Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation decisions and also produce insights into how and why those decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that GDP implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours

Pulse Wave Characteristics Based on Age and Body Mass Index (BMI) During Sitting Posture

peer reviewedMeasurement technologies of arterial parameters are mostly based on processing blood pulse wave which is an important representation of cardiac activity. The pulse wave is structured with forward and reflected waves which are affected by individual physiological parameters such as the blood intensity, the elasticity of the aorta, artery elasticity and the reflection location. The pulse wave is also an important parameter in invasive cuff-less blood pressure measurement methods. However, different physiological circumstances can lead to pulse waveforms with different characteristics including the curve factors, amplitude and time landmarks. In this study, the pulse wave signal is obtained by bio-impedance (BImp) via shoulder and photoplethysmography (PPG) from the left ear. Four age groups, as well as three (body mass index) BMI groups, are considered as physiological circumstances and the effect of them on five characteristics factors of the pulse wave, are compared. Overall, the results displayed a significant effect of the aging and BMI on the pulse wave’s characteristics. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.ARC: FT13010043

Systolic Time Interval Estimation Using Continuous Wave Radar with On-Body Antennas

The estimation of systolic time intervals (STIs) is done using continuous wave (CW) radar at 2.45 GHz with an on-body antenna. Motivation: In the state of the art, typically bioimpedance, heart sounds and/or ultrasound are used to measure STIs. All three methods suffer from insufficient accuracy of STI estimation due to various reasons. CW radar is investigated for its ability to overcome the deficiencies in the state of the art. Methods: Ten healthy male subjects aged 25-45 were asked to lie down at a 30° incline. Recordings of 60 s were taken without breathing and with paced breathing. Heart sounds, electrocardiogram, respiration, and impedance cardiogram were measured simultaneously as reference. The radar antennas were placed at two positions on the chest. The antennas were placed directly on the body as well as with cotton textile in between. The beat to beat STIs have been determined from the reference signals as well as CW radar signals. Results: The results indicate that CW radar can be used to estimate STIs in ambulatory monitoring. Significance: The results pave way to a potentially more compact method of estimating STIs, which can be integrated into a wearable device. © 2017 IEEE