Automated analysis of internal quantum efficiency using chain order regression

Spectral analysis of internal quantum efficiency (IQE) measurements of solar cells is a powerful method to identify performance-limiting mechanisms in photovoltaic devices. This analysis is usually performed using complex curve-fitting methods to extract various electrical and optical performance parameters. As these traditional fitting methods are not easy to use and are often sensitive to measurement noise, many users do not utilize the full potential of the IQE measurements to provide the key properties of their solar cells. In this study, we propose a simplified approach to analyze IQE curves of silicon solar cells using machine learning models that are trained to extract valuable information regarding the cell's performance and decoupling the parasitic absorption of the anti-reflection coating. The proposed approach is demonstrated to be a powerful characterization tool for solar cells as machine learning unlocks the full potential of IQE measurements

A BDI Agent Architecture for Dialogue Modelling and Coordination in a Smart Personal Assistant

In this paper, we discuss the architectural aspects of a Smart Personal Assistant (SPA) system that enables users to access a range of applications from a range of devices using multi-modal natural language dialogue. Each back-end application is a personal assistant specializing in one specific task such as e-mail or calendar management, and typically each has its own user model, enabling it to adapt to the user’s changing preferences. The PDA interface to the SPA must present the system as a single unified set of backend applications, enabling the user to conduct a dialogue in which it is easy to switch between these applications. Furthermore, the system’s interaction with the user must be tailored to their current device. The SPA is implemented using an agent platform and includes a special BDI coordinator agent with plans both for coordinating the actions of the individual assistants and for encoding the system’s dialogue model. The plan-based dialogue model is at a high level of abstraction, enabling the domain-independent plans in the dialogue model to be reused in different SPA systems. 1

Exploiting Concept Clumping for Learning in Adaptive Personal Assistants

Accurate learning of user preferences is important for personal assistants and poses significant challenges. Document stream and pattern mining techniques can discover up-to-date patterns and dependencies invaluable for timely user decisions, but they have to be fast and accurate, work with limited computational resources and be sufficiently robust in the presence of both concept drift and noise. This dissertation introduces several novel incremental algorithms that address the above requirements and exploit the phenomenon of local coherences in the data, called concept clumping. The main application areas addressed in this thesis are: (i) incremental categorization of e-mail in the presence of local changes in the data, (ii) construction of accurate calendar appointment suggestions in the presence of changes in user preferences and (iii) incremental categorization of news articles into multiple categories. We discovered that, while concept drift is an obstacle when addressing these problems, the associated phenomenon of clumping can be utilized by learners. We explore clumping in the data, define a number of clumping types and associated metrics demonstrating the existence of clumping in the data streams examined. We then define a number of domain specific learning algorithms and show that these methods have comparable performance to the best batch learning methods while requiring fewer computational resources