Existing learning-based single-image deraining networks mostly focus on the effect of rain streaks in rainy images on visual imaging， while ignoring the effect of fog on visual imaging due to the increase of humidity in the air in rainy environments， thus causing problems such as low generation quality and blurred texture detail information in the derained images. To address these problems， an asymmetric unsupervised end-to-end image deraining network model was proposed. It mainly consists of rain and fog removal network， rain and fog feature extraction network and rain and fog generation network， which form two different data domain mapping conversion modules： Rain-Clean-Rain and Clean-Rain-Clean. The above three sub-networks constituted two parallel transformation paths： the rain removal path and the rain-fog feature extraction path. In the rain-fog feature extraction path， a rain-fog-aware extraction network based on global and local attention mechanisms was proposed to learn rain-fog related features by using the global self-similarity and local discrepancy existing in rain-fog features. In the rain removal path， a rainy image degradation model and the above extracted rain-fog related features were introduced as priori knowledge to enhance the ability of rain-fog image generation， so as to constrain the rain-fog removal network and improve its mapping conversion capability from rain data domain to rain-free data domain. Extensive experiments on different rain image datasets show that compared to state-of-the-art deraining method CycleDerain， the Peak Signal-to-Noise Ratio （PSNR） is improved by 31.55% on the synthetic rain-fog dataset HeavyRain. The proposed model can adapt to different rainy scenarios， has better generalization， and can better recover the details and texture information of images.

With the proliferation of Artificial Intelligence （AI） computing power to the edge of the network and even to terminal devices， the computing power network of end-edge-supercloud collaboration has become the best computing solution. The emerging new opportunities have spawned the deep integration between end-edge-supercloud computing and the network. However， the complete development of the integrated system is unsolved， including adaptability， flexibility， and valuability. Therefore， a computing power network for ubiquitous AI named ACPN was proposed with the assistance of blockchain. In ACPN， the end-edge-supercloud collaboration provides infrastructure for the framework， and the computing power resource pool formed by the infrastructure provides safe and reliable computing power for the users， the network satisfies users’ demands by scheduling resources， and the neural network and execution platform in the framework provide interfaces for AI task execution. At the same time， the blockchain guarantees the reliability of resource transaction and encourage more computing power contributors to join the platform. This framework provides adaptability for users of computing power network， flexibility for resource scheduling of networking computing power， and valuability for computing power providers. A clear description of this new computing power network architecture was given through a case.

Concerning the uneven density distributed lightning location data, a lightning nowcasting model based on Ordering Points To Identify the Clustering Structure (OPTICS) algorithm was proposed. The model analyzed continuous period of lightning location data with OPTICS. It effectively filtered out the sparse points that would affect the lightning clouds distribution. Based on the lightning clusters produced by OPTICS, the model used dilate-corrode algorithm to restore real distribution of lightning clouds. Then future lightning location area was predicted according to the moving trend of lightning clouds. Furthermore, to overcome the traditional algorithm's drawback of consuming longer time, adjacent list and improved seed-list updating strategy were introduced into the OPTICS algorithm. The experimental results show that OPTICS based model is more applicable for lightning nowcasting, and achieves higher accuracy and lower time consumption.

Most of the existing methods to compute text similarity based on Vector Space Model (VSM) use TF-IDF scores as the weights of feature terms in text, which ignores the word sense relationships among feature terms and lead to inaccurate text similarity. To improve the accuracy of text similarities calculated by methods based on VSM, a new term weight computing method by integrating word sense was proposed in this paper. Firstly, word sense similarities among feature terms were computed based on the Chinese WordNet. And then, the TF-IDF weights were revised according to the word sense similarities for the purpose of reflecting both the frequency and the word sense of feature terms in text. The experimental results on the HIT IR-lab Multi-Document Summarization Corpus show that to use the weights calculated by the proposed method can efficiently improve the differentiation among document clusters.