Real-time Delay Research Articles

Evaluating the Performance of Right Turn Lanes at Signalized Intersection Using Traffic Simulation Model

The issue of traffic congestion at signalized intersections is a concern in transportation systems due to the growth of urban areas and increased vehicular transportation. To study the evolution of congestion and evaluate the traffic performance operation of signalized intersections under problematic congested and improved conditions, the microscopic simulation VISSIM software is utilized. The objectives of this paper are to evaluate operational techniques, build a simulation model, and produce a well-calibrated and validated model. The methodology procedure to evaluate the signalized intersection involves the application of a traffic simulation model to observe real-time delays and stopped vehicles. Using the VISSIM software Version 9 to create an intersection model and redesign geometry with an exclusive right turn to enhance the intersection functionality and reduce delay. Our research focused on the Al-Nakhala signalized intersection located in the southern part of Palestine urban street in Baghdad city. This intersection is one of the busiest along the corridor due to significant land-use changes in the study area, including residential, educational, or commercial areas generating daily pressure from additional trips and saturating the absorptive capacity of the intersections. The proposed scenario of an exclusive right–turn could reduce the queue length and vehicle delay at the signalized intersection, resulting in a more efficient traffic operation. As a result of the reduction in vehicle delay, the Level of service (LOS) for the north, west, and east approaches improved from F to D. However, there was only a slight improvement for the south approach, with the LOS changing from E to F. Nonetheless, there was a noticeable reduction in queue length and vehicle delay ranging from 25% to 50%. Doi: 10.28991/CEJ-2024-010-07-010 Full Text: PDF

Impact of Continuous Renal Replacement Therapy Initiation Time, Kidney Injury, and Hypervolemia in Critically Ill Children.

The mortality rate of pediatric patients who require continuous renal replacement therapy is approximately 42%, and outcomes vary considerably depending on underlying disease, illness severity, and time of dialysis initiation. Delay in the initiation of such therapy may increase mortality risk, prolong intensive care unit stay, and worsen clinical outcomes. In the pediatric intensive care unit of an urban level I trauma children's hospital, continuous renal replacement therapy initiation times and factors associated with delays in therapy were unknown. This quality improvement process involved a retrospective review of data on patients who received continuous dialysis in the pediatric intensive care unit from January 1, 2017, to December 31, 2021. The objectives were to examine the characteristics of the children requiring continuous renal replacement therapy, therapy initiation times, and factors associated with initiation delays that might affect unit length of stay and mortality. During the study period, 175 patients received continuous renal replacement therapy, with an average initiation time of 11.9 hours. Statistically significant associations were found between the degree of fluid overload and mortality (P < .001) and between the presence of acute kidney injury and prolonged length of stay (P = .04). No significant association was found between therapy initiation time and unit length of stay or mortality, although the average initiation time of survivors was 5.9 hours shorter than that of nonsurvivors. Future studies are needed to assess real time delays and to evaluate if the implementation of a standardized initiation process decreases initiation time.

Optimal tuning of PID controllers with derivative filter for stable processes using three points from the step response

This paper proposes a novel approach for tuning Proportional Integral Derivative (PID) controllers, utilizing experimental data obtained from an open-loop step input. We propose to use the measurements of the times taken to reach 5%, 35.3% and 85.3% of the final output, as well as the process static gain, to tune the controller. The tuning equations are applicable for a wide range of stable and over damped systems. Starting from an approximate model with three real poles and time delay (obtained from the measurements), the tuning equations approximate the controller that minimizes the Integral of Absolute Error (IAE) of the disturbance response. The designer can freely decide the Sensitivity Margin (Ms) to fix a desired robustness, as a difference with respect to other known methods where the user chooses among few predefined robustness values. The user can also select freely the derivative filter parameter, N, to find the required compromise between speed response and measurement noise amplification. For N=0 a PI controller is chosen. As N increases, a faster PID controller is selected, with higher noise amplification. We have also developed a web-based application and an Android application, downloadable for free, that implement the tuning equations as well as the whole required procedure.

Membership-Function-Based Secondary Frequency Regulation for Distributed Energy Resources in Islanded Microgrids With Communication Delay Compensation

Secondary frequency control is one of the most effective measures to ensure the stable operation of islanded microgrids (MGs). Most research on secondary frequency regulation has only focused on realizing steady-state operation objectives, that is, frequency restoration and power sharing. However, improving the dynamic performance of secondary frequency control is of great importance, especially in synchronous distributed energy resources. These synchronous units can introduce undesired oscillation modes, which may cause the instability conditions of MGs. To improve the dynamic performance of islanded MGs, a membership-function (MF) -based control strategy is proposed. The proposed strategy can trade-off between transient frequency regulation and frequency error elimination using the MF values calculated by the time-stamped synchronized measurements of distribution-level phasor measurement units. Besides, considering the time-varying communication delays in secondary frequency control loops, an adaptive delay compensator is proposed. The weights of the proposed compensator are updated by real-time delay measurements to compensate for the phase lag of control signals. Therefore, the adverse effect of communication delays on secondary frequency control is weakened effectively. Numerical simulations on an IEEE 34-bus system and a typical 40-bus islanded MG system demonstrate the advantages of the proposed method in the secondary frequency regulation of islanded MGs.

Real-Time Detection of Sleep Arousals with a Head-Mounted Accelerometer.

Wearable electroencephalography (EEG) enables real-time interactions with the sleeping brain in real-life settings. An important parameter to monitor during these interactions are sleep arousals, i.e. temporary increases in EEG frequency, that compose sleep dynamics, but are challenging to detect without delay. We describe the development of an EEG- and accelerometer(ACC)-based sensing approach to detect arousals in real-time. We investigated the ability of these sensing modalities to timely and accurately detect arousals. When evaluated on 6 nights of mobile recordings, ACC had a median real-time delay of 2 s and was therefore better suited for an early detection of arousals than EEG (4.7 s). The detection performance was independent of sleep stages, but worked better on longer arousals. Our results demonstrate that a head-mounted ACC might be a cost-effective and easy-to-integrate solution for arousal detection where short delays are important or EEG signals are not available.

The Hippocampus Contributes to Temporal Discounting When Delays and Rewards Are Experienced in the Moment.

Temporal discounting (TD) represents the mental devaluation of rewards that are available after a delay. Whether the hippocampus is critical for TD remains unclear, with marked discrepancies between animal and human studies: although animals with discrete hippocampal lesions display impaired TD, human participants with similar lesions show intact performance on classic intertemporal choice tasks. A candidate explanation for this discrepancy is that delays and rewards are experienced in the moment in animal studies but tend to be hypothetical in human studies. We tested this hypothesis by examining the performance of amnesic participants with hippocampal lesions (one female, six males) on a novel experiential intertemporal choice task that used interesting photographs occluded by thick lines as rewards (Patt et al., 2021). Using a logistic function to model indifference points data, we compared performance to that on a classic intertemporal choice task with hypothetical outcomes. Participants with hippocampal lesions displayed impaired patterns of choices in the experiential task but not in the hypothetical task. Specifically, hippocampal lesions were associated with decreased amplitude of the delay-reward trade-off, with persistent choice of the delayed option despite delay increase. These results help explain previous discrepancies across animal and human studies, indicating that the hippocampus plays a critical role in temporal discounting when the outcomes of decisions are experienced in the moment, but not necessarily when they are hypothetical.SIGNIFICANCE STATEMENT Impaired temporal discounting (TD) has been related to maladaptive behaviors, including substance dependence and nonadherence to medical treatment. There is consensus that TD recruits the brain valuation network but whether the hippocampal memory system is additionally recruited remains unclear. This study examined TD in hippocampal amnesia, providing a unique opportunity to explore the role of the hippocampus in cognition. Whereas most human studies have used hypothetical outcomes, this study used a novel experiential task with real-time delays and rewards. Results demonstrated hippocampal involvement in the experiential task, but not in a classic hypothetical task administered for comparison. These findings elucidate previous discrepancies between animal and human TD studies. This reconciliation is critical as animals serve as models of human neurocognition.

ISUOG Practice Guidelines (updated): fetal cardiac screening.

ISUOG Practice Guidelines (updated): fetal cardiac screening.

AN ENHANCED RESOURCE ALLOCATION SCHEME FOR LONG TERM EVOLUTION ADVANCED NETWORK

The emerging Long-Term Evolution Advanced (LTE-A) is an extended version of Long-Term Evolution (LTE) fourth-generation (4G) wireless broadband technologies that supports high speed transmission of multimedia applications, like video streaming, multimedia online game, Voice over IP (VoIP), browsing over the web etc. The Third Generation Partnership Project (3GPP) does not define a standard packet scheduling strategy in LTE-A. Therefore, it is expected for researchers and network service providers to design efficient schemes. This paper proposed an Enhanced Resource Allocation Scheme (E-RAS) that improves the packet scheduling strategy of LTE-A by addressing issues related to delay and fairness of users with both high and low channel conditions, for both Real Time (RT) and Non-Real Time (NRT) flows. The aim was achieved by using a fair spectrum allocation mechanism that consider the priority of RT flows of cell-centre users, without neglecting the throughput requirements of edge-centre users. Moreover, the proposed algorithm introduces an efficient traffic load balancing mechanism to efficiently regulate traffic load in LTE-A network. To evaluate the proposed scheme, simulation was conducted using the MATLAB programming with LTE-A system level simulator. The result obtained shows that the proposed scheme achieves better delay for RT and NRT traffics by 7% and 11% respectively as against the Resource Allocation Scheme to Optimize the Throughput (RASOT) scheme.

Design And Implementation Of Pose Recording System With Denavit Hartenberg Method In A 6-Dof Robot Rotaric

Robot developments have been integrated into several industrial and home appliances. To keep up with the development, UNIKA Atma Jaya created a group called PATRIOT as a head start, and started the development on ROTARIC robot. One of the early robot research discussed in this paper is to design and implement ROTARIC 6-DOF robot pose recording system which is drawn using forward kinematics with Denavit Hartenberg method. The system has a GUI with several function inside such as robot pose recording, 2-D display of the robot recorded pose using Forward Kinematics analysis, and playback recorded pose. The system uses several hardware that have been connected serially such as 6 Dynamixel MX-28 servo, OpenCM 9.04 +485 EXP microcontroller, and a Computer. The Software used in the system is C# .NET with WinForms App as the GUI and Arduino IDE. From the test done in this research, the recording and 2-D drawing system achieved error &lt;±1cm or 6.67% relative error caused by the resolution value while reading the servo position sensor and the value rounding inside the program. Meanwhile, the playback recording function that uses queue system achieved error of &lt;±1cm and error of ±5cm or 33.3% relative error when the recorded pose is played using real time delay used in recording. The error is caused by the robot failure to keep up with the speed of the recorded pose and the limited torque in servo number 2 in the robot.

2-Bit Wideband Electronically Controlled Reconfigurable Phased Array With Wide-Angle Beam-Scanning Capacity

In this paper, a 2-bit wideband electronically-controlled reconfigurable phased array with wide-angle beam-scanning capacity is proposed. Based on the current reversal mechanism and real-time delay design, a wideband 2-bit phase shifter is designed by cascading a wideband 180° phase shifter and a wideband 90° phase shifter. Then, a wideband 2-bit element is established by connecting the radiation area of the Vivaldi structure at the slot line of the phase shifter, and a metasurface slab with H-shaped metal layer is introduced to improve the scanning performance. Further, a planar wideband reconfigurable 2-bit array at 17 GHz has been designed and fabricated based on this element, and a stripline-based 1:16 power divider with the phase-shifting function is designed to simulate the space-fed spherical wave to avoid high sidelobe. Measured and simulated results are in good agreement. The measured results indicate that the main beam of the array can scan from -73° to 73° at 17 GHz by appropriately controlling the state of PIN diodes, and the gain fluctuation is only 2.18 dB. The measured 3-dB gain bandwidth of the array in the broadside direction is about 29.1% (from 14.7 GHz to 19.7 GHz). The wide-angle scanning and wideband performances are verified.

MLOps in freight rail operations

Railways are essential for freight transport due to their operational reliability advantages, but maintaining this advantage requires optimised railway infrastructure. Previous research has developed models to predict freight rail disruptions/disturbances and their associated delay times, in order to better understand the impact of multiple factors on them. However, because these models are built on static datasets, extracting real value from a model in a production environment remains difficult.This paper presents a methodology that demonstrates the potential of MLOps in automating the entire workflow, from data extraction to model deployment for real-time delay predictions in freight rail operations, including good practices of Continuous-Integration, Continuous-Delivery, and Continuous-Training, as well as a tool list for each process.Our research advances the field of railway operations by developing an entire MLOps workflow using data from the freight rail operations of the Luxembourgish National Freight Railway Company over a seventeen-month period. Furthermore, we employed a LightGBM model that had previously performed well in another study. This workflow can be automatically triggered to develop the processes and thus maintain an ML model capable of predicting delay times for CFL Multimodal operations in real-time.Our findings demonstrate that MLOps have the potential to automate the entire process, opening up new avenues for future research in this field. Although the methodology presented is intended to optimise freight rail operations for a specific company, it can be easily transferable to other railway companies or other transportation industries, such as aviation, shipping, and trucking.

Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways

Predictive analytics is an increasingly popular tool for enhancing decision-making processes but is in many business settings based on rule-based models. These rule-based models reach their limits in complex settings. This study compares the performance of a rule-based system with a customised LSTM encoder-decoder deep learning model for predicting train delays. For this, we use a purposefully built real-world dataset on railway transportation, where trains’ interdependence over the network makes delay prediction more difficult. Results show that the deep learning model, which incorporates rich spatiotemporal interdependency information in real-time, outperforms the rule-based system by 18%, with the difference increasing to above 23% with higher complexity. The study also dissects the performance difference across different settings: dense versus rural areas, peak versus off-peak hours, low versus high delay, and before versus during the COVID-19 pandemic. The deep learning model is implemented as a proof of concept for decision support within Belgium’s railway infrastructure company Infrabel.

Based on MOPSO Algorithm of Real-Time Traffic Signal Optimization Control for Intelligent Transportation Intersections

In order to improve the traffic efficiency of intersections, this paper establishes a multi-objective optimization model with the minimum real-time delay time and maximum capacity as the optimization objectives by applying multi-objective particle swarm optimization algorithm, and simulates the actual intersections. By optimizing the multi-objective model, a set of Pareto optimal solution sets can be obtained, and the optimal timing scheme can be determined by using multi-attribute decision-making algorithm and real-time update of signal timing configuration parameters to achieve dynamic optimization of signal timing. The simulation model of city road intersection is established by SUMO software. By using multi-objective particle swarm optimization algorithm to optimize the intersection simulation model, the traffic capacity of the simulated intersection has been greatly improved, with the average traffic capacity increasing by about 3.69% and the average vehicle delay time decreasing by about 21.35%.

Variational Bayesian Inference Time Delay Estimation for Passive Sonars

In passive sonars, distance and depth estimation of underwater targets is often limited by the accuracy of time delay estimations. The estimation accuracy of the existing methods of time delay estimation is limited by the uniform discrete grid (signal sampling rate). When a true time delay is out of the grid, the estimation accuracy deteriorates due to the mismatch between the real-time delay and the discrete grid. This paper proposes a new method for time delay estimation, which realizes the time delay estimation under the framework of variational Bayesian inference. The proposed method is grid-less, that is, continuous in the time domain. Unlike the popular grid-less compressive time delay estimation method, this method does not require parameter adjustment, and can automatically estimate the number of time delays, noise variance, and amplitude variance. The simulation results showed that the performance of the proposed method was superior to the reference state-of-the-art time delay estimation methods.

Patient diversion using real-time delay predictions across healthcare facility networks

Patient diversion using real-time delay predictions across healthcare facility networks

Characterisation of hydrodynamic trapping in microfluidic cross-slot devices for high strain rate applications.

Hydrodynamic trapping of a particle or cluster of particles based on contact and non-contact approaches has brought prominent insights to micro-nano scale applications. Of the non-contact methods, image-based real-time control in cross-slot microfluidic devices is one of the most promising potential platform for single cellular assays. Here, we report results from experiments conducted in two cross-slot microfluidic channels of different widths, with varying real-time delay of the control algorithm and different magnification. Sustained trapping of 5 μm diameter particles was achieved with high strain rates, of order 102 s-1, higher than in any previous studies. Our experiments show that the maximum attainable strain rate is a function of the real-time delay of the control algorithm and the particle resolution (pixel/μm). Therefore, we anticipate that with further reduced time delays and enhanced particle resolution, considerably higher strain rates can be attained, opening the platform to single cellular assay studies where very high strain rates are required.

Efficient Instruction Scheduling Using Real-time Load Delay Tracking

Issue time prediction processors use dataflow dependencies and predefined instruction latencies to predict issue times of repeated instructions. In this work, we make two key observations: (1) memory accesses often take additional time to arrive than the static, predefined access latency that is used to describe these systems. This is due to contention in the memory hierarchy and variability in DRAM access times, and (2) we find that these memory access delays often repeat across iterations of the same code. We propose a new processor microarchitecture that replaces a complex reservation-station-based scheduler with an efficient, scalable alternative. Our scheduling technique tracks real-time delays of loads to accurately predict instruction issue times and uses a reordering mechanism to prioritize instructions based on that prediction. To accomplish this in an energy-efficient manner we introduce (1) an instruction delay learning mechanism that monitors repeated load instructions and learns their latest delay, (2) an issue time predictor that uses learned delays and dataflow dependencies to predict instruction issue times, and (3) priority queues that reorder instructions based on their issue time prediction. Our processor achieves 86.2% of the performance of a traditional out-of-order processor, higher than previous efficient scheduler proposals, while consuming 30% less power.

ML-Based Delay Attack Detection and Isolation for Fault-Tolerant Software-Defined Industrial Networks.

Traditional security mechanisms find difficulties in dealing with intelligent assaults in cyber-physical systems (CPSs) despite modern information and communication technologies. Furthermore, resource consumption in software-defined networks (SDNs) in industrial organizations is usually on a larger scale, and the present routing algorithms fail to address this issue. In this paper, we present a real-time delay attack detection and isolation scheme for fault-tolerant software-defined industrial networks. The primary goal of the delay attack is to lower the resilience of our previously proposed scheme, SDN-resilience manager (SDN-RM). The attacker compromises the OpenFlow switch and launches an attack by delaying the link layer discovery protocol (LLDP) packets. As a result, the performance of SDN-RM is degraded and the success rate decreases significantly. In this work, we developed a machine learning (ML)-based attack detection and isolation mechanism, which extends our previous work, SDN-RM. Predicting and labeling malicious switches in an SDN-enabled network is a challenge that can be successfully addressed by integrating ML with network resilience solutions. Therefore, we propose a delay-based attack detection and isolation scheme (DA-DIS), which avoids malicious switches from entering the routes by combining an ML mechanism along with a route-handoff mechanism. DA-DIS increases network resilience by increasing success rate and network throughput.

Robust Real-Time Delay Predictions in a Network of High-Frequency Urban Buses

Providing transport users and operators with accurate forecasts on travel times is challenging due to a highly stochastic traffic environment. Public transport users are particularly sensitive to unexpected waiting times, which negatively affect their perception on the system’s reliability. In this paper we develop a robust model for real-time bus travel time prediction that departs from Gaussian assumptions by using Student- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> errors. The proposed approach uses spatiotemporal characteristics from the route and previous bus trips to model short-term effects, and date/time variables and Gaussian processes for long-run forecasts. The model allows for flexible modeling of mean, variance and kurtosis spaces. We propose algorithms for Bayesian inference and for computing probabilistic forecast distributions. Experiments are performed using data from high-frequency buses in Stockholm, Sweden. Results show that Student- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> models outperform Gaussian ones in terms of log-posterior predictive power to forecast bus delays at specific stops, which reveals the importance of accounting for predictive uncertainty in model selection. Estimated Student- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> regressions capture typical temporal variability between within-day hours and different weekdays. Strong spatiotemporal effects are detected for incoming buses from immediately previous stops, which is in line with many recently developed models. We finally show how Bayesian inference naturally allows for predictive uncertainty quantification, e.g. by returning the predictive probability that the delay of an incoming bus exceeds a given threshold.

Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment

We study a dynamic traffic assignment model, where agents base their instantaneous routing decisions on real-time delay predictions. We formulate a mathematically concise model and derive properties of the predictors that ensure a dynamic prediction equilibrium exists. We demonstrate the versatility of our framework by showing that it subsumes the well-known full information and instantaneous information models, in addition to admitting further realistic predictors as special cases. We complement our theoretical analysis by an experimental study, in which we systematically compare the induced average travel times of different predictors, including a machine-learning model trained on data gained from previously computed equilibrium flows, both on a synthetic and a real road network.