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Your search keyword '"Duan, Lijuan"' showing total 480 results
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480 results on '"Duan, Lijuan"'

52. Epileptic Seizure Prediction Based on Convolutional Recurrent Neural Network with Multi-Timescale

Author

Duan, Lijuan, Hou, Jinze, Qiao, Yuanhua, Miao, Jun, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cui, Zhen, editor, Pan, Jinshan, editor, Zhang, Shanshan, editor, Xiao, Liang, editor, and Yang, Jian, editor

Published
2019
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53. An Automated Method with Attention Network for Cervical Cancer Scanning

Author

Duan, Lijuan, Xu, Fan, Qiao, Yuanhua, Zhao, Di, Xu, Tongtong, Wu, Chunli, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Lin, Zhouchen, editor, Wang, Liang, editor, Yang, Jian, editor, Shi, Guangming, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, Chen, Xilin, editor, and Zhang, Yanning, editor

Published
2019
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61. Stereo Saliency Analysis Based on Disparity Influence and Spatial Dissimilarity

Author

Duan, Lijuan, Liang, Fangfang, Ma, Wei, Qiu, Shuo, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Zeng, Bing, editor, Huang, Qingming, editor, El Saddik, Abdulmotaleb, editor, Li, Hongliang, editor, Jiang, Shuqiang, editor, and Fan, Xiaopeng, editor

Published
2018
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62. Temporal Interval Regression Network for Video Action Detection

Author

Wang, Qing, Qing, Laiyun, Miao, Jun, Duan, Lijuan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Zeng, Bing, editor, Huang, Qingming, editor, El Saddik, Abdulmotaleb, editor, Li, Hongliang, editor, Jiang, Shuqiang, editor, and Fan, Xiaopeng, editor

Published
2018
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63. A Touching Character Database from Tibetan Historical Documents to Evaluate the Segmentation Algorithm

Author

Zhao, Quanchao, Ma, Long-long, Duan, Lijuan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Lai, Jian-Huang, editor, Liu, Cheng-Lin, editor, Chen, Xilin, editor, Zhou, Jie, editor, Tan, Tieniu, editor, Zheng, Nanning, editor, and Zha, Hongbin, editor

Published
2018
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64. A Novel Seizure Prediction Method Based on Generative Features

Author

Liu, Lili, Duan, Lijuan, Xiao, Ying, Qiao, Yuanhua, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Peng, Yuxin, editor, Yu, Kai, editor, Lu, Jiwen, editor, and Jiang, Xingpeng, editor

Published
2018
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65. A Novel EEG Signal Recognition Method Using Modified Optimal Electrodes Recombination Strategy

Author

Duan, Lijuan, Cui, Song, Liu, Lili, Qiao, Yuanhua, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Peng, Yuxin, editor, Yu, Kai, editor, Lu, Jiwen, editor, and Jiang, Xingpeng, editor

Published
2018
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69. Link Prediction Based on Data Augmentation and Metric Learning Knowledge Graph Embedding.

Author

Duan, Lijuan, Han, Shengwen, Jiang, Wei, He, Meng, and Qiao, Yuanhua

Subjects
KNOWLEDGE graphs, DATA augmentation, GIBBS sampling, COMPLETE graphs, FORECASTING, SEMANTICS
Abstract

A knowledge graph is a repository that represents a vast amount of information in the form of triplets. In the training process of completing the knowledge graph, the knowledge graph only contains positive examples, which makes reliable link prediction difficult, especially in the setting of complex relations. At the same time, current techniques that rely on distance models encapsulate entities within Euclidean space, limiting their ability to depict nuanced relationships and failing to capture their semantic importance. This research offers a unique strategy based on Gibbs sampling and connection embedding to improve the model's competency in handling link prediction within complex relationships. Gibbs sampling is initially used to obtain high-quality negative samples. Following that, the triplet entities are mapped onto a hyperplane defined by the connection. This procedure produces complicated relationship embeddings loaded with semantic information. Through metric learning, this process produces complex relationship embeddings imbued with semantic meaning. Finally, the method's effectiveness is demonstrated on three link prediction benchmark datasets FB15k-237, WN11RR and FB15k. [ABSTRACT FROM AUTHOR]

Published
2024
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74. Text Extraction for Historical Tibetan Document Images Based on Connected Component Analysis and Corner Point Detection

Author

Zhang, Xiqun, Duan, Lijuan, Ma, Longlong, Wu, Jian, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Yang, Jinfeng, editor, Hu, Qinghua, editor, Cheng, Ming-Ming, editor, Wang, Liang, editor, Liu, Qingshan, editor, Bai, Xiang, editor, and Meng, Deyu, editor

Published
2017
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75. A Text-Line Segmentation Method for Historical Tibetan Documents Based on Baseline Detection

Author

Li, Yanxing, Ma, Longlong, Duan, Lijuan, Wu, Jian, Barbosa, Simone Diniz Junqueira, Series editor, Chen, Phoebe, Series editor, Filipe, Joaquim, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Yuan, Junsong, Series editor, Zhou, Lizhu, Series editor, Yang, Jinfeng, editor, Hu, Qinghua, editor, Cheng, Ming-Ming, editor, Wang, Liang, editor, Liu, Qingshan, editor, Bai, Xiang, editor, and Meng, Deyu, editor

Published
2017
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83. Almost periodic solutions of memristive multidirectional associative memory neural networks with mixed time delays.

Author

Zhang, Yan, Qiao, Yuanhua, and Duan, Lijuan

Subjects
BIDIRECTIONAL associative memories (Computer science), BIOLOGICAL neural networks, EXPONENTIAL stability, LYAPUNOV functions
Abstract

Traditional biological neural networks cannot simulate the real situation of the abrupt synaptic connections between neurons while modeling associative memory of human brains. In this paper, the memristive multidirectional associative memory neural networks (MAMNNs) with mixed time-varying delays are investigated in the sense of Filippov solution. First, three steps are given to prove the existence of the almost periodic solution. Two new lemmas are proposed to prove the boundness of the solution and the asymptotical almost periodicity of the solution by constructing Lyapunov function. Second, the uniqueness and global exponential stability of the almost periodic solution of memristive MAMNNs are investigated by a new Lyapunov function. The sufficient conditions guaranteeing the properties of almost periodic solution are derived based on the relevant definitions, Halanay inequality and Lyapunov function. The investigation is an extension of the research on the periodic solution and almost periodic solution of bidirectional associative memory neural networks. Finally, numerical examples with simulations are presented to show the validity of the main results. [ABSTRACT FROM AUTHOR]

Published
2024
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84. Diagnosis of Esophageal Squamous Cell Carcinoma by High‐Performance Serum Metabolic Fingerprints: A Retrospective Study.

Author

Huang, Yida, Yang, Haijun, Li, Junkuo, Wang, Fuqiang, Liu, Wanshan, Liu, Yiwen, Wang, Ruimin, Duan, Lijuan, Wu, Jiao, Gao, Zhaowei, Cao, Jing, Bian, Fang, Zhang, Juxiang, Zhao, Fang, Yang, Shouzhi, Cao, Shasha, Yang, Aihua, Wang, Xueliang, Geng, Mingfei, and Hao, Anlin

Subjects
METABOLOMIC fingerprinting, SQUAMOUS cell carcinoma, DESORPTION ionization mass spectrometry, DIAGNOSIS, MEDICAL screening, MACHINE learning
Abstract

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric‐particle‐enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non‐ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area‐under‐the‐curves (AUCs) of 0.925–0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2–79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3–22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806–0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC. [ABSTRACT FROM AUTHOR]

Published
2024
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95. Modelling and Simulation Analysis of Power Flow Router in Energy Internet

Author

Hou Lei, Su Kun, Qing Cheng, Xia Tian, Yang Chao, and Duan Lijuan

Subjects
Environmental sciences, GE1-350
Abstract

With a large number of distributed generation (DG) and controllable load connected to the power grid under the background of energy Internet development, the security and stability of the grid are challenged. Power flow control has become a problem of the operation of the power grid. Power flow router (PFR) has become an important element of energy Internet. It is an effective way to realize the power flow control of active distribution network when abundant distributed resources are connected to the grid. In this paper, the steady-state and dynamic operation characteristics of PFR are studied, and the corresponding mathematical model is established. The effect of PFR on optimizing the power flow and improving the dynamic characteristics of distribution network is analysed by the 14-node distribution network case simulating.

Published
2021
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96. Dual-Teacher Feature Distillation: A Transfer Learning Method for Insomniac PSG Staging

Author

Duan, Lijuan, Zhang, Yan, Huang, Zhaoyang, Ma, Bian, Wang, Wenjian, and Qiao, Yuanhua

Abstract

Insomnia is the most common sleep disorder linked with adverse long-term medical and psychiatric outcomes. Automatic sleep staging plays a crucial role in aiding doctors to diagnose insomnia disorder. Only a few studies have been conducted to develop automatic sleep staging methods for insomniacs, and most of them have utilized transfer learning methods, which involve pre-training models on healthy individuals and then fine-tuning them on insomniacs. Unfortunately, significant differences in feature distribution between the two subject groups impede the transfer performance, highlighting the need to effectively integrate the features of healthy subjects and insomniacs. In this paper, we propose a dual-teacher cross-domain knowledge transfer method based on the feature-based knowledge distillation to improve the performance of sleep staging for insomniacs. Specifically, the insomnia teacher directly learns from insomniacs and feeds the corresponding domain-specific features into the student network, while the health domain teacher guide the student network to learn domain-generic features. During the training process, we adopt the OFD (Overhaul of Feature Distillation) method to build the health domain teacher. We conducted the experiments to validate the proposed method, using the Sleep-EDF database as the source domain and the CAP-Database as the target domain. The results demonstrate that our method surpasses advanced techniques, achieving an average sleep staging accuracy of 80.56% on the CAP-Database. Furthermore, our method exhibits promising performance on the private dataset.

Published
2024
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