@incollection{Hartmann, author = {Hartmann, Frank}, title = {Geschichte: Informationsdesign}, series = {Kompendium Informationsdesign}, booktitle = {Kompendium Informationsdesign}, publisher = {Springer}, address = {Berlin}, doi = {10.25643/bauhaus-universitaet.2266}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20140811-22669}, pages = {22 -- 51}, abstract = {Einf{\"u}hrung in Theoretische und gestalterische Grundlagen Kommunikation und Information sind Begriffe, die erst im 20. Jahrhundert ins Zentrum von Kultur und Technik ger{\"u}ckt sind. Nicht von Ungef{\"a}hr hat sich der Ausdruck Informationsgesellschaft etabliert - er bringt jene Ver{\"a}nderungen zum Ausdruck, die sich als Medienkultur {\"u}ber das etablierte Gef{\"u}ge der Industriekultur legt. Damit ist sowohl das breite Feld technischer Entwicklungen angesprochen, wie auch neuer Formen von Interaktion und Kommunikation. Die Geschichte des Informationsdesigns ist sowohl die eines neuen Gegenstandes mit der Bezeichnung "Information", als auch der damit verbundenen Ideen und Konzepte. Es gibt dabei neue kulturelle Objekte (Interfaces, Screens), deren Gestaltung ansteht, und es gibt mit den ver{\"a}nderten technischen Codierungen neue und sich {\"a}ndernde Kontexte, in denen das geschieht (konvergente Digitalmedien). Dabei ist es {\"u}berraschend, dass in der neueren Diskussion um die zunehmend technischen, zur hyperrealen Perfektion errechneten Bilder in unserer gegenw{\"a}rtigen Medienkultur offenbar v{\"o}llig vergessen wurde, auf den Beitrag des Informationsdesigns einzugehen. Seine Geschichte ist auch ein blinder Fleck in der aktuellen Medienwissenschaft und in der Medientheorie.}, subject = {Gestaltung}, language = {de} } @misc{SimonRitzLiehr, author = {Simon-Ritz, Frank and Liehr, Harald S.}, title = {Das Urheberrecht - ein Pulverfass f{\"u}r Lehre und Forschung}, doi = {10.25643/bauhaus-universitaet.1775}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20121130-17753}, abstract = {Radiodiskussion bei bauhaus.fm am 5. November 2012. Harald S. Liehr ist Lektor und Leiter der Niederlassung Weimar des B{\"o}hlau-Verlags (Wien / K{\"o}ln / Weimar), Dr. Frank Simon-Ritz ist Direktor der Universit{\"a}tsbibliothek der Bauhaus-Universit{\"a}t Weimar. Die Fragen stellten Ren{\´e} Tauschke und Jean-Marie Schaldach.}, subject = {Urheberrecht}, language = {de} } @phdthesis{Walsdorf, author = {Walsdorf, Joern}, title = {M-Learning: Lernen im mobilen Kontext an Hochschulen}, doi = {10.25643/bauhaus-universitaet.2136}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20140304-21361}, school = {Bauhaus-Universit{\"a}t Weimar}, abstract = {A fundamental characteristic of human beings is the desire to start learning at the moment of birth. The rather formal learning process that learners have to deal with in school, on vocational training or in university, is currently subject to fundamental changes. The increasing technologization, overall existing mobile devices, the ubiquitous access to digital information, and students being early adaptors of all these technological innovations require reactions on the part of the educational system. This study examines such a reaction: The use of mobile learning in higher education. Examining the subject m-learning first requires an investigation of the educational model e-learning. Many universities already established e-learning as one of their educational segments, providing a wide range of methods to support this kind of teaching. This study includes an empirical acceptance analysis regarding the general learning behavior of students and their approval of e-learning methods. A survey on the approval of m-learning supplements the results. Mobile learning is characterized by both the mobility of the communication devices and the users. Both factors lead to new correlations, demonstrate the potential of today's mobile devices and the probability to increase the learning performance. The dissertation addresses these correlations and the use of mobile devices in the context of m-learning. M-learning and the usage of mobile devices not only require a reflection from a technological point of view. In addition to the technical features of such mobile devices, the usability of their applications plays an important role, especially with regard to the limited display size. For the purpose of evaluating mobile apps and browser-based applications, various analytical methods are suitable. The concluding heuristic evaluation points out the vulnerability of an established m-learning application, reveals the need for improvement, and shows an approach to rectify the shortcoming.}, subject = {Mobile Learning}, language = {de} } @article{GhazvineiDarvishiMosavietal., author = {Ghazvinei, Pezhman Taherei and Darvishi, Hossein Hassanpour and Mosavi, Amir and Yusof, Khamaruzaman bin Wan and Alizamir, Meysam and Shamshirband, Shahaboddin and Chau, Kwok-Wing}, title = {Sugarcane growth prediction based on meteorological parameters using extreme learning machine and artificial neural network}, series = {Engineering Applications of Computational Fluid Mechanics}, volume = {2018}, journal = {Engineering Applications of Computational Fluid Mechanics}, number = {12,1}, publisher = {Taylor \& Francis}, doi = {10.1080/19942060.2018.1526119}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181017-38129}, pages = {738 -- 749}, abstract = {Management strategies for sustainable sugarcane production need to deal with the increasing complexity and variability of the whole sugar system. Moreover, they need to accommodate the multiple goals of different industry sectors and the wider community. Traditional disciplinary approaches are unable to provide integrated management solutions, and an approach based on whole systems analysis is essential to bring about beneficial change to industry and the community. The application of this approach to water management, environmental management and cane supply management is outlined, where the literature indicates that the application of extreme learning machine (ELM) has never been explored in this realm. Consequently, the leading objective of the current research was set to filling this gap by applying ELM to launch swift and accurate model for crop production data-driven. The key learning has been the need for innovation both in the technical aspects of system function underpinned by modelling of sugarcane growth. Therefore, the current study is an attempt to establish an integrate model using ELM to predict the concluding growth amount of sugarcane. Prediction results were evaluated and further compared with artificial neural network (ANN) and genetic programming models. Accuracy of the ELM model is calculated using the statistics indicators of Root Means Square Error (RMSE), Pearson Coefficient (r), and Coefficient of Determination (R2) with promising results of 0.8, 0.47, and 0.89, respectively. The results also show better generalization ability in addition to faster learning curve. Thus, proficiency of the ELM for supplementary work on advancement of prediction model for sugarcane growth was approved with promising results.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @unpublished{RezakazemiMosaviShirazian, author = {Rezakazemi, Mashallah and Mosavi, Amir and Shirazian, Saeed}, title = {ANFIS pattern for molecular membranes separation optimization}, volume = {2018}, doi = {10.25643/BAUHAUS-UNIVERSITAET.3821}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20181122-38212}, pages = {1 -- 20}, abstract = {In this work, molecular separation of aqueous-organic was simulated by using combined soft computing-mechanistic approaches. The considered separation system was a microporous membrane contactor for separation of benzoic acid from water by contacting with an organic phase containing extractor molecules. Indeed, extractive separation is carried out using membrane technology where complex of solute-organic is formed at the interface. The main focus was to develop a simulation methodology for prediction of concentration distribution of solute (benzoic acid) in the feed side of the membrane system, as the removal efficiency of the system is determined by concentration distribution of the solute in the feed channel. The pattern of Adaptive Neuro-Fuzzy Inference System (ANFIS) was optimized by finding the optimum membership function, learning percentage, and a number of rules. The ANFIS was trained using the extracted data from the CFD simulation of the membrane system. The comparisons between the predicted concentration distribution by ANFIS and CFD data revealed that the optimized ANFIS pattern can be used as a predictive tool for simulation of the process. The R2 of higher than 0.99 was obtained for the optimized ANFIS model. The main privilege of the developed methodology is its very low computational time for simulation of the system and can be used as a rigorous simulation tool for understanding and design of membrane-based systems. Highlights are, Molecular separation using microporous membranes. Developing hybrid model based on ANFIS-CFD for the separation process, Optimization of ANFIS structure for prediction of separation process}, subject = {Fluid}, language = {en} } @misc{Pessoa, type = {Master Thesis}, author = {Pessoa, Suelen}, title = {Why do the Archives archive? A journey from the hunko to the counter-ethnography and back}, doi = {10.25643/bauhaus-universitaet.4328}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20210112-43280}, school = {Bauhaus-Universit{\"a}t Weimar}, abstract = {A complex artistic research on the theme of cultural heritage and (neo)colonial processes of material and immaterial expropriation. Starting from the encounter with a phonographic relic at the Berliner Phonogramm-Archiv, the artist embarks on a journey to her own roots embodied in the practice of the Afro-Brazilian religion Candombl{\´e}. In the form of a theoretical treatise, an archive (photos, diagrams, maps, newspaper clippings, letters, documents), as well as a sound performance in the public space of the city of Weimar, several theoretical and performative elements are brought together in this transmedia artistic research that proposes a true decolonial practice.}, subject = {K{\"u}nstlerische Forschung}, language = {en} } @article{ShabaniSamadianfardSattarietal., author = {Shabani, Sevda and Samadianfard, Saeed and Sattari, Mohammad Taghi and Mosavi, Amir and Shamshirband, Shahaboddin and Kmet, Tibor and V{\´a}rkonyi-K{\´o}czy, Annam{\´a}ria R.}, title = {Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis}, series = {Atmosphere}, volume = {2020}, journal = {Atmosphere}, number = {Volume 11, Issue 1, 66}, doi = {10.3390/atmos11010066}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200110-40561}, pages = {17}, abstract = {Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters.}, subject = {Maschinelles Lernen}, language = {en} } @article{AbbaspourGilandehMolaeeSabzietal., author = {Abbaspour-Gilandeh, Yousef and Molaee, Amir and Sabzi, Sajad and Nabipour, Narjes and Shamshirband, Shahaboddin and Mosavi, Amir}, title = {A Combined Method of Image Processing and Artificial Neural Network for the Identification of 13 Iranian Rice Cultivars}, series = {agronomy}, volume = {2020}, journal = {agronomy}, number = {Volume 10, Issue 1, 117}, publisher = {MDPI}, doi = {10.3390/agronomy10010117}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200123-40695}, pages = {21}, abstract = {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.}, subject = {Maschinelles Lernen}, language = {en} } @article{FaroughiKarimimoshaverArametal., author = {Faroughi, Maryam and Karimimoshaver, Mehrdad and Aram, Farshid and Solgi, Ebrahim and Mosavi, Amir and Nabipour, Narjes and Chau, Kwok-Wing}, title = {Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship}, series = {Engineering Applications of Computational Fluid Mechanics}, volume = {2020}, journal = {Engineering Applications of Computational Fluid Mechanics}, number = {Volume 14, No. 1}, publisher = {Taylor \& Francis}, doi = {https://doi.org/10.1080/19942060.2019.1707711}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200110-40585}, pages = {254 -- 270}, abstract = {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.}, subject = {Fernerkung}, language = {en} } @article{NabipourMosaviBaghbanetal., author = {Nabipour, Narjes and Mosavi, Amir and Baghban, Alireza and Shamshirband, Shahaboddin and Felde, Imre}, title = {Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions}, series = {Processes}, volume = {2020}, journal = {Processes}, number = {Volume 8, Issue 1, 92}, publisher = {MDPI}, doi = {10.3390/pr8010092}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200113-40624}, pages = {12}, abstract = {Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.}, subject = {Maschinelles Lernen}, language = {en} }