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Parametric study of adaptive reinforcement learning for battery operations in microgrids
No full textReinforcement learning (RL) has been increasingly used for efficient energy management systems (EMSs) in microgrids. The battery storage system in the microgrid can be controlled using efficient policies derived from RL. However, little attention has been paid so far to the parametric study, which is a fundamental step for efficient implementation of such RL algorithms. Unlike previous works which focused on the implementation of different RL algorithms, this paper mainly demonstrates the parametric sensitivity study of the RL algorithms. It involves investigating the effects of (1) controllable state discretization, (2) exogenous state discretization, (3) action discretization, (4) exploration and exploitation parameters, and (5) decision intervals. Moreover, the performance of the ε-greedy randomized RL algorithm is compared against the adaptive Q-learning, derived from the adaptive approximate dynamic programming (ADP). In many microgrids utilizing solar energy and battery storage, energy management still relies on manually tuned and inefficient algorithms. This is largely due to the sensitivity of RL algorithm parameters to factors such as the specific EMS problem, environment, action-state discretization, exploration parameter and time step. We show the univariate and multivariate kernel density estimate (KDE) plots to study the RL algorithms’ performance concerning the rewards and variation of the battery state of charge (SoC) and the net power imported from the grid. Overall, the deterministic adaptive RL performs better as compared to the randomized ε-greedy algorithms in terms of rewards and simulation time. Higher discretization levels in the action space affect the convergence rate while lower discretization levels in the state space influence the performance of the algorithm. The proposed parametric analysis can be easily adapted to other EMS in more complex microgrids.This work was partially supported by the ESIF ERDF Cornwall New Energy project, project number: 05R16P00282. SD was partially supported by the ERDF Deep Digital Cornwall project number: 05R18P02820.Renewable Energ
Utilizing waste cast stone: compression strength analysis and implication for suitable cast stone production
Get PDFIn recent years, the concept of circular economy has become increasingly popular as a sustainable approach to resource management. The present study explores the potential of reusing cast stone waste generated during the production process, in alignment with the principles of the circular economy. Cast stone waste typically goes to landfill sites discarded as a byproduct and poses environmental challenges due to its non-biodegradable nature. This work focuses on reusing this waste material as a viable resource, thus mitigating the environmental impact. Through a series of compression test experiments on cast stone, the presented study investigates the effectiveness of incorporating cast stone waste into production. The findings of this study contribute to cost reduction in raw material and waste minimization promoting economic and environmental benefits.20th Global Conference on Sustainable Manufacturing (GCSM)Decarbonizing Value Chain
From Turing Test to Chinese Room Argument: how to apply artificial intelligence in aviation
Get PDFEuropean Association for Aviation Psychology Conference EAAP 35The emergence of artificial intelligence (AI) with advanced large language model (LLM) offers promising approaches for enhancing the capacity of textual analysis. Both the Turing Test and Chinese Room Argument explore AI’s understanding of human language, although both methodologies have dissimilar interpretation on AI’s ‘intelligence’. Current AI systems have demonstrated the capacity for achieving defined test goals for ‘intelligence’. The aviation industry is increasingly interested in adopting AI to improve efficiency, safety, and cost efficiency; with the Generative Pre-trained Transformers’ (GPT) capability to reduce resource-intensive analytics in accident causation classification. This study investigates the potential and challenges of using AI to analyze human factors involved in aviation accidents based on the Human Factors Analysis and Classification System (HFACS). Six subject-matter experts in aviation human factors and AI domain participated in this research. All participants were familiar with the HFACS framework to analyze aviation accident reports and the output of GPT which were based on the prompt engineering developed by the research team. This research creates a framework to perform its initial generation and training using GE 235 accident investigation report from Taiwan Transportation Safety Board (TTSB). Initial discoveries demonstrated that the AI model could populate the sub-dimensions of Level 1 HFACS framework with moderate accuracy, although there remains a high presence of hallucinations in generated outputs, with a lack of reproducibility in consecutive outputs with consistent input data. There are still different opinions on AI applications in real-world operations with ethics and safety concerns. While there is clear potential for GPT models to supplement accident analysis within the HFACS framework, there is still more work to integrate HFACS framework into GPT modelling for effective generation to accident data.Transportation Research Procedi
Improving human performance in flight operations using augmented reality and quick coherence technique
Get PDFBraithwaite, Graham R. - Associate SupervisorHuman errors continuously play a major role in the root cause of air accidents,
which brings up the need for effective improvements in pilots’ human
performance during flight operations. This research addresses two main
challenges of pilots’ interface to improve the non-technical performance in flight
operations from a system human factors perspective.
The first part is related to the human-computer interaction considerations in
modern flight decks. An innovative augmented reality (AR) interactive checklist is
developed. This AR application blends the virtual holographic checklist and
augmented guiding cues within the flight deck and allows multiple input modes of
gesture and voice. Compared to the traditional paper-based pre-landing
checklist, the voice-command AR can improve cognitive information processing
during landing operations. The voice input modality of AR provides a more
intuitive and flexible interface to improve human-computer interaction
effectiveness. It is also well-applicable in multitasking flight scenarios compared
to the gesture-command interaction.
In the second part, the interface between pilots and the societal environment is
investigated. The quick coherence technique (QCT) training is applied to address
the negative impacts on pilots’ psychological health and cognitive performance
during the COVID-19 pandemic. The QCT is based on paced breathing exercises
and heart rate variability biofeedback. Pilots practise the five-minute QCT not only
in day-to-day life but also during flight operations as a controlled rest strategy.
The two-month QCT training effectively increases well-being and decreases
perceived stress. Practising QCT in the flight deck improves pilots’ stress
resilience and cognitive functions, subsequently improving human performance
and aviation safety.
This research can provide promising solutions to improve human performance
and aviation safety. Furthermore, the AR and QCT applications have further
practical implications for enhancing pilots’ non-technical skills in real operational
scenarios. Also, the safety concerns of single pilot operations related to human
capacity can be effectively addressed.PhD in Transport System
Mid-IR standoff measurement of ageing-related spectroscopic changes in bitumen in the 6 µm (1700 cm−1) region. Part 1: measurement strategy and instrument design principles
Get PDFA strategy is described to make in-situ measurements of a spectroscopic marker of ageing in bitumen binders used on asphalt-paved roads. Oxidation of bitumen at the surface increases the number of carbonyl (C=O) bonds, and this can be measured in the 6 μm region (1700 cm−1) of the mid-infrared. A measurement strategy is proposed to make standoff measurements of surface reflectivity in this region, despite the challenge presented by numerous strong absorption lines from atmospheric water vapour within the optical path. An instrument design is described to make measurements at 4 discrete laser wavelengths, namely 1593.0, 1641.4 and 1731.3 cm−1 (around 6 µm) and at 2633.6 cm−1 (3.8 µm), the first 3 responding to carbonyl absorption and the fourth acting as a spectral reference that is substantially unaffected by ageing. Part 2 of this paper describes the implementation of such an instrument and its experimental performance.Funding for this work was provided by National Highways under the SPATS programme (Spectrographic Analysis of Pavements at Traffic Speed) Work Package 1-408 (reference PIN 601344) and the UK Engineering and Physical Science Research Council (Grant EP/N002520/01).Scientific Report
Understanding HRIS adoption: a psychosocial perspective on managerial engagement and system effectiveness
Get PDFThis article belongs to the Section Cognitive PsychologyHuman Resource Information Systems (HRISs) have become integral to contemporary organizational life, yet their successful adoption remains uneven and poorly understood. Existing models often focus on cognitive or technical determinants, overlooking how emotional and social factors shape user behavior in real-world settings. This study explores HRIS adoption through a psychosocial lens, centering the experiences of business line managers, key users who are often excluded from HRIS design, training, and research. Drawing on 25 qualitative interviews across five large UK-based organizations, this paper identifies six emergent themes related to interpersonal trust, role identity, leadership influence, organizational culture, emotional resistance, and the gap between expected usefulness and daily utility. Findings reveal that approaches which account for user emotions, perceived role clarity, and social context offer a more complete understanding of HRIS adoption than those based solely on intention or usability. By highlighting the role of interpersonal dynamics and subjective experience, this study challenges dominant technology adoption models and contributes to more human-centered perspectives in HRIS research and practice. This paper concludes by offering theoretical implications and practical guidance for designing HRIS strategies that reflect the psychosocial realities of implementation across diverse organizational environments.Psychology Internationa
A self-supervised point cloud completion method for digital twin smart factory scenario construction
Get PDFIn the development of digital twin (DT) workshops, constructing accurate DT models has become a key step toward enabling intelligent manufacturing. To address challenges such as incomplete data acquisition, noise sensitivity, and the heavy reliance on manual annotations in traditional modeling methods, this paper proposes a self-supervised deep learning approach for point cloud completion. The proposed model integrates self-supervised learning strategies for inferring missing regions, a Feature Pyramid Network (FPN), and cross-attention mechanisms to extract critical geometric and structural features from incomplete point clouds, thereby reducing dependence on labeled data and improving robustness to noise and incompleteness. Building on this foundation, a point cloud-based DT workshop modeling framework is introduced, incorporating transfer learning techniques to enable domain adaptation from synthetic to real-world industrial datasets, which significantly reduces the reliance on high-quality industrial point cloud data. Experimental results demonstrate that the proposed method achieves superior completion and reconstruction performance on both public benchmarks and real-world workshop scenarios, achieving an average CD-ℓ2 score of 15.96 on the 3D-EPN dataset. Furthermore, the method produces high-fidelity models in practical applications, providing a solid foundation for the precise construction and deployment of virtual scenes in DT workshops.Electronic
Cointegration-based pairs trading: identifying and exploiting similar exchange-traded funds
No full textWe examine the effectiveness of pairs trading using ETFs from 2000 to 2024, focusing on how cointegration stability affects profitability and risk. Analyzing 30 ETF pairs with different z-score thresholds, we find that lowering the threshold increases trading opportunities, boosting profits and Sharpe ratios but also raising volatility and drawdowns. Short trading windows, where cointegration holds, limit long-term profitability. While pairs trading captures short-term arbitrage, its success depends on cointegration stability. The study emphasizes the need for adaptive strategies, better pairs selection, and strong risk management for sustained profitability in changing markets.Journal of Asset Managemen
High-level airport assessment of noise and carbon emissions trade-off for novel aircraft - a holistic approach to decision making
No full textAs aviation demand grows, controlling carbon emissions is crucial for meeting 2050 net-zero targets. Complaints relating to airport noise have increased over recent years despite individual aircraft noise levels decreasing. Current aircraft designs face competing requirements to reduce fuel-burn or noise. To date, no existing high-level tools exist that determine the trade-offs between these two aircraft emissions at an individual aircraft or airport fleet level. This paper extends Powell's (2003) methodology, scaling the procedure for individual aircraft operations to enable a fleet-level assessment and allowing the impact of novel aircraft to be determined. The results of linear regression analysis were reproduced in AEDT (where Powell used INM) and included additional aircraft, such as business jets and propeller planes, to improve the accuracy for smaller, regional airports. A case study has been run for a UK airport with assumed noise levels of future aircraft based on Sustainable Aviation guidance, to demonstrate the change in noise and CO2 levels when introducing novel aircraft to the fleet. This has shown that the noise contour area increases when introducing novel hydrogen and electric aircraft compared to future low-noise technology designs with conventional propulsion mechanisms. The CO2 emissions were shown to decrease substantially when introducing novel hydrogen and electric aircraft, but increase for future low-noise technology designs with conventional propulsion mechanisms. This highlights the trade-offs between noise and CO2 and indicates that careful consideration must be given to future aircraft designs and fleet mixes if both emissions are to be reduced simultaneously.This research has been jointly funded by the Centre for Aeronautics, Faculty of Engineering and Physical Sciences, Cranfield University and Arup.AIAA Aviation Forum and Ascend 202
Airliner conceptual designs for the application of alternative fuels
No full textThis paper investigates the application of alternative fuels in tube-and-wing aircraft configurations. Potential alternative fuel choices include biofuel, liquid hydrogen, liquefied natural gas, and ammonia. A comprehensive mission range starting from 2000 nautical miles to 8500 nautical miles is explored, designed, and analyzed using GENUS, an in-house built multidisciplinary design analysis and optimization platform. This paper aims to give a comprehensive study of different alternative fuel options and capacity combinations over different mission ranges. The different gradients of these trends demonstrate the additional benefits of using alternative fuels. The objectives of this study are 1) to determine how different fuel properties affect the designs and performance of aircraft and 2) to quantify the reduction of greenhouse gas (GHG) emissions by utilizing alternative fuel. The results show cryogenic fuel, which requires fuselage fuel tanks and/or external fuel tanks, will noticeably increase energy consumption due to drag and weight penalties. Yet, the high specific energy density properties of liquid hydrogen (LH2) will offset the penalties and result in a reduction in specific energy consumption (MJ/pax/nm), especially in a high-capacity, long-range mission. A 20.8 and 13.7% reduction in specific energy consumption is calculated between the use of kerosene-based jet fuel and LX2 for a 400 and 800 pax design with an 8500 nm mission range, respectively. In addition, the use of LX2 and biofuel can significantly reduce life cycle GHG emissions if the fuel is produced through sustainable pathways.Journal of Aircraf