A Novice Method for Calibrating the Transient Model of an Automotive HVAC System

A novice method for calibrating the transient model of an automotive HVAC system is presented in this paper. Transient models can be of great importance in the development process of automotive HVAC control algorithms, especially model based ones, as it saves both time and effort. However, the calibration process is usually difficult and relies heavily on experience due to the complexity of the model. A set of customized measurement tools, which consists of several wireless temperature and humidity sensors and an OBD dongle, is used to capture time series data related to the HVAC system during normal driving. Parts of the time series data are then fed into an optimization algorithm to generate a cost function, which can be minimized when the measured data correspond to the simulation data generated by the transient model, while other parts of the data are remained for the validation step. A sensitivity analysis is then performed to find out which parameters in the HVAC transient model need to be optimized to calibrate the model. As the transient model is a physical network model which can be generally considered as a set of differential and algebraic equations, this presented method reduces the calibration process of a complex physical model into solving a common optimization problem. Therefore, various optimization algorithms and tools can be applied. The method is developed and tested during the modeling process of an automotive HVAC system. The efficiency of the modelling process is improved while the calibration results fit better with the measured data.

Characterizing personalized effects of family information on disease risk using graph representation learning

Family history is considered a risk factor for many diseases because it implicitly captures shared genetic, environmental and lifestyle factors. A nationwide electronic health record (EHR) system spanning multiple generations presents new opportunities for studying a connected network of medical histories for entire families. In this work we present a graph-based deep learning approach for learning explainable, supervised representations of how each family member's longitudinal medical history influences a patient's disease risk. We demonstrate that this approach is beneficial for predicting 10-year disease onset for 5 complex disease phenotypes, compared to clinically-inspired and deep learning baselines for a nationwide EHR system comprising 7 million individuals with up to third-degree relatives. Through the use of graph explainability techniques, we illustrate that a graph-based approach enables more personalized modeling of family information and disease risk by identifying important relatives and features for prediction