Dr Daniel Paluszczyszyn

Job: Senior Lecturer

Faculty: Technology

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

 

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 

  • Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas
    Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas Orun, A.; Elizondo, David; Goodyer, E.; Paluszczyszyn, D. 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 The file attached to this record is the author's final peer reviewed version.
  • Water Advisory Demand Evaluation and Resource Toolkit
    Water Advisory Demand Evaluation and Resource Toolkit Iliya, S.; Paluszczyszyn, D.; Goodyer, E.; Kubrycht, T. 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.
  • Neighbouring Link Travel Time Inference Method Using Artificial Neural Network
    Neighbouring Link Travel Time Inference Method Using Artificial Neural Network Luong H. Vu; Passow, Benjamin N.; Paluszczyszyn, D.; Deka, Lipika; Goodyer, E. 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. 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
    Water advisory demand evaluation and resource toolkit Paluszczyszyn, D.; Illya, S.; Goodyer, E.; Kubrycht, T.; Ambler, M. 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
    Modelling and simulation of water distribution systems with quantised state system methods Skworcow, P.; Ulanicki, Bogumil; Paluszczyszyn, D. 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.
  • A tool for practical simplification of water networks models
    A tool for practical simplification of water networks models Paluszczyszyn, D.; Skworcow, P.; Ulanicki, Bogumil 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. Open Access article
  • Advanced modelling and simulation of water distribution systems with discontinuous control elements
    Advanced modelling and simulation of water distribution systems with discontinuous control elements Paluszczyszyn, D. Water distribution systems are large and complex structures. Hence, their construction, management and improvements are time consuming and expensive. But nearly all the optimisation methods, whether aimed at design or operation, suffer from the need for simulation models necessary to evaluate the performance of solutions to the problem. These simulation models, however, are increasing in size and complexity, and especially for operational control purposes, where there is a need to regularly update the control strategy to account for the fluctuations in demands, the combination of a hydraulic simulation model and optimisation is likely to be computationally excessive for all but the simplest of networks. The work presented in this thesis has been motivated by the need for reduced, whilst at the same time appropriately accurate, models to replicate the complex and nonlinear nature of water distribution systems in order to optimise their operation. This thesis attempts to establish the ground rules to form an underpinning basis for the formulation and subsequent evaluation of such models. Part I of this thesis introduces some of the modelling, simulation and optimisation problems currently faced by water industry. A case study is given to emphasise one particular subject, namely reduction of water distribution system models. A systematic research resulted in development of a new methodology which encapsulate not only the system mass balance but also the system energy distribution within the model reduction process. The methodology incorporates the energy audits concepts into the model reduction algorithm allowing the preservation of the original model energy distribution by imposing new pressure constraints in the reduced model. The appropriateness of the new methodology is illustrated on the theoretical and industrial case studies. Outcomes from these studies demonstrate that the new extension to the model reduction technique can simplify the inherent complexity of water networks while preserving the completeness of original information. An underlying premise which forms a common thread running through the thesis, linking Parts I and II, is in recognition of the need for the more efficient paradigm to model and simulate water networks; effectively accounting for the discontinuous behaviour exhibited by water network components. Motivated largely by the potential of contemplating a new paradigm to water distribution system modelling and simulation, a further major research area, which forms the basis of Part II, leads to a study of the discrete event specification formalism and quantised state systems to formulate a 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 discontinuities 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 number of case studies and compared with results obtained from the Epanet2 simulator and OpenModelica. Although the current state-of-art of the simulation tools utilising the quantised state systems do not allow to fully exploit their potential, the results from comparison demonstrate that, if the second or third order quantised-based integrations are used, the quantised state systems approach can outperform the conventional water network simulation methods in terms of simulation accuracy and run-time.
  • Range extended for electric vehicle based on driver behaviour
    Range extended for electric vehicle based on driver behaviour Al-Doori, Moath; Paluszczyszyn, D.; Elizondo, David; Passow, Benjamin N.; Goodyer, E. N. 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.
  • Pump schedules optimisation with pressure aspects in complex large-scale water distribution systems
    Pump schedules optimisation with pressure aspects in complex large-scale water distribution systems Skworcow, P; Paluszczyszyn, D.; Ulanicki, Bogumil This paper considers optimisation of pump and valve schedules in complex large-scale water distribution networks (WDN), taking into account pressure aspects such as minimum service pressure and pressuredependent leakage. An optimisation model is automatically generated in the GAMS language from a hydraulic model in the EPANET format and from additional files describing operational constraints, electricity tariffs and pump station configurations. The paper describes in details how each hydraulic component is modelled. To reduce the size of the optimisation problem the full hydraulic model is simplified using module reduction algorithm, while retaining the nonlinear characteristics of the model. Subsequently, a nonlinear programming solver CONOPT is used to solve the optimisation model, which is in the form of Nonlinear Programming with Discontinuous Derivatives (DNLP). The results produced by CONOPT are processed further by heuristic algorithms to generate integer solution. The proposed approached was tested on a large-scale WDN model provided in the EPANET format. The considered WDN included complex structures and interactions between pump stations . Solving of several scenarios considering different horizons, time steps, operational constraints, demand levels and topological changes demonstrated ability of the approach to automatically generate and solve optimisation problems for a variety of requirements.
  • Range extended engine management system for electric vehicles
    Range extended engine management system for electric vehicles Paluszczyszyn, D.; Al-Doori, Moath; Manning, Warren; Elizondo, David; Gammon, Rupert; Goodyer, E. N.

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

Mechanical 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

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