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The effects of Electronic Stability Control (ESC) on fatal crash rates in the United States
Get PDFProblem: Electronic Stability Control (ESC) is believed to be among the most efficient vehicle safety interventions with reported effects around 50% for fatal single and rollover crashes. However, such estimates have used sample data, which have not controlled for the possibilities of self-selection, behavioral adaptation, increased access to the technology by less safe drivers, and the calculation of effects on very specific categories of crashes. Effects of ESC in the population can therefore be expected to be smaller than is currently believed. Method: National U.S. data for fatal crashes, driving exposure and other control factors, and market penetration of ESC over 1991–2021 were used to calculate whether the trends in fatalities over time in crash rates for singles, rollovers, and fatal crashes in general matched projections from estimates of effectiveness. Results: It was found that downward trends in the relevant crash types were generally present before ESC was introduced, and that the trends thereafter were weaker. Although some trends were consistent with effects of ESC, they were markedly smaller than the projected ones, and could be explained by other factors such as the number of vehicles per capita. At best, the effect for rollovers could be up to two-thirds of previous estimates, no effect was detected for singles, while for all fatal crashes results depended upon the type of analysis performed. These results conflict with conclusions in all published ESC crash sample studies, which have compared vehicles with and without ESC. This discrepancy can be explained by methodological errors in the previous studies using induced exposure methods and self-selected samples. Practical applications: Traffic safety may not be as much improved by technological interventions as believed. Alternative approaches to traffic safety are needed, which do not rely on technology that interferes with driver behavior.Engineering and Physical Sciences Research Council: grant number: EP/V026763/1Journal of Safety Researc
Preface for feature topic on human driver behaviours for intelligent vehicles
Get PDFEditorialAutomotive Innovatio
Guest Editorial: Selected papers from RADAR 2022—International Conference on Radar Systems (Edinburgh, UK)
Get PDFSpecial Issue: Selected Papers from RADAR 2022 ‐ International Conference on Radar Systems (Edinburgh, UK)It is our great pleasure to present you with this IET Radar, Sonar and Navigation special issue on the ‘Selected Papers from RADAR 2022—International Conference on Radar Systems (Edinburgh, UK)’.
RADAR 2022 took place at Murrayfield Stadium, Edinburgh, on 24–27 October 2022 as a prime opportunity for radar specialists at all career stages to update and enhance their knowledge on the latest developments in advanced radar systems. As such, RADAR 2022 was attended by over 250 delegates from 22 countries who joined the conference to explore the latest technologies in radar systems.IET Radar, Sonar & Navigatio
Temperature hotspot detection on printed circuit boards (pcbs) using ultrasonic guided waves—a machine learning approach
Get PDFThis paper addresses the challenging issue of achieving high spatial resolution in temperature monitoring of printed circuit boards (PCBs) without compromising the operation of electronic components. Traditional methods involving numerous dedicated sensors such as thermocouples are often intrusive and can impact electronic functionality. To overcome this, this study explores the application of ultrasonic guided waves, specifically utilising a limited number of cost-effective and unobtrusive Piezoelectric Wafer Active Sensors (PWAS). Employing COMSOL multiphysics, wave propagation is simulated through a simplified PCB while systematically varying the temperature of both components and the board itself. Machine learning algorithms are used to identify hotspots at component positions using a minimal number of sensors. An accuracy of 97.6% is achieved with four sensors, decreasing to 88.1% when utilizing a single sensor in a pulse–echo configuration. The proposed methodology not only provides sufficient spatial resolution to identify hotspots but also offers a non-invasive and efficient solution. Such advancements are important for the future electrification of the aerospace and automotive industries in particular, as they contribute to condition-monitoring technologies that are essential for ensuring the reliability and safety of electronic systems.Sensor
Multiclass sentiment prediction of airport service online reviews using aspect-based sentimental analysis and machine learning
Get PDFAirport service quality ratings found on social media such as Airline Quality and Google Maps offer invaluable insights for airport management to improve their quality of services. However, there is currently a lack of research analysing these reviews by airport services using sentimental analysis approaches. This research applies multiclass models based on Aspect-Based Sentimental Analysis to conduct a comprehensive analysis of travellers’ reviews, in which the major airport services are tagged by positive, negative, and non-existent sentiments. Seven airport services commonly utilised in previous studies are also introduced. Subsequently, various Deep Learning architectures and Machine Learning classification algorithms are developed, tested, and compared using data collected from Twitter, Google Maps, and Airline Quality, encompassing travellers’ feedback on airport service quality. The results show that the traditional Machine Learning algorithms such as the Random Forest algorithm outperform Deep Learning models in the multiclass prediction of airport service quality using travellers’ feedback. The findings of this study offer concrete justifications for utilising multiclass Machine Learning models to understand the travellers’ sentiments and therefore identify airport services required for improvement.Mathematic
Advancements in learning-based navigation systems for robotic applications in MRO hangar: review
Get PDFThe field of learning-based navigation for mobile robots is experiencing a surge of interest from research and industry sectors. The application of this technology for visual aircraft inspection tasks within a maintenance, repair, and overhaul (MRO) hangar necessitates efficient perception and obstacle avoidance capabilities to ensure a reliable navigation experience. The present reliance on manual labour, static processes, and outdated technologies limits operation efficiency in the inherently dynamic and increasingly complex nature of the real-world hangar environment. The challenging environment limits the practical application of conventional methods and real-time adaptability to changes. In response to these challenges, recent years research efforts have witnessed advancement with machine learning integration aimed at enhancing navigational capability in both static and dynamic scenarios. However, most of these studies have not been specific to the MRO hangar environment, but related challenges have been addressed, and applicable solutions have been developed. This paper provides a comprehensive review of learning-based strategies with an emphasis on advancements in deep learning, object detection, and the integration of multiple approaches to create hybrid systems. The review delineates the application of learning-based methodologies to real-time navigational tasks, encompassing environment perception, obstacle detection, avoidance, and path planning through the use of vision-based sensors. The concluding section addresses the prevailing challenges and prospective development directions in this domain.Sensor
Adaptive multivariate reusable launch vehicles reentry attitude control with pre-specified performance in the presence of unmatched disturbances
Get PDFThis paper introduces a novel adaptive multivariable attitude control method for a reusable launch vehicle (RLV) to track desired attitude trajectories in the presence of unknown external disturbances and uncertainties. Unlike most existing designs that overlook mismatched disturbances, this method employs an adaptive finite-time observer (AFO) to estimate the unknown states. Based on the outputs of the AFO and the prescribed performance function, a time-varying adaptive gain that is not overestimated is designed to establish the adaptive multivariable attitude control for the RLV system. The simulations demonstrate that the proposed approach successfully guides the RLV to follow desired attitude signals despite the presence of unmatched disturbances and uncertainties.Aerospace Science and Technolog
UV/TiO2 photocatalysis as post-treatment of anaerobic membrane bioreactor effluent for reuse
Get PDFAdvanced oxidation processes have been widely applied as a post-treatment solution to remove residual organic compounds in water reuse schemes. However, UV/TiO2 photocatalysis, which provides a sustainable option with no continuous chemical addition, has very rarely been studied to treat anaerobically treated effluents. In the current study, the removal of organics and nutrients from an anaerobic membrane bioreactor (AnMBR) effluent is evaluated during adsorption and photocatalysis processes under various conditions of TiO2 dose and UV intensity and compared to the effluent from an aerobic membrane bioreactor (AeMBR). The sequence for preferential adsorption on TiO2 was found to be phosphorus, inorganic carbon and then ammonia/organic carbon were found. The competing effect between the organics and nutrients, along with the low UV transmission efficiency caused by the need for high doses of TiO2, ultimately compromise the organic removal efficiency in the AnMBR permeate. TiO2 dosage was found to have a greater impact than UV intensity on improving the overall removal performance as nutrients are competing for the adsorption site but are not photodegraded. Under the same operational condition, the UV/TiO2 photocatalysis displayed a higher removal efficiency of organic matter and phosphorus in the AeMBR effluent due to a lower initial organics concentration and absence of ammonia as compared to the AnMBR effluent.Journal of Environmental Managemen
Exploiting Extended Reality under the Manufacturing as a Service paradigm
Get PDF5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023), 22-24 November 2023, Lisbon, PortugalThe advent of new technologies and concepts such as Extended Reality (XR), Cloud Manufacturing, Digital Twin, Industrial Internet of Things is completely changing the manufacturing landscape. Innovative digital tools are available to increase the competitiveness and efficiency of companies. The XR can support the Smart Operator in production, maintenance and training activities. Similarly, Manufacturing as a Service, which is a variation of Cloud Manufacturing, can make corporate architectural systems more flexible and scalable. In this paper, some preliminary results relating to the European 5G-ERA project and the synergistic work that the companies CAL-TEK, TWI and HAL Robotics are conducting is shown. An XR-oriented architecture is proposed, whose usefulness is emphasized by the use of the MaaS concept, and the implementation of a Mixed Reality application is shown, to support an on-site operator. Future developments concern the implementation of a Virtual Reality application for the off-site operator, and the use of the 5G-ERA middleware to exploit the potential of the MaaS paradigm.This research work was partly supported by the 5G-ERA (5G Enhanced Robot Autonomy - https://5g-era.eu/) Project, which received funding from the European Union's Horizon 2020 Research and Innovation programme (grant agreement No. 101016681)5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)Procedia Computer Scienc
Corrosion mechanisms of plasma welded Nickel aluminium bronze immersed in seawater
Get PDFNickel Aluminium Bronzes (NAB) are copper-based multi-phase alloys used extensively in marine applications. NAB is vulnerable to seawater corrosion, however the interaction between its corrosion mechanisms and real-world factors including biofouling, weld microstructure and residual stress are poorly understood. Seawater corrosion tests were performed on plasma-welded NAB in laboratory and marine environments, demonstrating that the retained β’ phase in the Heat Affected Zone (HAZ) experiences Selective Phase Corrosion (SPC), whereas crevice corrosion associated with SPC of the κIII phase occurs at biofouled and stressed areas of parent material. These factors, seldom simulated in physical tests, severely impact NAB’s corrosion resistance.The authors acknowledge the support of ANSTO in providing access to instruments, capabilities and facilities used in this via Proposal 13838. This work was supported by the EPSRC [grant number EP/R513179/1] and Babcock International [studentship number 2019 - 4720].Corrosion Scienc