81 research outputs found
Algorithms for Fault Detection and Diagnosis
Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of âAlgorithms for Fault Detection and Diagnosisâ, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions
Advances in Object and Activity Detection in Remote Sensing Imagery
The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms
Machine Learning in Sensors and Imaging
Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens
Models and analysis of vocal emissions for biomedical applications
This book of Proceedings collects the papers presented at the 4th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2005, held 29-31 October 2005, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies
LIDAR based semi-automatic pattern recognition within an archaeological landscape
LIDAR-Daten bieten einen neuartigen Ansatz zur Lokalisierung und Ăberwachung des kulturellen Erbes in der Landschaft, insbesondere in schwierig zu erreichenden Gebieten, wie im Wald, im unwegsamen GelĂ€nde oder in sehr abgelegenen Gebieten. Die manuelle Lokalisation und Kartierung von archĂ€ologischen Informationen einer Kulturlandschaft ist in der herkömmlichen Herangehensweise eine sehr zeitaufwĂ€ndige Aufgabe des Fundstellenmanagements (Cultural Heritage Management). Um die Möglichkeiten in der Erkennung und bei der Verwaltung des kulturellem Erbes zu verbessern und zu ergĂ€nzen, können computergestĂŒtzte Verfahren einige neue LösungsansĂ€tze bieten, die darĂŒber hinaus sogar die Identifizierung von fĂŒr das menschliche Auge bei visueller Sichtung nicht erkennbaren Details ermöglichen. Aus archĂ€ologischer Sicht ist die vorliegende Dissertation dadurch motiviert, dass sie LIDAR-GelĂ€ndemodelle mit archĂ€ologischen Befunden durch automatisierte und semiautomatisierte Methoden zur Identifizierung weiterer archĂ€ologischer Muster zu Bodendenkmalen als digitale âLIDAR-Landschaftâ bewertet. Dabei wird auf möglichst einfache und freie verfĂŒgbare algorithmische AnsĂ€tze (Open Source) aus der Bildmustererkennung und Computer Vision zur Segmentierung und Klassifizierung der LIDAR-Landschaften zur groĂflĂ€chigen Erkennung archĂ€ologischer DenkmĂ€ler zurĂŒckgegriffen. Die Dissertation gibt dabei einen umfassenden Ăberblick ĂŒber die archĂ€ologische Nutzung und das Potential von LIDAR-Daten und definiert anhand qualitativer und quantitativer AnsĂ€tze den Entwicklungsstand der semiautomatisierten Erkennung archĂ€ologischer Strukturen im Rahmen archĂ€ologischer Prospektion und Fernerkundungen. DarĂŒber hinaus erlĂ€utert sie Best Practice-Beispiele und den einhergehenden aktuellen Forschungsstand. Und sie veranschaulicht die QualitĂ€t der Erkennung von BodendenkmĂ€lern durch die semiautomatisierte Segmentierung und Klassifizierung visualisierter LIDAR-Daten. Letztlich identifiziert sie das Feld fĂŒr weitere Anwendungen, wobei durch eigene, algorithmische Template Matching-Verfahren groĂflĂ€chige Untersuchungen zum kulturellen Erbe ermöglicht werden. ResĂŒmierend vergleicht sie die analoge und computergestĂŒtzte Bildmustererkennung zu Bodendenkmalen, und diskutiert abschlieĂend das weitere Potential LIDAR-basierter Mustererkennung in archĂ€ologischen Kulturlandschaften
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