Automatic optimization of individual thermal comfort in indoor spaces shared by multiple occupants is difficult, because it requires understanding of the individual thermal comf…
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 and…
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 an…
This paper addresses a multi-label predictive fault classification problem for multidimensional time-series data. While fault (event) detection problems have been thoroughly stu…
This study evaluates the efficacy of machine learning (ML) methods to predict the compressive strength of field-placed concrete. We employ both field- and laboratory-obtained da…
Personal thermal comfort is the feeling that individuals have about how hot, cold or comfortable they are. Studies have shown that thermal comfort is a key component of human pe…
Exact pattern matching is a method of localizing arbitrarily sized patterns in time series data. To date, the problem of exact pattern matching has only been fully addressed for…
Anomalies in discrete manufacturing processes (DMPs) can result in reduced product quality, production delays, and physical danger to employees. It is difficult to detect anomal…
This paper addresses the problem of using unlabeled data in transfer learning. Specifically, we focus on transfer learning for a new unlabeled dataset using partially labeled tr…
Thermal comfort in office buildings is emerging as an important variable that can be used to maximize employee productivity. In this paper we propose a new Internet of Things (I…
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 line…
Improvements in sensor technology and data processing rates are leading to the collection of vast databases of time series data. These data sets are spawning new applications th…
This paper presents a chance constrained approach to extracting linear models from reference data to be used in subsequence identification or pattern matching. Due to the ordere…
Using terrain data and dynamical models is a promising approach to map-based passenger vehicle localization. In this approach, dynamical models are extracted from terrain data c…
This paper describes a novel method for the location of road vehicles using vehicle pitch data obtained from on-board sensors. The method encodes the road map data using linear …