Programming and Computer Software最新文献

Abstract

One of the open problems in modern control theory is synthesis of adaptive control for nonlinear systems with parametric uncertainty and analysis of stability of the corresponding closed-loop system. Fuzzy logic is one of the approaches that can take into account nonlinearity and uncertainty of the plant. Affine systems constitute a class of nonlinear systems often used to solve various practical problems. For this class, there are a number of methods for synthesis of controllers, in particular, a method based on the matrix Riccati equation with state-dependent coefficients. In this paper, for a given class of nonlinear systems, an adaptation mechanism of a neuro-fuzzy controller that approximates the control synthesized by the SDRE method is used for the first time. The main contribution of this work is the architecture of the neuro-fuzzy network and methods for its initialization. The proposed approach is applied to the model of a two-link manipulator with uncertain coefficients. The conducted numerical experiments demonstrate the effectiveness of the synthesized control in terms of the selected quality criteria.

Abstract

There is a wide range of fuzzing techniques for security assurance. Communication protocols and programs accepting complex structured data, including compilers, can be efficiently tested with a corresponding fuzzing approach. The main advantage of the fuzzing is actual data, which demonstrates the defects of the target software. The performance of fuzzing primarily depends on the quality of generated data. Existing fuzzing tools provide a fixed set of data generation algorithms, which doesn’t consider the specifics of the target program. There are several scenarios when the performance of the fuzzing may suffer. The target may execute different parts of the code based on a comparison of the offsets of the input data with some constant value. For such cases, random mutations will invalidate input data and lead to lower code coverage.

In this paper, we present a new approach for effective input data generation. We use static analysis to extract information regarding the input data offsets’ comparisons with constant values. Then we use this information during input data generation. Experimental evaluation of the developed method on several projects proves the efficiency of the developed method. Our approach allowed up to 91% increase in the number of executed paths for the same execution count.

Abstract

Various neuron approximations can be used to reduce the computational complexity of neural networks. One such approximation based on summation and maximum operations is a bipolar morphological neuron. This paper presents an improved structure of the bipolar morphological neuron that enhances its computational efficiency and a new approach to training based on continuous approximations of the maximum and knowledge distillation. Experiments were carried out on the MNIST dataset using a LeNet-like neural network architecture and on the CIFAR10 dataset using a ResNet-22 model architecture. The proposed training method achieves 99.45% classification accuracy on the LeNet-like model (the same accuracy as that provided by the classical network) and 86.69% accuracy on the ResNet-22 model compared with 86.43% accuracy of the classical model. The results show that the proposed method with log-sum-exp (LSE) approximation of the maximum and layer-by-layer knowledge distillation makes it possible to obtain a simplified bipolar morphological network that is not inferior to the classical networks.

Abstract

An approach for generating an optimal rule base of a fuzzy system is proposed, that relies on ellipsoidal clustering of observable data. Premises of fuzzy rules are formed by constructing projections of ellipsoids onto the coordinate axes while conclusions are formed either using ellipsoid axes or also based on projections. The idea of optimization is in using ellipsoids of the minimal volume that includes all points of the cluster. A comparative analysis of various ways to choose optimal parameters for ellipsoids covering the clusters is performed. The root-mean-square error is used to estimate the approximation accuracy of the resulting fuzzy system.

Abstract

This paper discusses the problem of classifying images of land impressions on discharged bullets in terms of the “match” and “non-match” categories. The research is aimed at improving the effectiveness of comparing land impression images by the congruent matching profile segments (CMPS) method. The scientific novelty of the approach is in supplementing the analysis with an additional independent feature, as well as in using the k-nearest neighbors algorithm at the final stage of trace comparison. The research shows that the accuracy of classification of the compared pairs of land impression images by the combined method is approximately 87%. The analysis by the CMPS method makes it possible to effectively compare land impression images with high resolution (approximately 1 μm per pixel). The research is of interest to developers of automated ballistic identification systems.

Abstract

The problem of vulnerability of artificial intelligence technologies based on neural networks is considered. It is shown that the use of neural networks generates a lot of vulnerabilities. Examples of such vulnerabilities are demonstrated, such as incorrect classification of images containing adversarial noise or patches, failure of recognition systems in the presence of special patterns in the image, including those applied to objects in the real world, training data poisoning, etc. Based on the analysis, the need to improve the security of artificial intelligence technologies is shown, and some considerations that contribute to this improvement are discussed.

Abstract

National Research and Education Networks (NRENs) are pivotal in modern research and education, providing a dynamic ecosystem for academic and scientific communities. These networks form the backbone for crucial infrastructures and services, propelling research, education, and innovation. This article explores the role of monitoring and advanced emulation in enhancing NREN performance. The experiences of ASNET-AM, the Armenian NREN, in leveraging these techniques are presented, highlighting the benefits and challenges.

Abstract

The article considers a single-channel non-Poisson queuing model GI|G|1|∞ with a FIFO “first-in-first-out” service discipline in stationary conditions and system load intensity less than one. One of the important concepts of the mathematical theory of reliability is the property of increasing the hazard rate (IHR) of homogeneous elements forming reliability systems, otherwise called the aging property. This problem is also of importent for the queuing theory. Two independent in the aggregate and independent of each other sequences: sequence of the waiting times before the start of servicing of actual calls and sequence of the waiting times of virtual calls, starting at time t, or, more precisely, sequence of the durations of the time intervals starting at time t and ending at the moment when the system is free from calls received into the system until time t, are considered. Using the properties of ladder points and ladder heights, as well as applying formulas of Takac’s, Cohen’s and Hook’s, it proved that the widely used in the theory of random walks two above-mentioned sequences of random variables are IHR.

Abstract

Prediction of earthquakes has long been one of the unsolved problems in seismology. The significance of this issue for the local population cannot be overstated. The entire republic is located within a high seismic risk zone. According to a recent study, strong earthquakes in this territory have a close relationship to planetary cycles. Using histograms of seismic event distribution, these connections are revealed for diurnal and lunar periods. To investigate the link, a method and tool for spatial and temporal monitoring of these processes was developed, integrating GIS data for active tectonic faults, seismic events, and planetary positions of the Sun and Moon. According to the study, most of the strongest earthquakes in the region occur during full or new moon periods. The nature of this phenomenon, however, is unclear. A study of the chemical composition of underground water during the Spitak 1988 earthquake reveals that it changes 4.5 months before the earthquake. The same thing happens before all the region’s strong earthquakes. This phenomenon has been named “geochemical quiescence,” and it has resulted in the discovery of stable earthquake precursors in the region. The time series of water chemical composition data obtained during the study shows that the mean of the series remains constant, but the standard deviation of the data changes. However, more sophisticated software is required to obtain a more accurate picture of seismic fault activation and its reliance on planetary cycles. More accurate solar system modeling, including the ability to switch between Geocentric and Heliocentric planetary systems, as well as visualization of tectonic fault position at any given time, will aid in understanding earthquake focal mechanism dependencies on planetary forces.

Abstract

Over time, the population and anthropogenic activities increase, adversely affecting our atmosphere. In particular, an increase in air temperature leads to a change in the number of various gases that fill the atmosphere, the growth and decrease of which can significantly affect the environment. Nitrogen dioxide (NO₂) is a common air pollutant from natural and human activities, with potential respiratory irritant effects at high levels. To effectively address these issues, there is a need for advanced tools and services that can analyze and visualize NO₂ air pollution data comprehensively. This article presents the development and implementation of a Data Analytics and Visualization Service tailored to Armenia and Belarus’s unique air quality dynamics. The service aims to provide actionable insights for policymakers, researchers, and the public. The studies show that NO₂ excesses in their troposphere for Armenia and Belarus. This increase is correlated with the rise in urban population, causing dramatic adverse effects on the atmosphere.