Communication Delays Research Articles

Social support from different sources and its relationship with stress in spaceflight analog environments

While there is a large body of research on social support in traditional work settings, less is known about how the unique context of long-term isolation and confinement affects perceived social support. The purpose of our research was to examine how perceptions of social support change over time, how they differ by source (i.e., public, organization, family/friends/colleagues, and other crewmembers), and the relationship between social support and stress. We collected data from 64 crewmembers in five spaceflight analog campaigns with restricted communication with outside sources. Results suggested that perceived social support declined over time for all sources, with declines more pronounced for external sources (public, organization, family/friends/colleagues) than for perceived support from other crewmembers. While perceived overall social support was unrelated to stress over time, social support from crewmembers was related to the stress levels reported by crewmembers in the evening. Our results are important as they: (a) empirically document the decline in perceived social support over time in extended isolation; (b) provide evidence for the critical importance of the role of fellow crewmembers in being able to provide social support in conditions of extended isolation and communication delay; and (c) underscore the need to more fully understand the dynamics between the crew and external sources of social support (public, organization, family/friends/colleagues) as well as how those relationships may be best supported for individuals who live and work in long-term isolation and confinement.

Networked Predictive Trajectory Tracking Control for Underactuated USV with Time-Varying Delays

This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system.

Designing of a wide-area power system stabilizer using an exponential distribution optimizer and fuzzy controller considering time delays

In this paper, explore the effectiveness of a new Wide Area Fuzzy Power System Stabilizer (WAFPSS), optimized using the Exponential Distribution Optimization (EDO) algorithm, and applied to an IEEE three-area, six-machine power system model. This research primarily focuses on assessing the stabilizer’s capability to dampen inter-area oscillations, a critical challenge in power grid operations. Through extensive simulations, the study demonstrates how the WAFPSS enhances stability and reliability under a variety of operational conditions characterized by different communication delay patterns. The application of the proposed stabilizer on this specific IEEE model provides a detailed insight into its performance in real-world scenarios, illustrating its adaptability and effectiveness in managing dynamic disturbances. The simulation results reveal that the proposed WAFPSS achieves significant reductions in the Integral Time Squared Error (ITSE), with improvements of 94.1%, 97.02%, and 98.18% in three distinct cases, showcasing its superior damping capability and robustness. The findings indicate that the advanced optimization techniques provided by the EDO algorithm significantly improve the stabilizer’s response, ensuring robust power system performance. This integration of WAMS with sophisticated control systems using fuzzy logic presents a strategic solution to managing the complexities faced by modern power networks, optimizing their stability in the face of increasing renewable integration and fluctuating demand.

Simultaneous power, delay, and crosstalk noise reduction for the BUS encoding method of ternary LWC plus quaternary TS

ABSTRACT This paper deals with one of the potential applications of multiple-valued logic (MVL) for low-power data transmission by ternary limited-weight codes (LWCs) and quaternary transition signalling (TS). It aims for a reduction in power, delay, and crosstalk noise. Crosstalk effects cause serious problems such as long propagation delay and high power consumption for intermediate and global wires inside a chip. This paper conducts an analytical investigation into an optimal situation. In order to analyse the effects of crosstalk and delay, communication delay types are classified based on the quaternary TS. The classification is particularly given for quaternary TS to make the mathematical analysis possible. In addition, this paper considers the reduction of bus delay, dynamic power, and crosstalk noise at the same time. Our mathematical analyses reveal that a set of 16 5-digit ternary LWC codewords with the maximum weight of two is the optimal solution. The MVL bus encoding decreases the switching activity and the average voltage swing by 37.5% and 58.27%, respectively. Furthermore, the quaternary TS method can reduce the dynamic power by up to 89.12%compared to when binary-level signalling (LS) is used. Finally, the proposed model reduces the effects of crosstalk noise because it eliminates rail-to-rail transitions.

A Simple Single-Ended Post-Fault Location Technique for DC Lines Based on Controlled Re-Energizations

Fault location in medium-voltage direct current (MVDC) systems is an essential yet underexplored area compared to high-voltage (HVDC) and low-voltage (LVDC) systems. MVDC systems, characterized by intermediate line lengths and fault resistances, as well as rapid fault clearance requirements, demand specialized solutions. This paper proposes a novel single-ended, offline fault location method based on controlled re-energizations after fault clearance. This approach employs a switched grounding resistor and a bypass connection through the current-limiting inductor to extract fault parameters from the discharge curves of two re-energization cycles. By analyzing the time constants derived from these curves, the method estimates fault location and resistance with high accuracy. The proposed method eliminates the need for additional active injection sources and circuit breaker modifications, ensuring seamless integration into existing MVDC infrastructure. Furthermore, the method avoids inter-terminal communication delays and sampling delays before fault clearance. Validation through electromagnetic transient simulations demonstrates fault location errors below 5% for fault resistances up to 50 Ω. Results show that the method performs better for faults farther from the active terminal, with the higher errors seen for short distances and elevated resistances. The proposed technique offers a robust and practical solution for post-fault location in DC lines.

Neural Network Output-Feedback Distributed Formation Control for NMASs Under Communication Delays and Switching Network

Neural Network Output-Feedback Distributed Formation Control for NMASs Under Communication Delays and Switching Network

A recursive filtering approach to power harmonic detection with stochastic communication delays: Tackling amplify-and-forward relays

This paper focuses on the power harmonic detection problem in smart grids with relay transmissions and stochastic communication delays. A model is established using an orthogonal vector to capture the dynamics of harmonic signals with direct current attenuation components. For the assurance of reliable information delivery, a strategy employing an amplify-and-forward relay with stochastic transmission power is used to schedule data from sensors to the remote filter. Furthermore, a set of random variables that obey Bernoulli distributions is used to characterize the stochastic nature of the communication delays. With the goal of achieving accurate power harmonic detection, a recursive filtering algorithm is aimed to be designed in the presence of the amplify-and-forward relay strategy and stochastic communication delays, which ensures that the desired filter gain parameter is derived recursively by minimizing the upper bound on the filtering error covariance. Ultimately, the effectiveness of the proposed filtering algorithm is demonstrated through simulation experiments on power harmonic detection, thereby verifying its capability in tracking harmonic dynamics.

Observer-based controller design for class of nonlinear wireless stochastic networked systems with communication delays and denial of service jamming attacks: comparison of observer position

Observer-based controller design for class of nonlinear wireless stochastic networked systems with communication delays and denial of service jamming attacks: comparison of observer position

Consensus of Multi-Agent Systems Under Unbounded Communication Delays Via Adaptive Distributed Observers

Consensus of Multi-Agent Systems Under Unbounded Communication Delays Via Adaptive Distributed Observers

Periodic Event-Triggered Optimal Output Consensus of Heterogeneous Multiagent Systems Subject to Communication Delays

Periodic Event-Triggered Optimal Output Consensus of Heterogeneous Multiagent Systems Subject to Communication Delays

Predictive Forwarding Rule Caching for Latency Reduction in Dynamic SDN

In mission-critical environments such as industrial and military settings, the use of unmanned vehicles is on the rise. These scenarios typically involve a ground control system (GCS) and nodes such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). The GCS and nodes exchange different types of information, including control data that direct unmanned vehicle movements and sensor data that capture real-world environmental conditions. The GCS and nodes communicate wirelessly, leading to loss or delays in control and sensor data. Minimizing these issues is crucial to ensure nodes operate as intended over wireless links. In dynamic networks, distributed path calculation methods lead to increased network traffic, as each node independently exchanges control messages to discover new routes. This heightened traffic results in internal interference, causing communication delays and data loss. In contrast, software-defined networking (SDN) offers a centralized approach by calculating paths for all nodes from a single point, reducing network traffic. However, shifting from a distributed to a centralized approach with SDN does not inherently guarantee faster route creation. The speed of generating new routes remains independent of whether the approach is centralized, so SDN does not always lead to faster results. Therefore, a key challenge remains: determining how to create new routes as quickly as possible even within an SDN framework. This paper introduces a caching technique for forwarding rules based on predicted link states in SDN, which was named the CRIMSON (Cashing Routing Information in Mobile SDN Network) algorithm. The CRIMSON algorithm detects network link state changes caused by node mobility and caches new forwarding rules based on predicted topology changes. We validated that the CRIMSON algorithm consistently reduces end-to-end latency by an average of 88.96% and 59.49% compared to conventional reactive and proactive modes, respectively.

Application of artificial intelligence based on state grid ESG platform in clean energy scheduling optimization

The randomness and volatility of existing clean energy sources have increased the complexity of grid scheduling. To address this issue, this work proposes an artificial intelligence (AI) empowered method based on the Environmental, Social, and Governance (ESG) big data platform, focusing on multi-objective scheduling optimization for clean energy. This work employs a combination of Particle Swarm Optimization (PSO) and Deep Q-Network (DQN) to enhance grid scheduling efficiency and clean energy utilization. First, the work analyzes the complexity and uncertainty challenges faced in clean energy scheduling within the current power system, highlighting the limitations of traditional methods in handling multi-objective optimization and real-time response. Consequently, the work introduces the ESG big data platform and leverages its abundant data resources and computational power to improve the scheduling decision-making process. Next, PSO is used for initial scheduling optimization and a mathematical model for clean energy scheduling optimization is constructed. To further enhance the dynamic response capability of the scheduling process, this work designs a dual-layer architecture. Among this architecture, DQN is responsible for adjusting the PSO initially optimized scheduling plan based on real-time data during the actual scheduling process, adapting it to the instantaneous change demand and renewable energy output characteristics. Finally, to verify the model’s effectiveness, this work conducts simulation analyses using real data from the state grid ESG big data platform. The results indicate that this method significantly improves clean energy utilization, increasing it from 62.4% (with traditional methods) to 87.7%, while reducing scheduling costs by 22%. With the increase in communication delay, the generation time of scheduling instruction increases significantly. This is because the algorithm needs to wait longer to receive the complete grid status information, which affects the generation speed of scheduling instructions. Moreover, this method demonstrates good adaptability and stability under different load demands and clean energy supply conditions. This work introduces a novel and efficient method for clean energy scheduling optimization by integrating PSO and DQN. It contributes to the overall improvement of clean energy utilization within the State Grid and provides a theoretical and empirical foundation for further research in related fields.

Association between chest-to-head circumference ratio at birth and childhood neurodevelopment: the Japan Environment and Children's Study.

Children born growth-restricted are well recognized to be at an increased risk of poor neurodevelopmental outcomes. This prospective study examined the influence of chest-to-head circumference ratio at birth on neurodevelopment in the first three years among children enrolled in the Japan Environment and Children's Study. We analyzed information of 84,311 children (43,217 boys, 41,094 girls). Children were divided into low, normal, and high chest-to-head circumference ratio groups. Neurodevelopment was assessed every six months (from 6 months to 3 years) using the Ages and Stages Questionnaire (Japanese translation), with delays defined as scores below 2 standard deviations from the mean. Additionally, we evaluated the contributions of chest and head circumference to the observed association. Linear mixed-effect regression revealed increased risk of delays in communication, gross motor, fine motor, problem-solving, and personal-social skills in the low-ratio group compared to the normal-ratio group. Adjusted risk ratios were in the range of 1.14 - 1.39 in boys and 1.16 - 1.37 in girls, with no such increase observed in the high-ratio group. The heightened risk in the low-ratio group was likely associated with a relatively narrow chest rather than a large head. The area under the ROC curves in predicting any developmental delay at three years for newborn measurements ranged from 0.513 to 0.526 in boys and 0.509 to 0.531 in girls. These findings suggest that a low chest-to-head circumference ratio may indicate children who are at risk for neurodevelopmental deficits. However, the ability to predict poor neurodevelopmental outcomes at three years of age is limited.

Self-triggered formation control of asynchronous multiple underwater unmanned vehicle under communication delay

Self-triggered formation control of asynchronous multiple underwater unmanned vehicle under communication delay

Highly Responsive Robotic Prosthetic Hand Control Considering Electrodynamic Delay.

As robots become increasingly integrated into human society, the importance of human-machine interfaces continues to grow. This study proposes a faster and more accurate control system for myoelectric prostheses by considering the Electromechanical Delay (EMD), a key characteristic of Electromyography (EMG) signals. Previous studies have focused on systems designed for wrist movements without attempting implementation. To overcome this, we expanded the system's capability to handle more complex movements, such as those of fingers, by replacing the existing four-channel wired EMG sensor with an eight-channel wireless EMG sensor. This replacement improved the number of channels and user convenience. Additionally, we analyzed the communication delay introduced by this change and validated the feasibility of utilizing EMD. Furthermore, to address the limitations of the SISO-NARX model, we proposed a MISO-NARX model. To resolve issues related to model complexity and reduced accuracy due to the increased number of EMG channels, we introduced ridge regression, improving the system identification accuracy. Finally, we applied the ZPETC+PID controller to an actual servo motor and verified its performance. The results showed that the system reached the target value approximately 0.240 s faster than the response time of 0.428 s without the controller. This study significantly enhances the responsiveness and accuracy of myoelectric prostheses and is expected to contribute to the development of practical devices in the future.

Distributed synchronous control of multiple electrohydraulic actuators with lumped uncertainty and communication delay

This study presents a distributed synchronous control in multiple electrohydraulic actuators (MEHAs), which ensures the motion consensus between the leader and the follower under a directed topology. Since the MEHAs system has many unknown uncertainties and communication delays, a terminal sliding mode observer (TSMO) is used to address hydraulic parametric uncertainties and load disturbance. Furthermore, one synchronized controller is designed by Lyapunov-Krasovskii method with backstepping iteration to ensure the synchronized errors of the MEHAs system converging to a zero neighborhood. Since some follower EHAs cannot directly acquire the position signal from the leader node, a demand estimation law is used to degrade the negative effect of unknown communication delay existed in the network topology. Finally, the effectiveness of the proposed synchronized controller is verified by a MEHAs platform with finite hydraulic nodes under constant and random communication delays.

Efficient Resource Allocation for Ultra Reliable Low Latency Communication Delay Minimization in Fifth Generation Networks

Efficient Resource Allocation for Ultra Reliable Low Latency Communication Delay Minimization in Fifth Generation Networks

A Resilient Event‐Triggered Control Scheme for Nonlinear Multi‐Agent Systems Under Communication Delays and Denial‐Of‐Service Attacks

ABSTRACTThis paper investigates the resilient event‐triggered control (ETC) for nonlinear multi‐agent systems in the presence of communication delays and denial‐of‐service (DoS) attacks. The communication delays and asynchronous DoS attack are considered for each communication link. Based on sampling‐receiving events and DoS attack events, flow and jump sets are formulated to describe the system's transmission time status. An independent event‐triggering mechanism (ETM) is designed for each communication link. Furthermore, a resilient control scheme is constructed in which sufficient conditions for achieving system input‐output stability are provided, including the constraints of frequency and DoS attack duration, the conditions of Lyapunov functions, and the maximally allowable transmission interval. Finally, the attitude cooperative control problem of multiple unmanned aerial vehicles (multi‐UAVs) is adopted as a simulation example. The simulation results show the effectiveness of the proposed scheme.

Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction

The limited coverage of terrestrial base stations and the limited transmission distance and onboard resources of satellite communications make it difficult to ensure the quality of communication services for marine users by relying only on satellites and terrestrial base stations. In contrast, UAVs, as flexible mobile communication nodes, have the capacity for dynamic deployment and real-time adjustment. They can effectively make up for the communication blind spots of traditional satellites and ground base stations in the marine environment, especially in the vast and unpredictable marine environment. Considering the mobility of maritime users, one can effectively reduce the communication delay and optimize the deployment scheme of UAVs by predicting their sailing trajectories in advance, thus enhancing the communication service quality. Therefore, this paper proposes a communication coverage model based on mobile user route prediction and a UAV dynamic deployment algorithm (RUDD). It aims to optimize the coverage efficiency of the maritime communication network, minimize the communication delay, and effectively reduce the energy consumption of UAVs. In this algorithm, the RUDD algorithm employs a modified Long Short-Term Memory (LSTM) network to predict the maritime user’s trajectory, utilizing its strengths in processing time-series data to provide accurate predictions. The prediction results are then used to guide the Proximal Policy Optimization (PPO) algorithm for the dynamic deployment of UAVs. The PPO algorithm can optimize the deployment strategy in dynamic environments, improve communication coverage, and reduce energy consumption. Simulation results show that the proposed algorithm can complement the existing satellite and terrestrial networks well in terms of coverage, with a communication coverage rate of more than 95%, which significantly improves the communication quality of marine users in areas far from land and beyond the reach of traditional networks, and enhances network reliability and user experience.

Distributed cooperative guidance law without numerical singularity with field-of-view angle constraint

Abstract A distributed cooperative guidance law without numerical singularities is proposed for the simultaneous attack a stationary target by multiple vehicles with field-of-view constraints. Firstly, the vehicle engagement motion model is transformed into a multi-agent model. Then, based on the state-constrained consensus protocol, a coordination control law with field-of-view (FOV) constraints is proposed. Finally, the cooperative guidance law has been improved to make it more suitable for practical application. Numerical simulations verified the effectiveness and robustness of the proposed guidance law in the presence of acceleration saturation, communication delays and measurement noise.