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The rapid development of Internet technology has ushered in the era of information overload. How to pick out information with excellent quality and reduce unnecessary browsing time is a problem to be solved urgently. In order to recommend information that users might be interested in, this paper presents a new personalized recommendation algorithm with the quality of service (QoS) constraints based...
Wind speed forecasting is critical to and challenging for wind energy industry. We present a combined AR-kNN regression model for short-term wind speed forecasting. Historical samples are selected to train the coefficients of a k-nearest-neighbor (kNN) regression model in order to capture the current variation pattern of wind speed. The training samples of the kNN model are combined with the recent...
Deep neural networks (DNNs) have been widely applied in speech recognition and enhancement. In this paper we present some experiments using deep rectifier neural networks for speech denoising. Rectified linear units (ReLUs) can make a sparse connection between hidden layers. We analyze the usage of regularization coefficient during training to encourage more sparseness. This method further improves...
Blocking artifact, characterized by visually noticeable changes in pixel values along block boundaries, is a common problem in block-based image/video compression, especially at low bitrate coding. Various post-processing techniques have been proposed to reduce blocking artifacts, but they usually introduce excessive blurring or ringing effects. This paper proposes a self-learning-based image/ video...
Swimmer tracking in swimming pools is a challenging vision task due to its varying complex background. Most moving object detection methods are developed for static or partial static backgrounds, and thus can not be applied in swimmer detection problems. This work presents an approach combining mean-shift clustering and cascaded boosting learning algorithm for swimmer detection. There are three main...
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