Iskanje po katalogu digitalne knjižnice

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
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Opis: Spatio-temporal forecasting is a rapidly evolving field, accelerated by the increasing accessibility of sensoring infrastructure and computational hardware, capable of processing the large amount of sampled data. Applications of spatio-temporal forecasts range from traffic, weather, air pollution forecasting and others. Emerging technologies employ deep learning architectures, such as graph, convolutional, recurrent and transformer neural networks. While the state-of-the-art methods provide accurate time series predictions, they are typically limited to providing forecasts only for the direct locations of sampling, whereas coverage of the entire area is often desired by the applications. In this work, we propose a method that addresses this challenge and improves on the shortcomings of related works, which have already tackled the task. The proposed graph convolutional recurrent neural network based method provides forecasts for arbitrary geolocations without available measurement data, formulating predictions based on contextual information of target geolocations and the time series data of nearby measurement geolocations. We evaluate the method on three real-world datasets from meteorological, traffic and air pollution domains, and gauge its performance against the state-of-the-art spatio-temporal forecasting methods. The proposed method achieves 12.26 %, 66.97 % and 42.89 % improvements in the mean absolute percentage errors on the three aforementioned datasets, compared to the best performing state-of-the-art method GConvGRU.
Ključne besede: spatio-temporal forecasting, graph recurrent neural networks, sparse geospatial sensor networks
Objavljeno v DKUM: 25.07.2025; Ogledov: 0; Prenosov: 6
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Opis: The influence of four main weather attributes on the content of alpha-acids of the hop cv. Aurora for the period 1994-2019 was studied. By analysing correlation coefficients, specific times of the year when the weather conditions affect the alpha-acid content with the goal of creating a forecasting model in Slovenia were identified. The most significant periods of weather that impacted the alpha-acid contents throughout the growing time of year are recognised as attributes of temperatures (T), rainfall (R) and sunshine (S) calculated from the 25th to 30th week (T2530, r = -0.78, P < 0.01; R2529, r = 0.72, P < 0.01 and S2529, r = -0.81, P < 0.01) and attributes of relative humidity (RH) from the 27th to 32nd week (RH2732, r = 0.82, P < 0.01). T2530 stands for the amount of active temperatures from June 18 to July 29. Likewise, R2530 matches to the precipitation (in mm or L/m2) during the same time period.
Ključne besede: alpha-acids, α-acids, weather attributes, cv. Aurora, forecasting, hop quality, brewing process, biosynthesis, vegetative period
Objavljeno v DKUM: 28.01.2025; Ogledov: 0; Prenosov: 6
Celotno besedilo (1,55 MB)
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Opis: Forecasting is important in many branches of logistics, including the logistics related to Tourism supply chains. With an increasing inflow of American tourists, planning and forecasting the US tourists' inflow to Slovenia have gained far more importance attention amongst scholars and practitioners. This study, therefore, was conducted to forecast the American tourists' inflow to Slovenia using one of the predictive models based on the exponential smoothing approach, namely Holt-Winters damped additive (HWDA) exponential smoothing method. The model was modified by several improvements, while the obtained results were generalized to other supply chain components. The results show that the forecasting system can predict well the observed inflow, while the methodology used to derive the model might have enriched the plethora of existing practical forecasting approaches in the tourism domain. Benchmarking demonstrates that the proposed model outperforms a competitive ARIMA model and official forecasts. The practical implications are also discussed in this paper.
Ključne besede: tourism, forecasting, Holt-Winters model, US tourists, supply chains, logistics
Objavljeno v DKUM: 22.08.2024; Ogledov: 42; Prenosov: 10
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Opis: At the Orlica fault in the Krško basin, combined barasol detectors were buried in six boreholes, two along the fault itself and four on either side of it, to measure and record radon activity, temperature and pressure in soil gas every 60 minutes for four years. Data collected have been analysed in a manner aimed at distinguishing radon anomalies resulting from environmental parameters (air and soil temperature, barometric pressure, rainfall) from those caused solely by seismic events. The following approaches have been used to identify anomalies: (i) ± 2σ deviation of radon concentration from the seasonal average, (ii) correlation between time gradients of radon concentration and barometric pressure, and (iii) prediction with regression trees within a machine learning program. In this paper results obtained with regression trees are presented. A model has been built in which the program was taught to predict radon concentration from the data collected during the seismically inactive periods when radon is presumably influenced only by environmental parameters. A correlation coefficient of 0.83 between measured and predicted values was obtained. Then, the whole data time series was included and a significantly lowered correlation was observed during the seismically active periods. This reduced correlation is thus an indicator of seismic effect.
Ključne besede: radon in soil gas, environmental parameters, earthquakes, correlation, regression trees, forecasting
Objavljeno v DKUM: 15.05.2018; Ogledov: 1784; Prenosov: 93
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