Impact of human factors for student pilots in approved flight training organizations in Korea

Statistics for aviation accidents in Korea show that the safety level of training flights is high. However, of the accidents that do occur, more than 80% occur due to human factors. Furthermore, because most causes of human factors-related accidents are “pilot error,” it is important for student pilots who will transport passengers to develop knowledge of safety and skills associated with human factors risk management to mitigate the risk of such accidents. To investigate the human factors that affect safety in training student pilots for flight, this study examined the correlation between events that are associated with accidents, differences according to the pilot’s experience level of flight training, and differences between student pilots who received flight training at approved collegiate flight education centers and those who did not. The study was conducted on human factors, focusing on the SHELL model. Using the SPSS software (ver. 17.0), correlation analyses, analyses of variance (ANOVA), and t-tests were conducted to generate statistical results. Briefly, the results of this study found that a student pilot’s natural ability and equipment in the cockpit are the important factors for safety for pilot on training flights. Additionally, the analysis of the differences between human factors according to the characteristics of student pilots’ groups shows that college student pilots are effected by immanent factors and organizational cultures. To date, there have been no accidents with related human casualties when training at collegiate “Approved Training Organizations” (ATOs) in Korea. However, accidents can occur at anytime and anywhere. Especially human factors, which cause most aviation accidents, have a wide reach and are impossible to eliminate. Because ATO is the starting point to lead the aviation industry of Korea, awareness of risks and initiatives to improve education/training of human factors is essential

A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weight bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.Comment: accepted for publication on IEEE Transactions on Wireless Communication