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16th International computer engineering Conference, ICENCO 2020

作者： Ibrahim, Abdelhameed Ali, Hesham Arafat Eid, Marwa M. El-Kenawy, El-Sayed M. Mansoura University Faculty of Engineering Computer Engineering and Control Systems Department Mansoura35516 Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt

ISBN: (纸本)9781728163925

Swarm-based techniques, a form of meta-heuristic techniques, are derived from the swarm system's social conduct in nature. A newly brought optimization algorithm is Harris Hawks Optimization (HHO) that is stimulated through looking conduct of Harris Hawks (agents) of finding food (optimal solution). Balancing between exploitative and exploratory processes of the original HHO algorithm is desirable for achieving better performance to many optimization problems. An algorithm, Chaotic Harris Hawks Optimization (CHHO), is proposed in this work for the unconstrained function optimization. The CHHO algorithm is presented based on adjusting the exploration mechanism of the original HHO algorithm. Ten chaotic maps are used to control this mechanism instead of random adjusting. The unimodal, multimodal, and multimodal based fixed-dimension benchmark functions are used to compare the CHHO algorithm with the HHO algorithm. Visibility of the CHHO algorithm for optimizing the unconstrained benchmark function, with the intelligible effect of the Gauss and Logistic chaotic maps over other maps, is shown in the experiments. © 2020 IEEE.

关键词： Optimization

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Quantized Distributed Nonconvex Optimization Algorithms with Linear Convergence under the Polyak–Lojasiewicz Condition

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IEEE Transactions on Automatic control 2025年

作者： Xu, Lei Yi, Xinlei Sun, Jiayue Shi, Yang Johansson, Karl H. Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Victoria Department of Mechanical Engineering VictoriaBCV8W 2Y2 Canada Tongji University Shanghai Institute of Intelligent Science and Technology Shanghai201804 China KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden

This paper considers distributed optimization for minimizing the average of local nonconvex cost functions, by using local information exchange over undirected communication networks. To reduce the required communication capacity, we introduce an encoder–decoder scheme. By integrating it with distributed gradient tracking and proportional integral algorithms, respectively, we then propose two quantized distributed nonconvex optimization algorithms. Assuming the global cost function satisfies the Polyak–Lojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that our proposed algorithms linearly converge to a global optimal point. Moreover, we show that a low data rate is sufficient to guarantee linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical examples. © 1963-2012 IEEE.

关键词： Cost functions

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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Chinese Automation Congress (CAC)

作者： Liu Hu Luefeng Chen Jie Hu Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration School of Automation Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan The Tokyo Institute of Technology Tokyo Japan

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method.

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Performance comparison of discrete-time robust adaptive controllers for grid-tied power converters under unbalanced grids

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2023年 4卷

作者： de Oliveira Evald, Paulo Jefferson Dias Hollweg, Guilherme Vieira da Silveira, Wagner Barreto Milbradt, Deise Maria Cirollini Tambara, Rodrigo Varella Gründling, Hilton Abílio Intelligent Systems and Control Group Federal University of Pelotas Pelotas - RS Brazil Department of Electrical and Computer Engineering University of Michigan Deaborn MI United States Power Electronics and Control Group Federal University of Santa Maria Brazil - RS Santa Maria - RS Brazil

Renewable energy generation systems largely use grid-tied power converters with LCL filters. A well-known issue in this kind of system is the resonance of the LCL filter, whose frequency is straightforwardly affected by grid condition. Variations on grid impedance tends to make the controller performance poor and until turn the closed-loop unstable. Therefore, some controllers are designed for specific operation ranges to ensure performance and global stability. However, a deep study of Model Reference Adaptive-based controllers for this kind of issue was not conducted yet. The aim of this paper is to fit this research gap. Thus, this paper presents a performance comparison of feasible discrete-time robust adaptive controllers, based on reduced order reference model, for grid-tied power converters with LCL filter under unbalanced grid conditions, providing an assessment of the benefits and drawbacks of each considered control strategy. The evaluated control laws are: Robust Model Reference Adaptive controller, Robust Model Reference-based Adaptive Super-Twisting Sliding Mode controller, Robust Adaptive One Sample Ahead Preview controller, and Robust Adaptive Proportional-Integral controller. Through performance comparison, it can be defined what controller is more adequate in face of grid condition, taking into consideration grid-injected currents quality and design complexity of controller. Experimental results of grid-injected current control of a grid-tied 5.8 kW Voltage-Source Inverter with LCL filter considering relevant grid voltage unbalance are presented to corroborate the controllers’ performance and discuss their reference tracking response, robustness to the parametric variation, exogenous disturbance rejection, parameters adaptability, and global stability. In evaluated scenarios, Robust Adaptive Proportional-Integral controller presented slower regulation dynamics between compared adaptive structures, but reduced tracking error, as well as lower total h

关键词： Adaptive PI controller Grid-tied converters LCL Filter One sample ahead preview controller Robust model reference adaptive control Super-Twisting sliding mode

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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Chinese Automation Congress (CAC)

作者： Jiazhuo Li Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Polish Academy of Sciences Systems Research Institute Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results.

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COMPUTATIONAL FLUID-STRUCTURE INTERACTION FRAMEWORK FOR SIMULATING CHARACTERISTIC DEFORMATIONS IN INSECT FLAPPING WINGS 9

COMPUTATIONAL FLUID-STRUCTURE INTERACTION FRAMEWORK FOR SIMU...

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9th International Conference on Computational Methods for Coupled Problems in Science and engineering, COUPLED PROBLEMS 2021

作者： Onishi, Minato Ishihara, Daisuke Department of Computer Science and System Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan Department of Intelligent and Control Systems Kyushu Institute of Technology Kyushu Institute of Technology 680-4 Kawazu Iizuka Fukuoka820-8502 Japan

In this study, a computational fluid-structure interaction (FSI) framework for characteristic deformations in insect's wings is proposed. The proposed framework consists of a pixel wing model using a structured shell finite element mesh, a projection method for the monolithic FSI monolithic equations using an algebraic splitting, and the FSI dynamic similarity law to measure dynamic similarity between model's and actual insect's flights. It is shown that the proposed framework can directly simulate passive feathering and cambering in insect's wings caused by the FSI, whose magnitudes are very close to those of actual insects. © 2021 9th International Conference on Computational Methods for Coupled Problems in Science and engineering, COUPLED PROBLEMS 2021. All rights reserved.

关键词： Fluid structure interaction

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Multilingual web traffic forecasting for network management using artificial intelligence techniques

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Results in engineering 2025年 26卷

作者： Samaan, Sama Salam Korial, Ayad E. Sarra, Raniya R. Humaidi, Amjad J. Computer Engineering Department University of Technology- Iraq Baghdad10001 Iraq Control and Systems Engineering Department University of Technology- Iraq Baghdad10001 Iraq

Web traffic forecasting is critical for ensuring an effective allocation of network resources for websites to avoid potential congestion and maintain service quality. Analyzing traffic forecasts helps network operators plan for future capacity needs to accommodate unexpected traffic loads. Multilingual web platforms present unique challenges due to varying traffic patterns across several languages. In this paper, modular architecture is proposed for multilingual web traffic forecasting using deep learning models. The architecture consists of multiple processes, from collecting the traffic data of the targeted website to preparing the data to be in a suitable form to feed the deep learning models. This study explores time-series forecasting methods using three deep learning models: long short-term memory (LSTM), gated recurrent units (GRU), and convolutional neural networks (CNN). A comprehensive dataset was collected from the BBC World Service websites in three languages—English, Arabic, and Persian—spanning the period from January 2017 to July 2024. The performance of the employed deep learning models is evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), relative root mean square error (RRMSE), and R-squared (R²). Results show that the CNN model outperformed the GRU and LSTM models in accuracy and variance explanation across all three language datasets. The CNN model demonstrated superior performance, achieving R² scores of 0.980, 0.989, and 0.994;MAE values of 47,195, 5090, and 6714;RRMSE values of 1.43 %, 1.90 %, and 1.86 %;and RMSE values of 61,372, 6342, and 9376 for the English, Arabic, and Persian websites, respectively. These results highlight the potential of deep learning models in capturing complicated patterns of multilingual web platforms, making them suitable for real-world network management. © 2025

关键词： Convolutional neural networks

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Asymmetric Feedback Learning in Online Convex Games

arXiv

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arXiv 2023年

作者： Wang, Zifan Yi, Xinlei Shen, Yi Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science Kth Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden The Lab for Information & Decision Systems Massachusetts Institute of Technology United States The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

This paper considers online convex games involving multiple agents that aim to minimize their own cost functions using locally available feedback. A common assumption in the study of such games is that the agents are symmetric, meaning that they have access to the same type of information or feedback. Here we lift this assumption, which is often violated in practice, and instead consider asymmetric agents;specifically, we assume some agents have access to first-order gradient feedback and others have access to the zeroth-order oracles (cost function evaluations). We propose an asymmetric feedback learning algorithm that combines the agent feedback mechanisms. We analyze the regret and Nash equilibrium convergence of this algorithm for convex games and strongly monotone games, respectively. Specifically, we show that our algorithm always performs between pure first-order and zeroth-order methods, and can match the performance of these two extremes by adjusting the number of agents with access to zeroth-order oracles. Therefore, our algorithm incorporates the pure first-order and zeroth-order methods as special cases. We provide numerical experiments on an online market problem for both deterministic and risk-averse games to demonstrate the performance of the proposed algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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GETAE: Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection

arXiv

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arXiv 2024年

作者： Truica, Ciprian-Octavian Apostol, Elena-Simona Marogel, Marius Paschke, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Splaiul Independentei 313 Bucharest060042 Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Fraunhofer Institute for Open Communication Systems Kaiserin-Augusta-Allee 31 Berlin10589 Germany

In today’s digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Graph embeddings

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Platoon-Based Distributed control for Automated Material Handling systems

Platoon-Based Distributed Control for Automated Material Han...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Alessandro Bozzi Jose-Fernando Jimenez Camilo Hernandez-Rodriguez Eliana-Maria Gonzalez-Neira Damien Trentesaux Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Italy Laboratory System and Materials for Mechatronics (SYMME) Université Savoie Mont Blanc Annecy France Industrial Engineering Department Pontificia Universidad Javeriana Bogota Columbia Laboratory of Industrial and Human Automation Control Mechanical Engineering and Computer Science (LAMIH) Université Polytechnique Hauts-de-France Valenciennes France

Smart manufacturing systems combine collaborative and autonomous control systems to provide optimal performance and respond to volatile demands. A key component of smart manufacturing is material management, as raw materials, work-in-progress, and finished products must be transported to the right location at the right time. However, during the production phase, it is imperative to establish an optimal traffic control scheme in order to avoid congestion and optimize material flow. This paper proposes a platooning-based control procedure implemented in a virtual agent-based manufacturing environment to increase the material flow efficiency on a manufacturing shop floor. An analysis of the production makespan and an indicator of the level of congestion in the material handling system was conducted in order to determine productivity improvements. An agent-based simulation is used to test the effectiveness of the novel platooning approach versus a traditional basic approach. A multivariate analysis of variance (MANOVA) reveals that the inclusion of a platoon-based procedure improves production performance and reduces congestion risks.

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