A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

Parametric study for influence of input parameters for analysis of fibre reinforced concrete slab-soil interaction

For geotechnical engineering and design of foundation elements of structures is important to properly determine the stress-deformation state of the subsoil. The calculation is most often done using the finite element method and the computational models. This article includes the parametric study for the selected type of concrete foundation structures. The article focuses attention to the calculation of the deformation of the slab with respect to the influences of individual input parameters (e.g. stiffness of concrete and subsoil, boundary condition, size of elements). Calculations are performed for two concrete types and three soil variants

Examining the results of the experimental solution with a focus to elastic effect area

Problematic of soil - structure - interaction is subject of research for many years, however satisfactory accuracy of numerical models has not been achieved yet. Therefore, it is necessary to examine this phenomenon. For this purpose, special testing equipment was built at Faculty of Civil Engineering in Ostrava and series of experimental tests were performed. One of the tested slabs was chosen and its numerical model was created in computational program Ansys 18.0. Numerical model consists of two parts - slab and soil, connected with a contact element. From the test and the model data, maximal deformations and deformations trough the cross section of the slab were compared. This paper examines whether the Hooke’s law is valid and can be used to express the test behavior and locate elastic effect area allowing linear modelling which can be useful for lowering computational time

Benchmark Analysis of Representative Deep Neural Network Architectures

This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity, computational complexity, memory usage, and inference time. The behavior of such performance indices and some combinations of them are analyzed and discussed. To measure the indices we experiment the use of DNNs on two different computer architectures, a workstation equipped with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson TX1 board. This experimentation allows a direct comparison between DNNs running on machines with very different computational capacity. This study is useful for researchers to have a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future; and for practitioners to select the DNN architecture(s) that better fit the resource constraints of practical deployments and applications. To complete this work, all the DNNs, as well as the software used for the analysis, are available online.Comment: Will appear in IEEE Acces

Embedding approach to modeling electromagnetic fields in a complex two-dimensional environment

An approach is presented to combine the response of a two-dimensionally inhomogeneous dielectric object in a homogeneous environment with that of an empty inhomogeneous environment. This allows an efficient computation of the scattering behavior of the dielectric cylinder with the aid of the CGFFT method and a dedicated extrapolation procedure. Since a circular observation contour is adopted, an angular spectral representation can be employed for the embedding. Implementation details are discussed for the case of a closed 434 MHz microwave scanner, and the accuracy and efficiency of all steps in the numerical procedure are investigated. Guidelines are proposed for choosing computational parameters such as truncation limits and tolerances. We show that the embedding approach does not increase the CPU time with respect to the forward problem solution in a homogeneous environment, if only the fields on the observation contour are computed, and that it leads to a relatively small increase when the fields on the mesh are computed as well