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3,542 results on '"Liu, Limin"'

1. Improved Memory Truncation Scheme for Quasi-Adiabatic Propagator Path Integral via Influence Functional Renormalization

Author

Liu, Limin, Ren, Jiajun, and Fang, Weihai

Subjects
Physics - Chemical Physics
Abstract

Accurately simulating non-Markovian quantum dynamics in system-bath coupled problems remains challenging. In this work, we present a novel memory truncation scheme for the iterative Quasi-Adiabatic Propagator Path Integral (iQuAPI) method to improve accuracy. Conventional memory truncation in iQuAPI discards all influence functional beyond a certain time interval, which is not effective for problems with a long memory time. Our proposed scheme selectively retains the most significant parts of the influence functional using the density matrix renormalization group algorithm. We validate the effectiveness of our scheme through simulations of the spin-boson model across various parameter sets, demonstrating faster convergence and improved accuracy compared to the conventional scheme. Our findings suggest that the new memory truncation scheme significantly advances the capabilities of iQuAPI for problems with a long memory time., Comment: 10 pages, 4 figures

Published
2024

6. Coaching doctors to improve ethical decision-making in adult hospitalized patients potentially receiving excessive treatment. The CODE stepped-wedge cluster randomized controlled trial

Author

Benoit, Dominique D., De Pauw, Aglaja, Jacobs, Celine, Moors, Ine, Offner, Fritz, Velghe, Anja, Van Den Noortgate, Nele, Depuydt, Pieter, Druwé, Patrick, Hemelsoet, Dimitri, Meurs, Alfred, Malotaux, Jiska, Van Biesen, Wim, Verbeke, Francis, Derom, Eric, Stevens, Dieter, De Pauw, Michel, Tromp, Fiona, Van Vlierberghe, Hans, Callebout, Eduard, Goethals, Katrijn, Lievrouw, An, Liu, Limin, Manesse, Frank, Vanheule, Stijn, and Piers, Ruth

Published
2024
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19. Publisher Correction: Coaching doctors to improve ethical decision-making in adult hospitalized patients potentially receiving excessive treatment. The CODE stepped-wedge cluster randomized controlled trial

Author

Benoit, Dominique D., De Pauw, Aglaja, Jacobs, Celine, Moors, Ine, Offner, Fritz, Velghe, Anja, Van Den Noortgate, Nele, Depuydt, Pieter, Druwé, Patrick, Hemelsoet, Dimitri, Meurs, Alfred, Malotaux, Jiska, Van Biesen, Wim, Verbeke, Francis, Derom, Eric, Stevens, Dieter, De Pauw, Michel, Tromp, Fiona, Van Vlierberghe, Hans, Callebout, Eduard, Goethals, Katrijn, Lievrouw, An, Liu, Limin, Manesse, Frank, Vanheule, Stijn, and Piers, Ruth

Published
2025
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22. Mask Reference Image Quality Assessment

Author

Xiao, Pengxiang, He, Shuai, Liu, Limin, and Ming, Anlong

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Understanding semantic information is an essential step in knowing what is being learned in both full-reference (FR) and no-reference (NR) image quality assessment (IQA) methods. However, especially for many severely distorted images, even if there is an undistorted image as a reference (FR-IQA), it is difficult to perceive the lost semantic and texture information of distorted images directly. In this paper, we propose a Mask Reference IQA (MR-IQA) method that masks specific patches of a distorted image and supplements missing patches with the reference image patches. In this way, our model only needs to input the reconstructed image for quality assessment. First, we design a mask generator to select the best candidate patches from reference images and supplement the lost semantic information in distorted images, thus providing more reference for quality assessment; in addition, the different masked patches imply different data augmentations, which favors model training and reduces overfitting. Second, we provide a Mask Reference Network (MRNet): the dedicated modules can prevent disturbances due to masked patches and help eliminate the patch discontinuity in the reconstructed image. Our method achieves state-of-the-art performances on the benchmark KADID-10k, LIVE and CSIQ datasets and has better generalization performance across datasets. The code and results are available in the supplementary material., Comment: 10 pages, 6 figures

Published
2023

27. An active jamming recognition algorithm based on ER-C-L network model

Author

ZHAO Zhongchen, LIU Limin, XIE Hui, HAN Zhuangzhi, JING He

Subjects
active jamming recognition, convolutional neural networks, compound feature, Military Science
Abstract

To solve the problem of low recognition accuracy of radar active jamming in strong noise environment,an algorithm for ER-C-L(Extended ResNet-CNN-LSTM) network model based on one-dimensional composite features is proposed. Firstly, the amplitude, instantaneous frequency, instantaneous envelope of power spectrum and their composite features are taken as network input to compare their recognition accuracy in ResNet-CNN model. The composite features of amplitude and instantaneous envelope of power spectrum with high detection probability and small data volume are selected as the optimal features. Then, the complex features are injammed into the ER-C-L network to identify six new active jamming models. Simulation experiments show that the recognition accuracy of jamming is 98.5% in strong noise environment within the JNR of -10 dB. Compared with other deep learning algorithms such as CNN, ResNet-CNN, extended ResNet-CNN and LSTM, it has higher interference recognition accuracy.

Published
2024
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28. Clustering-Induced Generative Incomplete Image-Text Clustering (CIGIT-C)

Author

Guo, Dongjin, Su, Xiaoming, Wang, Jiatai, Liu, Limin, Pei, Zhiyong, and Xu, Zhiwei

Subjects
Computer Science - Artificial Intelligence
Abstract

The target of image-text clustering (ITC) is to find correct clusters by integrating complementary and consistent information of multi-modalities for these heterogeneous samples. However, the majority of current studies analyse ITC on the ideal premise that the samples in every modality are complete. This presumption, however, is not always valid in real-world situations. The missing data issue degenerates the image-text feature learning performance and will finally affect the generalization abilities in ITC tasks. Although a series of methods have been proposed to address this incomplete image text clustering issue (IITC), the following problems still exist: 1) most existing methods hardly consider the distinct gap between heterogeneous feature domains. 2) For missing data, the representations generated by existing methods are rarely guaranteed to suit clustering tasks. 3) Existing methods do not tap into the latent connections both inter and intra modalities. In this paper, we propose a Clustering-Induced Generative Incomplete Image-Text Clustering(CIGIT-C) network to address the challenges above. More specifically, we first use modality-specific encoders to map original features to more distinctive subspaces. The latent connections between intra and inter-modalities are thoroughly explored by using the adversarial generating network to produce one modality conditional on the other modality. Finally, we update the corresponding modalityspecific encoders using two KL divergence losses. Experiment results on public image-text datasets demonstrated that the suggested method outperforms and is more effective in the IITC job., Comment: 13 pages,12 figures

Published
2022

29. Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation

Author

Wang, Jiatai, Xu, Zhiwei, Yang, Xuewen, Guo, Dongjin, and Liu, Limin

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: 1) It's difficult to learn latent representations that account for complementarity yet consistency without using label information; 2) and thus fails to take full advantage of the hidden information in incomplete data results in suboptimal clustering performance when complete data is scarce. In this paper, we propose Contrastive Incomplete Multi-View Image Clustering with Generative Adversarial Networks (CIMIC-GAN), which uses GAN to fill in incomplete data and uses double contrastive learning to learn consistency on complete and incomplete data. More specifically, considering diversity and complementary information among multiple modalities, we incorporate autoencoding representation of complete and incomplete data into double contrastive learning to achieve learning consistency. Integrating GANs into the autoencoding process can not only take full advantage of new features of incomplete data, but also better generalize the model in the presence of high data missing rates. Experiments conducted on \textcolor{black}{four} extensively-used datasets show that CIMIC-GAN outperforms state-of-the-art incomplete multi-View clustering methods.

Published
2022
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30. Design of Computer Network Security Defense System Based on Big Data

Author

Liu, Limin, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Swaroop, Abhishek, editor, Kansal, Vineet, editor, Fortino, Giancarlo, editor, and Hassanien, Aboul Ella, editor

Published
2024
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31. JWTKey: Automatic Cryptographic Vulnerability Detection in JWT Applications

Author

Xu, Bowen, Jia, Shijie, Lin, Jingqiang, Zheng, Fangyu, Ma, Yuan, Liu, Limin, Gu, Xiaozhuo, Song, Li, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tsudik, Gene, editor, Conti, Mauro, editor, Liang, Kaitai, editor, and Smaragdakis, Georgios, editor

Published
2024
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40. Review of Research on Rolling Bearing Health Intelligent Monitoring and Fault Diagnosis Mechanism

Author

WANG Jing, XU Zhiwei, LIU Wenjing, WANG Yongsheng, LIU Limin

Subjects
health monitoring, fault diagnosis, data distribution, signal analysis, data-driven, Electronic computers. Computer science, QA75.5-76.95
Abstract

As one of the most critical and failure-prone parts of the mechanical systems of industrial equipment, bearings are subjected to high loads for long periods of time. When they fail or wear irreversibly, they may cause accidents or even huge economic losses. Therefore, effective health monitoring and fault diagnosis are of great significance to ensure safe and stable operation of industrial equipment. In order to further promote the development of bearing health monitoring and fault diagnosis technology, the current existing models and methods are analyzed and summarized, and the existing technologies are divided and compared. Starting from the distribution of vibration signal data used, firstly, the relevant methods under uniform data distribution are sorted out, the classification, analysis and summary of the current research status are carried out mainly according to signal-based analysis and data-driven-based, and the shortcomings and defects of the fault detection methods in this case are outlined. Secondly, considering the problem of uneven data acquisition under actual working conditions, the detection methods for dealing with such cases are summarized, and different processing techniques for this problem in existing research are classified into data processing methods, feature extraction methods, and model improvement methods according to their different focuses, and the existing problems are analyzed and summarized. Finally, the challenges and future development directions of bearing fault detection in existing industrial equipment are summarized and prospected.

Published
2024
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42. A review of research on optical true time delay technology

Author

An Ting, Liu Limin, Han Chunhui, Zhu Sai, and Jiang Yunfeng

Subjects
microwave photonics, true time delay (ttd), optical true delay technology (ottd), optically controlled beamforming network, light controlled phased array, Applied optics. Photonics, TA1501-1820, Optics. Light, QC350-467
Abstract

Light controlled phased array has the advantages of fast response speed, compact system, diverse functions, and flexible control, and has been widely applied in many scientific and technological fields. Optical true delay technology (OTTD) is the most direct technical means to achieve phase delay of optical carrier signals, and it is also the most basic technical means to implement optical controlled beamforming systems. In order to fully understand the optical true delay technology, this article first elaborates on the principle of phased array antennas and the reasons for beam squint, and analyzes the impact of true delay on the performance of phased array radar. Then, the basic principle, technological progress, and related applications of optical true delay are introduced. Taking four common structures of optical true delay lines as examples, which are micro-ring resonant cavity array, grating true time delay line, multi-path switchable optical true time delay line (OTTDL), and wavelength selective optical delay line, their performance in delay accuracy, adjustable delay range, and frequency bandwidth are compared. Finally, the current problems and future development trends of optical controlled beamforming technology were summarized.

Published
2025
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