On problems of multicomponent system maintenance modelling

We present an overview of some recent developments in the area of mathematical modeling of maintenance decisions for multi-unit systems. The emphasis is on three main groups of multicomponent maintenance optimization models： the block replacement models, group maintenance models, and opportunistic maintenance models. Moreover, an example of a two-unit system maintenance process is provided in order to compare various maintenance policies.     

Upgrading a Granular Computing Based Data Mining Framework to a Relational Case

One of the popular methods to develop an algorithm for mining data stored in a relational structure is to upgrade an existing attribute‐value algorithm to a relational case. Current approaches to this problem have some shortcomings such as (1) a dependence on the upgrading process of the algorithm to be extended, (2) complicated redefinitions of crucial notions (e.g., pattern generality, pattern refinement), and (3) a tolerant limitation of the search space for pattern discovery. In this paper, we propose and evaluate a general methodology for upgrading a data mining framework to a relational case. This methodology is defined in a granular computing environment. Thanks to our relational extension of a granular computing based data mining framework, the three above problems can be overcome.     

Development of a robust canopy height model derived from ALS point clouds for predicting individual crown attributes at the species level

ABSTRACT

Tree crown attributes are important parameters during the assessment and monitoring of forest ecosystems. Canopy height models (CHMs) derived from airborne laser scanning (ALS) data have proved to be a reliable source for extracting different biophysical characteristics of single trees and at stand level. However, ALS-derived tree measurements (e.g., mean crown diameter) can be negatively affected by pits that appear in the CHMs. Thus, we propose a novel method for generating pit-free CHMs from ALS point clouds for estimating crown attributes (i.e., area and mean diameter) at the species level. The method automatically calculates a threshold for a pixel based on the range of height values within neighbouring pixels; if the pixel falls below the threshold then it is recognized as a pitted pixel. The pit is then filled with the median of the values of the neighbouring pixels. Manually delineated individual tree crowns (ITC) of four deciduous and two coniferous species on Colour Infrared (CIR) stereo images were used as a reference in the analysis. In addition, a variety of different algorithms for constructing CHMs were compared to investigate the performance of different CHMs in similar forest conditions. Comparisons between the estimated and observed crown area (R² = 0.95, RMSE% = 19.12% for all individuals) and mean diameter (R² = 0.92, RMSE% = 12.16% for all individuals) revealed that ITC attributes were correctly estimated by segmentation of the pit-free CHM proposed in this study. The goodness of matching and geometry revealed that the delineated crowns correctly matched up to the reference data and had identical geometry in approximately 70% of cases. The results showed that the proposed method produced a CHM that estimates crown attributes more accurately than the other investigated CHMs. Furthermore, the findings suggest that the proposed algorithm used to fill pits with the median of height observed in surrounding pixels significantly improve the accuracy of the results the species level due to a higher correlation between the estimated and observed crown attributes. Based on these results, we concluded that the proposed pit filling method is capable of providing an automatic and objective solution for constructing pit-free CHMs for assessing individual crown attributes of mixed forest stands.     

Vocal-based emotion recognition using random forests and decision tree

This paper proposes a new vocal-based emotion recognition method using random forests, where pairs of the features on the whole speech signal, namely, pitch, intensity, the first four formants, the first four formants bandwidths, mean autocorrelation, mean noise-to-harmonics ratio and standard deviation, are used in order to recognize the emotional state of a speaker. The proposed technique adopts random forests to represent the speech signals, along with the decision-trees approach, in order to classify them into different categories. The emotions are broadly categorised into the six groups, which are happiness, fear, sadness, neutral, surprise, and disgust. The Surrey Audio-Visual Expressed Emotion database is used. According to the experimental results using leave-one-out cross-validation, by means of combining the most significant prosodic features, the proposed method has an average recognition rate of \(66.28\%\), and at the highest level, the recognition rate of \(78\%\) has been obtained, which belongs to the happiness voice signals. The proposed method has \(13.78\%\) higher average recognition rate and \(28.1\%\) higher best recognition rate compared to the linear discriminant analysis as well as \(6.58\%\) higher average recognition rate than the deep neural networks results, both of which have been implemented on the same database.     

Interactions between fecal bacteria,bile acids and components of tomato pomace

Food Science and Biotechnology - The tomato pomace obtained during processing as a residue of tomato processing from large industry. The interactions between tomato pomace and fecal bacteria, bile...     

Influence of furfuryl moulding sand on flake graphite formation in surface layer of ductile iron castings

The influence of the use of moulding sand with furan resin, prepared both with fresh sand and reclaimed matrix, on the formation of a flake graphite formation at the surface layer of ductile iron castings has been investigated. A series of experimental heats of ductile iron cast in moulds made of moulding sand characterised by different levels of surface active elements (sulphur, oxygen) were performed. The effect of the wall thickness and the initial temperature of the metal in the mould cavity on the formation of flake graphite in the surface layer of the casting is shown in the paper. Investigations carried out by means of scanning electron microscopy (energy dispersive X-ray spectroscopy and wavelength dispersive X-ray spectroscopy) showed concentration of gradient profiles of surface active elements in the castings surface layer, which are responsible for their quality. Finally, it has been shown that there exists a significant effect of the quality of the sand on the formation of the flake graphite layer and the surface characteristics of ductile iron castings.     

Effects of the methods of pre-treatment before freezing on the retention of chlorophylls in frozen leaf vegetables prepared for consumption

The investigation included kale, New Zealand spinach and spinach. The evaluation covered the raw material; the raw material after blanching; the raw material after cooking; and frozen products prepared for consumption after 0, 4, 8 and 12 months of refrigerated storage. Both the traditional method of freezing (blanching before freezing) and the modified method of freezing (cooked before freezing) were used in the experiment, as well as two storage temperatures, T = −20 °C and T = −30 °C. The content of chlorophylls in fresh kale was four times that in New Zealand spinach and 1.5 times that in spinach. With the exception of New Zealand spinach, blanching and cooking significantly reduced the content of chlorophylls. In kale products prepared for consumption, the content of chlorophylls decreased in each successive stage of the investigation. In products of New Zealand spinach and spinach, the losses were usually not significant. After 12 months of refrigerated storage, frozen kale products prepared for consumption retained 52–65% of total chlorophylls compared with the content in the raw material; products of New Zealand spinach and spinach retained 66–71%. In kale and New Zealand spinach, the content of chlorophyll a decreased more rapidly than that of chlorophyll b, while in spinach the converse was true. The kale products obtained using the modified method contained more chlorophylls, while in the two spinach species their content was lower. The lower storage temperature resulted in a higher retention of chlorophylls in vegetables.     

Influence of yeast on the yield of fermentation and volatile profile of ‘Węgierka Zwykła’ plum distillates

The character of plum brandies depends on a unique aroma profile of the plum and the microbiota present on the surface of the fruits, as well as yeast used for fermentation. In this study, an evaluation of the effect of microorganisms applied for the fermentation of W?gierka Zwyk?a var. plum mashes and processing temperature (18 °C, 30 °C) on its efficiency and volatile profile, as well as taste and flavour of distillates obtained was performed. An estimation of the odour activity values (OAVs) of the volatile compounds was also conducted. Regardless of whether the fermentation was carried out using Saccharomyces bayanus wine yeast or by native microflora present on plums as well as raisins, the efficiency of this process was high and ranged between 91.7 and 96.7% of the theoretical efficiency. Especially rich in esters (among others ethyl acetate and isoamyl acetate) was the distillate derived after fermentation with the microflora of plums and raisins, at 18 °C. An evaluation of the individual aromatic effect of chemical compounds present in tested distillates, in terms of their OAVs, revealed that the highest OAVs were reached with isovaleraldehyde. Other compounds that showed aroma values >1 and possibly had an effect on the overall aroma of tested plum distillates were the following: hexanal, benzaldehyde, ethyl acetate, isoamyl acetate, ethyl benzoate, ethyl hexanoate, 1‐propanol, 2‐methyl‐1‐butanol, 3‐methyl‐1‐butanol and 1‐hexanol. The performed sensory ranking showed that the best rated distillate was the one obtained after fermentation with the indigenous microflora of plums and raisins, at 18 °C. Copyright © 2016 The Institute of Brewing & Distilling