Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

A self-adaptive weighted affinity propagation clustering for key frames extraction on human action recognition

In this paper, we propose a novel approach for key frames extraction on human action recognition from 3D video sequences. To represent human actions, an Energy Feature (EF), combining kinetic energy and potential energy, is extracted from 3D video sequences. A Self-adaptive Weighted Affinity Propagation (SWAP) algorithm is then proposed to extract the key frames. Finally, we employ SVM to recognize human actions on the EFs of selected key frames. The experiments show the information including whole action course can be effectively extracted by our method, and we obtain good recognition performance without losing classification accuracy. Moreover, the recognition speed is greatly improved.     

Functional and conformational characterisation of walnut protein obtained through AOT reverse micelles

Walnut protein was extracted from defatted walnut flour by bis (2‐ethylhexyl) sodium sulfosuccinate (AOT) reverse micellar system and alkali solution with isoelectric precipitation. The functional and conformational characteristics of two proteins were investigated and compared. Walnut protein obtained through AOT reverse micelles was found to have higher extraction yield, solubility, emulsifying activity, foam capacities, water‐ and oil‐holding capacity compared to the protein from alkali solution along with isoelectric precipitation, while the surface hydrophobicity (H₀), contents of disulphide bond (SS) and sulfhydryl group (SH) were relatively lower. The differences of H₀, SS and SH contents could be due to the conformational changes of walnut proteins using two extraction methods. FTIR spectra and data showed that the reverse micelles caused the decrease in α‐helix, β‐sheet, random and β‐turn of walnut protein, the increase in the side‐chain structure content, which could be responsible for the modification of functional properties.     

Determining Stability Constants Using the AKUFVE Technique

ABSTRACT

The AKUFVE techniques were developed by Rydberg and co-workers in the 1960s. The main aim was to be able to perform a series of liquid-liquid extraction data varying one or more parameters and at the same time achieve very pure phases. As such, this technique was later used for short-lived isotope studies in the SISAK system, but also as a standalone unit for a large number of thermodynamic studies of extraction systems both for fundamental understanding as well as more applied investigations. In this paper, the apparatus with modifications made over the decades is described. In addition, studies with stability constant determinations for the zirconium-water-acetylacetone system as well as lanthanide extraction using bromodecanoic acid are exemplified to demonstrate the potential use of the technique. The results shown clearly demonstrate the versatility and ability of the AKUFVE system.     

Development of green extraction process to produce antioxidant-rich extracts from purple coneflower

The present study was conducted to develop subcritical water extraction (SWE) of Echinacea purpurea flowers. The influence of temperature and extraction time on quality of extracts considering total phenols content, total flavonoids content, antioxidant capacity and extraction yield, was determined. Optimized extraction parameters for maximised investigated responses were as follows: 147.56 °C and 8.43 min. The experimental values agreed with the values predicted, thus indicating the adequacy of central composite experimental design for modelling the SWE of bioactive compounds from E. purpurea. Results of the study also highlighted the potential application of E. purpurea subcritical water extracts as a source of valuable bioactive compounds.     

Antioxidant and antimicrobial potential of cajazeira leaves (Spondias mombin) extracts

Cajazeira leaves (Spondias mombin) have their highlighted use as antioxidant and natural antimicrobial, which justifies the objective of this work to evaluate the biological activities of different extracts. In order to evaluate the antioxidant and antimicrobial potentials of the cajazeira leaves, extractions at low pressure and high pressure were performed. The low pressure extractions (PLE) were carried out using Soxhlet (SOX) and tip ultrasound, using different solvents. Supercritical fluid extraction (SFE) was evaluated at temperature of 40?60°C and pressure of 150?300 bar besides extraction with cosolvent. Higher yields were obtained with the use of more polar solvents at LPE. The extracts obtained by SOX with ethanol and others polar solvents presented the best TPC values and antioxidant activity. The extracts at LPE with hexane and ethyl acetate and SFE presented better antimicrobial activity. Through liquid chromatography of high efficiency, it was possible to identify compounds with recognized biological activity, like ellagic acid, gallic acid and catechin.     

Experimental Investigation and Thermodynamic Modeling of Glycerin/Methanol/Organic Solvent Systems

Glycerin is an important by‐product in biodiesel production. To increase its quality to be suitable for use it in other operations, e.g., the pharmaceutical industry, it needs to be purified. Therefore, the purification of glycerin by liquid‐liquid extraction of methanol using different solvents was investigated. It was shown that, in terms of separation, petroleum ether was more effective than toluene and toluene was more effective than n‐butanol. In addition to the experimental investigations, the NRTL and UNIQUAC thermodynamic models were used to predict the compositions of ternary mixtures of glycerin + methanol + organic solvent in glycerin‐rich and organic solvent‐rich phases. The results showed the high accuracy of the presented models and their consistency with the measured data.     

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.     

FarsBase-KBP: A knowledge base population system for the Persian Knowledge Graph

While most of the knowledge bases already support the English language, there is only one knowledge base for the Persian language, known as FarsBase, which is automatically created via semi-structured web information. Unlike English knowledge bases such as Wikidata, which have tremendous community support, the population of a knowledge base like FarsBase must rely on automatically extracted knowledge. Knowledge base population can let FarsBase keep growing in size, as the system continues working. In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabelled raw text, crawled from the Web. The proposed system consists of a set of state-of-the-art modules such as an entity linking module as well as information and relation extraction modules designed for FarsBase. Moreover, a canonicalization system is introduced to link extracted relations to FarsBase properties. Then, the system uses knowledge fusion techniques with minimal intervention of human experts to integrate and filter the proper knowledge instances, extracted by each module. To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of 22015 FarsBase triples and verified by human experts. The evaluation results demonstrate the efficiency of the proposed system.     

Negative statements considered useful

Knowledge bases (KBs) about notable entities and their properties are an important asset in applications such as search, question answering and dialog. All popular KBs capture virtually only positive statements, and abstain from taking any stance on statements not stored in the KB. This paper makes the case for explicitly stating salient statements that do not hold. Negative statements are useful to overcome limitations of question answering systems that are mainly geared for positive questions; they can also contribute to informative summaries of entities. Due to the abundance of such invalid statements, any effort to compile them needs to address ranking by saliency. We present a statistical inference method for compiling and ranking negative statements, based on expectations from positive statements of related entities in peer groups. Experimental results, with a variety of datasets, show that the method can effectively discover notable negative statements, and extrinsic studies underline their usefulness for entity summarization. Datasets and code are released as resources for further research.