Due to negative environmental impacts caused by the building industry, sustainable buildings have recently become one of the most investigated fields in research. As the design technique itself is mainly responsible for building performance, building energy design optimization is of particular interest. Several studies concentrate on systems, operation, and control optimization, complemented by passive strategies, specifically related to the envelope. In building physics, different architectural considerations, in particular, the building’s shape, are essential variables, as they greatly influence the performance of a building. Most scientific work that takes into consideration building geometry explores spaces without any energy optimization or calculates optimization processes of a few basic variables of simplified space geometries. Review studies mainly discuss the historic development of optimization algorithms, building domains, and the algorithm-system and software framework performance with coupling issues. By providing a systemized clustering of different levels of shape integration intensities, space creation principals, and algorithms, this review explores the current status of sustainability related shape optimization. The review proves that geometry design variable modifications and, specifically, shape generation techniques offer promising optimization potential; however, the findings also indicate that building shape optimization is still in its infancy.
This paper corresponds to the solution of some problems realized during ragweed identification experiments, namely the samples collected on the field by botanical experts did not match the initial conditions expected. Reflections and shadows appeared on the image, which made the segmentation more difficult, therefore also the classification was not efficient in previous study. In this work, unlike those solutions, which try to remove the shadow by restoring the illumination of image parts, the focus is on separating leaf and background points based on chromatic information, basically by examining the histograms of the full image and the border. This proposed solution filters these noises in the subspaces of hue, saturation and value space and their combination. It also describes a qualitative technique to select the appropriate values from the filtered outputs. With this method, the results of segmentation improved a lot.
Visual identification of objects is an important challenge today. Main target of frequently applied methods is to identify or classify complex objects. These methods are far less effective when objects are small and less complex, and thus less descriptor features are on hand. The main reason for this is that these features can significantly change on object occlusion or appearance of noise. The presented solution performs identification of simple, small (size is 17 × 13 pixels) objects with elliptical shape. High pass filtered normalized cross correlation is used for region of interest detection and a simple deep neural network is used for classification of selected regions. The proposed method detected objects on a noisy image with accuracy of 96.2%.
The building industry is responsible for a significant degree of energy consumption in the world, causing negative climate changes and energy supply uncertainties due to low energy efficiency as well as the high resource demand of construction. Consequently, energy design optimization has become an important research field. Passive design strategies are one of the most definitive factors concerning energy-related building development. The given architectural problem calls for a method that can create all potentially feasible building geometries, thus guaranteeing the optimal solution which is addressed in the current paper. To reach this requirement, the necessity of a modular space arrangement system and architectural selection rules were determined, focusing on the relationship between the rules and the generation of geometries with mathematical rigor. Next, the architecture-based congruency analysis performed, further reduced the number of simulation cases. With the simulations, it is illustrated how the building shape versions affect the heating energy demands: the performance of the configurations themselves. Results clearly illustrate the importance of the synthesis step of the architectural design.
Optimal building design in terms of comfort and energy performance means designing and constructing a building that requires the minimum energy demand under the given conditions while also providing a good level of human comfort. This paper focuses on replacing the complex energy and comfort simulation procedure with fast regression model-based processes that encounter the building shape as input. Numerous building shape descriptors were applied as inputs to several regression models. After evaluating the results, it can be stated that, with careful selection of building geometry describing design input variables, complex energy and comfort simulations can be approximated. Six different models with five different building shape descriptors were tested. The worst results were around R2 = 0.75, and the generic results were around R2 = 0.92. The most accurate prediction models, with the highest level of accuracy (R2 > 0.97), were linear regressions using 3rd power and dense neural networks using 1st power of inputs; furthermore, averages of mean absolute percentage errors are 1% in the case of dense neural networks. For the best performance, the building configuration was described by a discrete functional point cloud. The proposed method can effectively aid future building energy and comfort optimization processes.
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