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Superimposing Dynamic Range
(2009)
Replacing a uniform illumination by a high-frequent illumination enhances the contrast of observed and captured images. We modulate spatially and temporally multiplexed (projected) light with reflective or transmissive matter to achieve high dynamic range visualizations of radiological images on printed paper or ePaper, and to boost the optical contrast of images viewed or imaged with light microscopes.
Atlas der Datenkörper. Körperbilder in Kunst, Design und Wissenschaft im Zeitalter digitaler Medien
(2022)
Digitale Technologien und soziale Medien verändern die Selbst- und Körperwahrnehmung und verzerren, verstärken oder produzieren dabei spezifische Körperbilder. Die Beiträger*innen kartographieren diese Phänomene, fragen nach ihrer medialen Existenzweise sowie nach den Möglichkeiten ihrer Kritik. Dabei begegnen sie ihrer Neuartigkeit mit einer transdisziplinären Herangehensweise. Aus sowohl der Perspektive künstlerischer und gestalterischer Forschung als auch der Kunst-, Kultur- und Medienwissenschaft sowie der Psychologie und Neurowissenschaft wird die Landschaft rezenter Körperbilder und Techniken einer digitalen Körperlichkeit untersucht.
The current thesis presents research about new methods of citizen participation based on digital technologies. The focus on the research lies on decentralized methods of participation where citizens take the role of co-creators. The research project first conducted a review of the literature on citizen participation, its origins and the different paradigms that have emerged over the years. The literature review also looked at the influence of technologies on participation processes and the theoretical frameworks that have emerged to understand the introduction of technologies in the context of urban development. The literature review generated the conceptual basis for the further development of the thesis.
The research begins with a survey of technology enabled participation applications that examined the roles and structures emerging due to the introduction of technology. The results showed that cities use technology mostly to control and monitor urban infrastructure and are rather reluctant to give citizens the role of co-creators. Based on these findings, three case studies were developed. Digital tools for citizen participation were conceived and introduced for each case study. The adoption and reaction of the citizens were observed using three data collection methods.
The results of the case studies showed consistently that previous participation and engagement with informal citizen participation are a determinining factor in the potential adoption of digital tools for decentralized engagement. Based on these results, the case studies proposed methods and frameworks that can be used for the conception and introduction of technologies for decentralized citizen participation.
This report details the development of Horoskopos, a virtual planetarium for astrology. This project was an attempt to develop a learning tool for studying astrological concepts as connected to observational astronomy. The premise that astrology and observational astronomy were once inseparable from each other in ancient times guided the conceptualization of this tool as an interactive planetarium. The main references were existing software and applications for visualization in astrology and astronomy. Professional astrology teachers were consulted in order to understand better the state of astrological teaching and learning, as well as existing tools and practice. Horoskopos was built using the Unity3D development interface, which is based on the C# programming language. It also relied on the Swiss Ephemeris coding interface from Astrodienst. The development process was experimental and many of the needed skills were developed as needed. Usability tests were performed as new features were added to the interface. The final version of Horoskopos is fully usable, with many interactive visualization features and a defined visual identity. It was validated together with professional astrologers for its effectiveness in concept and visualization.
This work presents a concept of interactive machine learning in a human design process. An urban design problem is viewed as a multiple-criteria optimization problem. The outlined feature of an urban design problem is the dependence of a design goal on a context of the problem. We model the design goal as a randomized fitness measure that depends on the context. In terms of multiple-criteria decision analysis (MCDA), the defined measure corresponds to a subjective expected utility of a user. In the first stage of the proposed approach we let the algorithm explore a design space using clustering techniques. The second stage is an interactive design loop; the user makes a proposal, then the program optimizes it, gets the user’s feedback and returns back the control over the application interface.
The increasing success of BIM (Building Information Model) and the emergence of its implementation in 3D construction models have paved a way for improving scheduling process. The recent research on application of BIM in scheduling has focused on quantity take-off, duration estimation for individual trades, schedule visualization, and clash detection.
Several experiments indicated that the lack of detailed planning causes about 30% non-productive time and stacking of trades. However, detailed planning still has not been implemented in practice despite receiving a lot of interest from researchers. The reason is associated with the huge amount and complexity of input data. In order to create a detailed planning, it is time consuming to manually decompose activities, collect and calculate the detailed information in relevant. Moreover, the coordination of detailed activities requires much effort for dealing with their complex constraints.
This dissertation aims to support the generation of detailed schedules from a rough schedule. It proposes a model for automated detailing of 4D schedules by integrating BIM, simulation and Pareto-based optimization.
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.
The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.
The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively.
Der vorliegende Beitrag beschreibt die Problematik bei der Prognose verkehrsbedingter Schadstoff-Immissionen. Im Mittelpunkt steht die Entwicklung und der Aufbau einer Simulationsumgebung zur Evaluation von umweltorientierten Verkehrsmanagement-Strategien. Die Simulationsumgebung wird über die drei Felder Verkehr, Emission, Immission entwickelt und findet zunächst Anwendung in der Evaluation verkehrlicher Maßnahmen für die Friedberger Landstraße in Frankfurt am Main.