Computer programs and calibration with the PIBS technique for quantitative electron probe analysis using a lithium-drifted silicon detector

A FORTRAN IV program for on-line analysis of silicates and oxides by electron microprobe fitted with a Si(Li) detector is described. The method allows for conversion to off-line batch processing. The ZAF (matrix corrections) routine uses precalculated functions wherever possible: this reduces calculation time to a few seconds. Also a program for monitoring the performance of the Si(Li) detector is listed.     

A procedure for the correction of the effect of variation in incidence angle on AIRSAR data

Radar imaging is a valuable tool for the monitoring and management of tropical ecosystems. One of the obstacles to its successful usage is the variation of the radar backscatter with incidence angle caused by the side-looking mode of the sensor. Available research has modelled the effect of changes in incidence angle for soil, grass and scattering mechanisms in tree-covered areas. The effect varies depending on the scattering mechanism. To model the radar backscatter behaviour of a natural landscape, therefore, implies precise knowledge of the land-cover composition. A method for estimating and correcting the effect of changes in look angle on backscatter data is consequently proposed; it requires little field knowledge and encompasses the effects on most land-cover types in the study area. The method is based on the assumption that each line in azimuth direction contains a similar composition in regard to land-cover types. The backscatter frequency distribution of each azimuth line can then be employed to model and correct for the effect of variation in incidence angle. The results are evaluated using an existing land-cover classification to extract mean backscatter values for individual land-cover classes before and after the correction procedure. The correction procedure is shown to successfully adjust backscatter intensities to a nominal incidence angle for the vegetation communities in a coastal tropical savanna landscape in Australia's Northern Territory.     

A language for inquiring about the run-time behaviour of programs

This paper describes a language for studying the behaviour of programs, based upon the data collected while these programs are executed by a computer. Besides being a useful tool in debugging, the language is also valuable in the experimental evaluation of the complexity of algorithms, in studying the interdependence of conditionals in a program and in determining the feasibility of transporting programs from one machine to another. The program one wishes to analyse is written in an Algol 60-like language; when the program is executed it automatically stores, in a data base, the information needed to answer general questions about computational events which occurred during execution. This information consists (basically) of the list of labels passed while the program is being executed, and the current values of the variables. Since the list of labels is describable by regular expressions, these expressions can also be used to identify specific subparts of the list and therefore allow access to the values of the variables. This constitutes the basis for the design of the inquiry language. The user's questions are automatically answered by a processor which inspects the previously generated data base. The paper also presents examples of the use of the language and describes the implementation of its processor.     

A mixed effects log-linear model based on the Birnbaum-Saunders distribution

In lifetime data analysis and particularly in engineering reliability contexts, the Birnbaum-Saunders (BISA) density is often suggested as a suitable model; see Birnbaum and Saunders (1969), Mann et al. (1974), and Desmond (1985). A linear regression model, obtained from a logarithmic transformation of the response variable, is useful in studying the effect of covariates on the response variable; see Rieck and Nedelman (1991), Tsionas (2001) and Galea et al. (2004). In this paper, an extension of the log-linear regression model of Rieck and Nedelman (1991), which considers random effects, is introduced. From a Monte Carlo simulation study, the performance of various estimation and prediction methods are studied. The usefulness of the mixed log-linear model is stressed and compared to the pure fixed effects log-linear regression BISA model. The new model is used to analyze a real data set, for which a fixed effects model is inappropriate.     

Feature extraction and decision procedure for automated inspection of textured materials

This paper proposes a general system approach applicable to the automatic inspection of textured material. First, the input image is preprocessed in order to be independent of non-uniformities. A tone-to-texture transform is then performed by mapping the original grey level picture on a multivariate local feature sequence, which turns out to be normally distributed. More specifically, features derived with the help of the Karhunen-Loève decomposition of a small neighbourhood of each pixel are used. A decision as to conformity with a reference texture is arrived at by thresholding the Mahalanobis distance for every realization of the feature vector. It is shown that this approach is optimum under the Gaussian assumption in the sense that it has a minimum acceptance region for a fixed probability of false rejection.     

A fast procedure for the training of neural networks

A new training paradigm for artificial neural networks is described. The technique utilizes a polynomial approximation to the sigmoidal processing function and directly integrates principal components analysis (PCA) into the network training philosophy. A major benefit of the new technique is that off-line network training is ‘one-shot’, contrary to the standard iterative techniques available in the literature. Further training may be performed on-line in a recursive fashion, yielding an adaptive neural network. Additionally, the new philosophy incorporates a systematic procedure for determining the number of neurons in the hidden layer of the network. The training procedure is first described and the implications of the training philosophy discussed. Some results, including applications to industrial chemical processes, are then presented to highlight the power of the technique. The systems considered are a continuous stirred tank reactor and a polymerization reactor.     

基于偏差校正的建模法在BPA结晶过程中的应用

从推理控制的基本思路出发,采用推算出干扰量继而实现前馈控制的方法,本文提出了一种基于输出偏差校正(deviation correction)的建模方法.该方法与支持向量机(support vector machine,SVM)相结合,形成DC-SVM算法.将DC-SVM方法应用于双酚A结晶过程软测量模型中,所得到的回归模型输出与SVM方法相比.塔底双酚A组分的最大相对误差和平均相对误差分别从7.97%和2.4%降到5.51%和1.8%,塔底苯酚组分的最大相对误差和平均相对误差分别从4.93%和1.11%降到3.46%和0.96%.本文提出的DC-SVM建模方法,可以广泛应用在回归建模中,也适合应用于实践生产过程,但需要足够的数据作为建模的基础.     

A syntactic procedure for the recognition of glottal pulses in continuous speech

Speech prosody contains an important structural information for performing speech analysis and for extracting syntactic nuclei from spoken sentences. This paper describes a procedure based on a multichannel system of epoch filters for recognizing the pulses of glottal chord vibrations by an analysis of the speech waveform. Recognition is performed by a stochastic finite state automaton automatically inferred after experiments.     

A test electron model for the study of three dimensional magnetic reconnection effects

Understanding the mechanisms that enable the conversion of the explosive release of magnetic energy into the electron energization that is experimentally observed in space and laboratory plasmas represents a long-standing question in the study of magnetic reconnection.     

Experimental studies on a local rescheduling procedure for dynamic routing of autonomous decentralized AGV systems

This paper addresses a local rescheduling procedure for a distributed routing system of multiple Automated Guided Vehicles in dynamic environments where the requests for transportation are given in real time. The effectiveness of the proposed method is investigated for a transportation system of 143 nodes and 10–30 AGV systems in a semi-conductor fabricating bay. A distributed and parallel routing system (DPRS) is implemented and tested on an experimental five mobile robot system. The results demonstrate that the proposed method can reduce the total computation time by 39% compared with the conventional method without lowering the performance level. It is also experimentally verified that the rescheduling procedure can deal with disturbances in a significantly short computation time.     

基于AT-PGGAN的增强数据车辆型号精细识别

目的 车型识别在智能交通、智慧安防、自动驾驶等领域具有十分重要的应用前景。而车型识别中，带标签车型数据的数量是影响车型识别的重要因素。本文以"增强数据"为核心，结合PGGAN（progressive growing of GANs）和Attention机制，提出一种基于对抗网络生成数据再分类的网络模型AT-PGGAN（attention-progressive growing of GANs），采用模型生成带标签车型图像的数量，从而提高车型识别准确率。方法 该模型由生成网络和分类网络组成，利用生成网络对训练数据进行增强扩充，利用注意力机制和标签重嵌入方法对生成网络进行优化使其生成图像细节更加完善，提出标签重标定的方法重新确定生成图像的标签数据，并对生成图像进行相应的筛选。使用扩充的图像加上原有数据集的图像作为输入训练分类网络。结果 本文模型能够很好地扩充已有的车辆图像，在公开数据集StanfordCars上，其识别准确率相比未使用AT-PGGAN模型进行数据扩充的分类网络均有1%以上的提升，在CompCars上与其他网络进行对比，本文方法在同等条件下最高准确率达到96.6%，高于对比方法。实验结果表明该方法能有效提高车辆精细识别的准确率。结论 将生成对抗网络用于对数据的扩充增强，生成图像能够很好地模拟原图像数据，对原图像数据具有正则的作用，图像数据可以使图像的细粒度识别准确率获得一定的提升，具有较大的应用前景。     

A comparative analysis of score functions for multiple criteria decision making in intuitionistic fuzzy settings

The purpose of this paper was to conduct a comparative study of score functions in multiple criteria decision analysis based on intuitionistic fuzzy sets. The concept of score functions has been conceptualized and widely applied to multi-criteria decision-making problems. There are several types of score functions that can identify the mixed results of positive and negative parts in a bi-dimensional framework of intuitionistic fuzzy sets. Considering various perspectives on score functions, the present study adopts an order of preference based on similarity to the ideal solution as the main structure to estimate the importance of different criteria and compute optimal multi-criteria decisions in intuitionistic fuzzy evaluation settings. An experimental analysis is conducted to examine the relationship between the results yielded by different score functions, considering the average Spearman correlation coefficients and contradiction rates. Furthermore, additional discussions clarify the relative differences in the ranking orders obtained from different combinations of numbers of alternatives and criteria as well as different importance conditions.     

A gradient procedure for determining clusters of relatively high point density

We propose an algorithm for determining clusters of relatively high point density. The point density is defined by a Parzen estimate of the underlying probability density. A Gaussian bump is chosen for the Parzen window function. The algorithm puts points which can be connected by gradient lines to a maximum x₀ of the point density, into the same (gradient) cluster (around x₀). For this task a gradient procedure with step control is employed. We compare the procedure's convergence properties and computational expenses to those of other procedures for determining gradient clusters. Notes for choosing optimal standard deviations of the Gaussian bump are given.     

A new predictive model for compressive strength of HPC using gene expression programming

In this study, gene expression programming (GEP) is utilized to derive a new model for the prediction of compressive strength of high performance concrete (HPC) mixes. The model is developed using a comprehensive database obtained from the literature. The validity of the proposed model is verified by applying it to estimate the compressive strength of a portion of test results that are not included in the analysis. Linear and nonlinear least squares regression analyses are performed to benchmark the GEP model. Contributions of the parameters affecting the compressive strength are evaluated through a sensitivity analysis. GEP is found to be an effective method for evaluating the compressive strength of HPC mixes. The prediction performance of the optimal GEP model is better than the regression models.     

A computational procedure for response statistics-based optimization of stochastic non-linear FE-models

An approach for an efficient solution of response statistics-based optimization problems of non-linear FE systems under stochastic loading is presented. A sequential approximate optimization approach, where approximate stochastic analyses are used during portions of the optimization process, is implemented in the proposed formulation. In this approach, analytical approximations of the performance functions in terms of the design variables are considered during the optimization process. The analytical approximations are constructed by combining a mixed linearization approach with a stochastic response sensitivity analysis. The state of the system is defined in terms of the statistical second-moment characteristics of the structural response. The stochastic loading and the response of the system are represented by an orthogonal series expansion of the corresponding covariance matrices. In particular, a truncated Karhunen-Loève (K-L) expansion is applied. The system of non-linear equations is replaced by a statistical equivalent linear system. The evaluation of the K-L vectors is carried out by an efficient procedure that combines local linearization, modal analysis and static response of higher structural modes. An illustrative example is presented that shows the efficiency of the proposed methodology: it considers a building finite element model enforced with non-linear hysteretic devices and subject to a stochastic ground acceleration. Two types of problems are considered: a minimum structural weight design problem and an optimal non-linear device design problem.     

自整角机工艺误差的修正方法

在系统阐述自整角机工作原理的基础上 ,详细论述了在航空炮塔随动系统中 ,如何利用现有的自整角机来减小随动系统的误差以及消除假协调 ,从而使随动系统满足技战术指标。