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464 results on '"Cheng, Lianglun"'

51. Distributed low‐latency broadcast scheduling for multi‐channel duty‐cycled wireless IoT networks.

Author

Long, Peng, Wu, Yuhang, Chen, Quan, and Cheng, Lianglun

Subjects
INTERNET of things, DISTRIBUTED algorithms, VEHICULAR ad hoc networks, WIRELESS sensor networks, COMMUNICATION patterns, BROADCASTING industry, SCHEDULING, DATA structures, MULTICHANNEL communication
Abstract

Summary: Data broadcast is a fundamental communication pattern in wireless IoT networks, in which the messages are disseminated from a source node to the entire network. The problem of minimum latency broadcast scheduling (MLBS) which is aimed to generate a quick and conflict‐free broadcast schedule has not been extensively explored in duty‐cycled networks. The existing works either work in a centralized scheme or rely on a fixed tree for broadcasting. Additionally, they all employ a strict premise that each node can only utilize one channel for both transmitting and receiving messages. Thus, to address the issues mentioned above, we examine the first distributed broadcasting algorithm in multi‐channel duty‐cycled wireless IoT networks, without relying on a predetermined tree. First, the MLBS problem in such networks is defined and proved to be NP‐hard. Then, in order to avoid transmission conflicts between different links locally, two efficient data structures are designed to help compute the earliest time and channel of receiving messages without conflicts. Based on the above data structures, we introduce an efficient distributed broadcasting algorithm, which can generate a latency‐sensitive broadcast tree while calculating a collision‐free broadcast schedule, simultaneously. Finally, the theoretical analysis and simulations demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Published
2024
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60. Approximate Minimum-Transmission Broadcasting in Duty-Cycled WSNs

Author

Chen, Quan, Le, Tianbai, Cheng, Lianglun, Cai, Zhipeng, Gao, Hong, 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, Chellappan, Sriram, editor, Cheng, Wei, editor, and Li, Wei, editor

Published
2018
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62. Knowledge-integrated Multi-modal Movie Turning Point Identification.

Author

Wang, Depei, Xu, Ruifeng, Cheng, Lianglun, and Wang, Zhuowei

Subjects
REPRESENTATIONS of graphs, SPARSE graphs, ARTIFICIAL intelligence, MULTIMODAL user interfaces, TEXT recognition, VERBS
Abstract

The rapid development of artificial intelligence provides rich technologies and tools for the automated understanding of literary works. As a comprehensive carrier of storylines, movies are natural multimodal data sources that provide sufficient data foundations, and how to fully leverage the benefits of data remains a sustainable research hotspot. In addition, the efficient representation of multi-source data also poses new challenges for information fusion technology. Therefore, we propose a knowledge-enhanced turning points identification (KTPi) method for multimodal scene recognition. First, the BiLSTM method is used to encode scene text and integrate contextual information into scene representations to complete text sequence modeling. Then, the graph structure is used to model all scenes, which strengthens long-range semantic dependencies between scenes and enhances scene representations using graph convolution network. After, the self-supervised method is used to obtain the optimal number of neighboring nodes in sparse graph. Next, actor and verb knowledge involved in the scene text are added to the multimodal data to enhance the diversity of scene feature expressions. Finally, the teacher-student network strategy is used to train the KTPi model. Experimental results show that KTPi outperforms baseline methods in scene role recognition tasks, and ablation experiments show that incorporating knowledge into multimodal model can improve its performance. [ABSTRACT FROM AUTHOR]

Published
2024
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77. OptRS: An Optimized Algorithm Based on CRS Codes in Big Data Storage Systems

Author

Yin, Chao, Wang, Jianzong, Lv, Haitao, Cui, Zongmin, Cheng, Lianglun, Zhan, Qin, Li, Tongfang, 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, Wang, Guojun, editor, Zomaya, Albert, editor, Martinez, Gregorio, editor, and Li, Kenli, editor

Published
2015
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78. DistDL: A Distributed Deep Learning Service Schema with GPU Accelerating

Author

Wang, Jianzong, Cheng, Lianglun, 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, Cheng, Reynold, editor, Cui, Bin, editor, Zhang, Zhenjie, editor, Cai, Ruichu, editor, and Xu, Jia, editor

Published
2015
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82. Lightweight Convolutional Transformers Enhanced Meta Learning for Compound Fault Diagnosis of Industrial Robot

Author

Chen, Chong, Wang, Tao, Liu, Chao, Liu, Yuxin, Cheng, Lianglun, Chen, Chong, Wang, Tao, Liu, Chao, Liu, Yuxin, and Cheng, Lianglun

Abstract

Recent advance of deep learning has seen remarkable progress in compound fault diagnosis modeling for industrial robots. Nevertheless, the data scarcity of compound fault samples jeopardizes the modeling performance of deep learning algorithms. Meta-learning has become an effective tool in few-shot fault diagnosis modeling. However, due to the training instability of meta-learning, it is challenging to deploy advanced networks such as transformers as the base learner due to the extremely large model size. Therefore, this study proposes a lightweight convolutional transformers (LCT) network enhanced meta-learning (Meta-LCT) method to achieve accurate compound fault diagnosis with limited compound fault samples. Specifically, the LCT is first designed by taking the advantage of linear spatial reduction (LSR) attention and spatial pooling mechanism to achieve high computational efficiency. LCT is adopted as the base learner in the Meta-stochastic gradient descent (SGD) algorithm, and then, the meta-training is performed based on the single fault data. Subsequently, the limited compound fault samples are used in the meta testing stage to obtain a compound fault diagnosis model. An experimental study based on the real-world compound fault dataset of industrial robots is presented. The experimental results indicate that the proposed Meta-LCT can achieve the compound fault diagnosis accuracy of 81.1% when only 40 data samples in each compound fault category are available.

Published
2023

83. Learning From Incorrectness: Active Learning With Negative Pre-Training and Curriculum Querying for Histological Tissue Classification

Author

Hu, Wentao, Cheng, Lianglun, Huang, Guoheng, Yuan, Xiaochen, Zhong, Guo, Pun, Chi-Man, Zhou, Jian, and Cai, Muyan

Abstract

Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active learning framework called ICAL, which contains Incorrectness Negative Pre-training (INP) and Category-wise Curriculum Querying (CCQ) to address the above problem from the perspective of category-to-category and from the perspective of categories themselves, respectively. In particular, INP incorporates the unique mechanism of active learning to treat the incorrect prediction results that obtained from CCQ as complementary labels for negative pre-training, in order to better distinguish similar categories during the training process. CCQ adjusts the query weights based on the learning status on each category by the model trained by INP, and utilizes uncertainty to evaluate and compensate for query bias caused by inadequate category performance. Experimental results on two histological tissue classification datasets demonstrate that ICAL achieves performance approaching that of fully supervised learning with less than 16% of the labeled data. In comparison to the state-of-the-art active learning algorithms, ICAL achieved better and more balanced performance in all categories and maintained robustness with extremely low annotation budgets. The source code will be released at https://github.com/LactorHwt/ICAL.

Published
2024
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