The influence of temperature and humidity on the sensitivity of torque transducers

This paper presents a study of the effect of temperature and relative humidity on the sensitivity and the characteristics of torque transducers such as residual zero torque, reversibility and creep. Temperature and humidity coefficients of the sensitivity take positive as well as negative values. Linear dependence is found between the influence of temperature/relative humidity and the applied load. For some torque transducers, under temperature change the sensitivity has a transient overshoot reaching up to 3 times the steady amplitude. Also there is a small effect of temperature/relative humidity on the transducer’s parameters such as residual zero torque, reversibility and creep. Equations are presented to predict the effect of temperature/relative humidity changes on the sensitivity of torque transducers.     

An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images

Malignant and benign types of tumor infiltrated in human brain are diagnosed with the help of an MRI scanner. With the slice images obtained using an MRI scanner, certain image processing techniques are utilized to have a clear anatomy of brain tissues. One such image processing technique is hybrid self-organizing map (SOM) with fuzzy K means (FKM) algorithm, which offers successful identification of tumor and good segmentation of tissue regions present inside the tissues of brain. The proposed algorithm is efficient in terms of Jaccard Index, Dice Overlap Index (DOI), sensitivity, specificity, peak signal to noise ratio (PSNR), mean square error (MSE), computational time and memory requirement. The algorithm proposed through this paper has better data handling capacities and it also performs efficient processing upon the input magnetic resonance (MR) brain images. Automatic detection of tumor region in MR (magnetic resonance) brain images has a high impact in helping the radio surgeons assess the size of the tumor present inside the tissues of brain and it also supports in identifying the exact topographical location of tumor region. The proposed hybrid SOM-FKM algorithm assists the radio surgeon by providing an automated tissue segmentation and tumor identification, thus enhancing radio therapeutic procedures. The efficiency of the proposed technique is verified using the clinical images obtained from four patients, along with the images taken from Harvard Brain Repository.     

Probability of failure sensitivity analysis using polynomial expansion

Probability of failure sensitivity analysis is a subject of major interest in uncertainty based optimization. However, the computational effort required to obtain accurate results is frequently very high. In this work we present a novel strategy for probability of failure sensitivity analysis, that is based on polynomial expansions of both the performance function and its derivatives. Sensitivity analysis is then made using the obtained polynomial expansions together with standard expressions. Since the simulation step requires only evaluation of closed form polynomials, very large samples can be used to obtain accurate results with small computational effort. The proposed approach is expected to be efficient when polynomial expansion methods are suitable. Four numerical examples are presented in order to show the effectiveness of the proposed approach.     

Quantifying life safety Part II: Quantification of fire protection systems

In part I of the paper, an assembly building was analyzed in order to compute the failure probabilities, and thus the safety level, of current code-compliant buildings. In this second part, various fire protection systems are modeled within the fire and egress simulations in order to quantify their magnitude of impact. Since all fire protection systems can fail to perform as designed on demand, the potential failure along with its probability is accounted for in an event tree analysis. Comparing the resulting failure probabilities of the performance-based analyses with and without fire protection systems yields information about the magnitude of impact of the fire protection systems on the level of safety and hence allows a direct, objective, and quantitative comparison to other systems and designs. Accounting for the cost of the systems, a direct cost–benefit analysis can be conducted.     

Spatially-explicit integrated uncertainty and sensitivity analysis of criteria weights in multicriteria land suitability evaluation

We employ spatially-explicit uncertainty and sensitivity analysis to examine the robustness of land suitability evaluation. We use Monte Carlo simulation to sweep through criteria weight space, where weights are expressed using probability distributions. Multiple output suitability maps are generated and summarized using: an average suitability map, a standard deviation uncertainty map, and a number of sensitivity maps. We demonstrate how these surfaces help detect critical regions of suitability on the example of habitat suitability evaluation for a wetland plant. Areas of high average suitability and low uncertainty signify robust suitability sites, whereas high average suitability and high uncertainty characterize candidate areas. These candidate areas are potentially suitable but need further examination with variance-based sensitivity analysis, in which the variability of land suitability is decomposed and attributed to individual criteria weights. The resulting sensitivity maps delineate regions of weight dominance, where a particular weight greatly influences the uncertainty of suitability scores.     

Sensitivity analysis for thermocline thermal storage tank design

Concentrated solar power coupled with thermal energy storage is a promising approach for providing the world with clean, renewable, sustainable and cost-competitive power on a large scale. Thermocline thermal energy storage has been proposed as an efficient and cost-competitive alternative to the traditional two-tank design. The thermocline thickness is directly linked to the efficiency of the storage tank. Sensitivity analysis is thus applied to a model of the thermocline thickness to identify the parameters that influence it the most. Results indicate that the tank height along with the thermophysical properties of the solid filler material influence the tank efficiency the most, with fluid properties and having a secondary effect.     

Sensitivity of the stochastic response of structures coupled with vibrating barriers

The sensitivity of the stochastic response of linear behaving structures controlled by the novel Vibrating Barrier (ViBa) device is scrutinized. The Vibrating Barrier (ViBa) is a massive structure, hosted in the soil, calibrated for protecting structures by exploiting the structure–soil–structure interaction effect. Therefore the paper addresses the study of the sensitivity of soil–structure coupled systems in which the soil is modelled as a linear elastic medium with hysteretic damping. In order to accomplish efficient sensitivity analyses, a reduced model is determined by means of the Craig–Bampton procedure. Moreover, a lumped parameter model is used for converting the hysteretic damping soil model rigorously valid in the frequency domain to the approximately equivalent viscous damping model in order to perform conventional time-history analysis. The sensitivity is evaluated by determining a semi-analytical method based on the dynamic modification approach for the case of multi-variate stochastic input process. The ground motion is modelled as non-stationary zero-mean Gaussian random process defined by a given evolutionary Power Spectral Density function. The paper presents the sensitivity of the response statistics of a model of an industrial building, passively controlled by the ViBa, to relevant design parameters. Comparisons with pertinent Monte Carlo Simulation will show the effectiveness of the proposed approach.     

Parameterisation of a biodiesel plant process flow sheet model

This paper presents results of parameterisation of typical input–output relations within process flow sheet of a biodiesel plant and assesses parameterisation accuracy. A variety of scenarios were considered: 1, 2, 6 and 11 input variables (such as feed flow rate or a heater's operating temperature) were changed simultaneously, 3 domain sizes of the input variables were considered and 2 different surrogates (polynomial and high dimensional model representation (HDMR) fitting) were used. All considered outputs were heat duties of equipment within the plant. All surrogate models achieved at least a reasonable fit regardless of the domain size and number of dimensions. Global sensitivity analysis with respect to 11 inputs indicated that only 4 or fewer inputs had significant influence on any one output. Interaction terms showed only minor effects in all of the cases.     

FSAUA: A framework for sensitivity analysis and uncertainty assessment in historical and forecasted land use maps

Land change modelers often create future maps using reference land use map. However, future land use maps may mislead decision-makers, who are often unaware of the sensitivity and the uncertainty in land use maps due to error in data. Since most metrics that communicate uncertainty require using reference land use data to calculate accuracy, the assessment of uncertainty becomes challenging when no reference land use map for future is available. This study aims to develop a new conceptual framework for sensitivity analysis and uncertainty assessment (FSAUA) which compares multiple maps under various data error scenarios. FSAUA performs sensitivity analyses in land use maps using a reference map and assess uncertainty in predicted maps. FSAUA was applied using three well-known land change models (ANN, CART and MARS) in Delhi, India. FSAUA was found to be a practical tool for communicating the uncertainty with end-users who develop reliable planning decisions.