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arXiv 2021年

作者： Sun, Li Zhang, Zhongbao Ye, Junda Peng, Hao Zhang, Jiawei Su, Sen Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Ifm Lab Department of Computer Science University of California DavisCA United States Department of Computer Science University of Illinois at Chicago IL United States

Graph representation learning received increasing attentions in recent years. Most of the existing methods ignore the complexity of the graph structures and restrict graphs in a single constant-curvature representation space, which is only suitable to particular kinds of graph structure indeed. Additionally, these methods follow the supervised or semisupervised learning paradigm, and thereby notably limit their deployment on the unlabeled graphs in real applications. To address these aforementioned limitations, we take the first attempt to study the self-supervised graph representation learning in the mixed-curvature spaces. In this paper, we present a novel Self-supervised Mixed-curvature Graph Neural Network (SELFMGNN). To capture the complex graph structures, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces, and design hierarchical attention mechanisms for learning and fusing graph representations across these component spaces. To enable the self-supervisd learning, we propose a novel dual contrastive approach. The constructed mixed-curvature space actually provides multiple Riemannian views for the contrastive learning. We introduce a Riemannian projector to reveal these views, and utilize a welldesigned Riemannian discriminator for the single-view and cross-view contrastive learning within and across the Riemannian views. Finally, extensive experiments show that SELFMGNN captures the complex graph structures and outperforms state-of-the-art baselines. © 2021, CC BY-NC-SA.

关键词： Graph neural networks

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Guaranteed feasible control allocation using model predictive control

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control Theory and Technology 2019年第3期17卷 252-264页

作者： Mehdi NADERI Ali KHAKI SEDIGH Tor Arne JOHANSEN Center of Excellence in Industrial Control Faculty of Electrical Engineering K. N. Toosi University of Technology Seyyed Khandan Bridge Shariati Ave. Tehran Iran Center for Autonomous Marine Operation and Systems Department of Engineering Cybernetics Norwegian University of Science and Technology N7491 Trondheim Norway

This paper proposes a guaranteed feasible control allocation method based on the model predictive control. Feasible region is considered to guarantee the determination of the desired virtual control signal using the pseudo inverse methodology and is described as a set of constraints of an MPC problem. With linear models and the given constraints, feasible region defines a convex polyhedral in the virtual control space. In order to reduce the computational time, the polyhedral can be approximated by a few axis alig ned hypercubes. Employing the MPC with rectangular constraints substantially reduces the computational complexity .In two dimensions, the feasible region can be approximated by a few rectangles of the maximum area using numerical geometry techniques which are considered as the constraints of the MPC problem. Also, an active MPC is defined as the controller to minimize the cost function in the control horizon. Finally, several simulation examples are employed to illustrate the effectiveness of the proposed techniques.

关键词： control allocation feasible region actuator constraints model predictive control

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A Dual-Chaining Watermark Scheme for Data Integrity Protection in Internet of Things

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computers, Materials & Continua 2019年第3期58卷 679-695页

作者： Baowei Wang Weiwen Kong Wei Li Neal NXiong Jiangsu Engineering Centre of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologySchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Electronic Information and Control from Fujian Provincial Education Department Fuzhou350108China Department of Mathematics and Computer Science Northeastern State UniversityOklahomaUSA.

Chaining watermark is an effective way to verify the integrity of streaming data in wireless network environment,especially in resource-constrained sensor networks,such as the perception layer of Internet of Things ***,in all existing single chaining watermark schemes,how to ensure the synchronization between the data sender and the receiver is still an unsolved *** the synchronization points are attacked by the adversary,existing data integrity authentication schemes are difficult to work properly,and the false negative rate might be up to 50 *** the additional fixed group delimiters not only increase the data size,but are also easily detected by *** this paper,we propose an effective dual-chaining watermark scheme,called DCW,for data integrity protection in smart campus IoT *** proposed DCW scheme has the following three characteristics:(1)In order to authenticate the integrity of the data,fragile watermarks are generated and embedded into the data in a chaining way using dynamic grouping;(2)Instead of additional fixed group delimiters,chained watermark delimiters are proposed to synchronize the both transmission sides in case of the synchronization points are tampered;(3)To achieve lossless integrity authentication,a reversible watermarking technique is *** experimental results and security analysis can prove that the proposed DCW scheme is able to effectively authenticate the integrity of the data with free distortion at low cost in our smart meteorological Internet of Things system.

关键词： Dual-chaining reversible watermark integrity authentication Internet of things.

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 24

Binding in hippocampal-entorhinal circuits enables compositi...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Christopher J. Kymn Sonia Mazelet Anthony Thomas Denis Kleyko E. Paxon Frady Friedrich T. Sommer Bruno A. Olshausen Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Department of Electrical and Computer Engineering UC Davis Davis Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden and Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Intel Labs Santa Clara Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley and Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley

ISBN: (纸本)9798331314385

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. Code is available at https://***/SoniaMaz8/Hippocampal_enthorinal_circuit

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Wireless Acoustic Sensor Networks and Edge Computing for Rapid Acoustic Monitoring

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 64-74页

作者： Zhengguo Sheng Saskia Pfersich Alice Eldridge Jianshan Zhou Daxin Tian Victor C.M.Leung the Department of Engineering and Design University of Sussex Infineon Technologies AG the Department of Music University of Sussex Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems & Safety ControlSchool of Transportation Science and EngineeringBeihang University the Department of Electrical and Computer Engineering The University of British Columbia

Passive acoustic monitoring is emerging as a promising solution to the urgent, global need for new biodiversity assessment methods. The ecological relevance of the soundscape is increasingly recognised, and the affordability of robust hardware for remote audio recording is stimulating international interest in the potential for acoustic methods for biodiversity *** scale of the data involved requires automated methods,however, the development of acoustic sensor networks capable of sampling the soundscape across time and space and relaying the data to an accessible storage location remains a significant technical challenge, with power management at its core. Recording and transmitting large quantities of audio data is power intensive,hampering long-term deployment in remote, off-grid locations of key ecological interest. Rather than transmitting heavy audio data, in this paper, we propose a low-cost and energy efficient wireless acoustic sensor network integrated with edge computing structure for remote acoustic monitoring and in situ *** and computation of acoustic indices are carried out directly on edge devices built from low noise primo condenser microphones and Teensy microcontrollers, using internal FFT hardware support. Resultant indices are transmitted over a ZigBee-based wireless mesh network to a destination *** tests of audio quality, indices computation and power consumption demonstrate acoustic equivalence and significant power savings over current solutions.

关键词： Acoustic sensor networks edge computing energy efficiency

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On a network SIS model with opinion dynamics 21st

On a network SIS model with opinion dynamics

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21st IFAC World Congress 2020

作者： Xuan, Weihao Ren, Ruijie Paré, Philip E. Ye, Mengbin Ruf, Sebastian Liu, Ji School of Mechanical Engineering University of Leeds United Kingdom Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Faculty of Science and Engineering ENTEG University of Groningen Netherlands Center for Complex Networks Research Department of Psychology Northeastern University United States Department of Electrical and Computer Engineering Stony Brook University United States

This paper proposes a network continuous-time susceptible-infected-susceptible (SIS) model coupled with individual opinion dynamics, where the opinion dynamics models an individual's perceived severity of illness or perceived susceptibility. The effects of opinion dynamics on the network SIS model are studied by analyzing the limiting behaviors of the system model, equilibria of the system and their stability. Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license

关键词： Dynamics

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Sparse Large-Scale Fading Decoding in Cell-Free Massive MIMO Systems

arXiv

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tugfe Ai, Bo School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Department of Computer Science KTH Royal Institute of Technology KistaSE-16440 Sweden State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems are characterized by having many more access points (APs) than user equipments (UEs). A key challenge is to determine which APs should serve which UEs. Previous work has tackled this combinatorial problem heuristically. This paper proposes a sparse large-scale fading decoding (LSFD) design for CF mMIMO to jointly optimize the association and LSFD. We formulate a group sparsity problem and then solve it using a proximal algorithm with block-coordinate descent. Numerical results show that sparse LSFD achieves almost the same spectral efficiency as optimal LSFD, thus achieving a higher energy efficiency since the processing and signaling are reduced. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

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Indoor air quality monitoring system for proactive control of respiratory infectious diseases: Poster abstract 18

Indoor air quality monitoring system for proactive control o...

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18th ACM Conference on Embedded Networked Sensor Systems, SenSys 2020

作者： Ho, Yao-Hua Li, Pei-En Chen, Ling-Jyh Liu, Yu-Lun National Taiwan Normal University Department of Computer Science and Information Engineering Taipei Taiwan Academia Sinica Institute of Information Science Taipei Taiwan Taiwan Centers for Disease Control Epidemic Intelligence Center Taipei Taiwan

ISBN: (纸本)9781450375900

Disease surveillance is essential for the control of flu and respiratory infectious diseases including the novel coronavirus disease (COVID-19). Indoor air quality monitoring has been shown effective in understanding the effectiveness of airflow and circulation indoors to reduce the risk of infectious diseases. In this project, we developed low-cost indoor air quality monitoring devices and systems to tackle the disease surveillance problem. The monitoring device consists of a set of air quality sensors. By strategic deployment and real-time data analysis, the system is able to yield insightful air circulation information indoors. The real-time data analysis is performed on air quality for the indoor ventilation using Long Short-Term Memory (LSTM) on sensed data. A series of user-friendly visualization interfaces and chatbot applications are designed to interact with users and ensure the successful delivery of infection control information. Finally, we work closely with the Taiwan centers for Disease control (CDC) and conduct field experiments in 15 locations including hospitals, long-term care centers, schools with total of 144 IAQ devices. © 2020 ACM.

关键词： indoor air quality internet of thing respiratory disease

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Adaptive control for flow and volume regulation in multi-producer district heating systems

arXiv

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arXiv 2021年

作者： Martinez, Juan Eduardo Machado Cucuzzella, Michele Pronk, Nina Scherpen, Jacquelien M.A. The Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands The Department of Electrical Computer and Biomedical Engineering University of Pavia via Ferrata 5 Pavia27100 Italy

Flow and storage volume regulation is essential for the adequate transport and management of energy resources in district heating systems. In this letter, we propose a novel and suitably tailored-decentralized-adaptive control scheme addressing this problem whilst offering closed-loop stability guarantees. We focus on a system configuration comprising multiple heat producers, consumers and storage tanks exchanging energy through a common distribution network, which are features of modern and prospective district heating installations. The proposed controller is based on passivity, backstepping and (indirect) adaptive control theory. Copyright © 2021, The Authors. All rights reserved.

关键词： District heating

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Energy-Constrained Computation Offloading in Space-Air-Ground Integrated Networks using Distributionally Robust Optimization

arXiv

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arXiv 2022年

作者： Chen, Yali Ai, Bo Niu, Yong Zhang, Hongliang Han, Zhu The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In this paper, we consider a SAGIN, where multiple low-earthorbit (LEO) satellites providing connections to the cloud server, an unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge computing services are included. The UAV flies along a fixed trajectory to collect tasks generated by Internet of Things (IoT) devices, and forwards these tasks to a BS or the cloud server for further processing. To facilitate efficient processing, the UAV needs to decide where to offload as well as the proportion of offloaded tasks. However, in practice, due to the variability of environment and actual demand, the amount of arrival tasks is uncertain. If the deterministic optimization is utilized to develop offloading strategy, unnecessary system overhead or higher task drop rate may occur, which severely damages the system robustness. To address this issue, we characterize the uncertainty with a data-driven approach, and formulate a distributionally robust optimization problem to minimize the expected energy-constrained system latency under the worst-case probability distribution. Furthermore, the distributionally robust latency optimization algorithm is proposed to reach the suboptimal solution. Finally, we perform simulations on the realworld data set, and compare with other benchmark schemes to verify the efficiency and robustness of our proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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