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Dr Daniel Paluszczyszyn

Job: Senior Lecturer

Faculty: Computing, Engineering and Media

School/department: School of Engineering and Sustainable Development

Research group(s): De Montfort University Interdisciplinary Group in Intelligent Transport Systems (DIGITS)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 207 8939

E: paluszcol@dmu.ac.uk

W: http://www.linkedin.com/pub/daniel-paluszczyszyn/13/545/463

 

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Personal profile

Daniel Paluszczyszyn received the B.Eng. in Computer Engineering from the University of Zielona Gora, Poland, in 2003, and the M.Sc. in Systems and Control from the Coventry University, UK, in 2008.

From April 2008 till October 2009 he was a Research Assistant at Coventry University developing and implementing control strategies for a radiotherapy treatment machine. In 2015 he was awarded with the Ph.D. in Hydroinformatics from De Montfort University where he worked as a Research Fellow from 2011 to 2015 in a number of research and commercial projects.

Currently, he is working at De Montfort University as a Senior Lecturer in School of Engineering and Sustainable Development. His recent research interests consider various aspects of intelligent mobility including optimisation of the energy management system for low carbon vehicles and scheduling approaches to charge autonomous electric vehicles.

Research group affiliations

 

Institute of Artificial Intelligence (IAI)

Institute of Engineering Sciences (IES)

Publications and outputs

  • Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data
    dc.title: Estimation of Travel Times for Minor Roads in Urban Areas Using Sparse Travel Time Data dc.contributor.author: Vu, Luong H.; Passow, Benjamin N.; Paluszczyszyn, D.; Deka, Lipika; Goodyer, E. N. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas
    dc.title: Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas dc.contributor.author: Orun, A.; Elizondo, David; Goodyer, E.; Paluszczyszyn, D. dc.description.abstract: Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system dc.description: The file attached to this record is the author's final peer reviewed version.
  • Neighbouring Link Travel Time Inference Method Using Artificial Neural Network
    dc.title: Neighbouring Link Travel Time Inference Method Using Artificial Neural Network dc.contributor.author: Luong H. Vu; Passow, Benjamin N.; Paluszczyszyn, D.; Deka, Lipika; Goodyer, E. dc.description.abstract: This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods. dc.description: The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Water Advisory Demand Evaluation and Resource Toolkit
    dc.title: Water Advisory Demand Evaluation and Resource Toolkit dc.contributor.author: Iliya, S.; Paluszczyszyn, D.; Goodyer, E.; Kubrycht, T. dc.description.abstract: The purpose of this feasibility study is to determine if the application of computational intelligence can be used to analyse the apparently unrelated data sources (social media, grid usage, traffic/transportation and weather) to produce credible predictions for water demand. For this purpose the artificial neural networks were employed to demonstrate on datasets localised to Leicester city in United Kingdom that viable predictions can be obtained with use of data derived from the expanding Internet-of-Things ecosystem. The outcomes from the initial study are promising as the water demand can be predicted with accuracy of 0.346 m3 in terms of root mean square error.
  • Range extended engine management system for electric vehicles: Control design process
    dc.title: Range extended engine management system for electric vehicles: Control design process dc.contributor.author: Paluszczyszyn, D.; Al-Doori, M.; Manning, W.; Elizondo, David; Goodyer, E. dc.description.abstract: In this work a research is presented aimed to improve the mechanical performance models used to establish a range-extension methodology, and to introduce the use of computational intelligence to operate a real-time range extension engine management system to replace the current algorithmic approach. This paper describes the initial stage in design of the control strategy, taking into account a number of environmental factors in order to increase the range of series hybrid electric vehicles.
  • Improving numerical efficiency of water networks models reduction algorithm
    dc.title: Improving numerical efficiency of water networks models reduction algorithm dc.contributor.author: Paluszczyszyn, D.; Skworcow, P.; Ulanicki, Bogumil dc.description.abstract: Nowadays, it is common that water distribution network (WDN) models contain thousands of elements to accurately replicate hydraulic behaviour and topographical layout of real systems. Such models are appropriate for simulation purposes, however optimisation tasks are much more computationally demanding, hence simplified models are required. Variables elimination is a mathematical method for the reduction of such large-scale models described by non-linear algebraic equations. The approach benefits of preserving the non-linearity of the original WDN model and approximates the original model at wide range of operating conditions. However its compute-intensive nature demanded that its implementation should take into account the development in programming languages and the recently released libraries allowing an optimisation of the executable program for multi-core machines. This will ensure that model reduction application will be able to cope with complex topologies of large size networks. In this paper the process of design and development of the research software is described with focus put on the emerged computational research aspects. It is demonstrated that utilisation of parallel programming techniques and sparse matrices ordering algorithms drastically decrease computational time of the model simplification.
  • A tool for practical simplification of water networks models
    dc.title: A tool for practical simplification of water networks models dc.contributor.author: Paluszczyszyn, D.; Skworcow, P.; Ulanicki, Bogumil dc.description.abstract: This paper presents development of water network model reduction software, Simplifier2. The application can be integrated with other concepts applied to water distribution system or it can be used as a standalone tool for the purpose of the model simplification only. The utilisation of parallel programming techniques and sparse matrices ordering algorithms drastically increased the speed of simplification. Simplifier2 is able to reduce the water network model, consisting of several thousand elements, in less than 1 minute calculation time. Simplifier2 has been already successfully utilised in a number of research and commercial projects. dc.description: Open Access article
  • Range extended for electric vehicle based on driver behaviour
    dc.title: Range extended for electric vehicle based on driver behaviour dc.contributor.author: Al-Doori, Moath; Paluszczyszyn, D.; Elizondo, David; Passow, Benjamin N.; Goodyer, E. N. dc.description.abstract: Driver behaviour has been considered one of the main factors that contribute to increase fuel consumption, CO2 emissions, traffic accidents and causalities. Thus, the concept of detecting and classifying driver behaviour i s vital when tackling these challenges. Recognition of the driver behaviour is a difficult task as in the real-world, the driving behaviour is effected by many factors e.g. traffic, road conditions, duration of the journey etc. Many approaches have considered the use of Computational Intelligence techniques, to develop a driver behaviour detection system. In this paper we concentrate on the impact of driver behaviour on the energy consumption and thereby on the range of electric vehicles. A new architecture is proposed to show how computational intelligence techniques could interact with the contextual information collected from the vehicle, the driver and external environment. A neural network model is used to classify the driver behaviour, and then this classification is used in a fuzzy logic controller to make balanced managements to the range extender operation.
  • Water advisory demand evaluation and resource toolkit
    dc.title: Water advisory demand evaluation and resource toolkit dc.contributor.author: Paluszczyszyn, D.; Illya, S.; Goodyer, E.; Kubrycht, T.; Ambler, M. dc.description.abstract: Cities are living organisms, 24h / 7day, with demands on resources and outputs. Water is a key resource whose management has not kept pace with modern urban life. Demand for clean water and loads on waste water no longer fit diurnal patterns; and they are impacted by events that are outside the normal range of parameters that are taken account of in water management. This feasibility study will determine how the application of computational intelligence can be used to analyse a mix of data inputs to produce credible predictions for clean water demand and foul water outputs in urban areas. The data inputs will be social-media and gas and electricity usage, combined with meteorological and traffic movement data. These will deliver predictions of population density and activity over a subsequent 8 hours period, thus providing inputs to the water supply services on the future demand of fresh water supplies, and the subsequent load on waste water and sewerage systems. The innovation of this concept is the aggregation of social-media data with transport related data to deliver a toolkit that predicts population density in an urban area over the next 8 hours. The toolkit will output the predictions in an open-source manner to support interoperability; thus enabling the development of new applications. For the sake of feasibility study the obtained data sets are localised to Leicester city in United Kingdom. The created online database contains mix of historic and real-time data. Data sources which are monitored and collected in real-time are localised Twitter feeds, current gas and electricity usage on regional level, traffic information from in-situ sensors and from traffic monitoring institutions, weather forecast and rainfall data. To ease the work with such large dataset a graphical user interface was developed in Matlab software and employed capabilities its specialised toolboxes. The online database is based on the Microsoft Azure solution. The computational intelligence model currently developed consist of various topologies of artificial neural networks and support vector machine regression. Note that the final model will comprise at least two models with weighted outputs as initial studies suggested that one model may not capture all the possible trends that characterises the training data for artificial neural network. The created toolkit includes a sensitivity test unit to evaluate the importance or contribution of each of the input variable on the prediction accuracy of the model, and also as a means of comparing our approach with traditional methods of population and water prediction. The toolkit aims to provide predictions for different time intervals, e.g. hourly, daily, monthly and yearly. Embedded within the tool are variants of differential evolutionary and swarm intelligence optimisation algorithms for optimising the meta-parameters of the computational intelligence models and the weights of the combined model. To test the functionality of the developed tool along with appropriateness of the proposed approach for the water demand prediction, data obtained from the SmartSpaces website (http://smartspaces.dmu.ac.uk) were utilised. This website shows the energy performance of a selection of public buildings in Leicester such as De Montfort University campus buildings, Leicester City Council buildings, schools, libraries, leisure centres and others buildings in Leicester. The SmartSpaces website monitors at 30 min intervals temperature, usage of electricity, gas and water within the buildings on the list. While the number of monitored buildings on the SmartSpaces website is limited, it provided a convenient access to the data and thereby enabled development of initial models. For testing the functionality of the toolkit using historic data from the SmartSpace project, the inputs of the artificial neural network and support vector machine models include electricity, gas, temperature and two recent past water demand. The output is the predicted current water demand. The outcomes from the initial study seem promising as the water usage was predicted with an average mean square error of 0.119 in terms of cubic meters.
  • Modelling and simulation of water distribution systems with quantised state system methods
    dc.title: Modelling and simulation of water distribution systems with quantised state system methods dc.contributor.author: Skworcow, P.; Ulanicki, Bogumil; Paluszczyszyn, D. dc.description.abstract: The work in this paper describes a study of quantised state systems in order to formulate a new framework within which water distribution systems can be modelled and simulated. In contrast to the classic time-slicing simulators, depending on the numerical integration algorithms, the quantisation of system states would allow accounting for the iscontinuities exhibited by control elements in a more efficient manner, and thereby, offer a significant increase in speed of the simulation of water network models.The proposed approach is evaluated on a case study and compared against the results obtained from the Epanet2 simulator and OpenModelica.

Click here to view a full listing of Daniel Paluszczyszyn's publications and outputs. 

Research interests/expertise

Modelling, simulation and optimisation of various systems e.g. water distribution systems, hybrid electric vehicles and others within hybrid systems framework.

Areas of teaching

Control Engineering

Qualifications

PhD in Hydroinformatics

MSc in Systems and Control

BSc in Computer Engineering

Courses taught

Aeronautical Engineering, Electrical and Electronics Engineering

Membership of professional associations and societies

Fellow of the Higher Education Academy

Professional licences and certificates

Certified Internal auditor (ISO:9001), TUV NORD Server+ Certified Professional, CompTIA

ORCID number

0000-0003-2838-060X