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As a routine within the planning and operation of the electrical power system, Short-term load forecasting (STLF) is an essential issue in energy fields. Its prediction accuracy and precision specifically have an effect on the basic safety, stability and economic efficiency of the power system. Moreover, the actual forecasting result also has an impact on operations such as startup and shutdown of...
Deep Convolutional Neural Networks (DCNNs) have been demonstrated as effective models for understanding image content. The computation behind DCNNs highly relies on the capability of hardware resources due to the deep structure. DCNNs have been implemented on different large-scale computing platforms. However, there is a trend that DCNNs have been embedded into light-weight local systems, which requires...
Deep Convolutional Neural Networks (DCNN), a branch of Deep Neural Networks which use the deep graph with multiple processing layers, enables the convolutional model to finely abstract the high-level features behind an image. Large-scale applications using DCNN mainly operate in high-performance server clusters, GPUs or FPGA clusters; it is restricted to extend the applications onto mobile/wearable...
This paper proposes an occluded face detection technology based on the Adaboost algorithm. In this paper, we select moving regions for detection using a background subtraction method. The upper half and lower half parts of human face are detected respectively in moving regions by facial detector which was trained based on Adaboost algorithm and Haar features. Our experimental results indicate the...
Detection based on Hough Transform is a good way frequently employed to detect straight-line targets, but it bears problems as difficulty in selecting an appropriate threshold coefficient and target misjudgment. To solve those technical problems mentioned above, this paper proposes a novel algorithm based on Hough Transform and Mean Shift Multi-Scale Clustering (MSMSC-HT). Firstly, the outline of...
The scientific significance of automatic logo detection and recognition is more and more growing because of the increasing requirements of intelligent document image analysis and retrieval. In this paper, we introduce a system architecture which is aiming at segmentation-free and layout-independent logo detection and recognition. Along with the unique logo feature design, a novel way to ensure the...
The kernel function and parameters selection is a key problem in the research of support vector machine. After discussing the influence of support vector machine on kernel parameters and error penalty factors, a new kernel function CombKer was proposed and constructed. The CombKer kernel function is a kind of combination kernel function, which combines the Gaussian RBF kernel function that has the...
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