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2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Rivas-Blanco, Irene Del-Pulgar, Carlos J. Pérez Mariani, Andrea Tortora, Giuseppe Reina, Antonio J. University of Malaga Dept. Systems Engineering and Automation Malaga Spain Scuola Superiore Sant'Anna The BioRobotics Institute Dept. Excellence in Robotics & Ai Pisa Italy ABzero Srl Smart Medical Theatre Lab Pisa Italy

ISBN: (纸本)9798350322972

The use of large datasets is essential in surgical robotics to advance in the field of recognition and automation of surgical tasks. Furthermore, public datasets allow the comparison of different algorithms and methods to evaluate their performance. The objective of this work is to provide a complete dataset of three common training surgical tasks performed with the da Vinci Research Kit (dVRK). The dataset contains a total of 206 trials performed by twelve different subjects. For each trial, the dataset includes 154 kinematic variables from the dVRK (both master and slave sides) together with the associated video recorded with a camera. All the data has been carefully timestamped and provided in a readable csv format. A MATLAB interface integrated with ROS for using and replicating the data is also provided. © 2023 IEEE.

关键词： Large dataset

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Design of Polar Coding in Filtered-OFDM

Design of Polar Coding in Filtered-OFDM

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International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2022

作者： Piao, Jinnan Li, Dong Li, Zhibo Yang, Ming Liu, Jindi Yu, Xueting Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9789819904150

The filtered-orthogonal frequency division multiplexing (f-OFDM) is a new waveform configuration for the fifth generation wireless communication system, which satisfies different requirements and channel characteristics more flexibly. Polar codes are adopted as the channel coding scheme of enhance mobile broadband (eMBB) control channel scenario of 5G and is also the candidate coding scheme for ultra-reliable and low latency communications (URLLC) which is on services with strict delay constraints, such as industrial automation. In the paper, we introduce polar codes into f-OFDM system to increase the performance. The conception of channel polarization transformation is extended into the f-OFDM transmission system, where we divide the f-OFDM channels into binary-input channels with a two-stage channel polarization transformation. For the first stage of transformation, we divide a f-OFDM channel into a group of f-OFDM synthesized channels with the bit-interleaved modulation scheme. For the second stage of transformation, we decompose the f-OFDM synthesized channels into binary polarized channels with parallel channels polarization principle. The proposed scheme is evaluated via link simulation under AWGN and fading channels. The simulation results illustrate that the performance gain of polar codes is not affected by the f-OFDM compared with turbo coding scheme. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Orthogonal frequency division multiplexing

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An Improved Deep Learning Method-based Detection of Transmission Line Insulator Defects

An Improved Deep Learning Method-based Detection of Transmis...

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2023 International Conference on Computer, Artificial Intelligence, and Control Engineering, CAICE 2023

作者： Gao, Pengpei Song, Chunhe Yu, Shimao Wu, Tingting State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510665040

The insulator is a vital component of power equipment that aids in power transmission. Aiming at transmission line faults caused by missing insulator pieces, this paper proposed to divide insulator detection and defect location into two steps. Additionally, we add Squeeze-and-Excitation (SE) module into the Faster R-CNN model at the insulator detection stage, which could enhance insulator detection accuracy under similar model complexity and also improve the detection efficiency of the YOLOv3 model on insulator defect location. © 2023 SPIE.

关键词： Object detection

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Air-Ground Robots’ Cooperation-Based Mountain Glaciers Thickness Continuous Detection: systems And Applications 16th

Air-Ground Robots’ Cooperation-Based Mountain Glaciers Thic...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Zhong, Jikang Li, Peng Liu, Xu Zhao, Pinhui Jiang, Han Yang, Liying Li, Decai Bu, Chunguang He, Yuqing State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789819964826

In this article, we present a multi-robot continuous ice thickness measurement system that can operate in plateau glacial environments with natural slopes up to 30°, large crevasses of glacier, slippery snow/ice ground surface, and inevitable terrain undulations. The ground station operator can operate the unmanned ground vehicle (UGV) with improved safety driving ability by following the optimal driving advice provided by the driving skill learning model. The air-ground robot collaboration algorithm coordinated the operation of the UGV with the unmanned aerial vehicle (UAV) to improve the mobility of the UGV in complex glacier environments. We obtain multi-scale and multi-aspect environmental information through air-ground collaboration and use a lightweight environmental modeling method to obtain a three-dimensional model of the glacier surface and subglacial terrain. The system was applied in the Korchung Gangri Glacier in the Tibet Plateau in June 2022. The experimental results show that the system can operate in the extreme environment of plateau glaciers and collect high-precision continuous ice thickness distribution data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Thickness measurement

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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AGV semantic attack detection based on extended stochastic Petri net

AGV semantic attack detection based on extended stochastic P...

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作者： Zhang, Sichao Liang, Wei Zhang, Yinlong Wang, Kai State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

Semantic attack, as a covert and highly destructive network attack, seriously threatens the security of automatic guided vehicle (AGV) system. To make it worse, the dynamic change of wireless channel makes semantic attack detection more non-intractable. In order to solve the issues on the current semantic attack detection in wireless network environment, this paper proposes an extended stochastic Petri net (ESPN)-based detection method. It builds a stochastic Petri net (SPN) model for the normal operation process of AGV system, and detects the semantic attack by comparing the characteristics of relevance matrix, average dwell time and token density. Additionally, we innovatively add the wireless channel evaluation parameter in the SPN model, to estimate the current wireless channel state and eliminate the impact of wireless interference on attack detection. Eventually, an AGV attack detection system experimental platform has been developed to verify the effectiveness of the proposed method. The experimental results show that the proposed algorithm can effectively eliminate the impact of wireless channel interference on semantic attack detection, and has impressive detection performance. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Stochastic systems

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A Framework for Automatic Identification of Neural Network Structural Redundancy Based on Reinforcement Learning 5

A Framework for Automatic Identification of Neural Network S...

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5th International Conference on Computer Information Science and Artificial Intelligence, CISAI 2022

作者： Wu, Tingting Song, Chunhe Zeng, Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510662520

The increasing structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works usually compress models by removing unimportant filters based on the importance. However, the importance-based algorithms tend to ignore the parameters that extract edge features with small criterion values. And recent studies have shown that the existing criteria rely on norm and lead to similar model compression structures. Aiming at the problems of ignoring edge features and manually specifying the pruning rate in current importance-based model pruning algorithms, this paper proposes an automatic recognition framework for neural network structure redundancy based on reinforcement learning. First, we perform cluster analysis on the filters of each layer, and map the filters into a multidimensional space to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we use reinforcement learning to automatically optimize the cluster dimension, and then determine the optimal pruning rate for each layer to reduce the performance loss caused by pruning. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework. © 2023 SPIE.

关键词： Reinforcement learning

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Comparison Study of Different Types of Setting Reference Movements for Mechatronic Objects by Parametric Splines

Comparison Study of Different Types of Setting Reference Mov...

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2022 International Conference on Ocean Studies, ICOS 2022

作者： Gubankov, Anton Gornostaev, Igor Institute of Automation and Control Processes Feb Ras Sevastopol State University Robotics Laboratory Sevastopol Vladivostok Russia Institute of Marine Technology Problems Feb Ras Laboratory of Robotic Systems Vladivostok Russia

ISBN: (纸本)9781665473729

The paper presents a comparative study of various ways of specifying program movements for mechatronic objects (MO) along complex spatial trajectories defined by parametric Bezier splines, B-splines and third-order Catmull-Rom splines. An expression is obtained to describe the dependence of the rate of change of the parameter setting the desired position of the MO with the program speed of its movement, taking into account information about the type of trajectory. As a result, using this expression, which is universal for all the specified parametric splines of the third order, it is ensured that the MO moves along the trajectory exactly at a given speed while simultaneously changing its orientation as desired. The application of the methods considered in this paper does not require complex iterative calculations and allows one to perform all the necessary calculations in real time, even on low-power on-board computers of various robots and robotic complexes. Numerical modeling was carried out to analyze the features of the use of these splines in solving practical problems. © 2022 IEEE.

关键词： Trajectories

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Wave-motion compensation for USV–UAV cooperation: A model predictive controller approach

Wave-motion compensation for USV–UAV cooperation: A model p...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： José Martins Pedro Pereira Raul Campilho Andry Pinto ISEP - School of Engineering Polytechnic of Porto Centre for Robotics and Automation Systems of INESC TEC Porto Portugal Faculty of Engineering University of Porto Centre for Robotics and Automation Systems of INESC TEC Porto Portugal ISEP - School of Engineering Polytechnic of Porto INEGI Institute of Mechanical Engineering Porto Portugal

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Due to the difficult access to the maritime environment, cooperation between different robotic platforms operating in different domains provides numerous advantages when considering Operations and Maintenance (O&M) missions. The nest Uncrewed Surface Vehicle (USV) is equipped with a parallel platform, serving as a landing pad for Uncrewed Aerial Vehicle (UAV) landings in dynamic sea states. This work proposes a methodology for short term forecasting of wave-behaviour using Fast Fourier Transforms (FFT) and a low-pass Butterworth filter to filter out noise readings from the Inertial Measurement Unit (IMU) and applying an Auto-Regressive (AR) model for the forecast, showing good results within an almost 10-second window. These predictions are then used in a Model Predictive Control (MPC) approach to optimize trajectory planning of the landing pad roll and pitch, in order to increase horizontality, consistently mitigating around 80% of the wave induced motion.

关键词： Sea surface Fast Fourier transforms Trajectory planning Surface waves Noise Predictive models Prediction algorithms Autonomous aerial vehicles Forecasting Vehicle dynamics

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