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第33届中国控制与决策会议

作者： Zhenyu Ren Gang Peng Jin Yang Hao Wang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education

To realize a robust robotic grasping system for unknown objects in an unstructured environment,large amounts of grasp data and 3D model data for the object are *** reduce the time cost of data acquisition and labeling and increase the rate of successful grasps,we developed a self-supervised learning mechanism to control grasp tasks performed by ***,a manipulator automatically collects the point cloud for the objects from multiple perspectives to increase the efficiency of data *** complete point cloud for the objects is obtained by utilizing the hand-eye vision of the manipulator,and the TSDF ***,the point cloud data for the objects is used to generate a series of six-degrees-of-freedom grasp poses,and the force-closure decision algorithm is used to add the grasp quality label to each grasp pose to realize the automatic labeling of grasp ***,the point cloud in the gripper closing area corresponding to each grasp pose is obtained;it is then used to train the grasp-quality classification model for the *** results of performing actual grasping experiments demonstrate that the proposed self-supervised learning method can increase the rate of successful grasps for the manipulator.

关键词： Manipulator Grasping Self-Supervised Learning 3D Reconstruction

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Unequal Error Protection Rateless Codes for Spatial Audio Transmission

Unequal Error Protection Rateless Codes for Spatial Audio Tr...

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International Conference on Communication Technology (ICCT)

作者： Junpeng Yin Yusun Fu Haobo Huang Jinhui Tang Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering the Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Spatial audio is an important component of the metaverse. With the rapid increase of the spatial resolution and the number of objects, the data volume of the spatial side information has grown larger. However, the low bit rate transmission of spatial side information in the object-based audio coding methods leads to the low-quality reconstruction of spatial side information with limited bandwidth. To address this issue, this paper constructs a cascade design of spatial audio coding and unequal error protection (UEP) rateless codes. Furthermore, the further protection of specific spatial side information is proposed for different scenarios, which effectively addresses the limitation of traditional spatial audio coding in side information protection. For the situation where the code length K of different importance levels is different, we propose an adaptive degree distribution (ADD), adding K to the selection probability as an influencing factor, which has better performance than other degree distribution for short code length. ADD is used to replace the multiple degree distribution strategy of the traditional unequal error protection scheme, and the block error rate (BER) is reduced nearly 10 times compared with the traditional scheme.

关键词： Codes Error analysis Metaverse Distribution strategy Spatial audio Bit rate Symbols Communications technology Protection Spatial resolution

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Intervention of polyphenols on cod protein modification induced by α, β-unsaturated aldehydes in multi-interface system

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Food Research International 2025年 213卷 116570页

作者： Jiang, Kaiyu Yang, Ting-qi Tu, Juncai Liang, Yuxuan Liu, Sheng-Hai Wang, Meng-Hui Zhu, Beiwei Gong, Wei Shenzhen Key Laboratory of Food Nutrition and Health College of Chemistry and Environmental Engineering and Institute for Innovative Development of Food Industry Shenzhen University Shenzhen518060 China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen518060 China State Key Laboratory of Marine Food Processing and Safety Control Dalian Polytechnic University Dalian116034 China National Engineering Research Center of Seafood Collaborative Innovation Center of Seafood Deep Processing School of Food Science and Technology Dalian Polytechnic University Dalian116034 China

α, β-unsaturated aldehydes, generated by lipid peroxidation (LPO), are significant electrophiles with strong protein modification capacity in various seafood products. Polyphenols have been widely used as potent scavengers of α, β-unsaturated aldehydes to inhibit LPO-related protein deterioration. Compared with the homogenous aqueous system focused by most studies, heterogeneous multi-interface systems occurred more commonly in real food matrix. However, the role of interfaces in protein modification by α, β-unsaturated aldehydes and the intervention of polyphenols was still rarely reported. In this study, a multi-interface model system was established using cod protein (CP) solution and hexadecane, incorporating a clickable probe yne-ACR to simulate the modification of α, β-unsaturated aldehydes on CP, while six polyphenols were added to intervene this modification. Interfacial fluid dynamics results demonstrated that the interface significantly mediated the modification of CP by yne-ACR and polyphenol intervention, notably influencing the dynamics and expansion properties of CP at the interface. Stability analysis of the interface model system indicated that both yne-ACR and polyphenols intervention had minimal impact on the stability of system. Moreover, the interface provided a unique platform for both protein modification and polyphenol intervention, more significant modification by yne-ACR could be observed in interfacial protein than non-absorbed protein from the in-gel fluorescent imaging. The intervention effect of polyphenol could be closely linked to their structural characteristics since EC could markedly exacerbate the modification but EGCG effectively mitigate it. Our findings emphasized the distinct role of interfaces in protein modification by α, β-unsaturated aldehydes and polyphenol intervention and might offer potential regulation strategies for protein quality in multi-interface food systems. © 2025

关键词： Viscous flow

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Semi-supervised Long-tail Endoscopic Image Classification

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Chinese Medical Sciences Journal 2022年第3期37卷 171-180,I0002页

作者： Runnan Cao Mengjie Fang Hailing Li Jie Tian Di Dong School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Advaneed Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi'an 710126China

Objective To explore the semi-supervised learning(SSL) algorithm for long-tail endoscopic image classification with limited *** We explored semi-supervised long-tail endoscopic image classification in HyperKvasir,the largest gastrointestinal public dataset with 23 diverse ***-supervised learning algorithm FixMatch was applied based on consistency regularization and *** splitting the training dataset and the test dataset at a ratio of 4:1,we sampled 20%,50%,and 100% labeled training data to test the classification with limited *** The classification performance was evaluated by micro-average and macro-average evaluation metrics,with the Mathews correlation coefficient(MCC) as the overall *** algorithm improved the classification performance,with MCC increasing from 0.8761 to 0.8850,from 0.8983 to 0.8994,and from 0.9075 to 0.9095 with 20%,50%,and 100% ratio of labeled training data,*** a 20% ratio of labeled training data,SSL improved both the micro-average and macro-average classification performance;while for the ratio of 50% and 100%,SSL improved the micro-average performance but hurt macro-average *** analyzing the confusion matrix and labeling bias in each class,we found that the pseudo-based SSL algorithm exacerbated the classifier’ s preference for the head class,resulting in improved performance in the head class and degenerated performance in the tail *** SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification,especially when the labeled data is extremely limited,which may benefit the building of assisted diagnosis systems for low-volume ***,the pseudo-labeling strategy may amplify the effect of class imbalance,which hurts the classification performance for the tail class.

关键词： endoscopic image artificial intelligence semi-supervised learning long-tail distribution image classification

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Sliding Mode control for Quadrotor-Slung Load Transportation System with State Constraints*

Sliding Mode Control for Quadrotor-Slung Load Transportation...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Feng Ding Chong Sun Yong Ai Jian Huang School of Computer Science South-Central Minzu University Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises Wuhan Hubei China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and automation Huazhong University of Science and Technology Wuhan Hubei China

Quadrotor-slung load transportation is an important way to transport goods, but the swing of the load bring a great deal of challenges to the stability of the system. In this paper, a barrier Lyapunov function based sliding mode controller is proposed to limit the load swing, which also satisfies the speed limitation of the quadrotor. An inner-outer loop control strategy is proposed, where the outer one is the virtual position controller which guarantees the stability of the quadrotor while eliminating the load swing, and the inner one is the attitude controller which driver the actual force to the position controller to ensure the effectiveness of the system. Specifically, the dynamics of quadrotor-slung load transportation system is reconstructed as a cascade form. Then, a barrier Lyapunov function based sliding mode controller is designed to ensure the trajectory tracking errors uniformly bounded with state constraints valid. Finally, comparative simulation results illustrate the effectiveness of the proposed method.

关键词： Attitude control Trajectory tracking Simulation Force Dynamics Transportation Stability analysis

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Energy-Efficient Multi-UAV-Enabled MEC Systems with Space-Air-Ground Integrated Networks

arXiv

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arXiv 2024年

作者： Liu, Wenchao Zhang, Xuhui Ren, Jinke Shen, Yanyan Wang, Shuqiang Yang, Bo Guan, Xinping Cui, Shuguang Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China Southern University of Science and Technology Guangdong518055 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The School of Science and Engineering The Shenzhen Future Network of Intelligence Institute The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China

With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks. However, existing mobile computing networks struggle to support the rapidly growing computational needs of the MUs. Fortunately, space-air-ground integrated network (SAGIN) supported mobile edge computing (MEC) is regarded as an effective solution, offering the MUs multi-tier and efficient computing services. In this paper, we consider an SAGIN supported MEC system, where a low Earth orbit satellite and multiple unmanned aerial vehicles (UAVs) are dispatched to provide computing services for MUs. An energy efficiency maximization problem is formulated, with the joint optimization of the MU-UAV association, the UAV trajectory, the task offloading decision, the computing frequency, and the transmission power control. Since the problem is non-convex, we decompose it into four subproblems, and propose an alternating optimization based algorithm to solve it. Simulation results confirm that the proposed algorithm outperforms the benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Data-Driven Discovery of Stochastic Differential Equations

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Engineering 2022年第10期17卷 244-252页

作者： Yasen Wang Huazhen Fang Junyang Jin Guijun Ma Xin He Xing Dai Zuogong Yue Cheng Cheng Hai-Tao Zhang Donglin Pu Dongrui Wu Ye Yuan Jorge Gonçalves Jürgen Kurths Han Ding School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Mechanical Engineering University of KansasLawrenceKS 66045USA HUST-Wuxi Research Institute Wuxi 214174China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Department of Plant Sciences University of CambridgeCambridge CB23EAUK Luxembourg Centre for Systems Biomedicine University of LuxembourgBelvaux 4367Luxembourg Department of Physics Humboldt University of BerlinBerlin 12489Germany Department of Complexity Science Potsdam Institute for Climate Impact ResearchPotsdam 14473Germany

Stochastic differential equations(SDEs)are mathematical models that are widely used to describe complex processes or phenomena perturbed by random noise from different *** identification of SDEs governing a system is often a challenge because of the inherent strong stochasticity of data and the complexity of the system’s *** practical utility of existing parametric approaches for identifying SDEs is usually limited by insufficient data *** study presents a novel framework for identifying SDEs by leveraging the sparse Bayesian learning(SBL)technique to search for a parsimonious,yet physically necessary representation from the space of candidate basis *** importantly,we use the analytical tractability of SBL to develop an efficient way to formulate the linear regression problem for the discovery of SDEs that requires considerably less time-series *** effectiveness of the proposed framework is demonstrated using real data on stock and oil prices,bearing variation,and wind speed,as well as simulated data on well-known stochastic dynamical systems,including the generalized Wiener process and Langevin *** framework aims to assist specialists in extracting stochastic mathematical models from random phenomena in the natural sciences,economics,and engineering fields for analysis,prediction,and decision making.

关键词： Data-driven method System identification Sparse Bayesian learning Stochastic differential equations Random phenomena

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Quality Assessment of Electrocardiogram Signals Using Contrastive Learning

Quality Assessment of Electrocardiogram Signals Using Contra...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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Prognostic Prediction for Non-small-Cell Lung Cancer Based on Deep Neural Network and Multimodal Data 17th

Prognostic Prediction for Non-small-Cell Lung Cancer Based o...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Zhang, Zhong-Si Xu, Fei Jiang, Han-Jing Chen, Zhan-Heng 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 Wuhan430074 China Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University No. 4800 Caoan Road Shanghai201804 China

ISBN: (纸本)9783030845315

Non-small-cell lung cancer (NSCLC) is the most common lung cancer with poor prognosis. Prognostic prediction is significant in improving the prognosis of NSCLC patients. Clinical information and multi-omics data including gene expression, miRNA, copy number variations, and DNA methylation are closely related to NSCLC prognosis. In this study, we propose a deep neural network to conduct prognostic prediction for NSCLC patients based on all five types of data. Given the high dimensional features of the omics data, past works reduce the feature dimension by regression analysis or correlation sorting algorithms. A shortcoming of these methods is that only a small number of features are considered. To overcome it, we propose a convolution neural network-based feature transformation method, which considers all features and extracts the abstract representations of the omics data. Based on the representations, we predict the five-year survival status of NSCLC patients. The results show that our method achieves a more precise prediction than previous work, with the areas under the curve improved from 0.8163 to 0.8508 and the accuracy improved from 0.7544 to 0.8096. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Coverage control of multi-agent systems for ergodic exploration

Coverage control of multi-agent systems for ergodic explorat...

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Chinese control Conference (CCC)

作者： Zhulei Chen Li Xiao Qi Wang Zhuo Wang Zhigang Sun Key Laboratory of Imaging Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

In this paper, according to the ergodic theory, the ergodic metric is defined as the distance of the time average statistics of the agent's trajectories from the spatial distribution probability of the terrain. The feedback control law of the agent's dynamic is designed using the ergodic metric to achieve uniform or non-uniform coverage of a given area. At the same time, in order to expand the detection range, and obtain more complete and accurate environmental information and performed different types of tasks, the multi-agent system covers in the form of formation, and can adapt to move along the boundary. In this paper, the combination of the leader-follower method and the artificial potential field method are used to control the formation of a multi-agent system.

关键词： Manganese

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