Study of two-dimensional underwater particle velocity pickup sensor
Addressing the subject of a suspended co-oscillating vector hydrophone application platform, a new sound wave receiving theory model of underwater particle velocity pickup sensor was established. Based on the study of the inner sound field of an elastic cylinder vibrating freely under the action of sound waves in theory, the influence of material and geometry parameters on the frequency response of particle velocity has been analyzed. According to the results of parameter optimization and taking into account engineering needs, one sample of two-dimensional particle velocity pickup sensor has been designed and tested. By comparing the results of sensitivity and directivity tested in a calibration device, the general rules of theoretical analysis have been verified. The theory and experiments confirmed the feasibility of particle velocity pickup sensors in engineering applications.
We focus on the problem of learning semantics from multimedia data associated with broadcast video documents. In this paper we propose to learn semantic concepts from multimodal sources based on style and context detectors, in combination with statistical classier ensembles. As a case study we present our method for detecting the concept of news subject monologues. This approach had the best average precision performance amongst 26 submissions in the 2003 video track of the Text Retrieval Conference benchmark. Experiments were conducted with respect to individual detector contribution, ensemble size, and ranking mechanism. It was found that the combination of detectors is decisive for the nal result, although some detectors might appear useless in isolation. Moreover, by using a probabilistic ranking, in combination with a large classier ensemble, results can be improved even further.