Control Channel Research Articles

Observer-Based Active Control Strategy for Networked Switched Systems against Two-Channel Asynchronous DoS Attacks

This paper addresses the security issue of networked switched systems under two-channel asynchronous denial-of-service (DoS) attacks, where the measurement channel and the control channel are subject to DoS attacks independently. For the case of partial-state measurements, an observer-based active control strategy is proposed to mitigate the negative impact on the control performance and stability of the system caused by the attacks. In this strategy, a novel mode-dependent finite-time observer is designed to estimate the system state rapidly and accurately, the predictor and the buffer are designed to ensure that the control signals transmitted to the actuator can be updated even when the control channel is blocked. Compared to the earlier results on the active control strategy that only consider the case of full-state measurements and assume that the DoS signals followed specific patterns, our work only limits the frequency and duration of the DoS signals, which is more general and challenging. Furthermore, the switching signal is designed to ensure the input-to-state stability (ISS) of the networked switched system with the active control strategy under two-channel asynchronous DoS attacks and asynchronous switching behaviors. Finally, the effectiveness and the merits of our work are validated through an example and a comparative experiment.

Trade Facilitation at the Peru–Chile Land Border: Policy Impact of Digital Importation and Prearrival Declarations

This policy report examines the impact of the Peruvian Foreign Trade Public Policy implemented at the Santa Rosa Centre of Compliance (CAFSR) at the land border between Peru and Chile, presenting original research and quantitative analysis of statistics from the CAFSR at the Peruvian border collected by the National Superintendency of Customs and Tax Administration (SUNAT) from 2019 to 2022. The results show that customs compliance controls have been expedited, simplified, and modernised by both the digital importation process and mandatory prearrival customs declarations. However, the analysis calls for two further risk assessment strategies to be adopted by customs administrations in both countries. First, applying additional filters to identify fraud in prearrival customs declarations could expedite the release of low-risk consignments and help to ensure higher-risk consignments are subject to additional border restrictions. This paper suggests implementing an innovative blockchain technology that allows for the timely and accurate sharing of encrypted customs declarations to administrations in Peru and Chile. Second, upgrading infrastructure and logistics at the CAFSR could increase the capacity of the border post to facilitate increased binational trade.
 
 As a result, expediting the flow of goods and reducing time and costs facilitate trade. To maintain and enhance the advances, evaluating the infrastructure, logistics, and the automatic assignation of control channels is necessary. The flow of goods increased at CAFSR, even though the percentage per type of control (physical, documentary, or free) remains steady. Hence, the evaluation and adoption of this paper's recommendations are necessary as they also include the Smart Borders Project, announced to be executed up to 2024, aimed to automatize customs control and make it less intrusive and more intelligent through the extensive use of technology, risk assessment and data mining.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’ trajectory tracking guidance problem under wind field and external disturbances in the boost phase. Indeed, the velocity control in such trajectory tracking guidance systems of missiles is challenging. As our contribution, the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy. The global prescribed performance function, which guarantees the tracking error within the set range and the global convergence of the tracking guidance system, is first proposed based on the traditional PPF. Then, a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities. Meanwhile, an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem. A back propagation neural network (BPNN) extended state observer (BPNNESO) is employed in the inner loop to identify disturbances. The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors, convergence times, and overshoots.

DEFINITION OF MARKOV NETWORKS FROM THE POSITION OF THE THEORIES OF CATEGORIES AND N-CATEGORIES

The study develops new provisions of the theory of Markov algorithms. The area of use of the N-scheme of the Markov algorithm, developed to replace its wellknown y-scheme, is being expanded. The study solves the scientific problem of developing the Markov algorithm in the direction of processing not only words, but also morphisms. Such a problem has not been posed or solved before in the theory of Markov algorithms. Morphisms that differ from each other by types, where some are numeric and others are categorical, are proposed to be processed together. In the study of methods for processing morphisms in the Markov algorithm, it is proposed to use the wellknown in science and practice idea of weighing numerical morphisms. The very idea of weighting morphisms is considered more broadly as a task for the synthesis of a Markov network from separate components, such as an N-schema Markov algorithm for a single data source and a Markov system for many sources of big data. Markov networks are introduced into scientific circulation using the methodology adopted for artificial neural networks. This approach allows us to reduce the time for comprehending the main theoretical provisions of Markov networks, in comparison with artificial neural networks. The same technique is used to classify diagrams of Markov occurrences of words in each other, which are the basis for the synthesis of Markov networks. Approaches to weighting the values of morphisms in Markov networks do not fundamentally differ from those used in artificial neural networks. Diagrams of Markov occurrences are considered from category-theoretic positions, where words are assumed to be associated categories, otherwise, n-categories. For Markov networks, theoretical considerations are given on the activation of their elements by a secret morphism. The use of category-theoretic positions in Markov networks allows us to say that these networks are also activated by all associators used in the N-scheme of the Markov algorithm, as well as by the control channel, which is also an associator. At the same time, associators are considered as n-category objects, which distinguishes Markov networks from artificial neural networks. An N-scheme of the Markov algorithm and a Markov system with weight coefficients and thresholds for weighting the values of morphisms are synthesized. It is concluded that Markov networks provide joint processing of words and morphisms, and it is in the network that additional methods of processing morphisms are implemented that were not previously taken into account in the theory of Markov algorithms.

Top-down control of exogenous attentional selection is mediated by beta coherence in prefrontal cortex

Salience-driven exogenous and goal-driven endogenous attentional selection are two distinct forms of attention that guide selection of task-irrelevant and task-relevant targets in primates. Top-down attentional control mechanisms enable selection of the task-relevant target by limiting the influence of sensory information. Although the lateral prefrontal cortex (LPFC) is known to mediate top-down control, the neuronal mechanisms of top-down control of attentional selection are poorly understood. Here, we trained two rhesus monkeys on a two-target, free-choice luminance-reward selection task. We demonstrate that visual-movement (VM) neurons and nonvisual neurons or movement neurons encode exogenous and endogenous selection. We then show that coherent beta activity selectively modulates mechanisms of exogenous selection specifically during conflict and consequently may support top-down control. These results reveal the VM-neuron-specific network mechanisms of attentional selection and suggest a functional role for beta-frequency coherent neural dynamics in the modulation of sensory communication channels for the top-down control of attentional selection.

The engagement of autophagy in maniac disease.

Mania is a prevalent psychiatric disorder with undefined pathological mechanism. Here, we reviewed current knowledge indicating the potential involvement of autophagy dysregulation in mania and further discussed whether targeting autophagy could be a promising strategy for mania therapy. Accumulating evidence indicated the involvement of autophagy in the pathology of mania. One of the most well-accepted mechanisms underlying mania, circadian dysregulation, showed mutual interaction with autophagy dysfunction. In addition, several first-line drugs for mania therapy were found to regulate neuronal autophagy. Besides, deficiencies in mitochondrial quality control, neurotransmission, and ion channel, which showed causal links to mania, were intimately associated with autophagy dysfunction. Although more efforts should be made to either identify the key pathology of mania, the current evidence supported that autophagy dysregulation may act as a possible mechanism involved in the onset of mania-like symptoms. It is therefore a potential strategy to treat manic disorder by correting autophagy.

Design of an Open Channel for Flood and Gully Erosion Control: A Case Study of Amakohia-Uratta in Imo State, Nigeria

Flooding and gully erosion constitute a major problem associated with urbanization in the underlying watershed of an area. In this study, a trapezoidal channel hydraulic system was designed for flood and erosion control in Amakohia-Uratta, Owerri North, Imo state, Nigeria. A topographical survey of the study area was done. The result shows that the maximum elevation is 81.03 m, the minimum elevation is 78.21 m, the bed slope of the area is 0.02%, the area of the watershed is 1.061ha, and the maximum length of flow is 140m. Also, the results from the hydrological analysis using the rational method indicated that the rainfall intensity of 10 years duration was 105.3 mm/h with a peak discharge Qp of 0.17 m3/s, causing the flood and erosion, while the channel design capacity (discharge) Qd is 0.17 m3/s, calculated using trial and error methods. Finally, design parameters for the trapezoidal open channel were calculated. The result indicated that the channel width is 0.3 m, the design depth is 0.5 m, the freeboard is 0.1m, the total channel depth is 0.6m, the top width of the channel is 2.1 m and the side slope is 1.5:1. In all the design is appropriate to capture, carry and discharge the peak runoff to Nworie River a natural outlet.

Event-triggered robust distributed output feedback model predictive control for nonlinear MASs against false data injection attacks

In this paper, an event-triggered distributed output feedback model predictive control scheme for the nonlinear disturbed multiagent systems with sensor–controller channel false data injection attacks is proposed. To provide valid system states to the controller in the event of cyber attacks, a robust multivariate observer is designed to realize the estimation and separation of uncompromised system states, false data injection attacks, and measurement disturbances, simultaneously. Based on these reconstructed signals and a newly-designed linear robustness constraint, the distributed predictive controller is established to achieve smooth cooperative stabilization among agents. Meanwhile, an event-triggered mechanism is applied to save computing resources, and it restricts the error of predictive states and estimated states to guarantee the feasibility of the optimization control problem. Theoretical analyses on robustness and security for the nonlinear multiagent systems under event-triggered distributed output feedback model predictive control are presented. Finally, a simulation on two pairs of one-link flexible joint manipulator systems verifies the theoretical results.

Novel aminated F-Ce nanosheet mixed matrix membranes with controllable channels for CO2 capture

Membrane separation is a novel technology that has been widely studied for efficient CO2 capture. This study aims to synthesize novel porous amino-functionalized nanosheets using PEI (polyethyleneimine) (PEI-F-Ce) and PEI-F-Ce/polyethylene oxide (PEO) mixed matrix membranes (MMMs). PEI-F-Ce nanosheets were prepared via the electrostatic self-assembly method, allowing for the adjustment of both lateral and pore sizes of the nanosheets by varying the PEI concentration. The MMMs loaded with 2 % PEI-F-Ce-2.5 nanosheets exhibit excellent CO2 permeability (641 Barrer) and outstanding CO2/N2 selectivity (70.1). These values surpassed those of pure PEO membranes by 62 % and 53 %, respectively, exceeding the 2019 upper bound. PEI-F-Ce-2.5 nanosheets with a stable pore channel 3.406 nm improved CO2 transport and showed a large CO2 adsorption capacity of 1.32 mmol g−1, leading to enhanced the CO2/N2 reaction selectivity. Moreover, the reversible reaction of CO2 with –NH2 enhanced the CO2/N2 selectivity by 5.1 times at −20 °C, thereby highlighting its potential for cryogenic gas separation applications.

Study on channel prediction in IRS-assisted wireless communication systems

The use of intelligent reflecting surface (IRS) is being investigated for wireless communication in high frequency bands. By appropriately controlling the reflection coefficient of each IRS element, high quality communication can be achieved even in non-line-of-sight environment. The phase control of the IRS requires the determination of the optimal phase pattern based on the estimated channel, which is then fed back to the IRS through the control channel. To reduce channel estimation overhead, we propose a channel prediction method based on Gaussian process regression. We evaluate the proposed method performance on the points of signal-to-noise ratio (SNR) and confirm that the proposed approach can mitigate SNR degradation caused by terminal movement compared to the system without prediction.

Game-Theoretic Approach Based on Zone-based Routing Protocol (GTZRP) in Mobile Communication Networks

Background: A mobile communication network (MCN) consists of mobile and selfsupporting nodes in a network that communicates over wireless links in the network. These mobile nodes within communication range will communicate directly between them, while other nodes in the network need to support neighboring nodes through a routing protocol (EEZRP). Methods: The Routing Protocol (EEZRP) is used to reduce network topology routing. However, it leads to higher energy consumption. Since mobile nodes and self-supporting nodes have different communication ranges, there is no centralized system to manage the energy usage of mobile networks, and this high energy consumption in EEZR limits the successful data transfer rate of mobile networks over wireless links. Thus, this proposed work is a game-theoretic approach based on the zone-based routing protocol. Results: GTZRP for parallel conflict-driven broadcasts takes into account both energy consumption and a second channel for flow control and complex congestion that improve data transfer. Conclusion: Finally, simulation results show that the proposed GTZRP outperforms other routing protocols.

Data-Based Feedback Relearning Control for Uncertain Nonlinear Systems With Actuator Faults.

In this article, a data-based feedback relearning (FR) algorithm is developed for the uncertain nonlinear systems with control channel disturbances and actuator faults. Uncertain problems will influence the accuracy of collected data episodes, and in turn affect the convergence and optimality of the data-based reinforcement learning (RL) algorithm. The proposed FR algorithm can update the strategy online by relearning from the empirical data. The strategy can continuously approach the optimal solution, which improves the convergence and optimality of the algorithm. Moreover, based on the experience replay technology, a data processing method is designed to further improve the data utilization efficiency and the algorithm convergence. A neural network (NN)-based fault observer is used to achieve the model-free fault compensation. The polynomial activation function is redesigned by using the sigmoid function/hyperbolic tangent activation function, to reduce the difficulty of NNs design for an unknown nonlinear system and improve the generalization. In the face of disturbances and actuator faults, the control performance, algorithm convergence, and optimality of the proposed strategy can be well guaranteed through comparative simulation.

АНАЛІЗ МОЖЛИВОСТЕЙ ПРОТИДІЇ БЕЗПІЛОТНИМ ЛІТАЛЬНИМ АПАРАТАМ

This scientific work examines the main approaches to the modern classification of unmanned aerial vehicles. It was noted that in the zone of full-scale invasion in the east of Ukraine, Russian troops and private military companies of Russia use them to solve strategic, operational and tactical tasks. It was noted that with the development of modern technologies, it became possible to manufacture unmanned aerial vehicles with smaller dimensions, but with better capabilities for conducting constant reconnaissance, delivering massive surprise strikes using barrage ammunition, which made it much more difficult to fight them with air defense means and forces. All this is because traditional types of air defense weapons are designed to destroy large and distant targets, while modern drones consist of nano-, micro- and mini-devices flying at low and super low altitudes. Various means of destroying unmanned aerial vehicles were considered, a comparative analysis of modern countermeasures against unmanned aerial vehicles was carried out, and conclusions were drawn regarding the possibility of their comprehensive use in the Armed Forces of Ukraine. In this paper, an analysis of the possibilities of reducing the effectiveness of the use of unmanned aerial vehicles in the hybrid war waged by Russia in Ukraine today was carried out, the trends of the combat use of unmanned aerial vehicles in solving combat tasks in modern combat were revealed. This article also considers the possibilities of countering unmanned aerial vehicles that are controlled by an operator through control channels that are controlled automatically and with combined control. As a result of the conducted research, the possibilities for their destruction by various means of fire destruction were determined. Keywords: unmanned aerial vehicles, analysis, hybrid warfare, countermeasures, classification, trends, effectiveness, combat tasks, operator, control channels, research.

MBC-SS: A multi-band cooperative sidelink scheme for NR V2X networks

Vehicle-to-everything (V2X) sidelink (SL) communication at mmWave frequency is envisioned to satisfy the exchange of massive sensor data among vehicles. However, directional deafness due to inherent highly directional transmissions results in transmission collisions. Moreover, the high mobility of vehicles incurs significant beam training overhead for beam alignment and tracking. These issues cause performance degradation and pose challenges to SL scheme design. In this paper, we propose a multi-band cooperative SL scheme (MBC-SS) with the concurrent support of resource scheduling and beamforming, which offloads partial mmWave control signals to sub-6 GHz control channels and exploits spatial correlations between multiple frequencies. To enable such multi-band cooperation, centralized and distributed modes are developed for in-coverage and out-of-coverage deployments, respectively, whose advantages are twofold. First, centralized scheduling or receiver-side directional sensing in certain directions is adopted to avoid potential collisions during resource selection phase. Second, we take into account the spatial congruence and temporal dependence of channels to adaptively design training beams and select alignment beams. In addition, the performances of the proposed protocol are investigated theoretically and verified by simulations. Compared with the existing sub-6 GHz assisted scheme, the proposed distributed MBC-SS has an improvement of transmission reliability by 15% and throughput by 12.5% in highly dense scenarios. Meanwhile, lower delay and training overhead are achieved.

Laponite nanoclay-modified sacrificial composite ink for perfusable channel creation via embedded 3D printing

Vascularization is a major hurdle in developing in vitro thick tissue constructs. Fortunately, embedded sacrificial printing has emerged as a promising technology to fabricate perfusable channels. The objective of this study is to develop a Laponite nanoclay-modified polyvinyl alcohol (PVA) sacrificial composite ink that has improved printability, minimized interdiffusivity, and satisfactory post-printing removability during embedded printing of perfusable channels. With the addition of Laponite nanoclay, not only the printability of the PVA-Laponite nanoclay composite ink is significantly improved, but also the interdiffusion between the gelatin precursor solution and the PVA-Laponite nanoclay composite ink is largely mitigated. The diffusion of the gelatin precursor solution into the PVA-Laponite nanoclay composite ink is of great importance for printing controllable perfusable channels since the diffused gelatin may cross-link as the gelatin composite matrix does during and after printing, resulting in residual gelatin gel in the channel and altered channel dimensions. In particular, the PVA-2.0% Laponite nanoclay sacrificial ink shows good printability and minimized interdiffusivity while maintaining good post-printing removability, which can be a good sacrificial composite ink for thick perfusable tissue creations. The channel diameter reduction due to the gelatin solution diffusion can be satisfactorily predicted based on the unidirectional diffusion model. This study further demonstrates the feasibility of using the proposed sacrificial ink for vascular tissue engineering applications.

Enable the proactively load-balanced control plane for SDN via intelligent switch-to-controller selection strategy

The novelty of the software-defined network(SDN) is to separate the control plane and the data plane for easier manipulation of the network. The distributed control plane is designed to achieve more powerful computation capacity and address the single-point failure problem. However, it also poses a new challenge that how to arrange switch-controller associations effectively. The direct static configuration cannot adapt the time-varying requests from switches well, and it would result in imbalance problems on the control plane and cause long-tail latency. Thus, it is necessary to take proper actions to adjust the switch-to-controller association dynamically. Existing controller-based load balancing methods need to communicate with the switches frequently and incur not only high assumptions of the rare control channel of SDN but also high computation costs. In this paper, we provide a switch-based solution that puts Reinforcement Learning agents on all Switches (RLoS). Instead of setting static rules predefined by operators, RLoS makes each switch actively select the best controller. RLoS treats every switch as an independent agent with its own neural network and parameters. With the carefully designed training algorithm, the agents could choose their preferable controllers via their local information. The results show that even with partial observation, the RLoS still can achieve considerable improvement in the load balance among all controllers compared with those controller-based association benchmarks. Our RLoS could decrease the maximum response latency among controllers by about 5%∼15% under different scenarios on average.

Synthesis of load moment observerasynchronous electric drive with vector control

Purpose. The energy characteristics of asynchronous electric drives in steady-state operation are functions of three variables: the angular speed rotation of the rotor, the module of the vector flux coupling of the rotor and the moment load. The moment of load is an external disturbing influence, effective methods of its measurement are not available. Therefore, the purpose of this paper is to mathematically describe the observer of the state variables of the active power channel and the load moment on the shaft of an asynchronous electric drive with vector control. Methodology. To achieve the purpose of the work, the method of synthesis modal observers is applied using the field-oriented model of the asynchronous machine with the decomposition of its equations and taking into account the nonlinearities of cross-connections between the control channels. Results. As part of the relay-vector control system, a modal observer of the load moment is synthesized, the information from the output of which can be used to build algorithms for extreme control of an asynchronous electric drive. The asymptotic stability is proved by the method of mathematical modeling and is sufficient the accuracy of the observer of the load moment at different distributions of the roots its characteristic equation. Scientific novelty. Thepaper solves the actual scientific problem of establishing the regularities of the structural-algorithmic synthesis and tuning of the modal observer of the moment of load on the shaft of an asynchronous electric drive. Identification of the moment load on the shaft of an electric machine will allow to improve the quality indicators of extreme control systems of asynchronous electric drives. Practical significance. The obtained equations of the observer determine the load moment identification algorithm, the information about which can be used in two-channel vector control systems for asynchronous electric drives to build an external extreme control circuit, which improves the energy performance of the asynchronous machine. First of all, we are talking about minimizing the loss of active power or the consumption of reactive power an asynchronous machine when the load moment on its shaft changes.

Extending 5G to Narrow Spectrum Allocations

5G New Radio (NR) has been a great success story since its introduction. It offers high reliability, low communication latency, flexibility, and efficiency that further enable 5G networks to be used for mission critical applications. Currently, there are specialized networks serving industry verticals that would benefit from the NR properties. Key use cases include communications for critical infrastructure such as railways, public safety, and smart energy and infrastructure. These networks often use dedicated spectrum allocations that are narrower than the original minimum operating bandwidth of NR. In this paper, we describe how new flexibility is being introduced into the NR design to allow operation with bandwidths between about 3 and 5 MHz. As a use case study, we focus on railway communications, and study the co-existence of the Global System for Mobile Communications – Railway (GSM-R) and Future Railway Mobile Communication System (FRMCS) based on NR during the decade-long migration period. We discuss the changes required for the NR control channels and signaling and study the effect of the changes on the system performance.

Performance analysis in overlay-based cognitive D2D communications in 5G networks

With the advent of fifth-generation 5G networks, mobile communication data rates are steadily rising. The existing base stations (BSs) must reduce the loads with minimal cost and effort. With traffic demands constantly rising, available spectrum resources must be utilized effectively. One of the most exciting developments in 5G networks, particularly cellular networks, is the potential for devices to communicate directly with one another through a technology called device-to-device communication (D2D). With the transmission split between D2D users on one side and cellular users and the BS on the other, it will be effective in the rapidly expanding capacity and offload traffic from the BS. With the dynamic spectrum access in mind, this article proposes using cognitive radio direct-to-device (CRD2D) communication in 5G networks to make use of the unused spectrum. Throughput and latency are examined, for a carrier sense multiple access (CSMA) based medium access control (MAC) protocol that uses a common control channel (CCC) for device users to negotiate the data channel and to address contention among device users. Device users can take advantage of cognitive radio to access multiple data channels simultaneously in a shared interference zone. Since this property has not been utilized, in prior works, we use it to measure the possible delay and throughput. Cognitive radio capability applied to D2D communication and the allocation of a common control channel for device users significantly increases aggregated network throughput by more than 60% compared to individual D2D throughput without causing any harmful interference to cellular users. The wait time can also be scale back using this method.

Photonic Gateway and Protocol-Independent End-to-End Optical-Connection Provisioning in All-Photonic Metro-Access Converged Network

The All-Photonics Metro-Access Converged Network (APN) aims to achieve a flat and simple architecture that even covers access areas and represents the evolution of optical transport. Electrical processing functions such as exchange, multiplexing, and switching are uniformly placed at the border of the network segments, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> access, metro, and core networks, in current optical networks. These functions are placed in Data Centric Infrastructure (DCI) and accessed only when needed in the APN. By flexibly providing direct optical connections between user premises and/or DCI according to requests from users and services, the APN creates new types of use cases requiring end-to-end guaranteed wide-bandwidth and/or extremely low latency and jitter along with an information-computing platform. To provide optical paths originating from user terminals that launch client signals using protocols preferable to their applications, we propose a configuration for a novel optical edge node, the Photonic Gateway (GW), and a procedure based on remote control of user terminals to establish end-to-end optical paths that are independent of the client signal protocol. To verify these, we present results of a proof-of-concept experimental demonstration of establishing end-to-end optical paths. An auxiliary management control channel is employed in the protocol-independent access control method. We also describe future challenges facing the Photonic GW that must be addressed.