Author name cluster

Yongjun Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

EAAI Journal 2026 Journal Article

Knowledge graph-based operation and maintenance risk analysis and early warning approach for railway traction power supply systems

Shi Qiu
Xiaojian Li
Yongjun Chen
Weidong Wang
Jin Wang
Runan Cheng
Qasim Zaheer

The railway traction power supply system (RTPSS) is a critical component in the operation of electrified railways. However, as the network expands and maintenance cycles lengthen, it faces increasing operational risk. To enhance the accuracy of risk management and the timeliness of decision-making, this paper presents a risk analysis framework for the operation and maintenance (O&M) of RTPSS by utilizing knowledge graph technology. Initially, natural language processing (NLP) techniques are employed to handle massive fault data, constructing a systematic model to comprehensively represent the global modeling of multi-risk coupling mechanisms and cross-system cascade failures. Subsequently, a method for evaluating the early warning levels of risk events is proposed, which integrates multidimensional data. This method systematically assesses early warning levels by considering risk probability, risk loss data, and network topology data. Finally, the study outlines the process of mapping the early warning levels of RTPSS O&M risk onto knowledge graphs by dynamically integrating physical data with graph-based approaches. This approach enables maintenance personnel to quickly identify and comprehend the operational status of the RTPSS. Case study results demonstrate that the proposed method significantly enhances systematization, comprehensiveness, and observability, providing a more accurate and holistic tool for managing RTPSS O&M risk.

Details DOI

IJCAI Conference 2019 Conference Paper

Dense Transformer Networks for Brain Electron Microscopy Image Segmentation

Jun Li
Yongjun Chen
Lei Cai
Ian Davidson
Shuiwang Ji

The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods.

PDF Details

AAAI Conference 2019 Conference Paper

Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods

Hao Yuan
Yongjun Chen
Xia Hu
Shuiwang Ji

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose an approach to investigate the meaning of hidden neurons of the convolutional neural network (CNN) models. We first employ saliency map and optimization techniques to approximate the detected information of hidden neurons from input sentences. Then we develop regularization terms and explore words in vocabulary to interpret such detected information. Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden spatial locations. Additionally, we show that our approach can describe the decision procedure of deep NLP models.

PDF Details