Flow In Real Time Research Articles

Monitoring Slug Flow Using Distributed Acoustic Sensing Technology with Different Sensing Cable Configurations

Summary Monitoring slug flow in real time over long distances is essential for facility design, operation, and flow assurance management. In this study, we investigated the use of distributed acoustic sensing (DAS) technology for slug flow monitoring, exploring various cable designs and deployment methods. The experimentation encompassed the use of five distinctive fiber-optic cable deployment methods: three internal cables of varying designs (thin, flat, and thick), supplemented by two external cables—one placed straight atop the pipeline while the other was helically wrapped around it. All cables were connected consecutively and successfully captured the dynamic variation of the slugging phenomenon along the pipe. We introduce different data processing algorithms for slug flow characterization, including standard deviation (SD) downsampling, slug frequency determination, and semblance for velocity extraction. The experimental results indicate a higher slug frequency but smaller slug sizes near the inlet of upward inclined pipe, where most slug structures originate. The structure velocity shows a positive correlation with DAS amplitude or the maximum strain rate, which could be related to the slug size. The flat cable exhibited a heightened amplitude response to slugs, while the internal thin and the external straight cables provided the most distinct delineation of slug patterns. The internal thick cable provided the least sensitivity among all due to its design. Although the linear deployment of fiber-optic cables is more practical compared to helically wrapped ones, their resolution limits detailed analysis of more intricate slug characteristics for short pipelines, such as precise velocity measurements of slugs. Data from the helically wrapped cable can address these limitations, providing more comprehensive insights.

A feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach for PEMFC air supply system

With the application of proton exchange membrane fuel cell (PEMFC) in the field of transportation and energy, the requirements for air flow and pressure tracking performance, efficiency and stability of PEMFC air supply system become higher and higher. To improve PEMFC cathode air flow and pressure control performance under load variation and avoid surge of air compressor, a novel feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach is proposed. Firstly, optimal references of oxygen excess ratio (OER) and cathode air pressure under different load are calculated based on net output power of PEMFC system analysis. Then, an extended state observer is designed to estimate the cathode air pressure and flow in real time, a feedback linearization sliding mode based decoupling controller is designed to enhance OER and air pressure tracking control performance and robustness. Considering on the operating trajectory and surge line of air compressor, a fuzzy logic based anti-surge compensator is designed to prevent air compressor from surge by compensating both air flow and pressure simultaneously. The proposed control approach is implemented in PEMFC air supply control experiments and the results demonstrates its effectiveness.

Analysis of mathematical methods for describing financial flows: dynamic modeling of an innovative company

Purpose. Development of a dynamic mathematical model of an innovative company operating in accordance with Ukrainian legislation, allowing for the analysis and adjustment of its financial activities. Methodology. When developing a model and description of financial flows, a factor analysis of the necessary indicators of the financial condition of the enterprise in question was carried out, which were used in the calculations of the presented mathematical modeling. A mathematical apparatus has been developed for calculating financial indicators depending on time. Using inductive, deductive and logical methods, an analysis of the financial situation for the year was carried out. Findings. The main methods and tools for describing financial flows have been analyzed. Mathematical modeling of the innovative company is carried out, which allows analyzing the costs of the enterprise and determining the size of its free funds. The calculation of financial indicators of the enterprise, which are demonstrated in the form of tables, is performed and the dynamics is shown in charts and graphs. All calculations were made on the basis of existing Ukrainian legislation using modern information technologies. Originality. A mathematical dynamic model of changes in financial flows at the enterprise has been developed and implemented. The proposed model takes into account various items of expenses and profits in accordance with the state legislation. Calculations allow for dynamic analysis and determination of the impact of various indicators on the financial condition of the enterprise, which allows for faster adoption and implementation of decisions in managing financial flows. This dynamic model is a convenient tool for any enterprise in Ukraine. Practical value. The results that were obtained during the study can become the basis for creating the necessary digital tools for researching, analyzing and adjusting the financial flows of a particular enterprise. Now methods are available that allow analyzing and describing financial flows more accurately and forecasting their changes in the future. The methodology presented allows the creation of dynamic models that can account for complex dependencies between different indicators and forecast the behavior of financial flows in real time. The financial model allows the simulation of cash flows of planned activities and the evaluation of financial planning in advance, taking into account the conducted research. It is straightforward to use and allows the analysis of different scenarios of business development, significantly reducing the time required.

Machine learning method monitors non-Newtonian fluid flow in real time

Combining a contactless flow sensor with a neural-network algorithm allows accurate measurement and control of non-Newtonian fluids for healthcare and manufacturing applications.

MODELS FOR REAL-TIME TRAFFIC FLOW MANAGEABILITY AND DECISION-MAKING IN INTELLIGENT TRANSPORTATION SYSTEMS

This article explores models in Intelligent Transportation Systems for real-time traffic flow manageability, focusing on decision-making processes. It covers forecasting, planning, implementing, and controlling strategies to manage traffic flow and ease congestion. Traffic flow prediction models, like dynamic route guidance and traffic flow prediction, utilize historical data and real-time inputs for proactive decision-making. Traffic flow planning models, such as dynamic route guidance index and route efficiency factor, aid in route selection and signal timing optimization. In order to streamline the boundless complexity, the authors assume that it is effective to delineate the managerial capacity paradigm of intelligent transportation systems into the two separate scenarios of “stable and known situation” and “unstable and with large uncertainty situation”. The article proposes a hypothesis to improve the decision-making process in traffic flow. The distinction between these two situations is essential for the smooth running of the business and requires a thorough understanding of the traffic flow in real time, making decisions in intelligent transport systems in order to direct the traffic. The article focuses on data-driven decisions for smoother traffic flow.

Online mass-flow-rate measurement of high-intensity gas-conveyed particle flow for dry mineral processing

Measuring the mass flow rate of high-intensity gas-conveyed particle flow (GCPF) in real time is of great importance for enhancing efficiency and productivity in dry mineral processing circuits. It also poses challenges beyond the capability of most existing technologies. In this article, we introduce a novel, cost-effective, online mass flow rate measurement system utilizing a force transducer. A theoretical model of the relationship between the average impact force and the mass flow rate was developed using the Hertz theory of impact. A prototype sensing system was then designed and constructed, followed by a series of experiments to validate the theoretical model. The experimental results corroborated the proposed model and showed the accuracy of the measurement was within 2% for mass flow rates above 1.741 g/s. This demonstrated the system’s capability to measure the mass flow rate of GCPF in real-time using measurements of the average impact force.

Data Analysis and Congestion Prediction Model in Intelligent Transportation Systems

The relentless march of urbanization has led to a surge in population density and a dramatic increase in the number of motor vehicles, with traffic congestion emerging as a significant barrier to urban development. Intelligent Transportation Systems (ITS) utilize advanced information technology to monitor and analyze traffic flow in real time, playing a pivotal role in alleviating urban traffic pressures and enhancing road usage efficiency. This paper aims to propose a data analysis method based on machine learning algorithms for real-time traffic monitoring and congestion prediction, thereby providing scientific decision support for urban traffic management. The research encompasses methods of traffic data collection, including data on vehicle location, speed, and flow; preprocessing and feature extraction techniques for traffic data to improve data quality and extract features useful for congestion prediction; and the application of various machine learning algorithms to establish a traffic congestion prediction model. Additionally, this paper assesses and optimizes the model and tests it with actual traffic data to verify its effectiveness and practicality. The research findings indicate that the proposed machine learning-based method can effectively predict traffic congestion, providing a powerful tool for traffic management departments, aiding in preemptive measures to reduce traffic delays and improve the travel experience for citizens. This study not only enriches the field of intelligent transportation systems research but also provides theoretical foundations and technical support for urban traffic management practices.

MLFV: a novel machine learning feature vector method to predict characteristics of turbulent heat and fluid flow

PurposeThis study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.Design/methodology/approachIn this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.FindingsThe results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R2 ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.Practical implicationsThe method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.Originality/valueThe paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

An adaptive power control method for soft open points based on virtual impedance

The fluctuations in power output from distributed power sources are rapid and dramatic, causing voltage fluctuations in the distribution network that threaten the safety of electricity consumption. Soft open points (SOPs) can replace traditional contact switches and are expected to suppress voltage fluctuations. However, traditional power regulation methods based on the reference values are unable to address rapid and large voltage fluctuations. Therefore, this paper proposes an adaptive power control method for SOPs based on virtual impedance. The SOP is modeled as a series link of adjustable impedance and a voltage source. Then, the voltage difference component is used to calculate the reference for the DQ domain to regulate the power flow in real time automatically. By doing so, the proposed method can smooth the voltage fluctuations in the distribution network. Additionally, the virtual impedance is also optimized to minimize the power loss. Finally, the method is validated through simulation and experiments, demonstrating that this method can automatically regulate power and significantly reduce voltage fluctuations.

Graph-Based Traffic Forecasting with the Dynamics of Road Symmetry and Capacity Performance

Symmetry in traffic patterns is a fundamental aspect of intelligent transportation systems, aiming to precisely predict traffic flow in real time despite the complex interplay of spatial and temporal factors. This paper presents a novel method of traffic forecasting that incorporates parameters related to road symmetry into a Graph Convolution Network model. Our model is crafted to dynamically adjust to real-time changes in road conditions, including the presence of symmetric and asymmetric road layouts, which substantially influence traffic flow. We have developed a GCN model that not only accounts for standard traffic flow metrics but also integrates a matrix representing road symmetry. The model undergoes training and validation on the METR-LA dataset, showcasing a significant enhancement in prediction accuracy. In the comparative analysis of state-of-the-art methods, our model demonstrated a significant enhancement in performance, achieving 30.68% improvement in Mean Squared Error (MSE) and a 24.28% improvement in Mean Absolute Error (MAE) over the best-performing method. The implications of our research are profound for urban planners, traffic management systems, and navigation service providers, as it offers a more dependable tool for forecasting traffic conditions, aiding in road design, and refining route planning strategies based on the symmetry of road networks.

A distributionally robust optimization approach for the potassium fertilizer product transportation considering transshipment through crossdocks

Potassium fertilizer is an essential input for agricultural productivity, and plays a critical role in various plant processes, influencing water uptake, enzyme activation, and photosynthesis. The efficient delivery of potassium fertilizer products to the end-users, typically farmers and agricultural enterprises, is of utmost importance. Due to the long traveling distance between the supplier and customers, decision makers of supplier normally set up multiple crossdocks to aid in transition and storage of potassium fertilizer products. In this situation, making an optimal transportation plan as well as the inventory level of crossdocks will directly enhance the efficiency and effectiveness of long-distance transportation. In this study, we model this problem as a dynamic transportation problem with transshipment considering the uncertain time-varying customers’ demand. To characterize the demand information, we first construct an ambiguity set of customers’ demand based in limited historical data and then develop a distributionally robust optimization-based (DRO) framework to optimize the transportation plan and related inventory level of crossdocks simultaneously. We also propose a general approach to overcome the computational challenges of DRO by transforming the original DRO into a second-order cone programming based on duality theory. Additionally, we introduce linear decision rules to adjust the optimization strategy based on the new observed demand, thus lead the model to handle the dynamic information flow in real time. In case study, all involved data are collected or derived from a real potassium fertilizer company located at Western China. The results show our model reduces the cost by 18.07% compared to stochastic model (sample average approximation), indicating a significant effectiveness of our model on the improvement of delivery efficiency and cost saving in real dynamic logistic systems. Also, we conclude the managerial insight that decision-makers should develop a comprehensive strategy, including improving communication to ensure order status updates, planning rationally to evenly distribute orders, and proactively allocating resources to meet operational demands.

Enhancing Cybersecurity in Smart Manufacturing: A Comprehensive Approach

The industry 4.0 defines a new era of much efficient and intelligent manufacturing processes that works in integration with other advanced technologies like AI, Big data, Block chain, etc. The smart manufacturing industry has remarkably evolved due to integration of advanced technologies like cyber-physical systems (CPS), Internet of Things (IoT), digital twins that makes up a highly connected infrastructure where data flows in real time. However, the involvement of these technologies has also significantly increased the exposure of manufacturing industry to the cyber threats. This article investigates several security challenges and major threats that are associated with the manufacturing industry. It evaluates various security implementations including digital twins, machine learning, and block chain based on their effectiveness, scalability, and feasibility in terms of security. Through a comprehensive literature review, case study analysis of notable cyberattacks and qualitative exploratory methods, this study explores the best line of defence against the cyber threats. Our findings highlight the critical role of robust and adaptive multiple line of defense mechanisms in mitigating cyber risks. The research concludes by emphasizing the need for an effective security strategy that combines advanced technological solutions with continuous employee education and proactive threat management to safeguard smart manufacturing processes against evolving cyber threats.

Characterization of Gas-Liquid Two-Phase Slug Flow Using Distributed Acoustic Sensing in Horizontal Pipes.

This article discusses the use of distributed acoustic sensing (DAS) for monitoring gas-liquid two-phase slug flow in horizontal pipes, using standard telecommunication fiber optics connected to a DAS integrator for data acquisition. The experiments were performed in a 14 m long, 5 cm diameter transparent PVC pipe with a fiber cable helically wrapped around the pipe. Using mineral oil and compressed air, the system captured various flow rates and gas-oil ratios. New algorithms were developed to characterize slug flow using DAS data, including slug frequency, translational velocity, and the lengths of slug body, slug unit, and the liquid film region that had never been discussed previously. This study employed a high-speed camera next to the fiber cable sensing section for validation purposes and achieved a good correlation among the measurements under all conditions tested. Compared to traditional multiphase flow sensors, this technology is non-intrusive and offers continuous, real-time measurement across long distances and in harsh environments, such as subsurface or downhole conditions. It is cost-effective, particularly where multiple measurement points are required. Characterizing slug flow in real time is crucial to many industries that suffer slug-flow-related issues. This research demonstrated the DAS's potential to characterize slug flow quantitively. It will offer the industry a more optimal solution for facility design and operation and ensure safer operational practices.

IMPROVING THE EFFICIENCY OF TRAFFIC LIGHT CONTROL AT ROAD INTERSECTIONS

From the functional point of view, the intersection is the most complex element of the road network. It is here that the traffic flows in different directions cross, and various maneuvers take place. This indicates that the intersection is a place with an increased concentration of conflict situations and an increased risk of traffic accidents. At most high-flow intersections, traffic is controlled by traffic lights, and their inefficient operation can lead to unnecessarily long wait times and overall increase in traffic delays. The research analyzed a regulated intersection at the crossing of Stepana Bandery and Viacheslava Chornovola main streets in the city of Rivne. This intersection is characterized by constant significant traffic jams. To simulate traffic conditions at this intersection, the research used PTV Vissim software. Observations of traffic flows, their distribution by directions, and traffic light regulation parameters were used as initial data for modeling the intersection traffic scheme. The main problem that must be solved when considering isolated traffic light intersections is the calculation and optimization of the signal plan, which involves: determining the number of phases; determining cycle duration; distribution, i.e. determining parts of available green time for each phase; modeling of traffic situations that may arise due to the passage of priority vehicles, congestion of vehicles in intersection areas during peak periods or other situations. At the same time, it is necessary to achieve the best possible characteristics of the intersection functioning. According to the research results, the road conditions were modeled and the signal plan was optimized with the use of PTV Vissim software. Simulation modeling of the intersection included drawing a road network, installing traffic lights (signal controllers) with a description of their work (choosing the type of light signaling devices, creating signal groups and traffic light signals, parameters for coordinating signals), forming pedestrian zones and a node, performing calculations with subsequent analysis of the received data. In order to improve the efficiency of the intersection, two options for the operation of the traffic light controllers are offered: 1. Lengthening of the “green” phase, for the convenience of turning to the left (by reducing the phase of oncoming traffic in one direction by 5 seconds). The total duration of the cycle of 70 seconds will not change; 2. Implementation of the third phase – fully pedestrian in all directions, lasting 20 seconds. The total duration of the cycle will increase to 90 seconds. A more progressive measure can be the introduction of adaptive systems, which are based on new traffic monitoring technologies and allow obtaining accurate data on traffic flows in real time and performing adaptive control of traffic lights, that is, adapting the signal plan in real time to changes in traffic flows.

Calculation method of carbon flow distribution in load-intensive regional energy centers

With the development of the economy, people’s demand for green energy has increased significantly. However, the traditional single fossil energy supply system cannot meet the needs of low-carbon. Therefore, this study employs energy hub to establish a multi-energy flow network that enables the integration of carbon flow within the network. Additionally, by utilizing the multi-energy flow trend, a carbon flow tracking method is adopted to achieve real-time carbon flow calculation. Results show that this network calculates the electricity cost of 20043 yuan, gas cost of 67253 yuan, and carbon emission cost of 3152 yuan. Compared with the traditional energy flow system, gas cost is reduced by 4.3% and 1.7%, electricity cost by 21.3% and 15.0%, and carbon emission cost by 8.7% and 6.6%. The two-way sharing carbon flow calculation model calculates that the user side and power supply side of the node each bear half of the network loss, proving two-way sharing effectiveness. Test results on IEEE5 machine 14-node system show that the calculation method can accurately find high-emission and low-emission areas, making the carbon emission allocation between power generation and user more fair and reasonable. This research can effectively reduce emissions cost, accurately calculate emissions flow in real time, and facilitate reasonable emission reduction planning.

Safety and efficacy of dynamic catheter-directed cerebral digital subtraction angiography for diagnosis of bowhunter syndrome spectrum disorders: A systematic review of the literature.

Dynamic catheter-directed cerebral digital subtraction angiography (dcDSA) is the gold standard for diagnosing dynamic vascular occlusion syndromes such as bowhunter syndrome (BHS). Nonetheless, concerns about its safety exist and no standardized protocols have been published to date. We describe our methodology and insights regarding the use of dcDSA in patients with BHS. We also perform a systematic literature review to identify cases of typical and atypical presentations of BHS wherein dcDSA was utilized and report on any procedural complications related to dcDSA. Our study included 104 cases wherein dcDSA was used for the diagnosis of BHS. There were 0 reported complications of dcDSA. DcDSA successfully established diagnosis in 102 of these cases. Thirty-eight cases were deemed atypical presentations of BHS. Fourteen patients endorsed symptoms during neck flexion/extension. In eight cases, there was dynamic occlusion of bilateral vertebral arteries during a single maneuver. Three patients had multiple areas of occlusion along a single vertebral artery (VA). An anomalous entry of the VA above the C6 transverse foramen was observed in four patients. One patient had VA occlusion with neutral head position and recanalization upon contralateral lateral head tilt. Our study highlights the safety and diagnostic benefits of dcDSA in characterizing the broad spectrum of BHS pathology encountered in clinical practice. This technique offers a powerful means to evaluate changes in cerebral blood flow and cervical arterial morphology in real time, overcoming the constraints of static imaging methods. Our findings pave the way for further studies on dcDSA to enhance cross-sectional imaging methods for the characterization of BHS and other dynamic vascular occlusion syndromes.

DISTRIBUTION OF RESOURCES TO ENSURE ANTI-TERRORIST SECURITY OF SOCIALLY SIGNIFICANT FACILITIES

A new software package ‒ electronic testing ground ‒ designed to increase the anti-terrorist protection of socially significant objects is presented. The complex supports mathematical modeling of antagonistic conflict between the offender and risk recipients in the context of a terrorist attack. The complex is based on the author’s methodology for increasing security, based on countering a terrorist attack through the controlled movement of human flows (controlled evacuation) to security zones along safe trajectories. Active protection of risk recipients under attack conditions takes into account the capabilities of engineering and technical means of protection. Algorithms for modeling controlled movement are built on the conditions of minimizing damage to risk recipients in conditions of a terrorist attack. Active protection is advisable when the attack time horizon is about 10 minutes. The most important feature of the presented electronic test site is the design of movement trajectories of risk recipients in automatic mode, taking into account the dynamics of the development of a terrorist attack. A digital model of the protected object is built using the domestic BIM system Renga. To simulate the activity of the intruder and the movement of human flows in real time the building model is represented as a topological graph. Quantitative assessment of the level of terrorist security of each socially significant object is based on a comparison of the levels of threats and security. Correspondence between the level of security and the level of threats is decisive for identifying a set of objects whose protection system requires modernization. The software package is focused on solving practical problems. In particular, the distribution of limited resources allocated to improve anti-terrorist security. The practical orientation of the electronic training ground is demonstrated by the example of the distribution of funds for the modernization of the anti-terrorism security system for the buildings of an educational institution.

A numerical simulation research on fish adaption behavior based on deep reinforcement learning and fluid–structure coupling: Implementation of the “perceive-feedback-memory” control system

The autonomous swimming of fish in a complex flow environment is a nonlinear and intricate system, which is the focus and challenge in various fields. This study proposed a novel simulation framework for artificial intelligence fish. It employed a high-precision immersed boundary-lattice Boltzmann coupling scheme to simulate the interactions between fish and flow in real time, and utilized the soft actor-critic (SAC) deep reinforcement learning algorithm for fish brain decision-making module, which was further divided into a vision-based directional navigation and a lateral line-based flow perception modules, each matched with its corresponding macro-action space. The flow features were extracted using a deep neural network based on a multi-classification algorithm from the data perceived by the lateral line and were linked to the fish actions. The predation swimming and the various Kármán gait swimming were explored in terms of training, simulation, and generalization. Numerical results demonstrated significant advantages in the convergence speed and training efficiency of the SAC algorithm. Owing to the closed-loop “perceive-feedback-memory” mode, intelligent fish can respond in real-time to changes in flow fields based on reward-driven requirements and experience, and the accumulated experience can be directly utilized in other flow fields, and its adaptability, model training efficiency, and generalization were substantially improved.

Using EBPF to identify ransomware that use DGA DNS queries

In today's world, where the Internet has become an integral part of the functioning of government and corporate institutions, the integrity and availability of information is becoming a key issue for many organizations and individual users. The issue of protection against crypto viruses and attacks, in particular, using DGA (Domain Generation Algorithms), a method used by attackers to automatically generate domain names for client-server (Command & Control) communication in the DNS-based virus ecosystem, is particularly relevant, making it difficult to detect and block them due to the way DNS is used in modern computer networks. Given the growing number of attacks that use DGA, there is a need to develop new methods that are faster and can analyze large traffic flows in real time and provide functionality for detecting and blocking them. eBPF (Extended Berkeley Packet Filter) is a modern tool that allows you to create small programs to monitor and analyze various aspects of the system in real time, including network traffic. These programs are executed directly in the operating system kernel and/or at the network card level. In this study, we consider the possibility of using eBPF to detect DGA activity in DNS traffic. The goal is to determine the effectiveness of real-time ransomware detection. We developed a ransomware analysis lab environment where we developed eBPF-based modules, tested them, and simulated an attack. In addition, a cloud-based data analysis environment based on Splunk was set up and rules for detecting a DGA attack were developed based on this analysis. This article presents the results of developing an eBPF-based program for analyzing DNS traffic, conducting DGA attacks, and methods for detecting them. These results can be an important contribution to the development of strategies to protect against malicious attacks in the network.

PISketch: Finding Persistent and Infrequent Flows

Finding persistent and low-active activity periods is very helpful in practice, for example to detect intrusion activities. Most of the literature focuses on finding persistent flows or frequent flows. No previous work is able to find persistent and infrequent flows. In this paper, we propose a novel sketch data structure, PISketch, to find persistent and infrequent flows in real time. The key idea of is to define a weight and its Reward and Penalty System for each flow to combine and balance the information of both persistency and infrequency, and to keep high-weighted flows in a limited space through a strategy. We implement PISketch on P4, FPGA, and CPU platforms, and compare the performance of PISketch with two strawman solutions (On-Off + CM sketch, and PIE + CM sketch), in terms of finding persistent and infrequent flows. Our experimental results demonstrate the advantage of PISketch, by comparing it to two strawman solutions: 1) The F1 Score of is around 22.1% and 57.6% higher than two strawman solutions, respectively; 2) The Average Relative Error (ARE) of is around 820.9 (up to 1188.8) and 126.2 (up to 265.6) times lower than two strawman solutions, respectively; 3) The insertion throughput of is around 1.23 and 16.5 times higher than two strawman solutions, respectively. Moreover, we implement two concrete cases of PISketch through end-to-end experiments. All of our codes are available at GitHub.