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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9781643685489

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. © 2024 The Authors.

关键词： Robots

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Learning Communities from Equilibria of Nonlinear Opinion Dynamics 63

Learning Communities from Equilibria of Nonlinear Opinion Dy...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xing, Yu Bizyaeva, Anastasia Johansson, Karl H. Kth Royal Institute of Technology and with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden Cornell University Sibley School of Mechanical and Aerospace Engineering IthacaNY United States

ISBN: (纸本)9798350316339

This paper studies community detection for a nonlinear opinion dynamics model from its equilibria. It is assumed that the underlying network is generated from a stochastic block model with two communities, where agents are assigned with community labels and edges are added independently based on these labels. Agents update their opinions following a nonlinear rule that incorporates saturation effects on interactions. It is shown that clustering based on a single equilibrium can detect most community labels (i.e., achieving almost exact recovery), if the two communities differ in size and link probabilities. When the two communities are identical in size and link probabilities, and the intercommunity connections are denser than intra-community ones, the algorithm can achieve almost exact recovery under negative influence weights but fails under positive influence weights. Utilizing fixed point equations and spectral methods, we also propose a detection algorithm based on multiple equilibria, which can detect communities with positive influence weights. Numerical experiments demonstrate the performance of the proposed algorithms. © 2024 IEEE.

关键词： Nash equilibrium

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State-of-Health Estimation of Lithium-Ion Battery Based on Linguistic Description Considering Capacity Regeneration

State-of-Health Estimation of Lithium-Ion Battery Based on L...

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2023 China Automation Congress, CAC 2023

作者： Cui, Fangshu Li, Hui Wu, Kangjia Fan, Sitong Shi, Yuanhao School of Computer Science and Technology North University of China Taiyuan China School of Electrical and Control Engineering North University of China Taiyuan China

ISBN: (纸本)9798350303759

Owing to the wide application of lithium-ion batteries in industry, it is of great significance for accurate prediction of battery state of health (SOH) to ensure the safety and stability of equipment. The battery capacity regeneration (CR) during capacity degradation affects the accuracy of capacity prediction. This study proposes a hybrid estimation method for state of health of lithium-ion batteries based on linguistic description (LD) considering capacity regeneration. First, the capacity degradation data is decoupled into the trend component and the capacity regeneration component by the variational mode decomposition (VMD). Second, in order to improve the accuracy and reliability of battery capacity prediction, the capacity regeneration part is described by linguistic description. Finally, the trend part and the linguistic description sequence of capacity regeneration are predicted by support vector regression (SVR) model and long short term memory network with attention mechanism (Attn-LSTM) respectively, and their forecast results are superposed as the battery capacity prediction. The experimental results in NASA battery data demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： Lithium-ion batteries

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Comparison of Traffic control with Model Predictive control and Deep Reinforcement Learning 9

Comparison of Traffic Control with Model Predictive Control ...

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9th International Conference on control, Decision and Information Technologies, CoDIT 2023

作者： Imran, Muhammad Izzo, Riccardo Tortorelli, Andrea Liberati, Francesco University of Rome 'La Sapienza' Department of Computer Control and Management Engineering 'A. Ruberti' Via Ariosto 25 RM Rome00185 Italy

ISBN: (纸本)9798350311402

Traffic congestion is among the worst causes of pollution, and the time spent in traffic can cost the world tens of billions of dollars every year. Solutions to mitigate this problem are at hand thanks to the advent of advanced control techniques and artificial intelligence (AI). Traditional traffic light control strategies based on fixed timing of the green, yellow and red phases are simple to implement, but at the same time very inefficient, in particular for busy intersections. This paper discusses both a model predictive control (MPC) approach and a model-free deep reinforcement learning (DRL) algorithm for controlling the traffic lights at a single intersection, with the aim of improving the traffic flow. Firstly, a detailed linear mathematical model of an intersection is formulated and successively tested in a MPC framework;secondly, a DRL algorithm is proposed and verified by comparing it with the currently implemented baseline controller. Finally, the results for the three approaches, MPC, DRL and the baseline controller, are validated through the SUMO (Simulation of Urban Mobility) microscopic traffic simulator. © 2023 IEEE.

关键词： Model predictive control

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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computer Modeling in engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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An Automatic Object Attention Likelihood Map Correlation Filter for Visual Tracking 11

An Automatic Object Attention Likelihood Map Correlation Fil...

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11th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2023 - Proceedings

作者： Tang, Leinuo Xu, Xiaochun Wang, Xin Feng, Huibin Lin, Nixuan Minjiang University International Digital Economy College Fuzhou350108 China Minjiang University College of Computer and Control Engineering Fuzhou350108 China

ISBN: (纸本)9798350326277

Discriminative correlation filter-based trackers have achieved excellent performance. However, they are still suffered from the inherent boundary effect. To alleviate it, this paper proposes an automatic object attention likelihood map correlation filter. Specifically, an automatic object attention searching window is developed to highlight the object more precisely and suppress the background more appropriately, which divides the searching area into three regions and applies different object attention strategies on each area. Furthermore, combining it with the spatially regularized discriminative correlation filter, we propose the automatic object attention likelihood map correlation filter tracker. It further alleviates the boundary effect and adaptively generates a more precise tracking model frame by frame. The experimental evaluations on the popular OT2015 benchmark demonstrate that our proposed tracker gains outstanding tracking performance and offers a satisfying robustness to complex tracking scenarios. © 2023 IEEE.

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Bimodal Fusion Network for Basic Taste Sensation Recognition from Electroencephalography and Electromyography 48

Bimodal Fusion Network for Basic Taste Sensation Recognition...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Gao, Han Zhao, Shuo Li, Huiyan Liu, Li Wang, You Hu, Ruifen Zhang, Jin Li, Guang Zhejiang University State Key Laboratory of Industrial Control Technology China Changsha University of Science and Technology School of Computer and Communication Engineering China

ISBN: (纸本)9781728163277

Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals in taste sensation recognition. This paper proposes a bimodal fusion network (Bi-FusionNet) for recognizing basic taste sensations (sour, sweet, bitter, salty, umami, and blank). Two convolutional backbones with similar structures are designed to separately extract the single-modal features of EEG and EMG. Then, EEG and EMG features are concatenated for bimodal interaction and complementarity. Finally, three loss functions are adopted: a center loss for aggregating intra-class samples, a mean squared error loss for sequence positions for minimizing the difference between signals during the stimulation, and a softmax loss for minimizing the entropy of prediction and true labels. The results on the taste sensation dataset show that bimodal fusion improves recognition performance, and Bi-FusionNet outperforms single-modal methods and other fusion methods. Bi-FusionNet paves the way for the application of multimodal fusion in taste sensation recognition. © 2023 IEEE.

关键词： Electroencephalography Electromyography Feature Fusion Taste Sensation Recognition

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A Positive Influence Maximization Algorithm in Signed Social Networks

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computers, Materials & Continua 2023年第8期76卷 1977-1994页

作者： Wenlong Zhu Yang Huang Shuangshuang Yang Yu Miao Chongyuan Peng College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China College of Teacher Education Qiqihar UniversityQiqihar161006China

The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social *** positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely *** solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social *** overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)*** algorithm utilizes the ICPD model as well as the PRR set to select *** are two phases in *** is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR *** other is the node selection phase,which uses a greedy coverage algorithm to select optimal ***,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in *** on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

关键词： Positive influencemaximization polar relation information decay reverse influence sampling

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Global practical stabilization of the double integrator system with an imperfect sensor and subject to a bounded disturbance

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control Theory and Technology 2024年第3期22卷 468-478页

作者： Zhiyuan Zhu Yuanlong Li Zongli Lin Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai 200240China Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaP.0.Box 400743CharlottesvilleVA 22904-4743USA

This paper is concerned with global practical stabilization of the double integrator system with an imperfect sensor and subject to an additive bounded output *** imperfect sensor nonlinearity possesses the nonlinear characteristics of saturation and dead *** of the presence of output dead zone and the additive disturbance,the states cannot be expected to driven into an arbitrarily small neighborhood of the *** solve the global practical stabilization problem,we proposes a low gain-based linear dynamic output feedback law,under which the first state enters and remains in a bounded set whose size is depended on the bound of disturbance and the range of dead zone and the second state enters and remains in a pre-specified arbitrarily small set,both in finite *** results illustrate the effectiveness of our proposed control method.

关键词： Output saturation Disturbance rejection Output dead zone Low gain feedback-Linear output feedback Practical stabilization

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GLEm-Net: Unified Framework for Data Reduction with Categorical and Numerical Features

GLEm-Net: Unified Framework for Data Reduction with Categori...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： De Santis, Francesco Giordano, Danilo Mellia, Marco Damilano, Alessia Politenico di Torino Department of Control and Computer Engineering Torino Italy Bitron Electronic Division Quality and Process Digitalization Deparment Grugliasco Italy

ISBN: (纸本)9798350324457

In the era of Big Data, effective data reduction through feature selection is of paramount importance for machine learning. This paper presents GLEm-Net (Grouped Lasso with Embeddings Network), a novel neural framework that seamlessly processes both categorical and numerical features to reduce the dimensionality of data while retaining as much information as possible. By integrating embedding layers, GLEm-Net effectively manages categorical features with high cardinality and compresses their information in a less dimensional space. By using a grouped Lasso penalty function in its architecture, GLEm-Net simultaneously processes categorical and numerical data, efficiently reducing high-dimensional data while preserving the essential information. We test GLEm-Net with a real-world application in an industrial environment where 6 million records exist and each is described by a mixture of 19 numerical and 7 categorical features with a strong class imbalance. A comparative analysis using state-of-the-art methods shows that despite the difficulty of building a high-performance model, GLEm-Net outperforms the other methods in both feature selection and classification, with a better balance in the selection of both numerical and categorical features. © 2023 IEEE.

关键词： categorical features data reduction embeddings feature selection neural network

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