報告摘要：We consider that multi-earth observing satellites (EOSs) follow their orbits to observe a set of targets on the earth to complete the observing tasks. The problem is studied of achieving the maximal task observing ratio with least overlapped target for all EOSs. To describe the targets observing sequence of each EOS, we establish a individual task graph model according to the EOSs' configuration. In addition, based on the proposed task graph model, we design the global and local utility function, respectively. Based on the game theory, it is proved that each EOS's utility and the global utility satisfied the relationship of an exact potential game. To improve the target observing ratio, a distributed task scheduling model is proposed to find each agent's local optimal solution, which converges to the global optimum.
報告題目：Epileptic State Classification by Fusing Hand-crafted and Deep Learning EEG Features
報告摘要：Seizure onset detection and epileptic preictal prediction based on electroencephalogram (EEG) signals have been a challenge problem in the research community. We study a novel epileptic states classification algorithm based on the multichannel EEGs representation using multiple hand-crafted features. The frequency domain features (MAS and MPSD) and timescale features (WPFs) are combined together for multichannel EEG representation. Multiple diverse pre-trained DNNs have been adopted for feature transfer learning on the fused EEG image feature and a new hierarchical neural network has been developed for discriminative feature learning and epileptic state classification.
報告題目：Multi-modal Physiological Signals based Fear of Heights Analysis in Virtual Reality Scenes
報告摘要：Fear of heights (FoH) analysis and its association to physiological signals can better help understand people’s emotion and quantify human’s behaviour, which have been found important in many applications, such as disease analysis, affective computing, etc. Existing studies are mainly on how to alleviate FoH while little literature on FoH analysis has been reported in the past. In this paper, we present the studies of correlation on FoH to multi-modal physiological signals in the virtual and reality (VR) scenes. To stimulate the FoH to participants, 4 types of VR scenarioses that consist of the virtual scene of the VR game “Richie’s plank experience” and the realistic stimulus of hitting by basketball are adopted in the experiment. The synchronized eye movement (EMO), pupil, and electrocardiogram (ECG) of 17 healthy subjects with an even mix of men and women are recorded for FoH analysis. The multi-modal physiological signals based analysis reveals that: 1) the physiological features, including the pupil diameter, the power spectral densities (PSD) of EMO, pupil, ECG, the mean of EMO, etc., have obvious changes in different VR scenarioses (ground/high-altitude scenes), 2) machine learning models learning on multi-modal physiological signals combining with feature optimization can achieve.