AUTOMATIC CONTROL AND COMPUTER SCIENCES最新文献

A new type of dependency in databases, which is a generalization of inclusion dependencies, is considered. Traditionally, such dependencies are used in practice to ensure referential integrity. In this case, the constraint is established only between a pair of relations, the first of which is called the main relation, and the second one is the external relation. In practice, referential integrity often needs to be established for a larger number of relations, where several main relations and several external relations participate in the same constraint. This structure corresponds to an ultragraph. Generalized inclusion dependencies that take into account the presence of null values in external relations are justified. The system of axioms is obtained based on a study of the properties of typed dependencies. The consistency (soundness) and completeness of the system are proved.

This article presents a functional graph model of a network with adaptive topology, where the network nodes represent the graph vertices, and data exchange between the nodes is represented as edges. The dynamic nature of network interaction complicates the solution of the problem of monitoring and controlling the operation of a network with adaptive topology, which should be done in order to ensure a guaranteed correct network interaction. The importance of solving such a problem is justified by the creation of modern information and cyber-physical systems, which are based on networks with adaptive topology. The dynamic nature of links between nodes, on the one hand, makes it possible to provide self-regulation of the network and, on the other hand, significantly complicates the control over the network operation, because it is impossible to identify a single pattern of network interaction. On the basis of the developed model of the network with adaptive topology, a graph algorithm for link prediction is proposed, which is extended to the case of peer-to-peer networks. The algorithm is based on the significant parameters of network nodes characterizing both their physical characteristics (signal level and battery charge) and their characteristics as objects of network interaction (characteristics of the centrality of graph nodes). The correctness and adequacy of the developed algorithm is confirmed by the experimental results on modeling a peer-to-peer network with adaptive topology and its self-regulation when different nodes are removed.

Sole sufficient operators are of particular interest among functionally complete sets of Boolean functions. They have a wide range of applicability and are not limited to the binarity case. In this paper, we formulate conditions imposed on the Zhegalkin polynomial coefficients that are necessary and sufficient for the polynomial to correspond to a sole sufficient operator. The polynomial representation of constant-preserving Boolean functions is considered. It is shown that the properties of monotonicity and linearity do not need to be specifically considered when describing a sole sufficient operator. The concept of a dual remainder polynomial is introduced; the value of it allows one to determine the self-duality of a Boolean function. It is proven that a Boolean function preserving 0 and 1 or preserving neither 0 nor 1 is self-dual if and only if the dual remainder of the corresponding Zhegalkin polynomial is 0 for any sets of values of the function variables. The system of leading coefficients is obtained based on this fact. The solution of the system makes it possible to formulate a criterion for the self-duality of a Boolean function represented by a Zhegalkin polynomial, which imposes necessary and sufficient conditions on the polynomial coefficients. Thus, it is shown that Zhegalkin polynomials are a rather convenient tool for studying precomplete classes of Boolean functions.

Software development is often related to expanding functionality. To improve reliability in this case, it is necessary to minimize the change in the previously written code. For instrumental support of the evolutionary development of programs, a procedural-parametric programming paradigm is proposed, which makes it possible to increase the capabilities of the procedural approach. This allows extending both data and functions effortlessly. This paper considers the inclusion of procedural-parametric programming in the C language. Additional syntactic constructions are proposed to support the proposed approach. These constructions include: parametric generalizations, specializations of generalizations, generalizing functions, and specialization handlers. Their semantics, possibilities, and features of technical implementation are described. To check the possibilities of using this approach, models of procedural-parametric constructions in the C programming language are built. The example in this article demonstrates the flexible extension of the program and support of multiple polymorphism.

Mixed Boolean-arithmetic expressions (MBA expressions) with t integer n-bit variables are often used for program obfuscations. Obfuscation consists of replacing short expressions with longer equivalent expressions that seem to take the analyst more time to explore. This paper shows that to simplify linear MBA expressions (reduce the number of terms), a technique similar to the technique of decoding linear codes by information sets can be applied. Based on this technique, algorithms for simplifying linear MBA expressions are constructed: an algorithm for finding an expression of minimum length and an algorithm for reducing the length of an expression. Based on the length reduction algorithm, an algorithm is constructed that allows us to estimate the resistance of an MBA expression to simplification. We experimentally estimate the dependence of the average number of terms in a linear MBA expression returned by simplification algorithms on n, the number of decoding iterations, and the power of the set of Boolean functions, by which a linear combination with a minimum number of nonzero coefficients is sought. The results of the experiments for all considered t and n show that if before obfuscation the linear MBA expression contained r = 1, 2, 3 terms, then the developed simplification algorithms with a probability close to one allow using the obfuscated version of this expression find an equivalent one with no more than r terms. This is the main difference between the information set decoding technique and the well-known techniques for simplifying linear MBA expressions, where the goal is to reduce the number of terms to no more than 2^t. We also found that for randomly generated linear MBA expressions with increasing n, the average number of terms in the returned expression tends to 2^t and does not differ from the average number of terms in the linear expression returned by known simplification algorithms. The results obtained, in particular, make it possible to determine t and n for which the number of terms in the simplified linear MBA expression on average will not be less than the given one.

The authors propose an approach to generate keywords for Russian-language scientific texts using the mT5 (multilingual text-to-text transformer) model, fine-tuned on the Keyphrases CS&Math Russian text corpus. Automatic keyword selection is an urgent task in natural language processing, since keywords help readers search for articles and facilitate the systematization of scientific texts. In this paper, the task of selecting keywords is considered as a task of automatic text abstracting. Additional training of mT5 is carried out on the texts of abstracts of Russian-language scientific articles. The input and output data are abstracts and comma-separated lists of keywords, respectively. The results obtained using mT5 are compared with the results of several basic methods: TopicRank, YAKE!, RuTermExtract, and KeyBERT. The following metrics are used to present the results: F‑measure, ROUGE-1, and BERTScore. The best results on the test sample are obtained using mT5 and RuTermExtract. The highest F-measure is demonstrated by the mT5 model (11.24%), surpassing RuTermExtract by 0.22%. RuTermExtract shows the best result according to the ROUGE-1 metric (15.12%). The best results for BERTScore are also achieved by these two methods: mT5, 76.89% (BERTScore using the mBERT model); RuTermExtract, 75.8% (BERTScore based on ruSciBERT). The authors also assess the ability of mT5 to generate keywords that are not in the source text. The limitations of the proposed approach include the need to form a training sample for additional model training and probably the limited applicability of the additional trained model for texts in other subject areas. The advantages of keyword generation using mT5 are the absence of the need to set fixed values for the length and number of keywords, the need for normalization, which is especially important for inflected languages, and the ability to generate keywords that are not explicitly present in the text.

The motion control or trajectory tracking is a fundamental and benchmark problem while controlling the manipulator robots to move along a predefined trajectory. Extensive research in the literature has addressed the motion control problem for robotic manipulators. To address this challenge effectively, it becomes imperative to design suitable controllers and make adequate choices regarding the appropriate feedback gain values. In this study, we consider and investigate two PD-based nonlinear control laws for controlling the motion of manipulator robots, namely the PD-plus-compensation controller and the augmented one. We establish conditions for determining the feedback gains of these two controllers, ensuring tracking error stability. Moreover, conditions guaranteeing the uniqueness of the zero error of the closed-loop robotic system are developed. Finally, we consider a two-degree-of-freedom manipulator robot to reveal the effectiveness of the two proposed controllers in tracking a desired trajectory and hence evaluating their performance in addressing the motion control problem.

Efficient signal modulation schemes are crucial for optimizing communication performance in low Earth orbit (LEO) satellite systems. Traditionally, uniform modulation methods are applied across the satellite constellation, regardless of variations in satellite distances from ground stations. However, this approach leads to inefficient signal power utilization, particularly for satellites at higher altitudes. This paper proposes a novel solution to this issue by implementing dynamic modulation strategies tailored to specific altitude ranges of LEO satellites. Through dynamic modulation, we utilize binary phase-shift keying (BPSK) and quadrature phase-shift keying (QPSK) for satellites positioned farther from earth, while higher-order constellations are used for satellites in closer proximity. This altitude-dependent modulation scheme aims to improve power efficiency throughout the LEO satellite network. Our results demonstrate a significant enhancement in power efficiency across the LEO satellite system compared to the conventional uniform modulation scheme. The adaptability of this dynamic modulation approach to satellite altitude variations allows for more judicious utilization of signal power, thereby maximizing communication quality and network reliability. We validate the effectiveness of the proposed method through extensive simulations.

To address the issues of small target leakage, misdetection, and poor detection effect in helmet-wearing target detection, a small target helmet-wearing detection based on YOLOv7 is designed. Firstly, the space-to-depth (SPD) block is introduced to replace the convolution of the first down-sampling, and all subsequent Max Pooling (MP) structures are replaced with SPD structures, which can better capture the information of different depths in the graphs, and improve the performance of detecting small target helmets. Secondly, the use of ContentAware ReAssembly of Features (CARAFE) for the up-sampling section increases the receptive field, adapts to different types of feature maps, and improves the feature fusion capability; Then, SPPFCSPC is obtained by changing the connection method of SPPCSPC, and the spatial pyramid pooling module GhostSPPFCSPC is built by fusing a Ghost module and an SPPFCSPC module, in which the SPPCSPC module consists of two submodules, the spatial pyramid pooling (SPP) module and the connected spatial pyramid convolution (CSPC) module, which decreases the number of parameters and boosts the network performance in the meantime; Thirdly, the design uses SIoU to optimize the bounding box return loss function, which achieves faster convergence in the training phase, better performance in inference, and improved model accuracy and robustness. To demonstrate that the improved algorithm has better results than the original model with mAP boosted by 5.8%, accuracy boosted by 0.8%, parameter count decreased by 7.08%, and FPS up to 65 f/s.