2,911 research outputs found

    Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery

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    Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks

    Deposition dynamics and analysis of polyurethane foam structure boundaries for Aerial Additive Manufacturing

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    Additive manufacturing in construction typically consists of ground-based platforms. Introducing aerial capabilities offers scope to create or repair structures in dangerous or elevated locations. The Aerial Additive Manufacturing (AAM) project has developed a pioneering approach using Unmanned Aerial Vehicles (UAV, ‘drones’) to deposit material during self-powered, autonomous, untethered flight. This study investigates high and low-density foams autonomously deposited as structural and insulation materials. Drilling resistance, mechanical, thermal and microscopy tests investigate density variation, interfacial integrity and thermal stability. Autonomous deposition is demonstrated using a flying UAV and robotic arm. Results reveal dense material at interfaces and directionally dependent cell expansion during foaming. Cured interfacial regions are vulnerable to loading parallel to interfaces but resistant to perpendicular loading. Mitigation of trajectory printing errors caused by UAV flight disturbance is demonstrated by a stabilising end effector, with trajectory errors ≤10 mm. AAM provides a significant development towards on-site automation in construction

    Assessing the advancement of artificial intelligence and drones’ integration in agriculture through a bibliometric study

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    Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Unconsidered but influencing interference in unmanned aerial vehicle cabling system

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    The increasing complexity of electrical and electronic systems in unmanned aerial vehicles (UAVs) has raised concerns regarding unwanted electromagnetic interference (EMI) due to limited compartment space. Recent studies have highlighted the UAV cabling as the primary pathway for interference. This paper presents a novel approach to investigating the effects of interference power, polarization angle, and distance from the interference source on EMI in UAV cable systems. Measurements and simulations were performed to analyze the influence of these factors on the radiation received by the cable. A linear dipole antenna, operating at a frequency of 905 MHz, served as the radiation source, while a single wire cable pair terminated with a 50-ohm resistor was employed as the victim. The findings reveal that the power transmitted by the source, the distance between the cable and the source, and the polarization angle have a significant impact on the electromagnetic interference received by the cable. Notably, a perpendicular orientation of the cable to the interference source (antenna) in the far-field yielded a reduction of up to 15 dBm in EMI. The results underscore the necessity for more sophisticated models and comprehensive measurements to fully comprehend the diverse factors affecting polarization losses in practical scenarios

    A novel approach to intrusion detection using zero-shot learning hybrid partial labels

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    Computer networks have become the backbone of our interconnected world in today's technologically driven landscape. Unauthorized access or malicious activity carried out by threat actors to acquire control of network resources, exploit vulnerabilities, or undermine system integrity are examples of network intrusion. ZSL(Zero-Shot Learning) is a machine learning paradigm that addresses the problem of detecting and categorizing objects or concepts that were not present in the training data. . Traditional supervised learning algorithms for intrusion detection frequently struggle with insufficient labeled data and may struggle to adapt to unexpected assault patterns. In this article We have proposed a unique zero-shot learning hybrid partial label model suited to a large image-based network intrusion dataset to overcome these difficulties. The core contribution of this study is the creation and successful implementation of a novel zero-shot learning hybrid partial label model for network intrusion detection, which has a remarkable accuracy of 99.12%. The suggested system lays the groundwork for future study into other feature selection techniques and the performance of other machine learning classifiers on larger datasets. Such research can advance the state-of-the-art in intrusion detection and improve our ability to detect and prevent the network attacks. We hope that our research will spur additional research and innovation in this critical area of cybersecurity

    Advancements in Building Deconstruction: Examining the Role of Drone Technology and Building Information Modelling

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    Deconstructing a building with the help of drones and BIM (building information modelling) is becoming increasingly common as a more efficient, eco-friendly, and affordable alternative to the traditional techniques of building disassembly. This paper presents a systematic review following the methodology of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to investigate the role of drone technology and BIM in building deconstruction. A total of 10 studies were identified based on the integration of drone technology with BIM, all of which proved promising in enhancing the process of building deconstruction. The analysis of the 35 and 3 non-academic selected data reveals several key findings. Firstly, BIM is not commonly used in deconstruction or demolition processes, particularly in managing fixtures and fittings of buildings. Secondly, the adoption of deconstruction-oriented design methods and the use of drone technology can significantly reduce the negative environmental impacts of building demolition waste. Lastly, the limited implementation of design for deconstruction practices in the construction industry hinders the realisation of environmental, social, and economic benefits associated with this approach. Overall, this systematic review highlights the potential of drone technology and BIM in improving building deconstruction practices, while also identifying knowledge gaps and areas for further research and development on this topic

    Platform health management for aircraft maintenance – a review

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    Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions

    Exploration and countermeasures for the development of low-carbon agriculture: a study from Chongming District, Shanghai

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    To achieve the goals of carbon peaking and carbon neutrality, China is actively promoting carbon reduction in many areas. Agriculture is one of the main sources of greenhouse gas emissions, and promoting the development of low-carbon agriculture is a critical way to achieve carbon reduction targets. Taking Chongming District in Shanghai as an example, this study summarizes the experience of low-carbon agricultural development in Chongming and analyzes the problems and challenges faced during its development. Finally, based on the system dynamics method, the causal relationship of carbon emission in Chongming’s agricultural development is constructed, and feasible loop optimization suggestions are put forward

    Oil Spills- Where We Were, Where We Are, And Where We Will Be? A Bibliometric and Content Analysis Discourse

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    More frequently and in more ways than one might think, oil spills are a very common phenomenon. There were three major (>700 tonnes; Asia and Africa) and four minor oil spills only in 2022 (between 7 and 700 tonnes; North America, Asia, and Africa). Oil spills have been known to cause numerous negative ecological, societal, economic, and public health impacts. Not only this but oil spills require rapid response to contain and mitigate multidimensional damages caused. A SCOPUS search of the keyword ‘Oil Spills’ in ‘’Article title, Abstracts, and Keywords’ and ‘Article title’ results in 30529 and 9851 (as of March 4th, 2023) documents (Journal articles, Conference proceedings, Books, Book series, Trade journals, and Reports). In the year 2023 alone, the SCOPUS database had 297 documents at the time of writing. Such a massive database requires a retrospection of underlying and emerging themes for readers to understand the extant literature and to uncover future research agendas. This study is an attempt to conduct a bibliometric analysis of select ‘Oil spill’ publications. This investigation will involve performance analysis (performance of research constituents such as publication and citation evolution, leading authors, publications, affiliations, sources, and countries) and science mapping (relationship between research constituents by analyzing conceptual, intellectual, and social structures). VOSviewer and Biblioshiny The study will conclude future research trends by the content analysis of the fifteen most recent and cited documents
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