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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system. © 2023 IEEE.

关键词： genetic algorithm grey wolf optimizer Mobile edge computing resource allocation service migration

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Joint Optimization of Cache-Assisted Offloading and Resource Allocation in Mobile Edge Computing

Joint Optimization of Cache-Assisted Offloading and Resource...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Sun, Zhe Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations. © 2023 IEEE.

关键词： cloud computing Computation offloading particle swarm optimization vehicular edge computing

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Cost-Minimized Partial Computation Offloading in Cloud-Assisted Mobile Edge Computing Systems

Cost-Minimized Partial Computation Offloading in Cloud-Assis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers. © 2023 IEEE.

关键词： cloud computing computation offloading intelligent optimization algorithms Lévy flights Mobile edge computing

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Can Artificial Intelligence Detect the Intensity of Pain Caused by Local Dental Anesthetic Injections in Children? A Cross-Sectional Study

SSRN

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SSRN 2023年

作者： Alkhouli, Muaaz Al-Nerabieah, Zuhair Al-Issa, Sami Dashash, Mayssoon Faculty of Dentistry Damascus University Damascus Syria Department of Computer Engineering and Automation Faculty of Mechanical and Electrical Engineering Damascus University Damascus Syria

Background: Pain assessment in children is essential for effective management, particularly during dental procedures. This study aimed to validate an AI software that utilizes facial expression analysis for pain assessment in children undergoing dental injections. In addition, the study aimed to determine the Action Units associated with each level of pain. Methods: A total of 300 children aged 6-9 years were included in the study. Pain assessment was conducted using both the AI software and the widely used FLACC scale. The AI software used a CNN algorithm and detected six specific action units (AUs) associated with pain. Statistical analysis, including correlation and regression, was performed to evaluate the performance of the AI software and the agreement between the FLACC scores and AI scores. Results: The results showed a high sensitivity and specificity of the AI software in pain assessment, with an almost perfect aggrement observed between the AI scores and the FLACC scores (r = 0.921). The AI model accurately detected pain levels in children, with comparable results for boys and girls. The AUs utilized in the AI model (AU4, AU6, AU7, AU9, AU10, AU43) demonstrated significant correlations with increasing pain levels, as validated by the FLACC scale. Conclusion: The findings of this study support the validity and reliability of the AI software for pain assessment in children undergoing dental injections. The AI software demonstrated high sensitivity and specificity in detecting pain. The specific AUs used in the AI model showed significant associations with increasing pain levels. © 2023, The Authors. All rights reserved.

关键词： Artificial intelligence

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Frequency-Current Induction AC Electric Drive with Vector Correction of the Control Signal 4

Frequency-Current Induction AC Electric Drive with Vector Co...

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4th International Conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2022

作者： Meshcheryakov, Victor Boikov, Andrei Muravyev, Artem Bobrov, Maxim Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia Ogarev Mordovia State University Department of Electronic & Electrical Engineering Saransk Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

The problems of creating controlled electric drive systems based on an induction motor with a phase rotor, easy to manufacture, cheap to maintain and economical, remain relevant due to their fairly wide application on lifting and transport mechanisms (LTM), for example, on bridge metallurgical cranes and conveyors. At the same time, one of the important tasks is to increase the efficiency of AC electric drives with energy saving, which is achieved through the introduction of a frequency converter. At the same time, the parametric start-up control and frequency control systems currently used, made on the basis of starting resistors and controllers, are not economical in terms of energy consumption and do not allow to provide the required dynamic indicators. Currently, frequency converters with a DC link implemented on fully controlled IGBT transistors are mainly used to power regulated AC electric drives. The development of control systems for AC electric motors has led to the emergence of new algorithms for controlling the power switches of the frequency converter, which improve the quality of the voltage feeding the stator winding, reduce the number of high-frequency harmonics in the generated voltage and current of the stator. A promising direction for the development of frequency asynchronous electric drive systems based on an asynchronous motor with a phase rotor, characterized by the ability to measure the instantaneous values of the phase currents of the rotor, is the use of scalar frequency-current control systems with additional correction circuits of moment-forming variables, allowing to implement scalar control with vector correction of the three-phase signal setting the instantaneous values of phase currents of the stator. © 2022 IEEE.

关键词： Induction motors

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Energy-Optimized with Multi-Population Differential Annealed Optimization in Mobile Edge Computing

Energy-Optimized with Multi-Population Differential Annealed...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wu, Ruixuan Shi, Yuliang Yuan, Haitao Bi, Jing Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time. © 2023 IEEE.

关键词： computation offloading energy optimization Mobile edge computing multi-population optimization simulated annealing

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Latency-Minimized Computation Offloading in Vehicle Fog Computing with Improved Whale Optimization Algorithm

Latency-Minimized Computation Offloading in Vehicle Fog Comp...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xue, Xiangdong Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms. © 2023 IEEE.

关键词： computation offloading fog computing Lévy flight Vehicular networks whale optimization algorithm

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Network Anomaly Detection with Stacked Sparse Shrink Autoencoders and Improved XGBoost

Network Anomaly Detection with Stacked Sparse Shrink Autoenc...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Guan, Ziyue Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms. © 2023 IEEE.

关键词： autoencoders feature extraction Network anomaly detection particle swarm optimization XGBoost

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Practical engineering Education: Use of collaborative robots in the context of Industry 5.0

Practical Engineering Education: Use of collaborative robots...

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2023 IEEE IFEES World engineering Education Forum and Global engineering Deans Council, WEEF-GEDC 2023

作者： Vogel, Christian Lindner, Fabian Kratzsch, Alexander Zittau/Görlitz University of Applied Science Faculty of Electrical Engineering and Computer Science Zittau Germany Zittau/Görlitz University of Applied Science Faculty of Business Administration and Engineering Zittau Germany Zittau/Görlitz University of Applied Science Institute of Process Technology Process Automation and Measuring Technology Zittau Germany

ISBN: (纸本)9798350316025

This scientific paper examines the use of collaborative robots in the context of Industry 5.0 and its impact on practical engineering education. The aim of this paper is to highlight the importance as well as the challenges of using robots in Industry 5.0 and to analyze how this can be integrated into engineering education. The paper discusses various aspects such as the technological advances, the need for interaction between humans and robots, and the competencies that engineers need in an increasingly digitalized working world. Furthermore, practical methods and approaches in engineering education at the Zittau/Görlitz University of Applied Sciences are presented to prepare students for the requirements of Industry 5.0. Additionally, a draft of a future interdisciplinary learning factory 5.0 will be presented, in which networked technologies with the focus on robotics run through various scenarios in the context of Industry 5.0 at different locations. © 2023 IEEE.

关键词： engineering education

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Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal Neural Network

Web Traffic Anomaly Detection Using a Hybrid Spatio-temporal...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bi, Jing Xu, Lifeng Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China School of Engineering Dept. of Computer Science in Lyle Southern Methodist University DallasTX United States

ISBN: (纸本)9798350337020

Nowadays, rapid development of Internet has brought a sharp increase in traffic data. Abnormal traffic haS serious impact on network security. Traffic anomaly detection can be achieved by extracting characteristics of network traffic to detect anomalous intrusions, and therefore, anomaly detection algorithms are of great significance to maintenance of network security. This work proposes a hybrid spatio-temporal neural network with attention named CTGA to effectively identify anomalous traffic. CTGA combines a Convolutional neural network (CNN), a Temporal convolutional network (TCN), a bidirectional Gated recurrent unit network (BiGRU), and a self-Attention mechanism. It automatically extracts temporal and spatial features of sequences from raw data by sliding window preprocessing followed by CNN, TCN, BiGRU, and the self-attention mechanism to detect anomalous data. CNN is used to extract spatial features of time sequences and reduce the loss of spatial information. In the sequence, TCN obtains short-term features. Long-term dependencies in the data are captured by BiGRU, and the self-attention mechanism obtains important information in the sequence. Finally, experiments with the real-life Yahoo S5 dataset prove that CTGA outperforms other approaches substantially. © 2023 IEEE.

关键词： Anomaly detection bidirectional gated recurrent unit convolutional neural network self-attention temporal convolutional network

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