Dynamics of Physical Autonomous Robotic Network With Constant And Time-Varying Communication Delays

A Connected Autonomous Vehicle Network (CAVN) is an emerging paradigm that can reduce traffic congestion by allowing vehicles to cooperatively behave according to information out of the line of sight and improve traffic flow by decreasing inter-vehicular gaps on roadways. In this paper, the stability of CAVN with constant and time-varying communication delays is studied. When the time delay depends on time, the semi-discretization method is used to study the plant stability of the flow equilibrium of CAVN and construct approximate stability regions charts over control gain space. To study the influence of time-varying delay, a constant delay is considred as the average value of the time delay function. Then, explicit sufficient conditions are provided for the stability of the flow equilibrium and study the string stability of the CAVN. The proposed controller is validated using simulations and experimentally with a platoon of four autonomous robots under time-varying delays.

Optimization of Multimodal Discrete Network Design Problems Based on Super Networks

In this paper, we investigate the multimodal discrete network design problem that simultaneously optimizes the car, bus, and rail transit network, in which inter-modal transfers are achieved by slow traffic modes including walking and bike-sharing. Specifically, a super network topology is presented to signify the modal interactions. Then, the generalized cost formulas of each type of links in the super network are defined. And based on the above formulas a bi-objective programming model is proposed to minimize the network operation cost and construction cost with traffic flow equilibrium constraints, investment constraints and expansion constraints. Moreover, a hybrid heuristic algorithm that combines the minimum cost flow algorithm and simulated annealing algorithm is presented to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is evaluated through two numerical tests: a simple test network and an actual multimodal transport network.

Analysis and extension of the quadratic constitutive relation for RANS methods

The quadratic constitutive relation was proposed as an extension of minimal complexity to linear eddy-viscosity models in order to improve mean flow predictions by better estimating turbulent stress distributions. However, the successes of this modification have been relatively modest and are limited to improved calculations of flow along streamwise corners, which are influenced by weak secondary vortices. This paper revisits the quadratic constitutive relation in an attempt to explain its capabilities and deficiencies. The success in streamwise corner flows cannot be entirely explained by significant improvements in turbulent stress estimates in general, but is instead due to better prediction of the particular turbulent stress combinations which appear in the mean streamwise vorticity equation. As a consequence of this investigation, a new formulation of turbulent stress modification is proposed, which appears to better predict the turbulent stress distributions for a variety of flows: channel flow, equilibrium boundary layers, pipe flow, separated boundary layers and square duct flow.

Analysis of Traffic Flow Equilibrium in Urban Road Network Based on Floating Vehicle Data

Based on the floating vehicle data, this paper analyzes the equilibrium of urban road network traffic flow. This can guide traffic flow distribution and provide reliable basis for traffic control. This paper starts with the quantitative analysis of the traffic network equilibrium, on the basis of verifying the validity of the floating car data, divides the urban road network into regions, and constructs an analysis model of the traffic flow equilibrium of the urban road network. The urban road traffic distribution model is constructed in accordance with the number of road traffic segments. On this basis, gini coefficient index is introduced to judge the road network flow balance, which is used to analyze the balance of each sub-region. By means of traffic guidance, signal control and other traffic control means, the traffic flow in each sub-area is balanced, and the traffic flow in the whole road network becomes balanced.

Macroscopic Fundamental Diagram based Road Network Characteristics Identification and Simulation

As an inherent property of the road network, macroscopic fundamental diagram (MFD) method can effectively describe the traffic status of the urban roads and identify the relationship among key factors, such as traffic flow and occupancy. Currently, using MFD is easily affected by various network inner factors including topology and road density, so in this paper we propose a method to identify inner characteristic of road network and do a series of comparisons under different scenarios with fixed traffic input circumstance. The differential impact of data collector setting locations are discussed with a aim to reveal the respective location setting suitable for various networks conditions in initial; then road topology and density are designed in road network and simulated MFD performances with flow equilibrium affections. It is shown as the dispersion decreasing of link length or road density of network, the network exhibits better operation efficiency so as to increase the output of link flow and the dissipative ability of the road network. Meanwhile, the equivalent of entrances and exits is proved as another important factor has same impact on MFD.

A Research on an Efficient Cloud Scheduling with a Geo Microarray Data Set

Investigations on micro-array organisms for various researches have made a non discrete dealing of thousands of gene expressions achievable. For any applications, the results would be more accurate only when maximum count is analyzed within a predictable time and it is one of the unseen challenges in the field of bio medicine. The purpose of this data analysis is to regulate and control the activities of thousands of genes in our body. This paper develops a scheduling analysis of how effectively gene molecular patterns are taken into experimentation. This motivated our investigation in a new dimension for a cloud environment. This paper is about applying our previous works such as Workflow Shuffling and Hole Filling Algorithm (WSHF) [13], Agent Centric Enhanced Reinforcement learning algorithm (AGERL) [14], Heuristic Flow Equilibrium based Load Balancing (HFEL) [15] and Dynamic Resource Provisioning and Load Balancing (DRBLHS) [16] algorithms collaboratively for a Gene Express Omnibus dataset as a case study. The gene data’s plays an important role in monitoring the human activities and how well, the data has been processed in the cloud with minimum budget, time and minimum virtual machines. Finally, the efficiency of the system is analyzed in terms of resource utilization, completion time, response time, throughput and VM Migration time

Ideal flow of Markov Chain

Ideal flow network is a strongly connected network with flow, where the flows are in steady state and conserved. The matrix of ideal flow is premagic, where vector, the sum of rows, is equal to the transposed vector containing the sum of columns. The premagic property guarantees the flow conservation in all nodes. The scaling factor as the sum of node probabilities of all nodes is equal to the total flow of an ideal flow network. The same scaling factor can also be applied to create the identical ideal flow network, which has from the same transition probability matrix. Perturbation analysis of the elements of the stationary node probability vector shows an insight that the limiting distribution or the stationary distribution is also the flow-equilibrium distribution. The process is reversible that the Markov probability matrix can be obtained from the invariant state distribution through linear algebra of ideal flow matrix. Finally, we show that recursive transformation [Formula: see text] to represent [Formula: see text]-vertices path-tracing also preserved the properties of ideal flow, which is irreducible and premagic.