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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 753-757页

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Zhao, Hongxia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Syst Qingdao 266044 Peoples R China

There are gaps between education supply and industrial demand of talents in intelligent era. Thus, the shortage of artificial intelligence(AI) talents becomes the top challenges facing China's companies. Hence it is essential to implement educational reform to improve quality of education supply talents. However, educational reform is affected by multiple and intertwined factors. This research, therefore, developed a system dynamics-based model of education reform to sort out relationships among all factors and predict the influence of several factors on AI talents output. Then, based on this model, series of experiments are carried out to find main factors affecting cultivation of AI talents. Results show that: 1)this model could accurately simulate trends of output ratio AI talents under different educational reform measures;2) Measures including teaching mode reform, improving the application rate of new teaching model, speeding up the construction of new disciplines in higher education, developing AI, improving ethics of AI, and strengthening the integration of AI and education could play significant roles in promoting cultivation of AI talents.

关键词： System dynamics education reform simulation artificial intelligent talents cultivation

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Exploring painting synthesis with diffusion models 1

Exploring painting synthesis with diffusion models

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Yi, Da Guo, Chao Bai, Tianxiang Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

As a significant composition of art, fine art painting is becoming a research hotspot in machine learning community. With unique aesthetic value, paintings have quite different representations from natural images, making them irreplaceable. Meanwhile, the lack of training data is common in painting-related machine learning tasks. Therefore, the synthesis of fine art painting is meaningful and challenging work. There are two main types of generative models for image synthesis: generative adversarial networks (GANs) and likelihood-based models. GAN-based models can obtain high-quality samples but usually sacrifice diversity and training stability. Diffusion models are a class of likelihood-based models and have recently been shown to achieve state-of-the-art quality on the image synthesis tasks. In this paper, we explore generating fine art paintings by using diffusion models. We carried out the experiments on the partial impression paintings from the Wikiart dataset. The results demonstrate that the diffusion model can generate high-quality samples, and it is easy to train to cover more target distribution than the GAN-based methods. © 2021 IEEE.

关键词： Generative adversarial networks

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FrameNet: Tabular Image Preprocessing Based on UNet and Adaptive Correction 21st

FrameNet: Tabular Image Preprocessing Based on UNet and Adap...

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21st International Conference on Image Analysis and Processing (ICIAP)

作者： Wang, Yufei Du, Chen Xiao, Baihua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9783031064302;9783031064296

Detecting and recognizing objects in images with complex backgrounds and deformations is a challenging task. In this work, we propose FrameNet, while a deep table lines segmentation network based on our Res18UNet with an adaptive deformation correction algorithm for correcting the table lines. We use Itinerary/Receipt of E-ticket for Air Transport to evaluate our methods. The experiment results show that our Res18UNet can reduce the number of parameters and improve the speed of image segmentation without significantly reducing the segmentation accuracy, and our correction method can better correct the perspective deformation and some distorted tabular images with no dependence on pixel-level ground truth image. In addition, we also apply our model and method to VAT invoice dataset and prove that they also have better transfer ability.

关键词： Computer vision Deep learning Image rectification

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Interpretable Learning for Travel Behaviours in Cyber-Physical-Social-systems

Interpretable Learning for Travel Behaviours in Cyber-Physic...

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2022 Australian and New Zealand control Conference

作者： Qi, Hao Ye, Peijun Shandong Jiaotong Univ Jinan 250357 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

Interpretable learning is important for understanding human behavioral patterns in Cyber-Physical-Social-systems (CPSS). It facilitates smart decision-makings of intelligent algorithms so that the management of such human-machine hybrid systems can be efficient and optimal. Unlike the big data driven transportation management, this paper introduces a new interpretable learning method using fuzzy logic to semantically extract travel behaviors. Computational experiments based on actual traffic data indicate that our method is able to generate explicit rules, and these rules can be used to predict traffic patterns very well.

关键词： Deep Learning Interpretability Cyber-Physical-Social-System Travel Behavior

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Event-Triggered Energy Optimization of Wireless Sensor Networks 19

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Liu, Lu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665472432

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste. © 2022 IEEE.

关键词： Energy utilization

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LMI-based robust adaptive neural network control for Euler-Bernoulli beam with uncertain parameters and disturbances

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INTERNATIONAL JOURNAL OF control 2022年第1期95卷 1-10页

作者： Xing, Xueyan Yang, Hongjun Liu, Jinkun Wang, Shuquan Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Technol & Engn Ctr Space Utilizat Beijing Peoples R China

This paper is concerned with the stabilisation problem of an Euler-Bernoulli beam with uncertain parameters and disturbances. To correctly represent the beam's behaviour, the partial differential equations model is utilised for the control design of the beam without missing any high-order mode information. Then the linear matrix inequalities (LMIs) method is applied to the robust adaptive neural network control design to cope with systematic uncertainties and stabilise the beam system with disturbance compensation. Through resolving LMIs, feasible sets of designed control parameters can be effectively obtained without model linearisation. Finally, numerical simulations are done to validate the effectiveness of the proposed control.

关键词： Distributed parameter systems boundary control LMIs robust adaptive neural network control

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An FM*-Based Comprehensive Path Planning System for Robotic Floating Garbage Cleaning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systems 2022年第12期23卷 23821-23830页

作者： Kong, Shihan Wu, Zhengxing Qiu, Changlin Tian, Manjun Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot BIC ESATState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Minist Publ Secur PRC Res Inst 1 Beijing 100048 Peoples R China

A heuristic fast marching (FM*)-based comprehensive path planning system involving task allocation, initial planning, and replanning is presented for the robotic floating garbage cleaning mission. There are three primary contributions in this paper. First, to tackle the invalidation of the Euclidean distance metric in the obstacle environment, the task allocation is modeled as a travelling salesman problem (TSP) employing the FM*-based distance metric in order to obtain an optimal travel sequence. Second, to meet the maneuverability constraint from the surface robot and avoid the collision, a Gaussian filter is employed to adjust the curvature radius of the generated path. Third, for an efficient replanning, a neural network-based replanning point generator with the input of garbage movement vector is provided to strike a compromise for the distance cost and the computational burden. Moreover, a case study and a virtual obstacle experiment in the laboratory water tank demonstrate the feasibility of the proposed comprehensive path planning system. This work lays a firm foundation for the development of intelligent equipment for aquatic environment protection.

关键词： Task analysis Resource management Cleaning Path planning Robots Measurement Planning Autonomous vehicles path planning fast marching method task allocation replanning

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Drill the Cork of Information Bottleneck by Inputting the Most Important Data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第11期33卷 6360-6372页

作者： Peng, Xinyu Zhang, Jiawei Wang, Fei-Yue Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China Tsinghua Univ BNRist Dept Automat Beijing 100084 Peoples R China

Deep learning has become the most powerful machine learning tool in the last decade. However, how to efficiently train deep neural networks remains to be thoroughly solved. The widely used minibatch stochastic gradient descent (SGD) still needs to be accelerated. As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase. Based on this principle, we further study typicality sampling, an efficient data selection method, and propose a new explanation of how it helps accelerate the training process of the deep networks. We show that the fitting phase depicted in the IB theory will be boosted with a high signal-to-noise ratio of gradient approximation if the typicality sampling is appropriately adopted. Furthermore, this finding also implies that the prior information of the training set is critical to the optimization process, and the better use of the most important data can help the information flow through the bottleneck faster. Both theoretical analysis and experimental results on synthetic and real-world datasets demonstrate our conclusions.

关键词： Training Signal to noise ratio Mutual information Optimization Convergence Deep learning Tools Information bottleneck (IB) theory machine learning minibatch stochastic gradient descent (SGD) typicality sampling

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A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area

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IEEE INTELLIGENT TRANSPORTATION systems MAGAZINE 2023年第1期15卷 285-299页

作者： Ma, Yan Du, Wenbo Chen, Jun Thong, Yu Lv, Yisheng Cao, Xianbin Beihang Univ Key Lab Adv Technol Near Space Informat Syst Sch Elect & Informat Engn Beijing 100191 Peoples R China Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England Univ S Florida Dept Civil & Environm Engn Tampa FL 33620 USA Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

Affected by the nondeterministic nature of flight trajectories, the external environment, and airport operational conditions, the prediction of the estimated time of arrival (ETA) is one of the most challenging tasks for air traffic control in the terminal maneuvering area (TMA). Previous studies lack adequate utilization of the spatial and temporal behaviors embedded in continuous trajectories. We propose a novel spatiotemporal neural network model for estimating the time of arrival (STNN-ETA), which consists of three components: 1) trajectory pattern recognition, which classifies historical trajectories into several patterns/clusters; 2) trajectory prediction, which predicts a target flight’s subsequent positions based on trajectory pattern matching; and 3) arrival time prediction, in which nonlinear function and recurrent units are adopted to capture spatiotemporal features for prediction purposes. In the proposed model, we also utilize spatial and temporal attention mechanisms to focus on important features from radar echo maps and trajectory series, respectively, and suppress unnecessary ones for ETA prediction. To validate the effectiveness of the proposed method, we apply it to predict the ETA of flights within the Beijing TMA. Extensive experiments show that STNN-ETA outperforms the state-of-the-art models in terms of the mean absolute error.

关键词： Trajectory Predictive models Boosting Atmospheric modeling Spatiotemporal phenomena Meteorology Prediction algorithms

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Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 11453-11466页

作者： Zhou, Junjie Zhong, Shanlin Wu, Wei Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Beijing Key Lab Res & Applicat Robot Intelligence Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Generating various goal-oriented movements via the flexible muscle model of the musculoskeletal system as fast and accurately as possible is a pressing problem, which is also the basis of most human adaptive behaviors, such as reaching, catching, interception, and pointing. This article focuses on the adaptive motion generation of fast goal-oriented motion on the musculoskeletal system by implementing the speed-accuracy tradeoff (SAT) in a hierarchical motion learning framework. First, we introduce Fitts' Law into the modified basal ganglia circuit-inspired iterative decision-making model for achieving dynamic and adaptive decision making. Then, as a time constraint, the decision is decomposed into a series of supervised terms by the proposed striatal FSI-SPN interneuron circuit-inspired velocity modulator to implement the tradeoff smoothly on the musculoskeletal system. Finally, an improved policy gradient algorithm is suggested to generate the muscle excitations of the modulated motion via the proposed muscle co-contraction policy, which promotes general cooperation between flexor and extensor muscles. In experiments, a redundant musculoskeletal arm model is trained to perform the adaptive quick pointing movements. By combining the muscle co-contraction policy with SAT, our algorithm shows the most efficient training and the best performance in the adaptive motion generation among the other three popular reinforcement learning algorithms on the musculoskeletal model.

关键词： Muscles Adaptation models Task analysis Arms Mathematical model Integrated circuit modeling Basal ganglia Brain-inspired decision making Fitts' law motion generation musculoskeletal system speed-accuracy tradeoff (SAT)

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