Conceptual design of an airborne laser Doppler velocimeter system for studying wind fields associated with severe local storms

An airborne laser Doppler velocimeter was evaluated for diagnostics of the wind field associated with an isolated severe thunderstorm. Two scanning configurations were identified, one a long-range (out to 10-20 km) roughly horizontal plane mode intended to allow probing of the velocity field around the storm at the higher altitudes (4-10 km). The other is a shorter range (out to 1-3 km) mode in which a vertical or horizontal plane is scanned for velocity (and possibly turbulence), and is intended for diagnostics of the lower altitude region below the storm and in the out-flow region. It was concluded that aircraft flight velocities are high enough and severe storm lifetimes are long enough that a single airborne Doppler system, operating at a range of less than about 20 km, can view the storm area from two or more different aspects before the storm characteristics change appreciably

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio