Algorithm development for the application of ground-penetrating radar on asphalt pavement compaction monitoring

Ground-penetrating radar (GPR) is a promising non-destructive technique to be applied on monitoring the density change during asphalt pavement compaction. The utmost challenge of this application is the unknown effect of surface moisture, sprayed by the compactor during compaction, on GPR signals. To extract density information without the effect of surface moisture, a correction algorithm based on reference scan approach was developed. To evaluate the performance of the algorithm, a full-scale test site was constructed with compaction pass number from 0 to 10, and a large amount of GPR data were collected from the pavement with different surface moisture contents. A total of 22 cores were extracted for validation purposes. After applying the algorithm, it was found that the average density prediction error was reduced significantly. By using correction algorithm together with the density model, the density of asphalt pavement was obtained with high accuracy.     

城市生活垃圾压缩设备的压头及箱体设计与分析

 针对目前城市生活垃圾压缩设备普遍存在压缩效果不够理想的问题,提出一种新型压头和箱体结构,给出一种压缩效果和密闭性较好的城市生活垃圾压缩方式．通过优化传统垃圾压缩设备压头的结构,设计一种推铲形新型压头装置,提高垃圾压缩设备的压缩倍数,改善垃圾的压缩效果．对现有压缩设备箱体的结构和污水排放口进行了改进,设计一种与推铲形压头相匹配的垃圾压缩设备新型箱体．采用有限元分析方法,对设计的推铲形压头装置和箱体进行了分析．分析结果表明,同目前垃圾压缩设备使用的平压头和楔形压头相比较,设计的推铲形压头具有更好的压缩效果,为城市生活垃圾压缩设备压缩装置的设计和制造提供了理论基础．     

Balancing the Subjective and Objective Weights for Correlated Multiresponse Optimization

Desirability function approach is very popular for multiresponse optimization problems. However, the approach ignores the correlations among multiple responses and does not consider how to reasonably determine the relative weights of multiple responses. In this paper, an integrative desirability function approach is proposed to simultaneously consider the correlations among the responses and the weight determination method. For the proposed approach, the root mean square error performance is regarded as a new response, and then the seemingly unrelated regression estimation is utilized to fit the models. Through balancing the subjective and objective information, the proposed approach can be used to make more reasonable decisions for correlated multiresponse optimization. Two examples are employed to validate the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Fault simulation and response compaction in full scan circuits using HOPE

This paper presents results on fault simulation and response compaction on ISCAS 89 full scan sequential benchmark circuits using HOPE-a fault simulator developed for synchronous sequential circuits that employs parallel fault simulation with heuristics to reduce simulation time in the context of designing space-efficient support hardware for built-in self-testing of very large-scale integrated circuits. The techniques realized in this paper take advantage of the basic ideas of sequence characterization previously developed and utilized by the authors for response data compaction in the case of ISCAS 85 combinational benchmark circuits, using simulation programs ATALANTA, FSIM, and COMPACTEST, under conditions of both stochastic independence and dependence of single and double line errors in the selection of specific gates for merger of a pair of output bit streams from a circuit under test (CUT). These concepts are then applied to designing efficient space compression networks in the case of full scan sequential benchmark circuits using the fault simulator HOPE.     

A comprehensive Bayesian approach for model updating and quantification of modeling errors

This paper presents a comprehensive Bayesian approach for structural model updating which accounts for errors of different kinds, including measurement noise, nonlinear distortions stemming from the linearization of the model, and modeling errors due to the limited predictability of the latter. In particular, this allows the computation of any type of statistics on the updated parameters, such as joint or marginal probability density functions, or confidence intervals. The present work includes four main contributions that make the Bayesian updating approach feasible with general numerical models: (1) the proposal of a specific experimental protocol based on multisine excitations to accurately assess measurement errors in the frequency domain; (2) two possible strategies to represent the modeling error as additional random variables to be inferred jointly with the model parameters; (3) the introduction of a polynomial chaos expansion that provides a surrogate mapping between the probability spaces of the prior random variables and the model modal parameters; (4) the use of an evolutionary Monte Carlo Markov Chain which, in conjunction with the polynomial chaos expansion, can sample the posterior probability density function of the updated parameters at a very reasonable cost. The proposed approach is validated by numerical and experimental examples.     

基于FTA的老年人APP交互设计

目的提升老年人对APP的满意度,帮助老年人更好地接受和使用APP。方法提出基于故障树分析(FTA)的老年人APP交互设计方法。首先通过焦点小组确定导致错误发生的原因并建立故障树,对错误发生的原因进行合理分类;随后对故障树进行定性定量分析,计算故障树的最小切集和错误发生的概率,找到导致头号事件发生的核心因素;接着进行重要度分析,计算构造重要度,根据构造重要度值对引起错误发生的基本事件进行排序,为设计改良提供理论上的优先顺序;最后以老年人医疗APP为研究案例进行设计改良,并对改良后的结果进行错误率验证。结论该方法可以有效地发现老年人操作APP时出现障碍的原因,不仅可以帮助设计师找到较为合理的改良方案,而且还可以使老年人获得良好的交互体验。     

基于视觉认知出错的老年人自助售票机界面设计优化研究

目的 为减少老年人使用铁路自助售票机视觉认知出错,对自助售票界面进行优化设计。方法 梳理老年人视觉认知出错的类别及其相应的出错因子;募集30名老年人参加自助购票全过程模拟实验,结合实验后的访谈数据,提取老年人购票过程中可能出现的视觉认知出错问题,并进行分类;从视觉认知层面探讨购票出错的深层原因,分析现有自助售票界面中不符合老年人视觉认知加工特点的设计;提出铁路自助售票机界面优化策略,并对界面进行适老化改进和评估。结论 研究显示,老年人在自助售票界面视觉认知过程中,会因注意失效、知觉混淆和疏忽产生错过目标信息、错误理解目标信息、不理解目标信息语义、难以匹配原有认知经验等出错问题,这些问题通过合理组织与强化信息、增强目标信息语义理解度、匹配老年人认知经验和提升容错性等方法进行优化。优化后界面的可用性测试结果显示,从视觉认知出错角度进行适老化设计,是一种有效减少老年人操作错误、提高满意度的设计方法。     

An application of an expert system in layout compaction of VLSI design

Abstract

Recent research in knowledge‐based expert systems of VLSI design tools has concentrated on placement, routing, and cell generation. This paper presents an alternative application for artificial intelligence (AI) techniques on compaction design for a VLSI mask layout‐expert compactor. In order to overcome the shortcomings of iterative search through a large problem space within a working memory, and therefore, to speed‐up the runtime of compaction, a set of rule‐based region query operations and knowledge‐based techniques for the plane sweep method are proposed in this system. Experimental results have explored the possibility of using expert system technology (EST) to automate the compaction process by “reasoning” out the layout design and applying sophisticated expert rules to its knowledge base.     

Feature classification techniques in model-based object recognition

We describe a system that performs model-based recognition of the projections of generalized cylinders, and present new results on the final classification of the feature data. Two classification methods are proposed and compared. The first is a Bayesian technique that ranks the object space according to estimated conditional probability distributions. The second technique is a new feed-forward “neural” implementation that utilizes the back-propagation learning algorithm. The neural approach yields a 31.8% reduction in classification error for a database of twenty models relative to the Bayesian approach, although it does not provide an ordered ranking of the object space. The accuracy results of the neural approach represent a significant performance advance in feature-based recognition by perceptual organization without the use of depth information. Examples are provided using the results of a simple segmentation system applied to real image data.     

Analytical approach for the mix design optimisation of bituminous mixtures with crumb rubber

The present paper provides a basis for defining a mix design method specifically tailored to rubberised asphalt that takes into account the behaviour of crumb rubber. An analytical approach to quantifying the recovered deformation of crumb rubber in the post-compaction phase has been developed in order to adjust the number of gyrations proposed by the Superpave method for compacting specimens of hot mix asphalt using a Superpave gyratory compactor. The maximum allowable amount of rubber has been calculated. Finally, a step-by-step protocol has been proposed in order to fabricate and compact crumb rubber modified mixtures with the gyratory compactor.     

Soft fault detection and isolation in analog circuits: some resultsand a comparison between a fuzzy approach and radial basis functionnetworks

This paper provides a comparison between two techniques for soft fault diagnosis in analog electronic circuits. Both techniques are based on the simulation before test approach: a "fault dictionary" is a priori generated by collecting, signatures of different fault conditions. Classifiers, trained by the examples contained in the fault dictionary, are then configured to classify the measured circuit responses. The suggested classifiers have similar structures. The first is based on a fuzzy system, obtained by processing fault dictionary data for automatic generation of IF-THEN rules, and the second classifier is based on a radial basis function neural network. The two classifiers are used to detect and isolate faults both at the subsystem and component levels. The experimental results point out that both classifiers provide low classification errors in the presence of noise and nonfaulty components tolerance effects. The fuzzy approach provides better results due to an efficient generation method for the IF-THEN rules that allows adding IF parts in the input space regions where ambiguity occurs     

基于改进的最大似然法识别风力发电机转子的载荷参数

风机不对中故障是多载荷参数耦合而成,工程师难以凭经验了解载荷参数的大致范围。针对因转子结构参数和测试条件等不确定性参数的存在及载荷参数先验信息未知,最大似然法识别转子不对中故障的载荷参数时采用的搜索计算面临计算量大,且迭代过程中灵敏度会带来一些数值问题。引入搜索区间进退法到敏感矩阵法与最大似然法中,对最大似然法进行改进。改进后的最大似然法中的迭代适于处理复杂工程优化问题,避免传统迭代数值法对搜索空间的苛刻要求。以输入尺寸和输出测试响应具有随机性测量误差的风力发电机转子系统为例,在三种测量误差下的识别结果表明,改进的最大似然法提高了最大似然法的辨识效果,可以减少不确定性因素对识别结果的影响,鲁棒性好。     

Comments on Paper by Kurtz,Link, Tukey and Wallace

One way that a process can be out of statistical control is that the process mean can follow a cyclical pattern over time. This out-of-control condition leads to increased variability in the variable of interest over any production interval. Recently, a control chart based on a common time series technique, the periodogram, was proposed as a means of detecting cyclical behavior of the process mean. We show that this control chart is not able to detect certain periodicities and that it does not make use of all available information when multiple observations are taken at each sampling time. Using a multiple regression approach, we present modifications of the periodogram approach that produce control charts sensitive to a wider range of periodicities and use all available information. We compare the average run lengths of the procedures.     

Risk analysis of human error in interaction design by using a hybrid approach based on FMEA,SHERPA, and fuzzy TOPSIS

User experience is the focus of interaction design, and designing for errors is crucial for improving user experience. One method of designing for errors is to identify human errors and then initiate corrective actions on high-risk errors to reduce their adverse effects. In this study, we proposed a hybrid approach for risk analysis of human error concerning user experience of interactive systems. In this approach, systematic human error reduction and prediction approach (SHERPA) is first adopted to identify human error concerning user experience. Subsequently, failure mode and effect analysis (FMEA) is used to analyze the risk factors of the error, including occurrence, severity, and detection. Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is then used to calculate the risk priority number to rank the errors. Finally, corrective actions for high-risk errors are recommended. An in-vehicle information system was used to demonstrate the proposed approach. The results indicated that the proposed approach can effectively analyze the risk of human error concerning user experience and be used as a universal reliability approach for improving user experience in interaction design.     

基于KSLPP与RWKNN的旋转机械故障诊断

针对旋转机械高维故障特征集识别精度低的问题,提出基于核监督局部保留投影（Kernel Supervised Locality Preserving Projection, KSLPP）与ReliefF特征加权的K近邻（ReliefF Weighted K-Nearest Neighbor, RWKNN）分类器相结合的维数约简故障诊断方法。该方法首先应用KSLPP提取故障特征集中的非线性信息,同时在降维投影过程中充分利用类别信息,使降维后最小化类内散度,最大化类间分离度;随后,将降维后得到的低维敏感特征集输入RWKNN进行模式识别,RWKNN能够突出不同特征对分类的贡献率,强化敏感特征,弱化不相关特征,提升了分类精度和鲁棒性。最后,通过典型转子实验台的故障特征集验证了该方法的有效性。     

Two fault classification methods for large systems when available data are limited

In this paper, we consider the problem of fault diagnosis for a system with many possible fault types. Two approaches are presented that are useful for initial diagnosis of system-wide faults, assuming that no data are available before commissioning the system but the possibility of the occurrence of each symptom is known for each fault. The first method uses a fault tree approach to reduce the solution space before applying the geometric classification method, the assumption being that no unwanted symptoms are possible. This method is nonparametric and thus does not require any data to estimate the underlying distribution of faults and symptoms. The second method is based on the Bayes classification approach to utilize the subjective information and the limited data that may be available. The two methods are generic and applicable to a variety of industrial processes.     

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

This paper investigates the kernel entropy based extended Kalman filter (EKF) as the navigation processor for the Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS). The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed (or impulsive) interference errors, such as the multipath. The kernel minimum error entropy (MEE) and maximum correntropy criterion (MCC) based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the GPS. The standard EKF method is derived based on minimization of mean square error (MSE) and is optimal only under Gaussian assumption in case the system models are precisely established. The GPS navigation algorithm based on kernel entropy related principles, including the MEE criterion and the MCC will be performed, which is utilized not only for the time-varying adaptation but the outlier type of interference errors. The kernel entropy based design is a new approach using information from higher-order signal statistics. In information theoretic learning (ITL), the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to MSE. To improve the performance under non-Gaussian environments, the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error (MMSE) is utilized for mitigation of the heavy-tailed type of multipath errors. Performance assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.     

Analytical relations between effective material properties and microporosity: Application to bone mechanics

This work deals with the problem of reconstruction of microstructural parameters of viscoelastic composite material from measurements of its effective properties. The Stieltjes representation of the effective shear complex modulus of two-component composite material is exploited to recover information about structural parameters. This representation is derived from problem of torsion of a heterogeneous cylinder using asymptotic expansions method. The microstructural information is contained in the spectral measure in this analytical representation. The spectral function can be recovered from the measurements over a range of frequencies. The problem of reconstruction of the spectral measure is very ill-posed. Regularized algorithm is derived to ensure stability of the results. We apply the proposed method to recovery of porosity of cancellous bone from measurements of its effective shear modulus. Bone is modeled as a medium with a microstructure composed of two viscoelastic isotropic or/and transversely isotropic materials, with the components representing trabeculae (elastic component) and bone marrow (viscoelastic component). Porosity is understood as volume fraction of bone marrow. To verify the approach we apply it to analytically and numerically simulated response of a cylinder filled with a composite material with hexagonal microstructure. The proposed method does not use any specific assumptions about the microgeometry of the composite and is applicable to any two-phase composite medium.     

Built-in self-test for phase-locked loops

An effective built-in self-test (BIST) structure of a phase-locked loop (PLL) in digital applications is presented in this paper. The proposed BIST structure can identify possible faults in any block such as the phase detector, charge pump, loop filter, voltage-controlled oscillator and divide-by-N of the PLL. The key advantage of this approach is that it uses all existing blocks in PLL for measuring and testing, reducing the chip area overhead. Restated, the proposed approach does not alter any existing analog circuits. Rather, the proposed approach only adds some small circuits to the PLL and requires a slight modification of the digital part. The final test outputs are digital values which can increase the reliability of the proposed BIST structure. Physical chip design and fault simulation results indicate the characteristics of the proposed BIST structure, namely, high fault coverage (97.2%) and low area overhead (2.78%).     

Accuracy verification of a simple local three-dimensional displacement measurement method of DIC with two images coordinates

There are two methods applied for three-dimensional digital image correlation method to measure three-dimensional displacement. One is to measure the spatial coordinates of measuring points by analyzing the images. Then, the displacement vectors of these points can be calculated using the spatial coordinates of these points obtained at different stages. The other is to calibrate the parameters for individual measuring points locally. Then, the local displacements of these points can be measured directly. This study proposes a simple local three-dimensional displacement measurement method. Without any complicated distortion correction processes, this method can be used to measure small displacement in the three-dimensional space through a simple calibration process. A laboratory experiment and field experiment are carried out to prove the accuracy of this proposed method. Laboratory test errors of one-dimensional experiment are similar to the accuracy of the XYZ table; the error in Z-direction is only 0.0025% of the object distance. The measurement error of laboratory test is about 0.0033% of the object distance for local three-dimensional displacement measurement test. Test and analysis results of field test display that in-plane displacement error is only 0.12 mm, and the out-of-plane error is 1.1 mm for 20 m × 30 m measuring range. The out-of-plane error is only about 10 PPM of the object distance. These test and analysis results show that this proposed method can achieve very high accuracy under small displacement for both of laboratory and field tests.