Refine
Document Type
- Article (57)
- Doctoral Thesis (26)
- Part of a Book (16)
- Master's Thesis (7)
- Book (4)
- Preprint (3)
- Conference Proceeding (2)
- Habilitation (2)
- Report (2)
- Bachelor Thesis (1)
Institute
- Institut für Strukturmechanik (ISM) (42)
- Junior-Professur Bildtheorie (17)
- Professur Bauphysik (8)
- Professur Sozialwissenschaftliche Stadtforschung (7)
- Junior-Professur Organisation und vernetzte Medien (6)
- Institut für Europäische Urbanistik (5)
- Professur Bauchemie und Polymere Werkstoffe (3)
- Professur Denkmalpflege und Baugeschichte (3)
- Professur Modellierung und Simulation - Konstruktion (3)
- Bauhaus-Institut für zukunftsweisende Infrastruktursysteme (b.is) (2)
Keywords
- OA-Publikationsfonds2020 (27)
- Maschinelles Lernen (17)
- Machine learning (12)
- Künstlerische Forschung (10)
- Erdbeben (7)
- Deep learning (5)
- Theater (5)
- big data (5)
- Medien (4)
- Raumklima (4)
Year of publication
- 2020 (123) (remove)
Hydrological drought forecasting plays a substantial role in water resources management. Hydrological drought highly affects the water allocation and hydropower generation. In this research, short term hydrological drought forecasted based on the hybridized of novel nature-inspired optimization algorithms and Artificial Neural Networks (ANN). For this purpose, the Standardized Hydrological Drought Index (SHDI) and the Standardized Precipitation Index (SPI) were calculated in one, three, and six aggregated months. Then, three states where proposed for SHDI forecasting, and 36 input-output combinations were extracted based on the cross-correlation analysis. In the next step, newly proposed optimization algorithms, including Grasshopper Optimization Algorithm (GOA), Salp Swarm algorithm (SSA), Biogeography-based optimization (BBO), and Particle Swarm Optimization (PSO) hybridized with the ANN were utilized for SHDI forecasting and the results compared to the conventional ANN. Results indicated that the hybridized model outperformed compared to the conventional ANN. PSO performed better than the other optimization algorithms. The best models forecasted SHDI1 with R2 = 0.68 and RMSE = 0.58, SHDI3 with R 2 = 0.81 and RMSE = 0.45 and SHDI6 with R 2 = 0.82 and RMSE = 0.40.
The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms.
Energy‐Efficient Method for Wireless Sensor Networks Low‐Power Radio Operation in Internet of Things
(2020)
The radio operation in wireless sensor networks (WSN) in Internet of Things (IoT)applications is the most common source for power consumption. Consequently, recognizing and controlling the factors affecting radio operation can be valuable for managing the node power consumption. Among essential factors affecting radio operation, the time spent for checking the radio is of utmost importance for monitoring power consumption. It can lead to false WakeUp or idle listening in radio duty cycles and ContikiMAC. ContikiMAC is a low‐power radio duty‐cycle protocol in Contiki OS used in WakeUp mode, as a clear channel assessment (CCA) for checking radio status periodically. This paper presents a detailed analysis of radio WakeUp time factors of ContikiMAC. Furthermore, we propose a lightweight CCA (LW‐CCA) as an extension to ContikiMAC to reduce the Radio Duty‐Cycles in false WakeUps and idle listening though using dynamic received signal strength indicator (RSSI) status check time. The simulation results in the Cooja simulator show that LW‐CCA reduces about 8% energy consumption in nodes while maintaining up to 99% of the packet delivery rate (PDR).
Coronary Artery Disease Diagnosis: Ranking the Significant Features Using a Random Trees Model
(2020)
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
This thesis explores how cultural heritage plays a role in the development of urban identity by engaging both actively and passively with memory, i.e. remembering and forgetting. I argue that architectural heritage is a medium where specific cultural and social decisions form its way of presentation, and it reflects the values and interests of the period. By the process of remembering and forgetting, the meanings between inhabitant and object in urban environment are practiced, and the meanings are created.
To enable the research in narrative observation, cultural tourism management is chosen as the main research object, which reflects the alteration of interaction between the architectural heritage and urban identity. Identifying the role of heritage management, the definition of social resilience and the prospects of cultural heritage as a means of social resilience are addressed. Case region of the research is East Ger- many, thereby, the study examines the distinct approaches and objectives regarding heritage management under the different political systems along the German reunification process.
The framework is based on various theoretical paradigms to investigate the broad research questions: 1) What is the role of historic urban quarters in the revitalisation of East German towns? 2) How was the transition processed by cultural heritage management? 3) How did policy affect residents’ lives?
The case study is applied to macro level (city level: Gotha and Eisenach) and micro level study (object level: specific heritage sites), to analyse the performance of selective remembering and making tourist destination through giving significance to specific heritage. By means of site observations, archival research, qualitative inter- views, photographs, and discourse analysis on printed tourism materials, the study demonstrates that certain sites and characteristics of the city enable creating and focusing messages, which aids the social resilience.
Combining theory and empirical studies this thesis attempts to widen the academic discussion regarding the practice of remembering and forgetting driven by cultural heritage. The thesis argues for cultural heritage tourism as an element of social resilience and one that embraces the historic and cultural identity of the inhabitants.
Das Hauptziel der vorliegenden Arbeit war es, eine stetige Kopplung zwischen der ananlytischen und numerischen Lösung von Randwertaufgaben mit Singularitäten zu realisieren. Durch die inter-polationsbasierte gekoppelte Methode kann eine globale C0 Stetigkeit erzielt werden. Für diesen Zweck wird ein spezielle finite Element (Kopplungselement) verwendet, das die Stetigkeit der Lösung sowohl mit dem analytischen Element als auch mit den normalen CST Elementen gewährleistet.
Die interpolationsbasierte gekoppelte Methode ist zwar für beliebige Knotenanzahl auf dem Interface ΓAD anwendbar, aber es konnte durch die Untersuchung von der Interpolationsmatrix und numerische Simulationen festgestellt werden, dass sie schlecht konditioniert ist. Um das Problem mit den numerischen Instabilitäten zu bewältigen, wurde eine approximationsbasierte Kopplungsmethode entwickelt und untersucht. Die Stabilität dieser Methode wurde anschließend anhand der Untersuchung von der Gramschen Matrix des verwendeten Basissystems auf zwei Intervallen [−π,π] und [−2π,2π] beurteilt. Die Gramsche Matrix auf dem Intervall [−2π,2π] hat einen günstigeren Konditionszahl in der Abhängigkeit von der Anzahl der Kopplungsknoten auf dem Interface aufgewiesen. Um die dazu gehörigen numerischen Instabilitäten ausschließen zu können wird das Basissystem mit Hilfe vom Gram-Schmidtschen Orthogonalisierungsverfahren auf beiden Intervallen orthogonalisiert. Das orthogonale Basissystem lässt sich auf dem Intervall [−2π,2π] mit expliziten Formeln schreiben. Die Methode des konsistentes Sampling, die häufig in der Nachrichtentechnik verwendet wird, wurde zur Realisierung von der approximationsbasierten Kopplung herangezogen. Eine Beschränkung dieser Methode ist es, dass die Anzahl der Sampling-Basisfunktionen muss gleich der Anzahl der Wiederherstellungsbasisfunktionen sein. Das hat dazu geführt, dass das eingeführt Basissys-tem (mit 2 n Basisfunktionen) nur mit n Basisfunktion verwendet werden kann.
Zur Lösung diese Problems wurde ein alternatives Basissystems (Variante 2) vorgestellt. Für die Verwendung dieses Basissystems ist aber eine Transformationsmatrix M nötig und bei der Orthogonalisierung des Basissystems auf dem Intervall [−π,π] kann die Herleitung von dieser Matrix kompliziert und aufwendig sein. Die Formfunktionen wurden anschließend für die beiden Varianten hergeleitet und grafisch (für n = 5) dargestellt und wurde gezeigt, dass diese Funktionen die Anforderungen an den Formfunktionen erfüllen und können somit für die FE- Approximation verwendet werden.
Anhand numerischer Simulationen, die mit der Variante 1 (mit Orthogonalisierung auf dem Intervall [−2π,2π]) durchgeführt wurden, wurden die grundlegenden Fragen (Beispielsweise: Stetigkeit der Verformungen auf dem Interface ΓAD, Spannungen auf dem analytischen Gebiet) über-
prüft.
The longitudinal dispersion coefficient (LDC) plays an important role in modeling the transport of pollutants and sediment in natural rivers. As a result of transportation processes, the concentration of pollutants changes along the river. Various studies have been conducted to provide simple equations for estimating LDC. In this study, machine learning methods, namely support vector regression, Gaussian process regression, M5 model tree (M5P) and random forest, and multiple linear regression were examined in predicting the LDC in natural streams. Data sets from 60 rivers around the world with different hydraulic and geometric features were gathered to develop models for LDC estimation. Statistical criteria, including correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE), were used to scrutinize the models. The LDC values estimated by these models were compared with the corresponding results of common empirical models. The Taylor chart was used to evaluate the models and the results showed that among the machine learning models, M5P had superior performance, with CC of 0.823, RMSE of 454.9 and MAE of 380.9. The model of Sahay and Dutta, with CC of 0.795, RMSE of 460.7 and MAE of 306.1, gave more precise results than the other empirical models. The main advantage of M5P models is their ability to provide practical formulae. In conclusion, the results proved that the developed M5P model with simple formulations was superior to other machine learning models and empirical models; therefore, it can be used as a proper tool for estimating the LDC in rivers.
Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation.
In einer systematischen Interpretation von Vilém Flussers Werk schlägt die Arbeit vor, Flussers Ansatz als einen medienphilosophischen zu verstehen, insofern er das „wie“ der medienphilosophischen Fragestellung in den Mittelpunkt rückt. Medien werden nicht erst dann zu einem wesentlichen Bestandteil von Flussers Philosophie, wenn er sie explizit zum Gegenstand seiner Untersuchungen der gegenwärtigen Kultur und Gesellschaft oder historischer Rückblicke macht; Denken vollzieht sich immer in Medien oder medialen Praktiken, es wird nicht nur von ihnen (mit) geprägt – ohne Medien gäbe es kein Denken und umgekehrt verändert sich Philosophie mit den (jeweils) neuen Medien. Ausgehend von Begriffen oder eher Denkfiguren, die neben dem „was“ des jeweils verhandelten Themas auch das „wie“ der Reflexion selbst adressieren, wird der „Umbruch in der Struktur des Denkens“ zugleich als Beschreibung von Medienumbrüchen verstanden – mit dem Fluchtpunkt des Sprungs in das Universum der Komputation – und als Vollzug der gegenwärtigen Veränderung der „Methode des Denkens“. Flussers (Ver)Suche einer Reflexion, die nicht mehr durch das Medium Schrift strukturiert ist, sondern sowohl alten Medien wie dem Bild – bzw. Praktiken des Abbildens, Darstellens, Einbildens usw. – als auch neuen Medien – dem Komputieren – Geltung verschafft, laufen auf eine widersprüchliche Diagnose des neuen Universums der Komputation (anders: der technischen Bilder) hinaus : eine kybermetisch inspirierte Vision der frei modellierbaren Wirklichkeit(en) einerseits und die Dystopie einer Welt, in der Apparaten Denken, Wahrnehmen und Handeln beherrschen andererseits. Die Arbeit zeigt auf, wie Flusser zu dieser Aporie der Medienreflexion – die weit über Flussers Werk hinaus virulent bleibt – gelangt und wie sie, ausgehend von seiner Figur der Geste, im Sinne einer performativen Medienreflexion gelöst werden könnte.
Due to the importance of identifying crop cultivars, the advancement of accurate assessment of cultivars is considered essential. The existing methods for identifying rice cultivars are mainly time-consuming, costly, and destructive. Therefore, the development of novel methods is highly beneficial. The aim of the present research is to classify common rice cultivars in Iran based on color, morphologic, and texture properties using artificial intelligence (AI) methods. In doing so, digital images of 13 rice cultivars in Iran in three forms of paddy, brown, and white are analyzed through pre-processing and segmentation of using MATLAB. Ninety-two specificities, including 60 color, 14 morphologic, and 18 texture properties, were identified for each rice cultivar. In the next step, the normal distribution of data was evaluated, and the possibility of observing a significant difference between all specificities of cultivars was studied using variance analysis. In addition, the least significant difference (LSD) test was performed to obtain a more accurate comparison between cultivars. To reduce data dimensions and focus on the most effective components, principal component analysis (PCA) was employed. Accordingly, the accuracy of rice cultivar separations was calculated for paddy, brown rice, and white rice using discriminant analysis (DA), which was 89.2%, 87.7%, and 83.1%, respectively. To identify and classify the desired cultivars, a multilayered perceptron neural network was implemented based on the most effective components. The results showed 100% accuracy of the network in identifying and classifying all mentioned rice cultivars. Hence, it is concluded that the integrated method of image processing and pattern recognition methods, such as statistical classification and artificial neural networks, can be used for identifying and classification of rice cultivars.