Bus speed estimation by neural networks to improve the automatic fleet management

In the urban areas, public transport service interacts with the private mobility. Moreover, on each link of the urban public transport network, the bus speed is affected by a high variability over time. It depends on the congestion level and the presence of bus way or no. The scheduling reliability of the public transport service is crucial to increase attractiveness against private car use. A comparison between a Radial Basis Function network (RBF) and Multi layer Perceptron (MLP) was carried out to estimate the average speed, analysing the dynamic bus location data achieved by an AVMS (Automatic Vehicle Monitoring System). Collected data concern bus location, geometrical parameters and traffic conditions. Public Transport Company of Palermo provided these data

Modelling of advanced submicron gate InGaAs/InAIAs pHEMTS and RTD devices for very high frequency applications

InP based InAlAs/InGaAs pseudomorphic High Electron Mobility Transistors (pHEMTs) have shown outstanding performances, which makes them prominent in high frequency mm-wave and submillimeter-wave applications. However, conventional InGaAs/InAlAs pHEMTs have major drawbacks, i.e., very low breakdown voltage and high gate leakage current. These disadvantages degrade device performance, especially in Monolithic Microwave Integrated Circuit (MMIC) low noise amplifiers (LNAs). The optimisation of InAlAs/InGaAs epilayer structures through advanced bandgap engineering together with gate length reduction from 1 m into deep sub-μm regime is the key solution to enabled high breakdown and ultra-high speed, low noise pHEMT devices to be fabricated. Concurrently, device modelling plays a vital role in the design and analysis of pHEMT device and circuit performance. Physical modeling becomes essential to fully characterise and understand the underlying physical phenomenon of the device, while empirical modelling is significant in circuit design and predicts device’s characteristic performance. In this research, the main objectives to accurately model the DC and RF characteristics of the two-dimensional (2D) physical modelling for sub-μm gate length for strained channel InAlAs/InGaAs/InP pHEMT has been accomplished and developed in ATLAS Silvaco. All modelled devices were optimised and validated by experimental devices which were fabricated at the University of Manchester; the sub-micrometer devices were developed with T-gate using I-line optical lithography. The underlying device physics insight are gained, i.e, the effects of changes to the device’s physical structure, theoretical concepts and its general operation, hence a reliable pHEMT model is obtained. The kink anomalies in I-V characteristics was reproduced and the 2D simulation results demonstrate an outstanding agreement with measured DC and RF characteristics. The aims to develop linear and nonlinear models for sub-μm transistors and their implementation in MMIC LNA design is achieved with the 0.25 m In0.7Ga0.3As/In0.52Al0.48As/InP pHEMT. An accurate technique for the extraction of empirical models for the fabricated active devices has been developed and optimised using Advance Design System (ADS) software which demonstrate excellent agreement between experimental and modelled DC and RF data. A precise models for MMIC passive devices have also been obtained and incorporated in the proposed design for a single and double stage MMIC LNAs in C- and X-band frequency. The single stage LNA is designed to achieve maximum gain ranging from 9 to 13 dB over the band of operation while the gain is increased between 20 dB and 26 dB for the double stage LNA designs. A noise figure of less than 1.2 dB and 2 dB is expected respectively, for the C- and X-band LNA designed while retaining stability across the entire frequency bands. Although the RF performance of pHEMT is being vigorously pushed towards terahertz region, novel devices such as Resonant Tunnelling Diode (RTD) are needed to support future ultra-high speed, high frequency applications especially when it comes to THz frequencies. Hence, the study of physical modelling is extended to quantum modelling of an advanced In0.8Ga0.2As/AlAs RTD device to effectively model both large size and submicron RTD using Silvaco’s ATLAS software to reproduce the peak current density, peak-to-valley-current ratio (PVCR), and negative differential resistance (NDR) voltage range. The simple one-dimensional physical modelling for the RTD devices is optimised to achieve an excellent match with the fabricated RTD devices with variations in the spacer thickness, barrier thickness, quantum well thickness and doping concentration

Modifying Hidden Layer in Neural Network Models to Improve Prediction Accuracy: A Combined Model for Estimating Stock Price

Investment experts, who deal with stock price estimation, commonly look for the most accurate and appropriate statistical techniques to make decisions on investment. The aim of this study is to improve the accuracy of stock price prediction models through modifying the structure of a combined neural network model with time-series data, in which the main contribution is to insert the time-series analysis prediction into the hidden layer of the neural network. The proposed structure is made up of neural networks and time-series analysis, with variable reduction used to remove attributes with inter-correlations. Data has been collected over six years (72 months) from the Iranian stock market, including the number of trades, new-coin price, gold-18 price, US Dollar and Euro equivalent currencies, oil-index price, Brent-oil price, industry index, and balanced stock index, followed by developing the prediction models. Comparing the performance criteria of the proposed structure to the traditional ones in terms of the mean square and mean absolute errors revealed that inserting time-series estimated variables into hidden layers would improve the performance of neural network models to estimate stock prices for making investment decisions. Doi: 10.28991/HIJ-2022-03-01-05 Full Text: PD

Short term traffic flow prediction in heterogeneous condition using artificial neural network

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables. First published online: 16 Oct 201

Modelling of the Automatic Depth Control Electrohydraulic System Using RBF Neural Network and Genetic Algorithm

The automatic depth control electrohydraulic system of a certain minesweeping tank is complex nonlinear system, and it is difficult for the linear model obtained by first principle method to represent the intrinsic nonlinear characteristics of such complex system. This paper proposes an approach to construct accurate model of the electrohydraulic system with RBF neural network trained by genetic algorithm-based technique. In order to improve accuracy of the designed model, a genetic algorithm is used to optimize centers of RBF neural network. The maximum distance measure is adopted to determine widths of radial basis functions, and the least square method is utilized to calculate weights of RBF neural network; thus, computational burden of the proposed technique is relieved. The proposed technique is applied to the modelling of the electrohydraulic system, and the results clearly indicate that the obtained RBF neural network can emulate the complex dynamic characteristics of the electrohydraulic system satisfactorily. The comparison results also show that the proposed algorithm performs better than the traditional clustering-based method

Web and knowledge-based decision support system for measurement uncertainty evaluation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityIn metrology, measurement uncertainty is understood as a range in which the true value of the measurement is likely to fall in. The recent years have seen a rapid development in evaluation of measurement uncertainty. ISO Guide to the Expression of Uncertainty in Measurement (GUM 1995) is the primary guiding document for measurement uncertainty. More recently, the Supplement 1 to the "Guide to the expression of uncertainty in measurement" – Propagation of distributions using a Monte Carlo method (GUM SP1) was published in November 2008. A number of software tools for measurement uncertainty have been developed and made available based on these two documents. The current software tools are mainly desktop applications utilising numeric computation with limited mathematical model handling capacity. A novel and generic web-based application, web-based Knowledge-Based Decision Support System (KB-DSS), has been proposed and developed in this research for measurement uncertainty evaluation. A Model-View-Controller architecture pattern is used for the proposed system. Under this general architecture, a web-based KB-DSS is developed based on an integration of the Expert System and Decision Support System approach. In the proposed uncertainty evaluation system, three knowledge bases as sub-systems are developed to implement the evaluation for measurement uncertainty. The first sub-system, the Measurement Modelling Knowledge Base (MMKB), assists the user in establishing the appropriate mathematical model for the measurand, a critical process for uncertainty evaluation. The second sub-system, GUM Framework Knowledge Base, carries out the uncertainty evaluation process based on the GUM Uncertainty Framework using symbolic computation, whilst the third sub-system, GUM SP1 MCM Framework Knowledge Base, conducts the uncertainty calculation according to the GUM SP1 Framework numerically based on Monte Carlo Method. The design and implementation of the proposed system and sub-systems are discussed in the thesis, supported by elaboration of the implementation steps and examples. Discussions and justifications on the technologies and approaches used for the sub-systems and their components are also presented. These include Drools, Oracle database, Java, JSP, Java Transfer Object, AJAX and Matlab. The proposed web-based KB-DSS has been evaluated through case studies and the performance of the system has been validated by the example results. As an established methodology and practical tool, the research will make valuable contributions to the field of measurement uncertainty evaluation.Brunel Universit