Flow Classification Research Articles

Design of expert active knn classifier algorithm using flow stroop colour word test to assess flow state

Flow state assessment is essential to understand the involvement of an individual in a particular task assigned. If there is no involvement in the task assigned then the individual in due course of time gets affected either by psychological or physiological illnesses. The National Crime Records Bureau (NCRB) statistics show that non-involvement in the task drive the individual to a depression state and subsequently attempt for suicide. Therefore, it is essential to determine the decrease in flow level at an earlier stage and take remedial steps to recover them. There are many invasive methods to determine the flow state, which is not preferred and the commonly used non-invasive method is the questionnaire and interview method, which is the subjective and retroactive method, and hence chance to fake the result is more. Hence, the main objective of our work is to design an efficient flow level measurement system that measures flow in an objective method and also determines real-time flow classification. The accuracy of classification is achieved by designing an Expert Active k-Nearest Neighbour (EAkNN) which can classify the individual flow state towards the task assigned into nine states using non-invasive physiological Electrocardiogram (ECG) signals. The ECG parameters are obtained during the performance of FSCWT. Thus this work is a combination of psychological theory, physiological signals and machine learning concepts. The classifier is designed with a modified voting rule instead of the default majority voting rule, in which the contribution probability of nearest points to new data is considered. The dataset is divided into two sets, training dataset 75%and testing dataset 25%. The classifier is trained and tested with the dataset and the classification efficiency is 95%.

Predictive Congestion Control based on Collaborative Information Sharing for Vehicular Ad hoc Networks

Traffic jams are an essential and continuous challenge in our cities, responsible for socioeconomic and environmental concerns and an ambitious traffic jams management agenda is urgent. The distributed solutions in the literature for Traffic Management Systems (TMS) are heavily based on beacon messages or proactive communication protocols to share vehicular traffic information among vehicles. Thus, these solutions are not scalable when the number of vehicles increases in the network — when there are traffic jams. To overcome these problems, we propose a new VANET-based traffic management system named CoNeCT: Predictive Congestion Control based on Collaborative Information Sharing for Vehicular Ad hoc Networks. CoNeCT’s primary goal is to support vehicles’ collaboration in analyzing, predicting, and managing congestion. The proposed system was designed to decrease the number of messages by using a novel road segment load assessment that improves traffic flow classification. Vehicles aware of traffic conditions share it with their neighbors, and they can also request traffic views whenever necessary. Additionally, vehicles can detect significant traffic variations and predict future traffic conditions to improve roads’ overall traffic conditions, mitigating the congestion before it arises. Results obtained from an extensive performance analysis show CoNeCT’s ability to reduce traffic congestion with a low impact on the wireless communication medium, outperforming the state-of-art systems.

Classification and zoning of total evaporation flows for vegetation for the territory of the Marksovsky district of the Saratov region obtained according to the product MOD 16 ET in 2003-2017

This paper presents the results of the classification and zoning of the evapotranspiration fluxes for the growing season ETaw in the Marksovsky district of the Saratov region for the growing seasons of 2003-2017. The calculations for these types of spatial analysis were carried out in the ArcGIS Pro 2.8 software environment using the previously obtained two spatial arrays ETaw. To classify ETaw flows by land use types, we used raster datasets obtained at IIASA (International Institute for Applied Systems Analysis) for the territory of Russia as a result of processing and analyzing space monitoring data. Analysis of the classification results for the study area revealed a significant difference in ETaw flows in areas occupied by different types of land use. For zoning ETaw flows by distance from the left bank of the Volga River, the boundaries of seven zones were built at distances from its edge: 0-5 km; 5-10 km; 10-20 km; 20-30 km; 30-40 km; 40-50 km; 50-60 km. For the territories located within the indicated zones, the filtration of two ETaw flows was carried out, which revealed a decrease in their values as they moved away from the left bank of the Volga River

Efficient pipelined flow classification for intelligent data processing in IoT

The packet classification is a fundamental process in provisioning security and quality of service for many intelligent network-embedded systems running in the Internet of Things (IoT). In recent years, researchers have tried to develop hardware-based solutions for the classification of Internet packets. Due to higher throughput and shorter delays, these solutions are considered as a major key to improving the quality of services. Most of these efforts have attempted to implement a software algorithm on the FPGA to reduce the processing time and enhance the throughput. The proposed architectures, however, cannot reach a compromise among power consumption, memory usage, and throughput rate. In view of this, the architecture proposed in this paper contains a pipeline-based micro-core that is used in network processors to classify packets. To this end, three architectures have been implemented using the proposed micro-core. The first architecture performs parallel classification based on header fields. The second one classifies packets in a serial manner. The last architecture is the pipeline-based classifier, which can increase performance by nine times. The proposed architectures have been implemented on an FPGA chip. The results are indicative of a reduction in memory usage as well as an increase in speedup and throughput. The architecture has a power consumption of is 1.294w, and its throughput with a frequency of 233 ​MHz exceeds 147 Gbps.

Исследование информационного взаимодействия железнодорожного и морского транспорта в логистических цепях доставки груза

Purpose: To investigate the development of logistic chain of supplies from simple to maximal one, to consider informational flow evolution of logistic process till a single digital platform. To analyze the prerequisites for the transfer to a digital logistics and the level of development at the moment. To build the descriptive model of the interaction between a railway and sea transport. Methods: The research of the interaction between a station and a port is pursued in two major directions: information transfer, cargo traffic progression. The analysis of the development and classification of delivery chains is submitted. The descriptive models of document flow on import cargo traffic progression are built and analyzed. Results: The necessity of parallel development of transport and terminal complexes, RZD JCS infrastructure is pointed out for the transfer to network logistics stage. Information flow classification is presented. The descriptive model of the information interaction between a sea and port is built. Practical importance: The possibility to cut surplus document flow between a port and near-the-port station is shown, the need in such actions for the transfer to a single network platform is presented.

REAL-TIME MOVING VEHICLE COUNTER SYSTEM USING OPENCV AND PYTHON

— In view of highway management, intelligent vehicle recognition, and counting is becoming increasingly crucial. In highway monitoring, it is a crucial part that every vehicle should be observed and should go through vehicle monitoring and detection system. With today’s technology have great effect on this area the road surveillance camera gives a large library of traffic video footage has been available for examination. By taking these video footage from the cameras the paper will present a system that will achieve to calculate the vehicle their direction with using image subtractor and counters. The computer vision will advance the capability of the system to process on the videos and give desired output. The main idea behind using computer vision and open CV is that they will not disturb the traffic and monitoring part will be done smoothly. The vehicle counter-offered in this study is primarily based totally on an aggregate of image processing algorithms which includes item detection, edges detection, and frames differentiation. The machine has been applied with the use of Python. This paper explains how to use multiple libraries for real-time image processing to achieve traffic flow counting and classification. The proposed system differs the existing system by providing the feature to count the cars in the day and night mode.

Operational performance characteristics of an axial double baffles three channels classifier for coarse pulverized coal

In order to obtain the working principle of the axial direction classifier and raise its separation efficiency, the flow field and classification performance of the classifier was investigated by numerical simulation and experimental measurement. The particle size distribution at inlet, outlet and return pipe was measured by laser particle size analyzer and coupled with Rosin-Rammler model. The particle trajectory was analyzed using the discrete phase model. The tangential velocities played a dominant role in particles radial distribution mainly in the form of Rankine vortex. The axial velocities determine the vertical distribution of particles. The velocity distributions and turbulence influence the cutting particle size and separation efficiency mostly. The most suitable condition is baffles angle of 20° and inlet velocity of 19.3 m/s considering the load of the mill and the fineness of the pulverized coal at the inlet of the classifier.

Operational regimes in a confined pulsatory two-phase thermosyphon

The major loopholes in existing criteria used for the classification of various flow characteristics/patterns in the confined two-phase closed thermosyphons (TPCTs), and the additional accountable parameters have been recognized. The phenomenology of flow regimes in confined TPCTs, and the influence of confinement on their heat transfer characteristics need to be understood better. Also, the mitigative solutions to enhance the operational limits of a confined TPCT need to be developed. So, a visual and detailed study of the thermo-hydrodynamics of TPCTs is essential. The current research presents a confined pulsatory two-phase thermosyphon (PTPT), which is a hybrid cooling device derived by combining the ideologies of a pulsating heat pipe (PHP) and a TPCT. The functioning of PTPT involves thermosyphon phenomena in its evaporator coupled with oscillations of a liquid–vapor interface in its condenser. An experimental study of different flow regimes, viz. bubbly, bubbly/slug, geyser, slug, churn and annular flows, and associated heat transfer mechanisms observed in the PTPT at different applied thermal boundary conditions has been performed. The two-phase flow in PTPT has been rigorously mapped and an empirical flow regime map has been developed using the confinement and Jakob numbers for regime characterization. Contrary to the conventional notions, highly confined as well as unconfined flow patterns have been observed within a narrow range of the confinement number, with unconfined ones at the highest confinement number. The film-flow along with the corresponding instabilities, nucleation and growth of vapor bubbles, inter-regime transitional states, and associated pressure fluctuations have been studied in detail.

A feature selection-based method for DDoS attack flow classification

Distributed Denial of Service (DDoS) attacks still be a great threat to the availability of online servers. To defend against attacks, the challenge is not only detecting DDoS attacks as they occur but also identifying, and thus blocking the attack flows. However, existing classification methods cannot accurately and efficiently differentiate between attack flows and benign flows. In this paper, we propose a DDoS attack flow classification system, named SAFE, to accurately and quickly identify attack flows in network layer. First, SAFE chooses the optimal features by removing the redundant features and selecting the most informative features. Second, a threshold tuning method is proposed to identify the best threshold for each feature. Finally, an aggregated feature-based linear classifier is proposed to weight the selected features for classification. Since the proposed method monitors the flows in network layer, it can detect the traditional DDoS attack flows as well as the attack flows launched by Internet of Thing (IoT) devices. Comprehensive experiments are carried out on one IoT and two sophisticated DDoS attacks to evaluate the classification performance of the proposed method. The comparison results show that SAFE can achieve better classification performance than the state-of-the-art methods in terms of classification accuracy and efficiency.

Clinically viable myocardial CCTA segmentation for measuring vessel-specific myocardial blood flow from dynamic PET/CCTA hybrid fusion

BackgroundPositron emission tomography (PET)-derived LV MBF quantification is usually measured in standard anatomical vascular territories potentially averaging flow from normally perfused tissue with those from areas with abnormal flow supply. Previously we reported on an image-based tool to noninvasively measure absolute myocardial blood flow at locations just below individual epicardial vessel to help guide revascularization. The aim of this work is to determine the robustness of vessel-specific flow measurements (MBFvs) extracted from the fusion of dynamic PET (dPET) with coronary computed tomography angiography (CCTA) myocardial segmentations, using flow measured from the fusion with CCTA manual segmentation as the reference standard.MethodsForty-three patients’ 13NH3 dPET, CCTA image datasets were used to measure the agreement of the MBFvs profiles after the fusion of dPET data with three CCTA anatomical models: (1) a manual model, (2) a fully automated segmented model and (3) a corrected model, where major inaccuracies in the automated segmentation were briefly edited. Pairwise accuracy of the normality/abnormality agreement of flow values along differently extracted vessels was determined by comparing, on a point-by-point basis, each vessel’s flow to corresponding vessels’ normal limits using Dice coefficients (DC) as the metric.ResultsOf the 43 patients CCTA fully automated mask models, 27 patients’ borders required manual correction before dPET/CCTA image fusion, but this editing process was brief (2–3 min) allowing a 100% success rate of extracting MBFvs in clinically acceptable times. In total, 124 vessels were analyzed after dPET fusion with the manual and corrected CCTA mask models yielding 2225 stress and 2122 rest flow values. Forty-seven vessels were analyzed after fusion with the fully automatic masks producing 840 stress and 825 rest flow samples. All DC coefficients computed globally or by territory were ≥ 0.93. No statistical differences were found in the normal/abnormal flow classifications between manual and corrected or manual and fully automated CCTA masks.ConclusionFully automated and manually corrected myocardial CCTA segmentation provides anatomical masks in clinically acceptable times for vessel-specific myocardial blood flow measurements using dynamic PET/CCTA image fusion which are not significantly different in flow accuracy and within clinically acceptable processing times compared to fully manually segmented CCTA myocardial masks.

A modeling methodology to study the tributary-junction alluvial fan connectivity during a debris flow event

Abstract. Traditionally, interactions between tributary alluvial fans and the main river have been studied in the field and in the laboratory, giving rise to different conceptual models that explain their role in the sediment cascade. On the other hand, numerical modeling of these complex interactions is still limited because the broad debris flow transport regimes are associated with different sediment transport models. Even though sophisticated models capable of simulating many transport mechanisms simultaneously exist, they are restricted to research purposes due to their high computational cost. In this article, we propose a workflow to model the response of the Crucecita Alta alluvial fan in the Huasco Valley, located in the Atacama Desert, Chile, during an extreme storm event. Five different deposits were identified and associated with four debris flow surges for this alluvial fan. Using a commercial software application, our workflow concatenates these surges into one model. This study depicts the significance of the mechanical classification of debris flows to reproduce how an alluvial fan controls the tributary–river junction connectivity. Once our model is calibrated, we use our workflow to test if a channel is large enough to mitigate the impacts of these flows and the effects on the tributary–river junction connectivity.

The Clinical Efficacy of Tirofiban Combined with Ticagrelor and Aspirin in Treating Acute Myocardial Infarction by Percutaneous Coronary Intervention and Its Effect on Patients' Cardiac Function.

Objective To explore the clinical efficacy of tirofiban combined with ticagrelor and aspirin in acute myocardial infarction treatment by percutaneous coronary intervention and its effect on patients' cardiac function. Methods We selected 102 patients with acute myocardial infarction who came to The First Hospital of LanZhou University for treatment from July 2018 to May 2021. On the basis of conventional treatment, patients were separated into a joint group (tirofiban combined with ticagrelor and aspirin) comprising 55 cases and a control group (conventional ticagrelor and aspirin dual treatment) involving 47 cases. Blood flow classification of the two groups of patients was immediately recorded and compared after the myocardial infarction thrombolysis test (TIMI). Left ventricular function-related indicators, platelet-related parameters, neutrophil/lymphocyte ratio (NLR), red blood cell distribution width (RDW), and platelet/lymphocyte ratio (PLR) before treatment and 7 days after PCI were evaluated and compared between the groups before treatment and 3 months after treatment. ELISA was utilized to detect the serum levels of inflammatory factors, tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and hypersensitive C-reactive protein (hs-CRP) before and after treatment. Incidence of major adverse cardiovascular events (MACEs) and adverse reaction incidence was put into comparison between the two groups in the course of the 3-month follow-up period. Compared with the control group, the joint group accounted for more patients with TIMI blood flow classification level 3 (P < 0.05) and showed more drastic improvement on the left ventricular function, platelet-related parameters, and serum inflammatory factors (P < 0.05). Moreover, patients of the joint group suffered less fluctuation from RDW, NLR, and PLR (P < 0.05), and their incidence of MACE was drastically lower in contrast with the control group (P < 0.05). No notable changes were presented in terms of incidence of adverse reaction (P > 0.05). For patients who suffered from acute myocardial infarction and treated with percutaneous coronary intervention, the application of tirofiban combined with ticagrelor and aspirin could effectively reduce the incidence of no reflow or slow blood flow, improve myocardial perfusion function, and have marked curative effects. It is worthy of clinical promotion and application.

Development of an Integrate E-Medical System Using Software Defined Networking and Machine Learning

Scholars and medical professionals have recognizes the importance of electronic medical monitoring services for tracking elderly people's health. These platforms generate a large amount of data, requiring privacy and data security. on the contrary, Using Software Defined Networking (SDN) to maintain network efficiency and flexibility, which is especially important in the case of healthcare observation, could be a viable solution. Moreover, machine learning can additionally utilized as a game changing tool which incorporated with SDN for optimal level of privacy and security. Even so, integrating SDN into machine learning, which heavily relies on health sensors of patients, is incredibly difficult. In this paper, an Integrate Medical Platform (IMP) with a focus on SDN and Machine learning integration is proposed. We produce a platform that reduces complexity by identifying high level SDN regulations based on the extracted flow classes and utilizing machine learning traffic flow classification techniques. F or various types of traffic, We employ supervised learning approaches based on models that have already been trained. We use four algorithms for supervised learning: Random forest, Logistic Regression classifiers, K-NN, and SVM, with different characteristics. Finally, we evaluated IMP by using accuracy, precision, TPR, TNR, FPR, MAE, and energy consumption.

High-performance pseudo-anonymization of virtual power plant data on a CPU cluster

The considerable move towards the use of renewable energy resources has been provided by the digitization of energy systems with the help of virtual power plants (VPPs). However, due to the coincidence of this move with the introduction of new technologies in information and communications, joining these systems raises concerns about the privacy of personal data. The only real-world approach widely used in this case is to anonymize or pseudonymize the information associated with individuals in data received from distributed measurement devices. In this paper, we propose the method of classifying received data packets into different flows and assigning different access levels for each flow. This method makes data pseudonymous. Before this step, the received data, which has a different format, is unionized. To implement this idea, a tuple space flow classification algorithm is parallelized on a CPU cluster using MPI and OpenMP according to different scenarios. The CPU cluster consists of one head node and two computational nodes for packet classification operations. In this research, two scenarios have been used to run the CPU algorithm in parallel. The first scenario uses MPI and the second scenario uses a combination of MPI and OpenMP libraries. Also, the Tuple Space algorithm has been implemented on the computing systems using the mentioned libraries in the form of two scenarios using OpenMP and MPI. According to our results, the increase in the number of processor cores is linearly correlated with the increase in the speed of classification. Furthermore, while MPI uses more memory than OpenMP, it helps to achieve a higher speed of classification. In the combined method, the maximum speed of flow classification can be achieved if the number of processes and threads is equal to the number of processor cores. In other words, when the sum of processes and threads does not outnumber CPU cores, the least classification time and memory usage can be achieved.

Self-similar curve shortening flow in hyperbolic 2-space

We find and classify self-similar solutions of the curve shortening flow in standard hyperbolic 2-space. Together with earlier work of Halldorsson on curve shortening flow in the plane and Santos dos Reis and Tenenblat in the 2-sphere, this completes the classification of self-similar curve shortening flows in the constant curvature model spaces in 2-dimensions.

On classification of Morse-Smale flows on projective-like manifolds

In this paper, the problem of topological classification of gradient-like flows without heteroclinic intersections, given on a four-dimensional projective-like manifold, is solved. We show that a complete topological invariant for such flows is a bi-color graph that describes the mutual arrangement of closures of three-dimensional invariant manifolds of saddle equilibrium states. The problem of construction of a canonical representative in each topological equivalence class is solved.

Application of an integrated methodology for spatial classification of the environmental flow in the Bulgarian-Greek Rhodope Mountain Range

Application of an integrated methodology for spatial classification of the environmental flow in the Bulgarian-Greek Rhodope Mountain Range

Application of an integrated methodology for spatial classification of the environmental flow in the Bulgarian-Greek Rhodope Mountain Range

Application of an integrated methodology for spatial classification of the environmental flow in the Bulgarian-Greek Rhodope Mountain Range

On classification of Morse-Smale flows on projective-like manifolds

Решается проблема топологической классификации градиентно-подобных потоков без гетероклинических пересечений, заданных на четырехмерном проективно-подобном многообразии. Показывается, что полным топологическим инвариантом в этом классе является двуцветный граф потока, описывающий взаимное расположение замыканий трехмерных инвариантных многообразий седловых состояний равновесия потока. Решена проблема построения канонического представителя в каждом классе топологической эквивалентности. Библиография: 27 наименований.

Study on Flow Regime Classification of Gas-Liquid Two-Phase Flow with Bidirectional LSTM Networks

Study on Flow Regime Classification of Gas-Liquid Two-Phase Flow with Bidirectional LSTM Networks