Deep learning

We present a technique to analyze the cause of traffic congestion based on the traffic flow theory. We extract vehicle flows from traffic data, such as GPS trajectory and Vehicle Detector data. We detect vehicle flow changes utilizing the entropy from the information theory. Then, we build cumulative vehicle count curves (N-curve) that can quantify the flow of the vehicles in the traffic congestion area. The N-curves are classified into four different traffic congestion patterns by a convolutional neural network. Analyzing the causes and influence of traffic congestion is difficult and requires considerable experience and knowledge. Therefore, we present a visual analytics system that can efficiently perform a series of processes to analyze the cause and influence of traffic congestion. Through case studies, we have evaluated that our system can classify the causes of traffic congestion and can be used efficiently in road planning.

Paper: Pi M., Yeon H., Son H., Jang Y., "Visual Cause Analytics for Traffic Congestion", To appear in the IEEE Transactions on Visualization and Computer Graphics 

We propose a classification technique for gaze-written numbers as the hands-free interface. Since the gaze-writing is less accurate compared to the virtual keyboard typing, we apply Convolutional Neural Network (CNN) deep learning algorithm to recognize the gaze-writing and improve the classification accuracy. Besides, we create new gaze-writing datasets for training, gaze MNIST (gMNIST), by modifying the MNIST data with features of the gaze movement patterns. For the evaluation, we compare our approach with the basic CNN structures using the original MNIST dataset. Our study will allow us to have more options for the input interfaces and expand our choices in the hands-free environments.

Paper : Yoo, S., Jeong, D. K., & Jang, Y. (2019). The Study of a Classification Technique for Numeric Gaze-Writing Entry in Hands-Free Interface. IEEE Access, 7, 49125-49134. 

Patent: Jang Y., Yoo S., Jeong D. K., "Apparatus for analyzing user input data based on gaze Tracking and method thereof", Korean Patent 10-1987227 , June 3, 2019 

In this work, we apply and compare Deep Neural Network (DNN) and Convolutional Neural Network (CNN) deep learning algorithms for objective cybersickness measurement from EEG data. We also propose a data preprocessing for learning and signal quality weights allowing us to achieve high performance while learning EEG data with the deep learning algorithms. Besides, we analyze video characteristics where cybersickness occurs by examining the 360 video stream segments causing cybersickness in the experiments. Finally, we draw common patterns that cause cybersickness.

Paper: Jeong, D., Yoo, S., & Yun, J. (2019, March). Cybersickness Analysis with EEG Using Deep Learning Algorithms. In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR) (pp. 827-835). IEEE. 

Patent: Jang Y., Jeong D. K., Yoo S., "Method and apparatus for measuring VR sickness", Korean Patent 10-1987225, June 3, 2019