8,245 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

    Exploring acceptance of autonomous vehicle policies using KeyBERT and SNA: Targeting engineering students

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    This study aims to explore user acceptance of Autonomous Vehicle (AV) policies with improved text-mining methods. Recently, South Korean policymakers have viewed Autonomous Driving Car (ADC) and Autonomous Driving Robot (ADR) as next-generation means of transportation that will reduce the cost of transporting passengers and goods. They support the construction of V2I and V2V communication infrastructures for ADC and recognize that ADR is equivalent to pedestrians to promote its deployment into sidewalks. To fill the gap where end-user acceptance of these policies is not well considered, this study applied two text-mining methods to the comments of graduate students in the fields of Industrial, Mechanical, and Electronics-Electrical-Computer. One is the Co-occurrence Network Analysis (CNA) based on TF-IWF and Dice coefficient, and the other is the Contextual Semantic Network Analysis (C-SNA) based on both KeyBERT, which extracts keywords that contextually represent the comments, and double cosine similarity. The reason for comparing these approaches is to balance interest not only in the implications for the AV policies but also in the need to apply quality text mining to this research domain. Significantly, the limitation of frequency-based text mining, which does not reflect textual context, and the trade-off of adjusting thresholds in Semantic Network Analysis (SNA) were considered. As the results of comparing the two approaches, the C-SNA provided the information necessary to understand users' voices using fewer nodes and features than the CNA. The users who pre-emptively understood the AV policies based on their engineering literacy and the given texts revealed potential risks of the AV accident policies. This study adds suggestions to manage these risks to support the successful deployment of AVs on public roads.Comment: 29 pages with 11 figure

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Deep Multimodality Image-Guided System for Assisting Neurosurgery

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    Intrakranielle Hirntumoren gehören zu den zehn häufigsten bösartigen Krebsarten und sind für eine erhebliche Morbidität und Mortalität verantwortlich. Die größte histologische Kategorie der primären Hirntumoren sind die Gliome, die ein äußerst heterogenes Erschei-nungsbild aufweisen und radiologisch schwer von anderen Hirnläsionen zu unterscheiden sind. Die Neurochirurgie ist meist die Standardbehandlung für neu diagnostizierte Gliom-Patienten und kann von einer Strahlentherapie und einer adjuvanten Temozolomid-Chemotherapie gefolgt werden. Die Hirntumorchirurgie steht jedoch vor großen Herausforderungen, wenn es darum geht, eine maximale Tumorentfernung zu erreichen und gleichzeitig postoperative neurologische Defizite zu vermeiden. Zwei dieser neurochirurgischen Herausforderungen werden im Folgenden vorgestellt. Erstens ist die manuelle Abgrenzung des Glioms einschließlich seiner Unterregionen aufgrund seines infiltrativen Charakters und des Vorhandenseins einer heterogenen Kontrastverstärkung schwierig. Zweitens verformt das Gehirn seine Form ̶ die so genannte "Hirnverschiebung" ̶ als Reaktion auf chirurgische Manipulationen, Schwellungen durch osmotische Medikamente und Anästhesie, was den Nutzen präopera-tiver Bilddaten für die Steuerung des Eingriffs einschränkt. Bildgesteuerte Systeme bieten Ärzten einen unschätzbaren Einblick in anatomische oder pathologische Ziele auf der Grundlage moderner Bildgebungsmodalitäten wie Magnetreso-nanztomographie (MRT) und Ultraschall (US). Bei den bildgesteuerten Instrumenten handelt es sich hauptsächlich um computergestützte Systeme, die mit Hilfe von Computer-Vision-Methoden die Durchführung perioperativer chirurgischer Eingriffe erleichtern. Die Chirurgen müssen jedoch immer noch den Operationsplan aus präoperativen Bildern gedanklich mit Echtzeitinformationen zusammenführen, während sie die chirurgischen Instrumente im Körper manipulieren und die Zielerreichung überwachen. Daher war die Notwendigkeit einer Bildführung während neurochirurgischer Eingriffe schon immer ein wichtiges Anliegen der Ärzte. Ziel dieser Forschungsarbeit ist die Entwicklung eines neuartigen Systems für die peri-operative bildgeführte Neurochirurgie (IGN), nämlich DeepIGN, mit dem die erwarteten Ergebnisse der Hirntumorchirurgie erzielt werden können, wodurch die Gesamtüberle-bensrate maximiert und die postoperative neurologische Morbidität minimiert wird. Im Rahmen dieser Arbeit werden zunächst neuartige Methoden für die Kernbestandteile des DeepIGN-Systems der Hirntumor-Segmentierung im MRT und der multimodalen präope-rativen MRT zur intraoperativen US-Bildregistrierung (iUS) unter Verwendung der jüngs-ten Entwicklungen im Deep Learning vorgeschlagen. Anschließend wird die Ergebnisvor-hersage der verwendeten Deep-Learning-Netze weiter interpretiert und untersucht, indem für den Menschen verständliche, erklärbare Karten erstellt werden. Schließlich wurden Open-Source-Pakete entwickelt und in weithin anerkannte Software integriert, die für die Integration von Informationen aus Tracking-Systemen, die Bildvisualisierung und -fusion sowie die Anzeige von Echtzeit-Updates der Instrumente in Bezug auf den Patientenbe-reich zuständig ist. Die Komponenten von DeepIGN wurden im Labor validiert und in einem simulierten Operationssaal evaluiert. Für das Segmentierungsmodul erreichte DeepSeg, ein generisches entkoppeltes Deep-Learning-Framework für die automatische Abgrenzung von Gliomen in der MRT des Gehirns, eine Genauigkeit von 0,84 in Bezug auf den Würfelkoeffizienten für das Bruttotumorvolumen. Leistungsverbesserungen wurden bei der Anwendung fort-schrittlicher Deep-Learning-Ansätze wie 3D-Faltungen über alle Schichten, regionenbasier-tes Training, fliegende Datenerweiterungstechniken und Ensemble-Methoden beobachtet. Um Hirnverschiebungen zu kompensieren, wird ein automatisierter, schneller und genauer deformierbarer Ansatz, iRegNet, für die Registrierung präoperativer MRT zu iUS-Volumen als Teil des multimodalen Registrierungsmoduls vorgeschlagen. Es wurden umfangreiche Experimente mit zwei Multi-Location-Datenbanken durchgeführt: BITE und RESECT. Zwei erfahrene Neurochirurgen führten eine zusätzliche qualitative Validierung dieser Studie durch, indem sie MRT-iUS-Paare vor und nach der deformierbaren Registrierung überlagerten. Die experimentellen Ergebnisse zeigen, dass das vorgeschlagene iRegNet schnell ist und die besten Genauigkeiten erreicht. Darüber hinaus kann das vorgeschlagene iRegNet selbst bei nicht trainierten Bildern konkurrenzfähige Ergebnisse liefern, was seine Allgemeingültigkeit unter Beweis stellt und daher für die intraoperative neurochirurgische Führung von Nutzen sein kann. Für das Modul "Erklärbarkeit" wird das NeuroXAI-Framework vorgeschlagen, um das Vertrauen medizinischer Experten in die Anwendung von KI-Techniken und tiefen neuro-nalen Netzen zu erhöhen. Die NeuroXAI umfasst sieben Erklärungsmethoden, die Visuali-sierungskarten bereitstellen, um tiefe Lernmodelle transparent zu machen. Die experimen-tellen Ergebnisse zeigen, dass der vorgeschlagene XAI-Rahmen eine gute Leistung bei der Extraktion lokaler und globaler Kontexte sowie bei der Erstellung erklärbarer Salienzkar-ten erzielt, um die Vorhersage des tiefen Netzwerks zu verstehen. Darüber hinaus werden Visualisierungskarten erstellt, um den Informationsfluss in den internen Schichten des Encoder-Decoder-Netzwerks zu erkennen und den Beitrag der MRI-Modalitäten zur end-gültigen Vorhersage zu verstehen. Der Erklärungsprozess könnte medizinischen Fachleu-ten zusätzliche Informationen über die Ergebnisse der Tumorsegmentierung liefern und somit helfen zu verstehen, wie das Deep-Learning-Modell MRT-Daten erfolgreich verar-beiten kann. Außerdem wurde ein interaktives neurochirurgisches Display für die Eingriffsführung entwickelt, das die verfügbare kommerzielle Hardware wie iUS-Navigationsgeräte und Instrumentenverfolgungssysteme unterstützt. Das klinische Umfeld und die technischen Anforderungen des integrierten multimodalen DeepIGN-Systems wurden mit der Fähigkeit zur Integration von (1) präoperativen MRT-Daten und zugehörigen 3D-Volumenrekonstruktionen, (2) Echtzeit-iUS-Daten und (3) positioneller Instrumentenver-folgung geschaffen. Die Genauigkeit dieses Systems wurde anhand eines benutzerdefi-nierten Agar-Phantom-Modells getestet, und sein Einsatz in einem vorklinischen Operati-onssaal wurde simuliert. Die Ergebnisse der klinischen Simulation bestätigten, dass die Montage des Systems einfach ist, in einer klinisch akzeptablen Zeit von 15 Minuten durchgeführt werden kann und mit einer klinisch akzeptablen Genauigkeit erfolgt. In dieser Arbeit wurde ein multimodales IGN-System entwickelt, das die jüngsten Fort-schritte im Bereich des Deep Learning nutzt, um Neurochirurgen präzise zu führen und prä- und intraoperative Patientenbilddaten sowie interventionelle Geräte in das chirurgi-sche Verfahren einzubeziehen. DeepIGN wurde als Open-Source-Forschungssoftware entwickelt, um die Forschung auf diesem Gebiet zu beschleunigen, die gemeinsame Nut-zung durch mehrere Forschungsgruppen zu erleichtern und eine kontinuierliche Weiter-entwicklung durch die Gemeinschaft zu ermöglichen. Die experimentellen Ergebnisse sind sehr vielversprechend für die Anwendung von Deep-Learning-Modellen zur Unterstützung interventioneller Verfahren - ein entscheidender Schritt zur Verbesserung der chirurgi-schen Behandlung von Hirntumoren und der entsprechenden langfristigen postoperativen Ergebnisse

    Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning

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    This paper presents a solution to the challenge of mitigating carbon emissions from large-scale high performance computing (HPC) systems and datacenters that host machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to datacenter compute cycles and carbon emissions. We introduce Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets. Therefore, it is a promising solution toward achieving carbon neutrality in HPC systems and datacenters

    Object Segmentation and Reconstruction Using Infrastructure Sensor Nodes for Autonomous Mobility

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    This thesis focuses on the Lidar point cloud processing for the infrastructure sensor node that serves as the perception system for autonomous robots with general mobility in indoor applications. Compared with typical schemes mounting sensors on the robots, the method acquires data from infrastructure sensor nodes, providing a more comprehensive view of the environment, which benefits the robot's navigation. The number of sensors would not need to be increased even for multiple robots, significantly reducing costs. In addition, with a central perception system using the infrastructure sensor nodes navigating every robot, a more comprehensive understanding of the current environment and all the robots' locations can be obtained for the control and operation of the autonomous robots. For a robot in the detection range of the sensor node, the sensor node can detect and segment obstacles in its driveable area and reconstruct the incomplete, sparse point cloud of objects upon their movement. The complete shape by the reconstruction benefits the localization and path planning which follows the perception part of the robot's system. Considering the sparse Lidar data and the variety of object categories in the environment, a model-free scheme is selected for object segmentation. Point segmentation starts with background filtering. Considering the complexity of the indoor environment, a depth-matching-based background removal approach is first proposed. However, later tests imply that the method is adequate but not time-efficient. Therefore, based on the depth matching-based method, a process that only focuses on the drive-able area of the robot is proposed, and the computational complexity is significantly reduced. With optimization, the computation time for processing one frame of data can be greatly increased, from 0.2 second by the first approach to 0.01 second by the second approach. After background filtering, the remaining points for occurring objects are segmented as separate clusters using an object clustering algorithm. With independent clusters of objects, an object tracking algorithm is followed to allocate the point clusters with IDs and arrange the clusters in a time sequence. With a stream of clusters for a specific object in a time sequence, point registration is deployed to aggregate the clusters into a complete shape. And as noticed during the experiment, one of the differences between indoor and outdoor environments is that contact between objects in the indoor environment is much more common. The objects in contact are likely to be segmented as a single cluster by the model-free clustering algorithm, which needs to be avoided in the reconstruction process. Therefore an improvement is made in the tracking algorithm when contact happens. The algorithms in this thesis have been experimentally evaluated and presented

    Design and Advanced Model Predictive Control of Wide Bandgap Based Power Converters

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    The field of power electronics (PE) is experiencing a revolution by harnessing the superior technical characteristics of wide-band gap (WBG) materials, namely Silicone Carbide (SiC) and Gallium Nitride (GaN). Semiconductor devices devised using WBG materials enable high temperature operation at reduced footprint, offer higher blocking voltages, and operate at much higher switching frequencies compared to conventional Silicon (Si) based counterpart. These characteristics are highly desirable as they allow converter designs for challenging applications such as more-electric-aircraft (MEA), electric vehicle (EV) power train, and the like. This dissertation presents designs of a WBG based power converters for a 1 MW, 1 MHz ultra-fast offboard EV charger, and 250 kW integrated modular motor drive (IMMD) for a MEA application. The goal of these designs is to demonstrate the superior power density and efficiency that are achievable by leveraging the power of SiC and GaN semiconductors. Ultra-fast EV charging is expected to alleviate the challenge of range anxiety , which is currently hindering the mass adoption of EVs in automotive market. The power converter design presented in the dissertation utilizes SiC MOSFETs embedded in a topology that is a modification of the conventional three-level (3L) active neutral-point clamped (ANPC) converter. A novel phase-shifted modulation scheme presented alongside the design allows converter operation at switching frequency of 1 MHz, thereby miniaturizing the grid-side filter to enhance the power density. IMMDs combine the power electronic drive and the electric machine into a single unit, and thus is an efficient solution to realize the electrification of aircraft. The IMMD design presented in the dissertation uses GaN devices embedded in a stacked modular full-bridge converter topology to individually drive each of the motor coils. Various issues and solutions, pertaining to paralleling of GaN devices to meet the high current requirements are also addressed in the thesis. Experimental prototypes of the SiC ultra-fast EV charger and GaN IMMD were built, and the results confirm the efficacy of the proposed designs. Model predictive control (MPC) is a nonlinear control technique that has been widely investigated for various power electronic applications in the past decade. MPC exploits the discrete nature of power converters to make control decisions using a cost function. The controller offers various advantages over, e.g., linear PI controllers in terms of fast dynamic response, identical performance at a reduced switching frequency, and ease of applicability to MIMO applications. This dissertation also investigates MPC for key power electronic applications, such as, grid-tied VSC with an LCL filter and multilevel VSI with an LC filter. By implementing high performance MPC controllers on WBG based power converters, it is possible to formulate designs capable of fast dynamic tracking, high power operation at reduced THD, and increased power density

    ATR-FTIR Spectroscopy-Linked Chemometrics:A Novel Approach to the Analysis and Control of the Invasive Species Japanese Knotweed

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    Japanese knotweed (Reynoutria japonica), an invasive plant species, causes negative environmental and socio-economic impacts. A female clone in the United Kingdom, its extensive rhizome system enables rapid vegetative spread. Plasticity permits this species to occupy a broad geographic range and survive harsh abiotic conditions. It is notoriously difficult to control with traditional management strategies, which include repetitive herbicide application and costly carbon-intensive rhizome excavation. This problem is complicated by crossbreeding with the closely related species, Giant knotweed (Reynoutria sachalinensis), to give the more vigorous hybrid, Bohemian knotweed (Fallopia x Bohemica) which produces viable seed. These species, hybrids, and backcrosses form a morphologically similar complex known as Japanese knotweed ‘sensu lato’ and are often misidentified. The research herein explores the opportunities offered by advances in the application of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy-linked chemometrics within plant sciences, for the identification and control of knotweed, to enhance our understanding of knotweed biology, and the potential of this technique. ATR-FTIR spectral profiles of Japanese knotweed leaf material and xylem sap samples, which include important biological absorptions due to lipids, proteins, carbohydrates, and nucleic acids, were used to: identify plants from different growing regions highlighting the plasticity of this clonal species; differentiate between related species and hybrids; and predict key physiological characteristics such as hormone concentrations and root water potential. Technical advances were made for the application of ATR-FTIR spectroscopy to plant science, including definition of the environmental factors that exert the most significant influence on spectral profiles, evaluation of sample preparation techniques, and identification of key wavenumbers for prediction of hormone concentrations and abiotic stress. The presented results cement the position of concatenated mid-infrared spectroscopy and machine learning as a powerful approach for the study of plant biology, extending its reach beyond the field of crop science to demonstrate a potential for the discrimination between and control of invasive plant species

    Machine Learning based prediction of the effect of lay-up defects in the automated fiber placement

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    The use of Automated Fiber Placement is being widespread in the aerospace industry. The need of manufacturing large and complex structural composite components, it makes the use of this technology much more efficient than the conventional hand lay-up manufacturing. However, these components are still being manually inspected and the effect of the defects found is calculated with a simulation software. The scope of this thesis is to create a Machine Learning model that is able to calculate the effect on the effective stiffness for different defect configuration. This Machine Learning model should be provided with the geometrical defect characteristics in the laminate and it has to be able to predict, with a high level of accuracy, the effective stiffness of the laminate. Training this model with a big amount of different configuration defects generates the need to create a parametrized FE model of a composite laminate on the coupon level. The results show that a Multi Layer Perceptron architecture with two hidden layers. The first one with 281 nodes and the second one with 76 nodes which is able to predict the effective stiffness of a defective laminate coupon with an accuracy of 0,1 GPaL'ús del Automated Fiber Placement està estenent-se en la indústria aeroespacial. La necessitat de fabricar components estructurals compostos grans i complexes, fa que l'ús d'aquesta tecnologia sigui molt més eficient que la fabricació convencional amb col·locació manual. No obstant això, aquests components encara s'estan inspeccionant manualment i es calcula l'efecte dels defectes trobats amb software de simulació. L'abast d'aquesta tesi és crear un model de Machine Lerning que sigui capaç de calcular l'efecte en la rigidesa efectiva per diferents configuracions de defectes. Aquest model d'aprenentatge automàtic hauria de rebre les característiques geomètriques dels defectes en el laminat i de ser capaç de predir, amb un alt nivell de precisió, la rigidesa efectiva del laminat. Entrenar aquest model amb una gran quantitat de configuracions de defectes diferents genera la necessitat de crear un model FE parametritzat d'una laminació composta en el nivell de cupó. Els resultats mostren que una arquitectura de Multilayer Perceptron amb dues hidden layers. La primera amb 281 nodes i la segona amb 76 nodes, és capaç de predir la rigidesa efectiva d'un laminat defectuós amb una precisió de 0,1 GPaEl uso del Automated Fiber Placement se está expandiendo en la industria aeroespacial. La necesidad de fabricar grandes y complejos componentes estructurales de materiales compuestos, hace que el uso de esta tecnología sea mucho más eficiente que la fabricación manual convencional. Sin embargo, estos componentes siguen siendo inspeccionados manualmente y se calcula el efecto de los defectos encontrados con un software de simulación. El objetivo de esta tesis es crear un modelo de Machine Learning que sea capaz de calcular el efecto sobre la rigidez efectiva para diferentes configuraciones de defectos. A este modelo de aprendizaje automático se le deben proporcionar las características geométricas del defecto en el laminado y tiene que ser capaz de predecir, con un alto nivel de precisión, la rigidez efectiva del laminado. El entrenamiento de este modelo se debe de realizar con una gran cantidad de configuraciones de defectos diferentes. Este hecho genera la necesidad de crear un modelo de elementos finitos parametrizado de un laminado a nivel de cupón. Los resultados muestran que una arquitectura Multilayer Perceptron con dos hidden layers. La primera con 281 nodos y la segunda con 76 nodos que es capaz de predecir la rigidez efectiva de un coupon laminado defectuoso con una precisión de 0,1 GP
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