Traffic Flow Model Validation Using METANET, ADOL-C and RPROP

Macroscopic traffic flow model calibration is an optimisation problem typically solved by a derivative-free population based stochastic search methods. This paper reports on the use of a gradient based algorithm using automatic differentiation. The ADOL-C library is coupled with the METANET source code and this system is embedded within an optimisation algorithm based on RPROP. The result is a very efficient system which is able to be calibrate METANET's second order model by determining the density and speed equation parameters as well as the fundamental diagrams used. Information obtained from the system's Jacobian provides extra insight into the system dynamics. A 22 km site is considered near Sheffield, UK and the results of a typical calibration and validation process are reported

Nonlinear optimal control applied to coordinated ramp metering

The goal of this paper is to describe a generic approach to the problem of optimal coordinated ramp metering control in large-scale motorway networks. In this approach, the traffic flow process is macroscopically modeled by use of a second-order macroscopic traffic flow model. The overall problem of coordinated ramp metering is formulated as a constrained discrete-time nonlinear optimal control problem, and a feasible-direction nonlinear optimization algorithm is employed for its numerical solution. The control strategy's efficiency is demonstrated through its application to the 32-km Amsterdam ring road. A number of adequately chosen scenarios along with a thorough analysis, interpretation, and suitable visualization of the obtained results provides a basis for the better understanding of some complex interrelationships of partially conflicting performance criteria. More precisely, the strategy's efficiency and equity properties as well as their tradeoff are studied and their partially competitive behavior is discussed. The results of the presented approach are very promising and demonstrate the efficiency of the optimal control methodology for motorway traffic control problems

Alignment of liquid crystal/carbon nanotube dispersions for application in unconventional computing

We demonstrate the manipulation of single-walled carbon nanotube/liquid crystal composites using in-plane electric fields. The conductivity of the materials is shown to be dependant on the application of a DC bias across the electrodes. When the materials are subjected to this in-plane field, it is suggested that the liquid crystals orientate, thereby forcing the SWCNTs to follow in alignment. This process occurs over many seconds, since the SWCNTs are significantly larger in size than the liquid crystals. The opportunity for applying this material to unconventional computing problems is suggested

Application of a Multivariate Process Control Technique for Set-Up Dominated Low Volume Operations

In traditional high-volume manufacturing applications, the timing of control adjustments to processes is based on parametric Statistical Process Control (SPC) methods, such as Shewhart X & R charts. In high-value, high-complexity and low-volume industries, where production runs are in the order of tens rather than thousands, traditional SPC approaches are not easily applicable. A manufactured component's complexity, with multiple critical features to monitor, increases the difficulty for a process operator to maintain all of them within their design tolerances. In response to this, this paper presents a framework of nonparametric SPC, called multivariate Set-Up Process Algorithm (mSUPA), for managing control adjustment when required. mSUPA uses a simple to interpret traffic light system for alerting process operators when an adjustment is required. mSUPA is underpinned by multivariate statistics and probability theory for validating a process set up. The case of mSUPA application to a real industry process is discussed

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms

Evolution-in-Materio uses evolutionary algorithms (EA) to exploit the physical properties of unconfigured, physically rich materials, in effect transforming them into information processors. The potential of this technique for machine learning problems is explored here. Results are obtained from a mixture of single walled carbon nanotubes and liquid crystals (SWCNT/LC). The complex nature of the voltage/current relationship of this material presents a potential for adaptation. Here, it is used as a computational medium evolved by two derivative-free, population-based stochastic search algorithms, particle swarm optimisation (PSO) and differential evolution (DE). The computational problem considered is data classification. A custom made electronic motherboard for interacting with the material has been developed, which allows the application of control signals on the material body. Starting with a simple binary classification problem of separable data, the material is trained with an error minimisation objective for both algorithms. Subsequently, the solution, defined as the combination of the material itself and optimal inputs, is verified and results are reported. The evolution process based on EAs has the capacity to evolve the material to a state where data classification can be performed. PSO outperforms DE in terms of results’ reproducibility due to the smoother, as opposed to more noisy, inputs applied on the material

Computing with carbon nanotubes: optimization of threshold logic gates using disordered nanotube/polymer composites

This paper explores the use of single-walled carbon nanotube (SWCNT)/poly(butyl methacrylate) composites as a material for use in unconventional computing. The mechanical and electrical properties of the materials are investigated. The resulting data reveal a correlation between the SWCNT concentration/viscosity/conductivity and the computational capability of the composite. The viscosity increases significantly with the addition of SWCNTs to the polymer, mechanically reinforcing the host material and changing the electrical properties of the composite. The electrical conduction is found to depend strongly on the nanotube concentration; Poole-Frenkel conduction appears to dominate the conductivity at very low concentrations (0.11% by weight). The viscosity and conductivity both show a threshold point around 1% SWCNT concentration; this value is shown to be related to the computational performance of the material. A simple optimization of threshold logic gates shows that satisfactory computation is only achieved above a SWCNT concentration of 1%. In addition, there is some evidence that further above this threshold the computational efficiency begins to decrease

Evolution of Electronic Circuits using Carbon Nanotube Composites

Evolution-in-materio concerns the computer controlled manipulation of material systems using external stimuli to train or evolve the material to perform a useful function. In this paper we demonstrate the evolution of a disordered composite material, using voltages as the external stimuli, into a form where a simple computational problem can be solved. The material consists of single-walled carbon nanotubes suspended in liquid crystal; the nanotubes act as a conductive network, with the liquid crystal providing a host medium to allow the conductive network to reorganise when voltages are applied. We show that the application of electric fields under computer control results in a significant change in the material morphology, favouring the solution to a classification task