Flow Clusters Research Articles

Application of a Traffic Flow Prediction Model Based on Neural Network in Intelligent Vehicle Management

The ultimate direction of intelligent vehicle management is to achieve artificial intelligence (AI), and data mining is an important supporting technology for AI. The adoption of new AI technology can effectively improve operational efficiency and safety, especially in terms of performance. This paper takes the researches on traffic jam as an example and proposes one algorithm for combination forecasting model based on a segmentation algorithm for traffic flow sequence and BP neural network prediction. In this paper, it also introduces the traffic flow clustering analysis and mining algorithms for congestion events at the intersections. The blocking point algorithm is improved, and experimental analysis is performed through samples. Experimental results show that the algorithm use for combination forecasting model can greatly improve the real-time performance of short-term traffic flow prediction and significantly reduce the prediction error rate. Therefore, this algorithm has practical and innovative significance in the field of intelligent vehicle management.

The numerical analysis of particle-size distribution of clusters in shear flow at one-dimensional closed system and three-dimensional open system

Aimed at optimizing the resin-molding process, a method for numerically analyzing aggregation and dispersion behavior of the filler in resin composite was proposed. The flow of a resin composite during molding was calculated by using computational fluid dynamics (CFD), and particle-size distribution (PSD) of the cluster in each computational domain of the CFD model was estimated by solving the population balance equation (PBE). The proposed numerical-analysis method is based on the thixotropy model of Usui et al. In the thixotropy model, PSD is calculated by taking into account the aggregation and dispersion rate of the cluster by Brownian coagulation, shear coagulation, and shear breakage. Shear-breakage rate of the cluster is evaluated by solving the energy balance of bonding energy of the primary particles cut at the breakage and the drag applied to the cluster by the flow of the fluid. The composite viscosity was calculated using Krieger and Dougherty’s model based on apparent-solid-volume fraction estimated from the calculated PSD. To solve the PBE at low calculation cost, it was discretized using the fixed-pivot technique of Kumar and Ramkrishna. The proposed method was incorporated into the general-purpose CFD software FLOW-3D®, and its accuracy was proved.

Formation of atmospheric molecular clusters consisting of methanesulfonic acid and sulfuric acid: Insights from flow tube experiments and cluster dynamics simulations

In coastal regions and ocean areas, methanesulfonic acid (MSA; CH3SO3H) is present in considerable concentrations in the gas-phase and aerosols. It has been shown that MSA could contribute to growth and possibly form initial molecular cluster, which may lead to aerosol formation. However, quantitative concentrations and thermodynamic properties of MSA and sulfuric acid (SA; H2SO4) in the presence of water (W; H2O) remain largely uncertain. In this study, flow tube reactor was used to investigate the effects of each reactant on new particle formation (NPF) in a multi-component system consisting of MSA, SA, and W. Particles were measured for different combinations of reactants. It showed that a different order for reactant addition led to different experimental results, where the added MSA vapor to the SA-W binary system presented an obvious bimodal structure, for ternary system with SA added to the MSA-W, the similar bimodal phenomenon was not observed. The composition of clusters in the air flow was further analyzed by the commercial Atmospheric Pressure Interface Time-of-Flight Mass Spectrometer (APi-TOF-MS, Tofwerk AG), which is equipped with a homemade chemical ionization (CI) source, mass peaks corresponding to clusters that contain smaller MSA or SA molecules were clearly observed, indicating that these clusters are exist and stable. In addition, quantum chemistry calculation-based evaporation rate values were applied in a cluster dynamics model to yield formation rates of 2.6 × 102 cm−3 s−1 and cluster concentrations under different simulation conditions. This study could provide some insight into how acids interact in the atmosphere.

Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization

ABSTRACTAn origin-destination (OD) flow can be defined as the movement of objects between two locations. These movements must be determined for a range of purposes, and strong interactions can be visually represented via clustering of OD flows. Identification of such clusters may be useful in urban planning, traffic planning and logistics management research. However, few methods can identify arbitrarily shaped flow clusters. Here, we present a spatial scan statistical approach based on ant colony optimization (ACO) for detecting arbitrarily shaped clusters of OD flows (AntScan_flow). In this study, an OD flow cluster is defined as a regional pair with significant log likelihood ratio (LLR), and the ACO is employed to detect the clusters with maximum LLRs in the search space. Simulation experiments based on AntScan_flow and SaTScan_flow show that AntScan_flow yields better performance based on accuracy but requires a large computational demand. Finally, a case study of the morning commuting flows of Beijing residents was conducted. The AntScan_flow results show that the regions associated with moderate- and long-distance commuting OD flow clusters are highly consistent with subway lines and highways in the city. Additionally, the regions of short-distance commuting OD flow clusters are more likely to exhibit ‘residential-area to work-area’ patterns.

Analysis of big patient mobility data for identifying medical regions, spatio-temporal characteristics and care demands of patients on the move

BackgroundPatient mobility can be defined as a patient’s movement or utilization of a health care service located in a place or region other than the patient’s place of residence. Mobility provides freedom to patients to obtain health care from providers across regions and even countries. It is essential to monitor patient choices in order to maintain the quality standards and responsiveness of the health system, otherwise, the health system may suffer from geographic disparities in the accessibility to quality and responsive health care. In this article, we study patient mobility in a national health care system to identify medical regions, spatio-temporal and service characteristics of health care utilization, and demands for patient mobility.MethodsWe conducted a systematic analysis of province-to-province patient mobility in Turkey from December 2009 to December 2013, which was derived from 1.2 billion health service records. We first used a flow-based regionalization method to discover functional medical regions from the patient mobility network. We compare the results of data-driven regions to designated regions of the government in order to identify the areas of mismatch between planned regional service delivery and the observed utilization in the form of patient flows. Second, we used feature selection, and multivariate flow clustering to identify spatio-temporal characteristics and health care needs of patients on the move.ResultsMedical regions we derived by analyzing the patient mobility data showed strong overlap with the designated regions of the Ministry of Health. We also identified a number of regions that the regional service utilization did not match the planned service delivery. Overall, our spatio-temporal and multivariate analysis of regional and long-distance patient flows revealed strong relationship with socio-demographic and cultural structure of the society and migration patterns. Also, patient flows exhibited seasonal patterns, and yearly trends which correlate with implemented policies throughout the period. We found that policies resulted in different outcomes across the country. We also identified characteristics of long-distance flows which could help inform policy-making by assessing the needs of patients in terms of medical specialization, service level and type.ConclusionsOur approach helped identify (1) the mismatch between regional policy and practice in health care utilization (2) spatial, temporal, health service level characteristics and medical specialties that patients seek out by traveling longer distances. Our findings can help identify the imbalance between supply and demand, changes in mobility behaviors, and inform policy-making with insights.

Uplifted cool gas and heating by mixing in cooling flows

We analyze our earlier three-dimensional hydrodynamical numerical simulation of jet-inflated bubbles in cooling flow clusters, and find that dense gas that was not heated by the jets’ activity and that resides around the hot jet-inflated bubbles can be identified as uplifted gas as observed in some clusters. During the build up of the dense gas around the hot bubble, mixing of hot bubble gas with other regions of the intracluster medium (ICM) heats the ICM. The vortices that mix the ICM with the hot bubble gas also excite shock waves, sound waves and turbulence. Sound waves, shocks, turbulence and uplifted gas might be easier to detect than the mixing process and hence attract more attention, but we argue that the contributions of these processes to the heating of the ICM do not add up to the level of contribution of the mixing-heating process.

Jittering Jets in Cooling Flow Clusters

I put forth a scenario of jet-driven jittering-jets in cooling flows in galaxies and clusters of galaxies. By uplifting cool gas from the center of the cluster jets and the bubbles they inflate cause the direction of later jets to substantially change. Some uplifted cool clumps fall back to feed the active galactic nucleus, in what is termed the cold feedback mechanism, but with an average angular momentum that is largely inclined to that of the original jets. The proposed jittering jets scenario allows, over the cooling time of the intracluster medium (ICM), the heating by the mixing of hot jet-inflated bubble gas with the ICM to operate in all directions. The proposed scenario further supports the heating by mixing mechanism of the ICM. The feedback heating mechanism of the ICM chiefly refers to the energy cycle. The cold feedback mechanism introduces mass as another ingredient of the feedback cycle, as the mass accretion of future activity cycles is determined by the formation of perturbations by the present activity cycle. The jittering jets scenario that I propose here brings angular momentum to be another ingredient of the feedback cycle.

Safety analysis of traffic flow characteristics of highway tunnel based on artificial intelligence flow net algorithm

With the increasing development of traffic nowadays, traffic safety has become the focus of current research. Among them, highway tunnel is a special geographical obstacle on freeway, so it is very important to analyze its traffic flow security. Based on this, his paper studies the characteristics of highway tunnel traffic safety from the angle of artificial intelligence network algorithm; the improvements were made on the basis the classic FCM clustering algorithm in artificial intelligence net according to the unique complexity of highway tunnel; and an improved FCM clustering algorithm was proposed; then indexes, data and traffic flow clustering in freeway tunnel were studied in detail. Based on the actual traffic flow data, an example is given. According to the improved FCM clustering algorithm, the safety data of expressway tunnel was obtained, which was used to divide the safety area for highway tunnel. To sum up, the research in this paper can provide a strong theoretical basis for the safety characteristics of freeway tunnel traffic flow, and it is of great significance.

A Stepwise Spatio-Temporal Flow Clustering Method for Discovering Mobility Trends

Massive flows that represent the individual level of movements and communications can be easily obtained in the age of big data. Generalizing spatial and temporal flow patterns from such data is essential to demonstrate spatial connections and mobility trends. Clustering approaches provide effective methods to handle data sets that contain massive individual-level flows. However, existing flow clustering studies obscure the geometric properties of flow data, such as direction and length, which significantly indicate movement trends. In addition, temporal information is often ignored because previous approaches have mainly focused on the perspective of spatial clusters of flow data, resulting in a loss of temporal patterns. In this paper, we introduce new spatial and temporal similarity measurements between flows and propose a new clustering approach of flow data based on a stepwise strategy. This method can identify clusters from distinct flow distributions and discover significant spatio-temporal trends from large flow data. Simulated experiments with synthetic flows and a case study using Beijing taxi trip data are conducted to validate the usefulness of the proposed method.

A Multi-core architecture for a hybrid information system

This paper demonstrates our proposed Multi-core architecture for a hybrid information system (HIS) with the related work, system design, theories, experiments, analysis and discussion presented. Different designs on clusters, communication between different types of chips and clusters and network queuing methods have been described. Our aim is to achieve quality, reliability and resilience and to demonstrate it, our emphasis is on latency with messages communicated in our system – understand how it happens, what can trigger its increase, and then experiment with different types of focuses, including under Store-and-Forward Flow Control method, Wormhole flow control method, cluster size and message size to get a better understanding. Our analysis allows us to reduce latency and avoid its sharp increase. We justify our research contributions, particularly in the area of “traffic analysis and management” and “performance analysis of transmission control” of the HIS systems.

The perceptual dimensions of natural dynamic flow.

We measured perceptual judgments of category, material attributes, affordances, and similarity to investigate the perceptual dimensions underlying the visual representation of a broad class of natural dynamic flows (sea waves, smoke, and windblown foliage). The dynamic flows were looped 3-s movies windowed with circular apertures of two sizes to manipulate the level of spatial context. In low levels of spatial context (smaller apertures), human observers' judgments of material attributes and affordances were inaccurate, with estimates biased toward assumptions that the flows resulted from objects that were rigid, "pick-up-able," and not penetrable. The similarity arrangements showed dynamic flow clusters based partly on material, but dominated by color appearance. In high levels of spatial context (large apertures), observers reliably estimated material categories and their attributes. The similarity arrangements were based primarily on categories related to external, physical causes. Representational similarity analysis suggests that while shallow dimensions like color sometimes account for inferences of physical causes in the low-context condition, shallow dimensions cannot fully account for these inferences in the high-context condition. For the current broad data set of dynamic flows, the perceptual dimensions that best account for the similarity arrangements in the high-context condition are related to the intermolecular bond strength of a material's underlying physical structure. These arrangements are also best related to affordances that underlie common motor activities. Thus, the visual system appears to use an efficient strategy to resolve flow ambiguity; vision will sometimes rely on local, image-based, statistical properties that can support reliable inference of external physical causes, and other times it uses deeper causal knowledge to interpret and use flow information to the extent that it is useful for everyday action decisions.

Features of molecular-beam mass spectrometry registration of clusters in underexpanded supersonic jets

The structure of clustered supersonic underexpanded jets of molecular nitrogen and argon was measured by the method of molecular beam mass-spectrometry. Peculiarities of application of the molecular beam methods for recording the supersonic rarefied gas jets under the conditions of weak and developed condensation (i.e., in the presence of small and large clusters in jets) have been discovered, identified, and studied. An unusual shape of longitudinal and transverse cross sections of the clustered supersonic jets was revealed and explained when scanning with a molecular-beam system. It has been determined that small clusters and monomers are the sources of double-ionized monomers available near the flow axis, and dimer ions at the early stages of condensation, whereas another mechanism of such ion formation dominates, when large clusters area available in the flow. A marker for fixing the stage of formation of small clusters in a supersonic flow is proposed.

Gentle Heating by Mixing in Cooling Flow Clusters

Abstract We analyze 3D hydrodynamical simulations of the interaction of jets and the bubbles they inflate with the intracluster medium (ICM) and show that the heating of the ICM by mixing hot bubble gas with the ICM operates over tens of millions of years and hence can smooth the sporadic activity of the jets. The inflation process of hot bubbles by propagating jets forms many vortices, and these vortices mix the hot bubble gas with the ICM. The mixing, and hence the heating of the ICM, starts immediately after the jets are launched, but continues for tens of millions of years. We suggest that the smoothing of the active galactic nucleus (AGN) sporadic activity by the long-lived vortices accounts for the recent finding of a gentle energy coupling between AGN heating and the ICM.

Reactive Obstacle Avoidance for Highly Maneuverable Vehicles Based on a Two-Stage Optical Flow Clustering

This paper proposes a reactive obstacle avoidance approach based solely on image data from a monocular camera stream. By clustering and analyzing the optical flow, this approach is able to identify potential collisions with dynamic obstacles. Epipolar geometry is exploited to derive velocity commands that ensure a collision-free path for a highly maneuverable autonomous vehicle via a real-time optimizer. First, the underlying image processing and optimization principles are explained in detail, before simulation results show the general feasibility of the approach. Finally, real-world tests with the ROboMObil, the German Aerospace Center’s robotic electric vehicle, are provided to demonstrate its applicability.

A study of high-redshift AGN feedback in SZ cluster samples

We present a study of AGN feedback at higher redshifts ($0.3<z<1.2$) using Sunyaev-Zel'dovich (SZ) selected samples of clusters from the South-Pole Telescope and Atacama Cosmology Telescope surveys. In contrast to studies of nearby systems, we do not find a separation between cooling flow clusters and non-cooling flow clusters based on the radio luminosity of the central radio source. This lack may be due to the increased incidence of galaxy-galaxy mergers at higher redshift that triggers AGN activity. In support of this scenario, we find evidence for evolution in the radio luminosity function of the central radio source: while the lower-luminosity sources do not evolve much, the higher-luminosity sources show a strong increase in the frequency of their occurrence at higher redshifts. We interpret this evolution as an increase in high-excitation radio galaxies (HERGs) in massive clusters at $z>0.6$, implying a transition from HERG-mode accretion to lower-power low-excitation radio galaxy (LERG)-mode accretion at intermediate redshifts. Additionally, we use local radio-to-jet power scaling relations to estimate feedback power and find that half of the cooling flow systems in our sample probably have enough heating to balance cooling. However, we postulate that the local relations are likely not well suited to predict feedback power in high-luminosity HERGs, as they are derived from samples composed mainly of lower-luminosity LERGs.

Using genetic structure data and phylogenetic criteria in attributing prioritization scores for conservation of spontaneous Capparis spinosa L. populations from Tunisia

In Tunisia, the increasing pharmacological demand for spontaneous capers is actually leading to progressive forest overexploitation and genetic erosion of the species that is actually represented by scattered populations. Therefore, inventories with emphasis on the characterization, management and conservation of this medicinal plant are becoming a great need.In this work, genetic and phylogenetic criteria based on isozyme markers were used for the prioritization of spontaneous Capparis spinosa L. populations for conservation according to Vane-Wright and collaborators taxic method.Enzymatic loci varied in their ability to detect variation and population analyses indicated that most of the variation is partitioned between rather than within populations. Fst, gene flow values, PCA and cluster analysis also revealed significant differentiation between all populations.When combing phylogenetic and genetic criteria we were able to prioritize some of the populations for conservation. Therefore, the standardization and the summatory of all indices indicated that the populations Nebeur, Nahli and Dyr rank as a priority for conservation.So far, results of the present study appear to justify the in situ conservation strategy, where core areas completely free from perturbation would be defined for the populations with the highest priority for conservation, with the populations presenting unique alleles. This would guarantee the maintenance of most of the species’ genetic variation. The ex situ conservation management strategy would imply the setting up of specialized nurseries and national collections that are still missing in the country to counteract population fragmentation, demographic loss and genetic erosion threatening such spontaneous populations.

Partial jamming and non-locality in dense granular flows

Dense granular flows can exhibit non-local flow behaviours that cannot be predicted by local constitutive laws alone. Such behaviour is accompanied by the existence of diverging cooperativity length. Here we show that this length can be attributed to the development of transient clusters of jammed particles within the flow. By performing DEM simulation of dense granular flows, we directly measure the size of such clusters which scales with the inertial number with a power law. We then derive a general non-local relation based on kinematic compatibility for the existence of clusters in an arbitrary non-homogenous flow. The kinematic nature of this derivation suggests that non-locality should be expected in any material regardless of their local constitutive law, as long as transient clusters exist within the flow.

Object’s scale and rotation estimation using the clustering in tracking methods based on the optic flow calculation

Procedures of scale and rotation estimation of an object using the clustering are proposed. The pro-cedures are oriented to the application in tracking techniques based on sparse optic flow calculation. The new tracking method, which is called FCT (Flow Clustering Tracker) is developed using the mentioned procedures. The obtained results show its ability to achieve the compromise in robustness and accuracy with respect to the existing solutions. References 8, figures 5, tables. 2.

A New Static Data Flow Clustering Algorithm for Task Scheduling of Irregular Mesh in NoCs Based on Complex Networks

The majority of recent embedded systems are based on MPSoCs (Multi-Processors System on Chip) architectures. The topologies and the interconnections inside multi processors almost adopt NoCs (Networks on Chip) whose topology and task scheduling algorithm have a direct impact on its performances. In this paper, by using static data flow, a task scheduling algorithm which would automatically assign the application tasks onto different processors is proposed based on complex network. The goal of our algorithm is to replace the static data flow subnetwork by a single dynamic data flow actor such that the global performance in terms of latency and throughput is optimized. Through complex network, it greatly enhances the power of our algorithm in terms of avoiding deadlock, saving energy and providing for integration with more general models of computation. Experimental results show up to 60% performance improvement for real-world examples.

Visual motion perception for mobile robots through dense optical flow fields

Recent advances in visual motion detection and interpretation have made possible the rising of new robotic systems for autonomous and active surveillance. In this line of research, the current work discusses motion perception by proposing a novel technique that analyzes dense flow fields and distinguishes several regions with distinct motion models.The method is called Wise Optical Flow Clustering (WOFC) and extracts the moving objects by performing two consecutive operations: evaluating and resetting. Motion properties of the flow field are retrieved and described in the evaluation phase, which provides high level information about the spatial segmentation of the flow field. During the resetting operation, these properties are combined and used to feed a guided segmentation approach. The WOFC requires information about the number of motion models and, therefore, this paper introduces a model selection method based on a Bayesian approach that balances the model’s fitness and complexity. It combines the correlation of a histogram-based analysis with the decay ratio of the normalized entropy criterion. This approach interprets the flow field and gives an estimative about the number of moving objects.The experiments conducted in a realistic environment have proved that the WOFC presents several advantages that meet the requirements of common robotic and surveillance applications: is computationally efficient and provides a pixel-wise segmentation, comparatively to other state-of-the-art methods.