This paper aims to study the power control method for fairness energy efficiency (EE) improvement in cognitive radio networks (CRN) with interference channels, among one primary user (PU) shares the spectrum to multiple secondary users (SUs). The objective is to control the transmit power to maximize the minimum EE among all users subject to the quality of service (QoS) constraints. An extremely challenging non-convex max-min fraction optimization issue given due consideration. This work aims at developing an adaptive solving method based on deep learning (DL) techniques for the max-min EE optimization problem. To achieve such an objective, we construct a deep neural network (DNN), with the channel state information (CSI) being the input of DNN and the transmit power being the output of DNN. However, this faces two challenges. On the one hand, it is difficult to obtain label data. On the other hand, when DNNs are applied, it is very important to consider that QoS constraints should be met. These difficulties are circumvented in our work by designing an unsupervised learning strategy, in which a loss function is devised by combining the max-min EE objective and the QoS constraints via the barrier function method. The effectiveness of our proposed algorithm is ultimately demonstrated by the simulation results.
KEYWORDS: Data conversion, Transformers, Data modeling, Neural networks, Agriculture, Computer programming, Internet, Visual process modeling, Feature extraction, System identification
With the rapid development of precision agriculture and smart agriculture, the need to build an automatic identification and detection system for diseases and insect pests is increasing. Using computers to correctly label plant diseases and insect pests is an important prerequisite for achieving accurate classification of plant diseases and insect pests and ensuring system performance. In order to improve the accuracy of computer classification of plant pests and diseases, this paper proposes an automatic pest identification method based on the Vision Transformer (ViT). In order to avoid training overfitting, the plant diseases and insect pests data sets are enhanced by methods such as Histogram Equalization, Laplacian, Gamma Transformation, CLAHE, Retinex-SSR, and Retinex-MSR. Then use the enhanced data set to train the constructed ViT neural network, so as to realize the automatic classification of plant diseases and insect pests. The simulation results show that the constructed ViT network has a test recognition accuracy rate of 96.71% on the plant disease and insect pest public data set Plant_Village, which is about 1.00% higher than the Plant disease and pest identification method based on traditional convolutional neural networks such as GoogleNet and EfficentNetV2.
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