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Using ClassPad-technology in the education of students of electrical engineering (Fourier- and Laplace-Transformation)Paditz, Ludwig 09 May 2012 (has links) (PDF)
By the help of several examples the interactive work with the ClassPad330 is considered. The student can solve difficult exercises of practical applications step by step using the symbolic calculation and the graphic possibilities of the calculator. Sometimes several fields
of mathematics are combined to solve a problem. Let us consider the ClassPad330 (with the actual operating system OS 03.03) and discuss on some new exercises in analysis, e.g. solving a linear differential equation by the help of the Laplace transformation and using the inverse Laplace transformation or considering the Fourier transformation in discrete time (the Fast Fourier Transformation FFT and the inverse FFT). We use the FFT- and IFFT-function to study periodic signals, if we only have a sequence generated by sampling the time signal. We know several ways to get a solution. The techniques for studying practical applications fall into the following three categories: analytic, graphic and numeric. We can use the
Classpad software in the handheld or in the PC (ClassPad emulator version of the handheld).
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Using ClassPad-technology in the education of students of electricalengineering (Fourier- and Laplace-Transformation)Paditz, Ludwig 09 May 2012 (has links)
By the help of several examples the interactive work with the ClassPad330 is considered. The student can solve difficult exercises of practical applications step by step using the symbolic calculation and the graphic possibilities of the calculator. Sometimes several fields
of mathematics are combined to solve a problem. Let us consider the ClassPad330 (with the actual operating system OS 03.03) and discuss on some new exercises in analysis, e.g. solving a linear differential equation by the help of the Laplace transformation and using the inverse Laplace transformation or considering the Fourier transformation in discrete time (the Fast Fourier Transformation FFT and the inverse FFT). We use the FFT- and IFFT-function to study periodic signals, if we only have a sequence generated by sampling the time signal. We know several ways to get a solution. The techniques for studying practical applications fall into the following three categories: analytic, graphic and numeric. We can use the
Classpad software in the handheld or in the PC (ClassPad emulator version of the handheld).
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Estimating the load weight of freight trains using machine learningKongpachith, Erik January 2023 (has links)
Accurate estimation of the load weight of freight trains is crucial for ensuring safe, efficient and sustainable rail freight transports. Traditional methods for estimating load weight often suffer from limitations in accuracy and efficiency. In recent years, machine learning algorithms have gained significant attention and use cases within the railway industry due to their strong predictive capabilities for classification and regression tasks. This study aims to present a proof of concept in the form of a comparative analysis of five machine learning regression algorithms: Polynomial Regression, K-Nearest Neighbors, Regression Trees, Random Forest Regression, and Support Vector Regression for estimating the load weight of freight trains using simulation data. The study utilizes two comprehensive datasets derived from train simulations in GENSYS, a simulation software for modeling rail vehicles. The datasets encompasses various driving condition factors such as train speed, track conditions and running gear configurations. The algorithms are trained and evaluated on these datasets and their performance is evaluated based on the root mean squared error and R2 metrics. Results from the experiments demonstrate that all five machine learning algorithms show promising performance for estimating the load weight. Polynomial regression achieves the best result for both of the datasets when using many features of the datasets are considered. Random forest regression achieves the best result for both of the data sets when a small number features of the datasets are considered. Furthermore, it is suggested that the methodical approach of this study is examined on real world data from operating freight trains to assert the proof of concept in a real world setting. / Noggrann uppskattning av godstågens lastvikt är avgörande för att säkerställa säkra, effektiva och hållbara godstransporter via järnväg. Traditionella metoder för att uppskatta lastvikt lider ofta av begränsningar i noggrannhet och effektivitet. Under de senaste åren har maskininlärningsalgoritmer fått betydande uppmärksamhet och användningsfall inom järnvägsindustrin på grund av deras starka prediktiva förmåga för klassificerings- och regressionsproblem. Denna studie syftar till att presentera en proof of concept i form av en jämförande analys av fem maskininlärningalgoritmer för regression: Polynom regression, K-Nearest Neighbors, Regression träd, Random Forest Regression och Support Vector Regression för att uppskatta lastvikten för godståg med hjälp av simuleringsdata. Studien använder två omfattande dataset konstruerade från tågsimuleringar i GENSYS, en simuleringsprogramvara för modellering av järnvägsfordon. Dataseten omfattar olika körfaktorer såsom tåghastighet, spårförhållanden och vagns konfigurationer. Algoritmerna tränas och utvärderas på dessa dataset och deras prestanda utvärderas baserat på root mean squared error och R2 måtten. Resultat från experimenten visar att alla fem maskininlärningsalgoritmerna visar lovande prestanda för att uppskatta lastvikten. Polynom regression uppnår det bästa resultatet för båda dataset när många variabler i datan beaktas. Random Forest Regression ger det bästa resultatet för båda dataset när ett mindre antal variabler i datan beaktas. Det föreslås det att det metodiska tillvägagångssättet för denna studie undersöks på verklig data från aktiva godståg för att fastställa en proof of concept på en verklig världsbild.
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