Maintaining individual thermal comfort in indoor spaces shared by multiple occupants is difficult because it requires both intuition about the thermal properties of the room, as well as an understanding of the thermal comfort preferences of each individual. We explore an approach to optimizing individual thermal comfort within a group through temperature set-point optimization of HVAC equipment. We propose a weakly-supervised algorithm to learn the individual thermal comfort preferences and an autoencoding framework to learn static approximations of room thermodynamics. We further propose two approaches to learn a control law that sets the HVAC set-points subject to the preferred user temperatures. The proposed method is tested on a real data-set obtained from workers in an open office. The results show that, on average, the temperature in the room at each user's location can be regulated to within 0.5C of the user's desired temperature.

Rapid advances in information and communications technologies (ICT) have made it possible to deploy large collections of sensors to be used in traditional Supervisory Control and Data Acquisition (SCADA) systems and modern Internet of Things (IoT) installations. These sensors are intended for use in analytical formulas, AI algorithms, and traditional rule- based monitoring that determine optimal operation parameters, maintain smooth operation, or detect operation anomalies. Yet, advances in ICT do not always improve the reliability of data collection. Instead, the frequent use of consumer-grade sensors in IoT deployments, and an increasing array of customer choices often lead to inaccessibility of sensors or systematic absence of sensor readings. The lack of reliability in data collection leads to failures in the algorithms responsible for monitoring the system operation. We term this "the missing input problem", and discuss several state-of-the-art solutions. We specifically focus on the straightforward approach using standard imputation methods, as well as recent deep learning imputation methods. We show that none of the existing algorithms today perform very well in the face of missing sensors, and we outline several research directions that can lead to improvements.

This paper describes a dynamical model-based method for the localization of road vehicles using terrain data from the vehicle's onboard sensors. Road data are encoded using linear dynamical models and then, during travel, the location is identified through continuous comparison of a bank of linear models. The approach presented has several advantages over previous methods described in the literature. First, it creates computationally efficient linear model map representations of the road data. Second, the use of linear models eliminates the need for metrics during the localization process. Third, the localization algorithm is a computationally efficient approach that can have a bounded localization distance in the absence of noise, given certain uniqueness assumptions on the data. Fourth, encoding road data using linear models has the potential to compress the data, while retaining the sensory information. Finally, performing only linear operations on observed noisy data simplifies the creation of noise mitigation algorithms.