Radar Warning Receiver (RWR) is one of the basic pieces of combat equipment necessary for the electromagnetic situational awareness of aircraft in modern operations and requires good rapid performance and accuracy. This paper proposes a data processing flow for radar warning devices based on a hierarchical processing mechanism to address the issue of existing algorithms’ inability to balance real-time and accuracy. In the front-level information processing module, multi-attribute decision-making under intuitionistic fuzzy information (IFS) is used to process radar signals with certain prior knowledge to achieve rapid performance. In the post-level information processing module, an improved tri-training method is used to ensure accurate recognition of signals with low pre-level recognition accuracy. To improve the performance of tri-training in identifying radar emitters, the original algorithm is combined with the modified Hyperbolic Tangent Weight (MHTW) to address the problem of data imbalance in the radar identification problem. Simultaneously, cross entropy is employed to enhance the sample selection mechanism, allowing the algorithm to converge rapidly.

In real-world scenarios, the identification accuracy of radar pulse data is often hampered by limited recognition features, a large volume of data samples, and unknown signals. To tackle these issues, a Sample Recommendation-based Multidimensional View Collaborative (SR-MDVC) domain adaptation algorithmis proposed. Initially, by integrating transfer learning theory and the hierarchical emission model, unknown and known pulse signals are defined from the perspective of probability density functions （PDFs）. Subsequently，a sample recommendation algorithm based on user collaborative filtering algorithm is introduced. This algorithm effectively reduces the disparity in the PDFs of unknown and known radar pulses. Moreover, a collaborative domain adaptation algorithm for multidimensional perspective model is developed. The model adapts data in the statistical feature dimension and the geometric feature dimension, and is optimized by iterating results across different dimensions. Finally, the domain - adapted data is fed into the least squares support vector machines (LSSVM) to complete the identification of unknown radar pulses. The effectiveness of this method is verified by semi-physical experiments, which demonstrate the excellent recognition performance of the proposed method, with a recognition accuracy of more than 0.9 for unknown and unbalanced data, and a 4-fold improvement in computational efficiency.

Modern cognitive electronic reconnaissance methods for radar systems must contend with the complex electromagnetic environments arising from the deployment of multiple signal sources and radar countermeasures, which greatly limit access to the degree of prior information required to enable effective target recognition. The present work addresses this issue by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals. In contrast to conventional collaborative training approaches, which are suitable only for semisupervised learning, the proposed multiperspective collaborative clustering method performs unsupervised clustering, cluster label transfer, and dimensionality reduction by linear discriminant analysis iteratively based on the differences between the clustering results obtained from two signal perspectives radiation signal sorting can be conducted in a noncooperative context. The results of comparative experiments demonstrate that the proposed multiperspective sorting method can make full use of the difference information between basic signal characteristics and intrapulse features and thereby improve the accuracy of clustering-based radiation source sorting. Accordingly, the sorting ability of the proposed method is superior to those of other state-of-the-art clustering methods and that of the single-perspective clustering-based sorting method.

Unique ethnic minorities traditional patterns and texture in Gansu province and the related cultural relics item is with strong ethnic characteristics of national art, It is the crystallization of minority diligence and wisdom under the regional environment in Gansu. Study how digital media technology and Gansu Minority unique traditional motifs and patterns and other cultural resources of mutual integration, proposed Gansu minority traditional culture unique research strategy of protection and inheritance. １．Introduction Gansu is a multi-ethnic province.The province has 54 ethnic minorities, including the Hui, Tibetan, Dongxiang, Tu, Yugur, Bonan, Mongolian, Sarah ethnic Kazakh, Manchu and other 16 ethnic minorities living in Gansu for a long period of time, the Yugur, Dongxiang, Baoan for the Gansu region unique ethnic minorities, with the characteristics of Gansu regional culture. With the development of cultural heritage digitization process, changing the traditional way of information circulation and sharing.Gansu unique ethnic patterns and texture are hand-drawn form, has not yet combined with digital tools.It is very urgent and important to set up the database of the traditional patterns and texture of the ethnic minorities in Gansu and the digitalization of the cultural resources of the ethnic minorities in Gansu.