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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Xu, Xiaoyi Luo, Hao Chow, Mo-Yuen Huang, Cong-Sheng Jiang, Yuchen Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9798350303636

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： adaptive observer lithium-ion battery sensor bias sensor noises State-of-Charge (SOC) estimation

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Autonomous Surveillance Breakthrough by Implementing Facial Recognition in Dog Robots

Autonomous Surveillance Breakthrough by Implementing Facial ...

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2024 Intermountain engineering, Technology and Computing, IETC 2024

作者： Hafezi, Amir Zibran, Minhaz Deemyad, Taher Idaho State University Department of Mechanical and Measurement & Control Engineering PocatelloID United States Idaho State University Department of Computer Science PocatelloID United States

ISBN: (纸本)9798350373073

As the importance of security in today's world continues to escalate, technology's role in its enhancement has evolved significantly. From the inception of fixed security cameras monitored by individuals, advancements have steered towards autonomous threat recognition systems and the deployment of mobile robots for proactive security measures. Despite these technological strides, the exploration of autonomous robots in security has been relatively scant. This study takes a novel approach by repurposing the commercial GO2 Edu dog robot for security purposes, equipping it with a thermal camera and custom software for robust face detection in varying lighting conditions, aimed at achieving safer and more reliable face recognition. Furthermore, the mapping and navigational capabilities are studied by conducting experiments with a Turtlebot outfitted with a 2D RPLIDAR and depth camera using SLAM, laying the groundwork for the GO2 robot's autonomous operations. The results of these enhancements have shown to be promising in the context of bolstering security measures. The paper concludes with a discussion on potential future enhancements, paving the way for more sophisticated and autonomous security robots. © 2024 IEEE.

关键词： Face recognition

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Adaptive Fault Detection Scheme Using an Optimized Self-healing Ensemble Machine Learning Algorithm

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1145-1156页

作者： Levent Yavuz Ahmet Soran AhmetÖnen Xiangjun Li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML *** the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA D-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： Decision tree(DT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(LDA) logistic regression(LR) Naive Bayes(NB) self-healing algorithm

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Development and Analysis of Genetic Algorithm for Optimization of Continuous Cast Billets Warehousing Process

Development and Analysis of Genetic Algorithm for Optimizati...

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2024 International Russian Smart Industry Conference, SmartIndustryCon 2024

作者： Shchegolikhin, Ivan S. Andreev, Sergey M. Nosov Magnitogorsk State Technical University Department of Computer Engineering and Programming Magnitogorsk Russia Nosov Magnitogorsk State Technical University Department of Automated Control Systems Magnitogorsk Russia

ISBN: (纸本)9798350395044

Warehouses are an important logistic component of various companies. Warehouses may have different layouts, equipment and their own features. Optimization of warehouse operations can decrease overhead costs and increase the economic efficiency of industry. The technological process of steel sheet production at a metallurgical company includes two major stages: casting of liquid steel into billets and rolling of these billets on a rolling mill. In order to coordinate the productivity of these two stages and to be able to fulfill orders promptly, a warehouse of slabs (billets) is used. Billets of different parties and steel grades are stored in stacks. The billets are moved to the warehouse for storage and from the warehouse to the rolling mill by bridge cranes. In this paper the problem of optimization of the process of continuous cast billets storage is solved to minimize the number of slab handling operations. The problem is decomposed into two subproblems SLAP (Storage Location Assignment Problem) and CSP (Crane Scheduling Problem). A modified genetic algorithm was applied to solve the problem. Using a simulation model of the warehouse, the efficiency of the algorithm was evaluated using different approaches to take the restrictions imposed on the process of slab handling into account. It is shown that the most effective is the algorithm in which there is no restriction on the placement of stacks of billets belonging to one melting. © 2024 IEEE.

关键词： Genetic algorithms

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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Low-Complexity Hardware Architecture of an H.264-Based Video Encoder for FPGAs

Low-Complexity Hardware Architecture of an H.264-Based Video...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Tayyebi, Azam Hanna, Darrin Jones, Bryant Oakland University Department of Electrical and Computer Engineering Rochester United States Intrepid Control Systems Troy United States

ISBN: (纸本)9798350327595

In this paper, we present an architecture for a scalable, efficient, realtime intra H.264 video encoder implemented on an FPGA. Our architecture was designed to achieve a through-put of up to 2.3 Gbit/s using a parallel and pipelined architecture described in VHDL. All modules in the architecture are optimized to utilize minimum hardware area. A parameterized encoding system and flexible architecture is proposed to provide the ability to achieve different compression ratios ranging from 1.4 to 2 with varying size and power requirements. As a baseline, with no compression, the encoder required hardware resources equivalent to 18K logic gates. This work experimented with compression ratios up to 2 which required an equivalent of 31K logic gates. The encoder performs at frequency ranges of 159-183 MHz. © 2023 IEEE.

关键词： CAVLC H.264 integer-transform intra-prediction quantization

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Dynamic Modeling of Key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method

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Journal of Thermal Science 2024年第3期33卷 1216-1230页

作者： YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206China CHN Energy New Energy Technology Research Institute Co. Ltd.Beijing102209China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)***,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been ***,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB *** model parameters are determined based on the operating data of the unit and continuously optimized in real ***,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam ***,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB *** model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关键词： circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant

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Intelligent Execution of computer Vision Tasks in Delay-Constrained UAV-Aided Networks

Intelligent Execution of Computer Vision Tasks in Delay-Cons...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Varshney, Nancy Puligheddu, Corrado Chiasserini, Carla Fabiana Casetti, Claudio De, Swades Indian Institute of Technology Department of Electrical Engineering Delhi India Department of Electronics and Telecommunications Politecnico di Torino Italy Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (纸本)9798350370218

Autonomous unmanned aerial vehicles (UAVs) are crucial in critical target tracking and disaster management services. However, challenges arise due to limited channel capacity causing large transmission delays and constraints imposed by UAV batteries when running computationally intensive object detection and tracking algorithms. To address this, we propose in-telligent offloading computer vision tasks to a high-computational edge server at millimeter wave (mmWave) frequency. Transmission at mmWave needs large transmission power and is susceptible to blockages that necessitate considering the link quality in the offloading policy. Additionally, the timely processing of frames containing objects of interest is essential for context-based UAV operations to achieve a low frame drop rate. In this work, we present a delayed-reward reinforcement learning framework to determine the offloading policy of computer vision tasks in a delay-constrained environment. This approach considers the importance of the content within frames, which is unknown to the UAV. The objective is to jointly reduce UAV energy consumption and frame drop rates, leveraging statistical information of both the channel and the frame semantics. Through extensive simulations, we demonstrate by considering statistical information of the communication channel and frame semantics, we achieve approximately 45% energy savings compared to the UAV's energy consumption when processing all frames locally and maintaining the drop rate of delay-constrained frames below 5%. © 2023 IEEE.

关键词： Communication channel delay frame semantics offloading unmanned aerial vehicle (UAV)

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Receding-Horizon control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators

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SN computer Science 2023年第1期4卷 62页

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 71-73 Kassel 34121 Germany

This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach. © 2022, The Author(s).

关键词： Model approximation Neural networks Online control Optimization Reinforcement learning Stability

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Editorial: The Second International Workshop on Artificial Intelligence Systems in Education - AIxEDU 2

Editorial: The Second International Workshop on Artificial I...

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2nd International Workshop on Artificial INtelligent Systems in Education, AIxEDU 2024

作者： Casalino, Gabriella Limongelli, Carla Schicchi, Daniele Taibi, Davide Temperini, Marco Department of Computer Science University of Bari Aldo Moro Bari Italy Department of Civil Computer Science and Aeronautical Technologies Engineering Roma Tre University Rome Italy Institute for Educational Technology Italian National Research Council CNR-ITD Palermo Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

The Second International Workshop on Artificial Intelligence Systems in Education (AIxEDU) marks its 2024 edition in conjunction with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA), held from 25-28 November 2024 in Bolzano, Italy. This workshop brings together Artificial Intelligence and Education experts to explore the latest innovations and best practices in applying AI to educational contexts. Participants discuss recent trends, research initiatives, and emerging developments in AI technologies that aim to enhance teaching methodologies, support personalized learning, and address pressing educational challenges. The workshop emphasizes a multidisciplinary approach to understanding how AI systems can promote inclusion, improve learning outcomes, and empower educators while addressing ethical considerations and ensuring equitable access to education aligned with global sustainable development goals. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Adversarial machine learning

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