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Na Lu - IEEE Xplore Author Profile
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Na Lu

Publications
26
Citations
779
Publications by Year
20132025
Co-Authors:
Show All Co-Authors (42)
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Na Lu

Affiliation

Department of Automation Science and Engineering
Xi’an Jiaotong University
Xi’an, China

Publication Topics

Biography

Na Lu received the BS and PhD degrees from Xi’an Jiaotong University, Xi’an, Shaanxi, China, in 2002 and 2008, respectively. She received postdoc training with the University of Rochester, Rochester, NY, U.S. from 2011 to 2013. Currently, she is a professor with Xi’an Jiaotong University. Her research interests include machine vision, machine learning, brain cognitive science, and robotics.(Based on document published on 26 December 2024).
Publications
26
Citations
779
Publications by Year
20132025
Co-Authors:
Show All Co-Authors (42)
Author's Published Works

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Self-Correcting Clustering

Hanxuan Wang;Na Lu;Zixuan Wang;Yuxuan Yan;Gustavo Carneiro;Zhen Wang

IEEE Transactions on Knowledge and Data Engineering
Year: 2025 | Volume: 37, Issue: 3 | Journal Article |
The incorporation of target distribution significantly enhances the success of deep clustering. However, most of the related deep clustering methods suffer from two drawbacks: (1) manually-designed target distribution functions with uncertain performance and (2) cluster misassignment accumulation. To address these issues, a Self-Correcting Clustering (Self-CC) framework is proposed. In Self-CC, a ...Show More

Self-Correcting Clustering

Hanxuan Wang;Na Lu;Zixuan Wang;Yuxuan Yan;Gustavo Carneiro;Zhen Wang

Year: 2025 | Volume: 37, Issue: 3 | Journal Article |

Knowledge Distillation Based Dueling DQN for Real-Time Power Grid Dispatching Operation and Control

Xiaopeng Wang;Na Lu;Yunpeng Song

2024 3rd Asia Power and Electrical Technology Conference (APET)
Year: 2024 | Conference Paper |
Deep reinforcement learning (DRL) has proven to be effective for real-time Power grid Dispatching and Control (PDC) but requires intensive computing resources and has low efficiency. Most research employs expertise demonstrations to accelerate the training process, but neglects the rules derived from the experience. To boost the DRL efficiency with PDC rules, a Knowledge Distillation based Dueling...Show More

Knowledge Distillation Based Dueling DQN for Real-Time Power Grid Dispatching Operation and Control

Xiaopeng Wang;Na Lu;Yunpeng Song

Year: 2024 | Conference Paper |

System Summarization Based on Multimodal Language Model with Attention-Weighted Fusion

Yikun Li;Na Lu;Hao Guo

2024 China Automation Congress (CAC)
Year: 2024 | Conference Paper |
Power system briefings allow grid dispatchers to quickly understand the operating status of the power system and provide decision support, therefore the generation of power system briefings is of great importance to the stable operation of the power system. Although existing summary generation studies have made some progress, most of them have not focused on the power system domain, especially the...Show More

System Summarization Based on Multimodal Language Model with Attention-Weighted Fusion

Yikun Li;Na Lu;Hao Guo

Year: 2024 | Conference Paper |

Inverse Transfer Network With Frontier Point Restoration for EEG Transfer Classification

Xu Niu;Na Lu;Jianghong Kang

IEEE Transactions on Industrial Informatics
Year: 2024 | Volume: 20, Issue: 12 | Journal Article |
The transformation of data from the source domain into the target domain by minimizing their marginal and conditional distribution disparity is a common strategy in transfer learning. The distributions are measured using first-order, second-order, or higher-order data statistics. However, in brain-computer interface research, the scarcity of target-domain electroencephalograph (EEG) samples poses ...Show More

Inverse Transfer Network With Frontier Point Restoration for EEG Transfer Classification

Xu Niu;Na Lu;Jianghong Kang

Year: 2024 | Volume: 20, Issue: 12 | Journal Article |

Graph Continual Learning Network: An Incremental Intelligent Diagnosis Method of Machines for New Fault Detection

Shuhui Wang;Yaguo Lei;Na Lu;Bin Yang;Xiang Li;Naipeng Li

IEEE Transactions on Automation Science and Engineering
Year: 2024 | Early Access Article |
Streaming data of machines is continuously collected in practical applications, which produces new fault information with respect to the health change. Therefore, a lifelong-learning intelligent diagnosis model is desired for new fault type recognition based on the streaming data. However, existing research in intelligent fault diagnosis always treats new fault type detection and class incremental...Show More

Graph Continual Learning Network: An Incremental Intelligent Diagnosis Method of Machines for New Fault Detection

Shuhui Wang;Yaguo Lei;Na Lu;Bin Yang;Xiang Li;Naipeng Li

Year: 2024 | Early Access Article |

Model and Data Dual-Driven Double-Point Observation Network for Ultra-Short MI EEG Classification

Xu Niu;Na Lu;Ruofan Yan;Huan Luo

IEEE Journal of Biomedical and Health Informatics
Year: 2024 | Volume: 28, Issue: 6 | Journal Article |
Cited by: Papers (1)
Although deep networks have succeeded in various signal classification tasks, the time sequence samples used to train the deep models are usually required to reach a certain length. Especially, in brain computer interface (BCI) research, around 3.5s-long motor imagery (MI) Electroencephalography (EEG) samples are needed to obtain satisfactory classification performance. This time-span requirement ...Show More

Model and Data Dual-Driven Double-Point Observation Network for Ultra-Short MI EEG Classification

Xu Niu;Na Lu;Ruofan Yan;Huan Luo

Year: 2024 | Volume: 28, Issue: 6 | Journal Article |

A Time-Adaptive Method to Transient Stability Assessment Based on Reinforcement Learning

Xuecai Zhou;Na Lu;Xiaopeng Wang;Huan Luo;Ruofan Yan

2023 International Conference on Power System Technology (PowerCon)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The timely evaluation of post-contingency transient stability is crucial for power system control and decision-making. However, a major challenge lies in how to evaluate the stability within the shortest possible time. Most existing methods require a fixed length of time window for training an evaluation model, which may not be suitable for all cases. The fixed time window can delay the identifica...Show More

A Time-Adaptive Method to Transient Stability Assessment Based on Reinforcement Learning

Xuecai Zhou;Na Lu;Xiaopeng Wang;Huan Luo;Ruofan Yan

Year: 2023 | Conference Paper |

Multiple Object Tracking Based on Tracking Compensation for Low-Resolution Scenarios

Zhiyan Cui;Na Lu;Qian Wang;Jingjing Guo;Jiaming Yang

2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)
Year: 2022 | Conference Paper |
Multiple object tracking is one of the critical directions in computer vision research. In the application of vision-based tracking methods, cameras are sometimes installed far from the targets to obtain a global view. There would be a large number of targets in the videos with relatively low resolution, which increases the difficulty of visual tracking. Applying existing tracking methods directly...Show More

Multiple Object Tracking Based on Tracking Compensation for Low-Resolution Scenarios

Zhiyan Cui;Na Lu;Qian Wang;Jingjing Guo;Jiaming Yang

Year: 2022 | Conference Paper |

Optimization Based Distribution Network Fault Location Method Using PMU Information

Xiaopeng Wang;Na Lu;Huan Luo

2022 China International Conference on Electricity Distribution (CICED)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
As an important part of the construction of smart grids, distribution network fault location technology has received widespread attention to improve the level of grid automation. However, the complex structure and poor measurement conditions of distribution network have seriously restricted its application. By applying the synchronous vector information of Phasor Measurement Unit (PMU), this artic...Show More

Optimization Based Distribution Network Fault Location Method Using PMU Information

Xiaopeng Wang;Na Lu;Huan Luo

Year: 2022 | Conference Paper |

Transfer Relation Network for Fault Diagnosis of Rotating Machinery With Small Data

Na Lu;Huiyang Hu;Tao Yin;Yaguo Lei;Shuhui Wang

IEEE Transactions on Cybernetics
Year: 2022 | Volume: 52, Issue: 11 | Journal Article |
Cited by: Papers (61)
Many deep-learning methods have been developed for fault diagnosis. However, due to the difficulty of collecting and labeling machine fault data, the datasets in some practical applications are relatively much smaller than the other big data benchmarks. In addition, the fault data come from different machines. Therefore, on some occasions, fault diagnosis is a multidomain problem with small data, ...Show More

Transfer Relation Network for Fault Diagnosis of Rotating Machinery With Small Data

Na Lu;Huiyang Hu;Tao Yin;Yaguo Lei;Shuhui Wang

Year: 2022 | Volume: 52, Issue: 11 | Journal Article |

Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-Shot Fault Diagnosis of Machines

Saibo Xing;Yaguo Lei;Bin Yang;Na Lu

IEEE Transactions on Industrial Electronics
Year: 2022 | Volume: 69, Issue: 2 | Journal Article |
Cited by: Papers (51)
Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. Therefore, though massive data are accessible, most are data of the normal condition while fault data are still extremely limited. In other words, fault di...Show More

Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-Shot Fault Diagnosis of Machines

Saibo Xing;Yaguo Lei;Bin Yang;Na Lu

Year: 2022 | Volume: 69, Issue: 2 | Journal Article |

Knowledge Graph Model of Power Grid for Human-machine Mutual Understanding

Tao Yin;Na Lu

2020 Chinese Automation Congress (CAC)
Year: 2020 | Conference Paper |
Cited by: Papers (6)
In power grid research, the high complexity of the power generation and distribution requires the power grid system to be robust and resilient. To assure the stability of the power grid, massive human intervention is necessary currently, which is labor intensive and inflexible. However, considering the vital importance of the power grid, it is impossible to leave human being out of the power syste...Show More

Knowledge Graph Model of Power Grid for Human-machine Mutual Understanding

Tao Yin;Na Lu

Year: 2020 | Conference Paper |

Deep Embedded Clustering with Asymmetric Residual Autoencoder

Hanxuan Wang;Na Lu

2020 Chinese Automation Congress (CAC)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
Deep clustering methods have obtained excellent performance on clustering tasks with the benefit of feature representations learned with deep neural networks. Even though promising performance of deep clustering has been shown in different applications, the efficiency of the features achieved is limited by the symmetric structure of the autoencoders employed. Deeper autoencoder will lead to less r...Show More

Deep Embedded Clustering with Asymmetric Residual Autoencoder

Hanxuan Wang;Na Lu

Year: 2020 | Conference Paper |

A pipeline of stereo vision based visual odometer for unmanned aerial vehicle

Na Lu;Tao Yin

2020 Chinese Automation Congress (CAC)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Autonomous navigation is of critical importance for unmanned aerial vehicle (UAV). The navigation system of UAV is usually constructed based on Inertial Measurement Unit (IMU) and vision subsystem. The accumulative error in IMU is an inevitable issue. The application of visual information could assist to correct the localization error and make a more robust navigation system. However, the influenc...Show More

A pipeline of stereo vision based visual odometer for unmanned aerial vehicle

Na Lu;Tao Yin

Year: 2020 | Conference Paper |

Deep Learning Solutions for Motor Imagery Classification: A Comparison Study

Na Lu;Tao Yin;Xue Jing

2020 8th International Winter Conference on Brain-Computer Interface (BCI)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
Motor imagery classification has been widely applied in constructing brain computer interface to control the outside equipment as an alternative neural muscular pathway. EEG as the most popular non-invasive brain signal suffers from low signal to noise ratio and unpredictable pattern variation even for the same subject. To improve the classification accuracy of EEG based motor imageries, many deep...Show More

Deep Learning Solutions for Motor Imagery Classification: A Comparison Study

Na Lu;Tao Yin;Xue Jing

Year: 2020 | Conference Paper |

Common Spatial Patterns Based on the Quantized Minimum Error Entropy Criterion

Badong Chen;Yuanhao Li;Jiyao Dong;Na Lu;Jing Qin

IEEE Transactions on Systems, Man, and Cybernetics: Systems
Year: 2020 | Volume: 50, Issue: 11 | Journal Article |
Cited by: Papers (24)
Common spatial pattern (CSP) is a classic method commonly used in multichannel electroencephalogram (EEG) signal processing, which aims to extract effective features for binary classification by solving spatial filters that maximize the ratio of filtered dispersion between two classes. The aim of this paper is to improve the performance of the conventional CSP method, which will be badly influence...Show More

Common Spatial Patterns Based on the Quantized Minimum Error Entropy Criterion

Badong Chen;Yuanhao Li;Jiyao Dong;Na Lu;Jing Qin

Year: 2020 | Volume: 50, Issue: 11 | Journal Article |

A Temporal Convolution Network Solution for EEG Motor Imagery Classification

Na Lu;Tao Yin;Xue Jing

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)
Year: 2019 | Conference Paper |
Cited by: Papers (3)
EEG motor imagery recognition based brain computer interface has been an import scheme to construct an alternative pathway of the brain to the outside world. EEG signal is usually buried in noise and has very low signal to noise ratio (SNR), which has presented great challenge for efficient motor imagery classification. In addition, the large intra-subject and inter-subject signal variance toward ...Show More

A Temporal Convolution Network Solution for EEG Motor Imagery Classification

Na Lu;Tao Yin;Xue Jing

Year: 2019 | Conference Paper |

Smoke Detection Based on Dark Channel and Convolutional Neural Networks

Xiahao Shi;Na Lu;Zhiyan Cui

2019 5th International Conference on Big Data and Information Analytics (BigDIA)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
Smoke is an important sign of fire and could enable early fire detection. However, it could be hard to discriminate smoke in images because of the irregular shapes and density variation of the smoke. Background interference could also influence the performance of smoke detection methods. Moreover, it is difficult to collect large scale smoke dataset and the dataset used to train the classifier for...Show More

Smoke Detection Based on Dark Channel and Convolutional Neural Networks

Xiahao Shi;Na Lu;Zhiyan Cui

Year: 2019 | Conference Paper |

Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data

Na Lu;Yidan Wu;Li Feng;Jinbo Song

IEEE Journal of Biomedical and Health Informatics
Year: 2019 | Volume: 23, Issue: 1 | Journal Article |
Cited by: Papers (235)
Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. However, it is ha...Show More

Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data

Na Lu;Yidan Wu;Li Feng;Jinbo Song

Year: 2019 | Volume: 23, Issue: 1 | Journal Article |

Visual guided deep learning scheme for fall detection

Na Lu;Xiaodong Ren;Jinbo Song;Yidan Wu

2017 13th IEEE Conference on Automation Science and Engineering (CASE)
Year: 2017 | Conference Paper |
Cited by: Papers (9)
Fall detection is an important problem in the field of public health care, which is especially crucial for instant medical service delivery to the injured elderly due to falls. Ambient camera based fall detection has been a recognized non-intrusive and publicly acceptable method, where video data is employed to discriminate fall event from daily activities. Fall detection with videos usually requi...Show More

Visual guided deep learning scheme for fall detection

Na Lu;Xiaodong Ren;Jinbo Song;Yidan Wu

Year: 2017 | Conference Paper |

Correntropy induced metric based common spatial patterns

Jiyao Dong;Badong Chen;Na Lu;Haixian Wang;Nanning Zheng

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
Year: 2017 | Conference Paper |
Cited by: Papers (7)
Common spatial patterns (CSP) is a widely used method in the field of electroencephalogram (EEG) signal processing. The goal of CSP is to find spatial filters that maximize the ratio between the variances of two classes. The conventional CSP is however sensitive to outliers because it is based on the L2-norm. Inspired by the correntropy induced metric (CIM), we propose in this work a new algorithm...Show More

Correntropy induced metric based common spatial patterns

Jiyao Dong;Badong Chen;Na Lu;Haixian Wang;Nanning Zheng

Year: 2017 | Conference Paper |

Feature extraction by common spatial pattern in frequency domain for motor imagery tasks classification

Jie Wang;Zuren Feng;Na Lu

2017 29th Chinese Control And Decision Conference (CCDC)
Year: 2017 | Conference Paper |
Cited by: Papers (19)
Common spatial pattern (CSP) as a feature extraction algorithm has been successfully applied to classify EEG based motor imagery tasks in brain computer interface (BCI). Successful application of CSP depends on the character of input signals and the first and last m eigenvectors of projection matrix. In this study, we proposed a novel and robust feature extraction method designated frequency domai...Show More

Feature extraction by common spatial pattern in frequency domain for motor imagery tasks classification

Jie Wang;Zuren Feng;Na Lu

Year: 2017 | Conference Paper |

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines

Na Lu;Tengfei Li;Xiaodong Ren;Hongyu Miao

IEEE Transactions on Neural Systems and Rehabilitation Engineering
Year: 2017 | Volume: 25, Issue: 6 | Journal Article |
Cited by: Papers (336)
Motor imagery classification is an important topic in brain–computer interface (BCI) research that enables the recognition of a subject’s intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by electroencephalography (EEG) as nonstationary time series of low signal-to-noise ratio. Although a variety of methods have been previously developed to ...Show More

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines

Na Lu;Tengfei Li;Xiaodong Ren;Hongyu Miao

Year: 2017 | Volume: 25, Issue: 6 | Journal Article |

Clustering Tree-Structured Data on Manifold

Na Lu;Hongyu Miao

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2016 | Volume: 38, Issue: 10 | Journal Article |
Cited by: Papers (7)
Tree-structured data usually contain both topological and geometrical information, and are necessarily considered on manifold instead of euclidean space for appropriate data parameterization and analysis. In this study, we propose a novel tree-structured data parameterization, called Topology-Attribute matrix (T-A matrix), so the data clustering task can be conducted on matrix manifold. We incorpo...Show More

Clustering Tree-Structured Data on Manifold

Na Lu;Hongyu Miao

Year: 2016 | Volume: 38, Issue: 10 | Journal Article |

Clustering of tree-structured data

Na Lu;Yidan Wu

2015 IEEE International Conference on Information and Automation
Year: 2015 | Conference Paper |
Cited by: Papers (2)
Tree-structured data conveys both topological and geometrical information, which is strongly non-Euclidean and thus need be considered on manifold for parameterization and analysis. To address this problem and perform tree-structured data clustering, a novel parameterization method using the Topology-Attribute matrix (T-A matrix) is proposed which could enable tree analysis on matrix manifold. The...Show More

Clustering of tree-structured data

Na Lu;Yidan Wu

Year: 2015 | Conference Paper |

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