To solve the difficulty associated with radar signal classification in the case of few-shot signals, we propose an adaptive focus loss algorithm based on transfer learning. Firstly, we trained a one-dimensional convolutional neural network (CNN) with radar signals of three intra-pulse modulation types in the source domain, which were effortlessly obtained and had sufficient samples. Then, we transferred the knowledge obtained by the convolutional layer to nine types of few-shot complex intra-pulse modulation classification tasks in the target domain. We propose an adaptive focal loss function based on the focal loss function, which can estimate the parameters based on the ratio of hard samples to easy samples in the data set. Compared with other existing algorithms, our proposed algorithm makes good use of transfer learning to transfer the acquired prior knowledge to new domains, allowing the CNN model to converge quickly and achieve good recognition performance in case of insufficient samples. The improvement based on the focal loss function allows the model to focus on the hard samples while estimating the focusing parameter adaptively instead of tediously repeating experiments. The experimental results show that the proposed algorithm had the best recognition rate at different sample sizes with an average recognition rate improvement of 4.8%, and the average recognition rate was better than 90% for different signal-to-noise ratios (SNRs). In addition, upon comparing the training processes of different models, the proposed method could converge with the least number of generations and the shortest time under the same experimental conditions.

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model’s capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%.

Accurately classifying the intra-pulse modulations of radar emitter signals is important for radar systems and can provide necessary information for relevant military command strategy and decision making. As strong additional white Gaussian noise (AWGN) leads to a lower signal-to-noise ratio (SNR) of received signals, which results in a poor classification accuracy on the classification models based on deep neural networks (DNNs), in this paper, we propose an effective denoising method based on a denoising diffusion probabilistic model (DDPM) for increasing the quality of signals. Trained with denoised signals, classification models can classify samples denoised by our method with better accuracy. The experiments based on three DNN classification models using different modal input, with undenoised data, data denoised by the convolutional denoising auto-encoder (CDAE), and our method’s denoised data, are conducted with three different conditions. The extensive experimental results indicate that our proposed method could denoise samples with lower values of the SNR, and that it is more effective for increasing the accuracy of DNN classification models for radar emitter signal intra-pulse modulations, where the average accuracy is increased from around 3 to 22 percentage points based on three different conditions.

In the real world, radar specific emitter identification (SEI) faces complex open set recognition (OSR) scenarios, where traditional closed-set classifiers cannot distinguish between known and unknown categories similarly distributed in the feature space. Furthermore, feature overlap in the open space challenges the design of judgment thresholds. Different from most current approaches, we propose a dynamic boundary adversarial model based on convolutional prototype learning (CPL-DBA). It assigns magnetization weights of various directions and magnitudes to samples during training, depending on the features' distances from the prototype center. As a result, the model clusters samples that are closer to the prototype center while repelling extreme samples farther away. Additionally, we enhance the detection performance of unknown classes by introducing dynamic compact constraint terms. We design trainable closed dynamic boundaries for each known class prototype. This allows for adaptive expansion and contraction of the closed classification boundaries. Consequently, the dynamic boundaries improve the compactness of known features, which can also serve as detection thresholds for unknown classes. Extensive experimental results on the three simulated datasets, one simulator acquisition dataset, and one actual collected dataset indicate that the proposed dynamic framework achieves the best performance compared to other OSR methods.

Foreign secretarial major is not only a new major in adult colleges, but also a popular major. How to do well and make good use of this new major is an urgent problem for adult colleges and universities. Over the years, the author has known more than 30 adult colleges and universities in various regions and more than 20 enterprises, institutions of employment standards, found that the school training students and the current social needs of the talent standards there is a large contrast. Therefore, it is imperative to reform the teaching method and curriculum setting of foreign-related secretarial major.

With the continuous improvement of the degree of opening to the outside world, exchanges between countries are becoming more and more frequent. In order to better promote the extensive communication and cooperation with Japan in cultural and economic fields, it is necessary to fully respect the cultural and historical background of other countries in the process of translation, and make appropriate adjustments and transformations by using the perspective theory. Therefore, this kind of communication will greatly enrich and expand the connotation and extension of point of view transformation, so as to extend it to the practical translation work of various languages, so as to promote the further development of world economy, culture, education, science and technology.