Methodology for testing assets for impairment during the coronavirus pandemic

Accounting for the impairment of assets is one of the difficult issues in the preparation of financial statements. However, despite the considerable attention of domestic and foreign accounting science to tangible and intangible assets, the methodological apparatus for analyzing the procedure of asset impairment remains insufficiently developed. Issues that take into account the specifics of the development of the economic environment for the functioning of companies, the state and degree of the accounting and financial reporting system have not been worked out, which requires a comprehensive study of methodological issues of checking assets for impairment. Assessing whether an asset has decreased in value can be highly subjective and impairment can appear as a failure of directors, prompting management to underestimate the impairment loss. An impairment loss could have a material effect on a company's financial statements if the assets are overvalued. The main difficulties lie in recognizing when it is necessary to conduct impairment tests, applying the value in use and determining the cash-generating unit (CGU). In some cases, the application of the standard may be difficult, and therefore companies may inadvertently include distorted data in the reporting. The article discusses the application of the rules for determining indicators of impairment during the coronavirus pandemic. Particular attention is paid to the consideration of the principles and procedures of IFRS IAS 36, which apply to the impairment of assets in the form of a right of use. Based on the results of this study, several recommendations have been compiled for accountants who need to conduct an impairment test.

Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0

When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely and leave the position errors mainly unchanged. In this paper, a grid-distorted data assimilation, which consists of an image-morphing method and an ensemble-based variational assimilation, is designed for realigning a simulated dust plume to correct the position error. This newly developed grid-distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation configuration that focuses solely on intensity correction, a grid-distorted data assimilation that focuses on position correction only and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion only slightly improves the dust simulation, while the grid-distorted data assimilation effectively improves the dust simulation and forecasting. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecasting of dust concentrations.

INVESTIGATION OF PARAMETERS OF THE PROBLEM OF SPLINE APPROXIMATION OF NOISY DATA BY NUMERICAL METHODS OF OPTIMAL CONTROL

Currently, the problems of distortion of measurement data by noise and the appearance of un-certainties in quality criteria have caused increased interest in research in the field of spline approx-imation. At the same time, existing methods of minimizing empirical risk, assuming that the noise is a uniform distribution with heavier tails than Gaussian, limit the scope of application of these studies. The problem of estimating noise-distorted data is usually based on solving an optimi-zation problem with a function containing uncertainty arising from the problem of finding optimal parameters. In this regard, the estimation of distorted noise cannot be solved by classical methods. Aim. This study is aimed at solving and analyzing the problem of spline approximation of data under uncertainty conditions based on the parametrization of control and the gradient projec-tion algorithm. Methods. The study of the problem of spline approximation of noisy data is carried out by the method of approximation of the piecewise constant control function. In this case, para-metrization of the control is possible only for a finite number of break points of the first kind. In the framework of the experimental study, the gradient projection algorithm is used for the numerical solution of the spline approximation problem. The proposed methods are used to study the parameters of the problem of spline approximation of data under conditions of uncertain-ty. Results. The numerical study of the control parametrization approach and the gradient projec-tion algorithm is based on the developed software and algorithmic tool for solving the problem of the spline approximation model under uncertainty. To evaluate the noise-distorted data, numerical experiments were conducted to study the model parameters and it was found that increasing the value of the parameter α leads to an increase in accuracy, but a loss of smoothness. In addition, the analysis showed that the considered distribution laws did not change the accuracy and convergence rate of the algorithm. Conclusion. The proposed approach for solving the problem of spline approx-imation under uncertainty conditions allows us to determine the problems of distortion of measure-ment data by noise and the appearance of uncertainties in the quality criteria. The study of the model parameters showed that the constructed system is stable to the error of the initial approxima-tion, and the distribution laws do not significantly affect the accuracy and convergence of the gra-dient projection method.

Neural network training for vehicle fuel level control

The goal of the paper is to create a training model based on real raw noisy data and train a neural network to determine the behavior of the fuel level, namely, to determine the time and volume of vehicle refueling, fuel consumption / excessive consumption / drainage.Various algorithms and data processing methods are used in fuel control and metering systems to get rid of noise. In some systems, primary filtering is used by excluding readings that are out of range, sharp jumps and deviations, and averaging over a sliding window. Research is being carried out on the use of more complex filters than simple averaging – by example, the Kalman filter for data processing.When measuring the fuel level using various fuel level sensor the data is influenced by many external factors that can interfere with the measurement and distort the real fuel level. Since these interferences are random and have a different structure, it is very difficult to completely remove them using classical noise suppression algorithms. Therefore, we use artificial intelligence, namely a neural network, to find patterns, detect noise and correct distorted data. To correct distorted data, you first need to determine which data is distorted, classify the data.In the course of the work, the raw data on the fuel level were transformed for use in the neural network training model. To describe the behavior of the fuel level, we use 4 possible classes: fuel consumption is observed, the vehicle is refueled, the fuel level does not change (the vehicle is idle), the data is distorted by noise. Also, in the process of work, additional tools of the DeepLearning4 library were used to load data training and training a neural network. A multilayer neural network model is used, namely a three-layer neural network, as well as used various training parameters provided by the DeepLearning4j library, which were obtained because of experiments.After training the neural network was used on test data, because of which the Confusion Matrix and Evaluation Metrics were obtained.In conclusion, finding a good model takes a lot of ideas and a lot of experimentation, also need to correctly process and transform the raw data to get the correct data for training. So far, a neural network has been trained to determine the state of the fuel level at a point in time and classify the behavior into four main labels (classes). Although we have not reduced the error in determining the behavior of the fuel level to zero, we have saved the states of the neural network, and in the future we will be able to retrain and evolve our neural network to obtain better results.

ONLINE FUZZY CLUSTERING OF INCOMPLETE DATA USING CREDIBILISTIC APPROACH AND SIMILARITY MEASURE OF SPECIAL TYPE

Context. In most clustering (classification without a teacher) tasks associated with real data processing, the initial information is usually distorted by abnormal outliers (noise) and gaps. It is clear that “classical” methods of artificial intelligence (both batch and online) are ineffective in this situation.The goal of the paper is to propose the procedure of fuzzy clustering of incomplete data using credibilistic approach and similarity measure of special type. Objective. The goal of the work is credibilistic fuzzy clustering of distorted data, using of credibility theory. Method. The procedure of fuzzy clustering of incomplete data using credibilistic approach and similarity measure of special type based on the use of both robust goal functions of a special type and similarity measures, insensitive to outliers and designed to work both in batch and its recurrent online version designed to solve Data Stream Mining problems when data are fed to processing sequentially in real time. Results. The introduced methods are simple in numerical implementation and are free from the drawbacks inherent in traditional methods of probabilistic and possibilistic fuzzy clustering data distorted by abnormal outliers (noise) and gaps. Conclusions. The conducted experiments have confirmed the effectiveness of proposed methods of credibilistic fuzzy clustering of distorted data operability and allow recommending it for use in practice for solving the problems of automatic clusterization of distorted data. The proposed method is intended for use in hybrid systems of computational intelligence and, above all, in the problems of learning artificial neural networks, neuro-fuzzy systems, as well as in the problems of clustering and classification.

Position correction in dust storm forecast using LOTOS-EUROS v2.1: grid distorted data assimilation v1.0

When calibrating simulations of dust clouds, both the intensity and the position are important. Intensity errors arise mainly from uncertain emission and sedimentation strengths, while position errors are attributed either to imperfect emission timing, or to uncertainties in the transport. Though many studies have been conducted on the calibration or correction of dust simulations, most of these focus on intensity solely, and leave the position errors mainly unchanged. In this paper, a grid distorted data assimilation, which consists of an imaging morphing method and an ensemble-based variational assimilation, is designed for re-aligning a simulated dust plume to correct the position error. This new developed grid distorted data assimilation has been applied to a dust storm event in May 2017 over East Asia. Results have been compared for three configurations: a traditional assimilation that focuses solely on intensity correction, a grid distorted data assimilation that focuses on position correction only, and the hybrid assimilation that combines these two. For the evaluated case, the position misfit in the simulations is shown to be dominant in the results. The traditional emission inversion improves only slightly the dust simulation, while the grid distorted data assimilation effectively improves the dust simulation and forecast. The hybrid assimilation that corrects both position and intensity of the dust load provides the best initial condition for forecast of dust concentrations.