KEYWORDS: Matrices, Education and training, Field programmable gate arrays, Digital signal processing, Detection and tracking algorithms, Data modeling, Mathematical optimization, LCDs, Clocks, Power consumption
Intelligent applications such as digital recognition have problems in the process of training data and model inference, such as idle resources, low efficiency, high power consumption, etc. This paper mainly solves the problem by optimizing the LSTM model. The solution of the optimization model mainly includes the improvement of the matrix vector multiplication of the LSTM model and the improvement of the pruning algorithm. The improvement of the matrix vector multiplication of the LSTM model can reduce the calculation amount of the operation unit, Improve the recognition speed of the digital recognition system. The improvement of the pruning algorithm can reduce the resource consumption of the model parameters in storage, and can recognize more pictures and improve the recognition rate. The results of LSTM network implementation through training show that the speed of the system is about 250 times that of digital recognition using only the microcontroller core, and about 7.5 times that of general CPU.
For organ ultrasound examination, it is very important to accurately obtain the standard section of classified organ ultrasound images. In this paper, a method based on ResNet-cbam is proposed to identify the classified ultrasonic standard plane. By collecting thyroid ultrasound images, which are divided into TPTI transverse section isthm us, TPRT transverse section right thyroid lobe, TPLT transverse section left thyroid lobe and lateral thyroid lobe longitudinal section. After image denoising and enhancement preprocessing, several models are first used for experiments, which show that ResNet-cbam has the best classification and recognition effect. By constantly adjusting the ResNet-cbam model structure, the number of iterations comparative experiments on changing the learning rate and activation function show that the best experimental effect is when Resnet18-cbam and the learning rate lr is 0.001 and the activation function is relu() function. Finally, the accuracy of classification and recognition is 89%, which proves that ResNet-cbam can recognize the standard section of thyroid well.
Similarity calculation of short text is an important part of text classification. In view of the problem that statistical learning methods can only extract shallow features of short texts, but cannot express their deep semantics through the extracted features, a new method based on two-way mirror hole connection is proposed. A short text semantic similarity calculation model combining long short-term memory network (BiP-LSTM) and convolutional neural network (CNN). The word vector is pre-trained with the word vector attention mechanism, and the distance factor is introduced to solve the influence of the distance between words on the word sense association. When combining sentence-level features, an attention mechanism is added to make it contain more representative features. The experimental results show that, based on the Rotten_imdb and Quora Question Pairs datasets, the proposed short text similarity calculation method has an accuracy of 84.37% and an F1 value of 83.67%, which is better than other benchmark methods.
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