7 results on '"Lin, Zhuoyi"'
Search Results
2. GLIMG: Global and local item graphs for top-N recommender systems
- Author
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Lin, Zhuoyi, Feng, Lei, Yin, Rui, Xu, Chi, and Kwoh, Chee Keong
- Published
- 2021
- Full Text
- View/download PDF
3. Event-Triggered Sliding Mode Impulsive Control for Lower Limb Rehabilitation Exoskeleton Robot Gait Tracking.
- Author
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Liu, Yang, Peng, Shiguo, Zhang, Jiajun, Xie, Kan, Lin, Zhuoyi, and Liao, Wei-Hsin
- Subjects
SLIDING mode control ,ROBOTIC exoskeletons ,GAIT in humans ,ANKLE ,ARTIFICIAL satellite tracking ,LYAPUNOV functions ,MOVEMENT disorders ,FEEDFORWARD neural networks - Abstract
Lower limb rehabilitation exoskeleton robots (LLRERs) play an important role in lower limb rehabilitation training and assistance walking for patients with lower limb movement disorders. In order to reduce and eliminate adverse effects on the accuracy of human motion gait tracking during walking with an LLRER, which is caused by the gravity and friction, the periodic ground shock force, and the human–exoskeleton interaction force, this paper proposes a feedforward–feedback hybrid control strategy of sliding mode impulsive control with gravity and friction compensation, based on the event-triggered mechanism of Lyapunov function. Firstly, to realize high-precision gait tracking with bounded error, some constraints on controller parameters are deduced by analyzing the Lyapunov-based stability. Secondly, the Zeno behavior of impulsive event triggers is excluded by the analysis of three different cases of the triggering time sequence. Finally, the effectiveness of the proposed hybrid controller is verified by the numerical simulation of the LLRER human–exoskeleton integrated system based on a three-link simplified model. It shows that an event-triggered sliding mode impulsive control strategy with gravity and friction compensation can achieve complete gait tracking with bounded error and has excellent dynamic performance under the constraints. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
4. Information Theory-Based Feature Selection: Minimum Distribution Similarity with Removed Redundancy
- Author
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Zhang, Yu, Lin, Zhuoyi, and Kwoh, Chee Keong
- Subjects
Information theory ,Redundancy ,Feature ranking ,Feature selection ,Article - Abstract
Feature selection is an important preprocessing step in pattern recognition. In this paper, we presented a new feature selection approach in two-class classification problems based on information theory, named minimum Distribution Similarity with Removed Redundancy (mDSRR). Different from the previous methods which use mutual information and greedy iteration with a loss function to rank the features, we rank features according to their distribution similarities in two classes measured by relative entropy, and then remove the high redundant features from the sorted feature subsets. Experimental results on datasets in varieties of fields with different classifiers highlight the value of mDSRR on selecting feature subsets, especially so for choosing small size feature subset. mDSRR is also proved to outperform other state-of-the-art methods in most cases. Besides, we observed that the mutual information may not be a good practice to select the initial feature in the methods with subsequent iterations.
- Published
- 2020
5. Embedding-Augmented Generalized Matrix Factorization for Recommendation With Implicit Feedback.
- Author
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Feng, Lei, Wei, Hongxin, Guo, Qingyu, Lin, Zhuoyi, and An, Bo
- Subjects
MATRIX decomposition ,PSYCHOLOGICAL feedback ,FACTORIZATION ,RECOMMENDER systems - Abstract
Learning effective representations of users and items is crucially important to recommendation with implicit feedback. Matrix factorization is the basic idea to derive the representations of users and items by decomposing the given interaction matrix. However, existing matrix factorization based approaches share the limitation in that the interaction between user embedding and item embedding is only weakly enforced by fitting the given individual rating value, which may lose potentially useful information. In this article, we propose a novel augmented generalized matrix factorization approach that is able to incorporate the historical interaction information of users and items for learning effective representations of users and items. Despite the simplicity of our proposed approach, extensive experiments on four public implicit feedback datasets demonstrate that our approach outperforms state-of-the-art counterparts. Furthermore, the ablation study demonstrates that by using the historical interactions to enrich user embedding and item embedding for generalized matrix factorization, better performance, faster convergence, and lower training loss can be achieved. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
6. VirPreNet: a weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all eight segments.
- Author
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Yin, Rui, Luo, Zihan, Zhuang, Pei, Lin, Zhuoyi, and Kwoh, Chee Keong
- Subjects
PANDEMICS ,CONVOLUTIONAL neural networks ,INFLUENZA A virus ,INFLUENZA viruses ,VIRUS virulence ,INFLUENZA - Abstract
Motivation Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics. Results In this article, we propose a weighted ensemble convolutional neural network (CNN) for the virulence prediction of influenza A viruses named VirPreNet that uses all eight segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the eight-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble CNN is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNet's main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza virulence. Availability and implementation Codes and data to generate the VirPreNet are publicly available at https://github.com/Rayin-saber/VirPreNet. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
7. IAV-CNN: A 2D Convolutional Neural Network Model to Predict Antigenic Variants of Influenza A Virus.
- Author
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Yin R, Thwin NN, Zhuang P, Lin Z, and Kwoh CK
- Subjects
- Humans, Proteomics, Antigens, Viral genetics, Hemagglutinin Glycoproteins, Influenza Virus genetics, Neural Networks, Computer, Influenza A virus genetics, Influenza, Human
- Abstract
The rapid evolution of influenza viruses constantly leads to the emergence of novel influenza strains that are capable of escaping from population immunity. The timely determination of antigenic variants is critical to vaccine design. Empirical experimental methods like hemagglutination inhibition (HI) assays are time-consuming and labor-intensive, requiring live viruses. Recently, many computational models have been developed to predict the antigenic variants without considerations of explicitly modeling the interdependencies between the channels of feature maps. Moreover, the influenza sequences consisting of similar distribution of residues will have high degrees of similarity and will affect the prediction outcome. Consequently, it is challenging but vital to determine the importance of different residue sites and enhance the predictive performance of influenza antigenicity. We have proposed a 2D convolutional neural network (CNN) model to infer influenza antigenic variants (IAV-CNN). Specifically, we apply a new distributed representation of amino acids, named ProtVec that can be applied to a variety of downstream proteomic machine learning tasks. After splittings and embeddings of influenza strains, a 2D squeeze-and-excitation CNN architecture is constructed that enables networks to focus on informative residue features by fusing both spatial and channel-wise information with local receptive fields at each layer. Experimental results on three influenza datasets show IAV-CNN achieves state-of-the-art performance combining the new distributed representation with our proposed architecture. It outperforms both traditional machine algorithms with the same feature representations and the majority of existing models in the independent test data. Therefore we believe that our model can be served as a reliable and robust tool for the prediction of antigenic variants.
- Published
- 2022
- Full Text
- View/download PDF
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