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Research Square 2021年

作者： Xie, Jialiang Zhang, Shanli Wang, Honghui Wu, Dongrui School of Science Jimei University Xiamen361021 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control China

Two goals of multi-objective evolutionary algorithms are effectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey wolf optimization based self-organizing fuzzy multi-objective evolutionary algorithm. Grey wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto Sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on fifteen test problems were conducted to verify its effectiveness. Results show that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. © 2021, CC BY.

关键词： Self organizing maps

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Vision-based Autonomous Landing of Unmanned Aerial Vehicle on a Motional Unmanned Surface Vessel

Vision-based Autonomous Landing of Unmanned Aerial Vehicle o...

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第三十九届中国控制会议

作者： Zhe-Cheng Xu Bin-Bin Hu Bin Liu XD Wang Hai-Tao Zhang School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent ControlHuazhong University of Science and Technology

This paper has developed a vision-based control approach for autonomous tracking and landing on a motional Unmanned surface vessel（USV） with an unmanned aerial vehicle（UAV）. First, a novel three-stage visual detection method is proposed to estimate the relative 3 D position between UAV and USV, where the USV stays in a suitable view of the camera of the UAV. Then a PID controller is designed to regulate the attitude of the quadrotor and the movement of the vessel, which enables the successful landing procedure of the UAV on a motional USV. Finally, lake experiments are conducted to verify the effectiveness of the proposed algorithm.

关键词： Autonomous landing Unmanned aerial vehicle Unmanned surface vessel Computer vision

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Temporal context aggregation network for temporal action proposal refinement

arXiv

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arXiv 2021年

作者： Qing, Zhiwu Su, Haisheng Gan, Weihao Wang, Dongliang Wu, Wei Wang, Xiang Qiao, Yu Yan, Junjie Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China SenseTime Research Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shanghai AI Laboratory Shanghai China

Temporal action proposal generation aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet important task in the video understanding field. The proposals generated by current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval owing to the lack of efficient temporal modeling and effective boundary context utilization. In this paper, we propose Temporal Context Aggregation Network (TCANet) to generate high-quality action proposals through "local and global" temporal context aggregation and complementary as well as progressive boundary refinement. Specifically, we first design a Local-Global Temporal Encoder (LGTE), which adopts the channel grouping strategy to efficiently encode both "local and global" temporal inter-dependencies. Furthermore, both the boundary and internal context of proposals are adopted for frame-level and segment-level boundary regressions, respectively. Temporal Boundary Regressor (TBR) is designed to combine these two regression granularities in an end-to-end fashion, which achieves the precise boundaries and reliable confidence of proposals through progressive refinement. Extensive experiments are conducted on three challenging datasets: HACS, ActivityNet-v1.3, and THUMOS-14, where TCANet can generate proposals with high precision and recall. By combining with the existing action classifier, TCANet can obtain remarkable temporal action detection performance compared with other methods. Not surprisingly, the proposed TCANet won the 1st place in the CVPR 2020 - HACS challenge leaderboard on temporal action localization task. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification

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Nature communications 2025年第1期16卷 5027页

作者： Tao Liu Jiahao Liu Dong Li Shan Tan Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China. State Key Laboratory of Membrane Biology Tsinghua-Peking Center for Life Sciences Beijing Frontier Research Center for Biological Structure IDG/McGovern Institute for Brain Research New Cornerstone Science Laboratory School of Life Sciences Tsinghua University Beijing 100084 China. li-dong@***. Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China. shantan@***.

The objective of optical super-resolution imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination microscopy (SIM) is acknowledged as the optimal modality for live-cell super-resolution imaging. Although recent deep learning techniques have substantially advanced SIM, their transparency and reliability remain uncertain and under-explored, often resulting in unreliable results and biological misinterpretation. Here, we develop Bayesian deep learning (BayesDL) for SIM, which enhances the reconstruction of densely labeled structures while enabling the quantification of super-resolution uncertainty. With the uncertainty, BayesDL-SIM achieves high-fidelity distribution-informed SIM imaging, allowing for the communication of credibility estimates to users regarding the model outcomes. We also demonstrate that BayesDL-SIM boosts SIM reliability by identifying and preventing erroneous generalizations in various model misuse scenarios. Moreover, the BayesDL uncertainty shows versatile utilities for daily super-resolution imaging, such as error estimation, data acquisition evaluation, etc. Furthermore, we demonstrate the effectiveness and superiority of BayesDL-SIM in live-cell imaging, which reliably reveals F-actin dynamics and the reorganization of the cell cytoskeleton. This work lays the foundation for the reliable implementation of deep learning-based SIM methods in practical applications.

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A Pushing-Grasping Collaborative Method Based on Deep Q-Network Algorithm in Dual Viewpoints

Research Square

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Research Square 2021年

作者： Peng, Gang Liao, Jinhu Guan, Shangbin Yang, Jin Li, Xinde School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Ministry of Education China School of Automation South East University Nanjing China

In the field of intelligent manufacturing, robot grasping and sorting is an important content. However, in the traditional 2D camera-based robotic arm grasping method, the grasping efficiency is low and the grasping accuracy is low when facing the scene of stacking and occlusion. Insufficiency and other issues, a dual perspective-based deep reinforcement learning promotion and capture method is proposed. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual viewpoints is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point clouds from two viewpoints, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. © 2021, CC BY.

关键词： Manipulators

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Cell-Free Massive MIMO-OFDM for High-Speed Train Communications

arXiv

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arXiv 2022年

作者： Zheng, Jiakang Zhang, Jiayi Björnson, Emil Li, Zhetao Ai, Bo The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The Hunan International Scientific and Technological Cooperation Base of Intelligent Network Key Laboratory of Intelligent Computing & Information Processing Ministry of Education Xiangtan University Hunan411105 China The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

Cell-free (CF) massive multiple-input multiple-output (MIMO) systems show great potentials in low-mobility scenarios, due to cell boundary disappearance and strong macro diversity. However, the great Doppler frequency offset (DFO) leads to serious inter-carrier interference in orthogonal frequency division multiplexing (OFDM) technology, which makes it difficult to provide high-quality transmissions for both high-speed train (HST) operation control systems and passengers. In this paper, we focus on the performance of CF massive MIMO-OFDM systems with both fully centralized and local minimum mean square error (MMSE) combining in HST communications. Considering the local maximum ratio (MR) combining, the large-scale fading decoding (LSFD) cooperation and the practical effect of DFO on system performance, exact closed-form expressions for uplink spectral efficiency (SE) expressions are derived. We observe that cooperative MMSE combining achieves better SE performance than uncooperative MR combining. In addition, HST communications with small cell and cellular massive MIMO-OFDM systems are compared in terms of SE. Numerical results reveal that the CF massive MIMO-OFDM system achieves a larger and more uniform SE than the other systems. Finally, the train antenna centric (TA-centric) CF massive MIMO-OFDM system is designed for practical implementation in HST communications, and three power control schemes are adopted to optimize the propagation of TAs for reducing the impact of the DFO. Copyright © 2022, The Authors. All rights reserved.

关键词： MIMO systems

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Stability of partially unstable discrete-time switched positive nonlinear systems with delays

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Applied Mathematics and Computation 2025年 507卷

作者： Jinyuan Ni Yin Sheng Qiang Xiao Zhigang Zeng Tingwen Huang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

This paper addresses the stability issue of discrete-time switched positive nonlinear systems (SPNSs) characterized by partial unstable subsystems and constant delays. Firstly, we use a parameter to constrain the ratio of active durations of the stable and unstable modes, and derive the exponential stability conditions for SPNSs based on a key function. Then we analyze the relationship between convergence speed and the constant delay. Additionally, we obtain a stability criterion of SPNSs under a particular sequence of switching signals. Finally, we validate the results through a numerical example.

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A New control System Based on An Improved PID Neural Network controller for Landslide System control

A New Control System Based on An Improved PID Neural Network...

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第三十九届中国控制会议

作者： Ying Zhang Shu Sun Cheng Lian Xiaoping Wang the School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology the School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology the School of Automation Wuhan University of Technology IEEE

In this paper, a new control system based on the improved PID neural network controller（PIDNNC） is designed to control the landslide displacement. In the control system,the fuzzy comprehensive judgement model of landslide system is established to judge whether the system needs to be controlled and give the expected displacement. To solve the problem of random initialization in PIDNNC, This paper proposed an improved PIDNNC based on fuzzy PID controller（F-PIDNNC）.The fuzzy PID controller is used to obtain the proportional,integral and differential coefficients kp, ki and kd. Then the obtained parameters are taken as the initial weights of *** effectiveness of the proposed control system is simulated on Baishuihe and Shiliushubao landslides. The results show that the proposed control system based on F-PIDNNC is much accurate and faster than the traditional one-dimensional fuzzy PID controller and PIDNNC.

关键词： PID neural network Fuzzy PID controller Landslide control Stability evaluation

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Meineng Wang Luping Zhang Yu-An Huang Yangyuan Li Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Mathematics and Computer Science Yichun University Yichun China School of Information Engineering East China University of Technology Nanchang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets.

关键词： Sequential analysis Perturbation methods Clustering methods RNA Noise Clustering algorithms Sparse matrices Gene expression Unsupervised learning Periodic structures

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Fully Circuit Implementation of a two-layer Memristive Neural Network for Pattern Recognition

Fully Circuit Implementation of a two-layer Memristive Neura...

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International Conference on Information Science and Technology (ICIST)

作者： Mian Li Xiaoping Wang Zhanfei Chen School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665412667;9781665429412

In this paper, a fully circuit implementation of Memristive Neural Network (MNN) is proposed. The forward calculation of the network is based on winner-take-all (WTA) mechanism. The weight updating is achieved through the difference of pre-spike and post-spike, which is more close to the biological weight adjustment mechanism. The network is implemented in a full circuit without additional control units. The designed circuit consists of four modules. The memristive crossbar array module with one-memristor (1M) unit structure can effectively calculate the vector-matrix multiplication with only one step. The switch S is replaced by the transistor in the designed leaky-integrate-and-fire (LIF) module, which can control the integration and leakage of the membrane voltage and realize the lateral inhibition between output neurons. Connecting the integrated monostable trigger and the difference circuit, the post-spike generating module can output the required post-spike. The signal switch module realizes the switching of signals connected to memristors by using voltage-controlled switches. The combination of two modules validly realizes weight updating. The functions of four modules are verified separately. We performed a simulation experiment of 5×3 pixels image classification based on the designed circuit in PSPICE. The circuit output results and the high classification accuracy prove the circuit can be effectively applied in pattern recognition. The noise experiment shows the robustness of the designed circuit.

关键词： Training Neurons Switches Robustness Pattern recognition Integrated circuit modeling Task analysis

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