Set Of Characteristic Parameters Research Articles

Performance degradation assessment of rolling bearings for electrostatic monitoring based on IDDAE and ADPC

Abstract To improve the early degradation detection capability of the electrostatic monitoring system for rolling bearings, a performance degradation evaluation method based on improved deep denoising autoencoder (IDDAE) and adaptive density peak clustering (ADPC) is proposed in this paper. Firstly, the fusion of electrostatic charge signal features with conventional time-domain, frequency-domain and time-frequency-domain features constitutes the characteristic parameter set of the electrostatic monitoring system indicating the status of the bearings. Then, in order to improve the feature extraction ability of DAE, the deep network DDAE is constructed, and L1 regularisation and Dropout mechanism are applied to avoid overfitting in the deep network, so as to achieve non-linear mapping dimensionality reduction of high-dimensional features. Moreover, to eliminate the error caused by manually selecting the clustering centre, the parameters are adaptively determined by entropy value method and comprehensive optimisation search on the basis of DPC, thus avoiding the "chain effect" that occurs in traditional DPC when data are incorrectly aggregated due to incorrect assignment of clustering centres. Consequently, an improved ADPC algorithm is used to establish a model to measure the health status of bearings, calculate the Mahalanobis distance (MD) be-tween the test set and the cluster centre, and quantitatively characterize the degree of performance degradation of rolling bearings. Finally, combining the 3σ principle, a repair method that can satisfy online monitoring and adapt to spurious fluctuations in different situations is established on a sliding window to obtain an improved index IMD that can accurately characterise the rolling bearing degradation process. The experimental results show that the proposed method can identify early bearing degradation earlier and has better monotonicity, robustness and tendency than other performance degradation assessment methods.

Bearing fault diagnosis based on enhanced Canberra distance feature in SDP image

Abstract Feature enhancement is important in mechanical equipment fault diagnosis. A limited set of characteristic parameters is insufficient for diagnosing bearing signals with multiple fault types. The presence of noise increases the difficulty of extracting fault features from images. To address the challenge of diagnosing rolling bearing faults in complex environments, this study presents an enhanced weighted image fusion framework aimed at enhancing fault features within the images, which enables accurate diagnosis of bearing faults using a limited number of features. The proposed method encompasses four distinct stages. In the first stage, a symmetrized dot pattern method is employed to transform one-dimensional time-series data into two-dimensional images, visualizing the signal in a 2D format. In the second stage, image binarization and an improved weighted fusion method are utilized to simplify subsequent processing and enhance the image features. The third stage involves extracting the image’s contrast and maximum singular value to improve the Canberra distance calculation. Finally, the enhanced Canberra distance is used for classifying bearing faults. Performance testing of the image feature enhancement is conducted on various datasets containing rolling bearings. Comparative experiments with alternative enhancement methods demonstrate the superiority of the proposed improved weighted image fusion framework. Comparative experiments with the original Canberra distance validate the effectiveness of the enhanced Canberra distance. Additionally, experiments conducted in noisy environments confirm the robustness of the proposed approach. Furthermore, the image feature enhancement method is applied to other bearing datasets, and the experimental results demonstrate its effectiveness in enhancing fault feature representation and achieving accurate diagnosis of rolling bearings.

A Training-Free Estimation Method for the State of Charge and State of Health of Series Battery Packs under Various Load Profiles

To ensure the accuracy of state of charge (SOC) and state of health (SOH) estimation for battery packs while minimizing the amount of pre-experiments required for aging modeling and the scales of computation for online management, a decisive-cell-based estimation method with training-free characteristic parameters and a dynamic-weighted estimation strategy is proposed in this paper. Firstly, to reduce the computational complexity, the state estimation of battery packs is summed up to that of two decisive cells, and a new selection approach for the decisive cells is adopted based on the detection of steep voltage changes. Secondly, two novel ideas are implemented for the state estimation of the selected cells. On the one hand, a set of characteristic parameters that only exhibit local curve shrinkage with aging is chosen, which keeps the corresponding estimation approaches away from training. On the other hand, multiple basic estimation approaches are effectively combined by their respective dynamic weights, which ensures the estimation can maintain a good estimation accuracy under various load profiles. Finally, the experimental results show that the new method can quickly correct the initial setting deviations and have a high estimation accuracy for both the SOC and SOH within 2% for a series battery pack consisting of cells with obvious inconsistency.

Recognition of pulse-in-pulse modulation type of radar signal based on feature extraction

In order to solve the problem of identifying radar signal modulation types in a complex electromagnetic environment, this paper analyzes and evaluates some of the existing identification methods, such as CW, LFM, NLFM, BPSK, QPSK, FSK, FSK + PSK, and LFM + BPSK modulation types, and proposes a hierarchical decision recognition algorithm based on feature extraction. First, the signal is preprocessed by FFT transformation and STFT transformation. Second, a new set of radar signal recognition characteristic parameters is proposed to identify and classify different signals. Then, by analyzing the characteristics of different signals, the signals are identified and classified one by one. Finally, the usability of the proposed algorithm is obtained by comparing several commonly used recognition algorithms. The simulation results show that the recognition success rate of this algorithm can reach 95% when the SNR is better than −7 dB, which has certain industrial application value.

Decision support methodology for the selection of industrial robots using BWM and TOPSIS methods

This paper presents a methodology for assisting the decision of selecting an industrial robot from a set of alternatives. The approach is based on the use of a minimal set of robot characteristic parameters, as selection criteria. For the selection of the solution from the set of alternatives the BWM method is used as a first step to calculate the weights of the criteria. The weights resulting from the BWM application are then used to rank the alternatives by means of a simple additive weighting approach. The same weights calculated with BWM are then used to achieve the hierarchy of alternatives using the TOPSIS method. Both approaches ultimately showed the same option as the best of the set of alternatives.

What Are the Causes of Super Activity of Solar Active Regions?

Flare productivity varies among solar active regions (ARs). This study analyzed 20 ARs of contrasting sunspot areas and flare productivities to understand the super flare productivity of certain ARs. We used the flare index (FI) as an indicator of flare activity. We examined the pattern of morphological evolution of magnetic features. Further, we derived a set of magnetic feature parameters to quantitatively characterize ARs. Our study found that the correlation coefficient is the highest (r = 0.78) between FI and the length of the strong gradient polarity inversion line (SgPIL), while the coefficient is the lowest (r = 0.14) between FI and the total unsigned magnetic flux. For the selected ARs, this study also found that the super flare productive ARs have SgPILs (R value) longer (greater) than 50 Mm (4.5). These results suggest that flare productivity is mainly controlled by the size of the subregion that comprises close interaction of opposite magnetic polarities and is weakly correlated with the size of the whole ARs. Further, even though magnetic flux emergence is important, this study shows that it alone is insufficient to increase flare productivity. New emergence can drive either the interaction of like or opposite magnetic polarities of nonconjugate pairs (i.e., polarities not from the same bipole). In the former case, the magnetic configuration remains simple, and flare productivity would be low. In the latter case, the convergence of opposite magnetic fluxes of nonconjugate pairs results in a magnetic configuration with long SgPIL and an increase in flare productivity.

Data-Driven Algorithm Based on Energy Consumption Estimation for Electric Bus

The accurate estimation of battery state of charge (SOC) for modern electric vehicles is crucial for the range and performance of electric vehicles. This paper focuses on the historical driving data of electric buses and focuses on the extraction of driving condition feature parameters and data preprocessing. By selecting relevant parameters, a set of characteristic parameters for specific driving conditions is established, a process of constructing a battery SOC prediction model based on a Long short-term memory (LSTM) network is proposed, and different hyperparameters of the model are identified and adjusted to improve the accuracy of the prediction results. The results show that the prediction results can reach 1.9875% Root Mean Square Error (RMSE) and 1.7573% Mean Absolute Error (MAE) after choosing appropriate hyperparameters; this approach is expected to improve the performance of battery management systems and battery utilization efficiency in the field of electric vehicles.

Small Stochastic Data Compactification Concept Justified in the Entropy Basis.

Measurement is a typical way of gathering information about an investigated object, generalized by a finite set of characteristic parameters. The result of each iteration of the measurement is an instance of the class of the investigated object in the form of a set of values of characteristic parameters. An ordered set of instances forms a collection whose dimensionality for a real object is a factor that cannot be ignored. Managing the dimensionality of data collections, as well as classification, regression, and clustering, are fundamental problems for machine learning. Compactification is the approximation of the original data collection by an equivalent collection (with a reduced dimension of characteristic parameters) with the control of accompanying information capacity losses. Related to compactification is the data completeness verifying procedure, which is characteristic of the data reliability assessment. If there are stochastic parameters among the initial data collection characteristic parameters, the compactification procedure becomes more complicated. To take this into account, this study proposes a model of a structured collection of stochastic data defined in terms of relative entropy. The compactification of such a data model is formalized by an iterative procedure aimed at maximizing the relative entropy of sequential implementation of direct and reverse projections of data collections, taking into account the estimates of the probability distribution densities of their attributes. The procedure for approximating the relative entropy function of compactification to reduce the computational complexity of the latter is proposed. To qualitatively assess compactification this study undertakes a formal analysis that uses data collection information capacity and the absolute and relative share of information losses due to compaction as its metrics. Taking into account the semantic connection of compactification and completeness, the proposed metric is also relevant for the task of assessing data reliability. Testing the proposed compactification procedure proved both its stability and efficiency in comparison with previously used analogues, such as the principal component analysis method and the random projection method.

Prediction of Remaining Fatigue Life of In-Service Bridge Cranes

At present, bridge crane accidents occur frequently, resulting in significant losses and casualties; to ensure the safe use of in-service bridge cranes, it is necessary to predict the residual fatigue life of in-service bridge cranes. Firstly, a static analysis of the most dangerous working conditions of in-service bridge cranes is carried out to find the fatigue failure point. Subsequently, a three-parameter Weibull distribution model is established for the characteristic parameters affecting the acquisition of the stress spectrum. Latin hypercubic sampling is applied to randomly sample the characteristic parameters to produce a random sample set of characteristic parameters for use in obtaining the stress–time history. The amplitude and mean values of the stress spectra are obtained by cycle counting using the rainflow counting method. Finally, Forman’s formula and Miner’s continuous damage accumulation theory were used to derive the remaining life prediction equations for constant and variable amplitude loads. Comparing the remaining life obtained from the simulation and test, the error is about 9.145%, which proves that the remaining life obtained from the simulation is more accurate. The results show that the combined method of simulation and testing is feasible and can predict the remaining life more accurately. In the past, the prediction of residual life was performed with either testing or simulation, which is long and costly. Simulation is low-cost and takes a short time, but the accuracy is not high. In this paper, the combination method of testing and simulation improves the efficiency of production and reduces the cost of use.

Monitoring and detecting coal miners' fatigue status using MPA-LSSVM in the vision of smart mine

With booming computer technology and diverse computer-based smart applications, intelligent monitoring and detection of the fatigue state of coal miners (miners) has attracted extensive attention from enterprises. In order to accurately and fast knowledge the fatigue status of Miners and reduce production accidents. The article proposes a Fatigue Monitoring and Detection (FMD) model based on a fusion Machine Learning (ML) approach: Marine Predators Algorithm (MPA)-optimized Least Squares Support Vector Machine (LSSVM). Firstly, the physiological information of electroencephalogram (ECG) of coal miners before and after manual handling operations was collected using the MP160 recorder (multiconductance physiological 160) produced by BIOPAC, USA. Using the paired-samples t-test method, the characteristic indicators reflecting miners' fatigue were extracted from the ECG. Secondly, Principal Component Analysis (PCA) was used to optimize the selected feature indicators and establish the depth fatigue feature parameter set to characterize the fatigue level of miners. Finally, the proposed MPA-LSSVM-based FMD model is applied to recognize Miners' fatigue levels. The results show that the selected indexes can effectively reflect the fatigue status of Miners. The proposed MPA-LSSVM-based FMD model has higher recognition accuracy than SVM and LSSVM models (13.99% and 18.68% higher, respectively) and better robustness. Therefore, the proposed MPA-LSSVM-based FMD model can accurately and effectively identify the fatigue status of Miners.

Vibration Signal Analysis of Roadheader Based on Referential Manifold Learning

Roadheader is important large equipment in coal mining. The roadheader has a higher failure rate due to its harsh working environment and high working intensity. In this paper, we proposed a fault diagnosis method based on reference manifold (RM) learning by using the vibration signals of roadheader in the actual production process. First, health and fault vibration signals were extracted from a large number of field data. The abovementioned signals were analyzed by time domain and wavelet packet energy analysis and got the characteristic parameters of the signal which can form the characteristic parameter sets. RM method can reduce the dimension of the characteristic parameters, and the projection of different characteristic parameters was obtained. Finally, the health parameters and fault parameters of different characteristic parameters were segmented by linear discriminant analysis (LDA). It could get the different segment area range of characteristic parameters for health signals and fault signals. This method provides a set of fault analysis ideas and methods for equipment working under complex working conditions and improves the theoretical basis for fault type analysis.

Automatic Identification of Auroral Substorms Based on Ultraviolet Spectrographic Imager Aboard Defense Meteorological Satellite Program (DMSP) Satellite

An auroral substorm is an important physical process of energy accumulation and explosive release in the Earth’s magnetosphere, and is an important research object of space environment monitoring and space weather warnings. A westward traveling surge (WTS) is a typical auroral physical process of an auroral substorm. Its static characteristic is auroral folding at the polar boundary of an auroral oval and its dynamic characteristic is the westward motion of auroral folding. According to the static characteristic of a WTS, we defined a set of feature parameters based on the morphology and designed a set of automatic detection and discrimination methods; that is, the WTS was identified by using the extracted features and pattern recognition approaches. This approach was tested by using the aurora data of the ultraviolet auroral spectral imager of the Defense Meteorological Satellite Program (DMSP) satellite. The results showed that the accuracy rate of automatic recognition was 61.39%~63.61% and the precision rate was 55.52%~57.92%. The experimental results showed that the approach was effective at detecting the typical characteristics of an auroral substorm (WTS).

Research on WPD and DBSCAN-L-ISOMAP for circuit fault feature extraction

Abstract To solve the problem of feature extraction in electronic circuits due to the nonstationary and nonlinear characteristics of fault signals, a fault feature extraction method for electronic circuits is proposed, which combines wavelet packet analysis and an improved landmark ISOMAP mapping algorithm. The wavelet packet technology is used to decompose and reconstruct the fault feature signals at multiple levels. The extracted wavelet entropy is used to construct the feature vector matrix. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is used to calculate and screen the landmark points. The improved landmark ISOMAP is used to embed the high-dimensional fault feature parameter set into the low-dimensional eigenspace, extract the low-dimensional and sensitive fault feature subset, and apply the support vector machine to identify the fault. The fault diagnosis experiment of the three-phase VIENNA rectifier shows that compared with the principal component analysis method, the traditional ISOMAP method, and the landmark ISOMAP method, the landmark ISOMAP method based on DBSCAN clustering algorithm extracts the fault signal characteristics of electronic equipment more easily.

Uncertainty quantification of microcirculatory characteristic parameters for recognition of cardiovascular diseases

BackgroundCardiovascular disease is one of the leading causes of death worldwide. However, according to studies, 90% of cardiovascular diseases can be prevented. Cardiovascular function parameters are an important basis for the diagnosis of cardiovascular diseases. The pulse wave also contains a wealth of physiological and pathological information, which can reflect the trend of cardiac function parameters at an early stage, so the measurement and analysis of the pulse wave signal becomes more and more important. The wearable pulse signal acquisition device has gradually become a new trend. In the mobile health scenario, convenient use is the prerequisite for long-term and rapid health monitoring. The data containing diverse pulse wave signals is the basis for obtaining more comprehensive and accurate human physiopathological information. Accurate data analysis and processing is the key to realizing the important goal of cardiovascular health monitoring. ObjectiveBased on the concept of mobile health care, wearable devices are developed to obtain physiological signals. The zero-dimensional model and the optimization algorithm are combined to complete the uncertainty quantification of the microcirculation parameters. Then, a feature set containing the cardiovasvular parameters can be constructed. The machine learning algorithm can be used to build a model that can accurately realize cardiovascular disease identification. MethodsThis paper adopts laboratory-developed equipment to acquire the wrist pulse wave and fingertip volume pulse wave. A total of 323 samples were collected from healthy populations, hypertensive patients and patients with coronary heart disease (CHD). The pulse blood flow model in fingertip microcirculation is established, and the uncertainty quantification of model parameters is completed based on slime mold algorithm (SMA). After comparing and analyzing the performance of four algorithms on pulse wave classification, the identification model of cardiovascular diseases is established based on the microcirculatory characteristic parameter set and random forest algorithm (RF). ResultsRF showed good classification performance among the four classification algorithms. The identification accuracy of the model built on the microcirculatory characteristic parameter set and RF algorithm all reached more than 88%. The highest recognition accuracy was 95.51% for coronary heart disease samples, 92.11% for healthy samples, and 88.55% for hypertensive samples. It can be seen that the model based on RF algorithm has a good ability to distinguish the characteristic parameters in different cardiovascular health states. ConclusionsThe wearable device designed in this paper can facilitate the daily health monitoring of cardiovascular disease. By using the combination of the physical model and machine learning model, the uncertainty quantification of microcirculation parameters and the identification of cardiovascular disease was finally completed. The recognition model based on machine learning provides a new idea and method for the research of cardiovascular health monitoring through pulse waves.

A thermodynamic study on relationship between gas separation properties and microstructure of polyurethane membranes

The lattice fluid (LF) thermodynamic model and extended Vrentas’ free-volume (E-VSD) theory were coupled to study the gas separation properties of the linear thermoplastic polyurethane (TPU) membranes with different chemical structures by analyzing their microstructures. A set of characteristic parameters were extracted using the repeating unit of the TPU samples and led to prediction of reliable polymer densities (AARD < 6%) and gas solubilities. The viscoelastic parameters, which were obtained from the DMTA analysis, were also estimated the gas diffusion vs. temperature, precisely. The degree of microphase mixing based on the DSC analysis was in order: TPU-1 (4.84 wt%) < TPU-2 (14.16 wt%) < TPU-3 (19.92 wt%). It was found that the TPU-1 membrane had the highest degree of crystallinity, but showed higher gas solubilities and permeabilities because this membrane has the least degree of microphase mixing. These values, in combination with the gas permeation results, showed that the content of the hard segment along with the degree of microphase mixing and other microstructural parameters like crystallinity were the determinative parameters.

A Gas Emission Prediction Model Based on Feature Selection and Improved Machine Learning

This paper proposed a gas emission prediction method based on feature selection and improved machine learning, as traditional gas emission prediction models are neither accurate nor universally applicable. Through analysis, this paper identified 12 factors that affected gas emissions. A total of 30 groups of typical data for gas outflow were standardized, after which a full subset regression feature selection method was used to categorize 12 influencing factors into different regular patterns and select 18 feature parameter sets. Meanwhile, based on nuclear principal component analysis (KPCA), an optimized gas emission prediction model was constructed where the dimensionality of the original data was reduced. An optimized algorithm set was constructed based on the hybrid kernel extreme learning machine (HKELM) and the least squares support vector machine (LSSVM). The performance of feature parameters adopted in the prediction algorithm was evaluated according to certain metrics. By comparing the results of different sets, the final prediction sequence could be obtained, and a model that was composed of the optimal feature parameters was applied to the optimal machine learning algorithm. The results showed that the HKELM outperformed LSSVM in prediction accuracy, running speed, and stability. The root meant square error (RMSE) for the final prediction sequence was 0.22865, the determination coefficient (R2) was 0.99395, the mean absolute error (MAE) was 0.20306, and the mean absolute percentage error (MAPE) was 1.0595%. Every index of accuracy evaluation performed well and the constructed prediction model had high-prediction accuracy and a wide application.

Intelligent Height Adjustment Method of Shearer Drum Based on Rough Set Significance Reduction and Fuzzy Rough Radial Basis Function Neural Network

The intelligent adjustment method of the shearer drum is the key technology to improve the intelligent level and safety degree of fully mechanized mining face. This paper proposes a shearer drum intelligent height adjustment model based on rough set significance attribute reduction (AR) and fuzzy rough radial basis function neural network (FRRBFNN) optimized by adaptive immune genetic algorithm (AIGA). The model first selects the parameters of shearer process monitoring based on the importance attribute reduction algorithm of rough set, and establishes the attribute reduction set of shearer operation characteristic parameters and the drum height decision rule base. Next, a fuzzy rough radial basis function neural network determined by the decision rule space is proposed. By introducing the fuzzy rough membership function as the connection weight, the network can accurately describe the complex nonlinear relationship between the working characteristic parameters of the attribute shearer and the drum height, and measure the uncertainty of the coal seam distribution. Finally, to further optimize the performance of FRRBFNN, the adaptive immune genetic algorithm is introduced to optimize its parameters, to build a high-precision shearer drum height prediction system. For the evaluation method of the model, we use three indicators: mean absolute error, mean absolute percentage error, and root mean square error. Based on the measured data in Yujialiang area, Shaanxi Province, the experimental results show that—compared with the FRRBFNN and support vector regression (SVR) models, a gated current neural network (GRU), a radial basis function neural network (RBF), the memory strengthen long short-term memory (MSLSTM) model, and the adaptive fuzzy reasoning Petri net (AFRPN)—the MAE of the AR-AIGA-FRBFNN model for predicting the height of the left and right rollers are 18.3 mm and 17.2 mm, respectively; the MAPE is 0.96% and 0.93%, respectively; and the RMSE is 21.2 mm and 22.4 mm, respectively. The AR-AIGA-FRBFNN model is therefore more effective than the other considered methods.

Arctic Sea Ice and Open Water Classification From Spaceborne Fully Polarimetric Synthetic Aperture Radar

We present an approach for automatic classification of Arctic sea ice and open water from spaceborne C-band RADARSAT-2 fully polarimetric (HH, HV, VH, VV) synthetic aperture radar (SAR) imagery, based on the random forest (RF) model. The HH- and HV-polarized radar backscatter, incidence angle and optimal polarimetric features are inputs of the RF model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , we use a physics-based unsupervised scheme to generate well-annotated sea ice and open water samples. This scheme can alleviate labeling errors arising from visual interpretation or temporal-spatial mismatch between SAR images and operational ice charts. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Subsequently</i> , we estimate a set of characteristic polarimetric parameters and select the most representative features, using the recursive feature elimination method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Finally</i> , the RF model is trained and validated using more than one million labelled samples. Statistical validation results show that the overall sea ice and water classification accuracy is 99.94% and the Kappa coefficient is 0.999. We find that ice–water discrimination accuracy can be improved by about 4%–10%, when optimal polarimetric features are involved in the RF model input. Moreover, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">polarization difference</i> (PD, VV–HH) is found to be the foremost polarimetric parameter for distinguishing sea ice from open water. The proposed approach has capability of yielding satisfactory ice–water classification over the complicated marginal ice zone. High-resolution ice–water classification maps clearly show that sea ice leads and their surroundings are also well separated.

КОНЦЕПТУАЛЬНІ ЗАСАДИ ФОРМУВАННЯ ЦИРКУЛЯРНИХ ЦИКЛІВ У АГРОПРОДОВОЛЬЧИХ ЕКОНОМІЧНИХ СИСТЕМАХ

The article aims to supplement the methodological principles of the implementation of circular principles in the practical activity of economic entities based on the detailing of the cycles of a closed economic system and the definition of their key characteristics. The research results showed that activating the development of circular processes in the national economy is an objective requirement of the present and future. Based on the systematization of scientific literature, the author’s vision of the main concepts of circular economy development was proposed in the article. Under modern conditions, the concept of a "green" economy and global sustainable development was defined as the most common approach to working out the concepts and mechanisms of implementation into the practice of the circular economy principles. The closed cycle economy should be considered a tool for achieving the goals and objectives of the sustainable development of the national economy and the agro-food production sector. The system of cycles of a closed economy (cycles of closure, cycles of slowing down, and narrowing) was justified based on research on the features and principles of circular economy. According to each cycle of the closed-type economic system, the set of characteristic parameters of the circular economic system is presented here. The main sections of characteristic features of closed cycles were selected in the article: specific features, possible results, and promising priority drivers. Based on the study of various approaches and interpretations of the essence and content of the "circular economy" category, its author’s interpretation is presented. The strategic priorities of the further development of the circular economy, enshrined in the legislative initiatives of the EU countries, are investigated. A brief analysis of the existing regulatory and legal support for forming and stimulating the development of a closed economy in Ukraine has been carried out. The substantiation of the mechanisms and tools for the activation of the spread of the principles of circularity in the field of agro-food production in Ukraine forms the plane of further scientific research and development.

Comic Language Means in the English-Language Comic Books

The article is devoted to the study of language means used to create a pragmatic effect of humour and the comic in English-language comic books. The issue is considered in the context of studying the comic book not only as a creolised text and a special literary tradition, but also as a separate type of artistic discourse with a set of characteristic functional-linguistic parameters. Understanding the cognitive-discursive mechanism of humour in a graphic work is of particular interest in terms of optimising the creative and professional interaction between the artist and the author of the narrative text, who often act as a collective producer of modern comic book discourse. The empirical material of the study comprises examples of comic situations from various issues of comic books by Marvel and DC pub- lishing houses related to different topics, formats and time periods. Observation, quantitative analysis, the use of discursive means of the comic, linguistic-pragmatic analysis of humorous situations and generalisation of the obtained statistical groups and their interaction are used as methods of the research. In the article, the author proposes the definition of a comic book as a type of text and a separate type of artistic discourse, as well as distinguishes it from purely humorous types of graphic literature. The study differentiates between the terms “device” and “means” in the context of humorous pragmatics, offers a classification of comic means, as well as gives a brief description of them. In this article, an attempt is made to identify stylistic and functional-pragmatic features of the use of a number of means of creating a comic effect in the semantic structure of an English comic book. As a result of the study, the author formulates conclusions about the influence of the graphic component on the manifestation of the means of the comic, intertextual orientation and the interconnectedness of these means, as well as the functional load of humour in the English comic book.