Iskanje po katalogu digitalne knjižnice

Opis: People have been accompanied by colours throughout the history and through all periods of life. In different eras, colours were also associated with status symbols of different social classes or mythological beliefs. We are often addressed emotionally by colour combinations, since colour perception is always and exclusively a sensorial experience. Various colour combinations can have a pleasant effect on us or leave us cold as well as in a state of shock. All of the above, presented the starting point of this research. Detection and perception of sensations through colour was accomplished by preparing a questionnaire related to 12 selected colours. The research included 302 participants from Slovenia, of both sexes and different ages, born from 1940 to 2004. From the obtained results, it could be concluded how popular a certain colour is in a material, design and spiritual sense. Moreover, the results were validated and compared regarding the gender and age of participants, and further compared with the results of previous studies.
Ključne besede: colours perception, survey, colours popularity, colour of joy, colour of clothes, colour of sleeping area
Objavljeno v DKUM: 18.11.2025; Ogledov: 0; Prenosov: 0
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Opis: V magistrski nalogi smo obravnavali strižno trdnost lotnih spojev toplotnih izmenjevalcev, ki so bili izdelani iz nerjavnega jekla in spojeni s postopkom vakuumskega lotanja. V okviru naše raziskave so bili izvedeni statični in dinamični strižni testi, katerih rezultati so nam podali materialne lastnosti spoja in S-N diagram življenjske dobe. V nadaljevanju je bila izvedena metalografska analiza na nepretrganem vzorcu in vzorcu s statično ter dinamično obremenitvijo. Pri tem smo izvedli tudi meritev mikrotrdote na osnovnem materialu in v okolici spoja. Na vzorcih, ki so bili utrujani, pa smo izvedli še fraktografsko analizo v kombinaciji z EDS analizo mikrostrukture. S pomočjo analiz smo uspeli povezati mehanske lastnosti spojev z njihovo mikrostrukturo in opredeliti vpliv difuzije, utrjevanja ter morebitnih napak v spoju z njihovo nosilnostjo in življenjsko dobo.
Ključne besede: toplotni izmenjevalec, lotni spoj, statični in dinamični strižni testi, metalografska analiza, fraktografska analiza, mikrotrdota
Objavljeno v DKUM: 11.11.2025; Ogledov: 0; Prenosov: 7
Celotno besedilo (6,81 MB)

Opis: Skripta obravnavajo strojno učenje z vidika uporabe v inženirstvu, pri čemer temeljne koncepte povezujejo s praktičnimi primeri v okolju MATLAB. Predstavljeni so štirje temeljni pristopi strojnega učenja: nadzorovano učenje, nenadzorovano učenje, učenje z okrepitvijo in prenosno učenje. Za vsak pristop so podani temeljni koncepti, konkretni primeri uporabe ter naloge za samostojno delo. Poseben poudarek je na uporabi orodij, kot so Regression Learner, Classification Learner, Deep Network Designer in Reinforcement Learning Designer, s pomočjo katerih študenti razvijajo modele na podatkih, ki izvirajo iz realnih inženirskih primerov. Med njimi so obraba orodja, vibracije strojev, balansiranje sistemov in prepoznavanje predmetov. Skripta vključujejo tudi eksperimentalne podatkovne množice in praktične napotke za učenje, validacijo in izboljšavo modelov. Namenjena so študentom tehniških smeri ter vsem, ki želijo usvojiti uporabo metod strojnega učenja za reševanje konkretnih inženirskih problemov.
Ključne besede: strojno učenje, nadzorovano učenje, nenadzorovano učenje, učenje z okrepitvijo, prenosno učenje, MATLAB, inženirske aplikacije
Objavljeno v DKUM: 10.11.2025; Ogledov: 0; Prenosov: 7
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Opis: Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation.
Ključne besede: advanced drying technologies, continuous flow drying, time-series forecasting, LSTM, GRU, TCN, deep learning, statistical analysis, optimization of the drying process
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (3,02 MB)
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Opis: In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R2 test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage.
Ključne besede: additive manufacturing, machine learning, XG Boost, magnesium, relative density
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 5
Celotno besedilo (1,60 MB)
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