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Sora for foundation robots with parallel intelligence:three world models,three robotic systems

Frontiers of Information technology & Electronic engineering 2024年第7期25卷 917-923页

作者： Lili FAN Chao GUO Yonglin TIAN Hui ZHANG Jun ZHANG Fei-Yue WANG School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Electrical Engineering Wuhan UniversityWuhan 430072China

This paper outlines the initial steps and basic framework for developing foundation/infrastructure robots/robotics based on foundation models and parallel intelligence,as well as the potential applications of new art... 详细信息

关键词： artificial foundation intelligence

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Multi-Objective Optimization of Individual Pitch control for Blade Fatigue Load Reductions for a 15 MW Wind Turbine

Multi-Objective Optimization of Individual Pitch Control for...

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European control Conference (ECC)

作者： Manuel Lara Francisco Vázquez Jan-Willem van Wingerden Sebastiaan Paul Mulders Juan Garrido Department of Electrical Engineering and Automation University of Cordoba Cordoba Spain Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology Delft The Netherlands

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In order to mitigate periodic blade loads in wind turbines, recent research has analyzed different Individual Pitch control (IPC) approaches, which typically use the multi-blade coordinate (MBC) transformation. Some of these studies show that the introduction of an additional tuning parameter in the MBC, namely the azimuth offset, helps to decouple the nonrotating axes in the MBC transformation and enhances the IPC performance. However, these improvements have been studied without considering the increased control effort performed by the pitch signal, which is the main negative side effect of the IPC. This work addresses this trade-off between pitch signal effort and blade fatigue reduction for IPC applied to a wind turbine operating in the full load region. Here, two IPC schemes, with and without additional azimuth offset, are designed and applied to a 15 MW monopile offshore wind turbine simulated with OpenFAST software. The optimal tuning of the IPC parameters is performed by means of a multi-objective optimization solved by genetic algorithms. The optimization procedure minimizes two objective functions related to pitch signal effort and blade fatigue load. The resulting Pareto fronts show a range of optimal solutions for each IPC scheme. The selected optimal solution for IPC with azimuth offset compared to the optimal solution for IPC without offset achieves improvements of more than 10% in blade load reduction maintaining similar pitch signal effort.

关键词： Azimuth Blades Simulation Fatigue Linear programming Software Wind turbines

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Adaptive Fixed-time Fuzzy Tracking control of Uncertain Robot 27

Adaptive Fixed-time Fuzzy Tracking Control of Uncertain Robo...

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27th International Conference on automation and Computing, ICAC 2022

作者： Zhou, Beibei Zhu, Chengzhi Jiang, Yiming Guangzhou Jianxin Technology Co. Ltd Guangzhou510640 China South China University of Technology Key Laboratory of Autonomous Systems and Net-worked Control College of Automation Science and Engineering Guangzhou510640 China Hunan University National Engineering Laboratory for Robot Visual Perception and Control Hunan Changsha410082 China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

We propose a novel fixed-time fuzzy control strategy to achieve the trajectory tracking of the robot systems with uncertainties in this article. The tracking errors of the system can be convergent to a small area around zero with fixed-time convergence rate, combining with the fuzzy logic system and the backstepping technique. Besides, the boundedness of all the closed-loop system signals can be proved under the proposed controller. The superiority performance of the proposed control strategy has been shown by the simulation results. © 2022 IEEE.

关键词： Closed loop systems

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Semi-Global Robust Practical Output Voltage Tracking for Single-Phase UPS Inverters via Sampled-Data control

Semi-Global Robust Practical Output Voltage Tracking for Sin...

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第42届中国控制会议

作者： Kaiji Zeng Wei Liu Weijie Sun Weidong Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of Technology

This paper deals with the sampled-data semi-global robust practical output voltage tracking problem for single-phase voltage source uninterruptible power supply *** construct an aperiodic sampled-data state feedback control law in an internal model principle *** analysis shows that as long as the upper bound of the sampling time interval of the controller is less than an explicitly computable threshold,our problem can be handled by the proposed sampled-data control *** the tracking error is guaranteed to approach a given neighborhood of the origin asympotically.

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Correction of Images Projected on Non-White Surfaces Based on Deep Neural Network 14

Correction of Images Projected on Non-White Surfaces Based o...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Wang, Zewei Zhang, Jingjing Du, Xingzhuo Cao, Sihua Wei, Wenxuan School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9781510657564

When projecting onto a non-white surface, the projected image is distorted or color mixing by complex luminance and chrominance information, which makes the projection result different from the visual perception of the human eye. The purpose of projection image correction is to remove these effects, and traditional solutions usually estimate parameters from the collected projection samples, compute an inverse model of the projection imaging process, and try to fit a correction function. In this paper, a deep neural network-based projection image correction network (PICN) is designed to implicitly learn complex correction functions. PICN consists of a U-shaped backbone network, a convolutional neural network that extracts projected surface features, and a perceptual loss network that optimizes the correction results. Such a structure can not only extract the deep features and surface interference features of the projected image, but also make the corrected projected image more in line with human visual perception. In addition, we built a projector-camera system under the condition of a fixed global illumination environment for verification experiment, and proved the effectiveness of the proposed method by calculating the evaluation metrics of projected images before and after correction. © 2022 SPIE.

关键词： Deep neural networks

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Drug-target interaction prediction using high order nonlinear features via neural factorization machines 4

Drug-target interaction prediction using high order nonlinea...

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2022 4th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2022

作者： Qian, Quan Zhu, Yuan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

Medication design and repositioning are sped up by the prediction of drug-target interactions (DTIs). Two main kinds of prediction methods are commonly used, which are based on chemical structure feature extraction and deep learning methods. However, on the one hand, the DTI prediction approaches based on chemical structural feature extraction may not wholly explore the possible network characteristics in the data. On the other hand, many deep learning methods call for numerous layers of neural networks to be layered to learn higher-order feature interaction information. To sum up, the existing computation methods often have the limitations of gradient disappearance and overfitting. This study presents a novel method (JRD-NFM) by calculating Jaccard similarities, getting an eigenvector through Restarted random walk (RWR), and generating low-dimensional feature vectors by Disposition Component Analysis (DCA). Besides decoding the topological features and similarity information of target and drug node, it can also get the context information of a single network. Considering the advantages of Neural Factorization Machines (NFM) in extracting high-order nonlinear features and processing sparse data, this study use NFM to classifier the collection data to integrate drug and target biochemical structure information. The experimental results demonstrate that JRD-NFM can outperform widely used deep learning methods and conventional chemical structure approaches. It could provide fresh ideas for combining molecular structure and heterogeneous network data to predict DTIs. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Event-Triggered Output Voltage Practical Tracking control for Uninterruptible Power Supply systems

Event-Triggered Output Voltage Practical Tracking Control fo...

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第35届中国控制与决策会议

作者： Ben Wu Wei Liu Weijie Sun Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of Technology

In this paper,by using the output regulation theory,we research the output voltage practical tracking problem for uninterruptible power supply systems under event-triggered control *** configuring the desired poles for a controllable pair and solving the regulator equations,a state feedback gain and a feedforward gain can be obtained for designing the control ***,under event-triggered mechanism,a control law that can be applied to a digital platform directly is designed to solve our ***,it can be verified that the voltage tracking error can converge to a pre-specified neighborhood of the origin.

关键词： Uninterruptible power supply output regulation event-triggered control

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ADRC Combined with IMC for Non-Minimum Phase systems

ADRC Combined with IMC for Non-Minimum Phase Systems

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第42届中国控制会议

作者： Xiangyang Li Xindi Deng Senping Tian Wei Ai Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of Technology

The integrated control method,active disturbance rejection control(ADRC) combined with internal model control(IMC),is proposed for non-minimum phase *** ADRC combined with IMC is to reduce the influence of zeros on right half plant(RHP),system uncertainty and outer *** improved IMC is put in the inner loop and ADRC is adopted in the outer *** order to enhance the system's stability,an approximate zero phase error model of controlled plant(ZPEM) is used as the internal model in the IMC control loop where the feedback of the inner loop is the dynamic weighted filter sum of the real output and the ZPEM *** theoretical analysis and simulations verify effectiveness of the proposed method for non-minimum phase systems.

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