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Yinghui Zhang

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3 papers

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TIST Journal 2024 Journal Article

T-Distributed Stochastic Neighbor Embedding for Co-Representation Learning

Wei Chen
Hongjun Wang
Yinghui Zhang
Ping Deng
Zhipeng Luo
Tianrui Li

Co-clustering is the simultaneous clustering of the samples and attributes of a data matrix that provides deeper insight into data than traditional clustering. However, there is a lack of representation learning algorithms that serve this mechanism of co-clustering, and the current representation learning algorithms are limited to the sample perspective and lack the use of information in the attribute perspective. To solve this problem, in this article, ctSNE, a co-representation learning model based on t-distributed stochastic neighbor embedding, is proposed for unsupervised co-clustering, where ctSNE makes the dataset representation outputted more discriminative of row and column clusters (i.e. co-discrimination). On the basis of t-distributed stochastic neighbor embedding retaining the sample data distribution and local data structure, the philosophy of collaboration is introduced (i.e., row and column hidden relationship information) so that the ctSNE model is equipped with co-representation learning capability, which can effectively improve the performance of co-clustering. To prove the effectiveness of the ctSNE model, several classic co-clustering algorithms are used to check the co-representation performance of ctSNE, and a novel internal index based on an internal clustering index, known as total inertia, is proposed to demonstrate the effect of co-clustering. The numerous experimental results show that ctSNE has tremendous co-representation capability and can significantly improve the performance of co-clustering algorithms.

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TIST Journal 2022 Journal Article

Self-supervised Discriminative Representation Learning by Fuzzy Autoencoder

Wenlu Yang
Hongjun Wang
Yinghui Zhang
Zehao Liu
Tianrui Li

Representation learning based on autoencoders has received great concern for its potential ability to capture valuable latent information. Conventional autoencoders pursue minimal reconstruction error, but in most machine learning tasks such as classification and clustering, the discrimination of feature representation is also important. To address this limitation, an enhanced self-supervised discriminative fuzzy autoencoder (FAE) is innovatively proposed, which focuses on exploring information within data to guide the unsupervised training process and enhancing feature discrimination in a self-supervised manner. In FAE, fuzzy membership is applied to provide a means of self-supervised, which allows FAE can not only utilize AE’s outstanding representation learning capabilities but can also transform the original data into another space with improved discrimination. First, the objective function corresponding to FAE is proposed by reconstruction loss and clustering oriented loss simultaneously. Subsequently, Mini-Batch Gradient Descent is applied to infer the objective function and the detailed process is illustrated step by step. Finally, empirical studies on clustering tasks have demonstrated the superiority of FAE over the state of the art.

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EAAI Journal 2022 Journal Article

TSN-based routing and scheduling scheme for Industrial Internet of Things in underground mining

Yinghui Zhang
Jiamin Wu
Mingli Liu
Aiping Tan

Recently, there has been a growing interest in Time-Sensitive Networking (TSN) research in the Industrial Internet of Things(IIoT). The research on real-time and reliable IIoT transmission technology based on TSN has received extensive attention. Especially in some industrial scenarios, such as the safety monitoring of the underground construction environment, the real-time and reliability of data transmission is very important due to the long tunnel and complex structure in underground mining. It is a huge challenge to ensure that the data collected by the sensor has a slight end-to-end delay to the receiving end. A routing scheduling method is proposed based on TSN for IIoT applications in underground mining. Firstly, according to the characteristics of large-scale and distributed hybrid topology in mines and the delay constraints of TSN, the multi-hop routing cooperative scheduling problem S is defined. It is proved that the problem is NP-Hard. Secondly, two algorithms are proposed for problem S: (1) greedy algorithm based on local shortest delay; (2) heuristic algorithm based on an ant colony. Finally, a comparative experimental analysis of the proposed algorithm is carried out, proving that this paper’s algorithm performs better in terms of delay and jitter.

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