Schemes Of Networks Research Articles

Reinforcement learning-based pinning control for synchronization suppression in complex networks

Synchronization in complex networks is a ubiquitous and important phenomenon with implications in various fields. Excessive synchronization may lead to undesired consequences, making desynchronization techniques essential. Exploiting the Proximal Policy Optimization algorithm, this work studies reinforcement learning-based pinning control strategies for synchronization suppression in global coupling networks and two types of irregular coupling networks: the Watts-Strogatz small-world networks and the Barabási-Albert scale-free networks. We investigate the impact of the ratio of controlled nodes and the role of key nodes selected by the LeaderRank algorithm on the performance of synchronization suppression. Numerical results demonstrate the effectiveness of the reinforcement learning-based pinning control strategy in different coupling schemes of the complex networks, revealing a critical ratio of the pinned nodes and the superior performance of a newly proposed hybrid pinning strategy. The results provide valuable insights for suppressing and optimizing network synchronization behavior efficiently.

Analysis of the efficiency of block frame transmission in IEEE 802.11 computer networks

In infrastructure schemes of WLANs wireless computer networks, which are based on the use of the DSF (distributed coordination function) function and the CSMA/CA mechanism, the station STA (STAtion) sends a frame if the transmission channel is released after waiting for the end of the DIFS (distributed interframe space) interval and operation of the slot selec-tion mechanism for transmission (backoff mechanism). In case of collisions or damage to the frame by interference, the AP cannot decode the frame and does not send it back to the ACS station. The sending station STA waits for the re-ceipt of a potential ASK before the expiration of the corresponding timeout, and in case of receipt, starts the backoff procedure before transmission. The duration of the slot used in the backoff process depends on the speed of the physical layer technology. To reduce overhead, a transmission mechanism using frame blocks was proposed. This mechanism assumes that a block of frames intended for one recipient can be sent without con-firming the fact of correct reception by the AP access point of each frame separately. In this case, the sender (STA) competes for access to the channel for the first frame of the block. If it wins the access competition, the transmission of the first frame begins, and after receiving the ACK acknowledgment for it and a short SIFS separation interval, the STA transmits a whole block of frames, which is accompanied by a BAR service frame. The mechanism of frames block transmission in wireless computer networks IEEE 802.11 DCF with infrastructure topology has been analyzed. In protected mode, the depend-encies of network throughput on data transmission rate and the number of frames in a block are determined. It is shown that the mechanism of block transmission significantly increases the network throughput, especially in the range of higher transmission rates. With a marked increase in the intensity of interference the transmission of blocks becomes more complicated. Already with BER=10^-6 and the frames number of the order of 10 and more, in some cases there is a need to retransmit distorted in a block frames, and at BER=10^-5 there are many re-transmissions, so the resulting network throughput becomes small.

Algorithms for Сonstructing Matrixes of Routes of Pipeline Networks by Using the Method of Graph Theory

Использование средств информационных технологий и уровня автоматизации процессов управления значительно повышают роль технического прогресса и решают поставленные задачи Республикой Узбекистан по обеспечению целевым продуктом населению. Вместе с тем возможности современных компьютерных систем и развитие математических методов моделирования используются далеко не полностью из-за неформализации многих задач, а также несовершенства или отсутствия ряда алгоритмов, обеспечивающих возможность произвести анализ функционирования объекта, обработать поступающую информацию и принять соответствующие решения по оптимальному управлению многосвязных систем. В связи с этим разработка и исследование компьютерных моделей, вычислительных алгоритмов и на их основе создание объектно-ориентированных программных средств в этом направлении представляется актуальной проблемой дальнейшего развития автоматизации научных исследований для различных предметных областей, в частности трубопроводных систем. Всякая реальная энергетическая сеть, в том числе и газовая, по своей топологической структуре эквивалентна некоторому графу. Провести расчет и анализ несогласованности исходной информации сетей с более одним кольцом затруднительно. В этих случаях необходимо обратиться к законам Кирхгофа и соответствующим замыкающим соотношениям. Тем не менее, топология сети отличается разнообразием, чему свидетельствуют, в частности, схемы городских сетей теплоснабжения. В таких условиях основная тяжесть гидравлического или экономического расчета должна концентрироваться на стадии формирования топологии сети, для чего используются теории графов. В данной статье рассматриваются алгоритмы построения и оптимизации функционирования газопроводных многоконтурных сетей методом теории графов и программное обеспечение для оптимального управления, анализа и синтеза трубопроводных систем. Таким образом, разработанные вычислительные алгоритмы и компьютерная программа расчета можно применять для оптимизации функционирования газопроводных сетей, когда сеть имеет сложную лучистую и многокольцевую структуру. The use of information technology tools and the level of automation of management processes significantly increase the role of technical progress and solve the tasks set by the Republic of Uzbekistan to provide the population with the target product. At the same time, the capabilities of modern computer systems and the development of mathematical modeling methods are far from being fully used due to the informalization of many tasks, as well as the imperfection or absence of a number of algorithms that make it possible to analyze the functioning of an object, process incoming information and make appropriate decisions on the optimal control of multiply connected systems. . In this regard, the development and research of computer models, computational algorithms and, on their basis, the creation of object-oriented software in this direction seems to be an urgent problem for the further development of automation of scientific research for various subject areas, in particular pipeline systems. Any real energy network, including a gas network, is equivalent in its topological structure to a certain graph. It is difficult to calculate and analyze the inconsistency of the initial information of networks with more than one ring. In these cases, it is necessary to turn to Kirchhoff’s laws and the corresponding closing relations. However, the network topology is diverse, as evidenced, in particular, by the schemes of urban heating networks. In such conditions, the main burden of hydraulic or economic calculation should be concentrated on the stage of network topology formation, for which graph theories are used. This article discusses algorithms for constructing and optimizing the functioning of gas pipeline multiloop networks using the graph theory method and software for optimal control, analysis and synthesis of pipeline systems. Thus, the developed computational algorithms and a computer calculation program can be used to optimize the operation of gas pipeline networks when the network has a complex radiant and multi-ring structure.

Network planning of metro-based underground logistics system against mixed uncertainties: A multi-objective cooperative co-evolutionary optimization approach

Featured with zero-carbon, jam-free, and high-capacity, the utilization of metro systems for collaborative passenger-and-freight transport (i.e., the metro-based underground logistics system, M-ULS) has been recognized as a favorable alternative to realize automated goods movement in future megacities. This paper proposes an equilibrium chance-constrained programming (ECP) approach for the M-ULS network planning problem (MULNP) considering uncertain demand and costs. Based on the ongoing satellite city development project, the layout formation, facility components, and transportation workflows of a two-tier M-ULS network are designed. The joint decisions (i.e., location, allocation, capacity, inventory, spoke pipeline layout, and flow routing) are formulated as an ECP model with conjoint objectives of minimal fixed cost, operating cost, and penalty cost due to low operating load, wherein the chance-based constraints are further reformulated into crisp equivalent form. To yield high-quality solutions for MULNP problem, an improved multi-objective cooperative co-evolutionary algorithm (MoCC) incorporating non-dominated sorting and chromosomal recombination strategy is proposed and then validated via two groups of instances. Results from the Beijing case study suggest that the proposed method has computational advantages over MoPSO and NSGA-II in acquiring Pareto fronts of large-scale MULNP. The best configurations and layout schemes of the M-ULS networks in both uncertain and deterministic scenarios are analyzed. Based on the findings, several insights for real-world M-ULS network decision-making are provided.

Strong convergence and almost sure exponential stability of balanced numerical approximations to stochastic delay Hopfield neural networks

This paper deals with the balanced numerical schemes of the stochastic delay Hopfield neural networks. The balanced methods have a strong convergence rate of at least 12 and the balanced schemes have almost sure exponential stability under certain conditions. Under the Lipchitz and linear growth conditions, the balanced Euler methods are proved to have a strong convergence of order 12 in mean-square sense. Using the Lipchitz conditions on the various parameters of the model, based on the semimartingale convergence theorem and some reasonable assumptions, the balanced Euler methods of the stochastic delay Hopfield neural networks are proved to be almost sure exponentially stable. Numerical experiments are provided to illustrate the theoretical results which are derived in this paper. The computational efficiency of the balanced methods is demonstrated by numerical tests and compared to the Euler–Maruyama approximation scheme of the stochastic delay Hopfield neural networks. Furthermore, the obtained numerical results show that the balanced numerical methods of stochastic delay Hopfield neural networks are very efficient with the least error and have the best step size region for almost sure mean square stable.

Development of technologies for selecting energy-efficient power supply circuits of railway traction networks

The object of the study is the process of operation of traction and external power supply systems as objects of inextricable interconnection while reducing energy costs in the cost of railway transportation in real time. One of the most problematic areas is the technology for choosing energy-efficient power supply schemes for railway traction networks in real time. The methods of forming and transforming graphs of complex schemes of traction and external power supply systems and building expert control systems for the implementation of energy-saving technologies of electrified railways were further developed in the work. In the course of the study, to increase the efficiency of simulation modeling of electric traction networks, the statistical characteristics of the loads of feeders that supply the final boundary sections, stations, depot access tracks, railway junctions and idle voltages on the traction substation tires were obtained. Methods of calculation and modeling of traction power supply have been developed, which take into account the inseparable relationship with power systems and allow choosing rational modes with minimal power flows and energy losses. Proposed methods of managing the modes of operation of the traction power supply system based on a vague description of their states and an expert system that allow solving new problems. Including, the choice of energy-saving power supply schemes in the case of power flows, economic modes of network operation in case of intensification of the transportation process. Thanks to this, ways of reducing power consumption and minimizing energy losses of traction power supply systems are proposed, which allow minimizing power flows and power losses by adjusting load flow parameters and voltage levels of traction substations. And also to increase the energy efficiency of electrified railway lines. The technique of technical and economic feasibility of power supply schemes of traction networks and evaluation of the possibility of switching to cantilever or loop power supply schemes with parallel connection points has been developed. The implemented recommendations save about 25 thousand kWh per 1 km of two-track section.

Alternative feeder concepts in urban distribution networks

The paper considers various basic feeder schemes of urban distribution networks from the standpoint of economy, reliability, and technical limits. Presented are the mathematical models for the analysis of feeders’ annual costs, reliability performances, voltage drops and cable loading in normal and critical emergency situations. The suggested approach takes into account the uncertainty of various basic data by modeling them as grey inputs, which can have any value within the intervals of values experienced in practice. It was implied that adequate statistical data for the construction of probability distribution functions for uncertain parameters are missing, which is often the case. The proposed mathematical model could serve as a useful tool in planning new or expanding existing distribution networks. As a numerical illustration, the presented calculation methods have been applied for the analysis of the performances of the considered feeder schemes in the Belgrade area.

Energy-efficient routing in the proximity of a complicated hole in wireless sensor networks

A quest for geographic routing schemes of wireless sensor networks when sensor nodes are deployed in areas with obstacles has resulted in numerous ingenious proposals and techniques. However, there is a lack of solutions for complicated cases wherein the source or the sink nodes are located close to a specific hole, especially in cavern-like regions of large complex-shaped holes. In this paper, we propose a geographic routing scheme to deal with the existence of complicated-shape holes in an effective manner. Our proposed routing scheme achieves routes around holes with the (1+epsilon)-stretch. Experimental results show that our routing scheme yields the highest load balancing and the most extended network lifetime compared to other well-known routing algorithms as well.

Integrated Energy and Trust-Based Semi-Markov Prediction for Lifetime Maximization in Wireless Sensor Networks

Many of today’s computing and communication models are distributed systems that are composed of autonomous computational entities that communicate with each other, usually by passing messages. Distributed systems encompass a variety of applications and wireless sensor networks (WSN) is an important application of it. The tiny, multiple functionality and low power sensor nodes are considered to be interconnected in the WSN for efficient process of aggregating and transmitting the data to the base station. The clustering-based schemes of sensor networks are capable of organizing the network through the utilization of a specifically designated node termed as the cluster head for the objective of energy conservation and data aggregation. Further, the cluster head is responsible for conveying potential information collected by the cluster member nodes and aggregate them before transmitting it to the base station. In this paper, a Reliable Cluster Head Selection Technique using Integrated Energy and Trust-based Semi-Markov Prediction (RCHST-IETSMP) is proposed with the view to extend the lifetime of sensor networks. This proposed RCHST-IETSMP incorporated two significant parameters associated with energy and trust for effective selection of cluster head facilitated through the merits of Semi-Markoc prediction integrated with the Hyper Erlang distribution process. The simulation results of the proposed RCHST-IETSMP scheme is proving to be efficient in upholding the residual energy of the network and the throughput to a maximum level of 23% and 19% predominant to the trust and energy-based clustering schemes considered for investigation.

Analysis of Circuit Solutions for the Modernization of Distribution Networks for Housing and Communal Services

The article presents the results of the analysis of the main stages of the development of electric networks for housing and communal services (HCS) since the 50s of the last century and ending with the latest innovative developments, which are both at the stage of theoretical research and experimental tests in pilot projects implemented in existing networks. It is noted that the traditional 10(6)/0.4 kV structure with a dead-earth neutral on the low-voltage side, which is used both in industrial and utility networks, has disadvantages associated with increased power losses, as well as the need to use special technical means to ensure the required quality of electricity. It is shown that with the average value of the reverse sequence current coefficient equal to 0.3, the relative value of additional power losses in the low-voltage line from the presence of current in the neutral wire can reach 36% of the total amount of additional losses. The review and analysis of the four most promising areas of modern research, innovative developments and experiments on the structural optimization of utility networks is given: a network with an increased voltage of 6(10; 35) kV and a reduced voltage of 0.4 kV with an isolated neutral; a network with an increased voltage of 6 (10; 35) kV and a reduced voltage of 0.4 kV with an isolated neutral; 0,95/0,4 /0,23 A network with two levels of high voltage of 1.14 (0.66) kV and a reduced line voltage of 0.23 kV with a grounded neutral of transformers; Fundamentally new methods of transmitting electricity over a single wire at alternating and direct current of increased voltage are described. The necessity of choosing the most rational integrated circuit solution for the structure and schemes of utility networks, which will reduce the loss of electricity in the networks, improve the quality of electricity and electrical safety, is justified.

Flash Memory Array for Efficient Implementation of Deep Neural Networks

The advancement of artificial intelligence applications is promoted by developing deep neural networks (DNNs) with increasing sizes and putting forward higher computing power requirements of the processing devices. However, due to the process scaling of complementary metal–oxide–semiconductor technology approaches to the end and the bottleneck of data transmission in the von‐Neumann architecture, traditional processing devices are increasingly challenging to meet the requirements of deeper and deeper neural networks. In‐memory computing based on nonvolatile memories has emerged as one of the most promising solutions to overcome the bottleneck of data transmission in the von‐Neumann architecture. Herein, systematic implementation of the novel flash memory array‐based in‐memory computing paradigm for DNNs from the device level to the architecture level is presented. The methodology to construct multiplication‐and‐accumulation units with different structures, hardware implementation schemes of various neural networks, and the discussion of reliability are included. The results show the hardware implementations of the flash memory array‐based in‐memory computing paradigm for DNN own excellent characteristics such as low‐cost, high computing flexibility, and high robustness. With these advantages, in‐memory computing paradigms based on flash memory arrays show significant benefits to achieve high scalability and DNNs’ energy efficiency.

Habitat use and movements of a large dragonfly (Odonata: Anax imperator) in a pond network

Abstract Local movements of aquatic insects within the surroundings of waterbodies aim essentially to find food, mates, resting sites, or avoid predation. Distances moved are very variable among species and may also differ depending on sex or age at the intraspecific scale. Despite a large panel of studies on odonate activities near waterbodies, little is known about their movements and behaviour in the surrounding landscape matrix. This knowledge is, however, crucial to support management schemes of pond networks and allow improvement of connectivity between them. In this study, 87 individuals of the large dragonfly Anax imperator were identified with a unique code on the wings and monitored visually on five ponds during summers 2017 and 2018. Simultaneously, 54 individuals were equipped with radio transmitters and tracked for up to 15 days. We built Cormack Jolly Seber models to test which factors are important for movement and survival of individuals, and calculated home ranges. Additionally, we performed a step selection analysis on the telemetry data to identify the terrestrial habitats selected by A. imperator at the interface between a rural area and a suburban area in north‐western France. A reduction of the survival rate was observed on the day immediately following capture regardless of the marking method. Individuals equipped with radio transmitters had a lower estimated daily survival (0.78; 95% confidence interval = 0.70–0.85) compared to wing‐marked individuals (0.89; 95% confidence interval = 0.85–0.92). Wing loading and age were the main variables influencing dragonfly survival for both methods. The probability of movement between ponds was similar for both sexes, but radio‐tracking data showed that females moved significantly further away in the landscape matrix than males, with one female detected up to 1,902 m away from the release pond. Females also had a larger home range (mean 95% kernel: 50 ha) than males (mean 95% kernel: 5 ha). Reproductive behaviour of males and flying behaviour of females were positively related to air temperature. Individuals of both sexes were present on ponds more often than in all other habitats whatever their activity. High trees were the preferred place to rest when air temperatures were low, especially for females. Overall, this study highlights the importance of integrating neighbouring trees in management schemes of ponds. It also confirms that preservation of pond networks must include on several hectares of land around ponds to meet the aquatic and terrestrial needs of amphibious organisms such as odonates. Our radio‐tracking data provide a basis for further studies on the persistence of odonate meta‐populations in fragmented landscapes.

Basin systems of small rivers of Western Podillya: state, change tendencies, perspectives of nature management and nature protection optimization

The level of anthropogenization of the natural processes and the geocomponents of the basin geosystems of small rivers in Western Podillya has been estimated and rated as ecologically dangerous from the viewpoint of sustainable and conflict free functioning. The scales of the transformation of the components of natural environment by economic activities since 1774 were revealed using the method of comparative-geographic analysis of cartographic sources. The scales of deforestation were determined, as well as the scales of the influences of drainage meliorations on wetlands, river floodplains and riverbed complexes. It has been established that such transformations of the state of the components of landscape systems have caused the manifestation of a set of unfavorable processes and phenomena (lowering of ground water level, promoting desiccation, soil erosion and deflation, soil dehumification, decreasing landscape and biological diversity, etc.). Calculated indices of the anthropogenic modification of natural components testify that the strongest adversary impacts on river systems and basin landscapes are caused by agriculture, deforestation, and drainage meliorations. Our analysis of the current state of reclaimed lands in the basins of the rivers Dzhuryn and Nichlava confirmed the conclusions of B. I Kozlovsky on the effects of drainage reclamation on groundwater in drained lands and of the formation within them and around drainage systems of negative hydrogeological zones of different widths. In the absence of precipitation for 30-45 consecutive days in summer there is a sharp decrease in groundwater levels, and overdrying of soils, which causes the manifestation and intensification of deflation, shallowing and even drying of the upper reaches of rivers and streams. At the final stage of the study, a system of measures aimed at ensuring the sustainability of river basin geosystems was substantiated. The introduction of an optimization model of land use in the basin geosystem is one of the priority tasks in the context of negative changes in the water regime of watercourses and the water balance of river basin systems. Optimization measures provide for the transformation of the part of degraded and unproductive lands towards the grasslands and the planting of gardens (slopes up to 7°) and afforestation (surface steepness over 7°) to improve the quality of environment and to form the environmentally secure land use system. Regional indices of anthropogenic transformation for the existing and proposed land structure as a normative regional indices of nature utilization optimality are calculated. Substantiation of schemes of basin nature protection networks was based on taking into account the role of protected areas in maintaining certain functional features at the sources, in the middle and lower parts of river basins. Based on the results of field surveys, it is proposed to create nine protected areas within the Dzhuryn Basin and eight protected areas within the Nichlava river basin, which will increase the share of protected areas of the Dzhuryn basin to 8% (compared to present 4.8%) and Nichlava to 19%. At the same time, it is proposed to change the structure of the nature reserve fund of the Nichlava river basin, taking into account the existing high share (77%) of general zoological reserves, inefficient from the standpoint of conservation of natural complexes, instead creating six landscape reserves on an area of about 800 hectares. The paper considers the possibility of further development of the tourist and recreational sphere in the near-Dnister sections of the river basins of Dzhuryn and Nichlava, and proposes the creation of Borshchiv Regional landscape park in the picturesque valley of the Nichlava River.

Intuitive Estimation of Speed using Motion and Monocular Depth Information

Advances in deep learning make monocular vision approaches attractive for the autonomous driving domain. This work investigates a method for estimating the speed of the ego-vehicle using state-of-the-art deep neural network based optical flow and single-view depth prediction models. Adopting a straightforward intuitive approach and approximating a single scale factor, several application schemes of the deep networks are evaluated and meaningful conclusions are formulated, such as: combining depth information with optical flow improves speed estimation accuracy as opposed to using optical flow alone; the quality of the deep neural network results influences speed estimation performance; using the depth and optical flow data from smaller crops of wide images degrades performance. With these observations in mind, a RMSE of less than 1 m/s for ego-speed estimation was achieved on the KITTI benchmark using monocular images as input. Limitations and possible future directions are discussed as well.

ANALYSIS OF THE ELECTRICITY METERING SYSTEM FOR OWN ELECTRIC SUBSTATION NEEDS

In the article the researches of the use of electronic meters with standard nominal parameters for metering of substation own electric energy in the schemes of high and low voltage networks simultaneously with the use of the corresponding calculated coefficients on voltage for the purpose of operational control and determination of the reliability of measurement of electricity parameters.

Key pre-distribution scheme based on transversal design in large mobile fog networks with multi-clouds

Assuring the security of the communication channel is a significant issue in the fog networks. Designing an efficient and secure key management scheme with a common key among nodes is a critical factor. The available key distribution schemes of hierarchical networks are not scalable and impose high communication and computation overhead to networks. These schemes are impractical on limited resources’ environments like the internet of things (IoT) networks. In this paper, a hierarchical key pre-distribution scheme based on the transversal design and residual theorem (TD-R) is proposed for large mobile fog networks with multi-clouds. The objective of this paper to reveal that TD-R is the key pre-distribution scheme for establishing secure intra and inter-fog nodes’ communication channels and intra and inter-end nodes with moderate network connectivity. The obtained results indicate that the TD-R scheme increases network scalability in comparison with available schemes, while it decreases memory consumption and node storage overhead.

Selection of a Complex of Parameters of Distribution Electric Networks with Respect to Technical Limitations

The article contains an optimization analysis of the regularities of the formation of the parameters of urban distribution networks of 10 kV, taking into account the technical limitations by the method of criteria analysis, which revealed the feasibility of using a limited number of used cable cross-sections, depending on the density of the electrical load and on the schemes of urban distribution networks.

An Online Charging Scheme for Wireless Rechargeable Sensor Networks Based on a Radical Basis Function

The node energy consumption rate is not dynamically estimated in the online charging schemes of most wireless rechargeable sensor networks, and the charging response of the charging-needed node is fairly poor, which results in nodes easily generating energy holes. Aiming at this problem, an energy hole avoidance online charging scheme (EHAOCS) based on a radical basis function (RBF) neural network, named RBF-EHAOCS, is proposed. The scheme uses the RBF neural network to predict the dynamic energy consumption rate during the charging process, estimates the optimal threshold value of the node charging request on this basis, and then determines the next charging node per the selected conditions: the minimum energy hole rate and the shortest charging latency time. The simulation results show that the proposed method has a lower node energy hole rate and smaller charging node charging latency than two other existing online charging schemes.

Model Predictive Control Design for Dynamical Systems Learned by Echo State Networks

The objective of this letter is to analyze the stability properties, the training procedure and the use in predictive control schemes of echo state networks (ESNs), a specific class of recurrent neural networks. First, a sufficient condition guaranteeing incremental input-to-state stability ( $\delta ISS$ ) of ESNs is derived. Then, an automatic procedure to optimally tune the parameters of the ESN in the training phase is presented, which allows to enforce $\delta ISS$ . Finally, the application of the ESN as a model of the plant for predictive control purposes is studied. In particular, an asymptotically convergent observer is designed, and a model predictive controller with guaranteed stabilizing properties is devised for the solution to regulation problems. Simulation results on a nonlinear process for $pH$ neutralization confirm the effectiveness of the proposed control scheme.

Neural Network Approach for Multiple-Class Classification

Classification is a very important problem-solving technique. Given a set of attribute values about an object, a classifier determines which category the object belongs to. A classification task can be a binary or multiple classification; it can also be a single-label or multiple-label classification. Neural networks are an effective technique in the field of artificial intelligence. In this paper, we develop different schemes of neural networks for solving binary, multiple, single-label, and multiple-label classification problems. The ideas behind them are described in detail.