Maschinelles Lernen – Überblick und Abgrenzung

Frochte, Jörg

doi:10.3139/9783446459977.002

Cited by 4 publications

(4 citation statements)

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“…This decision is made according to its features' characteristics in order to achieve the highest information gain regarding the estimation of the respective label, e.g., the remaining travel time. The leaves consist of data for which a further split will not generate any additional information for the prediction of the respective label (Bose and Mahapatra 2001;Frochte 2019;Yu et al 2018). The DTR is a specialised type of the decision tree especially suitable for regression tasks.…”

Section: Selection Of ML Methods For Forecasting Remaining Travel Timementioning

confidence: 99%

“…The DTR is a specialised type of the decision tree especially suitable for regression tasks. Using an LR model, the relation between features and labels is represented for the data in each leaf separately (Frochte 2019). The suitability of DTR for short-term arrival time prediction is shown by Pani et al (2014).…”

Section: Selection Of ML Methods For Forecasting Remaining Travel Timementioning

confidence: 99%

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Robust berth scheduling using machine learning for vessel arrival time prediction

et al. 2022

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In this work, the potentials of data-driven optimization for the well-known berth allocation problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel assignments at the quay of a terminal, taking into account uncertainty regarding the actual vessel arrival times which may result from external influences as, e.g., cross wind and sea current. In order to achieve robustness, four different Machine Learning methods-from linear regression to an artificial neural network-are employed for vessel arrival time prediction in this work. The different Machine Learning methods are analysed and evaluated with respect to their forecast quality. The calculation and use of so-called dynamic time buffers (DTBs), which are derived from the different AIS-based forecasts and whose length depends on the estimated forecast reliability, in the berth scheduling model enhance the robustness of the resulting schedules considerably, as is shown in an extensive numerical study. Furthermore, the results show that also rather simple Machine Learning approaches are able to reach high forecast accuracy. The optimization model does not only lead to more robust solutions, but also to less actual waiting times for the vessels and hence to an enhanced service quality, as can be shown by studying the resulting schedules for real vessel data. Moreover, it turns out that the accuracy of the resulting berthing schedules, measured as the deviation of planned and actually realisable schedules, exceeds the accuracy of all forecasts which underlines the usefulness of the DTB approach.

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Section: Selection Of ML Methods For Forecasting Remaining Travel Timementioning

confidence: 99%

Section: Selection Of ML Methods For Forecasting Remaining Travel Timementioning

confidence: 99%

Robust berth scheduling using machine learning for vessel arrival time prediction

et al. 2022

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“…The notion of AI incorporates all advances in computer science, with an emphasis on intelligence and developing activities. An advanced Arti cial intelligence is powerful and reliable, and it interacts like a human in the sense that its intellectual layers are similar to the human brain [19]. A family of communication technology that uses arti cial neural networks (ANN), which are built on learning [20].…”

Section: Introductionmentioning

confidence: 99%

Ann Supported Study on the Performance and Slurry Erosion Resistance of Thermal Sprayed Wc20cr3c27ni Coatings

Bhosale¹,

Bhosale²,

Patil³

et al. 2023

SSRN Journal

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“…Common choices acc. to[28] for the sizes of the amount of data (here |D| = 𝑁 𝑆 is the number of data points within the whole data set) are: training data: 80% of |D|; test data: 10% of |D|; validation data: 10% of |D|. This approach ensures that the model's performance is evaluated on independent data and that the model does not overfit the training data.…”

mentioning

confidence: 99%

Predictive modelling and latent space exploration of steel profile overstrength factors using multi‐head autoencoder‐regressors

Kraus,

Müller,

Bischof

et al. 2023

ce papers

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This paper investigates the suitability and interpretability of a data‐driven deep learning algorithm for multi cross sectional overstrength factor prediction. For this purpose, we first compile datasets consisting of experiments from literature on the overstrength factor of circular, rectangular and square hollow sections as well as I‐ and H‐sections. We then propose a novel multi‐head encoder architecture consisting of three input heads (one head per section type represented by respective features), a shared embedding layer as well as a subsequent regression tail for predicting the overstrength factor. By construction, this multi‐head architecture simultaneously allows for (i) the exploration of the nonlinear embedding of different cross‐sectional profiles towards the overstrength factor within the shared layer, and (ii) a forward prediction of the overstrength factor given profile features. Our framework enables for the first time an exploration of cross‐section similarity w.r.t. the overstrength factor across multiple sections and hence provides new domain insights in bearing capacities of steel cross‐sections, a much wider data exploration, since the encoder‐regressor can serve as meta model predictor. We demonstrate the quality of the predictive capabilities of the model and gain new insights of the latent space of different steel sections w.r.t. the overstrength factor. Our proposed method can easily be transferred to other multi‐input problems of Scientific Machine Learning.

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Maschinelles Lernen – Überblick und Abgrenzung

Cited by 4 publications

References 0 publications

Robust berth scheduling using machine learning for vessel arrival time prediction

Robust berth scheduling using machine learning for vessel arrival time prediction

Ann Supported Study on the Performance and Slurry Erosion Resistance of Thermal Sprayed Wc20cr3c27ni Coatings

Predictive modelling and latent space exploration of steel profile overstrength factors using multi‐head autoencoder‐regressors

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