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arXiv 2017年

作者： Xi, Xuanyang Luo, Yongkang Li, Fengfu Wang, Peng Qiao, Hong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200031 China Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China LSEC Institute of Applied Mathematics AMSS Chinese Academy of Sciences Beijing100190 China

Visual saliency detection aims at identifying the most visually distinctive parts in an image, and serves as a pre-processing step for a variety of computer vision and image processing tasks. To this end, the saliency detection procedure must be as fast and compact as possible and optimally processes input images in a real time manner. It is an essential application requirement for the saliency detection task. However, contemporary detection methods often utilize some complicated procedures to pursue feeble improvements on the detection precession, which always take hundreds of milliseconds and make them not easy to be applied practically. In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images. It is an extremely simplified end-to-end deep neural network without any pre-processings and post-processings. When given an image, the network can directly predict a dense full-resolution saliency map (image-to-image prediction). It works like a compact pipeline which effectively simplifies the detection procedure. Our method is evaluated on six public datasets, and experimental results show that it can achieve comparable or better precision performance than the state-of-the-art methods while get a significant improvement in detection speed (35 FPS, processing in real time). Copyright © 2017, The Authors. All rights reserved.

关键词： Deep neural networks

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Real-Time Marker Localization Learning for GelStereo Tactile...

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IEEE International Conference on Robotics and Biomimetics

作者： Shujuan Liu Shaowei Cui Chaofan Zhang Yinghao Cai Shuo Wang University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science Intelligence Technology Chinese Academy of Sciences Shanghai China

ISBN: (纸本)9781665481106

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better realtime performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks.

关键词： Location awareness Sensitivity Object detection Network architecture Predictive models Robot sensing systems Real-time systems

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Approaches and solutions for Martian spacesuit design 68

Approaches and solutions for Martian spacesuit design

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68th International Astronautical Congress: Unlocking Imagination, Fostering Innovation and Strengthening Security, IAC 2017

作者： Lousada, J. Kamaletdinova, G.R. Patel, D. Lakmal, Y. Oluwafemi, F.A. De La Torre, A. Heshani, U. Onevsky, M.P. Skvortsov, S.A. DLR [German Aerospace Center Germany Scientific laboratory of design and modelling of complex engineering systems Tambov State Technical University 106 Sovetskaya street Tambov392000 Russia 400 Kent Street SydneyNSW2000 Australia C/o European Space Policy Institute Schwarzenbergplatz 6 Vienna1030 Austria Km 17 Airport Road Abuja437 Nigeria C/o European Space Policy Institute Schwarzenbergplatz 6 Vienna1030 Austria C/o European Space Policy Institute Schwarzenbergplatz 6 Vienna1030 Austria Department of Information processes and control Tambov State Technical University 106 Sovetskaya street Tambov392000 Russia Department of Information processes and control Tambov State Technical University 106 Sovetskaya street Tambov392000 Russia

ISBN: (纸本)9781510855373

The Human exploration of Mars is often seen as the next step in human exploration of the Solar System. Decades of scientific explorations related to Mars recorded data on the environment of the planet, including geological aspects, atmospheric composition, life aspects, and helped to create a foundation defining main hazards and security risk assessment. Based on data from decades of observation and robotic missions to Mars, many agencies and organizations around the world started planning the first human mission to Mars. Extravehicular Activities (EVAs) will play a crucial role in any mission either long or short term as human participation is an essential step in the future. Short term mission EVAs will allow to collect additional data about the planet and its environment, to run external experiments, and to work on setting up a part of future habitats if needed. Long term mission EVAs will help to run maintenance activities supporting the whole operation of the habitat or station and continue all needed scientific activities. Previous studies were dedicated to the analysis of possible hazards for the habitat, station and spacesuit. The most relevant hazards for the spacesuit design were defined including structural failures, power, thermal control and life support systems off-nominal situations (ONS), communication problems, loss in data management and problems related with humans in the crew. Previous works related to the logical and physical level of model of knowledge database for automated control system of the "artificial lungs" equipment helped to define basic conditions of human in this mission, dependence on psychophysiological state and needed level of preliminary testing. Potential solutions for listed ONS will be discussed in this paper in greater details. This paper will examine past experience in spacesuit design and its restrictions including aspects of mobility and operability, the conditions they are created to work in and its applicability in Mar

关键词： Fracture mechanics

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Learning Latent Causal Factors from the Intricate Sensor Feedback of Contact-Rich Robotic Assembly Tasks

SSRN

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SSRN 2023年

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To alleviate the low learning efficiency from the problematic observations, we propose introducing the concept of latent causal factors that cause the sensor observations and truly matter in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut assembly environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing intricate industrial challenges%. © 2023, The Authors. All rights reserved.

关键词： Robotic assembly

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Optimal control of a Delayed HIV Infection Model via Fourier Series

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Journal of Nonlinear Dynamics 2014年第1期2014卷

作者： Gh. Ghanbari M. H. Farahi Department of Applied Mathematics Ferdowsi University of Mashhad Mashhad *** Iranum.ac.ir The Center of Excellence on Modelling and Control Systems (CEMCS) Mashhad *** Iran

We present a delayed optimal control which describes the interaction of the immune system with the human immunodeficiency virus (HIV) and CD4 + T-cells. In order to improve the therapies, treatment and the intracellular delays are incorporated into the model. The optimal control in this model represents the efficiency of drug treatment in preventing viral production and new infections. The optimal pair of control and trajectories of this nonlinear delay system with quadratic cost functional is obtained by Fourier series approximation. The method is based on expanding time varying functions in the nonlinear delay system into their Fourier series with unknown coefficients. Using operational matrices for integration, product, and delay, the problem is reduced to a set of nonlinear algebraic equations.

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Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

Group Feature Learning and Domain Adversarial Neural Network...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chen-Chen Fan Haiqun Xie Liang Peng Hongjun Yang Zhen-Liang Ni Guan’an Wang Yan-Jie Zhou Sheng Chen Zhijie Fang Shuyun Huang Zeng-Guang Hou State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China First People's Hospital of Foshan Foshan China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF- DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group- level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system.

关键词： Deep learning Neural networks Medical services Human factors Sensitivity and specificity Feature extraction Robot sensing systems

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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IEEE International Conference on Robotics and Biomimetics

作者： Tian-Yu Xiang Xiao-Hu Zhou Xiao-Liang Xie Shi-Qi Liu Hong-Jun Yang Zhen-Qiu Feng Jian-Long Hao Zeng-Guang Hou The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The school of Information Shanxi University of Finance and Economics Taiyuan China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa Macau

ISBN: (纸本)9781665481106

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios.

关键词： Training Convolution Computational modeling Biological system modeling Streaming media Brain modeling Electroencephalography

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Group feature learning and domain adversarial neural network for aMCI diagnosis system based on EEG

arXiv

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arXiv 2021年

作者： Fan, Chen-Chen Xie, Haiqun Peng, Liang Yang, Hongjun Ni, Zhen-Liang Wang, Guanan Zhou, Yan-Jie Chen, Sheng Fang, Zhijie Huang, Shuyun Hou, Zeng-Guang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China Department of Neurology First People’s Hospital of Foshan FoshanGuangdong528000 China

— Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer’s disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system. © 2021, CC BY.

关键词： Diagnosis

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Erratum to: Analytical two-dimensional modeling of hydrogen–air mixture in catalytic micro-combustor

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Meccanica 2015年第12期50卷 3133-3133页

作者： Fanaee, S. A. Esfahani, J. A. Mechanical Engineering Department University of Birjand Birjand Iran Mechanical Engineering Department Center of Excellence on Modelling and Control Systems (CEMCS) Ferdowsi University of Mashhad Mashhad Iran

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

arXiv

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arXiv 2022年

作者： Liu, Shujuan Cui, Shaowei Zhang, Chaofan Cai, Yinghao Wang, Shuo The University of Science and Technology Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better real-time performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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