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arXiv 2020年

作者： Wang, Guojun Wu, Jian Tian, Bin Teng, Siyu Chen, Long Cao, Dongpu The State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130022 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 Beijing100190 China The Department of Computer Science Hong Kong Baptist University Hong Kong The School of Data and Computer Science Sun Yat-Sen University Guangdong Guangzhou510275 China The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point-the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://***/wangguojun2018/CenterNet3d. Copyright © 2020, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Correlation-based dynamics and systemic risk measures in the cryptocurrency market 16

Correlation-based dynamics and systemic risk measures in the...

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16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018

作者： Liang, Jiaqi Li, Linjing Zeng, Daniel Zhao, Yunwei State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China CNCERT/CC Beijing China

ISBN: (纸本)9781538678480

Cryptocurrency is a rapid developing financial technology innovation which has attracted a large number of people around the world. The high-speed evolution, radical price fluctuations of cryptocurrency, and the inconsistent attitudes of monetary authorities in different countries have triggered panic and chain reactions towards the application and adoption of cryptocurrency and have caused public security related events. So far, a lot of researches and analyses have focused on just one or only a few number of cryptocurrencies, a comprehensive analysis of the whole cryptocurrency market and its systemic risk is still lacking. In this paper, we analyze the dynamics and systemic risk of the cryptocurrency market based on the public available price history. We first validated that the correlation matrix and asset tree are good tools to analyze the risk and stability of the cryptocurrency market. Furthermore, consistent with public perception, our quantitative analysis reveals that the cryptocurrency market is relatively fragile and unstable. Our work is the first to investigate the systemic risk of the whole cryptocurrency market and may shed some light on cryptocurrency related investment decision, regulation, and legislation. © 2018 IEEE.

关键词： Blockchain

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Attention-based multi-hop reasoning for knowledge graph 16

Attention-based multi-hop reasoning for knowledge graph

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16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018

作者： Wang, Zikang Li, Linjing Zeng, Daniel Dajun Chen, Yue State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Institute of Automation Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China CNCERT/CC Beijing China

ISBN: (纸本)9781538678480

Knowledge graph plays an important role in detection, prediction, early warning, and other security related applications. A fundamental task in applying knowledge graph is the so-called multi-hop reasoning, which focuses on inferring new relations between entities. In this paper, we introduce attention mechanism to the classic compositional method. After finding reasoning paths between entities, we aggregate these paths' embeddings into one according to their attentions, and infer the relation of entities based on the combined embedding. Two experiments on NELL-995 dataset, fact prediction and link prediction, validated that our method outperforms all baselines. © 2018 IEEE.

关键词： Forecasting

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Experimental Application of a Networked Tracking control Method to the Discrete-Event Demonstrator HANS

Experimental Application of a Networked Tracking Control Met...

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control Technology and Applications (CCTA),

作者： Markus Zgorzelski Philipp Ostendarp Jan Lunze Institute of Automation and Computer Control Ruhr-Universität Bochum Germany

A networked tracking control method is experimentally evaluated at the discrete-event demonstrator HANS. controlled subsystems have to solve local tasks independently of each other and cooperative tasks collectively. Local tasks are specified by final states to be reached in the state space of a subsystem and cooperative tasks by state transitions that have to be executed synchronously. To solve the cooperative tasks, the controlled subsystems are extended by a decision unit and a synchronisation unit that recognise cooperative tasks, initiate the synchronisation and guarantee its collective execution. Depending on the current state and the local task, the networked subsystems decide when they temporarily have to connect to each other. An experimental evaluation on a prototypical manufacturing plant demonstrates the applicability of the proposed method under practical circumstances.

关键词： Task analysis Couplings Silicon Discrete-event systems Belts Observers Automata

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Influence of Time-Delay Mismatch for Robustness and Stability

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IFAC-PapersOnLine 2018年第14期51卷 248-253页

作者： Bányász, Cs. Keviczky, L. Bars, R. Institute of Computer Science and Control Hungarian Academy of Sciences H-1111 Budapest Kendeu 13-17 Hungary Department of Automation and Applied Informatics Budapest University of Technology and Economics H-1117 Budapest Magyar Tudósok krt 2 Q/B Hungary

In a practical control system the process always has a time delay. The uncertainty in the knowledge of the time delay i.e. the delay mismatch strongly influences the quality of the closed-loop control. It can cause unwanted instability and influences the robustness of the control. Stability region applicable for this uncertainty is investigated in this paper in connection with the required performance and robustness. © 2018

关键词： Delay control systems Convergence of numerical methods Quality control Robust control Robustness (control systems) Stability Time delay Timing circuits Closed loop control Delay mismatch performance Practical controls Stability regions Time delay uncertainties

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Two-timescale voltage control in distribution grids using deep reinforcement learning

arXiv

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arXiv 2019年

作者： Yang, Qiuling Wang, Gang Sadeghi, Alireza Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for distribution grids by judiciously coupling data-driven with physicsbased optimization. On a faster timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it;whereas, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the longterm discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47-bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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A statistical learning approach to reactive power control in distribution systems

arXiv

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arXiv 2019年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Construction of a Specialized CNC System for Thread Grinding Machines

Construction of a Specialized CNC System for Thread Grinding...

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International Russian automation Conference

作者： Georgi Martinov Ilya Kovalev Akram Al Khoury Institute of Automation and Robotics dept. Computer control systems MSTU STANKIN

ISBN: (纸本)9781538649398

The aim of this research is to construct a specialized system of thread grinding machines considering the features of thread grinding and the requirements to increase accuracy, the original structure of the control system for a thread grinder is proposed. This research will present an example of programming a software-implemented programmable logic controller to process the key press of operator's panel.

关键词： CNC system Thread grinding Soft PLC CNC system structure

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Critical technologies in the cluster of virtual and augmented reality

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IOP Conference Series: Materials Science and Engineering 2021年第1期1019卷

作者： V Zhmud A Liapidevskiy V Avrmachuk V Sayapin J N Sedinkin W Hardt Department of Automation Novosibirsk State Technical University Novosibirsk Russia General Director of Novosibirsk Institute of Program Systems Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Director of R&D Center of Novosibirsk Institute of Program Systems Novosibirsk Russia Vice-Director on Development of Novosibirsk Institute of Program Systems Novosibirsk Russia Department of Computer Science Technical University of Chemnitz Chemnitz Germany

The program 'Digital Economy of the Russian Federation', launched in 2019, contains virtual and augmented reality technologies in its list. Each such technology consists of many assistive technologies called sub-technologies. These assistive technologies make up a large list, while failure to obtain the required results in at least one of these technologies may lead to the fact that the integral technology will not be created. This article discusses a list of only critical assistive technologies, that is, precisely those technologies whose creation is a critical factor for creating a generalized (integral) technology. It is on these technologies that the main emphasis should be placed not only in financing their development, but also in the thorough planning of goals and objectives, in the selection of performers and in the acceptance of research results.

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Density Peak-based Pre-clustering Support Vector Machine for Multi-class Imbalanced Classification

Density Peak-based Pre-clustering Support Vector Machine for...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zonglin Di Qi Kang Daogang Peng Mengchu Zhou Department of Control Science and Engineering Tongji University Shanghai China College of Automation Engineering Shanghai University of Electric Power Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

Imbalanced classification using a support vector machine (SVM) is a normal but crucial problem in machine learning. Compared with binary classification, multiclass classification is much more complicated. Most existing studies on imbalanced classification using SVM focus on binary imbalanced classification; while only few of them look into imbalanced classification with multiple classes. Pre-clustering is a useful technique to prepare proper data from an imbalanced dataset for a classifier. It can be used to extract the feature of a dataset first and improve classification performance. Density peak based on Euclidean distance proves its effectiveness and generality in clustering. Motivated by this and the fact that the number of clusters is known in multi-class classification using a one-vs-rest strategy, we combine density peak clustering and SVM to propose a new pre-clustering method to perform effective imbalanced classification with multiple classes. Specifically, we transform a multi-class classification problem into several binary classification tasks. The results on 5 public datasets in terms of F-measure, G-mean and Area Under Curve (AUC) show its superiority over the original SVM and SVM with other methods including random under-sampling, Synthetic Minority Oversampling Technique, pre-clustering using K-Means and EasyEnsemble methods using either a one-vs-rest or one-vs-one strategy.

关键词： Support vector machines Feature extraction Nickel Testing Task analysis Euclidean distance Kernel

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