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Dr. William Hurst
Wageningen University & Research

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Journal article
Published: 30 April 2021 in Infrastructures
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Time-based smart home controllers govern their environment with a predefined routine, without knowing if this is the most efficient way. Finding a suitable model to predict energy consumption could prove to be an optimal method to manage the electricity usage. The work presented in this paper outlines the development of a prediction model that controls electricity consumption in a home, adapting to external environmental conditions and occupation. A backup geyser element in a solar geyser solution is identified as a metric for more efficient control than a time-based controller. The system is able to record multiple remote sensor readings from Internet of Things devices, built and based on an ESP8266 microcontroller, to a central SQL database that includes the hot water usage and heating patterns. Official weather predictions replace physical sensors, to provide the data for the environmental conditions. Fuzzification categorises the warm water usage from the multiple sensor recordings into four linguistic terms (None, Low, Medium and High). Partitioning clustering determines the relationship patterns between weather predictions and solar heating efficiency. Next, a hidden Markov model predicts solar heating efficiency, with the Viterbi algorithm calculating the geyser heating predictions, and the Baum–Welch algorithm for training the system. Warm water usage and solar heating efficiency predictions are used to calculate the optimal time periods to heat the water through electrical energy. Simulations with historical data are used for the evaluation and validation of the approach, by comparing the algorithm efficiency against time-based heating. In a simulation, the intelligent controller is 19.9% more efficient than a time-based controller, with higher warm water temperatures during the day. Furthermore, it is demonstrated that a controller, with knowledge of external conditions, can be switched on 728 times less than a time-based controller.

ACS Style

Daniel de Bruyn; Ben Kotze; William Hurst. A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating. Infrastructures 2021, 6, 67 .

AMA Style

Daniel de Bruyn, Ben Kotze, William Hurst. A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating. Infrastructures. 2021; 6 (5):67.

Chicago/Turabian Style

Daniel de Bruyn; Ben Kotze; William Hurst. 2021. "A Hidden Markov Model and Fuzzy Logic Forecasting Approach for Solar Geyser Water Heating." Infrastructures 6, no. 5: 67.

Journal article
Published: 24 November 2020 in IEEE Access
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This paper proposes a novel passenger car equivalent and capacity estimation methods that determine the effect of deceleration and acceleration performance of heavy goods vehicles on the traffic flow and estimate the capacity to facilitate rescheduling container carriers. The development of the new methods considers the driver’s perception of time and braking competency level, and the out of the box vehicle displacement approach. The safety gap between the following and leading vehicle should provide sufficient time and space for the driver to bring the vehicle safely to a standstill, to prevent accidents and facilitate enough space for maneuvering. As a case study, the authors have collected and utilized the automatic traffic counters data and the average annual daily flow data from manual counting for the road connecting the Liverpool containership port with North-West England and the rest of the UK. However, the capacity estimation method is suitable for all urban roads and streets that have controlled intersections in the UK and the USA. The authors have found that the passenger car equivalent method is directly proportional to the vehicle’s speed, and gross mass and the capacity method is inversely and directly proportional to perception time and braking competency level, respectively. Also, building an extra lane will allow meeting the ports targets.

ACS Style

Ahmed Adnan Makki; Trung Thanh Nguyen; Jun Ren; Dhiya Al-Jumeily; William Hurst. Estimating Road Traffic Capacity. IEEE Access 2020, 8, 228525 -228547.

AMA Style

Ahmed Adnan Makki, Trung Thanh Nguyen, Jun Ren, Dhiya Al-Jumeily, William Hurst. Estimating Road Traffic Capacity. IEEE Access. 2020; 8 (99):228525-228547.

Chicago/Turabian Style

Ahmed Adnan Makki; Trung Thanh Nguyen; Jun Ren; Dhiya Al-Jumeily; William Hurst. 2020. "Estimating Road Traffic Capacity." IEEE Access 8, no. 99: 228525-228547.

Journal article
Published: 13 October 2020 in Smart Cities
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Carbon emission is a prominent issue, and smart urban solutions have the technological capabilities to implement change. The technologies for creating smart energy systems already exist, some of which are currently under wide deployment globally. By investing in energy efficiency solutions (such as the smart meter), research shows that the end-user is able to not only save money, but also reduce their household’s carbon footprint. Therefore, in this paper, the focus is on the end-user, and adopting a quantitative analysis of the perception of 1365 homes concerning the smart gas meter installation. The focus is on linking end-user attributes (age, education, social class and employment status) with their opinion on reducing energy, saving money, changing home behaviour and lowering carbon emissions. The results show that there is a statistical significance between certain attributes of end-users and their consideration of smart meters for making beneficial changes. In particular, the investigation demonstrates that the employment status, age and social class of the homeowner have statistical significance on the end-users’ variance; particularly when interested in reducing their bill and changing their behaviour around the home.

ACS Style

William Hurst; Bedir Tekinerdogan; Ben Kotze. Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint. Smart Cities 2020, 3, 1173 -1186.

AMA Style

William Hurst, Bedir Tekinerdogan, Ben Kotze. Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint. Smart Cities. 2020; 3 (4):1173-1186.

Chicago/Turabian Style

William Hurst; Bedir Tekinerdogan; Ben Kotze. 2020. "Perceptions on Smart Gas Meters in Smart Cities for Reducing the Carbon Footprint." Smart Cities 3, no. 4: 1173-1186.

Journal article
Published: 03 September 2020 in IoT
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Smart meters have become a core part of the Internet of Things, and its sensory network is increasing globally. For example, in the UK there are over 15 million smart meters operating across homes and businesses. One of the main advantages of the smart meter installation is the link to a reduction in carbon emissions. Research shows that, when provided with accurate and real-time energy usage readings, consumers are more likely to turn off unneeded appliances and change other behavioural patterns around the home (e.g., lighting, thermostat adjustments). In addition, the smart meter rollout results in a lessening in the number of vehicle callouts for the collection of consumption readings from analogue meters and a general promotion of renewable sources of energy supply. Capturing and mining the data from this fully maintained (and highly accurate) sensing network, provides a wealth of information for utility companies and data scientists to promote applications that can further support a reduction in energy usage. This research focuses on modelling trends in domestic energy consumption using density-based classifiers. The technique estimates the volume of outliers (e.g., high periods of anomalous energy consumption) within a social class grouping. To achieve this, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Ordering Points to Identify the Clustering Structure (OPTICS) and Local Outlier Factor (LOF) demonstrate the detection of unusual energy consumption within naturally occurring groups with similar characteristics. Using DBSCAN and OPTICS, 53 and 208 outliers were detected respectively; with 218 using LOF, on a dataset comprised of 1,058,534 readings from 1026 homes.

ACS Style

William Hurst; Casimiro A. Curbelo Montañez; Nathan Shone. Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption. IoT 2020, 1, 92 -108.

AMA Style

William Hurst, Casimiro A. Curbelo Montañez, Nathan Shone. Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption. IoT. 2020; 1 (1):92-108.

Chicago/Turabian Style

William Hurst; Casimiro A. Curbelo Montañez; Nathan Shone. 2020. "Time-Pattern Profiling from Smart Meter Data to Detect Outliers in Energy Consumption." IoT 1, no. 1: 92-108.

Journal article
Published: 08 June 2020 in Future Internet
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Hospital critical infrastructures have a distinct threat vector, due to (i) a dependence on legacy software; (ii) the vast levels of interconnected medical devices; (iii) the use of multiple bespoke software and that (iv) electronic devices (e.g., laptops and PCs) are often shared by multiple users. In the UK, hospitals are currently upgrading towards the use of electronic patient record (EPR) systems. EPR systems and their data are replacing traditional paper records, providing access to patients’ test results and details of their overall care more efficiently. Paper records are no-longer stored at patients’ bedsides, but instead are accessible via electronic devices for the direct insertion of data. With over 83% of hospitals in the UK moving towards EPRs, access to this healthcare data needs to be monitored proactively for malicious activity. It is paramount that hospitals maintain patient trust and ensure that the information security principles of integrity, availability and confidentiality are upheld when deploying EPR systems. In this paper, an investigation methodology is presented towards the identification of anomalous behaviours within EPR datasets. Many security solutions focus on a perimeter-based approach; however, this approach alone is not enough to guarantee security, as can be seen from the many examples of breaches. Our proposed system can be complementary to existing security perimeter solutions. The system outlined in this research employs an internal-focused methodology for anomaly detection by using the Local Outlier Factor (LOF) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms for benchmarking behaviour, for assisting healthcare data analysts. Out of 90,385 unique IDs, DBSCAN finds 102 anomalies, whereas 358 are detected using LOF.

ACS Style

William Hurst; Aaron Boddy; Madjid Merabti; Nathan Shone. Patient Privacy Violation Detection in Healthcare Critical Infrastructures: An Investigation Using Density-Based Benchmarking. Future Internet 2020, 12, 1 .

AMA Style

William Hurst, Aaron Boddy, Madjid Merabti, Nathan Shone. Patient Privacy Violation Detection in Healthcare Critical Infrastructures: An Investigation Using Density-Based Benchmarking. Future Internet. 2020; 12 (6):1.

Chicago/Turabian Style

William Hurst; Aaron Boddy; Madjid Merabti; Nathan Shone. 2020. "Patient Privacy Violation Detection in Healthcare Critical Infrastructures: An Investigation Using Density-Based Benchmarking." Future Internet 12, no. 6: 1.

Journal article
Published: 27 January 2020 in IEEE Access
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Technological advancements in the field of electrical energy distribution and utilization are revolutionizing the way consumers and utility providers interact. In addition to allowing utility companies to monitor the status of their network independently in autonomous fashion, data collected by smart meters as part of the wider advanced metering infrastructure, can be valuable for third parties, such as government authorities. The availability of the information, the granularity of the data, and the real-time nature of the smart meter, means that predictive analytics can be employed to profile consumers with high accuracy and approximate, for example, the number of individuals living in a house, the type of appliances being used, or the duration of occupancy, to name but a few applications. This paper presents a machine learning model comparison for unemployment prediction of single household occupants, based on features extracted from smart meter electricity readings. A number of nonlinear classifiers are compared, and benchmarked against a generalized linear model, and the results presented. To ensure the robustness of the classifiers, we use repeated cross validation. The results revealed that it is possible to predict employability status with Area Under Curve (AUC) = 74%, Sensitivity (SE) = 54% and Specificity (SP) = 83%, using a multilayer perceptron neural network with dropout, closely followed by the results produced by a distance weighted discrimination with polynomial kernel model. This shows the potential of using the smart metering infrastructure to provide additional autonomous services, such as unemployment detection, for governments using data collected from an advanced and distributed Internet of Things (IoT) sensor network.

ACS Style

Casimiro A. Curbelo Montanez; William Hurst. A Machine Learning Approach for Detecting Unemployment Using the Smart Metering Infrastructure. IEEE Access 2020, 8, 22525 -22536.

AMA Style

Casimiro A. Curbelo Montanez, William Hurst. A Machine Learning Approach for Detecting Unemployment Using the Smart Metering Infrastructure. IEEE Access. 2020; 8 ():22525-22536.

Chicago/Turabian Style

Casimiro A. Curbelo Montanez; William Hurst. 2020. "A Machine Learning Approach for Detecting Unemployment Using the Smart Metering Infrastructure." IEEE Access 8, no. : 22525-22536.

Journal article
Published: 08 January 2020 in IEEE Access
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Fuel poverty has a negative impact on the wellbeing of individuals within a household; affecting not only comfort levels but also increased levels of seasonal mortality. Wellbeing solutions within this sector are moving towards identifying how the needs of people in vulnerable situations can be improved or monitored by means of existing supply networks and public institutions. Therefore, the focus of this research is towards wellbeing monitoring solution, through the analysis of gas smart meter data. Gas smart meters replace the traditional analogue electro-mechanical and diaphragm-based meters that required regular reading. They have received widespread popularity over the last 10 years. This is primarily due to the fact that by using this technology, customers are able to adapt their consumption behaviours based on real-time information provided by In-Home Devices. Yet, the granular nature of the datasets generated has also meant that this technology is ideal for further scalable wellbeing monitoring applications. For example, the autonomous detection of households at risk of energy poverty is possible and of growing importance in order to face up to the impacts of fuel poverty, quality of life and wellbeing of low-income housing. However, despite their popularity (smart meters), the analysis of gas smart meter data has been neglected. In this paper, an ensemble model is proposed to achieve autonomous detection, supported by four key measures from gas usage patterns, consisting of i) a tariff detection, ii) a temporally-aware tariff detection, iii) a routine consumption detection and iv) an age-group detection. Using a cloud-based machine learning platform, the proposed approach yielded promising classification results of up to 84.1% Area Under Curve (AUC), when the Synthetic Minority Over-sampling Technique (SMOTE) was utilised.

ACS Style

William Hurst; Casimiro Curbelo Montañez; Nathan Shone; Dhiya Al-Jumeily. An Ensemble Detection Model Using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities. IEEE Access 2020, 8, 7877 -7898.

AMA Style

William Hurst, Casimiro Curbelo Montañez, Nathan Shone, Dhiya Al-Jumeily. An Ensemble Detection Model Using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities. IEEE Access. 2020; 8 (99):7877-7898.

Chicago/Turabian Style

William Hurst; Casimiro Curbelo Montañez; Nathan Shone; Dhiya Al-Jumeily. 2020. "An Ensemble Detection Model Using Multinomial Classification of Stochastic Gas Smart Meter Data to Improve Wellbeing Monitoring in Smart Cities." IEEE Access 8, no. 99: 7877-7898.

Conference paper
Published: 08 January 2020 in Communications in Computer and Information Science
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Efforts of electrical utilities to respond to climate change require the development of increasingly sophisticated, integrated electrical grids referred to as the “smart grids”. Much of the smart grid effort focuses on integration of renewable generation into the electricity grid and on increased monitoring and automation of electrical transmission functions. However, a key component of smart grid development is the introduction of the smart electrical meter for all residential electrical customers. Smart meter (SM) deployment is the corner stone of the smart grid. In addition to adding new functionality to support system reliability, SMs provide the technological means for utilities to institute new programs to allow their customers to better manage and reduce their electricity use and to support increased renewable generation to reduce greenhouse emissions from electricity use. As such, this paper presents our research towards the study of a smart home environment and how the data produced is used to profile energy usage in homes. The validity of the data is justified through analysis of the profiles generated while consumers use energy during off peak and peak periods. By learning, understanding and feeding patterns of home behaviour, it is possible to educate the consumer regarding their energy usage, helping them to reduce costs but also the emissions from their home.

ACS Style

Mutinta Mwansa; William Hurst; Yuanyuan Shen. Towards Smart Meter Energy Analysis and Profiling to Support Low Carbon Emissions. Communications in Computer and Information Science 2020, 312 -322.

AMA Style

Mutinta Mwansa, William Hurst, Yuanyuan Shen. Towards Smart Meter Energy Analysis and Profiling to Support Low Carbon Emissions. Communications in Computer and Information Science. 2020; ():312-322.

Chicago/Turabian Style

Mutinta Mwansa; William Hurst; Yuanyuan Shen. 2020. "Towards Smart Meter Energy Analysis and Profiling to Support Low Carbon Emissions." Communications in Computer and Information Science , no. : 312-322.

Journal article
Published: 01 January 2020 in Scientia
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Agricultural weeds have the potential to cause significant crop loss. As such, conventional weed management practices have aimed to keep crop fields free from weeds through the broad application of herbicides. However, these practices have damaging consequences on the surrounding environment. Dr Rakesh Chandran and his team in the Agriculture and Natural Resources Department of West Virginia University have developed a more sustainable herbicide application regime that allows weeds to coexist with corn crops at acceptable levels, with the aim of improving environmental health without significantly sacrificing crop yield.

ACS Style

William Hurst. The Gamification of Business Productivity and Scientific Education. Scientia 2020, 1 .

AMA Style

William Hurst. The Gamification of Business Productivity and Scientific Education. Scientia. 2020; ():1.

Chicago/Turabian Style

William Hurst. 2020. "The Gamification of Business Productivity and Scientific Education." Scientia , no. : 1.

Conference paper
Published: 24 July 2019 in Advanced Data Mining and Applications
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ACS Style

William Hurst; Casimiro Curbelo Montañez; Dhiya Al-Jumeily. Age Group Detection in Stochastic Gas Smart Meter Data Using Decision-Tree Learning. Advanced Data Mining and Applications 2019, 593 -605.

AMA Style

William Hurst, Casimiro Curbelo Montañez, Dhiya Al-Jumeily. Age Group Detection in Stochastic Gas Smart Meter Data Using Decision-Tree Learning. Advanced Data Mining and Applications. 2019; ():593-605.

Chicago/Turabian Style

William Hurst; Casimiro Curbelo Montañez; Dhiya Al-Jumeily. 2019. "Age Group Detection in Stochastic Gas Smart Meter Data Using Decision-Tree Learning." Advanced Data Mining and Applications , no. : 593-605.

Reference work
Published: 28 June 2019 in Encyclopedia of Computer Graphics and Games
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ACS Style

William Hurst; Kieran Latham; Darryl O’Hare; Andrew Sands; Robert John Gandy. Virtual Reality Proton Beam Therapy Unit: Case Study on the Development. Encyclopedia of Computer Graphics and Games 2019, 1 -5.

AMA Style

William Hurst, Kieran Latham, Darryl O’Hare, Andrew Sands, Robert John Gandy. Virtual Reality Proton Beam Therapy Unit: Case Study on the Development. Encyclopedia of Computer Graphics and Games. 2019; ():1-5.

Chicago/Turabian Style

William Hurst; Kieran Latham; Darryl O’Hare; Andrew Sands; Robert John Gandy. 2019. "Virtual Reality Proton Beam Therapy Unit: Case Study on the Development." Encyclopedia of Computer Graphics and Games , no. : 1-5.

Journal article
Published: 30 January 2019 in Future Internet
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Visualising complex data facilitates a more comprehensive stage for conveying knowledge. Within the medical data domain, there is an increasing requirement for valuable and accurate information. Patients need to be confident that their data is being stored safely and securely. As such, it is now becoming necessary to visualise data patterns and trends in real-time to identify erratic and anomalous network access behaviours. In this paper, an investigation into modelling data flow within healthcare infrastructures is presented; where a dataset from a Liverpool-based (UK) hospital is employed for the case study. Specifically, a visualisation of transmission control protocol (TCP) socket connections is put forward, as an investigation into the data complexity and user interaction events within healthcare networks. In addition, a filtering algorithm is proposed for noise reduction in the TCP dataset. Positive results from using this algorithm are apparent on visual inspection, where noise is reduced by up to 89.84%.

ACS Style

Aaron Boddy; William Hurst; Michael Mackay; Abdennour El Rhalibi; Thar Baker; Casimiro Adays Curbelo Montañez. An Investigation into Healthcare-Data Patterns. Future Internet 2019, 11, 30 .

AMA Style

Aaron Boddy, William Hurst, Michael Mackay, Abdennour El Rhalibi, Thar Baker, Casimiro Adays Curbelo Montañez. An Investigation into Healthcare-Data Patterns. Future Internet. 2019; 11 (2):30.

Chicago/Turabian Style

Aaron Boddy; William Hurst; Michael Mackay; Abdennour El Rhalibi; Thar Baker; Casimiro Adays Curbelo Montañez. 2019. "An Investigation into Healthcare-Data Patterns." Future Internet 11, no. 2: 30.

Journal article
Published: 21 January 2019 in Behaviour & Information Technology
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ACS Style

Carl Chalmers; William Hurst; Michael Mackay; Paul Fergus. Identifying behavioural changes for health monitoring applications using the advanced metering infrastructure. Behaviour & Information Technology 2019, 38, 1154 -1166.

AMA Style

Carl Chalmers, William Hurst, Michael Mackay, Paul Fergus. Identifying behavioural changes for health monitoring applications using the advanced metering infrastructure. Behaviour & Information Technology. 2019; 38 (11):1154-1166.

Chicago/Turabian Style

Carl Chalmers; William Hurst; Michael Mackay; Paul Fergus. 2019. "Identifying behavioural changes for health monitoring applications using the advanced metering infrastructure." Behaviour & Information Technology 38, no. 11: 1154-1166.

Conference paper
Published: 02 November 2018 in Advances in Intelligent Systems and Computing
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Worldwide, the number of people living with self-limiting conditions, is increasing. The resulting strain on healthcare resources means that providing 24-h monitoring for patients is a challenge. As this problem escalates, caring for an ageing population will become more demanding over the next decade, and the need for new, innovative and cost effective home monitoring technologies are now urgently required. In the UK, a national rollout of smart meters is underway. Energy suppliers are committed to the installation and configuration of over 50 million smart meters by 2020. Smart meters enable detailed monitoring of energy usage. The research presented in this paper provides an innovative approach, which addresses the limitations of current Ambient Assistive Living technologies. The approach demonstrates that the detection of both normal and abnormal patient behaviour is possible by correlating the detection of individual electrical appliances and mapping them to an individual’s Activities of Daily Living.

ACS Style

Carl Chalmers; William Hurst; Michael Mackay; Paul Fergus; Dhiya Al-Jumeily; Bryony Kendall. Intelligent Health Monitoring Using Smart Meters. Advances in Intelligent Systems and Computing 2018, 1104 -1113.

AMA Style

Carl Chalmers, William Hurst, Michael Mackay, Paul Fergus, Dhiya Al-Jumeily, Bryony Kendall. Intelligent Health Monitoring Using Smart Meters. Advances in Intelligent Systems and Computing. 2018; ():1104-1113.

Chicago/Turabian Style

Carl Chalmers; William Hurst; Michael Mackay; Paul Fergus; Dhiya Al-Jumeily; Bryony Kendall. 2018. "Intelligent Health Monitoring Using Smart Meters." Advances in Intelligent Systems and Computing , no. : 1104-1113.

Conference paper
Published: 29 September 2018 in Advances in Intelligent Systems and Computing
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The growing Internet of Things (IoT), the increasing use of sensor technology and the digitisation of traditionally isolated analogue devices are transforming manufacturing and private dwellings in the UK. This ongoing revolution is often referred to as Industry 4.0, where real-time data informs the product value chain and digital applications are used for automating service allocation. Within this emerging environment, good practice is essential for productivity. Yet, the access to good practice guides and information is a challenge. Consequently, in this paper, the Productivity Accelerator (ProAccel) platform design is proposed. The system is a modular cloud-based multimedia platform that has the goal of helping UK businesses improve their productivity. ProAccel employs advanced machine learning and gamification techniques to revolutionise the way productivity information is shared.

ACS Style

William Hurst; Nathan Shone; David Tully; Qi Shi; Carl Chalmers; Jamie Hulse; Darryl O’Hare. Developing a Productivity Accelerator Platform to Support UK Businesses in the Industry 4.0 Revolution. Advances in Intelligent Systems and Computing 2018, 517 -525.

AMA Style

William Hurst, Nathan Shone, David Tully, Qi Shi, Carl Chalmers, Jamie Hulse, Darryl O’Hare. Developing a Productivity Accelerator Platform to Support UK Businesses in the Industry 4.0 Revolution. Advances in Intelligent Systems and Computing. 2018; ():517-525.

Chicago/Turabian Style

William Hurst; Nathan Shone; David Tully; Qi Shi; Carl Chalmers; Jamie Hulse; Darryl O’Hare. 2018. "Developing a Productivity Accelerator Platform to Support UK Businesses in the Industry 4.0 Revolution." Advances in Intelligent Systems and Computing , no. : 517-525.

Conference paper
Published: 17 October 2017 in Proceedings of the 1st International Conference on Medical and Health Informatics 2017
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ACS Style

Aaron Boddy; William Hurst; Michael Mackay; Abdennour El Rhalibi. A study into data analysis and visualisation to increase the cyber-resilience of healthcare infrastructures. Proceedings of the 1st International Conference on Medical and Health Informatics 2017 2017, 32 .

AMA Style

Aaron Boddy, William Hurst, Michael Mackay, Abdennour El Rhalibi. A study into data analysis and visualisation to increase the cyber-resilience of healthcare infrastructures. Proceedings of the 1st International Conference on Medical and Health Informatics 2017. 2017; ():32.

Chicago/Turabian Style

Aaron Boddy; William Hurst; Michael Mackay; Abdennour El Rhalibi. 2017. "A study into data analysis and visualisation to increase the cyber-resilience of healthcare infrastructures." Proceedings of the 1st International Conference on Medical and Health Informatics 2017 , no. : 32.

Conference paper
Published: 12 October 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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3D scanning and printing has the potential to revolutionise the world. It offers a bridge between the virtual environment and the tangible world. The use of 3D scanners to capture and recreate defining objects is known as 3D virtualisation. It involves capturing a real-life scene using laser technology and representing its geometry using 3D modelling software or 3D printers. Despite being a relatively young technology, 3D printing has now become accessible and a part of modern industry. The printing of a 3D generated model can change the way in which an individual understands a concept, environment or communicates an idea. This has multiple benefits, for education, skills development, training and within the construction industry. However, using this technology relies on the operator having the skills and training required to generate accurate 3D models, and account for errors in the mesh after scanning. As such, this paper details the development into an automated 3D scanning system, and a cloud-based printing platform, where models are intelligently printed by multiple devices. Its development allows the readiness of 3D printing capabilities to unskilled users, who have no education or training in 3D model construction. Objects can be instantly manipulated and transferred into free-to-use open source graphic software. The access to detailed 3D model construction has never been so accessible to the untrained.

ACS Style

Darryl O’Hare; William Hurst; David Tully; Abdennour El Rhalibi. A Study into Autonomous Scanning for 3D Model Construction. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 182 -190.

AMA Style

Darryl O’Hare, William Hurst, David Tully, Abdennour El Rhalibi. A Study into Autonomous Scanning for 3D Model Construction. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; ():182-190.

Chicago/Turabian Style

Darryl O’Hare; William Hurst; David Tully; Abdennour El Rhalibi. 2017. "A Study into Autonomous Scanning for 3D Model Construction." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 182-190.

Conference paper
Published: 20 March 2017 in 2016 4th International Conference on Enterprise Systems (ES)
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Electrocardiograms (ECG) contain biological information which is unique to the individual. In this paper, an ECG identification system, which uses Frequency Rank Order Statistics (FROS) as a feature extraction method and Back-Propagation Neural Network (BPNN) classifiers to identify 'other classes', is proposed. FROS handle different ECG states and BPNN classifiers, with random input weights, are used to generate a relatively high accuracy model for the identification system. Additionally, in the output layer, classified patterns are categorized according to the maximum value of the output layer nodes. Similar data is grouped into one category for the final identification result. Experiments show that the BPNN classifier produces more accurate results than an SVM and Bayesian classifier achieve on average. The proposed approach also out-performs SVMNN and LVQNN. The identification system, put forward in this paper, may be applied to an intelligent vehicular system, as an application example.

ACS Style

Kuo-Kun Tseng; Dachao Lee; William Hurst; Fang-Yin Lin; W.H. Ip. Frequency Rank Order Statistic with Unknown Neural Network for ECG Identification System. 2016 4th International Conference on Enterprise Systems (ES) 2017, 160 -167.

AMA Style

Kuo-Kun Tseng, Dachao Lee, William Hurst, Fang-Yin Lin, W.H. Ip. Frequency Rank Order Statistic with Unknown Neural Network for ECG Identification System. 2016 4th International Conference on Enterprise Systems (ES). 2017; ():160-167.

Chicago/Turabian Style

Kuo-Kun Tseng; Dachao Lee; William Hurst; Fang-Yin Lin; W.H. Ip. 2017. "Frequency Rank Order Statistic with Unknown Neural Network for ECG Identification System." 2016 4th International Conference on Enterprise Systems (ES) , no. : 160-167.

Original articles
Published: 12 January 2017 in Systems Science & Control Engineering
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File sharing applications, which operate as a form of Peer-to-Peer (P2P) network, are popular amongst users and developers due to their heterogeneity, decentralized approach and rudimentary deployment features. However, they are also used for illegal online activities and often are infested with malicious content such as viruses and contraband material. This brings new challenges to forensic investigations in detecting, retrieving and examining the P2P applications. Within the domain of P2P applications, the Invisible Internet Project (IP2) is used to allow applications to communicate anonymously. As such, this work discusses its use by network node operators and known attacks against privacy or availability of I2P routers. Specifically, we investigate the characteristics of I2P networks in order to outline the security flaws and the issues in detecting artefacts within the I2P. Furthermore, we present a discussion on new methods to detect the presence of I2P using forensic tools and reconstruct specific I2P activities using artefacts left over by network software.

ACS Style

Behnam Bazli; Maxim Wilson; William Hurst. The dark side of I2P, a forensic analysis case study. Systems Science & Control Engineering 2017, 5, 278 -286.

AMA Style

Behnam Bazli, Maxim Wilson, William Hurst. The dark side of I2P, a forensic analysis case study. Systems Science & Control Engineering. 2017; 5 (1):278-286.

Chicago/Turabian Style

Behnam Bazli; Maxim Wilson; William Hurst. 2017. "The dark side of I2P, a forensic analysis case study." Systems Science & Control Engineering 5, no. 1: 278-286.

Chapter
Published: 01 January 2017 in Handbook of Research on Advancing Cybersecurity for Digital Transformation
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Over the last decade, Internet of Things (IoTs) have brought radical changes to the means and forms of communication for monitoring and control of a large number of applications including Smart Grid (SG). Traditional energy networks have been modernized to SGs to boost the energy industry in the context of efficient and effective power management, performance, real-time control and information flow using two-way communication between utility provides and end-users. However, integrating two-way communication in SG comes at the cost of cyber security vulnerabilities and challenges. In the context of SG, node compromise is a severe security threat due to the fact that a compromised node can significantly impact the operations and security of the SG network. Therefore, in this chapter, Key Management Scheme for Communication Layer in the Smart Grid (KMS-CL-SG) has proposed. In order to achieve a secure end-to-end communication we assign a unique key to each node in the group.

ACS Style

Bashar Alohali; Kashif Kifayat; Qi Shi; William Hurst. A Key Management Scheme for Secure Communications Based on Smart Grid Requirements (KMS-CL-SG). Handbook of Research on Advancing Cybersecurity for Digital Transformation 2017, 242 -265.

AMA Style

Bashar Alohali, Kashif Kifayat, Qi Shi, William Hurst. A Key Management Scheme for Secure Communications Based on Smart Grid Requirements (KMS-CL-SG). Handbook of Research on Advancing Cybersecurity for Digital Transformation. 2017; ():242-265.

Chicago/Turabian Style

Bashar Alohali; Kashif Kifayat; Qi Shi; William Hurst. 2017. "A Key Management Scheme for Secure Communications Based on Smart Grid Requirements (KMS-CL-SG)." Handbook of Research on Advancing Cybersecurity for Digital Transformation , no. : 242-265.