000 Informatik, Wissen, Systeme
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000 Informatik, Informationswissenschaft, allgemeine Werke
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- 001 Wissen
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002 Das Buch
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004 Datenverarbeitung; Informatik
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005 Computerprogrammierung, Programme, Daten
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006 Spezielle Computerverfahren
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- In Zusammenarbeit mit der Bauhaus-Universität Weimar (86)
- Graduiertenkolleg 1462 (34)
- Institut für Strukturmechanik (16)
- Professur Angewandte Mathematik (13)
- Professur Informatik im Bauwesen (5)
- Institut für Konstruktiven Ingenieurbau (4)
- Juniorprofessur Stochastik und Optimierung (3)
- Geschichte und Theorie der Visuellen Kommunikation (2)
- Institut für Mathematik-Bauphysik (2)
- Professur Computer Vision in Engineering (2)
Keywords
- Angewandte Informatik (147)
- Angewandte Mathematik (147)
- Computerunterstütztes Verfahren (146)
- Architektur <Informatik> (75)
- Computer Science Models in Engineering; Multiscale and Multiphysical Models; Scientific Computing (74)
- Building Information Modeling (35)
- Data, information and knowledge modeling in civil engineering; Function theoretic methods and PDE in engineering sciences; Mathematical methods for (robotics and) computer vision; Numerical modeling in engineering; Optimization in engineering applications (34)
- Maschinelles Lernen (3)
- Finite-Elemente-Methode (2)
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For this paper, the problem of energy/voltage management in photovoltaic (PV)/battery systems was studied, and a new fractional-order control system on basis of type-3 (T3) fuzzy logic systems (FLSs) was developed. New fractional-order learning rules are derived for tuning of T3-FLSs such that the stability is ensured. In addition, using fractional-order calculus, the robustness was studied versus dynamic uncertainties, perturbation of irradiation, and temperature and abruptly faults in output loads, and, subsequently, new compensators were proposed. In several examinations under difficult operation conditions, such as random temperature, variable irradiation, and abrupt changes in output load, the capability of the schemed controller was verified. In addition, in comparison with other methods, such as proportional-derivative-integral (PID), sliding mode controller (SMC), passivity-based control systems (PBC), and linear quadratic regulator (LQR), the superiority of the suggested method was demonstrated.
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.
Rapid advancements of modern technologies put high demands on mathematical modelling of engineering systems. Typically, systems are no longer “simple” objects, but rather coupled systems involving multiphysics phenomena, the modelling of which involves coupling of models that describe different phenomena. After constructing a mathematical model, it is essential to analyse the correctness of the coupled models and to detect modelling errors compromising the final modelling result. Broadly, there are two classes of modelling errors: (a) errors related to abstract modelling, eg, conceptual errors concerning the coherence of a model as a whole and (b) errors related to concrete modelling or instance modelling, eg, questions of approximation quality and implementation. Instance modelling errors, on the one hand, are relatively well understood. Abstract modelling errors, on the other, are not appropriately addressed by modern modelling methodologies. The aim of this paper is to initiate a discussion on abstract approaches and their usability for mathematical modelling of engineering systems with the goal of making it possible to catch conceptual modelling errors early and automatically by computer assistant tools. To that end, we argue that it is necessary to identify and employ suitable mathematical abstractions to capture an accurate conceptual description of the process of modelling engineering systems.
FCS-MBFLEACH: Designing an Energy-Aware Fault Detection System for Mobile Wireless Sensor Networks
(2019)
Wireless sensor networks (WSNs) include large-scale sensor nodes that are densely distributed over a geographical region that is completely randomized for monitoring, identifying, and analyzing physical events. The crucial challenge in wireless sensor networks is the very high dependence of the sensor nodes on limited battery power to exchange information wirelessly as well as the non-rechargeable battery of the wireless sensor nodes, which makes the management and monitoring of these nodes in terms of abnormal changes very difficult. These anomalies appear under faults, including hardware, software, anomalies, and attacks by raiders, all of which affect the comprehensiveness of the data collected by wireless sensor networks. Hence, a crucial contraption should be taken to detect the early faults in the network, despite the limitations of the sensor nodes. Machine learning methods include solutions that can be used to detect the sensor node faults in the network. The purpose of this study is to use several classification methods to compute the fault detection accuracy with different densities under two scenarios in regions of interest such as MB-FLEACH, one-class support vector machine (SVM), fuzzy one-class, or a combination of SVM and FCS-MBFLEACH methods. It should be noted that in the study so far, no super cluster head (SCH) selection has been performed to detect node faults in the network. The simulation outcomes demonstrate that the FCS-MBFLEACH method has the best performance in terms of the accuracy of fault detection, false-positive rate (FPR), average remaining energy, and network lifetime compared to other classification methods.
Hochschulwege 2015
(2017)
Die in diesem Tagungsband zusammengeführten Beiträge beschäftigen sich mit dem Spannungsfeld, das sich zwischen externen Förderprogrammen, Veränderungsprojekten und den Zielen, Strukturen und Bedingungen der jeweiligen Hochschule ergibt. In diesem Spannungsfeld kommt es unweigerlich zu Reibungen, da vorhandene Strukturen und Ziele in Konflikt mit neuen Vorhaben und Ideen geraten. Ein Teil der Projekte stellt allein durch ihr finanzielles Volumen und die daraus resultierende Wirkkraft die tradierten Verhältnisse zwischen Lehre, Forschung und den wissenschaftsstützenden Bereichen in Frage und teils auf den Kopf. Die leitenden Fragen der Tagung und der hier versammelten Beiträge waren daher: Wie bringen Hochschulen ihre individuellen Ziele mit denen der bundesweiten Programme oder länderspezfifischer Fördermaßnahmen überein? Wie gehen Hochschulen mit ihren Projekten um? Wie vollzieht sich Veränderung an den Hochschulen? Und schließlich: Was bleibt von den Impulsen, die Projekte setzen? Die in diesem Tagungsband versammelten Beiträge geben darauf erste, auf dem bisherigen Erfahrungswissen basierende Antworten. Sie setzen sich intensiv mit den Faktoren auseinander, die den Erfolg von Veränderungsprozessen und Projekten befördern oder behindern können und leiten daraus Empfehlungen für Gestaltungsprozesse an Hochschulen ab.
30. Forum Bauinformatik
(2018)
Die Bauhaus-Universität Weimar ist seit langer Zeit mit dem Forum Bauinformatik eng verbunden. So wurde die Veranstaltung 1989 hier durch den Arbeitskreis Bauinformatik ins Leben gerufen und auch das 10. und 18. Forum Bauinformatik (1998 bzw. 2006) fand in Weimar statt. In diesem Jahr freuen wir uns daher besonders, das 30. Jubiläum an der Bauhaus-Universität Weimar ausrichten zu dürfen und viele interessierte Wissenschaftler und Wissenschaftlerinnen aus dem Bereich der Bauinformatik in Weimar willkommen zu heißen.
Das Forum Bauinformatik hat sich längst zu einem festen Bestandteil der Bauinformatik im deutschsprachigen Raum entwickelt. Dabei steht es traditionsgemäß unter dem Motto „von jungen Forschenden für junge Forschende“, wodurch insbesondere Nachwuchswissenschaftlerinnen und ‑wissenschaftlern die Möglichkeit geboten wird, ihre Forschungsarbeiten zu präsentieren, Problemstellungen fachspezifisch zu diskutieren und sich über den neuesten Stand der Forschung zu informieren. Zudem wird eine ausgezeichnete Gelegenheit geboten, in die wissenschaftliche Gemeinschaft im Bereich der Bauinformatik einzusteigen und Kontakte mit anderen Forschenden zu knüpfen.
In diesem Jahr erhielten wir 49 interessante und qualitativ hochwertige Beiträge vor allem in den Themenbereichen Simulation, Modellierung, Informationsverwaltung, Geoinformatik, Structural Health Monitoring, Visualisierung, Verkehrssimulation und Optimierung. Dafür möchten wir uns ganz besonders bei allen Autoren, Co-Autoren und Reviewern bedanken, die durch ihr Engagement das diesjährige Forum Bauinformatik erst möglich gemacht haben. Wir danken zudem Professor Große und Professor Díaz für die Unterstützung bei der Auswahl der Beiträge für die Best Paper Awards.
Ein herzliches Dankeschön geht an die Kollegen an der Professur Informatik im Bauwesen der Bauhaus-Universität Weimar für die organisatorische, technische und beratende Unterstützung während der Planung der Veranstaltung.
During the previous decades, the upcoming demand for security in the digital world, e.g., the Internet, lead to numerous groundbreaking research topics in the field of cryptography. This thesis focuses on the design and analysis of cryptographic primitives and schemes to be used for authentication of data and communication endpoints, i.e., users. It is structured into three parts, where we present the first freely scalable multi-block-length block-cipher-based compression function (Counter-bDM) in the first part. The presented design is accompanied by a thorough security analysis regarding its preimage and collision security. The second and major part is devoted to password hashing. It is motivated by the large amount of leaked password during the last years and our discovery of side-channel attacks on scrypt – the first modern password scrambler that allowed to parameterize the amount of memory required to compute a password hash. After summarizing which properties we expect from a modern password scrambler, we (1) describe a cache-timing attack on scrypt based on its password-dependent memory-access pattern and (2) outline an additional attack vector – garbage-collector attacks – that exploits optimization which may disregard to overwrite the internally used memory. Based on our observations, we introduce Catena – the first memory-demanding password-scrambling framework that allows a password-independent memory-access pattern for resistance to the aforementioned attacks. Catena was submitted to the Password Hashing Competition (PHC) and, after two years of rigorous analysis, ended up as a finalist gaining special recognition for its agile framework approach and side-channel resistance. We provide six instances of Catena suitable for a variety of applications. We close the second part of this thesis with an overview of modern password scramblers regarding their functional, security, and general properties; supported by a brief analysis of their resistance to garbage-collector attacks. The third part of this thesis is dedicated to the integrity (authenticity of data) of nonce-based authenticated encryption schemes (NAE). We introduce the so-called j-IV-Collision Attack, allowing to obtain an upper bound for an adversary that is provided with a first successful forgery and tries to efficiently compute j additional forgeries for a particular NAE scheme (in short: reforgeability). Additionally, we introduce the corresponding security notion j-INT-CTXT and provide a comparative analysis (regarding j-INT-CTXT security) of the third-round submission to the CAESAR competition and the four classical and widely used NAE schemes CWC, CCM, EAX, and GCM.
We apply keyquery-based taxonomy composition to compute a classification system for the CORE dataset, a shared crawl of about 850,000 scientific papers. Keyquery-based taxonomy composition can be understood as a two-phase hierarchical document clustering technique that utilizes search queries as cluster labels: In a first phase, the document collection is indexed by a reference search engine, and the documents are tagged with the search queries they are relevant—for their so-called keyqueries. In a second phase, a hierarchical clustering is formed from the keyqueries within an iterative process. We use the explicit topic model ESA as document retrieval model in order to index the CORE dataset in the reference search engine. Under the ESA retrieval model, documents are represented as vectors of similarities to Wikipedia articles; a methodology proven to be advantageous for text categorization tasks. Our paper presents the generated taxonomy and reports on quantitative properties such as document coverage and processing requirements.
We present StarWatch, our application for real-time analysis of radio astronomical data in Virtual Environment. Serving as an interface to radio astronomical databases or being applied to live data from the radio telescopes, the application supports various data filters measuring signal-to-noise ratio (SNR), Doppler's drift, degree of signal localization on celestial sphere and other useful tools for signal extraction and classification. Originally designed for the database of narrow band signals from SETI Institute (setilive.org), the application has been recently extended for the detection of wide band periodic signals, necessary for the search of pulsars. We will also address the detection of week signals possessing arbitrary waveforms and present several data filters suitable for this purpose.
Cyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.