Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
4 results
Search Results
Article Citation - WoS: 3Citation - Scopus: 5Deterioration of Pre-War and Rehabilitation of Post-War Urbanscapes Using Generative Adversarial Networks(SAGE Publications, 2023) Çiçek, Selen; Turhan, Gözde Damla; Taşer, AybükeThe urban built environment of contemporary cities confronts a constant risk of deterioration due to natural or artificial reasons. Especially political aggression and war conflicts have significant destructive effects on architectural and cultural heritage buildings. The post-war urbanscapes demonstrate the striking effects of the armed conflicts during the hot war encounters. However, the residues of the urbanscapes become the actual indicators of damage and loss. Since today we can make future predictions using a variety of machine learning algorithms, it is possible to represent hybrid projections of urban heterotopias. In this context, this research proposes to explore dystopian post-war projections for modern cities based on their architectural styles and demonstrate the utopian scenarios of rehabilitation possibilities for the damaged urban built environment of post-war cities by using generative adversarial network (GAN) algorithms. Two primary datasets containing the post-war and pre-war building facades have been given as the input data for the CycleGAN and pix2pix GAN models. Thus, two different image-to-image GAN models have been compared regarding their ability to produce legible building facade projections in architectural features. Besides, the machine learning process results have been discussed in terms of cities' utopian and dystopian future predictions, demonstrating the war conflicts' immense effects on the built environment. Moreover, the immediate consequence of the destructive aggression on tangible and intangible architectural heritage would become visible to inhabitants and policymakers when the AI-generated rehabilitation potentials have been exposed.Article Citation - WoS: 6Citation - Scopus: 9Performance Analysis of Data-Driven and Model-Based Control Strategies Applied To a Thermal Unit Model(MDPI Multidisciplinary Digital Publishing Institute, 2017) Turhan, Cihan; Simani, Silvio; Zajic, Ivan; Gökçen Akkurt, GüldenThe paper presents the design and the implementation of different advanced control strategies that are applied to a nonlinearmodel of a thermal unit. A data-driven grey-box identification approach provided the physically-meaningful nonlinear continuous-time model, which represents the benchmark exploited in this work. The control problem of this thermal unit is important, since it constitutes the key element of passive air conditioning systems. The advanced control schemes analysed in this paper are used to regulate the outflow air temperature of the thermal unit by exploiting the inflow air speed, whilst the inflow air temperature is considered as an external disturbance. The reliability and robustness issues of the suggested control methodologies are verified with a Monte Carlo (MC) analysis for simulating modelling uncertainty, disturbance and measurement errors. The achieved results serve to demonstrate the effectiveness and the viable application of the suggested control solutions to air conditioning systems. The benchmark model represents one of the key issues of this study, which is exploited for benchmarking different model-based and data-driven advanced control methodologies through extensive simulations. Moreover, this work highlights the main features of the proposed control schemes, while providing practitioners and heating, ventilating and air conditioning engineers with tools to design robust control strategies for air conditioning systems.Article Citation - WoS: 54Citation - Scopus: 69Strength Prediction of High-Strength Concrete by Fuzzy Logic and Artificial Neural Networks(American Society of Civil Engineers (ASCE), 2014) Tayfur, Gökmen; Erdem, Tahir Kemal; Kırca, ÖnderHigh-strength concretes (HSC) were prepared with five different binder contents, each of which had several silica fume (SF) ratios (0-15%). The compressive strength was determined at 3, 7, and 28 days, resulting in a total of 60 sets of data. In a fuzzy logic (FL) algorithm, three input variables (SF content, binder content, and age) and the output variable (compressive strength) were fuzzified using triangular membership functions. A total of 24 fuzzy rules were inferred from 60% of the data. Moreover, the FL model was tested against an artificial neural networks (ANNs) model. The results show that FL can successfully be applied to predict the compressive strength of HSC. Three input variables were sufficient to obtain accurate results. The operators used in constructing the FL model were found to be appropriate for compressive strength prediction. The performance of FL was comparable to that of ANN. The extrapolation capability of FL and ANNs were found to be satisfactory.Article Citation - WoS: 8Citation - Scopus: 9Modeling of Polygalacturonase Enzyme Activity and Biomass Production by Aspergillus Sojae Atcc 20235(Springer Verlag, 2009) Tokatlı, Figen; Tarı, Canan; Ünlütürk, Mehmet; Göğüş, NihanAspergillus sojae, which is used in the making of koji, a characteristic Japanese food, is a potential candidate for the production of polygalacturonase (PG) enzyme, which of a major industrial significance. In this study, fermentation data of an A. sojae system were modeled by multiple linear regression (MLR) and artificial neural network (ANN) approaches to estimate PG activity and biomass. Nutrient concentrations, agitation speed, inoculum ratio and final pH of the fermentation medium were used as the inputs of the system. In addition to nutrient conditions, the final pH of the fermentation medium was also shown to be an effective parameter in the estimation of biomass concentration. The ANN parameters, such as number of hidden neurons, epochs and learning rate, were determined using a statistical approach. In the determination of network architecture, a cross-validation technique was used to test the ANN models. Goodness-of-fit of the regression and ANN models was measured by the R 2 of cross-validated data and squared error of prediction. The PG activity and biomass were modeled with a 5-2-1 and 5-9-1 network topology, respectively. The models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 value of 0.83, whereas the regression models predicted enzyme activity with an R 2 of 0.84 and biomass with an R 2 of 0.69.