Flow In Real Time Research Articles

Intelligent transportation systems as cyberphysical systems in transport

In paper we offer a report on the results of the comparative analysis of existing concepts of the functioning and development of Smart Logistic. In paper shown that at present ITS is the most developed concept for the implementation of Smart Logistic and, in its essence, approaches the essence of the CPS concept as intelligent automatic (or close to automatic) control systems for physical objects and processes of various nature. Within the framework of the CPS concept, through test studies using various AI methods (GA, ESA, AA and ACAmod), it was determined that the most effective method for optimizing the route of freight traffic is ACAmod, which allows dynamic routing of logistics flows in real time, taking into account the non-stationary dynamics of TF. One of the promising areas for further research, the authors consider the creation of an intelligent system for supporting decisions on transport and logistics management of freight traffic in real time taking into account the influence of external factors of various nature on the transportation processes. The creation of such a system should be based on a modern interpretation of the concept of ITS as a CPS for Smart Logistics. In addition, in this report within the concept of the Cyber-Physical Systems are show the results of determining of the most effective method for optimizing of logistics flows in real time, taking into account the non-stationary dynamics of traffic flows

Diffuse Correlation Spectroscopy: A Review of Recent Advances in Parallelisation and Depth Discrimination Techniques

Diffuse correlation spectroscopy is a non-invasive optical modality used to measure cerebral blood flow in real time, and it has important potential applications in clinical monitoring and neuroscience. As such, many research groups have recently been investigating methods to improve the signal-to-noise ratio, imaging depth, and spatial resolution of diffuse correlation spectroscopy. Such methods have included multispeckle, long wavelength, interferometric, depth discrimination, time-of-flight resolution, and acousto-optic detection strategies. In this review, we exhaustively appraise this plethora of recent advances, which can be used to assess limitations and guide innovation for future implementations of diffuse correlation spectroscopy that will harness technological improvements in the years to come.

A Lightweight and Accurate Method for Detecting Traffic Flow in Real Time

Traffic flow detection provides significant information for intelligent transportation systems. However, as the mainstream research direction, vision-based traffic flow detection methods currently face the challenges of a trade-off between accuracy and speed. Furthermore, it is crucial that modularization be incorporated into the system design process to enhance the maintainability and flexibility of the system. To achieve this, we propose a modular design method that divides this task into three parts: vehicle detecting, vehicle tracking, and vehicle counting. As an important link of the system, vehicle detection greatly influences the accuracy and speed of the system. We therefore introduce a lightweight network called feature adaptive fusion-YOLOX, which is based on YOLOX. Specifically, in order to eliminate redundant information brought by bilinear interpolation, we propose a feature-level upsampling method called channel to spatial, which enables upsampling without additional calculations. Based on this module, we design a lightweight, multi-scale feature fusion module, feature adaptive fusion pyramid network (FAFPN). Compared with PA-FPN, FAFPN reduces FLOPs by 61% and parameters of the neck by 50% while maintaining comparable or even slightly improved performance. Through experimental tests, the traffic flow detection method proposed in this paper achieves high accuracy and adaptability in a series of traffic surveillance videos in different types of weather and perspectives and can realize traffic flow detection in real time.

Visual analytics of route recommendation for tourist evacuation based on graph neural network

The overcrowding of scenic spots not only threatens tourists’ safety but also affects the travel experience. Traditional methods for addressing tourist overload have involved limited access and guided evacuation. While limited access has been effective, it often results in a diminished tourist experience. Moreover, the existing guided evacuation rarely considers the impact on tourists’ experience, resulting in a low willingness to cooperate and making it difficult to estimate evacuation effort efficiency. To solve these problems, this paper proposed a tourist evacuation route recommendation algorithm based on a graph neural network considering the similarity of tourism styles (PER-GCN) and designed a visualization system to simulate and analyse evacuation efficiency. First, the interaction matrix of tourists and scenic spots was constructed using graph mining to extract the high-order interaction information. In the output layer, the similarity between scenic spots and tourism styles was calculated to further improve the accuracy of scenic spot recommendations. Second, due to route complexity and the real-time carrying capacity of scenic spots, the researchers optimized the evacuation routes. Finally, taking the West Lake spot as the case study, the effectiveness of PER-GCN was verified. Additionally, a visualization system was designed to monitor tourist flow in real time and analyse tourist portraits according to the clustering results of scenic spot styles. In addition, the evacuation efficiency of scenic spots was analysed by adjusting the parameters of tourists’ willingness to cooperate, evacuation batch, and the weight of route complexity and scenic spot carrying capacity.

Leveraging machine learning for predicting and monitoring clogging in laser cladding processes: An exploration of neural sensors

This study addresses the development of smart neural sensors to predict the powder mass flow and track clogging in real time during laser cladding. The challenges posed by powder granulometry and challenging environmental conditions that can lead to delivery failures are considered. An extensive experimental setup was conducted that included manipulation of key factors, such as laser power, travel speed, Z-step, N-layers, nozzle-to-substrate distance, and two types of process patterns. The mass flow rate of the powder was used as an independent variable to evaluate the predictive ability of the neural sensor with respect to the mass flow rate. Several models were trained and evaluated with different datasets and images of the cladding equipment. The model that integrated all data and images showed the best accuracy and precision also showed a strong predictive power for real-time estimation of the powder mass flow rate. Considering two practical rules—an error detection time of no more than one second and a confidence interval of less than 1.8 g/min—two strategies were proposed to meet these criteria. The first recommends the use of the comprehensive “all-features” model, while the second proposes a simplified model (with Z-step, N-slices, and the external camera as inputs) for efficient real-time error detection. The study provides an understanding of powder clogging prediction in laser cladding and suggests strategies for leaders in the field. Future research should validate these results and test these models in different environments to predict complex cladding properties and support the development of stand-alone laser cladding systems.

Experimental Study of the Hydrodynamics of an Open Channel with Algae Attached to the Side Wall

The construction of large-scale water diversion projects has effectively alleviated the current situation of the uneven distribution of water resources in China. However, due to the siltation of very fine sediment and organic matter on the side wall of an open channel, and the slow velocity of the side wall flow field, it is easy for epipelic algae to be produced, which affects water quality. Because prototype observation cannot be used to predict the series of flow in real time, and the calculation of the mathematical model is affected by parameter limitations, these two methods often cannot truly reflect the hydrodynamic characteristics of an open channel with epipelic algae. Therefore, by referring to the design parameters of the water diversion project channel, this study took the epipelic algae growing on the side wall of an open channel as the research object and used the scale of 1:30 to carry out a generalized flume experiment. Through the analysis of the physical characteristics of the prototype sample, and the simulation of the cohesive force between the oblique side wall and the epipelic algae, multi-group and multi-series hydrodynamic tests were carried out. The velocity distribution law and flow field distribution law were analyzed. The research results show that the presence of epipelic algae has a certain hindering effect on the flow velocity and significantly reduces the range of the peak velocity of the channel along the water depth. The position of the maximum velocity on the vertical line of the channel flow appears at the relative water depth of 0.6. In the case of small flow, the epipelic algae group only reduces the average flow rate of the channel by 5~6%; in the case of large flow, the effect of epipelic algae on the channel flow rate is minimal. This paper includes important scientific guiding significance and practical value for the regulation of water quantity and water quality safety, as well as the protection of long-distance projects.

Flood modelling for a real-time decision support system of the covered Lower Paillons River, Nice, France

Abstract Nice Metropolis in Alpes Maritimes, France is prone to flood. The city is crossed by the Lower Paillons River (LPR). Its discharge for a return period of 100 years is estimated at 794 m3/s. Part of the river is covered by 2 km. In addition, there are two retention storages in the river bed and a floodable road tunnel on the left bank. Due to the increase in urban development, flood management is challenging. An existing decision support system, Aquavar, uses DHI Mike tools to reproduce runoff for the Lower Var River in the same region. To extend this system to the LPR and reinforce flood management, a new modelling tool adapted to the characteristics of the LPR is needed. Consequently, this research utilizes the DHI MIKEPLUS tool to develop a 1D -2D coupled model for real-time flood management. The results demonstrate that flood events like those in 2017 and 2019 were correctly reproduced. The linear regression R2 is above 0.8 for all stations. It was also estimated that the covered river should stay clean to avoid widespread flooding in the urban area. Overall, the model is useful for simulating flow in real time and can help sustain urban development.

Analysis of the Influence and Propagation Law of Urban Rail Transit Disruptions: A Case Study of Beijing Rail Transit

In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers.

Inducing and disrupting flow during music performance.

Flow is defined as a state of total absorption in an activity, involving focused attention, deep engagement, loss of self-conscious awareness, and self-perceived temporal distortion. Musical flow has been associated with enhanced performance, but the bulk of previous research has investigated flow mechanisms using self-report methodology. Thus, little is known about the precise musical features that may induce or disrupt flow. This work aims to consider the experience of flow from a music performance perspective in order to investigate these features and introduces a method of measuring flow in real time. In Study 1, musicians reviewed a self-selected video of themselves performing, noting first, where in the performance they recalled "losing themselves" in the music, and second, where their focused state was interrupted. Thematic analysis of participant flow experiences suggests temporal, dynamic, pitch and timbral dimensions associated with the induction and disruption of flow. In Study 2, musicians were brought into the lab and recorded while performing a self-selected musical composition. Next, participants were asked to estimate the duration of their performance, and to rewatch their recordings to mark those places in which they recalled "losing themselves in the moment." We found that the proportion of performance time spent in flow significantly correlated with self-reported flow intensity, providing an intrinsic measure of flow and confirming the validity of our method to capture flow states in music performance. We then analyzed the music scores and participants' performed melodies. The results showed that stepwise motion, repeated sequence, and a lack of disjunct motion are common to flow state entry points, whereas disjunct motion and syncopation are common to flow state exit points. Overall, such initial findings suggest directions that warrant future study and, altogether, they have implications regarding utilizing flow in music performance contexts.

Hierarchical User-Driven Trajectory Planning and Charging Scheduling of Autonomous Electric Vehicles

Autonomous electric vehicles (A-EVs), regarded as one of the innovations to accelerate transportation electrification, have sparked a flurry of interest in trajectory planning and charging scheduling. In this regard, this work employs mobile edge computing (MEC) to design a decentralized hierarchical algorithm for finding an optimal path to the nearby A-EV parking lots (PLs), selecting the best PL, and executing an optimal charging scheduling. The proposed model makes use of unmanned aerial vehicles (UAVs) to assist edge servers in trajectory planning by surveying road traffic flow in real time. Furthermore, the target PLs are selected using a user-driven multiobjective problem to minimize the cost and waiting time of A-EVs. To tackle the complexity of the optimization problem, a greedy-based algorithm has been developed. Finally, charging/discharging power is scheduled using a local optimizer based on the PLs’ real-time loads, which minimizes the deviation of the charging/discharging power from the average load. The obtained results show that the proposed model can handle charging/discharging requests of on-move A-EVs and bring fiscal and nonfiscal benefits for A-EVs and the power grid, respectively. Moreover, it observed that user satisfaction in terms of traveling time and traveling distance is increased by using the edge-UAV model.

Load Balancing With Deadline-Driven Parallel Data Transmission in Data Center Networks

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor data generated by soft real-time IoT applications has been moved to data centers for storage and data analysis. Large amounts of these data are required to be processed within a given deadline to ensure application performance. Therefore, meeting the transmission deadlines of data flows for soft real-time applications has always been crucial yet challenging to current data centers. Recent progress has demonstrated that adopting parallel data transmission over multipath data center network combining with effective load balancing can achieve a high bisection network bandwidth, thus speeding up the network transfer of data flows. Nevertheless, the deadline miss ratios (DMRs) of these flows are not lowered as expected since the existing load balancing schemes are naturally agnostic to the deadline requirement. They are either unable to reroute traffic flexibly or aimlessly reroute these deadline-restrained flows, regardless of their urgent levels and path conditions. To address these inefficiencies, we propose a deadline-aware load-balancing scheme, namely, DLB, which perceives the deadline requirements and helps the urgent flows to timely switch to those faster transmission paths to complete quickly. Specifically, DLB computes the urgent level for each flow in real time to judge if the switch needs to make proactive rerouting. When a flow is nonurgent, DLB does not proactively change its transmission path, leaving more available paths to those flows with higher urgent levels. When a flow becomes extremely urgent, it immediately switches to those light-loaded paths to finish its data transmission before its deadline as far as possible. Experimental results of NS2 simulations and real testbed implementations show that DLB reduces the DMRs by up to 50% compared to the state-of-the-art data center load-balancing schemes, while only induces trivial overhead during deployment.

РАЗРАБОТКА МЕТОДИКИ ОПРЕДЕЛЕНИЯ РАЦИОНАЛЬНОГО ВЫБОРА ДЛИТЕЛЬНОСТИ РАЗРЕШАЮЩЕГО СИГНАЛА СВЕТОФОРНОГО РЕГУЛИРОВАНИЯ НА ОСНОВАНИИ НЕЙРОННОЙ СЕТИ

The article discusses a method for recognizing a person's face based on convolutional neural networks to develop a method for determining the rational choice of the duration of the permissive traffic light signal based on a neural network. The advantage of using convolutional neural networks is that they provide robustness to changes in face recognition conditions, as well as the ability to process a large database. The topology of the used convolutional neural network and the methodology for its training are substantiated.A fragment of a description of the program code is given that adjusts the duration of the permissive traffic light signal for pedestrian flows in real time. Methods are indicated to increase the recognition accuracy of a convolutional neural network.

Distributed Multi‐MMW Radar Fusion for Target Detection and Tracking in Highway Traffic Environment

High‐resolution millimeter‐wave (MMW) radar is viewed as a low‐cost and highly reliable sensor compared to camera, lidar, etc., in moving scenarios and thus has been selected by highway stakeholders as an important roadside detector to detect the movement of traffic vehicles and monitor traffic flow in real time. However, the echo signal of MMW radar in complex highway environment contains not only the signal reflected by target but also spurious signals and other interference signals, which significantly affects the estimation of the target movement state. To solve this problem, an improved vehicle tracking method is designed to simultaneously estimate the polar angle and polar radius in coordinator of MMW radar. Moreover, considering the movement patterns of target vehicles in dynamic uncertain traffic situations, a set of state space models, such as CA, CV, and CT are combined to represent the vehicle movement. In addition, based on the enhanced detection performance of a single radar, the combination of multiple MMW radars’ information was performed to determine the sequential trajectory of the target vehicle on the continuous road sections; then, the historical trajectory of the target vehicle was correlated and fused. Real experiments in highway scenarios show that the method used in this study is effective in deriving the trajectory of the vehicle and improving the positioning accuracy and reliability when the vehicle performs heavy maneuvers.

Printing conformal and flexible copper networks for multimodal pressure and flow sensing.

Flexible multimodal sensors with ultrasensitive detection capabilities are an indispensable component of wearable electronics and are highly sought-after involving a wide range of signal monitoring such as artificial skin and soft robotics. Here we report a flexible and wireless multimodal sensor using low-temperature additive manufacturing of copper nanoplates on elastic polyurethane substrates for temperature, pressure, and flow monitoring. The positive temperature coefficient and piezoresistive performance of the copper nanoplate network translates to a reliable temperature, steady-state and dynamic pressure/flow sensing for detecting pressures as small as 0.64 Pa with a response time of 130 ms, as well as velocity detection ranging from 2.5-6.8 m s-1. Additionally, by incorporating a printed antenna, it enables a self-powered, battery-free system, offering a wireless readout of printed multimodal sensors with superior real-time sensing performance in conjunction with wearable flexibility.

High-Sensitivity Signal Processing Circuit Integrated Coil Antenna Sensor Toward Measurements of the Water Fraction in the Oil–Water Two-Phase Flow

The measurement of water fraction in the oil-water two-phase flow is important for the processing control of oil production. In this paper, a coil antenna based high-sensitivity inductive sensor is proposed to measure the water fraction in the oil-water two-phase flow in real time. It employs a wire-wound coil antenna, an associated signal processing circuit, and a sensor fixture to support and hold them. The change of water fraction in the flow causes the change in equivalent inductance of the coil antenna. By adding external capacitors to the coil antenna, a parallel inductance-capacitance resonant circuit is formed, and it efficiently converts the inductance change to the voltage change, which improves the measurement sensitivity. The specified signal processing circuit picks up the voltage change, realizes the AC/DC signal conversion, and amplifies DC small signals. Then the measurement theory is analyzed, and the transduction model of coil inductance change to voltage output is given. To verify the proposed inductive sensor, a prototype is fabricated, and a measurement platform is built in which the generated oil-water flow has a water fraction ranging from 0 to 100%. Experimental results show that the proposed inductive sensor can measure the water fraction in real time with a measurement resolution of 1% and a measurement standard deviation of less than 1.2%.

A Decision Tree-Based Online Traffic Classification Method for QoS Routing in Data Center Networks

In data center networks, how to ensure the quality of service (QoS) of traffic effectively has become an important optimization goal. Traffic classification has been widely studied to guarantee network flow QoS since it provides a basis for the network performance level requirement of different flow types. However, traditional network traffic classification methods, which can only identify traffic type offline, have no ability for online classification and the in-time QoS guarantee. To solve this problem, we propose an online traffic classification for QoS routing (OCQR) framework based on programmable data plane in this paper. Firstly, in order to improve the accuracy of flow classification and reduce the impact on line-rate packet forwarding, the way of machine learning model offline training and online deployment is used to classify the network flow in real time. Based on the hardware foundation of programmable switch and the complexity of machine learning algorithm, a CART decision tree model is used to classify network flows. Secondly, in order to actually deploy and run the decision tree model in the programmable switch with limited computing and storage resources, we prune the decision tree model and optimize the leaf nodes to reduce the size of the decision tree and improve the classification recall rate. Finally, by building a P4 simulation environment, the recall rate of OCQR’s classified scheduling of some application flows reaches more than 98%, which is consistent with the evaluation results of the offline dataset. At the same time, compared with the classification method based on SDN, OCQR does not require additional bandwidth occupation, so it has less impact on network performance.

The growing importance of ultrasonography in cosmetic dermatology: An update after the 23rd IMCAS Annual World Congress (2022).

Ultrasound (US) has been used for many years in the field of medicine. Many specialties have embraced US as a quick, painless, and relatively inexpensive tool to assist the clinician in determining anatomy, pathology, and aid in diagnostic or therapeutic procedures. US allows for precise mapping of cutaneous and subcutaneous structures in the face, in particular vascular structures. The use of US leads to reduced chances of complications and clinical failures, rendering more safety and high quality. US is considered the first-imaging technique for dealing with fillers and managing their potential complications. US can be deployed for vascular mapping, safe placement of fillers, and directed low-dose hyaluronidase reversal of vascular adverse events. It is a noninvasive imaging modality that provides a good definition for studying the skin, deeper layers, and blood flow in real time. In other words, we go from static to dynamic anatomy. In addition, US can guide with the application of botulinum toxin, in order to define the muscular planes. US may contribute to a more personalized procedure, better cosmetic results, and help to avoid complications. In general, physicians tend to use it for prevention. Last, for research purposes, US examination provides valuable information on the behavior, longevity, and interaction of the filler within the tissues. This new approach for US-guided treatments is a very practical and an effective method in cosmetic dermatology. As doctors, we owe it to our patients to do our best to prevent any harm. We feel that in near future, US will be an essential diagnostic tool in any dermatology or cosmetic doctor's office to both ensure safety and provide legal protection for the professional.

Decentralized Optimal Power Flow for time-varying network topologies using machine learning

Distributed Energy Resources (DERs) have been continuously increasing over time. When DERs are uncontrolled, technical challenges arise such as over-voltage and congestion. Therefore, Distribution System Operators (DSO) would benefit from controlling PV inverters and batteries. However, the communication infrastructure required to control DERs in a centralized manner by solving Optimal Power Flow in real time is not developed and is intractable. Furthermore, as new DERs are deployed, a new question arises on how to manage these agents given the time-varying environment (new nodes and branches in the graph). This work addresses this gap by proposing a machine learning framework to design controllers that are robust to topological changes for each new inverter-connected DER. We show that the “Learned controller” strategy always enables a more economical operation in comparison to using utilities’ standards. Results show savings ranging from 113% up to 487%. The strengths of the controller we propose are its interpretability, ease of implementation and decentralized nature. From these results we propose guidelines for the DSO to design controllers for new DERs deployed in time-varying networks.

A multi-shift phase angle control strategy for bidirectional wireless power transmission system

Using the charging and discharging capability of battery-based loads and a two-way wireless energy transmission system, grid-load balancing can be achieved in both directions. The system can be applied to many locations by deploying power flow in real time. In the scenario of vehicle network interconnection, it realizes peak-shaving and valley-filling to optimize grid dispatching capability; in the scenario of modular satellite interconnection, it realizes load balancing and effectively improves energy utilization. Therefore, a suitable control strategy is needed to precisely change the output power flow and the output power magnitude. A method to coordinate multiple phase shifting angles in the system hierarchy to achieve power flow regulation is proposed for a bidirectional wireless power transmission system with a dual full-bridge topology. In this paper, we first model the bidirectional transmission system and analyze the relationship between each phase shift angle and active and reactive power. The traditional control strategy is optimized by reducing the number of control loop variables and using the system power factor angle instead of the difficult-to-control external phase shift angle, while ensuring the voltage gain for optimal system efficiency. Finally, only two control loops are used to control the direction and magnitude of the output power respectively, and the effectiveness and correctness of the control strategy are verified by using the corresponding simulation experiments to achieve smooth switching of the bidirectional electric power transmission power flow.

Real-time estimation and prediction of unsteady flows using reduced-order models coupled with few measurements

The estimation and prediction of unsteady flows in real time offers significant advantages for the monitoring and active control of complex hydrodynamic and aerodynamic systems, such as wind turbine blades, hydrofoils and aircraft wings. A new data assimilation algorithm is proposed for the estimation and prediction of unsteady flows, coupling in real time onboard measurements and fluid dynamics simulations at minimal computational expense. The procedure combines a Proper Orthogonal Decomposition Galerkin method, a model under location uncertainty stochastic closure, and a particle filtering scheme. The algorithm is validated using case studies of two- and three-dimensional wake flows at low and moderate Reynolds numbers respectively. Following an initial learning window to train the algorithm, and using only a single measurement point, our method is shown to perform well against conventional reduced data assimilation algorithms for up to 14 vortex shedding cycles.