Human Factors Analysis of Air Traffic Safety Based on HFACS-BN Model

traffic control (ATC) performance is important to ensure flight safety and the sustainability of aviation growth. To better evaluate the performance of ATC, this paper introduces the HFACS-BN model (HFACS: Human factors analysis and classification system BN: Bayesian network), which can be combined with the subjective information of relevant experts and the objective data of accident reports to obtain more accurate evaluation results. The human factors of ATC in this paper are derived from screening and analysis of 142 civil and general aviation accidents/incidents related to ATC human factors worldwide from 1980 to 2019, among which the most important 25 HFs are selected to construct the evaluation model. The authors designed and implemented a questionnaire survey based on the HFACS framework and collected valid data from 26 frontline air traffic controllers (ATCO) and experts related to ATC in 2019. Combining the responses with objective data, the noisy MAX model is used to calculate the conditional probability table. The results showed that, among the four levels of human factors, unsafe acts had the greatest influence on ATC Performance (79.4%), while preconditions for safe acts contributed the least (40.3%). The sensitivity analysis indicates the order of major human factors influencing the performance of ATC. Finally, this study contributes to the literature in terms of methodological development and expert empirical analysis, providing data support for human error management intervention of ATC in aviation safety. Document type: Articl

The influence of graphical user interface on motion onset brain-computer interface performance and the effect of data augmentation on motor imagery brain-computer interface

Motor Imagery Brain Computer Interface (MI BCI) is one of the most frequently used BCI modalities, due to the versatility of its applications. However, it still has unresolved issues like time-consuming calibration, low information transfer rate, and inconsistent performance across individuals. Combining MI BCI with Motion Onset Visual Evoked Potential (mVEP) BCI in a hybrid structure may solve some of these problems. Combining MI BCI with more robust mVEP BCI, would increase the degrees of freedom thereby increasing the information transfer rate, and would also indirectly improve intrasubject consistency in performance by replacing some MI-based tasks with mVEP. Unfortunately, due to Covid -19 pandemic experimental research on hybrid BCI was not possible, therefore this thesis focuses on two BCI separately. Chapter 1 provides an overview of different BCIs modalities and the underlying neurophysiological principles, followed by the objectives of the thesis. The research contributions are also highlighted. Finally, the thesis outlines are presented at the end of this chapter. Chapter 2 presents a comprehensive state of the art to the thesis, drawing on a wide range of literature in relevant fields. Specifically, it delves into MI BCI, mVEP BCI, Deep Learning, Transfer Learning (TL), Data Augmentation (DA) and Generative Adversarial Networks (GANs). Chapter 3 investigates the effect of graphical elements, in online and offline experiments. In the offline experiment, graphical elements such as the color, size, position, and layout were explored. Replacing a default red moving bar with a green and blue bar, changing the background color from white to gray, and using smaller visual angles did not lead to statistically significant improvement in accuracy. However, the effect size of η2 (0.085) indicated a moderate effect for these changes of graphical factors. Similarly, no statistically significant difference was found for the two different layouts in online experiments. Overall, the mVEP BCI has achieved a classification accuracy of approximately 80%, and it is relatively impervious to changes in graphical interface parameters. This suggests that mVEP is a promising candidate for a hybrid BCI system combined with MI, that requires dynamic, versatile graphical design features. In Chapter 4, various DA methods are explored, including Segmentation and Recombination in Time Domain, Segmentation and Recombination in Time-Frequency Domain, and Spatial Analogy. These methods are evaluated based on three feature extraction approaches: Common Spatial Patterns, Time Domain Parameters (TDP), and Band Power. The evaluation was conducted using a validated BCI set, namely the BCI Competition IV dataset 2a, as well as a dataset obtained from our research group. The methods are effective when a small dataset of single subject are available. All three DA methods significantly affect the performance of the TDP feature extraction method. Chapter 5 explored the use of GANs for DA in combination with TL and cropped training strategies using ShallowFBCSP classifier. It also used the same validated dataset (BCI competition IV dataset 2a) as in Chapter 4. In contrast to DA method explored in Chapter 4, this DA is suitable for larger datasets and for generalizing training based on other people’s data. Applying GAN-based DA to the dataset resulted on average in a 2% improvement in average accuracy (from 68.2% to 70.7%). This study provides a novel method to enable MI GAN training with only 40 trials per participant with the rest 8 people’s data for TL, addressing the data insufficiency issue for GANs. The evaluation of generated artificial trials revealed the importance of inter-class differences in MI patterns, which can be easily identified by GANs. Overall the thesis addressed the main practical issues of both mVEP and MI BCI paving the way for their successful combination in future experiments

A PML method for signal-propagation problems in axon

This work is focused on the modelling of signal propagations in myelinated axons to characterize the functions of the myelin sheath in the neural structure. Based on reasonable assumptions on the medium properties, we derive a two-dimensional neural-signaling model in cylindrical coordinates from the time-harmonic Maxwell's equations. The well-posedness of model is established upon Dirichlet boundary conditions at the two ends of the neural structure and the radiative condition in the radial direction of the structure. Using the perfectly matched layer (PML) method, we truncate the unbounded background medium and propose an approximate problem on the truncated domain. The well-posedness of the PML problem and the exponential convergence of the approximate solution to the exact solution are established. Numerical experiments based on finite element discretization are presented to demonstrate the theoretical results and the efficiency of our methods to simulate the signal propagation in axons

The Partitioning Technique of Power System to Improve the Observability of Sub-System for Multi-area State Estimation

An accurate state estimation plays an essential role in power system operation and planning in energy management systems. However, existing multi-area state estimation researches have not focused on the importance of system clustering. The clustering mechanism divides or partitions a system according to user-defined criteria. Few published research works have mentioned the importance of considering the electrical properties of a power system while devising their partitioning methods. To the best of our knowledge, these publications have not considered the application of such a concept to multi-area state estimation. This research attempts to model a partitioning technique of the power system whose purpose is to ensure the sub-system observability prior to the multi-area state estimation. Hence, the accuracy of the multi-area state estimation could be improved. A modified genetic algorithm-based phasor measurement unit (PMU) placement is introduced in this thesis, which includes the electrical distance-based additional PMU installation technique. The modified partitioning method is introduced in this thesis based on a genetic algorithm partitioning algorithm. In the modified partitioning method, a proposed genetic index is proposed aiming to include the consideration of system observability, which employs the proposed PMU placement technique to represent the sub-system observability. In addition to the partitioning method, few publications have considered the state estimation by only employing PMU measurements. Furthermore, few publications have considered employing the noise statistics estimation technique to state estimation to improve the convergence of the estimation process. A cubature Kalman filter-based algorithm (CKF) is used in the thesis to solve the state estimation problem where only PMU measurements data are employed. An online noise statistic estimation technique is incorporated into the CKF to improve convergence. A modified two-level MASE is introduced to implement the modified CKF. The modified partitioning method is applied to the multi-area state estimation algorithm. By employing all the techniques introduced in this thesis, a considerable improvement of accuracy and convergence can be achieved

Threshold Selection Based on the SNR in Time of Arrival Localization System

Threshold-based time of arrival (TOA) estimation is a technique for high-precision indoor localization. Existing threshold selection methods, such as xed thresh- old and normalized threshold methods, do not consider the signal-to-noise radio (SNR) value at the receiver. This is not desired for high-precision positioning. A proper threshold value depends on the statistics of the received signal. The dominating parameter that a ects the statistics of the receiver is the SNR. In this thesis, we propose an adaptive threshold selection method which takes the SNR value at the receiver into consideration, resulting in an increased precision of the timing detection ( rst peak detection). The performance of the proposed method is evaluated in simulation. The simulation uses ultra wideband transmitted signal, and adopts the IEEE 802.15.3a channel model. Performance of the normalized threshold method and the proposed selection threshold method are compared