Experimental Methodology for Obtaining the Flow Curve of Sheet Materials in a Wide Range of Strains

An experimental approach for determining the stress–strain curve over a large range of strains through tensile test is introduced. The novel aspect of the proposed approach is to apply different degrees of cold working on the sheet metal specimens before tensile test. By adding the pre‐strain derived from cold working to the strain from tensile test, the corresponding stress–strain curves can be shifted to large strains. Since the variation trend of these curves is in high coincidence with the results from compression test, it is convinced that the stress–strain curve of tensile test over a large range of strains can be experimentally extrapolated based on such an approach, especially aiming at medium‐thick sheet metal. Based on the tensile test results, five different extrapolation models were evaluated with respect to different sampling dataset. It was revealed that the true stress–plastic strain curve over a given strain range could be approximated well by the extrapolation models of Ludwik, Ghosh, and Hocket‐Sherby based on sampling data points of standard tensile test combined with a prescribed data point from tensile test after cold working.     

Full second-order chromatographic/spectrometric data matrices for automated sample identification and component analysis by non-data-reducing image analysis

A data analysis method is proposed for identification and for confirmation of classification schemes, based on single- or multiple-wavelength chromatographic profiles. The proposed method works directly on the chromatographic data without data reduction procedures such as peak area or retention index calculation. Chromatographic matrices from analysis of previously identified samples are used for generating a reference chromatogram for each class, and unidentified samples are compared with all reference chromatograms by calculating a resemblance measure for each reference. Once the method is configured, subsequent sample identification is automatic. As an example of a further development, it is shown how the method allows identification of characteristic sample components by local similarity calculations thus finding common components within a given class as well as component differences between classes from the reference chromatograms. This feature is a valuable aid in selecting components for further analysis. The identification method is demonstrated on two data sets: 212 isolates from 41 food-borne Penicillium species and 61 isolates from 6 soil-borne Penicillium species. Both data sets yielded over 90% agreement with accepted classifications. The method is highly accurate and may be used on all sorts of chromatographic profiles. Characteristic component analysis yielded results in good agreement with existing knowledge of characteristic components, but also succeeded in identifying new components as being characteristic.     

An Outlier Identification and Judgment Method for an Improved Neural‐Network BOF Forecasting Model

Due to the lack of simple and effective data filtering method for multi‐variable and numerous samples in BOF endpoint forecasting model, a method of outlier identification and judgment was introduced and applied to data screens for improving BOF endpoint forecasting model. The outside values as potential outliers are calculated using the method of five‐number summary which is a robust estimation of the population parameter, and then the potential outliers are judged with the clustering method. By comparing the exceptional data from clustering analysis with the outside values from the five‐number summary, the intersection of these two groups is regarded as the final outliers to be deleted; in addition, the exceptional data but not outside values are regarded as final exceptional data to be further analyzed; and the outside values but not exceptional data are regarded as final outliers to be deleted too. Finally, to verify the data selection, an improved BP‐based neural network model is used to predict the end‐point carbon content and temperature. By using this data pretreatment method, the absolute values of the mean and maximum training residuals of endpoint carbon and temperature decreased by 26.7%, 41% and 17.3%, 34.5% respectively; and those of the prediction decreased by 10%, 44.9% and 9.4%, 22.9% respectively. It is shown that the proposed method improves effectively the neural network model for BOF endpoint forecasting.     

Free-Vibration Analysis and Material Constants Identification of Laminated Composite Sandwich Plates

Free vibration of symmetrically laminated composite sandwich plates with elastic edge restraints is studied via the Rayleigh–Ritz approach. The proposed Rayleigh–Ritz method is constructed on the basis of the layer-wise linear displacement theory. The accuracy of the method in predicting natural frequencies of composite sandwich plates with different boundary conditions is verified by the results reported in the literature or the experimental data obtained in this study. The proposed method is then applied to the material constant identification of free composite sandwich plates using the first six theoretical natural frequencies of the plates. In the identification process, trial material constants are used in the present method to predict the theoretical natural frequencies, a frequency discrepancy function is established to measure the sum of the squared differences between the experimental and theoretical natural frequencies, and a stochastic global minimization algorithm is used to search for the best estimates of the material constants by making the frequency discrepancy function a global minimum. Applications of the material constant identification technique are demonstrated by means of several examples.     

Development of the giant fiber neuron of Drosophila melanogaster

The origins and vicissitudes of envy are discussed from the viewpoints of Boris and the Kleinians. Their ideas, coupled with a relational perspective of the therapeutic process, enrich our understanding and inform our work concerning the emergence, processing, and working through of envy in the therapy group. A variable in the negative therapeutic reaction, envy can be destructive to the therapy process. It is proposed that envy, accompanied by shame and guilt, is likely to enter the group via enactments. They are fueled by projective identification, which, if ignored, impede the continuation of the group process. Four clinical vignettes illustrate how envy enters the group and how the group functions as a container and transformational object as it processes the projective identifications and works through the enactments.     

基于Kmeans–BP神经网络的KR工序终点铁水硫含量预测模型

针对KR工序终点铁水硫含量预测问题,提出一种基于Kmeans聚类分析和BP神经网络（BPNN）相结合的建模方法。首先,通过Kmeans聚类对KR工序生产数据进行模式识别和分类,构建不同工况特征的数据集;然后,基于BP神经网络,针对不同数据集训练预测模型;最后,将不同数据集的预测模型进行集成,形成最终的终点铁水硫含量预测模型,实现对不同铁水条件和工况条件的预测。利用某钢铁企业实际生产数据,分别用基于脱硫反应动力学、BP神经网络和Kmeans–BPNN方法建立的预测模型,对KR工序终点铁水硫含量进行预测。结果表明,Kmeans–BPNN的KR工序终点硫含量预测模型的精度显著高于脱硫反应动力学和BP神经网络的预测模型。      

Case-Based Reasoning Approach to Construction Safety Hazard Identification: Adaptation and Utilization

Risk assessment, consisting of hazard identification and risk analysis, is an important process that can prevent costly incidents. However, due to operational pressures and lack of construction experience, risk assessments are frequently poorly conducted. In order to improve the quality of risk assessments in the construction industry, it is important to explore the use of artificial intelligence methods to ensure that the process is efficient and at the same time thorough. This paper describes the adaptation process of a case-based reasoning (CBR) approach for construction safety hazard identification. The CBR approach aims to utilize past knowledge in the form of past hazard identification and incident cases to improve the efficiency and quality of new hazard identification. The overall approach and retrieval mechanism are described in earlier papers. This paper is focused on the adaptation process for hazard identification. Using the proposed CBR approach, for a new work scenario (the input case), a most relevant hazard identification tree and a set of incident cases will be retrieved to facilitate hazard identification. However, not all information contained in these cases are relevant. Thus, less relevant information has to be pruned off and all the retrieved information has to be integrated into a hazard identification tree. The proposed adaptation is conducted in three steps: (1) pruning of the retrieved hazard identification tree; (2) pruning of the incident cases; and (3) insertion of incident cases into the hazard identification tree. The adaptation process is based on the calculation of similarity scores of indexes. A case study based on actual hazard identifications and incident cases is used to validate the feasibility of the proposed adaptation techniques.     

Cytostatic activity develops during meiosis I in oocytes of LT/Sv mice

Clinicians and patients make many decisions in situations where optimal treatment is uncertain. Despite well-published advantages of clinical trials for reducing such uncertainties, a trial may not be conducted because the sample size indicated by classical, hypothesis-testing methods is so large that no one institution could afford to sponsor the trial. By explicitly taking into consideration the costs and benefits of a trial, Bayesian statistical methods permit estimation of the value to a health care organization conducting a randomized trial instead of continuing to treat patients in the absence of more information. This paper describes a method for calculating the cost-benefit to a health care organization conducting a clinical trial, and the expected sample size to adequately resolve the uncertainties about which treatment is better. The method is illustrated in the case of a proposed clinical trial of a drug to prevent multiorgan system failure and death in patients admitted to the Stanford University surgical intensive care unit. This method should permit health care organizations to evaluate a proposed trial's expected cost-benefit and the expected sample size that will resolve the question of interest, and thereby assist in the process of deciding whether to conduct the trial.     

基于PSO-RBF神经网络的轧辊偏心在线辨识

在实际生产中,轧辊偏心往往会导致带材厚度的波动,降低带材的质量。离线辨识轧辊偏心的控制方法在实际生产中效果不明显甚至会其反作用。为了使轧辊偏心能够在线自适应辨识,提出了一种基于改进粒子群算法优化的RBF神经网络的在线辨识方式,建立在线训练模型,对轧辊偏心信号进行在线辨识研究并与未采用该算法的在线辨识方式进行对比。结果表明,基于改进粒子群算法优化的辨识方式速度更快、精度更高,能迅速地辨识出生产过程中的轧辊偏心信号的变化,达到了期望的结果。     

Handwritten billet number recognition algorithm based on edge extraction

Character recognition has always been a hot topic in the field of computer vision.However,it is often difficult to obtain high-precision results in the actual scene owing to factors such as lighting conditions and imaging angle.Aiming at the problem of handwritten billet identification in the steel industry,this paper proposes the use of the canny edge extraction method to enhance the contour characteristics of characters.This technique is combined with the object detection network to achieve the automatic identification of blank square numbers and solve the problem of automatic tracking of billet logistics in the production process.The proposed algorithm is applied to the site with more than 2019 images containing characters in the test set.Results show that the proposed algorithm has good practical application potential.     

Fuzzy Clustering Model for Estimating Haulers’ Travel Time

This paper presents a two-step fuzzy clustering method for estimating haulers’ travel time. The proposed method provides a generic tool that can be incorporated in models dedicated for estimating earthmoving production. The estimated travel time takes into account the acceleration and deceleration in the transition zones. The developed method utilizes linear regression and fuzzy subtractive clustering. Seven factors influencing haulers’ travel time were first identified and their significance was then quantified using linear regression. The regression analysis was performed utilizing 180 training cases, generated using commercially available software for different models of haulers. The data were generated randomly to represent a wide range of possible combinations of factors affecting travel time of haulers across different types of road segments. The training data were subsequently used in the development of the proposed method. Unoptimized subtractive clustering, optimized Takagi–Sugeno zeroth-order subtractive clustering, and optimized Takagi–Sugeno first-order subtractive clustering were used in estimating haulers’ travel time. Their performance was evaluated using 36 test cases, also generated randomly in a similar manner to those utilized for training. The optimized Takagi–Sugeno first-order subtractive clustering model was found to outperform the other two, and was accordingly used in the proposed method. A numerical example is presented to demonstrate the use of the developed method and illustrate its accuracy.     

New Project Procurement Process

In the United Kingdom, partnering was proposed by Sir Michael Latham as a means of achieving 30% cost savings in a suitable organizational climate. How this is achieved within the construction industry involves not only a mutuality of purpose between project personnel, but appropriately applied methods of process. This paper addresses the issues surrounding the way forward in partnering. By applying systematic processes to selecting partners on a short-term construction project, a case study demonstrates that cooperative working can be developed and conflict can be avoided. A unique and rigorous selection methodology known as the project delivery process was adopted for the Forton Lake Opening Bridge Millennium project in the United Kingdom, and demonstrates how significant improvements may be achieved in line with worldwide improvement targets.     

转炉终点预测模型中异常数据检验的研究

针对转炉终点神经网络预测模型数据预处理过程中多变量、大样本的特点,介绍并应用了一种识别及检验异常数据的方法;筛选了神经网络模型的数据样本集数据;并初步验证了筛选结果。经过筛选,神经网络预测模型训练集数据训练误差绝对值的平均值及最大值分别下降了267%和41%;测试集数据测试误差的平均值及最大值分别下降10%和45%。结果表明,该方法对于转炉预测模型的数据筛选行之有效,对转炉预测模型的进一步完善有一定的实用价值。     

Determining the chlorine content in the blast-furnace system

The production of ferrous metals and the extraction and processing of raw materials are environmentally hazardous. Besides atmospheric and water pollution, many solid wastes are formed. Chlorine plays a special role in blast-furnace processes. The chlorine compounds present in the blast-furnace gas lead to the corrosion of pipelines, air heaters, and tuyeres in the furnace. The behavior of chlorine in the blast furnace determines the sinter properties during the reduction process. If the content of mineral chlorine (chlorine bound with metals) in residues at filters and dust traps is monitored, the toxicity of the wastes may be assessed, and the production processes may be evaluated. The chlorine content is determined by various physical methods: atomic-emission spectrometry, mass spectrometry with inductively bound plasma, and neutron-activated analysis. However, chemical methods have been widely adopted: titrimetric, photometric, electrochemical, and ion-chromatographic methods. It is important to develop fast and inexpensive methods of determining chlorine for the analysis of materials encountered in technological systems, including metallurgical systems. In the present work, attention focuses on samples of blast-furnace slag from PAO Tulachermet and dusty wastes from PAO Kosogorskii Metallurgicheskii Zavod. X-ray fluorescent methods are used for preliminary analysis of the sample composition. The interfering influence of other components is considered. Ionometric determination of chlorine by means of a crystalline chloride-selective electrode is proposed. The samples are decomposed by high-temperature sintering in a mixture with Na₂CO₃ and ZnO and subsequent leaching with water. The details of this method are described. The results of the analysis are confirmed by variation in the sample weight (n = 10; t _tab = 2.26, P = 0.95) and also by comparison with X-ray fluorescent data. The precision of ionometric determination of chlorine in the blast-furnace system is assessed. The working range of the chlorine concentrations observed is 0.037–1.340 wt %.     

Adaptive Parametric Identification Scheme for a Class of Nondeteriorating and Deteriorating Nonlinear Hysteretic Behavior

The adaptive parametric identification of deteriorating and nondeteriorating nonlinear hysteretic phenomena is considered using a generalization of Masing model based on the observed memory behavior of distributed element models. The model permits a parametric identification to be performed using nonlinear optimization techniques for arbitrary response time histories. A changing objective function, defined as the normalized force estimation error over a shifting window of recent data, is employed so that classic nonlinear optimization techniques can be used for the adaptive identification problem. A variation of the steepest descent method is used with significant modifications. To achieve the best performance for any given problem, a set of a priori numeric tests are suggested to design the identification scheme. The design identification scheme exhibits a very good performance in identifying the correct values of the parameters and is rather robust in dealing with noise. The proposed approach has applications to adaptive identification of much wider types of nonlinear rate-dependent hysteretic behavior. Also, the set of guidelines proposed by the authors is a contribution toward having more effective autonomous identification schemes, using minimal information about the model and input.     

System Identification of Partially Restrained Composite Plates Using Measured Natural Frequencies

A nondestructive evaluation technique established on the basis of a global minimization method is presented for the system identification of laminated composite plates partially restrained by elastic edge supports. Six natural frequencies extracted from the vibration data of the flexibly restrained plate are used to identify the system parameters of the plate. In the identification process, the trial system parameters are used in the Rayleigh–Ritz method to predict the theoretical natural frequencies of the plate, an error function is established to measure the sum of the differences between the experimental and theoretical predictions of the natural frequencies, and the global minimization method is used to search for the best estimates of the parameters by making the error function a global minimum. The accuracy and efficiency of the proposed technique in identifying the parameters of several flexibly supported plates made of different composite materials are studied via both theoretical and experimental approaches. The excellent results obtained in this study have validated the applicability of the proposed technique.     

Damage Detection Utilizing the Damage Index Method to a Benchmark Structure

This paper addresses the first generation benchmark problem on structural health monitoring developed by the ASCE Task Group on Structural Health Monitoring. The focus of the problem is a four-story model of an existing physical model at the University of British Columbia where simulated data are used for the system identification. Modal parameters were extracted using the frequency domain decomposition method. Rather than relying on data from the undamaged structure, a new proposed methodology based on ratios between stiffness and mass values from the eigenvalue problem is presented to identify the undamaged state of the structure. Once the structural identification is complete, the damage index method is used to detect the location and severity of damage. By not relying on undamaged structure information, this approach may be applicable to existing structures that may already incorporate some amount of damage.     

A pilot study of psychotherapist trainees' alpha-amylase and cortisol levels during treatment of recently suicidal clients with borderline traits.

Psychotherapists often experience stress while providing psychotherapy, in particular when working with difficult presentations such as suicidality. As part of a larger study on the treatment of recently suicidal college students with borderline traits, 6 therapists in training collected their own salivary samples for alpha-amylase (AA) and cortisol (C) analyses immediately before and after sessions with 2 selected clients. On average, samples were collected for the same therapist–patient dyad throughout the year-long study to ensure that data reflected therapist responses across stages of treatment. Therapists also completed a working alliance questionnaire and rated perceived session difficulty immediately after each selected session. Contrary to expectations, therapists demonstrated elevated levels of stress as measured by AA and C at presession relative to postsession levels. Greater session difficulty was related to more pronounced declines in AA, whereas a stronger working alliance was linked to more pronounced reductions in C. Results suggest that physiological stress responses while working with recently suicidal clients with borderline traits occur primarily in terms of session anticipatory anxiety, whereas AA and C changes may be affected differently by factors such as session difficulty and working alliance. This is a pilot study, limited by its sample size, but the design, findings, and inclusion of physiological measures present an initial step in an essential line of research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

An RFID system for roll number identification

Improving efficiency in roll grinding process is a critical issue in the steel mill.Each roll has to be grinded to a well-defined profile and then to be measured for quality check.However,the surface conditions are inspected by using different on-line inspection probes equipped on the grinder.The roll number is a unique information which can be used to merge with a huge amount of measurement data so that the condition of each roll is fully monitored.One of the key factors that hinder the efficiency in such process is the errors and time consumption due to human intervention.In order to mitigate these issues and to realize the fully automatic grinding process,radio frequency identification technology(RFID) could be a solution and has been developed in this paper.It is well known that when an RFID tag is placed directly upon a metallic object,in the absence of a gap or a substrate,it functions rather poorly and even becomes totally dysfunctional.This limitation,in turn,poses a real barrier to the use of the RFID on metallic objects.This paper proposes a miniature RFID tag antenna design for application on roll number identification.The experimental tests show that the maximum read range of the proposed RFID tag placed on a roll is approximately 1.5m and the overall size is only 32×18×3.2 mm~3.An RFID system for roll number identification was used in a roll shop and several remarkable improvements were achieved,including the completely automatic grinding process and the error-free identification,as well as the high personnel safety operation.