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IEEE International Conference on Networking, Sensing and Control

作者： Meijia Wang Haitao Yuan Zhenwei Kuang Hanbo Ma Jing Bi Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

As the Internet and big data technologies advance, a tremendous amount of data is generated daily. Efficient network operations are essential for handling such data. Accurately predicting future network traffic in real-time enables prompt response from cloud infrastructure and efficient traffic scheduling and allocation, ultimately reducing costs, preventing economic losses, and optimizing the performance of downstream facilities. However, predicting network traffic in large-scale data centers is challenging due to the multidimensional, nonlinear, and high-volatility nature of the time series. Traditional prediction methods, e.g., regression algorithms, struggle to capture non-linear features effectively. Many deep learning models face issues such as gradient explosion or vanishing during their training. Current commonly used prediction methods fail to fully uncover vital information about the frequency domain features in the time series. To do so, this work proposes a novel network traffic prediction model that combines a Savitzky Golay filter, sequence decomposition, multi-scale attention, a temporal convolutional network, an autocorrelation mechanism, and bidirectional long short-term memory. It can accurately predict future network traffic by adaptively extracting important features without requiring excessive feature engineering of the original data. It performs end-to-end sequence prediction, feature selection, and automatic learning of data features and temporal dependencies to achieve accurate time series prediction and avoid redundant data processing. Experiments involving ablation studies and comparisons with advanced prediction models are performed with the Google cluster trace. Experimental results show that the proposed model improves the prediction accuracy by at least 38.52% over the state-of-the-art models.

关键词： Data centers Biological system modeling Time series analysis Telecommunication traffic Predictive models Feature extraction Prediction algorithms Autocorrelation Data mining Long short term memory

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Integrating Dynamic Digital Twins: Enabling Real-Time Connectivity for IoT and Virtual Reality

Integrating Dynamic Digital Twins: Enabling Real-Time Connec...

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Simulation Winter Conference

作者： Lejla Erdal Ammar Gubartalla Paulo Victor Lopes Huizhong Cao Guodong Shao Per Lonnehed Henri Putto Abbe Ahmed Sven Ekered Björn Johansson Dept. of Industrial and Materials Science Chalmers University Gothenburg Sweden Dept. Computer Science Aeronautics Institute of Technology São José dos Campos Brazil NIST Gaithersburg Maryland US PTC Gothenburg Sweden Rockwell Automation Gothenburg Sweden

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

When Industry 4.0 technology is applied, it can increase resilience in production systems with interconnected machines and workers. As it is still evolving, research and implementation gaps due to issues such as costs, security, and a lack of use cases and domain expertise for enabling Digital Twins, need to be bridged. This paper proposes a framework connecting an Internet of Things platform with Digital Twin-compliant software based on ISO 23247. The simulation software provides cognitive support for the user who is immersed in the Digital Twin through Virtual Reality, and provides applications such as remote control and exploration of what-if scenarios. Connectivity with an Internet of Things platform facilitates real-time bi-directional communication, collaboration, monitoring, and assistance. A proof-of-concept use case based on an assembly line for a lab-scale drone factory was used for validation of the deployed Digital Twin based on ISO 23347.

关键词： ISO Standards Virtual reality Real-time systems Software Digital twins Safety Security Assembly Monitoring Remote control

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Coordinating Cooperative Perception in Urban Air Mobility for Enhanced Environmental Awareness

Coordinating Cooperative Perception in Urban Air Mobility fo...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Timo Häckel Luca Von Roenn Nemo Juchmann Alexander Fay Rinie Akkermans Tim Tiedemann Thomas C. Schmidt Dept. of Computer Science Hamburg University of Applied Sciences Hamburg Germany Institute of Automation Technology Helmut Schmidt University Hamburg Germany Dept. of Automotive and Aeronautical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The trend for Urban Air Mobility (UAM) is growing with prospective air taxis, parcel deliverers, and medical and industrial services. Safe and efficient UAM operation relies on timely communication and reliable data exchange. In this paper, we explore Cooperative Perception (CP) for Unmanned Aircraft Systems (UAS), considering the unique communication needs involving high dynamics and a large number of UAS. We propose a hybrid approach combining local broadcast with a central CP service, inspired by centrally managed U-space and broadcast mechanisms from automotive and aviation domains. In a simulation study, we show that our approach significantly enhances the environmental awareness for UAS compared to fully distributed approaches, with an increased communication channel load, which we also evaluate. These findings prompt a discussion on communication strategies for CP in UAM and the potential of a centralized CP service in future research.

关键词： Communication channels Market research Reliability Aircraft Vehicle dynamics Public transportation

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Coordinating Cooperative Perception in Urban Air Mobility for Enhanced Environmental Awareness

arXiv

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arXiv 2024年

作者： Häckel, Timo von Roenn, Luca Juchmann, Nemo Fay, Alexander Akkermans, Rinie Tiedemann, Tim Schmidt, Thomas C. The Dept. of Computer Science Hamburg University of Applied Sciences Hamburg Germany The Institute of Automation Technology Helmut Schmidt University Hamburg Germany The Dept. of Automotive and Aeronautical Engineering Hamburg University of Applied Sciences Hamburg Germany

关键词： Air mobility

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Fractional Order Ultra-Local Model-Based Sliding-Mode-Assissted Disturbance Observer and Model-Free Control

Fractional Order Ultra-Local Model-Based Sliding-Mode-Assiss...

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Automatics and Informatics (ICAI), International Conference

作者： Liuchang Zhang Haoping Wang Yang Tian Xincheng Zhuang Ahmad Taher Azar Saim Ahmed Dept. School of Automation Nanjing University of Science and Technology Nanjing China Dept. Automated Systems & Soft Computing Lab Prince Sultan University Riyadh Saudi Arabia Dept. College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

This paper puts forward a fractional order sliding-mode-assisted disturbance observer-based model-free control (FO-SAMDOMFC) methodology for fractional order system with disturbance. First, the system dynamic model is rewritten as a fractional order ultra-local model (ULM), and a SMADO is designed to estimate the lumped disturbance term. Then, A closed-loop control law is proposed on the basis of the ULM with the objective of guaranteeing the convergence of the tracking error. Next, a proof of the stability of the closed-loop system is given based on the fractional extension of Lyapunov’s direct method. Finally, the simulation compared with other controllers based on MATLAB/Simulink is realized, and the simulation results validate the proposed method.

关键词： Accuracy System dynamics Simulation Noise Mathematical models Disturbance observers Stability analysis Noise measurement MATLAB Informatics

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haitao Yuan Shen Wang Yaofei Ma Jing Bi Jinhong Yang Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China College of Computer Beijing University of Technology Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization.

关键词： Energy consumption Federated learning Collaboration Simulated annealing Programming Genetics Servers Resource management Particle swarm optimization Optimization

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Control of Multilevel Converters for High Penetration of Renewable Energies 12

Control of Multilevel Converters for High Penetration of Ren...

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12th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2021

作者： Pouresmaeil, Mobina Sepehr, Amir Sangrody, Reza Taheri, Shamsodin Pouresmaeil, Edris Aalto University Dept. of Elec. Eng. and Automation Espoo Finland Islamic Azad University Dept. of Elec. and Computer Eng. Firoozkooh Iran Dept. of Eng. and Computer Science Gatineau Canada

ISBN: (纸本)9780738142371

Increasing integration of converter-interfaced renewable power generation challenges the stability of the frequency in the modern power systems due to the lack of inertia in this type of generators. In this regard, this paper establishes a control method for controlling multilevel converters as an interface between renewable power generation and the power grid, which enables them to operate as voltage-controlled sources, thus grid-forming capabilities like conventional synchronous generators (SGs). In fact, the proposed control technique mimics the behavior of synchronous generators in the control loop of the neutral point clamped (NPC) converter. Consequently, supportive functionalities for frequency stability i.e., inertia and frequency/power oscillation damping will be provided by the multilevel converter as a virtual synchronous generator (VSG). Simulation results in Matlab/Simulink verifies the well-designed inertia and damping features for the interfaced converter as well as appropriate performance of multilevel converter in reducing total harmonic distortion (THD). © 2021 IEEE.

关键词： Synchronous generators

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jinhong Yang Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

关键词： Energy consumption Quantum computing Multitasking Energy efficiency Internet Manufacturing Optimization Sorting Industrial Internet of Things Genetic algorithms

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm.

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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An Adaptive Multi-Stage Evolution Algorithm for High-Dimensional Expensive Problems

An Adaptive Multi-Stage Evolution Algorithm for High-Dimensi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Boyuan Zhang Rui Lai Guanghong Gong Haitao Yuan Jinhong Yang Jia Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem.

关键词： Adaptation models Costs Switches Gaussian processes Genetics Iron Space exploration Particle swarm optimization Optimization Robots

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