Green mine construction is the main melody of mining development and problems such as safe production, energy saving and consumption reduction need to be solved urgently. The working conditions of the mill are complex in the process of grinding. Aiming at the problems existing in the feature extraction and load prediction of the mill, a signal-processing method based on adaptive chirp mode decomposition (ACMD) and a standardized variable distance classifier (SVD) is proposed. Firstly, the recursive framework of the ACMD method is used to obtain the initial frequency of mill vibration signals. Secondly, the initial frequency is used to reconstruct the high-resolution component of the mill vibration signal through the iterative frame in the ACMD method. The frequency corresponding to the frequency domain peak of the reconstructed signal is then selected as the mill load feature vector. Finally, with consideration to the influence of standard deviation and standardized variable factors on the feature vectors, a standardized variable distance classifier is proposed. The feature vectors of the mill load are input into the SVD model for training, and the state types of the mill load are obtained. The method is applied to the grinding experiment and the results show that the frequency-domain features obtained by the mill vibration signal-processing method based on ACMD-SVD are obvious, which has high accuracy in the identification of mill load types, and provides a new idea for the extraction of mill load features and prediction of the mill load.

The most important distinctive features and some morphological and bionomie characteristic of Liriomyza huidobrensis are presented in this paper.

The concept of cointegration that enables the proper statistical analysis of long-run comovements between unit root processes has been of great interest to numerous economic investigators since it was introduced. However, investigation of short-run comovement between economic time series seems equally important, especially for economic decision-makers. The concept of common features and based on it the idea of two additional reduced rank structure forms in a VEC model (the strong and the weak one) may be of some help. The strong form reduced rank structure (SF) takes place when at least one linear combination of the first differences of the variables exists, which is white noise. However, when this assumption seems too strong, the weaker case can be considered. The weak form appears when the linear combination of first differences adjusted for long-run efects exists, which is white noise.

The main focus of this paper is a Bayesian analysis of the VEC models involving the weak form of reduced rank restrictions.

After the introduction and discussion of the said Bayesian model, the presented methods will be illustrated by an empirical investigation of the price – wage spiral in the Polish economy.

The paper presents a comparative study of music features derived from audio recordings, i.e. the same music pieces but representing different music genres, excerpts performed by different musicians, and songs performed by a musician, whose style evolved over time. Firstly, the origin and the background of the division of music genres were shortly presented. Then, several objective parameters of an audio signal were recalled that have an easy interpretation in the context of perceptual relevance. Within the study parameter values were extracted from music excerpts, gathered and compared to determine to what extent they are similar within the songs of the same performer or samples representing the same piece.

An automatic analysis of product reviews requires deep understanding of the natural language text by machine. The limitation of bag-of-words (BoW) model is that a large amount of word relation information from the original sentence is lost and the word order is ignored. Higher-order-N-grams also fail to capture the long-range dependency relations and word order information. To address these issues, syntactic features extracted from the dependency relations can be used for machine learning based document-level sentiment classification. Generalization of syntactic dependency features and negation handling is used to achieve more accurate classification. Further to reduce the huge dimensionality of the feature space, feature selection methods based on information gain (IG) and weighted frequency and odds (WFO) are used. A supervised feature weighting scheme called delta term frequency-inverse document frequency (TF-IDF) is also employed to boost the importance of discriminative features using the observed uneven distribution of features between the two classes. Experimental results show the effectiveness of generalized syntactic dependency features over standard features for sentiment classification using Boolean multinomial naive Bayes (BMNB) classifier.

In order to achieve accurate identification and segmentation of ore under complex working conditions, machine vision and neural network technology are used to carry out intelligent detection research on ore, an improved Mask RCNN instance segmentation algorithm is proposed. Aiming at the problem of misidentification of stacked ores caused by the loss of deep feature details during the feature extraction process of ore images, an improved Multipath Feature Pyramid Network (MFPN) was proposed. The network firstly adds a single bottom-up feature fusion path, and then adds with the top-down feature fusion path of the original algorithm, which can enrich the deep feature details and strengthen the fusion of the network to the feature layer, and improve the accuracy of the network to the ore recognition. The experimental results show that the algorithm proposed in this paper has a recognition accuracy of 96.5% for ore under complex working conditions, and the recall rate and recall rate function values reach 97.4% and 97.0% respectively, and the AP75 value is 6.84% higher than the original algorithm. The detection results of the ore in the actual scene show that the mask size segmented by the network is close to the actual size of the ore, indicating that the improved network model proposed in this paper has achieved a good performance in the detection of ore under different illumination, pose and background. Therefore, the method proposed in this paper has a good application prospect for stacked ore identification under complex working conditions.

This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, k-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring.

We studied the embryology of Withania somnifera (L.) Dunal by light microscopy in order to reveal specific embryological features of the genus, and compared the results with embryological data on other members of the family Solanaceae. The key embryological characters of W. somnifera include dicotyledonous-type anther wall formation, simultaneous cytokinesis in pollen mother cells, binucleate tapetal cells, 2-celled mature pollen, anatropous, tenuinucellate and unitegmic ovules, polygonum-type embryo sac formation, the presence of an endothelium, and cellular endosperm formation. We give the first report of the dicotyledonous mode of anther wall formation (previously described as basic type) for the species. Comparative study suggests that anther wall formation, number of nuclei in tapetal cells, number of cells in mature pollen, mode of embryo sac formation and endosperm development are the most variable embryological features in Solanaceae. Some of these embryological features of W. somnifera should be of value for comparative study of related species and their phylogenetic relationships within the family.

The aim of this research was to study the biodiversity of cyanobacteria and microalgae in hydro-terrestrial habitats from the area of Hornsund fjord (Svalbard archipelago). This research is particularly important, because hitherto no complex research (including all taxonomic groups) has previously been conducted on the cyanobacterial and microalgal flora in Arctic water ecosystems. The research was conducted during the summer seasons of 2011 and 2013. Shannon’s diversity index was used to describe species diversity and evenness. Data on cyanobacteria and microalgae were analyzed using the MVSP and PCA. Additionally, a basic analysis of the physicochemical properties of water in the studied ecosystems was performed. A total of 506 taxa were noted in the studied hydro-terrestrial habitats. The most numerous group was cyanobacteria, constituting 35% of all recorded taxa. Ochrophyta and Chlorphyta were almost equally numerous (percentage again as for cyanobacteria). Nineteen types of assemblages were noted in all studied hydro-terrestrial habitats. The diversity of cyanobacteria and microalgae and the assemblages formed by them were used to determine the characteristics of the studied ecosystems. Each type of water ecosystem was represented by specific phycoflora and assemblages. Ecological parameters along with biological data (the diversity of cyanobacteria and microalgae) allowed us to sort the studied hydro-terrestrial habitats by similarity. Our analyses clearly distinguished water ecosystem groups differing in species composition determining their trophic status. The research shows the usefulness of cyanobacteria and microalgae diversity defined by the Shannon-Weaver index for characterizing bodies of water and determining the trophic status of these habitats.

Currently the recidivism rate in Ukraine. This indicates failure to achieve the goal of punishment – correction of the convict. The purpose of the article is to research the problems of resocialization of convicts, taking into consideration the psychological characteristics of the person serving the sentence. The subject of research: the subject of research is the resocialization of convicts. The following scientific methods were used to study the international experience of resocialization of convicts, to prove the hypotheses, to formulate conclusions: dialectical method, monographic method, logical method, comparative method, generalization method, system and structural method. The results of the research: it was found out that serving a certain term of imprisonment or life imprisonment affects convicts and leads to a change in their psychology in completely different ways. It is proved that the process of resocialization should be set up during the selection of convict’s type and size of punishment (taking into account the circumstances of the case, the perpetrator personality and criminogenic risks that may contribute to recidivism), continue during punishment (using training, work and communication, and providing psychological support to overcome possible psychological crises) and finish after the release from penitentiary institutions (with control over the released, employment assistance or the provision of temporary residence).

The paper presents qualitative, Bayesian model used 10 determine some interdependencies between sorption features for mineral soils in southern Poland. Sorption properties are very important, crucial for measure or fertility, nutrient retention capacity, and the capacity to protect groundwater from coutaminution. Cation exchange capacity (CFC) is a commonly applied indicator otihc soils conditions or vulncrahilitv. Base saturation (BS) is an important clement of hazard degree assessment in soils lying within reach of impact acidifying agents. The considered soils represented different valuation classes and differed in their typology. The Bayesian model is used lor interdependences assessment.

Complex gaps may be formed when carrying out live working in substations, while the discharge characteristics of complex gaps are different from those of single gaps. This paper focuses on the prediction of critical 50% positive switching impulse breakdown voltage ( U _{50–crit +} of phase-to-phase complex gaps formed in 220 kV substations. Firstly, several electric field features were defined on the shortest discharge path of the complex gap to reflect the electric field distribution. Then support vector machine (SVM) prediction models were established according to the connection between electric field distribution and breakdown voltage. Finally, the U _50–crit¸+ data of the complex gap were obtained through twice electric field calculations and predictions. The prediction results show that the minimum U _{50–crit +} of phase-to-phase complex gaps is 1147 kV, and the critical position is 0.9 m away from the high voltage conductor, accounting for 27% of the whole gap. Both critical position and voltage are in good agreement with the values provided in IEC 61472.

In the field of medicine there is a need for the automatic detection of retinal disorders. Blindness in older persons is primarily caused by Central Retinal Vein Occlusion (CRVO). It results in rapid, irreversible eyesight loss, therefore, it is essential to identify and address CRVO as soon as feasible. Hemorrhages, which can differ in size, pigment, and shape from dot-shaped to flame hemorrhages, are one of the earliest symptoms of CRVO. The early signs of CRVO are, hemorrhages, however, so mild that ophthalmologists must dynamically observe such indicators in the retina image known as the fundus image, which is a challenging and time-consuming task. It is also difficult to segment hemorrhages since the blood vessels and hemorrhages (HE) have the same color properties also there is no particular shape for hemorrhages and it scatters all over the fundus image. A challenging study is needed to extract the characteristics of vein deformability and dilatation. Furthermore, the quality of the captured image affects the efficacy of feature Identification analysis. In this paper, a deep learning approach for CRVO extraction is proposed.

In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90%.

The paper considers the problem of increasing the generalization ability of classification systems by creating an ensemble of classifiers based on the CNN architecture. Different structures of the ensemble will be considered and compared. Deep learning fulfills an important role in the developed system. The numerical descriptors created in the last locally connected convolution layer of CNN flattened to the form of a vector, are subjected to a few different selection mechanisms. Each of them chooses the independent set of features, selected according to the applied assessment techniques. Their results are combined with three classifiers: softmax, support vector machine, and random forest of the decision tree. All of them do simultaneously the same classification task. Their results are integrated into the final verdict of the ensemble. Different forms of arrangement of the ensemble are considered and tested on the recognition of facial images. Two different databases are used in experiments. One was composed of 68 classes of greyscale images and the second of 276 classes of color images. The results of experiments have shown high improvement of class recognition resulting from the application of the properly designed ensemble.

The paper presents the fusion approach of different feature selection methods in pattern recognition problems. The following methods are examined: nearest component analysis, Fisher discriminant criterion, refiefF method, stepwise fit, Kolmogorov-Smirnov criteria, T2-test, Kruskall-Wallis test, feature correlation with class, and SVM recursive feature elimination. The sensitivity to the noisy data as well as the repeatability of the most important features are studied. Based on this study, the best selection methods are chosen and applied in the process of selection of the most important genes and gene sequences in a dataset of gene expression microarray in prostate and ovarian cancers. The results of their fusion are presented and discussed. The small selected set of such genes can be treated as biomarkers of cancer.

Individual identification of similar communication emitters in the complex electromagnetic environment has great research value and significance in both military and civilian fields. In this paper, a feature extraction method called HVG-NTE is proposed based on the idea of system nonlinearity. The shape of the degree distribution, based on the extraction of HVG degree distribution, is quantified with NTE to improve the anti-noise performance. Then XGBoost is used to build a classifier for communication emitter identification. Our method achieves better recognition performance than the state-of-the-art technology of the transient signal data set of radio stations with the same plant, batch, and model, and is suitable for a small sample size.

The paper discusses a way of choosing the design features (geometry, the rate of grinding and thrust) of ring-ball mills. Various methods of calculating the optimal rate of grinding have been compared. Basing on experimental investigations on the pilot-plant and industrial scale, the influence of the angular velocity and the thrust on the mill have been verified, and the interdependence between the rate of grinding and the thrust of the grinding elements have been explained.

The empirical-analytic model of milling in a ring-ball mill has been presented. It concerns the interaction of the basic design features of the grinding unit (geometry, rate of grinding and thrust of the balls) on the maximum efficiency of the mill. The production of pulverized coal was expressed by the product of the flux of material drawn in by the balls and the so-called "grinding effect of the balls" (defined by the increase of the mass fraction of dust in its flux). The kinematic quantities (among others, the flux of loose material drawn in by the balls) have been calculated on the basis of a simple analytical description of the flow of particles and some parametrical assumptions. The grinding properties of coal have been determined making use of laboratory tests of its cruising by the rollers. Some verifications of the grinding model on the experimental test stand with a ring-ball mill have been presented. The test stand is installed at the Institute of Power Engineering and Turbomachinery of the Silesian University of Technology.

In order to explore the impact of coal and gangue particle size changes on recognition accuracy and to improve the single particle size of coal and gangue identification accuracy of sorting equipment, this study established a database of different particle sizes of coal and gangue through image gray and texture feature extraction, using a relief feature selection algorithm to compare different particle size of coal and gangue optimal features of the combination, and to identify the points and particle size of coal and gangue. The results show that the optimal features and number of coal and gangue are different with different particle sizes. Based on visible-light coal and gangue separation technology, the change of coal and gangue particle size cause fluctuations in the recognition accuracy, and the fluctuation of recognition accuracy will gradually decrease with increases in the number of features. In the process of particle size classification, if the training model has a single particle size range, the recognition accuracy of each particle size range is low, with the highest recognition accuracy being 98% and the average recognition rate being only 97.2%. The method proposed in this paper can effectively improve the recognition accuracy of each particle size range. The maximum recognition accuracy is 100%, the maximum increase is 4%, and the average recognition accuracy is 99.2%. Therefore, this method has a high practical application value for the separation of coal and gangue with single particle size.

The aim of the paper is to evaluate the development of the Geoeducation Center in Kielce and to define ways and stage of creating its tourist brand. It is a new tourist attraction in the Świętokrzyskie region, which also plays role of informal education. Every year, this object is visited by approximately 40,000. tourists. Research has shown that the Geoeducation Center from the beginning of its operation consistently creates all the elements that make up the brand equity: awareness, perceived quality, associations and loyalty.

In 1993 Engle and Kozicki proposed the notion of common features of which one example is a serial correlation common feature. We say that stationary, non-innovation processes exhibit common serial correlation when there exists at least one linear combination of them which is an innovation. Later on in 1993 Vahid and Engle combined the notions of cointegration among I(1) processes with common serial correlation within their first differences. It is commonly known that cointegrated time series have vector error correction (VEC) representation. The existence of common serial correlation leads to an additional reduced rank restriction imposed on the VEC model’s parameters. This type of restriction was later termed a strong form (SF) reduced rank structure, as opposed to a weak one introduced in 2006 by Hecq, Palm and Urbain.

The main aim of the present paper is to construct the Bayesian vector error correction model with these additional strong form restrictions.

The empirical validity of investigating both the short- and long-run co-movements between macroeconomic time series will be illustrated by the analysis of the price-wage nexus in the Polish economy.

At present, most of the existing target detection algorithms use the method of region proposal to search for the target in the image. The most effective regional proposal method usually requires thousands of target prediction areas to achieve high recall rate.This lowers the detection efficiency. Even though recent region proposal network approach have yielded good results by using hundreds of proposals, it still faces the challenge when applied to small objects and precise locations. This is mainly because these approaches use coarse feature. Therefore, we propose a new method for extracting more efficient global features and multi-scale features to provide target detection performance. Given that feature maps under continuous convolution lose the resolution required to detect small objects when obtaining deeper semantic information; hence, we use rolling convolution (RC) to maintain the high resolution of low-level feature maps to explore objects in greater detail, even if there is no structure dedicated to combining the features of multiple convolutional layers. Furthermore, we use a recurrent neural network of multiple gated recurrent units (GRUs) at the top of the convolutional layer to highlight useful global context locations for assisting in the detection of objects. Through experiments in the benchmark data set, our proposed method achieved 78.2% mAP in PASCAL VOC 2007 and 72.3% mAP in PASCAL VOC 2012 dataset. It has been verified through many experiments that this method has reached a more advanced level of detection.