Water leakage forecasting: The application of a modified fuzzy evolving algorithm

This paper investigates the use of evolving fuzzy algorithms in forecasting. An evolving Takagi-Sugeno (eTS) algorithm, which is based on a recursive version of the subtractive algorithm is considered. It groups data into several clusters based on Euclidean distance between the relevant independent variables. The Mod eTS algorithm, which incorporates a modified dynamic update of cluster radii while accommodating new available data is proposed. The created clusters serve as a base for fuzzy If-Then rules with Gaussian membership functions which are defined using the cluster centres and have linear functions in the consequent i.e., Then parts of rules. The parameters of the linear functions are calculated using a weighted version of the Recursive Least Squares algorithm. The proposed algorithm is applied to a leakage forecasting problem faced by one of the leading UK water supplying companies. Using the real world data provided by the company the forecasting results obtained from the proposed modified eTS algorithm, Mod eTS, are compared to the standard eTS algorithm, exTS, eTS+ and fuzzy C-means clustering algorithm and some standard statistical forecasting methods. Different measures of forecasting accuracy are used. The results show higher accuracy achieved by applying the algorithm proposed compared to other fuzzy clustering algorithms and statistical methods. Similar results are obtained when comparing with other fuzzy evolving algorithms with dynamic cluster radii. Furthermore the algorithm generates typically a smaller number of clusters than standard fuzzy forecasting methods which leads to more transparent forecasting models

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Hydrogen Fuel Cell Emergency Power System: Installation and Performance of Plug Power GenCore 5B48 Unit

RES Master´s Thesis Verkefnið er unnið í tengslum við Háskóla Íslands og Háskólann á AkureyriBackup systems are crucial elements of modern electrical grids. They are used in places where an interruption in power supply can cause significant damage, e.g. in hospitals, banks or telecommunication towers. There are many solutions for how emergency power can be delivered. Hydrogen fuel cells are an emerging technology with great potential for the future. Fuel cells combine the advantages of batteries and diesel generators, and eliminate some of their significant disadvantages. They can work as long as they are supplied with fuel via a simple and efficient electrochemical reaction and at the same time they are quiet, produce no emissions and require minimum maintenance. The aim of this thesis is to present the idea of hydrogen fuel cells as reliable backup power systems. The work consisted of two parts: one practical, the other theoretical. The first part includes the background of energy security, emergency power sources, fuel cell systems backup power market, as well as an introduction to fuel cell technology, principles of operation and hydrogen safety. The practical part of this project is focused on the Plug Power GenCore 5B48 fuel cell backup power unit, its description, installation, operation, safety precautions and performance characteristics. The necessary hydrogen infrastructure was built according to safety codes and standards. The performance and reliability of the system was assessed. The system’s behavior was stable except for several minor problems during start-up which required intervention. The measured efficiency of the fuel cell stack and the whole system at the maximum available load of 1.65kW was 42.5% and 35.8% respectively. It was noted that the auxiliary load of the system has great influence on the overall performance of the system, especially at low output power. Noted fuel consumption was 13slm at 1kW and fuel utilization efficiency was estimated at around 99%. A cold start-up analysis was conducted and described based on the output data. During the first few minutes of operation the system required additional power to warm the fuel cell stack. The transition analysis focused on the ability of the system to provide power in case of a sudden outage. It was working well with batteries, as the fuel cell needed approximately 15 seconds to be ready to completely take over the power demand. Reliability and availability were assessed to be 96.8% and 79.9% respectively. It has to be pointed out that it was not possible to completely determine the system’s performance during some of the failure scenario and operation under different load because of the limitations of time and budget

A human factors approach to enhanced machine learning in cars

Using machine learning techniques, it is possible to learn and subsequently automate certain driver-focused features in consumer vehicles. A human factors approach is taken to review current machine learning systems. Subsequently, it is found that current methods used for machine learning involve long learning times and might not be sufficient to grasp a true understanding of interaction, routine and feature use - a new method is proposed. Issues surrounding trust and acceptance in automation are also explored and recommendations made