TY - JOUR A1 - Band, Shahab S. A1 - Janizadeh, Saeid A1 - Saha, Sunil A1 - Mukherjee, Kaustuv A1 - Khosrobeigi Bozchaloei, Saeid A1 - Cerdà, Artemi A1 - Shokri, Manouchehr A1 - Mosavi, Amir Hosein T1 - Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data JF - Land N2 - Piping erosion is one form of water erosion that leads to significant changes in the landscape and environmental degradation. In the present study, we evaluated piping erosion modeling in the Zarandieh watershed of Markazi province in Iran based on random forest (RF), support vector machine (SVM), and Bayesian generalized linear models (Bayesian GLM) machine learning algorithms. For this goal, due to the importance of various geo-environmental and soil properties in the evolution and creation of piping erosion, 18 variables were considered for modeling the piping erosion susceptibility in the Zarandieh watershed. A total of 152 points of piping erosion were recognized in the study area that were divided into training (70%) and validation (30%) for modeling. The area under curve (AUC) was used to assess the effeciency of the RF, SVM, and Bayesian GLM. Piping erosion susceptibility results indicated that all three RF, SVM, and Bayesian GLM models had high efficiency in the testing step, such as the AUC shown with values of 0.9 for RF, 0.88 for SVM, and 0.87 for Bayesian GLM. Altitude, pH, and bulk density were the variables that had the greatest influence on the piping erosion susceptibility in the Zarandieh watershed. This result indicates that geo-environmental and soil chemical variables are accountable for the expansion of piping erosion in the Zarandieh watershed. KW - Maschinelles Lernen KW - Bayes-Verfahren KW - Naturkatastrophe KW - random forest KW - support vector machine KW - geoinformatics KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20210122-43424 UR - https://www.mdpi.com/2073-445X/9/10/346 VL - 2020 IS - volume 9, issue 10, article 346 SP - 1 EP - 22 PB - MDPI CY - Basel ER - TY - JOUR A1 - Brombach, Benjamin A1 - Bruns, Erich A1 - Bimber, Oliver T1 - Subobject Detection through Spatial Relationships on Mobile Phones N2 - We present a novel image classification technique for detecting multiple objects (called subobjects) in a single image. In addition to image classifiers, we apply spatial relationships among the subobjects to verify and to predict locations of detected and undetected subobjects, respectively. By continuously refining the spatial relationships throughout the detection process, even locations of completely occluded exhibits can be determined. Finally, all detected subobjects are labeled and the user can select the object of interest for retrieving corresponding multimedia information. This approach is applied in the context of PhoneGuide, an adaptive museum guidance system for camera-equipped mobile phones. We show that the recognition of subobjects using spatial relationships is up to 68% faster than related approaches without spatial relationships. Results of a field experiment in a local museum illustrate that unexperienced users reach an average recognition rate for subobjects of 85.6% under realistic conditions. KW - Objekterkennung KW - Smartphone KW - Subobjekterkennung KW - Räumliche Beziehungen KW - Neuronales Netz KW - Museumsführer KW - Subobject Detection KW - Spatial Relationships KW - Neural Networks KW - Museum Guidance Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20081007-14296 ER - TY - THES A1 - Bruns, Erich T1 - Adaptive Image Classification on Mobile Phones N2 - The advent of high-performance mobile phones has opened up the opportunity to develop new context-aware applications for everyday life. In particular, applications for context-aware information retrieval in conjunction with image-based object recognition have become a focal area of recent research. In this thesis we introduce an adaptive mobile museum guidance system that allows visitors in a museum to identify exhibits by taking a picture with their mobile phone. Besides approaches to object recognition, we present different adaptation techniques that improve classification performance. After providing a comprehensive background of context-aware mobile information systems in general, we present an on-device object recognition algorithm and show how its classification performance can be improved by capturing multiple images of a single exhibit. To accomplish this, we combine the classification results of the individual pictures and consider the perspective relations among the retrieved database images. In order to identify multiple exhibits in pictures we present an approach that uses the spatial relationships among the objects in images. They make it possible to infer and validate the locations of undetected objects relative to the detected ones and additionally improve classification performance. To cope with environmental influences, we introduce an adaptation technique that establishes ad-hoc wireless networks among the visitors’ mobile devices to exchange classification data. This ensures constant classification rates under varying illumination levels and changing object placement. Finally, in addition to localization using RF-technology, we present an adaptation technique that uses user-generated spatio-temporal pathway data for person movement prediction. Based on the history of previously visited exhibits, the algorithm determines possible future locations and incorporates these predictions into the object classification process. This increases classification performance and offers benefits comparable to traditional localization approaches but without the need for additional hardware. Through multiple field studies and laboratory experiments we demonstrate the benefits of each approach and show how they influence the overall classification rate. N2 - Die Einführung von Mobiltelefonen mit eingebauten Sensoren wie Kameras, GPS oder Beschleunigungssensoren, sowie Kommunikationstechniken wie Bluetooth oder WLAN ermöglicht die Entwicklung neuer kontextsensitiver Anwendungen für das tägliche Leben. Insbesondere Applikationen im Bereich kontextsensitiver Informationsbeschaffung in Verbindung mit bildbasierter Objekterkennung sind in den Fokus der aktuellen Forschung geraten. Der Beitrag dieser Arbeit ist die Entwicklung eines bildbasierten, mobilen Museumsführersystems, welches unterschiedliche Adaptionstechniken verwendet, um die Objekterkennung zu verbessern. Es wird gezeigt, wie Ojekterkennungsalgorithmen auf Mobiltelefonen realisiert werden können und wie die Erkennungsrate verbessert wird, indem man zum Beispiel ad-hoc Netzwerke einsetzt oder Bewegungsvorhersagen von Personen berücksichtigt. T2 - Adaptive Bilderkennung auf Mobiltelefonen KW - Kontextbezogenes System KW - Bilderkennung KW - Ubiquitous Computing KW - Mobile Computing KW - Maschinelles Sehen KW - Museumsführer KW - Handy KW - Wegrouten KW - Positionsbestimmung KW - PhoneGuide KW - Bluetooth tracking KW - pathway awareness KW - museum guidance system KW - PhoneGuide Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20100707-15092 ER - TY - JOUR A1 - Bruns, Erich A1 - Bimber, Oliver T1 - Phone-to-Phone Communication for Adaptive Image Classification N2 - In this paper, we present a novel technique for adapting local image classifiers that are applied for object recognition on mobile phones through ad-hoc network communication between the devices. By continuously accumulating and exchanging collected user feedback among devices that are located within signal range, we show that our approach improves the overall classification rate and adapts to dynamic changes quickly. This technique is applied in the context of PhoneGuide – a mobile phone based museum guidance framework that combines pervasive tracking and local object recognition for identifying a large number of objects in uncontrolled museum environments. KW - Peer-to-Peer-Netz KW - Bilderkennung KW - Museumsführer KW - Ad-hoc-Netz KW - Phone-to-phone communication KW - adaptive image classification KW - mobile ad-hoc networks KW - museum guidance system Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20080722-13685 ER - TY - RPRT A1 - Bruns, Erich A1 - Bimber, Oliver T1 - Adaptive Training of Video Sets for Image Recognition on Mobile Phones N2 - We present an enhancement towards adaptive video training for PhoneGuide, a digital museum guidance system for ordinary camera–equipped mobile phones. It enables museum visitors to identify exhibits by capturing photos of them. In this article, a combined solution of object recognition and pervasive tracking is extended to a client–server–system for improving data acquisition and for supporting scale–invariant object recognition. KW - Objektverfolgung KW - Neuronales Netz KW - Handy KW - Objekterkennung KW - Museum KW - Anpassung KW - mobile phones KW - object recognition KW - neural networks KW - museum guidance KW - pervasive tracking KW - temporal adaptation Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-8223 ER - TY - RPRT A1 - Bruns, Erich A1 - Brombach, Benjamin A1 - Bimber, Oliver T1 - Mobile Phone Enabled Museum Guidance with Adaptive Classification N2 - Although audio guides are widely established in many museums, they suffer from several drawbacks compared to state-of-the-art multimedia technologies: First, they provide only audible information to museum visitors, while other forms of media presentation, such as reading text or video could be beneficial for museum guidance tasks. Second, they are not very intuitive. Reference numbers have to be manually keyed in by the visitor before information about the exhibit is provided. These numbers are either displayed on visible tags that are located near the exhibited objects, or are printed in brochures that have to be carried. Third, offering mobile guidance equipment to visitors leads to acquisition and maintenance costs that have to be covered by the museum. With our project PhoneGuide we aim at solving these problems by enabling the application of conventional camera-equipped mobile phones for museum guidance purposes. The advantages are obvious: First, today’s off-the-shelf mobile phones offer a rich pallet of multimedia functionalities ---ranging from audio (over speaker or head-set) and video (graphics, images, movies) to simple tactile feedback (vibration). Second, integrated cameras, improvements in processor performance and more memory space enable supporting advanced computer vision algorithms. Instead of keying in reference numbers, objects can be recognized automatically by taking non-persistent photographs of them. This is more intuitive and saves museum curators from distributing and maintaining a large number of physical (visible or invisible) tags. Together with a few sensor-equipped reference tags only, computer vision based object recognition allows for the classification of single objects; whereas overlapping signal ranges of object-distinct active tags (such as RFID) would prevent the identification of individuals that are grouped closely together. Third, since we assume that museum visitors will be able to use their own devices, the acquisition and maintenance cost for museum-owned devices decreases. KW - Objektverfolgung KW - Neuronales Netz KW - Handy KW - Objekterkennung KW - Museum KW - Anpassung KW - Mobiltelefone KW - Museumsführer KW - Adaptive Klassifizierung KW - Ad-hoc Sensor-Netzwerke KW - mobile phones KW - object recognition KW - museum guidance KW - adaptive classification KW - ad-hoc sensor networks Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-9406 ER - TY - THES A1 - Bunte, Andreas T1 - Entwicklung einer ontologiebasierten Beschreibung zur Erhöhung des Automatisierungsgrades in der Produktion N2 - Die zu beobachtenden kürzeren Produktlebenszyklen und eine schnellere Marktdurchdringung von Produkttechnologien erfordern adaptive und leistungsfähige Produktionsanlagen. Die Adaptivität ermöglicht eine Anpassung der Produktionsanlage an neue Produkte, und die Leistungsfähigkeit der Anlage stellt sicher, dass ausreichend Produkte in kurzer Zeit und zu geringen Kosten hergestellt werden können. Durch eine Modularisierung der Produktionsanlage kann die Adaptivität erreicht werden. Jedoch erfordert heutzutage jede Adaption manuellen Aufwand, z.B. zur Anpassung von proprietären Signalen oder zur Anpassung übergeordneter Funktionen. Dadurch sinkt die Leistungsfähigkeit der Anlage. Das Ziel dieser Arbeit ist es, die Interoperabilität in Bezug auf die Informationsverwendung in modularen Produktionsanlagen zu gewährleisten. Dazu werden Informationen durch semantische Modelle beschrieben. Damit wird ein einheitlicher Informationszugriff ermöglicht, und übergeordnete Funktionen erhalten Zugriff auf alle Informationen der Produktionsmodule, unabhängig von dem Typ, dem Hersteller und dem Alter des Moduls. Dadurch entfällt der manuelle Aufwand bei Anpassungen des modularen Produktionssystems, wodurch die Leistungsfähigkeit der Anlage gesteigert und Stillstandszeiten reduziert werden. Nach dem Ermitteln der Anforderungen an einen Modellierungsformalismus wurden potentielle Formalismen mit den Anforderungen abgeglichen. OWL DL stellte sich als geeigneter Formalismus heraus und wurde für die Erstellung des semantischen Modells in dieser Arbeit verwendet. Es wurde exemplarisch ein semantisches Modell für die drei Anwendungsfälle Interaktion, Orchestrierung und Diagnose erstellt. Durch einen Vergleich der Modellierungselemente von unterschiedlichen Anwendungsfällen wurde die Allgemeingültigkeit des Modells bewertet. Dabei wurde gezeigt, dass die Erreichung eines allgemeinen Modells für technische Anwendungsfälle möglich ist und lediglich einige Hundert Begriffe benötigt. Zur Evaluierung der erstellten Modelle wurde ein wandlungsfähiges Produktionssystem der SmartFactoryOWL verwendet, an dem die Anwendungsfälle umgesetzt wurden. Dazu wurde eine Laufzeitumgebung erstellt, die die semantischen Modelle der einzelnen Module zu einem Gesamtmodell vereint, Daten aus der Anlage in das Modell überträgt und eine Schnittstelle für die Services bereitstellt. Die Services realisieren übergeordnete Funktionen und verwenden die Informationen des semantischen Modells. In allen drei Anwendungsfällen wurden die semantischen Modelle korrekt zusammengefügt und mit den darin enthaltenen Informationen konnte die Aufgabe des jeweiligen Anwendungsfalles ohne zusätzlichen manuellen Aufwand gelöst werden. KW - Ontologie KW - Metamodell KW - Interoperabilität KW - OWL KW - Industrie 4.0 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20201215-43156 ER - TY - THES A1 - Coulon, Carl-Helmut T1 - Strukurorientiertes Fallbasiertes Schließen T1 - Structure oriented case based reasoning N2 - Das Ziel dieser Arbeit war es, durch Verwendung geeigneter vorhandener CAD-Pläne die Bearbeitung neuer CAD-Pläne zu unterstützen. Entstanden ist ein generischer Ansatz zum fallbasierten Schließens. Da in CAD-Plänen die räumliche Struktur eine wichtige Rolle spielt, ist das Konzept auf strukturorientierte Anwendungen ausgerichtet. Deshalb bezeichne ich es als ein Konzept zum " strukturorientierten fallbasierten Schließen". Die Arbeit spezifiziert das Minimum an Wissen, welches zur Suche und Wiederverwendung von Fällen benötigt wird, wie das darüber hinausgehende Wissen verarbeitet wird, welche Zusammenhänge es zum Beispiel zwischen Vergleichs- und Anpassungswissen gibt und wie man das Wissen modellieren kann. Zur Erläuterung wird das benötigte Wissen anhand verschiedener Anwendungen dargestellt. Das in der Arbeit vorgestellte Konzept erlaubt die Ergänzung, Detaillierung und Korrektur einer Anfrage. Die beiden entscheidenden Algorithmen dienen dem Vergleich von Anfrage und Fall und der Anpassung der Information des Falles zur Modifikation der Anfrage. N2 - The task of this thesis was the computer supported reuse of known CAD-designs in order to create new CAD-designs. The developed solution contains a generic approach to case based reasoning. Due to the relevance of spatial structures in CAD-designs the approach focusses on structure oriented applications. Therefore it is called an approach for „structure oriented case based reasoning". This thesis specifies the kind of the minimum knowledge required for retrieval and reuse of cases, how to integrate additional knowledge, relations between knowledge needed for comparision and adaption and how to model the knowledge. For illustration the required knowledge is described for different applications. The developed concept allows to extend, detail and correct a given query. The two most important algorithms are used to compare cases and query and to reuse the information found in a case to modify a query. KW - Fallbasiertes Schließen KW - Bauplanung KW - CAD KW - Problemlösen KW - Wissensmodellierung KW - Strukturvergleich KW - Anpassung KW - Knowledge modelling KW - comparison of structure KW - adaption Y1 - 1997 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20040212-265 ER - TY - RPRT A1 - Föckler, Paul A1 - Zeidler, Thomas A1 - Bimber, Oliver T1 - PhoneGuide: Museum Guidance Supported by On-Device Object Recognition on Mobile Phones N2 - We present PhoneGuide – an enhanced museum guidance approach that uses camera-equipped mobile phones and on-device object recognition. Our main technical achievement is a simple and light-weight object recognition approach that is realized with single-layer perceptron neuronal networks. In contrast to related systems which perform computational intensive image processing tasks on remote servers, our intention is to carry out all computations directly on the phone. This ensures little or even no network traffic and consequently decreases cost for online times. Our laboratory experiments and field surveys have shown that photographed museum exhibits can be recognized with a probability of over 90%. We have evaluated different feature sets to optimize the recognition rate and performance. Our experiments revealed that normalized color features are most effective for our method. Choosing such a feature set allows recognizing an object below one second on up-to-date phones. The amount of data that is required for differentiating 50 objects from multiple perspectives is less than 6KBytes. KW - Neuronales Netz KW - Objekterkennung KW - Handy KW - Museum KW - Mobile phones KW - object recognition KW - neural networks KW - museum guidance Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-6500 ER - TY - JOUR A1 - Harirchian, Ehsan A1 - Lahmer, Tom A1 - Kumari, Vandana A1 - Jadhav, Kirti T1 - Application of Support Vector Machine Modeling for the Rapid Seismic Hazard Safety Evaluation of Existing Buildings JF - Energies N2 - The economic losses from earthquakes tend to hit the national economy considerably; therefore, models that are capable of estimating the vulnerability and losses of future earthquakes are highly consequential for emergency planners with the purpose of risk mitigation. This demands a mass prioritization filtering of structures to identify vulnerable buildings for retrofitting purposes. The application of advanced structural analysis on each building to study the earthquake response is impractical due to complex calculations, long computational time, and exorbitant cost. This exhibits the need for a fast, reliable, and rapid method, commonly known as Rapid Visual Screening (RVS). The method serves as a preliminary screening platform, using an optimum number of seismic parameters of the structure and predefined output damage states. In this study, the efficacy of the Machine Learning (ML) application in damage prediction through a Support Vector Machine (SVM) model as the damage classification technique has been investigated. The developed model was trained and examined based on damage data from the 1999 Düzce Earthquake in Turkey, where the building’s data consists of 22 performance modifiers that have been implemented with supervised machine learning. KW - Erdbeben KW - Maschinelles Lernen KW - earthquake vulnerability assessment KW - rapid visual screening KW - machine learning KW - support vector machine KW - buildings KW - OA-Publikationsfonds2020 Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200707-41915 UR - https://www.mdpi.com/1996-1073/13/13/3340 VL - 2020 IS - volume 13, issue 13, 3340 PB - MDPI CY - Basel ER -