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Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning

Al-Sammarraie Mustafa A.J., Gierz Łukasz Adam, Özbek Osman, Kırılmaz Hasan

Advances in Science and Technology. Research Journal

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2024

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Vol. 18, no 5

EN

The relationship between the power consumed in the engine and the power take-off shaft of a maize silage harvester is critical to understanding the efficiency and performance of the harvester. The power consumed in the engine directly affects the power available for use on the P.T.O shaft, which is the power source for the suspended silage harvesters. The research aims to predict the power consumption of the P.T.O shaft based on the power consumption of the tractor engine at different operating parameters, which are two applications of the P.T.O shaft (540 and 540E rpm) and two forward speeds (1.8 and 2.5 km/h) using machine learning algorithms. The best results in terms of engine power consumption were achieved in the 540E P.T.O application, and the forward speed was 1.8 km/h. The results also gave a correlation between the power consumed by the engine and the P.T.O shaft of 87%. Regarding prediction algorithms, the Tree algorithm gave the highest prediction accuracy of 98.8%, while the KNN, SVM, and ANN algorithms gave an accuracy of 98.1, 60, and 60%, respectively.

2

Network Intrusion Detection Using Machine Learning Techniques

Almutairi Yasmeen S., Alhazmi Bader, Munshi Amr

Advances in Science and Technology. Research Journal

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2022

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Vol. 16, no 3

193--206

EN

Intrusion detection systems (IDS) are essential for the protection of advanced communication networks. These systems were primarily designed to identify particular patterns, signatures, and rule violations. Machine Learning and Deep Learning approaches have been used in recent years in the field of network intrusion detection to provide promising alternatives. These approaches can discriminate between normal and anomalous patterns. In this paper, the NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) benchmark data set has been used to evaluate Network Intrusion Detection Systems (NIDS) by using different machine learning algorithms such as Support Vector Machine, J48, Random Forest, and Naïve Bytes with both binary and multi-class classification. The results of the application of those techniques are discussed in details and outperformed previous works.

3

Speciation of Population in Neuroevolutionary Ship Handling

Lacki M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2010

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Vol. 4, no. 2

211--216

EN

This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behavior in ship maneuvering. Simulated helmsman is treated as an individual in population, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current situation and choose one of the available actions. The individual improves his fitness function with reaching destination and decreases its value for hitting an obstacle. Neuroevolutionary approach is used to solve this task. Speciation of population is proposed as a method to secure innovative solutions.

4

Reinforcement Learning in Ship Handling

Łącki M.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

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2008

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Vol. 2, no. 2

157--160

EN

This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behaviour in ship manoeuvring. Simulated model of ship is treated as an agent, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current state and choose one of the available actions. The agent gets positive reward for reaching destination and negative reward for hitting an obstacle. Few reinforcement learning algorithms are considered. Experimental results based on simulation program are presented for different layouts of possible routes within restricted area.