Psychophysical considerations in permissible loads

Psychophysics is identified as one of the criteria that has been used with increasing frequency in developing recommendations for permissible loads. A critique of psychophysics is offered, citing both advantages and disadvantages of the method. The relationship between permissible workloads and low-back pain is reviewed. A distinction is made between low-back pain, low-back impairment, low-back disability and low-back compensation. It is suggested that the setting of permissible workloads in industry, regardless of the criteria used, will have little impact on the incidence of low-back pain, but a significant effect upon low-back disability and low-back compensation.     

Ergonomie guidelines for the prevention of low back pain at the workplace

The aim of this paper is to discuss the usefulness of common ergonomie guidelines for preventing low back pain at the workplace. Sixteen recent ergonomie, biomechanical and epidemiological books were reviewed to obtain common ergonomie guidelines for both static and dynamic work (e.g., sitting, lifting), and to obtain an overview of commonly-observed individual and work-related risk factors of low back pain. The results show that the aspects of work for which ergonomie guidelines are presented generally correspond to work-related risk factors as shown by epidemiological studies. However, in quantitative terms the guidelines show a great variety, possibly due to differences in criteria. In certain cases, it is not clear whether or not the guidelines are based on back load or back pain data, due to lack of references. It appears that many guidelines are based upon a combination of back load criteria and other criteria, although it is unknown how these criteria are combined. Hence, many guidelines do not apply specifically to low back pain.

With certain exceptions, most guidelines do not take into account individual factors, although epidemiological studies indicate that several factors such as age, strength, fitness, psychosocial factors, and history of back pain should be considered. However, because of the qualitative character of most current epidemiological studies, results cannot be readily implemented into quantitative ergonomie guidelines.

The above general results are discussed with examples. It is concluded that there is a great need for ergonomie guidelines that apply specifically to low back pain, and for quantitative epidemiological data on which these guidelines may be based.     

On estimating simple probabilistic discriminative models with subclasses

Discriminative subclass models can provide good estimates of complex ‘continuous to discrete’ conditional probabilities for hybrid Bayesian network models. However, the conventional approach of specifying deterministic ‘hard’ subclasses via unsupervised clustering can lead to inaccurate models. The multimodal softmax (MMS) model is presented as a new probabilistic discriminative subclass model that overcomes this unreliability. By invoking fully probabilistic latent ‘soft’ subclasses, MMS permits learning via standard statistical methods without requiring explicit clustering/relabeling of data. MMS is also shown to be closely related to the mixture of experts model and the generative Gaussian mixture classifier. Synthetic and benchmark classification results demonstrate the MMS model’s correctness and usefulness for hybrid probabilistic modeling.     

A binomial software reliability model based on coverage of structural testing criteria

A new approach to software reliability modeling is discussed where variables indirectly related with software reliability are used to provide additional information for the modeling process. Previous studies, empirical and theoretical evidences, and results from experiments indicate that there is a strong relationship between software reliability and coverage of program elements required to be exercised by structural testing criteria. This paper develops a binomial type coverage-based software reliability model through the definition of a coverage-based failure rate function. The Binomial software reliability Model Based on Coverage—BMBC—is proposed and discussed. In the BMBC test data between failures is used instead of time as independent variable; the model was assessed with test data from a real application, making use of the following structural testing criteria: all-nodes, all-edges, and potential-uses—a data-flow based family of testing criteria. The results from our experiments have shown that our modeling approach has some advantages over some traditional reliability models and points to a very promising research direction in software reliability.

An integrated package for drought monitoring,prediction and analysis to aid drought modeling and assessment

Due to severe drought events and disastrous impacts in recent decades, substantial efforts have been devoted recently to drought monitoring, prediction and risk analysis for aiding drought preparedness plans and mitigation measures. Providing an overview of these aspects of drought research, this study presents an integrated R package and illustrates a wide range of its applications for drought modeling and assessment based on univariate and multivariate drought indices for both operational and research purposes. The package also includes statistical prediction of drought in a probabilistic manner based on multiple drought indicators, which serves as a baseline for drought prediction. The univariate and multivariate drought risk analysis of drought properties and indices is also presented. Finally, potential extensions of this package are also discussed. The package is provided freely to public to aid drought early warning and management.     

Evidence-based guidelines for assessment of software development cost uncertainty

Several studies suggest that uncertainty assessments of software development costs are strongly biased toward overconfidence, i.e., that software cost estimates typically are believed to be more accurate than they really are. This overconfidence may lead to poor project planning. As a means of improving cost uncertainty assessments, we provide evidence-based guidelines for how to assess software development cost uncertainty, based on results from relevant empirical studies. The general guidelines provided are: 1) Do not rely solely on unaided, intuition-based uncertainty assessment processes, 2) do not replace expert judgment with formal uncertainty assessment models, 3) apply structured and explicit judgment-based processes, 4) apply strategies based on an outside view of the project, 5) combine uncertainty assessments from different sources through group work, not through mechanical combination, 6) use motivational mechanisms with care and only if greater effort is likely to lead to improved assessments, and 7) frame the assessment problem to fit the structure of the relevant uncertainty information and the assessment process. These guidelines are preliminary and should be updated in response to new evidence.     

Towards exposure limits for working postures and musculoskeletal symptoms – a prospective cohort study

Occupational postures are considered to be an important group of risk factors for musculoskeletal pain. However, the exposure-outcome association is not clear yet. Therefore, we aimed to determine the exposure-outcome association of working postures and musculoskeletal symptoms. Also, we aimed to establish exposure limits for working postures. In a prospective cohort study among 789 workers, intensity, frequency and duration of postures were assessed at baseline using observations. Musculoskeletal pain was assessed cross-sectionally and longitudinally and associations of postures and pain were addressed using logistic regression analyses. Cut-off points were estimated based on ROC-curve analyses. Associations were found for kneeling/crouching and low-back pain, neck flexion and rotation and neck pain, trunk flexion and low-back pain, and arm elevation and neck and shoulder pain. The results provide insight into exposure-outcome relations between working postures and musculoskeletal symptoms as well as evidence-based working posture exposure limits that can be used in future guidelines and risk assessment tools.

Practitioner Summary: Our study gives insight into exposure-outcome associations of working postures and musculoskeletal symptoms (kneeling/crouching and low-back pain, neck flexion/rotation and neck pain, trunk flexion and low-back pain, and arm elevation and neck and shoulder pain). Results furthermore deliver evidence-based postural exposure limits that can be used in guidelines and risk assessments.     

Parameter estimation and uncertainty analysis for a watershed model

Where numerical models are employed as an aid to environmental management, the uncertainty associated with predictions made by such models must be assessed. A number of different methods are available to make such an assessment. This paper explores the use of three such methods, and compares their performance when used in conjunction with a lumped parameter model for surface water flow (HSPF) in a large watershed.Linear (or first-order) uncertainty analysis has the advantage that it can be implemented with virtually no computational burden. While the results of such an analysis can be extremely useful for assessing parameter uncertainty in a relative sense, and ascertaining the degree of correlation between model parameters, its use in analyzing predictive uncertainty is often limited. Markov Chain Monte Carlo (MCMC) methods are far more robust, and can produce reliable estimates of parameter and predictive uncertainty. As well as this, they can provide the modeler with valuable qualitative information on the shape of parameter and predictive probability distributions; these shapes can be quite complex, especially where local objective function optima lie within those parts of parameter space that are considered probable after calibration has been undertaken. Nonlinear calibration-constrained optimization can also provide good estimates of parameter and predictive uncertainty, even in situations where the objective function surface is complex. Furthermore, they can achieve these estimates using far fewer model runs than MCMC methods. However, they do not provide the same amount of qualitative information on the probability structure of parameter space as do MCMC methods, a situation that can be partially rectified by combining their use with an efficient gradient-based search method that is specifically designed to locate different local optima.All methods of parameter and predictive uncertainty analysis discussed herein are implemented using freely-available software. Hence similar studies, or extensions of the present study, can be easily undertaken in other modeling contexts by other modelers.     

复杂科学与工程问题仿真的隐式微积分建模

针对现代科学与工程仿真遇到愈来愈多难以用经典微积分建模方法描述的复杂问题,在理论研究和工程实践中提出各种含有多个经验参数的唯象偏微分方程模型,或直接采用统计模型来描述.这些模型的物理意义不是很清楚且参数多,其中部分人为参数缺乏物理意义.因此,利用描述问题的基本解或统计分布构造隐式微积分控制方程.这里"隐式"是指可以不需要或难以推导出该控制方程的显式微积分表达式.该方法仅需微积分控制方程的基本解和相应的边界条件就可以进行数值仿真计算.称该方法为隐式微积分方程建模.考虑多相软物质热传导的幂律行为,采用分数阶里斯(Riesz)势核函数为基本解构造稳态问题的隐式分数阶微积分方程模型并进行数值验证.此外,以列维(Lévy)稳态统计分布的概率密度函数为基本解,构造反常扩散现象的隐式分数阶微积分方程模型.该研究的主要数值计算技术基于径向基函数的配点方法.     

Signal extraction and knowledge discovery based on statistical modeling

In the coming post IT era, the problems of signal extraction and knowledge discovery from huge data sets will become very important. For these problems, the use of good model is crucial and thus the statistical modeling will play an important role. In this paper, we show two basic tools for statistical modeling, namely the information criteria for the evaluation of the statistical models and generic state-space model which provides us with a very flexible tool for modeling complex and time-varying systems. As examples of these methods we shall show some applications in seismology and macro economics.     

Development of Box–Jenkins type time series models by combining conventional and orthonormal basis filter approaches

A unified scheme for developing Box–Jenkins (BJ) type models from input–output plant data by combining orthonormal basis filter (OBF) model and conventional time series models, and the procedure for the corresponding multi-step-ahead prediction are presented. The models have a deterministic part that has an OBF structure and an explicit stochastic part which has either an AR or an ARMA structure. The proposed models combine all the advantages of an OBF model over conventional linear models together with an explicit noise model. The parameters of the OBF–AR model are easily estimated by linear least square method. The OBF–ARMA model structure leads to a pseudo-linear regression where the parameters can be easily estimated using either a two-step linear least square method or an extended least square method. Models for MIMO systems are easily developed using multiple MISO models. The advantages of the proposed models over BJ models are: parameters can be easily and accurately determined without involving nonlinear optimization; a prior knowledge of time delays is not required; and the identification and prediction schemes can be easily extended to MIMO systems. The proposed methods are illustrated with two SISO simulation case studies and one MIMO, real plant pilot-scale distillation column.     

A modeling approach to aid in designing robotized manufacturing cells

This article describes an approach for modeling the work cycle of robotized manufacturing cells. The approach incorporates the logic of GERT networks and the computational techniques of recursion models, but extends these fundamental concepts to provide a resource-based model of cell operations. Since the approach gives approximate estimates of performance, accuracy is evaluated in comparison with simulation results. A comprehensive, but hypothetical, example is given to describe use of the model as an aid in cell design. Typical applications involve “what if” studies to evaluate end-of-arm effectors, cell layout, a limited supply of tooling, equipment capabilities (e.g., speeds), and sequence of operations in cells employing one or more robots.     

Small-signal modeling and controller design of energy sharing controlled distributed battery system

This paper presents small-signal modeling, analysis and closed-loop controller design guidelines for a distributed battery energy storage system with energy sharing controller which has recently been presented in the literature in order to achieve cell balancing with high cell balancing speed and energy efficiency. The derived small signal models provide deeper insight into the dynamics of the energy sharing controlled battery system under different operating modes, including discharge mode, constant current charging mode and constant voltage charging mode. Based on the derived small signal models, closed-loop controller design guidelines are provided based on rule-of-thumb frequency-domain design criteria. The small signal models and designed controllers are validated by MATLAB®/SIMULINK simulation and experimental prototype results.     

Building Sugeno-type models using fuzzy discretization andorthogonal parameter estimation techniques

This paper develops a new approach to building Sugeno-type models. The essential idea is to separate the premise identification from the consequence identification, while these are mutually related in the previous methods. A fuzzy discretization technique is suggested to determine the premise of the model, and an orthogonal estimator is provided to identify the consequence of the model. The orthogonal estimator can provide information about the model structure, or which terms to include in the model, and final parameter estimates in a very simple and efficient manner. The well-known gas furnace data of Box and Jenkins is used to illustrate the proposed modeling approach and to compare its performance with other statistical and fuzzy modeling approaches. It shows that the performance of the new approach compares favorably with these existing techniques     

Application of statistical information criteria for optimal fuzzymodel construction

Theoretical studies have shown that fuzzy models are capable of approximating any continuous function on a compact domain to any degree of accuracy. However, constructing a good fuzzy model requires finding a good tradeoff between fitting the training data and keeping the model simple. A simpler model is not only easily understood, but also less likely to overfit the training data. Even though heuristic approaches to explore such a tradeoff for fuzzy modeling have been developed, few principled approaches exist in the literature due to the lack of a well-defined optimality criterion. In this paper, we propose several information theoretic optimality criteria for fuzzy models construction by extending three statistical information criteria: 1) the Akaike information criterion [AIC] (1974); 2) the Bhansali-Downham information criterion [BDIC] (1977); and 3) the information criterion of Schwarz (1978) and Rissanen (1978) [SRIC]. We then describe a principled approach to explore the fitness-complexity tradeoff using these optimality criteria together with a fuzzy model reduction technique based on the singular value decomposition (SVD). The role of these optimality criteria in fuzzy modeling is discussed and their practical applicability is illustrated using a nonlinear system modeling example     

The kernel density estimate/point distribution model (KDE-PDM) for statistical shape modeling of automotive stampings and assemblies

The ability to effectively model dimensional variation of stampings and assemblies is an important tool for manufacturers to investigate, assess and control quality levels of their products. Statistical Process Control (SPC) and Six-Sigma approaches use the assumptions of statistical independence and normally distributed data to create quality process control guidelines which are predominantly used in industry. Multivariate statistical techniques such as Principal Components Analysis (PCA) have been more recently applied to automotive body assembly analysis in order to capture the surface co-linearity present in the dimensional variation of stampings and assemblies. This paper combines the Point Distribution Model, which is based on PCA, and Kernel Density Estimation to provide a statistical shape model (the KDE–PDM) that can deal with high dimensional data sets, represent correlated variation modes, and provide accurate estimates of the underlying shape distribution. Examples from FE simulation and production case studies are presented to highlight the advantages of the KDE–PDM over two other statistical shape models: the univariate shape model, and the original PDM. The KDE–PDM's capabilities make it particularly suited to variation monitoring and diagnosis of high dimensional measurement data sets made available by optical measurement devices, and some suggestions for its implementation are also presented.     

Research to reality: a critical review of the validity of various criteria for the prevention of occupationally induced low back pain disability

Criteria have been suggested to reduce the incidence, or severity of low back pain disability. Five underlying theories for such criteria have been identified in the literature, and a critical review of the validity of these criteria has been carried out. Despite attributions elsewhere, peer-reviewed validation of the various criteria range from modest to nil. The need for a validation criterion for use in workplace design in order to reduce low back pain disability, or severity, is identified and the need for an international protocol to allow cross-study validation of present and future criteria is suggested.