Real-time State Information Research Articles

Admission control for a capacitated supply system with real-time replenishment information

Control towers can provide real-time information on logistic processes to support decision making. The question however, is how to make use of it and how much it may save. We consider this issue for a company supplying expensive spare parts and which has limited production capacity. Besides deciding on base stock levels, it can accept or reject customers. The real-time status information is captured by a k-Erlang distributed replenishment lead time. First we model the problem with patient customers as an infinite-horizon Markov decision process and minimize the total expected discounted cost. We prove that the optimal policy can be characterized using two thresholds: a base work storage level that determines when ordering takes place and an acceptance work storage level that determines when demand of customers should be accepted. In a numerical study, we show that using real-time status information on the replenishment item and adopting admission control can lead to significant cost savings. The cost savings are highest when the optimal admission threshold is a work storage level with a replenishment item halfway in process. This finding is different from the literature, where it is stated that the cost increase of ignoring real-time information is negligible under either the lost sales or the backordering case. Next we study the problem where customers are of limited patience. We find that the optimal admission policy is not always of threshold type. This is different from the literature which assumes an exponential production lead time.

Multi-Agent Bifurcation Consensus-Based Multi-Layer UAVs Formation Keeping Control and Its Visual Simulation

This paper focuses on the mult-layer unmanned aerial vehicles (UAVs) formation keeping control issue based on multi-agent bifurcation consensus. Taking the two-layer formation as an example, due to delays in receiving and responding to signals, the intra-layer and inter-layer control schemes under the dynamic properties need to be considered. Firstly, the UAV heading autopilot is designed to fully consider the control of the angle of attack and yaw angle, which is utilized to deal with the translational and angular motion of UAV. Based on the heading autopilot, the stability and Hopf bifurcation conditions of UAVs heading dynamic system are discussed to get real-time state information and generate control input. Secondly, the leader-following formation combined with the virtual control structure to obtain a virtual hierarchical control strategy, and then a bifurcation consensus criterion. The results shown that when UAVs heading control system achieves bifurcation consensus. The formation maintains the shape while showing interesting periodic oscillation motion behavior. Finally, a numerical example is presented to demonstrate the effectiveness of the presented control strategy. Particularly, visualization simulation using 3ds Max software makes the results more intuitive and flexible.

Universal Energy Solution for Triboelectric Sensors Toward the 5G Era and Internet of Things.

The launching of 5G technology provides excellent opportunity for the prosperous development of Internet of Things (IoT) devices and intelligent wireless sensor nodes. However, deploying of tremendous wireless sensor nodes network presents a great challenge to sustainable power supply and self-powered active sensing. Triboelectric nanogenerator (TENG) has shown great capability for powering wireless sensors and work as self-powered sensors since its discovery in 2012. Nevertheless, its inherent property of large internal impedance and pulsed "high-voltage and low-current" output characteristic seriously limit its direct application as stable power supply. Herein, a generic triboelectric sensor module (TSM) is developed toward managing the high output of TENG into signals that can be directly utilized by commercial electronics. Finally, an IoT-based smart switching system is realized by integrating the TSM with a typical vertical contact-separation mode TENG and microcontroller, which is able to monitor the real-time appliance status and location information. Such design of a universal energy solution for triboelectric sensors is applicable for managing and normalizing the wide output range generated from various working modes of TENGs and suitable for facile integration with IoT platform, representing a significant step toward scaling up TENG applications in future smart sensing.

Smart Passenger Center: Real-Time Optimization of Urban Public Transport

This paper introduces the Smart Passenger Center (SPaCe), MerMec's solution for real-time optimization of urban public transport. The proposed system, based on cutting-edge artificial intelligence technologies, provides real-time vehicle status information and passenger activity monitoring. All of the information acquired from the vehicles is used to optimize the performance of the vehicle fleet for real-time traffic management.The purpose of the paper is to explain the reasons and benefits of such Intelligent Transport System (ITS) with a deep understanding of its architecture and advanced functionalities.Additionally, a practical application of the data collected by SPaCe is proposed for the task of bus timetable optimization.The goal is to schedule bus trips in a way that maximizes multiple conflicting goals such as service quality and operating costs, based on real time collected data.The reported results show how the developed optimization system can support the decision-making process to balance the interests between passengers and public transport agencies.

A non-stationary channel prediction method for UAV communication network with error compensation

In an unmanned aerial vehicle (UAV) communication network, especially for mission-critical applications, ultra-reliable and low-latency communication (URLLC) of the control links has essential implications for realizing collision avoidance and real-time control of UAVs. However, the UAVs’ mobility and the channel environment’s variability may make the wireless channels highly non-stationary. Real-time and accurate Channel Status Information (CSI) acquisition is a critical challenge to implementing URLLC for UAV control links. Predicting the characteristics of non-stationary channels is profitable for formulating communication strategies to mitigate the effects of future channel fading. Nevertheless, the difficulty lies in extracting accurate prediction models for non-stationary channels to obtain accurate CSI. This paper proposes a more precise prediction by utilizing the CSI obtained as feedback from the receiver and introducing error correction. The method contains three stages, CSI collection and processing, channel tracking, and error correction. First, the transmitter collects the CSI feedback from the receiver and converts it to a stationary series. Secondly, the recent historical CSIs are used to track the time evolution of the channel based on an autoregressive integrated moving average (ARIMA) model. Next, the Gaussian process regression (GPR) model is used to establish the prediction error regression model and obtain a more accurate prediction. Finally, the effectiveness of the proposed method is verified based on a UAV wireless communication network application scenario in an urban environment. Simulation results show that the proposed method outperforms other methods regarding RMSE and reliability in high-dynamic UAV communication scenarios. Significantly when the channel changes rapidly, this method can respond to the changes faster and predict the CSI more accurately.

Dynamic Periodic Event-Triggered Consensus Protocols for Linear Multiagent Systems With Network Delay

In this article, we investigate the event-triggered consensus problem for general linear multiagent systems with bounded communication network delay. To reduce the communication frequency, we propose some dynamic periodic event-triggering strategies, under which an auxiliary dynamic variable and a weighting parameter are introduced for each agent to build its event-triggering condition. Then, a distributed adaptive consensus protocol, which includes a time-varying coupling weight for each edge, is designed based on the proposed event-triggering strategies. With the well-designed updating laws of dynamic variables, coupling weights and weighting parameters, we can theoretically guarantee the consensus of the concerned multiagent systems. Our event-triggering strategies do not require agents to continuously verify the event-triggering conditions or read their neighbors’ real-time state information. Moreover, Zeno behavior is inherently excluded as the interevent intervals are bounded from below by one period in the periodic event-triggering framework. Two numerical examples are provided to verify the effectiveness of the proposed event-triggered consensus protocols.

SDN Control-Enabled and Time-Quantum-Driven Max-Pressure Approach for Intersection Management in Smart City

Congestion optimization, at an intersection, needs real-time traffic state information from the neighboring intersections. Existing actuated traffic light controllers (TLCs) act on local traffic data. On the same line, max-pressure-based TLCs have been proved effective to act in real-time. However, such TLCs act only on queue length or travel time, which are unable to optimize the congestion as they act only on local traffic data. Furthermore, such TLCs suffer from the starvation problem; if a road lane, at an intersection, continuously receives enormous vehicles during peak hours, then it gets continuous green phase duration, and other lanes continue with red phase duration for a longer time. To overcome this, we propose an efficient quantized max-pressure-based TLC utilizing software-defined networking (SDN) to mitigate the abovementioned issues and maintain the traffic flow in the city. The proposed SDN-enabled quantized max-pressure controller (SDN-QMPC) takes real-time vehicular dynamics as inputs and generates the traffic light phase duration in the form of dynamic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time quantum</i> by accounting for heterogeneous traffic states. Roadside units/cameras are considered vehicle sensing entities for the real-life implementations to account vehicular dynamics, such as vehicle speed, vehicle position, etc. These parameters become inputs for SDN-QMPC to generate SDN control-enabled TLC signals. An extensive simulation study has been carried out on an Indian city map using an open-source simulator, i.e., simulation of urban mobility. The comparative results against the state-of-the-models prove the effectiveness of SDN-QMPC.

Soldier health and position monitoring system using GPS and IOT technology

The fighter Wellbeing and Position Global Positioning Framework allows the military to track the current GPS position of officers and also monitors their health status, including internal temperature and heartbeat. This system also includes an additional feature that enables a soldier to request physical assistance or send a distress signal to the military if needed. The GPS modem transmits the latitude and longitude positions in a connected pattern, aiding the military in tracking the officer's current location. The system is highly beneficial for obtaining real-time health status information and providing immediate assistance to soldiers. In today's world, all nations prioritize their security, and soldiers, as the backbone of any armed forces, often lose their lives due to lack of medical aid during emergencies. Furthermore, soldiers engaged in missions or special operations might become disoriented on war fields, losing contact with authorities. To address these concerns, we developed this project. By utilizing a Wireless Body Area Sensor Network (WBANS) consisting of temperature and heartbeat sensors, we can monitor the soldiers' health status whenever necessary. Additionally, through GPS, we can track the precise location of the soldiers as needed. The use of an oxygen level sensor enables us to monitor the environmental conditions so that necessary aid can be provided by medical professionals. Communication between the soldiers and medical personnel is established via the Internet of Things (IoT). Any anomalies detected by the Remote Body Area Sensor Network (WBASNs) act as triggers for the IoT device to establish a connection between the soldier and the base unit, transmitting current location and health status to the receiver. To effectively convey this data, we have employed this equipment to create a cost-effective, lightweight, portable, and accurate defense system for soldiers.

Fully distributed consensus control for second-order multi-agent systems based on adaptive dynamic clock communication

This paper proposes a novel event-triggered control strategy based on an adaptive dynamic clock to address the consensus problem of second-order multi-agent systems with undirected communication topology. The agent determines the triggering time according to its dynamic clock, adaptively resets the clock, and broadcasts its state information to neighbors at the triggering time. Each agent only obtains the status information of its neighbors at the triggering time and updates the clock and control signals based on its own state and the status of its neighbors at the triggering time. It does not require real-time status information from neighbors and does not rely on any global communication topology information. The designed control strategy is completely distributed and effectively avoids continuous communication. Using the Lyapunov stability analysis method and algebraic graph theory, it is proved that the proposed control strategy can ensure that the system is asymptotically stable and does not exhibit Zeno behavior. Finally, the effectiveness of the proposed dynamic event-triggered control strategy is demonstrated by a simulation example.

DSOQR: Deep Reinforcement Learning for Online QoS Routing in SDN-Based Networks

With the rapid development of mobile communication technology, there are an increasing number of new network applications and services, and the existing best-effort routing algorithms cannot meet the quality-of-service (QoS) requirements of these applications and services. QoS-routing optimization solutions based on a software-defined network (SDN) are often targeted at specific network scenarios and difficult to adapt to changing business requirements. The current routing algorithms based on machine learning (ML) methods can generally only handle discrete, low-dimensional action spaces and use offline network data for training, which is not effective for dynamic network environments. In this study, we propose DSOQR, which is an online QoS-routing framework based on deep reinforcement learning (DRL) and SDN. DSOQR collects network status information in real time through the software-defined paradigm and carries out on-policy learning. Under this framework, we further propose SA3CR, which is a QoS-routing algorithm based on SDN and asynchronous advantage actor-critic (A3C). The SA3CR algorithm can dynamically switch routing paths that meet the conditions according to the current network status and the needs of different service types to ensure the QoS of target traffic. Experimental results show that DSOQR is effective and that the SA3CR algorithm has better performance in terms of delay, throughput, and packet loss rate.

Fault Diagnosis Algorithm of Transformer and Circuit Breaker in Traction Power Supply System Based on IoT

Transformers and circuit breakers are essential equipment in traction power supply systems. Once a fault occurs, it will affect the train’s regular operation and even threaten passengers’ personal safety. Therefore, it is essential to diagnose the faults of the transformers and circuit breakers of the traction power supply system. At present, power companies have made many achievements in fault diagnosis of power equipment, but there are still problems with real-time and accuracy. The Internet of Things (IoT) is a technology that connects different types of terminal devices for information exchange and communication to achieve intelligence. It includes data acquisition and transmission, information interaction, processing, and decision-making from bottom to top. It uses sensor terminals to obtain real-time status information on electrical equipment. Moreover, it conducts real-time monitoring and intelligent processing of the equipment status of the traction power supply system. In this paper, the multi-data fusion technology of the IoT combines the real-time information of electrical equipment with fault diagnosis to realize the fault diagnosis of transformers and circuit breakers. First, we built an equipment fault diagnosis system based on the multi-terminal data fusion technology of the IoT. Secondly, the transformer fault diagnosis model is established. We adopt the BP neural network algorithm based on particle swarm optimization (PSO) to realize transformer fault diagnosis and use PSO to optimize the feature subset to improve the diagnosis performance. Finally, the fault diagnosis model of the vacuum circuit breaker is established. We select the current change and time node as typical fault feature quantities and use the PSO–BP neural network algorithm to realize the fault diagnosis of the circuit breaker.

Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion

To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Attention Mechanism, and combines a Dilated Convolution Module (DCM) with LSTM to recognize complex signals of multiple sensors. By introducing an attention mechanism, the recognition performance of the network was improved. Finally, the real-time status information of the production line was obtained by integrating attention weight. Experimental results show that for the custom multi-sensor dataset of A-class insulation board production line, the proposed CNN-LSTM fault diagnosis method achieved 98.97% accuracy. Compared with other popular algorithms, the performance of the proposed CNN-LSTM model performed excellently in each evaluation index is better.

Fully Distributed Formation Control of General Linear Multiagent Systems Using a Novel Mixed Self- and Event-Triggered Strategy

In this study, the state formation control issue of general linear networked multi-agent systems (MASs) is considered. Combining self-triggered strategy with general event-triggered strategy, a novel fully distributed asynchronous mixed self- and event-triggered control strategy is proposed, which can make the MASs achieve the prescribed state formation structure. The control strategy proposed in this study does not depend on any global network information. Therefore, no matter how large scale of the network is, the control strategy is feasible. Meanwhile, the control strategy uses the sampled state information at triggering instants instead of the real-time state information, which efficiently eliminates continuous communication among agents. Different from the existing studies, the event-triggered detector starts to work if and only if the next self-triggering instant comes in the control strategy proposed in this article. Thus, the control strategy can save more communication resources between sensor and event-triggered detector within the same inter-event interval compared with the previous event-triggered strategies. In addition, the control strategy designs a exponential decay term in the triggering function to rule out the unexpected Zeno behavior and reduce the triggering number. Finally, the numerical simulation result of multi-robot formation is given, which demonstrates the feasibility and performance of the proposed control strategy.

Square-root cubature Kalman filter based power system dynamic state estimation

The state estimator’s real-time state information plays a vital role in monitoring and control of the power system. As a result, one of the primary requirements of the estimator is to provide accurate state estimates with good convergence under varied system operating conditions. In this context, dynamic state estimation (DSE) consistently tracks the power system dynamics and delivers real-time system state evolution. In literature, this dynamic state estimation (SE) problem is generally solved using an extended Kalman filter (EKF). The EKF approach neglects the non-linear mathematical functions of network equations by linearly approximating the measurement equations. This leads to degradation of SE accuracy during a sudden change in load conditions. In this paper, a new derivative-free method based on square-root cubature Kalman filter (SCKF) is proposed to overcome the limitations of the EKF technique and achieves improved performance. The proposed SCKF technique has been tested on IEEE 14-bus, IEEE 30-bus, and IEEE 57-bus systems under various operating conditions. The effectiveness of the SCKF technique has been compared with the traditional EKF and cubature Kalman filter (CKF) techniques using different performance indices. The obtained results demonstrate the versatility of the proposed SCKF technique.

Dynamic Scheduling of Intelligent Group Maintenance Planning under Usage Availability Constraint

Maintenance, particularly preventive maintenance, is a crucial measure to ensure the operational reliability, availability, and profitability of complex industrial systems such as nuclear asset, wind turbines, railway trains, etc. Powered by the continuous advancement of sensor technology, condition-based group maintenance has become available to enhance the execution efficiency and accuracy of maintenance plans. The majority of existing group maintenance plans are static, which require the prescheduling of maintenance sequences within fixed windows and, thus, cannot fully utilize real-time health information to ensure decision-making responsiveness. To address this problem, this paper proposes an intelligent group maintenance framework that is capable of dynamically and iteratively updating all component health information. A two-stage analytical maintenance model was formulated to capture the comprehensive impact of scheduled maintenance and opportunistic maintenance through failure analyses of both degradation and lifetime components. The penalty functions for advancing or postponing maintenance were calculated based on the real-time state and age information of each component in arbitrary groups, and the subsequent grouping of the time and sequence of components to be repaired were iteratively updated. A lifetime maintenance cost model was formulated and optimized under a usage availability constraint through the sequential dynamic programming of group sequences. Numerical experiments demonstrated the superior performance of the proposed approach in cost control and availability insurance compared with conventional static and periodic maintenance approaches.

Dynamic Quantization Driven Synchronization of Networked Systems Under Event-Triggered Mechanism

This paper focuses on the synchronization control issue of networked systems. Due to the imperfect network environment, continuous or precise transmission is impractical, and intermittent communication scheme under state quantization needs to be considered. First, an easy-to-implement dynamic quantizer, which possesses finite quantization level, is designed to deal with the imprecise information sharing. Next, based on the designed quantizer, a dynamic estimator is introduced to estimate the real-time state information and generate control inputs. An event-based communication scheme is utilized to ensure that the estimate error does not exceed a certain threshold. Then, detailed design of the dynamically quantized controller is given to achieve exact synchronization, and corresponding distributed design is also provided to improve the feasibility. Moreover, some results for practical synchronization are derived. Finally, the validity of our theoretical results is illustrated by two numerical examples.

Estimation of berthing state of maritime autonomous surface ships based on 3D LiDAR

During the process of automatic berthing, the maritime autonomous surface ship (MASS) needs to sense its own status relative to the berth in real-time and accurately to avoid collisions. This study proposes a berthing state perception method based on 3D LiDAR for MASS. First, a method for estimating the ship state based on point cloud registration is established. The accuracy of this method is verified through a comparison with GPS/IMU integrated navigation. Then, the detailed calculation methods for five types of berthing status are proposed, which including the distance, transversal velocity, longitudinal velocity, vessel-approaching angle and yaw rate. Besides, a berthing perception framework based on the robot operation system (ROS) is constructed. Finally, a test platform was established, and berthing experiments were conducted. The experimental results indicated that the average relative error between the calculated results of the berthing state perception method and actual measured value is 2.85%, with a calculation frequency of 10 Hz, which meets the accuracy and real-time requirements of the ship berthing sensing. The method proposed in this paper can not only assist other perception units, such as GPS/IMU, to form a redundant sensing system, but also provide accurate and real-time berthing status information for the control unit.

Operational planning in service control towers – heuristics and case study

We study performance improvement in multi-echelon, closed loop spare part supply chains using operational interventions based on real-time status information. Our objective is to minimize the total cost relevant costs, consisting of intervention costs and the backorder costs. In this paper, we focus on proactive interventions, aiming to avoid stockouts. We assume that all reactive interventions are fixed. Proactive interventions that we study include lateral transshipments, emergency shipments, stock reservations, expediting part repairs, and early new buys of parts. These interventions are invoked by using alert generation, when the supply chain status deviates from the plan. We propose heuristic rules to generate alerts. We also develop heuristic rules for the choice of operational interventions. We model and test our heuristics in a simulation test bed, based on data of a global IT company by using the case data in Germany. Numerical experiments reveal the following key insights: (i) downstream interventions – proactive lateral and emergency shipments – have most impact in reducing costs, (ii) communicating losses in the supply chain (no returns, failed repairs) for early new buys has positive impact on fill rates at negligible costs, and (iii) expedite repair and stock reservations using the proposed rules is not profitable.

How Blockchain Will Improve Private Ambulance Service Challenge

The ambulance service system for the patient's transport to the hospital is essential for the healthcare system. However, traffic congestion increases the urban population, a centralized management system, and increased demand for prehospital emergency services challenge the health system; there is a risk of lack of information sharing and information reliability, the consequences of which may directly and detrimentally affect patient well-being. This paper has built a blockchain-based ambulance model to solve these problems. The model can solve existing problems by decentralizing centralized ambulance system information and processes. All users in the emergency medical services (EMS) network can participate and use the service in the blockchain, using real-time state information such as patient location, ambulance, and ambulance attendants. To effectively implement blockchain in the medical sector, the ambulance business logic for a smart contract must reflect and confirm the operation of the defined functions suggested in this paper.

RESEARCH OF LSTM MODEL-BASED INTELLIGENT GUIDANCE OF FLIGHT AIRCRAFT

The breakthrough of artificial intelligence provides a new technical approach for the research of aircraft reentry guidance. Aiming at the disadvantage in predictor-corrector guidance, where a large amount of integrations need to be calculated in the prediction step and the bank angle amplitude is iteratively solved based on secant method in the correction step. All the above calculation are difficult to meet real-time demand. Moreover, the dynamic equations need to be integrated both in the longitudinal and lateral guidance, which exist an obvious redundant calculation. In this paper, we propose LSTM (long short term memory)-based intelligent guidance technology. On the one hand, the integration of the dynamic equations in longitudinal guidance is no longer required to predict the range, that is, the prediction step is eliminated, which will greatly reduce the amount of integral calculation and increase the calculation speed. On the other hand, the amplitude of the bank angle will be no longer iteratively solved based on the secant method, that is, the correction step is eliminated. Based on the natural advantages of deep learning both in neural network mapping capabilities and real-time performance, bank angle command is generated by the output of a trained LSTM model based on the real-time state information of the gliding vehicle. At the same time, the longitudinal and lateral guidance periods in the traditional predictor-corrector guidance will be merged into one period, which has the advantage to ensure that the guidance system meets real-time requirements for online using. Monte Carlo simulation analysis show that the proposed method has the advantages both in accuracy and calculation speed under the condition of initial state error and aerodynamic parameter perturbation. The interdisciplinary fusion of guidance technology and artificial intelligence is a hot research direction, which will greatly promote the development of guidance and control of aircraft.