8,367 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    An empirical investigation of the relationship between integration, dynamic capabilities and performance in supply chains

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    This research aimed to develop an empirical understanding of the relationships between integration, dynamic capabilities and performance in the supply chain domain, based on which, two conceptual frameworks were constructed to advance the field. The core motivation for the research was that, at the stage of writing the thesis, the combined relationship between the three concepts had not yet been examined, although their interrelationships have been studied individually. To achieve this aim, deductive and inductive reasoning logics were utilised to guide the qualitative study, which was undertaken via multiple case studies to investigate lines of enquiry that would address the research questions formulated. This is consistent with the author’s philosophical adoption of the ontology of relativism and the epistemology of constructionism, which was considered appropriate to address the research questions. Empirical data and evidence were collected, and various triangulation techniques were employed to ensure their credibility. Some key features of grounded theory coding techniques were drawn upon for data coding and analysis, generating two levels of findings. These revealed that whilst integration and dynamic capabilities were crucial in improving performance, the performance also informed the former. This reflects a cyclical and iterative approach rather than one purely based on linearity. Adopting a holistic approach towards the relationship was key in producing complementary strategies that can deliver sustainable supply chain performance. The research makes theoretical, methodological and practical contributions to the field of supply chain management. The theoretical contribution includes the development of two emerging conceptual frameworks at the micro and macro levels. The former provides greater specificity, as it allows meta-analytic evaluation of the three concepts and their dimensions, providing a detailed insight into their correlations. The latter gives a holistic view of their relationships and how they are connected, reflecting a middle-range theory that bridges theory and practice. The methodological contribution lies in presenting models that address gaps associated with the inconsistent use of terminologies in philosophical assumptions, and lack of rigor in deploying case study research methods. In terms of its practical contribution, this research offers insights that practitioners could adopt to enhance their performance. They can do so without necessarily having to forgo certain desired outcomes using targeted integrative strategies and drawing on their dynamic capabilities

    ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting

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    Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment. The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable

    Integrating materials supply in strategic mine planning of underground coal mines

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    In July 2005 the Australian Coal Industry’s Research Program (ACARP) commissioned Gary Gibson to identify constraints that would prevent development production rates from achieving full capacity. A “TOP 5” constraint was “The logistics of supply transport distribution and handling of roof support consumables is an issue at older extensive mines immediately while the achievement of higher development rates will compound this issue at most mines.” Then in 2020, Walker, Harvey, Baafi, Kiridena, and Porter were commissioned by ACARP to investigate Australian best practice and progress made since Gibson’s 2005 report. This report was titled: - “Benchmarking study in underground coal mining logistics.” It found that even though logistics continue to be recognised as a critical constraint across many operations particularly at a tactical / day to day level, no strategic thought had been given to logistics in underground coal mines, rather it was always assumed that logistics could keep up with any future planned design and productivity. This subsequently meant that without estimating the impact of any logistical constraint in a life of mine plan, the risk of overvaluing a mining operation is high. This thesis attempts to rectify this shortfall and has developed a system to strategically identify logistics bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout a life of mine plan. By identifying any logistics constraints as early as possible, the best opportunity to rectify the problem at the least expense is realised. At the very worst if a logistics constraint was unsolvable then it could be understood, planned for, and reflected in the mine’s ongoing financial valuations. The system developed in this thesis, using a suite of unique algorithms, is designed to “bolt onto” existing mine plans in the XPAC mine scheduling software package, and identify at a strategic level the number of material delivery loads required to maintain planned productivity for a mining operation. Once an event was identified the system then drills down using FlexSim discrete event simulation to a tactical level to confirm the predicted impact and understand if a solution can be transferred back as a long-term solution. Most importantly the system developed in this thesis was designed to communicate to multiple non-technical stakeholders through simple graphical outputs if there is a risk to planned production levels due to a logistics constraint

    China-US Competition

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    This open access edited book brings together a closer examination of European and Asian responses to the escalating rivalry between the US and China. As the new Cold War has surfaced as a perceivable reality in the post-COVID era, the topic itself is of great importance to policymakers, academic researchers, and the interested public. Furthermore, this manuscript makes a valuable contribution to an under-studied and increasingly important phenomenon in international relations: the impact of the growing strategic competition between the United States and China on third parties, such as small and middle powers in the two arguably most affected regions of the world: Europe and East Asia. The European side has been under-studied and explicitly comparative work on Europe and East Asia is extremely rare. Given that the manuscript focuses heavily on recent developments—and because many of these developments have been quite dramatic—there are very few publications that cover the same topics

    EU Data Governance: Preserving Global Privacy in the Age of Surveillance

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    The thesis explores the EU’s Global Data Protection Regulation (GDPR), its human rights approach to data privacy, and its diffusion around the world. It asks the question: why would any nation, authoritarian or democratic, adopt Europe’s data privacy framework as a model for their country’s data governance? Accessing the theoretical frameworks of the Brussels Effect and the New Interde-pendence Approach, the research considers country case studies on China, Japan, and the US, comparing the different motivations and structural conditions that dictate how these three countries have adopted and adapted the GDPR framework. It finds a vastly different set of conditions for adopting the GDPR data privacy framework, none of which can be explained fully by either the Brussels Effect or the New Interdependence Approach. It also finds that none of the three countries embrace the language of human rights in their data privacy legislation. Of all the three countries, Japan has converged most closely with the GDPR in letter and spirit over time. While China’s legislation bears all the key features of the GDPR, the de facto reality is that data privacy regulation is a tool of state control. The United States case shows how a changing global environment forced the U.S. legislators to retreat from their market-driven approach to data governance in the direction of GDPR-like regulation

    Crashworthiness Optimization using difference-based equivalent static Loads - Sizing and Topology Optimization of Structures subjected to Crash

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    Structural optimization of crash related problems usually involves nonlinearities in geometry, material, and contact. For such kinds of problems, the sensitivities are either not available or very expensive to compute. Efficient gradient-based optimizers can then not be employed directly. The Difference-based Equivalent Static Load (DiESL) method provides a procedure to circumvent the sensitivity calculation of the original nonlinear dynamic problem by creating linear auxiliary load cases enabling gradient-based optimization. Each linear auxiliary load case then represents one specific time step of the original nonlinear dynamic problem. In this thesis various extensions of the DiESL method are presented and the method is compared to several other relevant approaches in this field. It is demonstrated how an appropriate selection of the time steps in each cycle can improve the DiESL method's approximation quality. For this purpose, the time steps are selected adaptively such that an appropriate curve, indicating the structure's nonlinear behavior, is fitted by the selected time steps. It turns out that this leads to better optimization results and more reliable convergence behavior. The DiESL method also enables the adaption of path-dependent structural properties of the original nonlinear dynamic problem like material stiffness in each linear auxiliary load case. In this thesis, an adaption of the Young’s modulus and Poisson's ratio on element level in the linear auxiliary load cases corresponding to the local plasticization in the nonlinear dynamic problem is tested. Therefore, a bilinear material model is employed in the auxiliary load cases. Here, the test examples indicate that an observable improvement can only be obtained if the material of the nonlinear dynamic problem is also idealized bilinearily and the portion of elements in the elastic and the plastic range is balanced such that the structure’s behavior is not dominated by one of both. Crashworthiness design usually involves two contradictory objectives: the structure's stiffness as well as its energy absorption behavior. To be able to address the latter, an approach for handling crash forces with the DiESL method is developed and tested using sizing optimization examples. The respective results are validated by comparing them to the theoretically known optimum or other state of the art methods. Moreover, the DiESL method is extended to topology optimization utilizing the Solid Isotropic Material with Penalization approach (SIMP). The method is tested using three examples. The first is a rigid pole colliding with a simple beam structure, where the intrusion of the pole is minimized. The initial velocity of the pole is varied in order to examine the influence of inertia effects on the optimized structures. It is shown that the results differ significantly depending on the chosen initial velocity and, consequently, that they exhibit inertia effects. Moreover, considerable improvement in terms of the resulting objective function's value could be achieved employing the DiESL method when compared with the standard ESL method for high initial velocities. The second example is an extruded rocker colliding with a rigid pole, where also the intrusion of the pole is minimized. The DiESL method yields equally good results as the Graph and Heuristic Topology optimization (GHT) approach does. However, the number of nonlinear analyses necessary to achieve convergence is significantly smaller when using the DiESL method. Finally, a rail reinforced by an additive manufactured rib is optimized. Here, several optimization runs are executed. The reaction force is maximized, while the mass of the rib is constrained to various fractions of the original rib's mass. This formulation aims to find designs where the original rib's mass and thus the related production cycle time is reduced, while its stiffness is almost maintained. In doing so a mass reduction of 30% could be achieved

    Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

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    Safe navigation is a key objective for autonomous applications, particularly those involving mobile tasks, to avoid dangerous situations and prevent harm to humans. However, the integration of a risk management process is not yet mandatory in robotics development. Ensuring safety using mobile robots is critical for many real-world applications, especially those in which contact with the robot could result in fatal consequences, such as agricultural environments where a mobile device with an industrial cutter is used for grass-mowing. In this paper, we propose an explicit integration of a risk management process into the design of the software for an autonomous grass mower, with the aim of enhancing safety. Our approach is tested and validated in simulated scenarios that assess the effectiveness of different custom safety functionalities in terms of collision prevention, execution time, and the number of required human interventions

    New Computational Methods for Automated Large-Scale Archaeological Site Detection

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    Aquesta tesi doctoral presenta una sèrie d'enfocaments, fluxos de treball i models innovadors en el camp de l'arqueologia computacional per a la detecció automatitzada a gran escala de jaciments arqueològics. S'introdueixen nous conceptes, enfocaments i estratègies, com ara lidar multitemporal, aprenentatge automàtic híbrid, refinament, curriculum learning i blob analysis; així com diferents mètodes d'augment de dades aplicats per primera vegada en el camp de l'arqueologia. S'utilitzen múltiples fonts, com ara imatges de satèl·lits multiespectrals, fotografies RGB de plataformes VANT, mapes històrics i diverses combinacions de sensors, dades i fonts. Els mètodes creats durant el desenvolupament d'aquest doctorat s'han avaluat en projectes en curs: Urbanització a Hispània i la Gàl·lia Mediterrània en el primer mil·lenni aC, detecció de monticles funeraris utilitzant algorismes d'aprenentatge automàtic al nord-oest de la Península Ibèrica, prospecció arqueològica intel·ligent basada en drons (DIASur), i cartografiat del patrimoni arqueològic al sud d'Àsia (MAHSA), per a la qual s'han dissenyat fluxos de treball adaptats als reptes específics del projecte. Aquests nous mètodes han aconseguit proporcionar solucions als problemes comuns d'estudis arqueològics presents en estudis similars, com la baixa precisió en detecció i les poques dades d'entrenament. Els mètodes validats i presentats com a part de la tesi doctoral s'han publicat en accés obert amb el codi disponible perquè puguin implementar-se en altres estudis arqueològics.Esta tesis doctoral presenta una serie de enfoques, flujos de trabajo y modelos innovadores en el campo de la arqueología computacional para la detección automatizada a gran escala de yacimientos arqueológicos. Se introducen nuevos conceptos, enfoques y estrategias, como lidar multitemporal, aprendizaje automático híbrido, refinamiento, curriculum learning y blob analysis; así como diferentes métodos de aumento de datos aplicados por primera vez en el campo de la arqueología. Se utilizan múltiples fuentes, como lidar, imágenes satelitales multiespectrales, fotografías RGB de plataformas VANT, mapas históricos y varias combinaciones de sensores, datos y fuentes. Los métodos creados durante el desarrollo de este doctorado han sido evaluados en proyectos en curso: Urbanización en Iberia y la Galia Mediterránea en el Primer Milenio a. C., Detección de túmulos mediante algoritmos de aprendizaje automático en el Noroeste de la Península Ibérica, Prospección Arqueológica Inteligente basada en Drones (DIASur), y cartografiado del Patrimonio del Sur de Asia (MAHSA), para los que se han diseñado flujos de trabajo adaptados a los retos específicos del proyecto. Estos nuevos métodos han logrado proporcionar soluciones a problemas comunes de la prospección arqueológica presentes en estudios similares, como la baja precisión en detección y los pocos datos de entrenamiento. Los métodos validados y presentados como parte de la tesis doctoral se han publicado en acceso abierto con su código disponible para que puedan implementarse en otros estudios arqueológicos.This doctoral thesis presents a series of innovative approaches, workflows and models in the field of computational archaeology for the automated large-scale detection of archaeological sites. New concepts, approaches and strategies are introduced such as multitemporal lidar, hybrid machine learning, refinement, curriculum learning and blob analysis; as well as different data augmentation methods applied for the first time in the field of archaeology. Multiple sources are used, such as lidar, multispectral satellite imagery, RGB photographs from UAV platform, historical maps, and several combinations of sensors, data, and sources. The methods created during the development of this PhD have been evaluated in ongoing projects: Urbanization in Iberia and Mediterranean Gaul in the First Millennium BC, Detection of burial mounds using machine learning algorithms in the Northwest of the Iberian Peninsula, Drone-based Intelligent Archaeological Survey (DIASur), and Mapping Archaeological Heritage in South Asia (MAHSA), for which workflows adapted to the project’ s specific challenges have been designed. These new methods have managed to provide solutions to common archaeological survey problems, presented in similar large-scale site detection studies, such as the low precision in previous detection studies and how to handle problems with few training data. The validated approaches for site detection presented as part of the PhD have been published as open access papers with freely available code so can be implemented in other archaeological studies
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