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Data for "The influence of different abiotic conditions on the concentrations of free and conjugated (masked) deoxynivalenol and zearalenone in stored wheat"
No full textThis study aims to examine the impact of storage conditions of water activities 0.93, 0.95, 0.98 aw and temperature 20-25 °C on (a) the concentrations of DON and ZEN and their respective glucosides/conjugates and (b) the concentrations of emerging mycotoxins in both naturally contaminated and irradiated wheat grains inoculated with Fusarium graminearum to ascertain any potential increases in toxicity in the wheat grains.FoodBioSystems: biological processes across the Agri-Food system from pre-farm to post-for
Supporting data for 'An insight into the hormonal interplay regulating pigment changes and colour development in the peel of ‘Granny Smith’, ‘Opal®’ and ‘Royal Gala’ apples'
No full textThis data set contains physiological (colour, size, total soluble content) and biochemical data (including plant hormones, indivicual sugars, anthocyanins) of three different apple cultivars. It also includes the gene expression of gene involved in the ethylene pathway.Spanish Agencia Estatal de Investigacio
Enhanced hyperspectral sharpening through improved relative spectral response characteristic (R-SRC) estimation for long-range surveillance applications
No full textThe fusion of low-spatial-resolution hyperspectral images (LRHSI) with high-spatial-resolution multispectral images (HRMSI) for super-resolution (SR), using coupled non-negative matrix factorization (CNMF), has been widely studied in the past few decades. However, the matching of spectral characteristics between the LRHSI and HRMSI, which is required before they are jointly factorized, has rarely been studied. One objective of this work is to study how the relative spectral response characteristics (R-SRC) of the LRHSI and HRMSI can be better estimated, particularly when the SRC of the latter is unknown. To this end, three variants of enhanced R-SRC algorithms were proposed, and their effectiveness was assessed by applying them for sharpening data using CNMF. The quality of the output was assessed using the L1-norm-error (L1NE) and receiver operating characteristics (ROC) of target detections performed using the adaptive coherent estimator (ACE) algorithm. Experimental results obtained from two subsets of a real scene revealed a two- to three-fold reduction in the reconstruction error when the scenes were sharpened by the proposed R-SRC algorithms, in comparison with Yokoya’s original algorithm. Experiments also revealed that a much higher proportion (by one order of magnitude) of small targets of 0.015 occupancy in the LRHSI scene could be detected by the proposed R-SRC methods compared with the baseline algorithm, for an equal false alarm rate. These results may suggest the possibility of SR to allow long-range surveillance using low-cost HSI hardware, particularly when the remaining issues of the occurrence of large reconstruction errors and comparatively higher false alarm rate for ‘rare’ species in the scene can be understood and resolved in future research.Electronic
Engine strip and build lab: a practical approach to learning gas turbine engines
Get PDFThe world is currently undergoing a technological transformation with numerous innovative concepts emerging. This shift is driven by remarkable advancements in artificial intelligence and the urgent need for decarbonisation. With this comes a growing demand for skilled engineers who can actively contribute at any stage within the life cycle of a product. This can be the generation of new concepts at low Technology Readiness Levels or contributing actively to their development and operational safety. This paper explores the integration of a 1-day practical activity to reinforce theoretical concepts learned within a classroom-based environment. Small groups of students were given the opportunity of engaging with a small helicopter engine (Rolls-Royce Gnome engine) through the disassembly and reassembly of the exhaust and power turbine section while following the manufacturer’s manual and ensuring industrial norms for safe practice. This hands-on activity included an introduction to tooling, a Gnome familiarisation activity, and an introduction to inspection techniques. Based on the feedback recorded, the students experienced a notable improvement in their basic understanding by effectively reinforcing knowledge acquired within the classroom through active engagement with an actual gas turbine engine.The Aeronautical Journa
Battery temperature prediction in electric vehicles using Bayesian regularization
Get PDFThis study is focused on developing a new temperature prediction model to be used in battery thermal management systems. Electric vehicle (EV) application is considered as a case study however, the proposed model is applicable in other applications too. The final goal is to improve batteries’ performance, durability, and safety. By specifically examining two types of batteries, which are Lithium Iron Phosphate (LFP) and Nickel Cobalt Aluminum (NCA), the proposed model utilizes Bayesian Regularization to precisely predict variations in the battery’s surface temperature in an EV application. The present study experimentally evaluates the accuracy of the proposed model for prediction of the batteries’ surface temperature under various conditions. According to the results, average errors of less than 0.1°C and 0.3°C are achieved when predicting the batteries’ surface temperature in 30 and 90 seconds ahead. This study is expected to have an impact on the advancement of EVs’ battery technologies by improving the battery’s performance and safety.Innovate UKThis work was co-funded by the UKRI Faraday Battery Challenge project called Next Generation LFP Cathode Material (NEXLFP) and the High-performance LFP Cathode Active Material (HiCAM) project funded by the Advanced Propulsion Centre (APC) and the Innovate UK.In addition, Abbas Fotouhi acknowledges funding from the Faraday Institution (Industrial Fellowships FIIF 003 and FIIF-014).2024 20th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD
Dataset for Arbitrary-Order Unstructured Finite-Volume Methods for Implicit Large Eddy Simulation of Turbulent Flows with Adaptive Dissipation/Dispersion Adjustment (ADDA)
No full textThis dataset contains the folders for each of the simulations of this article including:
4.1 (1_CALIBRATION_ER)
5.1 (2 TGV_SUBSONIC)
5.2 (3_SD7003)
5.3 (4_TGV_SUPERSONIC)
5.4 (5_CYLINDER)
Each folder contains the following:
1) meshes used to run the simulations
2) UCNS3D.DAT parameter files for UCNS3D CFD SOFTWARE (freely available at https://github.com/ucns3d-team/UCNS3D)
3) output files in tecplot format/or vtu (that can be used in tecplot/paraview/visit post-processing software packages)
4) restart/checkpoint filesEP/X035484/1; EP/W037092/
Analysis and testing of a flyable micro flapping-wing rotor with a highly efficient elastic mechanism
Get PDFA Flapping-Wing Rotor (FWR) is a novel bio-inspired micro aerial vehicle configuration, featuring unique wing motions which combine active flapping and passive rotation for high lift production. Power efficiency in flight has recently emerged as a critical factor in FWR development. The current study investigates an elastic flapping mechanism to improve FWRs’ power efficiency by incorporating springs into the system. The elastic force counteracts the system inertia to accelerate or decelerate the wing motion, reducing the power demand and increasing efficiency. A dynamic model was developed to simulate the unique kinematics of the FWR’s wing motions and its elastic mechanism, considering the coupling of aerodynamic and inertial forces generated by the wings, along with the elastic and driven forces from the mechanism. The effects of the spring stiffness on the aerodynamic performance and power efficiency were investigated. The model was then verified through experimental testing. When a spring stiffness close to the mechanical system resonance was applied, the power efficiency of the test model increased by 16% compared to the baseline model without springs, generating an equivalent average lift. With an optimal elastic flapping mechanism for greater lift and lower power consumption, the FWR was fully constructed with onboard power and a control receiver weighing 27.79 g, successfully achieving vertical take-off flight. The current model produces ten times greater lift and has nearly double the wing area of the first 2.6 g flyable FWR prototype.Biomimetic
A review of Bayes filters with machine learning techniques and their applications
No full textA Bayes filter is a widely used estimation algorithm, but it has inherent limitations. Performance can degrade when the dynamics are highly nonlinear or when the probability distribution of the state is unknown. To mitigate these issues, machine learning (ML) techniques have been incorporated into many Bayes filters, due to their advantage of being able to map between the input and the output without explicit instructions. In this review, we reviewed 90 papers that proposed the use of ML techniques with Bayes filters to improve estimation performance. This review provides an overview of Bayes filters with ML techniques, categorised according to the role of ML, remaining challenges and research gaps. In the concluding section of this review, we point out directions for future research.Information Fusio
A robust adaptive PID-like controller for quadrotor unmanned aerial vehicle systems
Get PDFThis paper introduces a stable adaptive PID-like control scheme for quadrotor Unmanned Aerial Vehicle (UAV) systems. The PID-like controller is designed to closely estimate an ideal controller to meet specific control objectives, with its gains being dynamically adjusted through a stable adaptation process. The adaptation process aims to reduce the discrepancy between the ideal controller and the PID-like controller in use. This method is considered model-free, as it does not require knowledge of the system’s mathematical model. The stability analysis performed using a Lyapunov method demonstrates that every signal in the closed-loop system is Uniformly Ultimately Bounded (UUB). The effectiveness of the proposed PID-like controller is validated through simulations on a quadrotor for path following, ensuring accurate monitoring of the target positions and yaw angle. Simulation results highlight the performance of this control scheme.Aerospac
Advanced semantic segmentation of aircraft main components based on transfer learning and data-driven approach
Get PDFThe implementation of Smart Airport and Airport 4.0 visions relies on the integration of automation, artificial intelligence, data science, and aviation technology to enhance passenger experiences and operational efficiency. One essential factor in the integration is the semantic segmentation of the aircraft main components (AMC) perception, which is essential to maintenance, repair, and operations in aircraft and airport operations. However, AMC segmentation has challenges from low data availability, high-quality annotation scarcity, and categorical imbalance, which are common in practical applications, including aviation. This study proposes a novel AMC segmentation solution, employing a transfer learning framework based on a sophisticated DeepLabV3 architecture optimized with a custom-designed Focal Dice Loss function. The proposed solution remarkably suppresses the categorical imbalance challenge and increases the dataset variability with manually annotated images and dynamic augmentation strategies to train a robust AMC segmentation model. The model achieved a notable intersection over union of 84.002% and an accuracy of 91.466%, significantly advancing the AMC segmentation performance. These results demonstrate the effectiveness of the proposed AMC segmentation solution in aircraft and airport operation scenarios. This study provides a pioneering solution to the AMC semantic perception problem and contributes a valuable dataset to the community, which is fundamental to future research on aircraft and airport semantic perception.The Visual Compute