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15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

作者： Mahdi Al-Adhami, Marwah Saad Long, Jiang Wang, Wei Wang, Lei Beihang University School of Automation Science and Electrical Engineering Beijing China Beihang University School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China

ISBN: (纸本)9781728151694

This paper investigates the leaderless consensus problem for high-order nonlinear systems with unmatched unknown parameters and intermittent actuator faults. Under directed topology condition, a novel adaptive fault tolerant leaderless consensus control scheme is proposed. In order to achieve the leaderless consensus under directed topology condition, an auxiliary system, which is related to the individual states and neighboring states, is introduced in each agent. On the other hand, to compensate the effect of intermittent actuator faults on the system stability of each agent, a local parameter estimator is introduced to estimate a lumped unknown parameter related to the lower bound of the effectiveness of actuator. It is shown that with the proposed adaptive fault tolerant leaderless consensus control scheme, all the closed-loop signals are globally uniformly bounded and all the agents' outputs can reach a consensus. © 2020 IEEE.

关键词： System stability

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A Vision-Based Method for Vehicle Forward Collision Warning

A Vision-Based Method for Vehicle Forward Collision Warning

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Chinese Intelligent Systems Conference, CISC 2019

作者： Zhang, Yanfei Wang, Zhangyu Zhou, Bin Yu, Guizhen Hu, Chaowei Zhang, Li School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beihang University Beijing100191 China

ISBN: (纸本)9789813296978

Forward Collision Warning (FCW) system can automatically measure the distance between obstacles and provide early warning, which can effectively provide safety guarantee for vehicle travel and reduce the probability of traffic accidents. The vision-based approaches are always popular because vision sensors have the characteristics of low cost and rich image information. In this paper, a vision-based forward collision warning method is proposed. The method contains three main stages: (1) detect obstacle based on multi-feature fusion using convolution neural networks (CNN), (2) estimate the relative distance, relative velocity from vehicles and collision time, (3) design obstacles hazard level discrimination strategy for different road scenarios. The algorithm is tested on our own dataset and the experiment results showed that the method has good feasibility and robustness. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Hazards

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Quantum graph convolutional neural networks

arXiv

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arXiv 2021年

作者： Zheng, Jin Gao, Qing Lü, Yanxuan School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data. In this paper, we propose a novel quantum graph convolutional neural network (QGCN) model based on quantum parametric circuits and utilize the computing power of quantum systems to accomplish graph classification tasks in traditional machine learning. The proposed QGCN model has a similar architecture as the classical graph convolutional neural networks, which can illustrate the topology of the graph type data and efficiently learn the hidden layer representation of node features as well. Numerical simulation results on a graph dataset demonstrate that the proposed model can be effectively trained and has good performance in graph level classification tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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Vision-Based Vehicle Detection in Foggy Days by Convolutional Neural Network

Vision-Based Vehicle Detection in Foggy Days by Convolutiona...

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Chinese Intelligent Systems Conference, CISC 2019

作者： Yu, Guizhen Wang, Sifen Li, Mingxing Guo, Yaxin Wang, Zhangyu School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789813296978

Vehicle detection plays an important role in advanced driving assisted system and autonomous driving system. However, the existing vehicle detection methods are not robust in harsh environments, especially in foggy environment. To solve this problem, a vision-based vehicle detection structure using convolutional neural network is presented to detect the vehicle in foggy days. In our vehicle detection structure, a pair of encoders and decoders is used to estimate atmospheric illumination and transmissivity, and to establish the defogging image firstly. And then, the vehicle detection is implemented by a proposed vehicle detection method which predict the left-top key point as well as the right- bottom key point of the vehicle, thus get the bounding box of the vehicle. To verify the effectiveness of the new method, a data set based on the video generated from PreScan simulation platform is set up. And the new vehicle detection method is tested in multiple scenarios such as left turn, right turn, uphill, downhill. Experimental results show that our vehicle detection structure can effectively detect vehicles in foggy days. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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DependLoc: A Dependency-based Framework For Bug Localization

DependLoc: A Dependency-based Framework For Bug Localization

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Asia-Pacific Conference on Software Engineering

作者： Wei Yuan Binhang Qi Hailong Sun Xudong Liu Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China

ISBN: (数字)9781728195537

ISBN: (纸本)9781728195544

As software systems are becoming larger and more complex, debugging poses great challenges to software developers and maintainers. Among various efforts on easing the burden of debugging, bug localization techniques are developed to help locate where a bug occurs in source code files automatically. Information retrieval and deep neural network techniques are often adopted in existing research to achieve bug localization through capturing the textual or semantic similarity between bug reports and source code files. At the same time, some domain- specific eatures in software engineering are also utilized to locate the buggy files. However, the dependency relationship between classes (A depends on B if A references B) is not considered or utilized by existing approaches. In this work, we propose a novel framework DependLoc for bug localization which leverages the dependency relationship among source code files. DependLoc is based on the observation that buggy files may not be highly similar to a bug report but have a dependency relationship with one or more files that are quite similar to the bug report. DependLoc adopts a customized Ant Colony algorithm to quantify the intrinsic dependency relationship (called reference heat) and designs a segment-based encoder to learn this feature. Experimental results on six widely-used benchmark datasets for bug localization show that our approach outperforms the state-of-the-art methods, and Accuracy@10 is improved by 4% on average.

关键词： Location awareness Computer bugs Debugging Benchmark testing Software systems Software reliability Software engineering

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Dynamic network embedding via incremental skip-gram with negative sampling

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Science China(Information Sciences) 2020年第10期63卷 89-107页

作者： Hao PENG Jianxin LI Hao YAN Qiran GONG Senzhang WANG Lin LIU Lihong WANG Xiang REN Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Computer Science University of Southern California

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O（1/n;）, and the second order moment of the objective difference can be stabilized in order of O（1）. Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up to

关键词： dynamic network embedding bound and convergence analysis multi-label classification link prediction

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Erratum to "Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle"

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IEEE transactions on biomedical circuits and systems 2025年第1期19卷 238页

作者： Jing Liang Yuanqi Hu Hefei Innovation Research Institute Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing and School of Integrated Circuits Science and Engineering Beihang University Beijing China

In [1], in section III.E of the article, we calculate the equivalent tunnelling current according to equation (4) by using the value of Cg, eff as 1.679 fF, which is about 4.6 times smaller than the correct value. This leads to the wrong equivalent impedance value obtained in the final Fig. 10 is about 4.6 times larger than the correct value, and the equivalent impedance should be about 2.2 PΩ at this size, so according to the basis of the above, the article should be corrected as follows.

关键词： Circuits and systems Cutoff frequency Tunneling

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data-Driven Control Based on Function Approximation with Binomial Coefficients

Data-Driven Control Based on Function Approximation with Bin...

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第33届中国控制与决策会议

作者： Guoliang Zhu Lei Chen Kexin Liu Jinhu Lü School of Automation Science and Electrical Engineering Beihang Uniersity Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Advanced Innovation Center for Big Data and Brain Computing Beihang University

This paper investigates data-driven control methods based on function approximation with binomial coefficients and sampling *** propose two approximate models and corresponding controllers formed from sampled *** shows that the information truncated in the first approximate model is saved in the latter with parameter estimation *** results validate the effectiveness of proposed approaches.

关键词： Sampled-data driven systems Function approximation Approximate theory Adaptive control Binomial coefficients

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Nonreciprocal coherent coupling of nanomagnets by exchange spin waves

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Nano Research 2021年第7期14卷 2133-2138页

作者： Hanchen Wang Jilei Chen Tao Yu Chuanpu Liu Chenyang Guo Song Liu Ka Shen Hao Jia Tao Liu Jianyu Zhang Marco A.Cabero Z Qiuming Song Sa Tu Mingzhong Wu Xiufeng Han Ke Xia Dapeng Yu Gerrit E.W.Bauer Haiming Yu Fert Beijing Institute School of Integrated Circuit Science and EngineeringBeijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Max Planck Institute for the Structure and Dynamics of Matter 22761 HamburgGermany Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing 100190China Shenzhen Institute for Quantum Science and Engineering(SIQSE) and Department of PhysicsSouthern University of Science and Technology(SUSTech)Shenzhen 518055China Department of Physics Beijing Normal UniversityBeijing 100875China Department of Physics Colorado State UniversityFort CollinsColorado 80523USA Kavli Institute of Nanoscience Delft University of Technology2628 CJ DelftThe Netherlands Institute for Materials Research WPI-AIMR and CSNRTohoku UniversitySendai 980-8577Japan Zernike Institute for Advanced Materials University of GroningenGroningenNijenborgh 49747 AG GroningenThe Netherlands

Nanomagnets are widely used to store information in non-volatile spintronic *** waves can transfer information with low-power consumption as their propagations are independent of charge ***,to dynamically couple two distant nanomagnets via spin waves remains a major challenge for *** we experimentally demonstrate coherent coupling of two distant Co nanowires by fast propagating spin waves in an yttrium iron garnet thin film with sub-50 nm *** in two nanomagnets are unidirectionally phase-locked with phase shifts controlled by magnon spin torque and spin-wave *** coupled system is finally formulated by an analytical theory in terms of an effective non-Hermitian *** results are attractive for analog neuromorphic computing that requires unidirectional information transmission.

关键词： spintronics nanomagnets spin waves coherent coupling nonreciprocity

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Stochastic computing Implemented by Skyrmionic Logic Devices

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Physical Review Applied 2020年第5期13卷 054049-054049页

作者： Haoyang Zhang Daoqian Zhu Wang Kang Youguang Zhang Weisheng Zhao Fert Beijing Research Institute Beijing Advanced Innovation Center for Big Data and Brain Computing School of Microelectronics Beihang University Beijing 100191 China

Magnetic skyrmions, which are a topologically nontrivial spin texture, have been considered as promising information carriers in future electronic devices because of their nanoscale size, low depinning current density, and high motion velocity. Despite the broad interest in skyrmion racetrack memory, researchers have recently been exploiting logic functions enabled by using the particlelike behavior of skyrmions. These functions can be applied to unconventional computing, such as stochastic computing (SC), which treats data as probabilities and is superior to binary computing due to its simplicity of logic operation. In this paper, we demonstrate SC implemented by the use of skyrmionic logic devices. We propose a skyrmionic and-or logic device as a multiplier in the stochastic domain and two skyrmionic multiplexer (MUX) logic devices as stochastic adders. With the assistance of voltage-controlled magnetic anisotropy (VCMA), precise control of skyrmion collisions is not required in the skyrmionic and-or logic device, thus improving the operational robustness. In the two MUX logic devices, skyrmions can be driven by the Zhang-Li torque or the spin-orbit torque (SOT). In particular, we can regulate the skyrmion motion flexibly by using VCMA or voltage-controlled Dzyaloshinskii-Moriya interaction in the SOT case. Furthermore, a 3-bit stochastic multiplier and adder are demonstrated by micromagnetic simulations. Simulations in synthetic antiferromagnets show that the performance of our skyrmionic logic gates can be optimized through the use of advanced materials. Our work opens up possibilities for implementing SC using skyrmionic logic devices.

关键词： Magnetic anisotropy Skyrmions Spin dynamics Spin torque Spintronics Devices Nanostructures Stochastic networks

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