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Ping Deng

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5 papers

2 author rows

IROS Conference 2025 Conference Paper

Numerical Optimization-based Kinematics with Pose Tracking Control for Continuum Robots

Rui Peng
Ping Deng
Duo Tang
Peng Lu

In this paper, we employ multiple IMUs into a triple-section continuum manipulator to precisely capture the attitude data of each section’s end disk. Leveraging the sensory and mechanical hardware system, we construct a sophisticated coordinate transformation scheme to accurately identify the detailed configuration states of the manipulator. Additionally, we introduce a numerical optimization strategy to develop a unified forward and inverse kinematic modeling framework, ensuring both iterative efficiency and accuracy. Through the IMUs’ real-time attitude feedback, we implement a closed-loop controller, enhancing the manipulator’s operational robustness and agility. In our experimental evaluations, we assess the convergence performance of both forward and inverse kinematics within a simulated environment and validate the precision of these kinematic models through real-time experiments on an actual continuum manipulator. Moreover, we evaluate the performance of the proposed controller by examining its accuracy during the manipulator’s continuous motions and analyzing its response characteristics. In contrast to previous research on continuum robots in the literature, we pioneer a fully integrated kinematic control architecture that is successfully implemented on a physical continuum robotic system.

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EAAI Journal 2025 Journal Article

Symmetric non-negative matrix factorization-based deep representation algorithm for multi-view clustering

Ping Deng
Xinying Zhou
Ji Xu
Wei Huang
Jie Wang
Dexian Wang
Tianrui Li

Symmetric Non-negative Matrix Factorization (SNMF) shows significant advantages in clustering task due to its unique mathematical properties. However, it still has several key limitations: (1) the single optimization scheme of traditional multiplicative update rule limits the flexibility of the algorithm; (2) linear factorization leads to insufficient representation ability for complex nonlinear features; (3) lack of learning rate guidance mechanism. These factors together constrain the algorithm representation learning ability in complex data. To address these issues, this paper proposes a SNMF-based Deep Representation algorithm for Multi-view Clustering (SNDRMvC). First, the matrix elements are decoupled, and the stochastic gradient descent as well as nonlinear activation function are used to implement non-negative matrix update. Then, based on the corresponding gradients of the elements and nonlinear function, the neural network learning mechanism is introduced into the SNMF update rule to construct a novel framework SNMF-based deep representation network for optimizing SNMF. This network aims to update the elements in the low-dimensional matrix of each view and fuse the low-dimensional matrices of multiple views to derive a consensus matrix. Finally, extensive experiments conducted on several public datasets demonstrate that the proposed algorithm exhibits notable advantages in clustering performance. We provide the code at: https: //github. com/Code706/SNDRMvC.

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TIST Journal 2024 Journal Article

DNSRF: Deep Network-based Semi-NMF Representation Framework

Dexian Wang
Tianrui Li
Ping Deng
Zhipeng Luo
Pengfei Zhang
Keyu Liu
Wei Huang

Representation learning is an important topic in machine learning, pattern recognition, and data mining research. Among many representation learning approaches, semi-nonnegative matrix factorization (SNMF) is a frequently-used one. However, a typical problem of SNMF is that usually there is no learning rate guidance during the optimization process, which often leads to a poor representation ability. To overcome this limitation, we propose a very general representation learning framework (DNSRF) that is based on a deep neural net. Essentially, the parameters of the deep net used to construct the DNSRF algorithms are obtained by matrix element update. In combination with different activation functions, DNSRF can be implemented in various ways. In our experiments, we tested nine instances of our DNSRF framework on six benchmark datasets. In comparison with other state-of-the-art methods, the results demonstrate the superior performance of our framework, which is thus shown to have a great representation ability.

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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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TCS Journal 2007 Journal Article

Non-unique probe selection and group testing

Feng Wang
Hongwei David Du
Xiaohua Jia
Ping Deng
Weili Wu
David MacCallum

A minimization problem that has arisen from the study of non-unique probe selection with group testing technique is as follows: Given a binary matrix, find a d -disjunct submatrix with the minimum number of rows and the same number of columns. We show that when every probe hybridizes to at most two viruses, i. e. , every row contains at most two 1s, this minimization is still MAX SNP-complete, but has a polynomial-time approximation with performance ratio 1 + 2 / ( d + 1 ). This approximation is constructed based on an interesting result that the above minimization is polynomial-time solvable when every probe hybridizes to exactly two viruses.

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