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Yang Liu, Nam-kyun Im, Qiang Zhang and Guibing Zhu
This paper investigates the automatic berthing problem of underactuated surface vessels in the case of uncertain dynamics and yaw rate limitation, given the importance of yaw rate control and the unmeasurable hydrodynamic parameters of the vessel at low ...
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Dokkyun Yi, Jaehyun Ahn and Sangmin Ji
A machine is taught by finding the minimum value of the cost function which is induced by learning data. Unfortunately, as the amount of learning increases, the non-liner activation function in the artificial neural network (ANN), the complexity of the a...
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Bingbing Qiu, Guofeng Wang, Yunsheng Fan, Dongdong Mu and Xiaojie Sun
In the presence of modeling uncertainties and input saturation, this paper proposes a practical adaptive sliding mode control scheme for an underactuated unmanned surface vehicle (USV) using neural network, auxiliary dynamic system, sliding mode control ...
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Chimango Nyasulu, Awa Diattara, Assitan Traore, Abdoulaye Deme and Cheikh Ba
To ensure continued food security and economic development in Africa, it is very important to address and adapt to climate change. Excessive dependence on rainfed agricultural production makes Africa more vulnerable to climate change effects. Weather inf...
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Yang Zheng, Jieyu Zhao, Yu Chen, Chen Tang and Shushi Yu
With the widespread success of deep learning in the two-dimensional field, how to apply deep learning methods from two-dimensional to three-dimensional field has become a current research hotspot. Among them, the polygon mesh structure in the three-dimen...
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Hosang Han and Jangwon Suh
The accurate prediction of soil contamination in abandoned mining areas is necessary to address their environmental risks. This study employed a combined model of machine learning and geostatistics to predict the spatial distribution of soil contaminatio...
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Andrea D?Ambrosio and Roberto Furfaro
This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal control pr...
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José Luis Olazagoitia, Jesus Angel Perez and Francisco Badea
Accurate modeling of tire characteristics is one of the most challenging tasks. Many mathematical models can be used to fit measured data. Identification of the parameters of these models usually relies on least squares optimization techniques. Different...
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