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arXiv 2022年

作者： O'Connor, Daniel L. Siang, Lim C. Elnawawi, Shams Control Consulting Inc. Great FallsMT United States Department of Process Control Engineering Refinery BurnabyBC Canada College of Computing Georgia Institute of Technology AtlantaGA United States

In industrial model predictive controllers (MPCs), models generated from regression-based system identification methods typically contain small or even physically non-existent degrees of freedom. control issues can arise when the steady-state optimizer uses these small degrees of freedom to calculate targets for plant operation due to matrix ill-conditioning. Mathematical techniques like Relative Gain Array (RGA) and Singular Value Decomposition (SVD) are helpful for analyzing controller gain interactions and identifying conditioning issues, which can be corrected relatively easily in small models. However, these techniques are difficult and tedious to apply for larger, more complex models. This paper describes a novel, non-iterative, RGA-based, binning technique for discretizing the gain matrix and quickly solving 2×2 conditioning issues for any model size, while guaranteeing gain adjustments below a certain threshold. Higher order interactions are also discussed. © 2022, CC BY.

关键词： Singular value decomposition

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The Impact of Recommendation Systems on Opinion Dynamics: Microscopic versus Macroscopic Effects

arXiv

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arXiv 2023年

作者： Lanzetti, Nicolas Dörfler, Florian Pagan, Nicolò The Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Zürich8003 Switzerland The Social Computing Group Department of Informatics University of Zürich Zürich8003 Switzerland

Recommendation systems are widely used in web services, such as social networks and e-commerce platforms, to serve personalized content to the users and, thus, enhance their experience. While personalization assists users in navigating through the available options, there have been growing concerns regarding its repercussions on the users and their opinions. Examples of negative impacts include the emergence of filter bubbles and the amplification of users’ confirmation bias, which can cause opinion polarization and radicalization. In this paper, we study the impact of recommendation systems on users, both from a microscopic (i.e., at the level of individual users) and a macroscopic (i.e., at the level of a homogenous population) perspective. Specifically, we build on recent work on the interactions between opinion dynamics and recommendation systems to propose a model for this closed loop, which we then study both analytically and numerically. Among others, our analysis reveals that shifts in the opinions of individual users do not always align with shifts in the opinion distribution of the population. In particular, even in settings where the opinion distribution appears unaltered (e.g., measured via surveys across the population), the opinion of individual users might be significantly distorted by the recommendation system. © 2023, CC BY.

关键词： Recommender systems

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Wind Power Potential Estimation by Using the Statistical Models-Adama, Ethiopia 8th

Wind Power Potential Estimation by Using the Statistical Mod...

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8th EAI International Conference on Advancement of Science and Technology, ICAST 2020

作者： Ayenew, Endalew Biru, Getachew Mulatu, Asrat Hampannavar, Santoshkumar Center of Excellence for Sustainable Energy College of Electrical and Mechanical Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Electrical Power and Control Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia School of Electrical and Electronics Engineering REVA University Bangalore India

ISBN: (纸本)9783030806200

This paper is aimed to statistically estimate wind power that can be converted to electrical power. It is important to have an inclusive fact of wind phenomena to efficiently plan the generation of power from the wind. To estimate wind power potential, this paper includes daily average wind speeds, monthly average wind speeds, and related wind power density, and frequency distribution based on wind speed probability frequency, Weibull and Rayleigh distributions. The two parameters for Weibull distribution were found out using data from the Adama wind farm site. The yearly average wind power densities for wind velocity frequency distribution, the Weibull distribution, and the Rayleigh distribution models 412 W/m2, 370 W/m2, and 532 W/m2 respectively were estimated using wind speed statistics of the 2018 year at the ADAMA wind farm site. The value of estimated wind power density by Rayleigh distribution models is equivalent to maximum power density of the site. The result of this study shows that the selected site has utility-scale potential wind power. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Wind power

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A Gps/Bds Based Train Positioning Method Using Non-Differential Range Measurements

SSRN

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SSRN 2024年

作者： Jiang, Wei Zhou, ZiJian Wang, Jian Cai, BaiGen Rizos, Chris Liu, Dan The State Key Laboratory of Rail Traffic Control and Safety School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Institute of Computing Technology China Academy of Railway Sciences Corporation Limited Beijing100081 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China The School of Civil and Environmental Engineering University of New South Wales Sydney2052 Australia

Compared with pseudorange measurement, carrier phase tracking is much less noisy, which can provide accurate positioning solutions. The existing carrier phase-based positioning methods rely on precise ephemeris or base station, which may cause poor real-time and increase complexity in the system. To simplify the calculation model and achieve accurate navigation solutions with a stand-alone receiver, a new GPS/BDS based real-time non-differential positioning method is proposed. Due to the need to estimate the ambiguity terms of carrier phase measurements, the dimension of the state is larger than that of the measurement, the system observability is evaluated via the observability degrees. Both the train test on Beijing- Shenyang high speed railway and simulation were carried out, and the results confirm that the multi-constellation method can improve the positioning accuracy compared with stand-alone cases. The position and velocity Root Mean Square of the proposed method are (0.9022 m, 0.9140 m, 1.1621 m) and (0.0818 m/s, 0.0958m/s, 0.0289m/s) in the north, east, down directions. The proposed method has improved positioning performance in terms of accuracy and observability compared with different combinations of measurement methods. © 2024, The Authors. All rights reserved.

关键词： Observability

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Chinese News Title Classification Based on Lightweight Improved ERNIE-RCNN

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Science Technology and engineering 2025年第2期25卷 649-656页

作者： Li, Li Zhang, Zhi-Xin Wang, Xiao-Long School of Control and Computer Engineering North China Electric Power University Baoding071003 China Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding071003 China

Aiming at the problems that the large-scale pre-training language model faces when dealing with news headlines, such as huge parameters, inefficient use of contextual semantic features and circular convolution neural network’s neglect of the importance of initial input elements, a news headline classification method that combines ERNIE (enhanced representation through knowledge integration) of mixture-of-expert model and recurrent convolution neural network with attention mechanism were proposed. Firstly, the text was encoded with the help of MoE’s improved ERNIE technology, and then the text was classified with attention RCNN (recurrent convolutional neural networks) on the basis of preserving the word order and characteristics of the text. In order to improve the classification ability, RCNN was improved by calculating the input fusion context weight. In the process of calculating the weights of experts in MoE, Gumbel-Softmax was selected as a new gating function to improve the traditional Softmax function, so as to better control the smoothness. According to the experimental results, it is found that compared with the traditional classification methods, the classification method proposed in this study shows significant advantages and greatly reduces the number of parameters. On this basis, the F1 value is increased by 0. 51% compared with the traditional model. After the ablation experiment, the feasibility of this classification method in the classification task has been confirmed. © 2025 Beijing Kexue Jishu yu Gongcheng Zazhishe. All rights reserved.

关键词： Convolutional neural networks

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Editorial:Special Topic on Federated Learning for IoT and Edge computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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High-Order Local Discontinuous Galerkin Method with Multi-Resolution WENO Limiter for Navier-Stokes Equations on Triangular Meshes

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Communications in Computational Physics 2023年第5期33卷 1217-1239页

作者： Yizhou Lu Jun Zhu Shengzhu Cui Zhenming Wang Linlin Tian Ning Zhao State Key Laboratory of Mechanics and Control for Aerospace Structures Nanjing University of Aeronautics and AstronauticsJiangsu 210000P.R.China Key Laboratory of Mathematical Modeling and High Performance Computing of Air Vehicles Nanjing University of Aeronautics and AstronauticsJiangsu 210000P.R.China Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design Nanjing University of Aeronautics and AstronauticsJiangsu 210000P.R.China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsJiangsu 210000P.R.China

In this paper,a new multi-resolution weighted essentially non-oscillatory(MR-WENO)limiter for high-order local discontinuous Galerkin(LDG)method is designed for solving Navier-Stokes equations on triangular *** MR-WENO limiter is a new extension of the finite volume MR-WENO *** new limiter uses information of the LDG solution essentially only within the troubled cell itself,to build a sequence of hierarchical L^(2)projection polynomials from zeroth degree to the highest degree of the *** an example,a third-order LDGmethod with associated same orderMR-WENO limiter has been developed in this paper,which could maintain the original order of accuracy in smooth regions and could simultaneously suppress spurious oscillations near strong shocks or contact *** linear weights of such new MR-WENO limiter can be any positive numbers on condition that their summation is *** is the first time that a series of different degree polynomials within the troubled cell are applied in a WENO-type fashion to modify the freedom of degrees of the LDG solutions in the troubled *** MR-WENO limiter is very simple to construct,and can be easily implemented to arbitrary high-order accuracy and in higher dimensions on unstructured *** spatial reconstruction methodology improves the robustness in the numerical simulation on the same compact spatial stencil of the original LDG methods on triangular *** classical viscous examples are given to show the good performance of this third-order LDG method with associated MR-WENO limiter.

关键词： Local discontinuous Galerkin method multi-resolution WENO limiter triangular meshes Navier-Stokes equations

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MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing

arXiv

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arXiv 2023年

作者： Huang, Haitong Liu, Cheng Liu, Bo Xue, Xinghua Li, Huawei Li, Xiaowei State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Beijing Institite of Control Engineering Beijing100190 China

To ensure resilient neural network processing on even unreliable hardware, comprehensive reliability analysis against various hardware faults is generally required before the deep neural network models are deployed, and efficient error injection tools are highly demanded. However, most existing fault injection tools remain rather limited to basic fault injection to neurons and fail to provide fine-grained vulnerability analysis capability. In addition, many of the fault injection tools still need to change the neural network models and make the fault injection closely coupled with normal neural network processing, which further complicates the use of the fault injection tools and slows down the fault simulation. Due to the various fault injection implementations and error metrics, it makes the comparison between different fault-tolerant study difficult. To this end, we propose MRFI, a highly configurable multi-resolution fault injection tool for deep neural networks. It enables users to modify an independent fault configuration file rather than neural network models for the fault injection and vulnerability analysis. Particularly, it integrates extensive fault analysis functionalities from different perspectives and enables multi-resolution investigation of the vulnerability of neural networks. In addition, it does not modify the major neural network computing framework of PyTorch. Hence, it allows parallel processing on GPUs naturally and exhibits fast fault simulation according to our experiments. Moreover, we also have the fault injection calibrated with fault simulation with architectural details and validate the accuracy of the proposed fault injection. Finally, MRFI is also open sourced on Github1 © 2023, CC BY.

关键词： Neural network models

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Multi-Target Tracking Using a Swarm of UAVs by Q-learning Algorithm

Multi-Target Tracking Using a Swarm of UAVs by Q-learning Al...

引用

Sensor Signal Processing for Defence (SSPD)

作者： Seyed Ahmad Soleymani Shidrokh Goudarzi Xingchi Liu Lyudmila Mihaylova Wenwu Wang Pei Xiao Centre for Vision Speech and Signal Processing (CVSSP) The University of Surrey UK School of Computing and Engineering University of West London UK Department of Automatic Control and Systems Engineering The University of Sheffield UK Institute for Communication Systems (5GIC) The University of Surrey UK

This paper proposes a scheme for multiple un-manned aerial vehicles (UAVs) to track multiple targets in challenging 3-D environments while avoiding obstacle collisions. The scheme relies on Received-Signal-Strength-Indicator (RSSI) measurements to estimate and track target positions and uses a Q-Learning (QL) algorithm to enhance the intelligence of UAVs for autonomous navigation and obstacle avoidance. Considering the limitation of UAVs in their power and computing capacity, a global reward function is used to determine the optimal actions for the joint control of energy consumption, computation time, and tracking accuracy. Extensive simulations demonstrate the effectiveness of the proposed scheme, achieving accurate and efficient target tracking with low energy consumption.

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Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition

Bio-inspired Swarm Intelligence: a Flocking Project With Gro...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Song Tianbo Hu Weijun Cai Jiangfeng Liu Weijia Yuan Quan He Kun Ira A. Fulton Schools of Engineering Arizona State University Tempe USA Faculty of Applied Mathematics and Control Processes Saint Petersburg State University Saint Petersburg Russia Department of Chemistry Lomonosov Moscow State University Moscow Russia Allen School of Engineering & Computing Trine University Angola USA Faculty of Engineering The Open University of China Hangzhou China College of Computing Illinois Institute of Technology Chicago USA

With the help of the adaptive gain control algorithm and the flocking SWARM algorithm with adversarial agents, the project described in this article makes the entire system communication-free while maintaining the same functionality and performance. It outlines the entirety of this biologically inspired swarm intelligence's thought process, from inception to conclusion (Flocking algorithm). The entire project is dependable and practically impenetrable under a variety of conditions. The neighbor observation for group conjecture is then strengthened by the use of the adaptive gain control technique and a partial Kalman filter. After the system has been stabilized, use the built-in camera of the SWARM drone to introduce the Object Recognition method in computer vision. As a result, the system enters the real world from the simulation level.

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