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Although many methods are available to forecast short-term electricity load based on small scale data sets, they may not be able to accommodate large data sets as electricity load data becomes bigger and more complex in recent years. In this paper, a novel machine learning model combining convolutional neural network with K-means clustering is proposed for short-term load forecasting with improved...
It is interesting to determine the number of signals impinging upon a large array with small samples. We tackle this problem by using linear shrinkage coefficients of signal and noise subspaces, ending up with two shrinkage coefficient–based detectors (SCDs) for source enumeration. It is proved that the noise shrinkage coefficients are asymptotically Gaussian distributed as the number of antennas...
The reuse-rate of vector register is one of the most important aspects that influence the SIMD performance. However, the reuse of vector register probably can lead ton on-continuous memory access and non-hit of cache. Based on register reuse analysis, we establish a cost analysis that guide the multiple code generation. Then we perform NPB test based on different problem-scale and the result shows...
Crowd density analysis is crucial for crowd monitoring and management. This paper proposes a novel method for crowd density analysis. According to the framework, input images are firstly divided into patches, and each patch is associated with a density label based on its texture features. Finally, local information is synthesized for global density estimation. Local image content is described by features...
Automatic target recognition (ATR) is an important task in image application. A classifier for the airplane recognition based on the merits of rough set theory (RST)and directed acyclic graph support vector machines (DAGSVM) is proposed in this paper. RST can mine useful information from a large number of data and generate decision rules without prior knowledge. DAGSVM have better classification performances...
Crowd estimation is crucial for crowd monitoring and control. It differs from pedestrian detection or people counting in that no individual pedestrian can be properly segmented in the image. This paper describes a novel and efficient system for crowd density estimation, based on local image texture analysis. A novel indication of local binary pattern feature vector called Advanced LBP is proposed...
Local binary pattern (LBP) is a powerful texture descriptor that is gray-scale and rotation invariant. In this paper, an extension of the original LBP is proposed. LBP operator is adopted in multi-layer block domain, instead of pixel domain. Meanwhile, feature dimension is effectively reduced by dual-histogram LBP (DH-LBP). Combining merits of the two, we propose the advanced LBP (ALBP) and use that...
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