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Trajectory shaping guidance for impact angle control of planetary hopping robots
Get PDFThis paper presents a novel optimal trajectory-shaping control concept for a planetary hopping robot. The hopping robot suffers from uncontrolled in-flight and undesired after-landing motions, leading to a position drift at landing. The proposed concept thrives on the Generalized Vector Explicit (GENEX) guidance, which can generate and shape the optimal trajectory and satisfy the end-point constraints like the impact angle of the velocity vector. The proposed concept is used for a thruster-based hopping robot, which achieves a range of impact angles, reduces the position drift at landing due to the undesired in-flight and after-landing motions, and handles the error in initial hopping angles. The proposed approach’s conceptual realization is illustrated by lateral acceleration generated using thruster orientation control. Extensive simulations are carried out on horizontal and sloped surfaces with different initial and impact angle conditions to demonstrate the effect of impact angle on the position drift error and the viability of the proposed approach.Frontiers in Robotics and A
Quantifying the interrelationship between friction, wear, and noise: a comparative study on aluminum, brass, and steel
Get PDFFriction-induced wear and noise affect the performance and lifespan of industrial components, yet models often address them separately. This study proposes a model linking wear volume, coefficient of friction (COF), and noise. Ball-on-disc tribometer tests on 6082 aluminum, UNS C38500 brass, and 304 stainless steel were conducted under various loads and speeds. Key findings reveal thermal expansion affects wear in aluminum but minimally impacts brass and steel. The aluminum-based equation also predicts noise for brass and steel, with errors under 10 % within 5–15 N loads and 0.21–0.63 m/s speeds, suggesting broader applicability. This model provides a simplified approach to linking friction, wear, and noise, offering potential improvements in wear monitoring and noise control for mechanical systems.Tribology Internationa
Data-centric predictive control with tuna swarm optimization-backpropagation neural networks for enhanced wind turbine performance
Get PDFWind energy is a significant renewable resource, but its efficient harnessing requires advanced control systems. This study presents a Data-Centric Predictive Control (DPC) system, enhanced by a Tuna Swarm Optimization-Backpropagation Neural Network (TSO-BPNN) for predictive wind turbine control. It's like a smart tool that uses innovative fusion of deep learning, predictive Control, and reinforcement learning. Unlike traditional control methods, the proposed approach uses real-time data to optimize turbine performance in response to fluctuating wind conditions.
The system is validated using simulations on the FAST platform, which demonstrate its superior performance in two critical operational regions. Specifically, in Region II, where the objective is to maximize power extraction from the wind, the DPC achieves a 1.07 % reduction in overshoot and an improvement of 36.14 units in steady-state error compared to traditional methods. The response time remains comparable to existing Model Predictive Control (MPC) strategies, ensuring real-time applicability without sacrificing efficiency. In Region III, where maintaining constant power output is crucial, the DPC outperforms both the baseline and MPC methods, reducing overshoot by 0.58 % and improving accuracy by 17.27 units compared to the baseline method. These results highlight the effectiveness of the proposed DPC system in optimizing turbine performance under variable wind conditions, offering a significant improvement over traditional methods in both accuracy and control precision.Renewable Energ
Charting the course: standardization of quality assurance in digital twin applications across product lifecycle
Get PDFDigital twins hold immense promise in accelerating scientific discovery and revolutionizing industries. The use cases for digital twins are diverse and proliferating, with applications across multiple areas of science, technology, and society, and their potential is wide-reaching. Despite the growing use of quality assurance approaches, standards, and frameworks in digital twins, the promise of digital twin benefits remains more aspirational than reality. Consequently, the confidence level in the prediction of these models is questionable. There is a lack of guidance on establishing standardization and interoperability as a foundation for integration of platforms, systems, and stakeholders within the digital twin ecosystem. To address this gap, we propose guidance for standardized quality assurance of digital twins in simulating product lifecycles, aiming to enhance regulatory decision-making. This proposed guidance is a step towards development of framework for standardized quality assurance of digital twins.57th CIRP Conference on Manufacturing Systems 2024 (CMS 2024)Procedia CIR
Modelling the innovation-decision process for hydrogen homes: an integrated model of consumer acceptance and adoption intention
Get PDFAs the global energy transition progresses, a range of drivers and barriers will continue to shape consumer attitudes and behavioural intentions towards emerging low-carbon technologies. The innovation-decision process for technologies composing the residential sector such as hydrogen-fuelled heating and cooking appliances is inherently governed by the complex interplay between perceptual, cognitive, and emotional factors. In response, this study responds to the call for an integrated research perspective to advance theoretical and empirical insights on consumer engagement in the domestic hydrogen transition. Drawing on online survey data collected in the United Kingdom, where a policy decision on ‘hydrogen homes’ is set for 2026, this study systematically explores whether an integrated modelling approach supports higher levels of explanatory and predictive power. Leveraging the foundations of the unified theory of domestic hydrogen acceptance, the analysis suggests that production perceptions, public trust, perceived relative advantage, safety perceptions, knowledge and awareness, and positive emotions will shape consumer support for hydrogen homes. Conversely, perceived disruptive impacts, perceived socio-economic costs, financial perceptions, and negative emotions may impede the domestic hydrogen transition. Consumer acceptance stands to significantly shape deployment prospects for hydrogen boilers and hobs, which are perceived to be somewhat advantageous to natural gas appliances from a technological and safety perspective. The study attests to the predictive benefits of adopting an integrated theoretical perspective when modelling the early stages of the innovation-decision process, while acknowledging opportunities for leveraging innovative research approaches in the future. As national hydrogen economies gain traction, adopting a neuroscience-based approach may help deepen scientific understanding regarding the neural, psychological, and emotional signatures shaping consumer perspectives towards hydrogen homes.Engineering and Physical Sciences Research CouncilThis research was supported by the UK Research and Innovation Engineering and Physical Sciences Research Council (UKRI-EPSRC) Grant EP/T518104/1, and sponsored by Cadent Gas Ltd.International Journal of Hydrogen Energ
Machine learning-based environment-aware GNSS integrity monitoring for urban air mobility
Get PDFThe increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks.This study was funded by the Ministry of National Education of Turkey.Drone
Data "Detection of sugar syrup adulteration in honey using DNA barcoding"
No full textHoney is a valuable and nutritious food product, but it is at risk to fraudulent practices such as the addition of cheaper syrups including corn, rice, and sugar beet syrup. Honey authentication is of the utmost importance, but current methods are faced with challenges due to the large variations in natural honey composition (influenced by climate, seasons and bee foraging), or the incapability to detect certain types of plant syrups to confirm the adulterant used. Molecular methods such as DNA barcoding have shown great promise in identifying plant DNA sources in honey and could be applied to detect plant-based sugars used as adulterants. In this work DNA barcoding was successfully used to detect corn and rice syrup adulteration in spiked UK honey with novel DNA markers. Different levels of adulteration were simulated (1-30%) with a range of different syrup and honey types, where adulterated honey was clearly separated from natural honey even at 1% adulteration level. Moreover, the test was successful for multiple syrup types and effective on honeys with different compositions. These results demonstrated that DNA barcoding could be used as a sensitive and robust method to detect common sugar adulterants and confirm syrup species origin in honey, which can be applied alongside current screening methods to improve existing honey authentication tests. The datasets provided are the raw data from qPCR tests and HPLC analysis
Vertical-horizontal actor collaboration in governance network: a systematic review
Get PDFThis research proposes a categorization framework to analyse multi-actor collaboration variations in governance network. It examines both vertical (government levels) and horizontal (sectors) interactions. This study reviewed 92 academic articles, adhering to the PRISMA methodology and utilizes 2 × 2 and "who-how-what" frameworks to capture the breadth of vertical-horizontal collaboration arrangements. The study identifies a rise in multi-actor collaborations and a dominance of qualitative research. It exposes research gaps: limited cross-sectoral and international actor studies, and under-researched sectors beyond environment. The proposed framework, capturing actors, levers, purposes, and context, aims to guide future research on vertical-horizontal actor collaboration in governance network.Ministry of FinanceThis work was supported by Lembaga Pengelola Dana Pendidikan (LPDP) of the Ministry of Finance of Indonesia (grant no. 20210521356570).Public Organization Revie
Pressures for sub-supplier sustainability compliance: the importance of target markets in textile and garment supply chains
Get PDFWe propose that sub-supplier sustainability compliance in developing economies' textile and garment supply chains can be more effectively realized by understanding sub-suppliers' target markets. We introduce the concept of sub-suppliers' customer share of production as the share of production that sub-suppliers sell to “exporting” direct suppliers that cater to the international market vis-à-vis “local” direct suppliers that cater to the domestic market. Through this concept and qualitative evidence, we offer a model outlining that as sub-suppliers sell more to exporting direct suppliers, they encounter increased coercive, competitive, and collaborative pressures for sustainability compliance. This article contributes to the multi-tier sustainable supply chain management literature by illustrating how target markets exert pressures for sub-supplier sustainability compliance, and why some sub-suppliers are more inclined to invest in sustainability compliance, some decouple from it, and others invest beyond compliance. We conclude with business strategy guidelines for managers in textile and garment supply chains
A deep mixture of experts network for drone trajectory intent classification and prediction using non-cooperative radar data
Get PDFThe intent prediction of unmanned aerial vehicles (UAVs) also known as drones is a challenging task due to the different mission profiles and tasks that the drone can perform. To alleviate this issue, this paper proposes a deep mixture of experts network to classify and predict drones trajectories measured from non-cooperative radars. Telemetry data of open-access datasets are converted to simulated radar tracks to generate a pool of heterogeneous trajectories and construct three independent datasets to train, validate, and test the proposed architecture. The network is composed of two main components: i) a deep network that predicts the class associated to the input trajectories and ii) a set of deep experts models that learns the extreme bounds of the trajectories in different future time steps. The proposed approach is tested and compared with different deep models to verify its effectiveness under different flight profiles and time-windows.2023 IEEE Symposium Series on Computational Intelligence (SSCI