1. Temporal and statistical insights into multivariate time series forecasting of corn outlet moisture in industrial continuous-flow drying systemsMarko Simonič, Simon Klančnik, 2025, original scientific article Abstract: 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.
Keywords: advanced drying technologies, continuous flow drying, time-series forecasting, LSTM, GRU, TCN, deep learning, statistical analysis, optimization of the drying process
Published in DKUM: 03.11.2025; Views: 0; Downloads: 3
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2. Predicting wine quality under changing climate : An integrated approach combining machine learning, statistical analysis, and systems thinkingMaja Borlinič Gačnik, Andrej Škraba, Karmen Pažek, Črtomir Rozman, 2025, original scientific article Abstract: Climate change poses significant challenges for viticulture, particularly in regions known for producing high-quality wines. Wine quality results from a complex interaction between climatic factors, regional characteristics, and viticultural practices. Methods: This study integrates statistical analysis, machine learning (ML) algorithms, and systems thinking to assess the extent to which wine quality can be predicted using monthly weather data and regional classification. The dataset includes average wine scores, monthly temperatures and precipitation, and categorical region data for Slovenia between 2011 and 2021. Predictive models tested include Random Forest, Support Vector Machine, Decision Tree, and linear regression. In addition, Causal Loop Diagrams (CLDs) were constructed to explore feedback mechanisms and systemic dynamics. Results: The Random Forest model showed the highest prediction accuracy (R2 = 0.779). Regional classification emerged as the most influential variable, followed by temperatures in September and April. Precipitation did not have a statistically significant effect on wine ratings. CLD models revealed time delays in the effects of adaptation measures and highlighted the role of perceptual lags in growers’ responses to climate signals. Conclusions: The combined use of ML, statistical methods, and CLDs enhances understanding of how climate variability influences wine quality. This integrated approach offers practical insights for winegrowers, policymakers, and regional planners aiming to develop climate-resilient viticultural strategies. Future research should include phenological phase modeling and dynamic simulation to further improve predictive accuracy and system-level understanding.
Keywords: wine quality, machine learning, climate change, viticulture, Slovenia, terroir, statistical analysis, causal loop diagrams, system thinking
Published in DKUM: 18.08.2025; Views: 0; Downloads: 11
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3. Quantifying power system frequency quality and extracting typical patterns within short time scales below one hourYounes Mohammadi, Boštjan Polajžer, Roberto Chouhy Leborgne, Davood Khodadad, 2024, original scientific article Abstract: This paper addresses the lack of consideration of short time scales, below one hour, such as sub-15-min and sub1-hr, in grid codes for frequency quality analysis. These time scales are becoming increasingly important due to
the flexible market-based operation of power systems as well as the rising penetration of renewable energy
sources and battery energy storage systems. For this, firstly, a set of frequency-quality indices is considered,
complementing established statistical indices commonly used in power-quality standards. These indices provide
valuable insights for quantifying variations, events, fluctuations, and outliers specific to the discussed time
scales. Among all the implemented indices, the proposed indices are based on over/under frequency events (6
indices), fast frequency rise/drop events (6 indices), and summation of positive and negative peaks (1 index), of
which the 5 with the lowest thresholds are identified as the most dominant. Secondly, k-means and k-medoids
clustering methods in a learning scheme are employed to identify typical patterns within the discussed time
windows, in which the number of clusters is determined based on prior knowledge linked to reality. In order to
clarify the frequency variations and patterns, three frequency case studies are analyzed: case 1 (sub-15-min scale,
10-s values, 6 months), case 2 (sub-1-hr scale, 10-s values, 6 months), and case 3 (sub-1-hr, 3-min values, the
year 2021). Results obtained from the indices and learning methods demonstrate a full picture of the information
within the windows. The maximum value of the highest frequency value minus the lowest one over the windows
is about 0.35 Hz for cases 1 and 2 and 0.25 Hz for case 3. Over-frequency values (with a typical 0.1% threshold)
slightly dominates under-frequency values in cases 1 and 2, while the opposite is observed in case 3. Medium
fluctuations occur in 35% of windows for cases 1 and 2 and 41% for case 3. Outlier values are detected using the
quartile method in 70% of windows for case 2, surpassing the other two cases. About six or seven typical patterns
are also extracted using the presented learning scheme, revealing the frequency trends within the short time
windows. The proposed approaches offer a simpler alternative than tracking frequency single values and also
capture more comprehensive information than existing approaches that analyze the aggregated frequency values
at the end of the specific time windows without considering the frequency trends. In this way, the network
operators have the possibility to monitor the frequency quality and trends within short time scales using the most
dominant indices and typical patterns.
Keywords: quantifying power system frequency quality, statistical indices, pattern extracting, machine learning, short time scales, renewable energy sources
Published in DKUM: 23.08.2024; Views: 50; Downloads: 41
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4. Organization in finance prepared by stohastic differential equations with additive and nonlinear models and continuous optimizationPakize Taylan, Gerhard-Wilhelm Weber, 2008, original scientific article Abstract: A central element in organization of financal means by a person, a company or societal group consists in the constitution, analysis and optimization of portfolios. This requests the time-depending modeling of processes. Likewise many processes in nature, technology and economy, financial processes suffer from stochastic fluctuations. Therefore, we consider stochastic differential equations (Kloeden, Platen and Schurz, 1994) since in reality, especially, in the financial sector, many processes are affected with noise. As a drawback, these equations are hard to represent by a computer and hard to resolve. In our paper, we express them in simplified manner of approximation by both a discretization and additive models based on splines. Our parameter estimation refers to the linearly involved spline coefficients as prepared in (Taylan and Weber, 2007) and the partially nonlinearly involved probabilistic parameters. We construct a penalized residual sum of square for this model and face occuring nonlinearities by Gauss-Newton's and Levenberg-Marquardt's method on determining the iteration step. We also investigate when the related minimization program can be written as a Tikhonov regularization problem (sometimes called ridge regression), and we treat it using continuous optimization techniques. In particular, we prepare access to the elegant framework of conic quadratic programming. These convex optimation problems are very well-structured, herewith resembling linear programs and, hence, permitting the use of interior point methods (Nesterov and Nemirovskii, 1993).
Keywords: stochastic differential equations, regression, statistical learning, parameter estimation, splines, Gauss-Newton method, Levenberg-Marquardt's method, smoothing, stability, penalty methods, Tikhonov regularization, continuous optimization, conic quadratic programming
Published in DKUM: 10.01.2018; Views: 1436; Downloads: 155
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