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Parallel mining operating systems: From digital twins to mining intelligence 1

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Parallel mining operating systems: From digital twins to min...

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

作者： Chen, Long Hu, Xiaoming Wang, Ge Cao, Dongpu Li, Lingxi Wang, Fei-Yue Sun Yat-sen University Guangzhou510006 China Waytous Inc. Qingdao266109 China College of Intelligence and Computing Tianjin University Tianjin300350 China Institute of Systems Engineering Macau University of Science and Technology 999078 China Indiana University-Purdue University ECE Department IndianapolisIN46202 United States Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665433372

With the rapid development and modernization requirement of global coal industry, there is an emerging need for intelligent and unmanned mining systems. In this paper, the Intelligent Mining Operating System (IMOS) is proposed and developed, based on the parallel management and control of mining operating infrastructure that integrates the intelligent mining theory, the ACP-based (Artificial societies, Computational experiments, Parallel execution) parallel intelligence approaches, and the new generation of artificial intelligence (AI) technologies. To satisfy the intelligent and unmanned demand of open-pit mines, the IMOS architecture is developed by integrating the theory of digital quadruplets. The main subsystems and functions of IMOS are elaborated in detail, including a single-vehicle operating subsystem, multi-vehicle collaboration subsystem, vehicle-road collaboration subsystem, unmanned intelligent subsystem, dispatch management subsystem, parallel management and control subsystem, supervisory subsystem, remote takeover subsystem, and communication subsystem. The IMOS presented in this paper is the first integrated solution for intelligent and unmanned mines in China, and has been implemented over ten main open pits in the past few years. Its deployment and utilization will effectively improve the production efficiency and safety level of open-pit mines, promote the construction of ecological mines, and bring great significance to the realization of sustainable mining development. © 2021 IEEE.

关键词： Computation theory

OpenDR: An Open Toolkit for Enabling High Performance, Low Footprint Deep Learning for Robotics

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arXiv 2022年

作者： Passalis, Nikolaos Pedrazzi, S. Babuska, R. Burgard, W. Dias, D. Ferro, F. Gabbouj, M. Green, O. Iosifidis, A. Kayacan, E. Kober, J. Michel, O. Nikolaidis, N. Nousi, P. Pieters, R. Tzelepi, M. Valada, A. Tefas, A. Dept. of Informatics Aristotle University of Thessaloniki Greece Cyberbotics Switzerland Dept. of Cognitive Robotics Delft University of Technology Netherlands Dept. of Computer Science University of Freiburg Germany PAL Robotics Spain The units of Computing Sciences and Automation Technology and Mechanical Engineering Tampere University Finland Agrointelli Denmark The Department of Electrical and Computer Engineering Aarhus University Denmark

Existing Deep Learning (DL) frameworks typically do not provide ready-to-use solutions for robotics, where very specific learning, reasoning, and embodiment problems exist. Their relatively steep learning curve and the different methodologies employed by DL compared to traditional approaches, along with the high complexity of DL models, which often leads to the need of employing specialized hardware accelerators, further increase the effort and cost needed to employ DL models in robotics. Also, most of the existing DL methods follow a static inference paradigm, as inherited by the traditional computer vision pipelines, ignoring active perception, which can be employed to actively interact with the environment in order to increase perception accuracy. In this paper, we present the Open Deep Learning Toolkit for Robotics (OpenDR). OpenDR aims at developing an open, non-proprietary, efficient, and modular toolkit that can be easily used by robotics companies and research institutions to efficiently develop and deploy AI and cognition technologies to robotics applications, providing a solid step towards addressing the aforementioned challenges. We also detail the design choices, along with an abstract interface that was created to overcome these challenges. This interface can describe various robotic tasks, spanning beyond traditional DL cognition and inference, as known by existing frameworks, incorporating openness, homogeneity and robotics-oriented perception e.g., through active perception, as its core design principles. Copyright © 2022, The Authors. All rights reserved.

关键词： Robotics

A Survey of Pretraining on Graphs: Taxonomy, Methods, and Applications

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arXiv 2022年

作者： Xia, Jun Zhu, Yanqiao Du, Yuanqi Li, Stan Z. School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Computer Science George Mason University United States

Pre-trained Language Models (PLMs) such as BERT have revolutionized the landscape of natural language processing (NLP). Inspired by their proliferation, tremendous efforts have been devoted to pre-trained graph models (PGMs) recently. Owing to its huge model parameters, PGMs can capture abundant knowledge from massive labeled and unlabeled graph data. The knowledge implicitly encoded in model parameters can benefit various downstream tasks and help to alleviate several fundamental issues of learning on graphs. In this paper, we provide a comprehensive survey for PGMs. We first briefly present the limitations of graph representation learning and thus introduce the motivation for graph pre-training. Next, we systematically categorize existing PGMs based on a taxonomy from five different perspectives including the history, model architectures, pre-training strategies, tunning strategies and applications. Finally, we outline several promising research directions that can serve as a guideline for future studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Taxonomies

Enhancing Force Sensing Capabilities in Exoskeleton Interfaces Using Compliant Actuator-Sensor Units - A User Study

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Enhancing Force Sensing Capabilities in Exoskeleton Interfac...

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Hosam Alagi Nikola Fischer Kai Behrends Iris Ftirst-Walter Jürgen Becker Michael Beigl Franziska Mathis-Ullrich Björn Hein Intelligent Process Automation and Robotics Laboratory (IPR) Institute for Anthropomatics and Robotics (IAR) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institute of Telematics (TM) - Pervasive Computing Systems (TECO) at Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Institut fuer Technik der Informationsverarbeitung (ITIV) at Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany Karlsruhe University of Applied Sciences Karlsruhe Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Reliable interaction force sensing between an exoskeleton and the human body is crucial for personalized support and efficient control. Poor donning and disturbances during movement lead to lower force measurement quality and thus to unfavorable use. In this work, we present an active smart human-robot interface by utilizing compliant actuator-sensor units (CASUs) that can be worn over clothing. These units combine capacitive force sensing and actuation using Shape-Memory Alloy (SMA) to actively compensate disturbances and improve force measurement by tightening the interface and reestablishing optimal contact. We present a pilot user study with five subjects, investigating the performance of an exoskele-ton interface featuring three integrated CASUs to compensate for non-ideal donning configurations and displacements during daily activities of walking, sitting down, and climbing the stairs. The results for a passive interface show a decrease in holding forces and force signal quality during activities. Our active interface, on the other hand, effectively compensates for disturbances, restoring high signal quality by the mean of peak-to-peak value up to 300% which leads to more distinct signals.

关键词： Legged locomotion Force measurement Cooling Force Exoskeletons Metals Stairs Robot sensing systems Sensors Reliability

Terms Development of Additive Manufacturing

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Terms Development of Additive Manufacturing

Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Tariku Sinshaw Tamir Gang Xiong Jingchao Jiang Zhen Shen Ehtisham Lodhi Hub Ali Li Wan Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao China Ten Dimensions (Guangdong) Technology Co. Ltd Foshan China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

Nowadays, additive manufacturing has been increasingly developed and applied in many fields. As a rapid development of additive manufacturing, similar terms are appeared, such as 3D printing, freeform fabrication, rapid tooling, rapid manufacturing, direct digital manufacturing, layer manufacturing, additive process, 4D printing, 3D bioprinting, and 3D biofabrication. This paper reports and analyze the development of these terms. Moreover, it tries to give the readers a comprehensive understanding of these terms and their development.

关键词： Fabrication Additives Three-dimensional displays Three-dimensional printing Market research Digital twins Biological materials

BrainNNExplainer: An interpretable graph neural network framework for brain network based disease analysis

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arXiv 2021年

作者： Cui, Hejie Dai, Wei Zhu, Yanqiao Li, Xiaoxiao He, Lifang Yang, Carl Department of Computer Science Emory University Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Department of Computer Science Princeton University Department of Computer Science and Engineering Lehigh University

MSC Codes 68T07, 68T45, 68T20Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience. GNNs are promising to model complicated network data, but they are prone to overfitting and suffer from poor interpretability, which prevents their usage in decision-critical scenarios like healthcare. To bridge this gap, we propose BrainNNExplainer, an interpretable GNN framework for brain network analysis. It is mainly composed of two jointly learned modules: a backbone prediction model that is specifically designed for brain networks and an explanation generator that highlights disease-specific prominent brain network connections. Extensive experimental results with visualizations on two challenging disease prediction datasets demonstrate the unique interpretability and outstanding performance of BrainNNExplainer. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

Regularly Truncated M-estimators for Learning with Noisy Labels

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arXiv 2023年

作者： Xia, Xiaobo Lu, Pengqian Gong, Chen Han, Bo Yu, Jun Yu, Jun Liu, Tongliang The Sydney AI Center School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia The Australian AI Institute Faculty of Engineering and IT The University of Technology Sydney BroadwayNSW2007 Australia The PCA Lab The Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing Hong Kong Polytechnic University Hong Kong The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China The Department of Automation University of Science and Technology of China Hefei230026 China

The sample selection approach is very popular in learning with noisy labels. As deep networks "learn pattern first", prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, while the large-loss examples are treated as mislabeled ones and excluded from network parameter updates. However, such a procedure is arguably debatable from two folds: (a) it does not consider the bad influence of noisy labels in selected small-loss examples;(b) it does not make good use of the discarded large-loss examples, which may be clean or have meaningful information for generalization. In this paper, we propose regularly truncated M-estimators (RTME) to address the above two issues simultaneously. Specifically, RTME can alternately switch modes between truncated M-estimators and original M-estimators. The former can adaptively select small-losses examples without knowing the noise rate and reduce the side-effects of noisy labels in them. The latter makes the possibly clean examples but with large losses involved to help generalization. Theoretically, we demonstrate that our strategies are label-noise-tolerant. Empirically, comprehensive experimental results show that our method can outperform multiple baselines and is robust to broad noise types and levels. The implementation is available at https://***/xiaoboxia/RTM_LNL. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs

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arXiv 2021年

作者： Xiao, Jiaren Dai, Quanyu Xie, Xiaochen Lam, James Kwok, Ka-Wai Department of Mechanical Engineering The University of Hong Kong Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Automation Harbin Institute of Technology Shenzhen China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China

The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train together with the scarce available labeled nodes. However, most existing methods require the information of all nodes, including those to be predicted, during model training, which is not practical for dynamic graphs with newly added nodes. To address this issue, an adversarially regularized graph attention model is proposed to classify newly added nodes in a partially labeled graph. An attention-based aggregator is designed to generate the representation of a node by aggregating information from its neighboring nodes, thus naturally generalizing to previously unseen nodes. In addition, adversarial training is employed to improve the model’s robustness and generalization ability by enforcing node representations to match a prior distribution. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods. The code is available at https://***/JiarenX/AGAIN. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

Discover and align taxonomic context priors for open-world semi-supervised learning 23

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Discover and align taxonomic context priors for open-world s...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yu Wang Zhun Zhong Pengchong Qiao Xuxin Cheng Xiawu Zheng Chang Liu Nicu Sebe Rongrong Ji Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China School of Computer Sceince University of Nottingham United Kingdom School of Electronic and Computer Engineering Peking University Shenzhen China and Department of Information Engineering and Computer Science University of Trento Italy School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China and Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Department of Automation Tsinghua University Beijing China Department of Information Engineering and Computer Science University of Trento Italy School of Electronic and Computer Engineering Peking University Shenzhen China and Peng Cheng Laboratory Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China

Open-world Semi-Supervised Learning (OSSL) is a realistic and challenging task, aiming to classify unlabeled samples from both seen and novel classes using partially labeled samples from the seen classes. Previous works typically explore the relationship of samples as priors on the pre-defined single-granularity labels to help novel class recognition. In fact, classes follow a taxonomy and samples can be classified at multiple levels of granularity, which contains more underlying relationships for supervision. We thus argue that learning with single-granularity labels results in sub-optimal representation learning and inaccurate pseudo labels, especially with unknown classes. In this paper, we take the initiative to explore and propose a uniformed framework, called Taxonomic context prIors Discovering and Aligning (TIDA), which exploits the relationship of samples under various granularity. It allows us to discover multi-granularity semantic concepts as taxonomic context priors (i.e., sub-class, target-class, and super-class), and then collaboratively leverage them to enhance representation learning and improve the quality of pseudo labels. Specifically, TIDA comprises two components: i) A taxonomic context discovery module that constructs a set of hierarchical prototypes in the latent space to discover the underlying taxonomic context priors; ii) A taxonomic context-based prediction alignment module that enforces consistency across hierarchical predictions to build the reliable relationship between classes among various granularity and provide additions supervision. We demonstrate that these two components are mutually beneficial for an effective OSSL framework, which is theoretically explained from the perspective of the EM algorithm. Extensive experiments on seven commonly used datasets show that TIDA can significantly improve the performance and achieve a new state of the art. The source codes are publicly available at https://***/rain305f/TIDA.

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