Electric load forecasting by using dynamic neural network

Electrical energy is fundamental for the wellbeing and for the economic development of any country. However, all countries must ensure access to essential resources and ensure the continuity of its supply. Due to the non-storable nature of electrical energy, the amount of consumed active power should always be equal the produced active power just to avoid power system frequency deviation problem. In order to keep the relationship production–consumption relation in compliance with different standards and to secure profitable operations of power system, electric load consumption must be predicted and controlled instantaneously. Several statistical and classical techniques are proposed in the literature but unfortunately all these methods are not accurate in a satisfactory manner. In this paper, a dynamic neural network is used for the prediction of daily power consumption. The suitability and the performance of the proposed approach is illustrated and verified with simulations on load data collected from French Transmission System Operator (RTE) website. The obtained results show that the accuracy and the efficiency are improved comparatively to conventional methods widely used in this field of research.     

Cyclostationarity-Based Modulation Classification of Linear Digital Modulations in Flat Fading Channels

Modulation classification is an intermediate step between signal detection and demodulation, and plays a key role in various civilian and military applications. In this correspondence, higher-order cyclic cumulants (CCs) are explored to discriminate linear digital modulations in flat fading channels. Single- and multi-antenna CC-based classifiers are investigated. These benefit from the robustness of the CC-based features to unknown phase and timing offset. Furthermore, the latter provides significant performance improvement due to spatial diversity used to combat the fading effect. Classifier performances are investigated under a variety of channel conditions. In addition, analytical closed-form expressions for the cyclic cumulant polyspectra of linearly digitally modulated signals affected by fading, carrier frequency and timing offsets, and additive Gaussian noise are derived, along with a condition for the oversampling factor to avoid aliasing in the cycle and spectral frequency domains.     

Identifying the period of a step change in high‐yield processes

Quality control charts have proven to be very effective in detecting out‐of‐control states. When a signal is detected a search begins to identify and eliminate the source(s) of the signal. A critical issue that keeps the mind of the process engineer busy at this point is determining the time when the process first changed. Knowing when the process first changed can assist process engineers to focus efforts effectively on eliminating the source(s) of the signal. The time when a change in the process takes place is referred to as the change point. This paper provides an estimator for a period of time in which a step change in the process non‐conformity proportion in high‐yield processes occurs. In such processes, the number of items until the occurrence of the first non‐conforming item can be modeled by a geometric distribution. The performance of the proposed model is investigated through several numerical examples. The results indicate that the proposed estimator provides a reasonable estimate for the period when the step change occurred at the process non‐conformity level. Copyright © 2009 John Wiley & Sons, Ltd.     

A modified sorting task to investigate consumer perceptions of extra virgin olive oils

A two-stage sorting task was used to probe Californian consumers’ opinions of twenty-five extra virgin olive oils based on visual assessments of the bottles. The modification of the simple sorting task aimed to encourage consumers to further discriminate products through sub-groupings of products within the groups they had already made. Sorting data were analyzed using a 3-way extension of classical multidimensional scaling called DISTATIS which explores the level of consumer agreement as well as the structure of the extra virgin olive oil products. Consumer agreements were explained by 46% of the variances found in the ways consumers sorted the bottles. Decreased amount of explained variance from the first stage to the second stage of the sorting task was observed in the olive oil product structures. Consumers were asked to describe the characteristics defining each group formed. Consumer comments were analyzed qualitatively prior to statistical analysis and were later used to understand the sorting results. Despite background differences in the usage of olive oil products, the majority of the consumers perceived the product set similarly. The two-stage sorting task allowed subjects to provide more criteria or multidimensional views of their perception of the products.     

An investigation into the effects of lime on compressive and shear strength characteristics of fiber-reinforced clays

To meet the ever-increasing construction demands around the world during recent years,reinforcement and stabilization methods have been widely used by geotechnical engineers to improve the performances and behavior of fine-grained soils.Although lime stabilization increases the compressive strength of soils,it reduces the soil ductility at the same time.Recent research shows that random fiber inclusion modifies the brittleness of soils.In the current research,the effects of lime and polypropylene(PP) fiber additions on such characteristics as compressive and shear strengths,failure strain,secant modulus of elasticity(E_(50)) and shear strength parameters of mixtures were investigated.Kaolinite was treated with 1%,3% and 5% lime by dry weight of soil and reinforced with 0.1% monovalent PP fibers with the length of 6 mm.Samples were prepared at optimum conditions and cured at 35℃ for 1 d,7 d and28 d at 90% relative humidity and subsequently subjected to uniaxial and triaxial compression tests(UCT and TCT) under cell pressures of 25 kPa,50 kPa and 100 kPa.Results showed that inclusion of random PP fibers to clay-lime mixtures increases both compressive and shear strengths as well as the ductility.Lime content and curing period were found to be the most influential factors.Scanning electron microscopy(SEM) analysis showed that lime addition and the formation of cementitious compounds bind soil particles and increase soil/fiber interactions at interface,leading to enhanced shear strength.The more ductile the stabilized and reinforced composition,the less the cracks in roads and waste landfill covers.     

Parameter estimation and prediction uncertainties for multi-response kinetic models with uncertain inputs

Error-in-variables model (EVM) methods are used for parameter estimation when independent variables are uncertain. During EVM parameter estimation, output measurement variances are required as weighting factors in the objective function. These variances can be estimated based on data from replicate experiments. However, conducting replicates is complicated when independent variables are uncertain. Instead, pseudo-replicate runs may be performed where the target values of inputs for repeated runs are the same, but the true input values may be different. Here, we propose a method to estimate output-measurement variances for use in multivariate EVM estimation problems, based on pseudo-replicate data. We also propose a bootstrap technique for quantifying uncertainties in resulting parameter estimates and model predictions. The methods are illustrated using a case study involving n-hexane hydroisomerization in a well-mixed reactor. Case-study results reveal that assumptions about input uncertainties can have important influences on parameter estimates, model predictions and their confidence intervals.     

A special finite element for the analysis of the singularity in a bimaterial containing a crack perpendicular to the interface

It is shown that the singularity of stresses near the tip of crack in an elastic bi-material can be obtained by using degenerate triangular elements, the shape functions of which are derived from those classical isoparametric elements. A four node triangular element is tested on two different samples. The results obtained are compared with those which exist already in bibliography.     

Epithelial Layer Estimation Using Curvatures and Textural Features for Dysplastic Tissue Detection

Boundary effect in digital pathology is a phenomenon where the tissue shapes of biopsy samples get distorted during the sampling process. The morphological pattern of an epithelial layer is greatly affected. Theoretically, the shape deformation model can normalise the distortions, but it needs a 2D image. Curvatures theory, on the other hand, is not yet tested on digital pathology images. Therefore, this work proposed a curvature detection to reduce the boundary effects and estimates the epithelial layer. The boundary effect on the tissue surfaces is normalised using the frequency of a curve deviates from being a straight line. The epithelial layer’s depth is estimated from the tissue edges and the connected nucleolus only. Then, the textural and spatial features along the estimated layer are used for dysplastic tissue detection. The proposed method achieved better performance compared to the whole tissue regions in terms of detecting dysplastic tissue. The result shows a leap of kappa points from fair to a substantial agreement with the expert’s ground truth classification. The improved results demonstrate that curvatures have been effective in reducing the boundary effects on the epithelial layer of tissue. Thus, quantifying and classifying the morphological patterns for dysplasia can be automated. The textural and spatial features on the detected epithelial layer can capture the changes in tissue.