The realization of short-term load forecasting is the basis of system planning and decision-making, and it is an important index to evaluate the safety and economy of power grid.In order to accurately predict the power load under the influence of many factors, a new short-term power load prediction method based on fuzzy support vector machine and similar daily linear extrapolation is proposed, which combinesthe method of fuzzy support vector machine and linear extrapolation of similar days. The method first selects similar days according to the effect of integrated weather and time on load. Then the fuzzy membership of the training sample is obtained by the normalization processing, and the daily maximum and minimum load is predicted by the fuzzy support vector machine. Finally, the load prediction value is obtained by combining the load trend curve obtained by the similar daily linear extrapolation method. and this method is feasible and effective for short-term forecasting of power load.
A deep learning speech enhancement algorithm based on dynamic hybrid feature and adaptive mask and DSP implementation is proposed in this paper, which solves the problem of feature loss and improves the performance of speech enhancement. The dynamic features incorporate the log Mel power spectrum, Mel cepstral coefficients, and Multiresolution Auditory Cepstral Coefficients (MRACC) and capture the speech transient information by deriving the derivatives to comprehensively represent the nonlinear structure of speech and reduce distortion. To make the system improve the speech quality while reducing the speech distortion as much as possible, a soft mask that can be adaptively adjusted considering the signal-to-noise ratio information is proposed, which can be automatically adjusted according to the different speech signal-to-noise ratio information to obtain the mask value under the corresponding signal-to-noise ratio conditions, and phase difference information that can improve the speech intelligibility is incorporated in it. Then, an improved deep neural network model is designed to effectively improve the speech enhancement performance. Finally, the hardware and algorithm software design of the DSP-based speech enhancement system is given. Experimental simulations are carried out for multiple voices in different noise backgrounds. The experimental results indicate that the performance indexes of the proposed method are significantly improved compared with the existing speech enhancement methods, which verifies the feasibility and superiority of the proposed method.
As science and technology continue to develop, Chinese character image recognition technology is being used in a wide range of fields. This computer-based technology is a practical way of automatically recognizing images of text. Typically used in Chinese character education, it provides a new form of human–computer interaction for students. In addition, multimedia technology can provide a rich learning environment for students, which can present information about Chinese characters in the form of pictures, sounds, and videos, thus compensating for the disadvantages of learning Chinese characters by rote in the traditional educational process. The combination of Chinese character image recognition technology and multimedia technology can not only enrich the process of learning Chinese characters, but also promote students’ motivation to learn, thus providing a new and more modern approach to Chinese character education. Based on the study of Chinese character image recognition technology, this research combines it with multimedia information, to achieve the image recognition of Chinese character and multimedia information representation. The combined technology can provide significant references for course design and Chinese learners.
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