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第33届中国控制与决策会议

作者： Bocheng Chen Gang Peng Dingxin He Cheng Zhou Bin Hu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Shantui Construction Machinery Co. Ltd.

On the one hand,traditional visual SLAM does not consider dynamic objects in the scene,on the other hand,deep learning technology has been widely used in computer *** paper combines the two organically,and proposes an algorithm that uses dynamic object detection to improve the robustness of visual SLAM in a dynamic ***,we use the object detection network integrated into the attention mechanism to detect the dynamic target in the key ***,we follow the optical flow detection to further determine the dynamic feature points in the scene and eliminate ***,we use the static feature points for camera tracking to achieve highly robust monocular visual *** method described in this paper can not only eliminate dynamic feature points,but also retain as many static feature points as *** method described in this paper is compared with the original ORB-SLAM2 algorithm and DS-SLAM algorithm,and tested with public data *** results show that the method described in this paper can effectively eliminate the influence of dynamic objects on the visual SLAM algorithm.

关键词： Visual SLAM dynamic object detection deep learning optical flow detection attention mechanism

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Reduced-order interval observer based consensus for MASs with time-varying interval uncertainties

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AUTOMATICA 2022年 135卷 109989-109989页

作者： Wang, Xiaoling Wang, Xiaofan Su, Housheng Lam, James Nanjing Univ Posts & Telecommun Coll Automat & AI Nanjing 210023 Peoples R China Jiangsu Engn Lab IOT Intelligent Robots IOTRobot Nanjing 210023 Peoples R China Shanghai Univ Dept Automat Shanghai 200072 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Image Proc & Intelligent Control Key Lab Educ Minist China Luoyu Rd 1037 Wuhan 430074 Peoples R China Univ Hong Kong Dept Mech Engn Pokfulam Rd Hong Kong Peoples R China

In this paper, the consensus problem of a continuous-time uncertain multi-agent system (MAS) with time-varying interval uncertainties in the system matrix and unknown initial states is investigated via output feedback. Relating only to the interval bounds on the uncertainties, a reduced-order framer, which includes two Luenberger-like observers, is first derived. Then, a distributed control algorithm relying only on the information of the framers associated with each agent and its neighbors is proposed. It is shown that the control algorithm can render the reduced-order framer to be an exponential reduced-order interval observer, and simultaneously drive the uncertain MAS to reach robust consensus exponentially. Finally, numerical simulations for a large-scale MAS are used to verify the theoretical results. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Consensus Interval observer Multi-agent system Reduced-order observer Uncertain system

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Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix

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BRIEFINGS IN BIOINFORMATICS 2022年第3期23卷 bbac113-bbac113页

作者： Jiang, Hanjing Huang, Yabing Li, Qianpeng Huazhong Univ Sci & Technol Inst Artificial Intelligence Key Lab Image Informat Proc & Intelligent Control Sch Artificial Intelligence & AutomatEduc Minist Wuhan 430074 Peoples R China Wuhan Univ Dept Pathol Renmin Hosp Wuhan 430060 Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

Limitations of bulk sequencing techniques on cell heterogeneity and diversity analysis have been pushed with the development of single-cell RNA-sequencing (scRNA-seq). To detect clusters of cells is a key step in the analysis of scRNA-seq. However, the high-dimensionality of scRNA-seq data and the imbalances in the number of different subcellular types are ubiquitous in real scRNA-seq data sets, which poses a huge challenge to the single-cell-type *** propose a meta-learning-based model, SiaClust, which is the combination of Siamese Convolutional Neural Network (CNN) and improved spectral clustering, to achieve scRNA-seq cell type detection. To be specific, with the help of the constrained Sigmoid kernel, the raw high-dimensionality data is mapped to a low-dimensional space, and the Siamese CNN learns the differences between the cell types in the low-dimensional feature space. The similarity matrix learned by Siamese CNN is used in combination with improved spectral clustering and t-distribution Stochastic Neighbor Embedding (t-SNE) for visualization. SiaClust highlights the differences between cell types by comparing the similarity of the samples, whereas blurring the differences within the cell types is better in processing high-dimensional and imbalanced data. SiaClust significantly improves clustering accuracy by using data generated by nine different species and tissues through different scNA-seq protocols for extensive evaluation, as well as analogies to state-of-the-art single-cell clustering models. More importantly, SiaClust accurately locates the exact site of dropout gene, and is more flexible with data size and cell type.

关键词： scRNA-seq Sigmoid kernel Siamese CNN improved spectral clustering

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Exploiting learnable joint groups for hand pose estimation

arXiv

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arXiv 2020年

作者： Li, Moran Gao, Yuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent AI Lab United States

In this paper, we propose to estimate 3D hand pose by recovering the 3D coordinates of joints in a group-wise manner, where less-related joints are automatically categorized into different groups and exhibit different features. This is different from the previous methods where all the joints are considered holistically and share the same feature. The benefits of our method are illustrated by the principle of multi-task learning (MTL), i.e., by separating less-related joints into different groups (as different tasks), our method learns different features for each of them, therefore efficiently avoids the negative transfer (among less related tasks/groups of joints). The key of our method is a novel binary selector that automatically selects related joints into the same group. We implement such a selector with binary values stochastically sampled from a Concrete distribution, which is constructed using Gumbel softmax on trainable parameters. This enables us to preserve the differentiable property of the whole network. We further exploit features from those less-related groups by carrying out an additional feature fusing scheme among them, to learn more discriminative features. This is realized by implementing multiple 1x1 convolutions on the concatenated features, where each joint group contains a unique 1x1 convolution for feature fusion. The detailed ablation analysis and the extensive experiments on several benchmark datasets demonstrate the promising performance of the proposed method over the state-of-the-art (SOTA) methods. Besides, our method achieves top-1 among all the methods that do not exploit the dense 3D shape labels on the most recently released FreiHAND competition at the submission date. The source code and models are available at https://***/moranli-aca/LearnableGroups-Hand. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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A Multi-Level Thresholding image Segmentation Based on an Improved Artificial Bee Colony Algorithm 2nd

A Multi-Level Thresholding Image Segmentation Based on an Im...

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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Xia, Xingyu Gao, Hao Hu, Haidong Lan, Rushi Pun, Chi-Man The Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Department of Computer and Information Science University of Macau China Beijing Institute of Control Engineering Beijing China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9783030177621

As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation function is separable which means each variable is independent. Due to its one-dimensional search strategy and relative power global but poorer local search abilities, ABC could find an acceptable but not precise segmentation results. For making more precise search and further enhancing the achievements on image segmentation, we propose an Otsu segmentation method based on a new ABC algorithm with an improved scout bee strategy. Different from the traditional scout bee strategy, we use a local search strategy when a bee stagnates for a defined value. The experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm. © 2020, Springer Science and Business Media Deutschland GmbH. All rights reserved.

关键词： image segmentation

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Topology Prediction Improvement of α-helical Transmembrane Proteins Through Helix-tail Modeling and Multiscale Deep Learning Fusion

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JOURNAL OF MOLECULAR BIOLOGY 2020年第4期432卷 1279-1296页

作者： Feng, Shi-Hao Zhang, Wei-Xun Yang, Jing Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci Shanghai 200240 Peoples R China Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China

Transmembrane proteins (TMPs) play important roles in many biological processes, such as cell recognition and communication. Their structures are crucial for revealing complex functions but are hard to obtain. A variety of computational algorithms have been proposed to fill the gap by predicting structures from primary sequences. In this study, we mainly focus on alpha-helical TMP and develop a multiscale deep learning pipeline, MemBrain 3.0, to improve topology prediction. This new protocol includes two submodules. The first module is transmembrane helix (TMH) prediction, which features the capability of accurately predicting TMH with the tail part through the incorporation of tail modeling. The prediction engine contains a multiscale deep learning model and a dynamic threshold strategy. The deep learning model is comprised of a small-scale residue based residual neural network and a large-scale entire-sequence-based residual neural network. Dynamic threshold strategy is designed to binarize the raw prediction scores and solve the under-split problem. The second module is orientation prediction, which consists of a support vector machine (SVM) classifier and a new Max-Min assignment (MMA) strategy. One typical merit of MemBrain 3.0 is the decision mode composed of the dynamic threshold strategy and the MMA strategy, which makes it more effective for hard TMHs, such as half-TMH, back-to-back TMH, and long-TMH. Systematic experiments have demonstrated the efficacy of the new model, which is available at: ***/bioinf/MemBrain/. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： membrane protein bioinformatics structure prediction topology deep learning

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A Model Reference Adaptive controller with Feedforward Decoupling for Active Magnetic Bearing Rotor System 4

A Model Reference Adaptive Controller with Feedforward Decou...

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4th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2019

作者： Yi, Jian Ren, Gui-Ping Zhang, Hai-Tao Wu, Yue Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China

ISBN: (纸本)9781728119076

Active magnetic bearing (AMB) rotor system is widely applied in the industry for its remarkable advantages. However, it is a typical open-loop unstable mechatronics system suffering from nonlinear and couple characteristic. In this paper, the feedforward decoupling method is used to deal with the couple relationship of different axes. And the model reference adaptive control (MRAC) method is applied to improve the control performance and robustness and overcome the uncertainties. This control method makes the system output reach to the reference model by the designed adaptive law. Simulation results illustrate the effectiveness of the proposed scheme. © 2019 IEEE.

关键词： Model reference adaptive control

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Global Ultimate Mittag-Leffler Lag Quasi-Synchronization of Delayed Fractional-Order Memristive Neural Networks with Switching Jumps Mismatch via Pinning control

Global Ultimate Mittag-Leffler Lag Quasi-Synchronization of ...

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第三十九届中国控制会议

作者： Jia Jia Zhigang Zeng School of Artificial Intelligence and Automation the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology School of Control Science and Engineering Shandong University

In this paper, a novel pinning controller, which is applied to the selected partial neurons with partial errors needed, is designed for global ultimate Mittag-Leffler lag quasi-synchronization(GUMLQS) of delayed fractional-order memristive neural networks(FMNNs) with switching jumps mismatch. Since the right-hand sides of the equations of FMNNs are discontinuous,FMNNs have no solution in the ordinary sense, and Filippov solutions are adopted for FMNNs. With the assistance of fractional Halanay inequality, a Lyapunov function in quadratic form and the theory of fractional derivative, an LMI-based GUMLQS criterion is derived. For a prescribed GUMLQS error bound, the control gain matrices can be calculated out via a group of ***, the sufficient condition for the feasibility of the LMIs is obtained, which is an important basis for the selection of controlled neurons. Finally, a numerical example shows that the designed pinning controller can achieve GUMLQS between master-slave FMNNs with the prescribed ultimate error bound.

关键词： Memristive Neural Networks(MNNs) Fractional-Order Pinning control Global Ultimate Mittag-Leffler Lag Quasi-Synchronization(GUMLQS)

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Adaptive Type-2 Fuzzy Neural-Network control for Teleoperation Systems With Delay and Uncertainties

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第10期28卷 2543-2554页

作者： Kebria, Parham Mohsenzadeh Khosravi, Abbas Nahavandi, Saeid Wu, Dongrui Bello, Fernando Deakin Univ Inst Intelligent Syst Res & Innovat Waurn Ponds Vic 3216 Australia Huazhong Univ Sci & Technol Sch Automat Minist Educ Image Proc & Intelligent Control Key Lab Wuhan 430074 Peoples R China Imperial Coll London Ctr Engagement & Simulat Sci London SW7 2AZ England Chelsea & Westminster Hosp Acad Surg London SW10 9NH England

Interacting with human operators, remote environment, and communication networks, teleoperation systems are considerably suffering from complexities and uncertainties. Managing these is of paramount importance for safe and smooth performance of teleoperation systems. Among the countless solutions developed by researchers, type-2 fuzzy (T2F) algorithms have shown an outstanding performance in modeling complex systems and tackling uncertainties. Moreover, artificial neural networks (NNs) are well known for their adaptive learning potentials. This article proposes an adaptive interval type-2 fuzzy neural-network control scheme for teleoperation systems with time-varying delays and uncertainties. The T2F models are developed based on the experimental data collected from a teleoperation setup over a local computer network. However, the resulted controller is evaluated on an intercontinental communication network through the Internet between Australia and Scotland. Moreover, the slave robot and the remote workspace are completely different and unforeseen. Stability and performance of the proposed control is analyzed by Lyapunov-Krasovskii method. Comprehensive comparative studies demonstrate that the proposed controller outperforms traditional techniques in experimental evaluations.

关键词： Uncertainty Robots Adaptation models Delays Data collection Adaptive systems Artificial neural networks Adaptive control neural network (NN) time delay teleoperation systems type-2 fuzzy (T2F) models uncertainties

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Video-rate 3D imaging of living cells using Fourier view-channel-depth light field microscopy

COMMUNICATIONS BIOLOGY

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COMMUNICATIONS BIOLOGY 2023年第1期6卷 1259页

作者： Yi, Chengqiang Zhu, Lanxin Sun, Jiahao Wang, Zhaofei Zhang, Meng Zhong, Fenghe Yan, Luxin Tang, Jiang Huang, Liang Zhang, Yu-Hui Li, Dongyu Fei, Peng Huazhong Univ Sci & Technol Sch Opt & Elect Informat Wuhan Natl Lab Optoelect Adv Biomed Imaging Facil Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Britton Chance Ctr Biomed Photon MoE Key Lab Biomed Photon Adv Biomed Imaging FacilWuhan Natl Lab Optoelect Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol State Educ Commiss Key Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Tongji Hosp Tongji Med Coll Dept Hematol Wuhan 430030 Hubei Peoples R China

Interrogation of subcellular biological dynamics occurring in a living cell often requires noninvasive imaging of the fragile cell with high spatiotemporal resolution across all three dimensions. It thereby poses big challenges to modern fluorescence microscopy implementations because the limited photon budget in a live-cell imaging task makes the achievable performance of conventional microscopy approaches compromise between their spatial resolution, volumetric imaging speed, and phototoxicity. Here, we incorporate a two-stage view-channel-depth (VCD) deep-learning reconstruction strategy with a Fourier light-field microscope based on diffractive optical element to realize fast 3D super-resolution reconstructions of intracellular dynamics from single diffraction-limited 2D light-filed measurements. This VCD-enabled Fourier light-filed imaging approach (F-VCD), achieves video-rate (50 volumes per second) 3D imaging of intracellular dynamics at a high spatiotemporal resolution of similar to 180 nm x 180 nm x 400 nm and strong noise-resistant capability, with which light field images with a signal-to-noise ratio (SNR) down to -1.62 dB could be well reconstructed. With this approach, we successfully demonstrate the 4D imaging of intracellular organelle dynamics, e.g., mitochondria fission and fusion, with similar to 5000 times of observation.

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