Real-time State Information Research Articles

A Dynamic Day-To-Day Departure Time and Route Choice Model for Bounded-Rational Individuals

Accurate prediction of travellers’ day-to-day departure time and route choice is critical in advanced traffic management systems. There have been several related works about route choice with the assumption that the departure time for individual travellers is known beforehand. With real-time traffic state information provided by navigation systems and previous historical experience, travellers will dynamically update their departure time, which is neglected in existing works. In this study, we aim to describe travellers’ spatial-temporary choice behaviour taking navigation information into account and propose a bounded-rational day-to-day dynamic learning and adjustment model. The new model contains three steps. First, the real-time navigation guidance on each discrete day is obtained, and the self-learned experience of travellers’ choices with navigation information is presented; then, the day-to-day revision process of the choices is derived to maximize departure and route choice prospect; next, by aggregating each individual’s behaviour and calculating route choice probability, a bounded-rational continuous day-to-day dynamic model is provided. Numerical experiments suggest that the proposed model converges to a spatial-temporal oscillating equilibrium not a fixed-point stable status, and the final equilibrium trend is different from classical user equilibrium. The findings of the study are helpful to improve the prediction accuracy of traffic state in urban street networks.

An energy-saving routing algorithm for opportunity networks based on asynchronous sleeping mode

The opportunistic network is based on the idea of "Store-Carry-Forward" between nodes which haven't complete communication links. It uses the encounter opportunity presented by node mobility to establish a route to realize communication. The traditional Epidemic algorithm uses the flooding mechanism which greatly increases the energy consumption of nodes, and it is often unable to supplement the energy of nodes at any time in the application environment of the opportunistic network. Therefore, this paper proposes an asynchronous sleep mechanism, which allows nodes to judge whether to enter the sleep state according to their own energy situation, real-time state and other information. It ensures that the nodes do not miss the possible communication opportunities by dynamically controlling the sleep time and wake-up time. The simulation results show that the proposed EASE algorithm can guarantee the network performance and greatly reduce the energy consumption of nodes compared with the classical opportunistic network routing algorithms.

Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model

Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme.

Car-following model of connected and autonomous vehicles considering both average headway and electronic throttle angle

The connected and automated vehicle (CAV) is regarded as an effective way to improve traffic efficiency and safety, which can utilize vehicle-to-vehicle (V2V) communication technology to obtain real-time status information from multiple preceding vehicles. In view of the car-following characteristic of CAV in a V2V communications environment, an extended car-following model AHT-FVD is proposed which takes both average headway and electronic throttle angle difference into account. The stability of this model is examined via linear stability analysis. It is found that the proposed model has a larger stability region than both the full velocity difference (FVD) model and throttle-based FVD (T-FVD) model. Namely, this AHT-FVD model can effectively stabilize traffic flow and alleviate traffic congestion in theory. Moreover, a series of numerical simulations are carried out to explore how average headway together with electronic throttle angle difference influences the stability of traffic flow. Simulation results show that increasing either the average headway weight or the electronic throttle angle difference control signal coefficients can yield higher traffic flow stability. Simulation result is highly consistent with theoretical analysis.

A passenger risk assessment method based on 5G-IoT

For the airports worldwide, it is important to establish a "passenger integrity system" based on the basic information of passengers and their related credit system. Correspondingly, this paper develops a new risk assessment model for the passenger graded security check by introducing several new technologies to obtain the passengers’ real-time status information as well as historical data. We first propose to deploy a variety of 5G-IoT devices to monitor the passengers in real time, including high-definition cameras, millimeter-wave security detectors, etc. We then rely on machine learning to analyze the passenger risk level and integrate improved analytic hierarchy process (AHP) with group decision theory, namely GD-AHP. According to the risk level, the passengers can be classified into known, ordinary and dangerous targets. The differentiated handling of different targets could significantly save the time of security check and improve the passenger experience.

Congestion-aware WiFi offload algorithm for 5G heterogeneous wireless networks

First, the key technologies of 5G heterogeneous wireless networks are introduced. Based on the 5G heterogeneous network fusion architecture, heterogeneous network technologies are used to achieve the integration between different access networks, so that they can cooperate with each other, coordinate interference, and improve spectrum resources. Utilization and transmission efficiency; At the same time, an enhanced ADNSF congestion-aware WiFi(WIreless FIdelity) offloading system suitable for 5G networks is proposed. The congestion-aware WiFi offloading system can obtain network status information in real time. Analyzed the shortcomings of 3GPP’s existing congestion-aware WiFi offload strategy, combined with the user-centric network characteristics of 5G, proposed a congestion-aware WiFi offload algorithm, and modeled the access strategy as the effective capacity In the non-cooperative game model, a distributed algorithm and a dynamic value algorithm are designed respectively to maximize network utility. Simulation shows that the proposed distributed algorithm doubles the total effective capacity of the network compared to the device full activation scheme; compared with the distributed algorithm, the proposed dynamic value algorithm improves the total effective capacity of the network. This algorithm can effectively improve user satisfaction and achieve fine management of congested WiFi offloads

Toward a methodology of requirements definition for prognostics and health management system to support aircraft predictive maintenance

Aircraft maintenance has been further developed with predictive maintenance instead of solely condition-based maintenance. Prognostics and health management (PHM) with advanced technologies can utilize real-time and historical health state information to provide actionable information, enabling predictive maintenance decision-making. In this case, the methodology of how to design the PHM systems is an issue to be faced. The state of the art has provided several conceptual design methodologies and associated methods to support the conceptual requirements development of PHM systems. However, there is no rigorous process available for requirements definition. Existing options for requirements derivation are lacking details, which restricting PHM system design and development. This constitutes a major drawback and hurdle towards the successful design of PHM systems in practice. This paper consequently proposes a methodology for the systematic derivation of system requirements towards PHM system development. Besides, this methodology defines detailed processes for requirements definition, and positions mean through which various categories of requirements can be derived through appropriate analyses in detail. Sequences of interoperability requirements categories and associated flow-down perspectives are identified. To evaluate the applicability, this paper undertakes the case study of requirements definition for a generic PHM system, which provides a comprehensive application of the methodology. Designers can perform requirements definition under this methodology as guidance towards the design of a successful PHM system, providing solutions for predicting remaining useful life (RUL) to support aircraft predictive maintenance.

Key Algorithms of Video Target Detection and Recognition in Intelligent Transportation Systems

With the popularization of video detection and recognition systems and the advancement of video image processing technology, the application research of intelligent transportation systems based on computer vision technology has received more and more attention. It comprehensively utilizes image processing, pattern recognition, artificial intelligence and other technologies. It also involves processing and analyzing the video image sequence collected by the detection system, intelligently understanding the video content and making processing, and dealing with various problems such as accident information judgment, pedestrian and vehicle classification, traffic flow parameter detection, and moving target tracking. It promotes intelligent transportation systems to be more intelligent and practical, and provides comprehensive, real-time traffic status information for traffic management and control. Therefore, the research on the method of traffic information detection based on computer vision has important theoretical and practical significance. The detection and recognition of video targets is an important research direction in the field of intelligent transportation and computer vision. However, due to the background complexity, illumination changes, target occlusion and other factors in the detection and recognition environment, the application still faces many difficulties, and the robustness and accuracy of detection and recognition need to be further improved. In this paper, several key problems in video object detection and recognition are studied, including accurate segmentation of target and background, shadow in complex scenes; accurate classification of extracted foreground targets; and target recognition in complex background. In response to these problems, this paper proposes a corresponding solution.

Maintenance Optimization Model with Sequential Inspection Based on Real-Time Reliability Evaluation for Long-Term Storage Systems

For long-term storage systems such as rockets and missiles, most of the relevant models and algorithms for inspection and maintenance currently focus on analysis based on periodic inspection. However, considering factors such as the complexity of the degradation mechanisms of these systems, the constraints imposed by failure risk, and the uncertainty caused by environmental factors, it is preferable to dynamically determine the inspection intervals based on real-time status information. This paper investigates the issue of maintenance optimization modelling for long-term storage systems based on real-time reliability evaluation. First, the Wiener process is used to establish a performance degradation model for one critical unit of such a system, and a closed-form expression for the real-time reliability distribution is obtained by using the first-hitting-time theory. Second, sequential inspection intervals are dynamically determined by combining the real-time reliability function with a real-time reliability threshold for the system. Third, a maintenance optimization model is established for the critical unit based on update process theory. An analytical expression for the expected total cost rate is derived, and then, the real-time reliability threshold and the preventive maintenance threshold for the unit are jointly optimized by means of Monte Carlo simulation, with the lowest expected total cost rate as the optimization goal. Finally, two examples of a gyroscope and an alloy blade that are commonly used in the long-term storage systems are considered, and the validity of the proposed model is illustrated by means of a sensitivity analysis of the relevant parameters.

A Review: Prognostics and Health Management in Automotive and Aerospace

Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily.

Two-stage stochastic approximation for dynamic rebalancing of shared mobility systems

Mobility systems featuring shared vehicles are often unable to serve all potential customers, as the distribution of demand does not coincide with the positions of vehicles at any given time. System operators often choose to reposition these shared vehicles (such as bikes, cars, or scooters) actively during the course of the day to improve service rate. They face a complex dynamic optimization problem in which many integer-valued decisions must be made, using real-time state and forecast information, and within the tight computation time constraints inherent to real-time decision-making. We first present a novel nested-flow formulation of the problem, and demonstrate that its linear relaxation is significantly tighter than one from existing literature. We then adapt a two-stage stochastic approximation scheme from the generic SPAR algorithm due to Powell et al., in which rebalancing plans are optimized against a value function representing the expected cost (in terms of fulfilled and unfulfilled customer demand) of the future evolution of the system. The true value function is approximated by a separable function of contributions due to the rebalancing actions carried out at each station and each time step of the planning horizon. The new algorithm requires surprisingly few iterations to yield high-quality solutions, and is suited to real-time use as it can be terminated early if required. We provide insight into this good performance by examining the mathematical properties of our new flow formulation, and perform rigorous tests on standardized benchmark networks to explore the effect of system size. We then use data from Philadelphia’s public bike sharing scheme to demonstrate that the approach also yields performance gains for real systems.

Joint Communication and Control for Small Underactuated USV Based on Mobile Computing Technology

To satisfy the requirement of the future ocean communication and maritime transportation, in this paper, an integrated space-air-ground-sea communication control system based on mobile computing technology is proposed to rescue a water container. Specifically, unmanned surface vehicle (USV), as a flexible and lightweight intelligent device deployment integration platform, is the main tool to move towards the water container. However, how to sense the real-time navigation status information of the USV, integrate the information, and eventually distribute it to the ground control center, is still a difficult point for us. To successfully rescue the water container, three aspects of research are needed: information collection of the USV, information fusion, and data distribution to the ground control center. For the information collection part, we mainly use nine-axis, ultrasonic, global positioning system (GPS), and ultra wide band (UWB) sensors to complete the information collection for the USV. For the information fusion part, we set up a computational model to solve it. The USV computational model in previous works is mainly based on large ships, ignoring the modeling of wind, wave, and current for small underactuated USV. We use the classical Nomoto model, combined with the actual any slight wind, wave, and current forces on the USV, and finally combined with the proportion integration differentiation (PID) algorithm to model the USV. For the data distribution to the ground control center part, we have set up a communication protocol for the USV so that it can communicate normally. In addition, we wrote a Browser/Server (B/S) architecture to enable the ground control center to communicate with the host computer. Finally, our proposed control and communication schemes are carried out on actual USV. The real experiments show that the proposed schemes can reduce the ship surge and satisfy the ship navigation.

Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

Real-time traffic status information provides good references for urban traffic control and management. Travel time is easy to understand and widely employed in representing traffic status. With significantly improved positioning accuracy and coverage, trajectory data collected from GPS-equipped probe vehicles have great potential for traffic state recognition. This paper presents a machine learning enabled travel time estimation method based on the GPS-equipped probe vehicles data. This research considers the spatial-temporal relevancy while solving the travel time allocation problem: the travel time of target segment might be associated with its previous travel times and/or the traffic states of nearby relevant segments. After data normalization and network clustering, an artificial neural network (ANN) algorithm considering such spatial-temporal relevancy was conducted to infer the travel time distribution among the traveled segments within one path. Furthermore, a weighted summation of the travel time estimation result from various trajectories was calculated to better represent the segment travel time in one time step. The proposed method was evaluated by evaluating the estimation results with automatic vehicle identification obtained ground truth. The experimental results illustrated that by utilizing the ANN to consider the spatial-temporal relevancy, the proposed method is effective and efficient in estimating the travel time.

Statistical and Numerical Robust State Estimator for Heavily Loaded Power Systems

Real-time state information provided by the state estimator plays a major role in power system monitoring and control. As a result, the convergence of the estimator under various system operating conditions becomes one of the key requirements. In this paper, a state estimation framework that achieves both statistical and numerical robustness is proposed. This estimation framework also generalizes several well-known estimators, including the weighted least squares estimator, the least absolute value estimator, the Huber Maximum-likelihood-estimator, and the Schweppe-type Huber generalized Maximum-likelihood (SHGM)-estimator. The statistical robustness of each estimator has been studied analytically through the total influence function. In addition, the dependence between the statistical robustness of an estimator and the numerical robustness of the iterative algorithm is investigated. To enhance the numerical robustness of the iterative algorithm that solves the SHGM-estimator, a fourth-order Levenberg–Marquardt approach-based SHGM (LM-SHGM) estimator is proposed. It integrates the statistical robustness of that estimator to address various types of bad data and the numerical robustness of the LM approach to handle highly stressed operating conditions. Numerical results carried out on the IEEE test systems demonstrate the good convergence and robustness of the proposed approach under various operating conditions.

Dynamic and adaptive multi-path routing algorithm based on software-defined network

Recently, there has been a surge of the video services over the Internet. However, service providers still have difficulties in providing high-quality video streaming due to the problem of scheduling efficiency and the wide fluctuations of end-to-end delays in the existing multi-path algorithms. To solve these two problems affecting video transmission quality, networks are expected to have the capability of dynamically managing the network nodes for satisfying quality-of-service requirements, which is a challenging issue for media streaming applications. Against this changing network landscape, this article proposes a dynamic and adaptive multi-path routing algorithm under three constraints (packet loss, time delay, and bandwidth) that are based on software-defined network for centralized routing computations and real-time network state updating in multimedia applications. Compared with related multi-path routing proposals, dynamic and adaptive multi-path routing makes efficient use of the latest global network state information achieved by the OpenFlow controller and calculates the optimal routes dynamically according to the real-time status information of the link. Moreover, our proposed algorithm can significantly reduce the computational overhead of the controller while completing a fine-grained flow balance. Experimental results show that dynamic and adaptive multi-path routing significantly outperforms other existing scheduling approaches in achieving a 35%–70% improvement in quality-of-service.

QoS-Predicted Energy Efficient Routing for Information-Centric Smart Grid: A Network Calculus Approach

As the proportion of information content in the power grid increased, information-centric smart grid (iCenS) has drawn much attention in recent years. By introducing features of information-centric networking on decentralization, multi-path transmission, and in-network caching to distributed power systems, the iCenS enabled more efficient demand response, disruption tolerant, and mobility support. However, due to the strict quality of services (QoSs) requirement of power grid and a large number of resource-limited infrastructures existed in iCenS, it still faces the following challenges. First, multi-path addressing will cause redundant packets and the transmission of duplicate packet consumes the node energy which may lead to link failure; the delayed transaction of key component information like tele-protection will induce electricity accidents. Second, it is hard to model the traffic of iCenS without introducing extra computing burden because of its interest-driven feature and constantly updated forwarding information base. In this paper, we propose a QoS-predicted energy efficient routing (QPER) scheme for iCenS, which leverage on the network calculus. Upon collecting the real-time state information of the iCenS nodes, the QPER rapidly predicts the QoS boundary and energy consumption of each link for upcoming traffic flows by establishing the service curve and arrival curve. The simulation results validate that our method is superior to traditional multicast routing algorithm in terms of delay, energy consumption, packet loss, and others. Simultaneously, providing various routing methods for different service information can ensure the stability and minimize the security risks.

A Dynamic Simulation Model for the Total Packaging System Cost Analysis in an International Supply Chain

To implement a new packaging system for an international supply chain, a packaging and supply chain manager should have a good understanding of the total packaging cost concept. Especially for the reusable packaging system, a poor investment may result in enormous loss of assets and efficiency throughout the supply chain. This paper compares one-way and reusable packaging systems. For the reusable packaging system, two options, company-owned or rental, were considered. A dynamic simulation method was used for calculating the number of reusable plastic containers (totes) and total packaging cost, based on activity-based cost data provided by an automotive company. The variables considered include costs of shipping containers, distance, and transport time required for a minimum 2-year operation. Five scenarios were tested which evaluated system time and cost, resource utilization for the process and number of units processed. The greatest benefit of using the dynamic simulation is that it provides real-time status information about the packaging inventory and various cost-benefit scenarios in the target supply chain. This paper identified the cost associated with each level of various activities (e.g., cost per line item picked, cost per delivery, etc.) which enables to show a clearer picture of the true packaging and logistics costs, so any inefficiency in a supply chain due to adoption of different packaging system. The results of this simulation can help in analyzing the interactive and coherent behavior between different packaging and supply chain systems.

Hardware for dynamic quantum computing.

We describe the hardware, gateware, and software developed at Raytheon BBN Technologies for dynamic quantum information processing experiments on superconducting qubits. In dynamic experiments, real-time qubit state information is fed back or fed forward within a fraction of the qubits' coherence time to dynamically change the implemented sequence. The hardware presented here covers both control and readout of superconducting qubits. For readout, we created a custom signal processing gateware and software stack on commercial hardware to convert pulses in a heterodyne receiver into qubit state assignments with minimal latency, alongside data taking capability. For control, we developed custom hardware with gateware and software for pulse sequencing and steering information distribution that is capable of arbitrary control flow in a fraction of superconducting qubit coherence times. Both readout and control platforms make extensive use of field programmable gate arrays to enable tailored qubit control systems in a reconfigurable fabric suitable for iterative development.

Development and application of maintenance decision-making support system for aircraft fleet

With the arrival of on-board monitoring systems for aircraft structures, maintainers have access to a near real-time assessment of component health. Due to the traditional reliability assessment methods for aircraft structures lack of the ability to make full use of real-time status information collected by the monitoring systems, a new reliability evaluation method based on the real-time load information is proposed for aircraft structural reliability. Condition-based-maintenance is a maintenance scheme which recommends maintenance decisions according to equipment status collected by monitoring systems over a period of time. As most researches on condition-based maintenance for aircraft focused on solely minimizing maintenance cost or maximizing the availability of single aircraft, a multi-objective decision-making model based on condition-based-maintenance concentrated on both minimizing the maintenance cost and maximizing the availability of a fleet (total number of available aircraft in fleet) is established for an aircraft fleet. Finally, a maintenance decision-making support system (MDMSS) is developed based on the proposed model. The system integrates the process of data acquisition (real-time status information of aircraft), data processing (reliability assessment of aircraft structures) as well as maintenance decision-making. Therefore, it simplifies the complex process of equipment management and takes a step further in engineering application.

A Sleeping and Offloading Optimization Scheme for Energy-Efficient WLANs

In this letter, we propose an access point (AP) sleeping and user offloading optimization scheme to improve energy efficiency in densely deployed WLANs. Through real trace analysis, we investigate AP energy efficiency to obtain the sleep-awake threshold, which is used to select sleep or awake APs according to real-time status information monitored on controller. Moreover, we formulate the user offloading problem as a reverse auction process to optimize energy efficiency of APs involved in offloading. Simulation results demonstrate that, comparing to traditional methods, our scheme can achieve up to 20% energy saving while maintaining effective system coverage and throughput.