Zhe Zhang

Auxiliary Decision Technology and Application of Power Grid Fault Disposal Based on Knowledge Understanding of Fault Preplan

2020 · 2020 5th International Conference on Power and Renewable Energy (ICPRE)

Combined with the characteristics of power grid fault disposal, auxiliary decision-making technology and implementation architecture of power grid fault disposal that based on knowledge understanding of fault preplan are proposed. Natural language processing technology is …

An improved algorithm of TFIDF combined with Naive Bayes

2022

The TF-IDF algorithm is often used for the extraction of keywords of articles, but it only considers the information of word frequency, which limits the choice of keywords. In order to improve the efficiency of …

Smoothing Advantage Learning

2022 · arXiv (Cornell University)

Advantage learning (AL) aims to improve the robustness of value-based reinforcement learning against estimation errors with action-gap-based regularization. Unfortunately, the method tends to be unstable in the case of function approximation. In this paper, we …

Unified Domain Adaptive Semantic Segmentation

2023 · arXiv (Cornell University)

Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled target domain. The majority of existing UDA-SS works typically consider images whilst recent attempts have extended …

Coincident learning for unsupervised anomaly detection of scientific instruments

2024 · Machine Learning Science and Technology

Abstract Anomaly detection is an important task for complex scientific experiments and other complex systems (e.g. industrial facilities, manufacturing), where failures in a sub-system can lead to lost data, poor performance, or even damage to …

C-DPSS: Channel dual-phase sparsity pruning framework for spiking neural networks

2026 · Neurocomputing

Spiking Neural Networks (SNNs) have emerged as an essential paradigm for brain-inspired computing, achieving superior energy efficiency on neuromorphic hardware. However, as network scale increases, SNNs encounter growing challenges in deployment efficiency. While structured pruning …