Research on an intelligent fault diagnosis method for nuclear power plants based on ETCN-SSA combined algorithm

Utilizing fault diagnosis methods is crucial for nuclear power professionals to achieve efficient and accurate fault diagnosis for nuclear power plants (NPPs). The performance of traditional methods is limited by their dependence on complex feature extraction and skilled expert knowledge, which can be time-consuming and subjective. This paper proposes a novel intelligent fault diagnosis method for NPPs that combines enhanced temporal convolutional network (ETCN) with sparrow search algorithm (SSA). ETCN utilizes temporal convolutional network (TCN), self-attention (SA) mechanism and residual block for enhancing performance. ETCN excels at extracting local features and capturing time series information, while SSA adaptively optimizes its hyperparameters for superior performance. The proposed method's performance is experimentally verified on a CPR1000 simulation dataset. Compared to other advanced intelligent fault diagnosis methods, the proposed one demonstrates superior performance across all evaluation metrics. This makes it a promising tool for NPP intelligent fault diagnosis, ultimately enhancing operational reliability.

Non-autoregressive Generative Models for Reranking Recommendation

In a multi-stage recommendation system, reranking plays a crucial role by modeling the intra-list correlations among items.The key challenge of reranking lies in the exploration of optimal sequences within the combinatorial space of permutations. Recent research proposes a generator-evaluator learning paradigm, where the generator generates multiple feasible sequences and the evaluator picks out the best sequence based on the estimated listwise score. Generator is of vital importance, and generative models are well-suited for the generator function. Current generative models employ an autoregressive strategy for sequence generation. However, deploying autoregressive models in real-time industrial systems is challenging. Hence, we propose a Non-AutoRegressive generative model for reranking Recommendation (NAR4Rec) designed to enhance efficiency and effectiveness. To address challenges related to sparse training samples and dynamic candidates impacting model convergence, we introduce a matching model. Considering the diverse nature of user feedback, we propose a sequence-level unlikelihood training objective to distinguish feasible from unfeasible sequences. Additionally, to overcome the lack of dependency modeling in non-autoregressive models regarding target items, we introduce contrastive decoding to capture correlations among these items. Extensive offline experiments on publicly available datasets validate the superior performance of our proposed approach compared to the existing state-of-the-art reranking methods. Furthermore, our method has been fully deployed in a popular video app Kuaishou with over 300 million daily active users, significantly enhancing online recommendation quality, and demonstrating the effectiveness and efficiency of our approach.