法的定位精度和运行时间、不同数据收集模式的定位精度、不同特征的定位精度以及探索了后处理对定位精度的提升效果。最终通过实验可知,基于栅格化的随机森林分类模型是效果最好的方法,能够达到15~20m的平均误差和10m的中位误差,比前期回归算法在2G和4G数据分别实现了39.46%和54.28%的精度提升,取得与GPS定位接近的定位精度。     

All unidirectional error-detecting code for universal applications

A code has been devised with a minimum distance D(c) = 2 to detect all unidirectional errors possible during communication. The ten decimal digits 0 to 9 are encoded using code words, each of which have seven bits. This is an unordered code with the minimum complexity in encoding and decoding to identify a codeword. The code is framed employing the uniform weight policy. This unique feature makes our code a complete unidirectional error-detecting code. In addition, the code proposed can correct one unidirectional error automatically by the proposed receiving-end logic. In addition, a uniform weighted code is suggested for ASCII which can detect all unidirectional errors with a limited 1-bit error correction ability     

基于Kalman滤波的地图匹配方法

地图匹配方法利用电子地图的地理数据对GPS定位数据进行校正,可有效提高定位数据的精度。本文首先采用最近点估计方法计算道路垂直方向的GPS误差,对该误差采用Kalman滤波方法获得GPS偏差估计,以此偏差估计修正GPS测量值并进行地图匹配。实验表明利用该方法可将定位数据准确地匹配到道路上,使匹配后的定位精度得到提高。     

基于粒子滤波的GPS/DR组合导航算法研究

对于车载GPS/DR(全球定位系统/航位推算)组合导航系统的算法研究,大多采用扩展卡尔曼滤波,但是应用扩展卡尔曼滤波时,非线性系统必须要进行线性化,从而导致滤波结果有较大的误差.为此将粒子滤波算法用于车载GPS/DR组合导航系统中,并建立了GPS/DR组合导航系统的状态方程和观测方程.为了检验其有效性,将两种方法分别对车载GPS/DR组合导航系统进行滤波仿真,仿真实验结果表明粒子滤波比扩展卡尔曼滤波有更好的滤波效果,更能减少定位误差.     

Improving cooperative trajectory mapping applications with encounter-based error correction

Cooperative trajectory mapping is an emerging technique that allows users to create such a map by using data that are collected from each other's mobile phones. This technique has been proposed for many applications, such as people localisation, public transportation tracking and traffic monitoring. Unlike the traditional Global Positioning System, cooperative trajectory mapping only requires information about the departure distance and moving direction from the previously reported position. This avoids problems such as the high-energy consumption of GPS and weak GPS signals in indoor conditions. However, the new technique also brings about other problems, such as measurement errors in cooperative trajectory mapping, which is when a measurement error causes the spatial relations among users to be wrong. We propose an encounter-based error correction algorithm to efficiently reduce measurement errors. Extensive simulation experiments are carried out to validate our solutions.     

A case history analysis of software error cause-effectrelationships

Approximately 700 errors in four commercial measuring-control software products were analyzed, and the cause-effect relationships of errors occurring during software development were identified. The analysis method used defined appropriate observation points along the path leading from cause to effect of a software error and gathered the corresponding data by analyzing each error using fault tree analysis. Each observation point's data were categorized, and the relationships between two adjoining points were summarized using a cross-indexing table. Four major error-occurrence mechanisms were identified; two are related to hardware and software interface specification misunderstandings, while the other two are related to system and module function misunderstandings. The effects of structured analysis and structured design methods on software errors were evaluated     

数据质量检测规则挖掘方法

数据质量规则是检测数据库质量的关键。为从关系数据库中自动发现数据质量规则,并以其为依据检测错误数据,研究质量规则表示形式及其评估度量,提出以数据项分组及其可信度为依据的最小质量规则计算准则、挖掘算法以及采用质量规则检测错误数据的思路。该数据质量规则形式借鉴关联规则的可信度评估机制、条件函数依赖的表达能力,统一描述函数依赖、条件函数依赖、关联规则等,具有简洁、客观、全面、检测异常数据准确等特性。与相关研究相比,降低挖掘算法的时间复杂度,提高检错率。用实验证明该方法的有效性和正确性。     

Error Detection and Fault Tolerance in ECSM Using Input Randomization

For some applications, elliptic curve cryptography (ECC) is an attractive choice because it achieves the same level of security with a much smaller key size in comparison with other schemes such as those that are based on integer factorization or discrete logarithm. For security reasons, especially to provide resistance against fault-based attacks, it is very important to verify the correctness of computations in ECC applications. In this paper, error-detecting and fault-tolerant elliptic curve cryptosystems are considered. Error detection may be a sufficient countermeasure for many security applications; however, fault-tolerant characteristic enables a system to perform its normal operation in spite of faults. For the purpose of detecting errors due to faults, a number of schemes and hardware structures are presented based on recomputation or parallel computation. It is shown that these structures can be used for detecting errors with a very high probability during the computation of the elliptic curve scalar multiplication (ECSM). Additionally, we show that using parallel computation along with either PV or recomputation, it is possible to have fault-tolerant structures for the ECSM. If certain conditions are met, these schemes are more efficient than others such as the well-known triple modular redundancy. Prototypes of the proposed structures for error detection and fault tolerance have been implemented, and experimental results have been presented.     

A new hierarchical moving curve-fitting algorithm for filtering lidar data for automatic DTM generation

Recent advances in laser scanning hardware have allowed rapid generation of high-resolution digital terrain models (DTMs) for large areas. However, the automatic discrimination of ground and non-ground light detection and ranging (lidar) points in areas covered by densely packed buildings or dense vegetation is difficult. In this paper, we introduce a new hierarchical moving curve-fitting filter algorithm that is designed to automatically and rapidly filter lidar data to permit automatic DTM generation. This algorithm is based on fitting a second-degree polynomial surface using flexible tiles of moving blocks and an adaptive threshold. The initial tile size is determined by the size of the largest building in the study area. Based on an adaptive threshold, non-ground points and ground points are classified and labelled step by step. In addition, we used a multi-scale weighted interpolation method to estimate the bare-earth elevation for non-ground points and obtain a recovered terrain model. Our experiments in four study areas showed that the new filtering method can separate ground and non-ground points in both urban areas and those covered by dense vegetation. The filter error ranged from 4.08% to 9.40% for Type I errors, from 2.48% to 7.63% for Type II errors, and from 5.01% to 7.40% for total errors. These errors are lower than those of triangulated irregular network filter algorithms.     

Approach to weak signal detection via over-sampling and bidirectional saw-tooth shaped function in wearable devices

Many biological signals that reflect the human health are relatively weak, and high-resolution Analog-Digital Converters (ADCs) are required to measure them. The over-sampling technology is a method for increasing the accuracy, equal to the accuracy of high resolution ADCs, when an ADC has low resolution. For high resolution ADCs, their accuracy from the over-sampling technology would break its maximum limitation. During our signal collection, there are two items which affect our results: 1. measurement error; 2. non-linear characteristics at the end points. In over-sampling method of detecting weak signal of wearable devices by using the unidirectional saw-tooth shaped function, the magnitude of the shaped function is as close as possible to the integer multiple of the quantization step size to reduce the additional measurement error. To reduce the difficulty of the adding shaped function, which features nonlinearity of the starting point, a new method of detecting the weak signal by adding the bidirectional saw-tooth shaped function is proposed. The experimental results show this method is feasible for improving the ADC resolution.     

Fault tolerance design in JPEG 2000 image compression system

The JPEG 2000 image compression standard is designed for a broad range of data compression applications. The new standard is based on wavelet technology and layered coding in order to provide a rich feature compressed image stream. The implementations of the JPEG 2000 codec are susceptible to computer-induced soft errors. One situation requiring fault tolerance is remote-sensing satellites, where high energy particles and radiation produce single event upsets corrupting the highly susceptible data compression operations. This paper develops fault tolerance error-detecting capabilities for the major subsystems that constitute a JPEG 2000 standard. The nature of the subsystem dictates the realistic fault model where some parts have numerical error impacts whereas others are properly modeled using bit-level variables. The critical operations of subunits such as discrete wavelet transform (DWT) and quantization are protected against numerical errors. Concurrent error detection techniques are applied to accommodate the data type and numerical operations in each processing unit. On the other hand, the embedded block coding with optimal truncation (EBCOT) system and the bitstream formation unit are protected against soft-error effects using binary decision variables and cyclic redundancy check (CRC) parity values, respectively. The techniques achieve excellent error-detecting capability at only a slight increase in complexity. The design strategies have been tested using Matlab programs and simulation results are presented.