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arXiv 2023年

作者： Yue, Xinwei Song, Meiqi Ouyang, Chongjun Liu, Yuanwei Li, Tian Hou, Tianwei The Key Laboratory of Information and Communication Systems Ministry of Information Industry China The Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing100101 China The School of Electrical and Electronic Engineering University College Dublin DublinD04 V1W8 Ireland The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The 54th Research Institute of China Electronics Technology Group Corporation Hebei Shijiazhuang050081 China The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Erlangen91054 Germany

Active reconfigurable intelligent surface (ARIS) is a promising way to compensate for multiplicative fading attenuation by amplifying and reflecting event signals to selected users. This paper investigates the performance of ARIS assisted non-orthogonal multiple access (NOMA) networks over cascaded Nakagami-m fading channels. The effects of hardware impairments (HIS) and reflection coefficients on ARIS-NOMA networks with imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) are considered. More specifically, we develop new precise and asymptotic expressions of outage probability and ergodic data rate with ipSIC/pSIC for ARIS-NOMA-HIS networks. According to the approximated analyses, the diversity orders and multiplexing gains for couple of non-orthogonal users are attained in detail. Additionally, the energy efficiency of ARIS-NOMA-HIS networks is surveyed in delay-limited and delay-tolerant transmission schemes. The simulation findings are presented to demonstrate that: i) The outage behaviors and ergodic data rates of ARIS-NOMA-HIS networks precede that of ARIS aided orthogonal multiple access (OMA) and passive reconfigurable intelligent surface (PRIS) aided OMA;ii) As the reflection coefficient of ARIS increases, ARIS-NOMA-HIS networks have the ability to provide the strengthened outage performance;and iii) ARIS-NOMA-HIS networks are more energy efficient than ARIS/PRIS-OMA networks and conventional cooperative schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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A Simplified Variant for Graph Convolutional Network Based Knowledge Graph Completion Model

A Simplified Variant for Graph Convolutional Network Based K...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yashen Wang Qiang Li Yi Zhang Xuecheng Zhang Zhuoya Ju Jian Sun National Engineering Laboratory for Risk Perception and Prevention (RPP) Key Laboratory of Cognition and Intelligence Technology (CIT) China Academy of Electronics and Information Technology Artificial Intelligence Institute of CETC Beijing China The State Key Laboratory for Management and Control of Complex Systems Scientific and Technological Innovation Center of ARI Institute of Automation Chinese Academy of Sciences Beijing China CETC Academy of Electronics and Information Technology Group Co. Ltd. Beijing China Khoury College of Computer Science Northeastern University U.S.A Scientific and Technological Innovation Center of ARI Beijing China Qi An Xin Technology Group Inc. Beijing China

Knowledge graphs (KGs) serve as useful resources for various applications including machine learning, data mining, and artificial intelligence. Knowledge Graph Completion (KGC) aims at reasoning over known (observed) facts and infer the missing (unobserved) relationships, to improve KG's coverage. Many recent works have laid stress on leveraging Graph Convolutional Networks (GCNs) for KGC task, which obtain good representation learning ability for graph-structured data, and have achieved satisfactory results. However, GCN-based researches continue to improve these lines of models by introducing an increasingly complex feature space. Especially, the hyper-parameter space of conventional GCN-based KGC model is too huge, resulting in low computational efficiency and excessive storage pressure. To overcome this problem, this paper investigates a simplified and generic variant for GCN-based KGC model, and shows that simple changes in the architecture of the underlying architecture (with a Subtraction Operator and an Addition Operator), can compte with state-of-the-art GCN-based models. Therefore, this paper proposes a easy-to-accomplish plug-in unit, which could be loaded into any current GCN-based KGC architectures, and help them maximize the maintenance of native model's original performance while significantly reducing parameter scale.

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Deep Rank-Consistent Pyramid Model for Enhanced Crowd Counting

arXiv

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arXiv 2022年

作者： Gao, Jiaqi Huang, Zhizhong Lei, Yiming Shan, Hongming Wang, James Z. Wang, Fei-Yue Zhang, Junping The Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China The Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Fudan University Shanghai200433 China The Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai201210 China The College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Institute of Systems Engineering Macau University of Science and Technology China Qingdao Academy of Intelligent Industries Qingdao266109 China

Most conventional crowd counting methods utilize a fully-supervised learning framework to establish a mapping between scene images and crowd density maps. They usually rely on a large quantity of costly and time-intensive pixel-level annotations for training supervision. One way to mitigate the intensive labeling effort and improve counting accuracy is to leverage large amounts of unlabeled images. This is attributed to the inherent self-structural information and rank consistency within a single image, offering additional qualitative relation supervision during training. Contrary to earlier methods that utilized the rank relations at the original image level, we explore such rank-consistency relation within the latent feature spaces. This approach enables the incorporation of numerous pyramid partial orders, strengthening the model representation capability. A notable advantage is that it can also increase the utilization ratio of unlabeled samples. Specifically, we propose a Deep Rank-consistEnt pyrAmid Model (DREAM), which makes full use of rank consistency across coarse-to-fine pyramid features in latent spaces for enhanced crowd counting with massive unlabeled images. In addition, we have collected a new unlabeled crowd counting dataset, FUDAN-UCC, comprising 4,000 images for training purposes. Extensive experiments on four benchmark datasets, namely UCF-QNRF, ShanghaiTech PartA and PartB, and UCF-CC-50, show the effectiveness of our method compared with previous semi-supervised methods. The codes are available at https://***/bridgeqiqi/DREAM. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Direct Synthesis of High Silica Ssz-16 Zeolite with Extraordinarily Superior Performance in Nh3-Scr Reaction

SSRN

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SSRN 2023年

作者： Han, Shichao Rao, Wei Hu, Junyi Tang, Xiaomin Ma, Ye Du, Jinpeng Liu, Zhongqi Wu, Qinming Ma, Yanhang Meng, Xiangju Shan, Wenpo Xiao, Feng-Shou He, Hong Center for Excellence in Regional Atmospheric Environment Institute of Urban Environment Chinese Academy of Sciences Xiamen361021 China Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control Ningbo Urban Environment Observation and Research Station Institute of Urban Environment Chinese Academy of Sciences Ningbo315800 China State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics National Center for Magnetic Resonance in Wuhan Key Laboratory of Magnetic Resonance in Biological Systems Wuhan Institute of Physics and Mathematics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan430071 China School of Physical Science and Technology Shanghai Key Laboratory of High-resolution Electron Microscopy ShanghaiTech University Shanghai201210 China State Key Joint Laboratory of Environment Simulation and Pollution Control Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China Key Lab of Applied Chemistry of Zhejiang Province Department of Chemistry Zhejiang University Hangzhou310007 China

The development of Cu ion-exchanged small-pore zeolites with excellent performance and high hydrothermal stability in NH3-SCR reaction is of great importance. However, many frameworks of the small-pore zeolites are Al-rich, for instance, Cu-SSZ-16 zeolite with Si/Al ratio always no more than 5, showing relatively low hydrothermal stability. In this work, inspired by theoretical calculation for the interaction between organic structural directing agents (OSDAs) and zeolite framework, high silica SSZ-16 zeolite with Si/Al ratio at 6.7 was directly synthesized using an economical diquaternary alkylammonium cation as an OSDA. After exchanged with Cu ion, the Cu-SSZ-16 catalyst shows extraordinarily superior performance in NH3-SCR reaction even after hydrothermal aging at 900 °C. The designed synthesis combined with excellent performance as well as high hydrothermal stability of the high silica SSZ-16 zeolite might provide an attractive alternative candidate for NH3-SCR catalyst on diesel vehicles and non-road mobile machinery in the future. © 2023, The Authors. All rights reserved.

关键词： Zeolites

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Path Planning System for Smart Cars Used in Education

Path Planning System for Smart Cars Used in Education

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Guangyu Xiong Lulu Niu Yanfu Tian Jiehan Zhou Babak Mohajeri Timo R. Nyberg Silk Road Business School Sanya University(USY) China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Articial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Oulu Oulu Finland Aalto University Espoo Finland

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we developed a path planning system for smart cars for teaching electronic engineering or computer science, which consists of the interactive platform for smart cars development and path planning. Designed by Visual C++, the interactive platform can call Matlab engine, allows users to choose path optimization algorithms such as genetic or A-star(A*) algorithm for different tasks and control smart cars through serial ports. The simulation and practice demonstrate that our interactive platform can help learners to plan paths and control intelligent vehicles without specially designing a user interface.

关键词： Education Path planning Automobiles Task analysis Optimization Programming profession Matlab

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Solving Fokker-Planck equation using deep learning

arXiv

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arXiv 2019年

作者： Xu, Yong Zhang, Hao Li, Yongge Zhou, Kuang Liu, Qi Kurths, Jürgen Department of Applied Mathematics Northwestern Polytechnical University Xi’an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an710072 China Department of Engineering Mechanics Northwestern Polytechnical University Xi’an710072 China Center for Mathematical Sciences School of Mathematics and Statistics HuazhongUniversity of Science and Technology Wuhan430074 China Potsdam Institute for Climate Impact Research Potsdam14412 Germany Department of Physics Humboldt University Berlin Berlin12489 Germany

The probability density function of stochastic differential equations is governed by the Fokker-Planck (FP) equation. A novel machine learning method is developed to solve the general FP equations based on deep neural networks. The proposed algorithm does not require any interpolation and coordinate transformation, which is different from the traditional numercial methods. The main novelty of this paper is that penalty factors are introduced to overcome the local optimization for the deep learning approach, and the corresponding setting rules are given. Meanwhile, we consider a normalization condition as a supervision condition to effectively avoid that the trial solution is zero. Several numerical examples are presented to illustrate performances of the proposed algorithm, including one- and two-dimensional systems. All the results suggest that the deep learning is quite feasible and effective to calculate the FP equation. Further, influences of the number of hidden layers, the penalty factors, and the optimization algorithm are discussed in detail. These results indicate that the performances of the machine learning technique can be improved through constructing the neural networks appropriately. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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nonlocal quark condensate from Dyson-Schwinger Equation and its contributions to the gluon vacuum polarization based on OPE approach

arXiv

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arXiv 2021年

作者： Huang, Jing-Hui Duan, Xue-Ying Huan, Chen Wang, Guang-Jun Hu, Xiang-Yun School of Institute of Geophysics and Geomatics China University of Geosciences Lumo Road 388 Wuhan430074 China School of Automation China University of Geosciences Lumo Road 388 Wuhan430074 China School of Mathematics and Physics China University of Geosiciences Lumo Road 388 Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

The operator-product expansion(OPE) could be employed to obtain the lowest-order, nonlocal quark scalar condensate component of gluon vacuum polarization. In particular, nonlocal quark scalar condensate can be calculated by solving Dyson-Schwinger Equation(DSE) of QCD. Then, field-theoretic aspects of the gluon vacuum polarization and nonperturbative gluon propagator will be considered in the Landau gauge of the Lorentz gauge fixing. The gluon propagator we obtained is finite in the infrared domain where the single gluon mass mg can be determined. Our results of the ratio mg/ΛQCD the range of that from 1.33 to 1.39 agree with previous determinations for this ratio. Besides, the analytic structure of the gluon propagators from the OPE's result is explored. Our numerical analysis of the gluon' Schwinger function finds clear evidence of the positivity violations in the gluon propagator. In addition, a new method for obtaining the chemical potential dependence of the gluon vacuum polarization and the dressed gluon propagator is developed. © 2021, CC BY.

关键词： Polarization

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Fast control Strategy of Grid-forming Inverter Based on Power Feedforward

Fast Control Strategy of Grid-forming Inverter Based on Powe...

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Chinese control Conference (CCC)

作者： Sheng Zhang Zhijian Fang Qine Liu Muchao Xiang Dingbang Huang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China State Grid Hubei Electric Power Co. Ltd Xiangyang Power Supply Company Xiangyang China Electric Power Scientific Research Institute of State Grid Hubei Electric Power Co. Ltd Wuhan China

Grid-forming inverter is widely used in grid-connected systems of distributed generation because of its frequency and voltage support capacity and good stability in microgrid, but its large inertia will affect the dynamic response speed of grid-forming inverter. In order to solve this problem, this paper introduces the loop that affects the dynamic response of grid-forming inverter, and carries out small signal modeling for active loop, analyzes the dynamic performance indicators and determinants of typical second-order system. Thus, a method of adding power feedforward coefficient to the forward channel of the power loop is designed, and the response speed of the system with or without feedforward coefficient under the unit step response is compared. Finally, the simulation results show that adding the power feedforward coefficient can improve the response speed of the grid-forming inverter during startup and power switching, then achieves the effect of fast control.

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Wheel Hub Automatic Production System Based on Industrial Robots

引用

Journal of Physics: Conference Series 2021年第4期1885卷

作者： Bin Zhang Zongxue Lu Qiang Wang Shiyi Cui Guangxin Jia Jun Yang School of Automation Southeast University 210096 P.R. China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 P. R. China Lianyungang JARI Automation CO. LTD. Lianyungang 222006 China

In this thesis, a control system for a wheel hub machining line based on a Fanuc robot, which includes adopting Siemens 300PLC as the main control unit, performing wheel circumferential positioning and center correction based on vision technology, and controlling the machine tool to perform real-time tool compensation based on the measured data, is researched and designed to achieve fully automatic unmanned wheel processing.

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Multi-Objective Squirrel Search Algorithm for EEG Feature Selection

SSRN

引用

SSRN 2022年

作者： Wang, Chao Li, Songjie Shi, Miao Zhao, Jie Acharya, U. Rajendra Xie, Neng-Gang Cheong, Kang Hao College of Civil Engineering and Architecture Anhui Polytechnic University Anhui Wuhu241000 China Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems Anhui Ma’anshan 243002 China Department of Management science and Engineering Anhui University of Technology Anhui Maanshan243002 China 8 Somapah Road Singapore487372 Singapore Department of Mechanical Engineering Anhui University of Technology Maanshan243002 China Department of Electronics and Computer Engineering Ngee Ann Polytechnic S599489 Singapore Department of Biomedical Engineering School of Science and Technology SUSS University Singapore Department of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

Feature selection plays a critical role in the application of Brain Computer Interface (BCI) systems. Many methods have been used to solve the feature selection problem, but they model it as a single-objective problem, considering only classification accuracy or number of features. To close this critical gap, we improve the squirrel search algorithm by combining it with the grid method, and propose a Multi-Objective Squirrel Search Algorithm (MOSSA) to solve the feature selection problem in BCI. We conduct experiments on three publicly available motion imagery datasets, and the experimental results reveal the best classification results of the method on dataset 1. The average classification accuracy of dataset 2 is 96.71%, with the number of selected features reduced to 18 on average. The highest classification accuracy of dataset 3 is 83.57% on the training set and 82.86% on the test set. In addition, we compare MOSSA with other algorithms and the results show the superiority of our proposed method in solving the feature selection problem. Finally, we combine MOSSA with an online application of BCI, where subjects visualise controlling the robot to perform the corresponding actions by the left and right hand movements. The average recognition rate of the three subjects is approximately 70%. In summary, the MOSSA is an effective method for solving the feature selection problem and is useful for the development of online © 2022, The Authors. All rights reserved.

关键词： Brain computer interface

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