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Network Traffic Prediction with Decomposition and Multi-Scale Autocorrelation in Large-Scale Cloud Data Centers

Network Traffic Prediction with Decomposition and Multi-Scal...

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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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Concurrent imaging of large-scale vasculature, myelin content and cell density of post-mortem human brain with serial sectioning PSOCT-2PM

Concurrent imaging of large-scale vasculature, myelin conten...

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Optical Coherence Tomography, OCT 2022

作者： Chang, Shuaibin Wang, Hui Yang, Jiarui Fischl, Bruce Chen, Shih-Chi Boas, David A. Chen, Ichun Anderson Dept. of Electrical and Computer Engineering Boston University 8 St Mary's St Boston02215 United States Dept. of Biomedical Engineering 44 Cummington Mall Boston University Boston02215 United States Dept. of Radiology A.A. Martinos Center for Biomedical Imaging Massachusetts General Hospital 13th Street Boston02129 United States Dept. of Mechanical and Automation Engineering Chinese University of Hong Kong N.T. Shatin Hong Kong

A dual-modality microscope combining polarization sensitive optical coherence tomography (PSOCT) and two photon microscopy (2PM) is built for block-face imaging of large human brain blocks, which enriches the biologic... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical tomography

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Monitoring and Forecasting Quality Indicators of Predough Production in the Bakery Industry

Monitoring and Forecasting Quality Indicators of Predough Pr...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Nataliia Lutska Nataliia Zaiets Lidiia Vlasenko Andrii Zhyltsov dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of electromagnetic systems The Institute of Electrodynamics of the National Academy of Sciences of Ukraine Kyiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The work demonstrates the process of developing a machine learning model for a system for monitoring and controlling the quality of predough at a bakery. This system will provide continuous analysis of the predough parameters, such as temperature, humidity, lifting force and titranic acidity, which will ensure timely detection of deviations in the quality of the predough and avoid production problems. In addition, it will help automate the quality control process and ensure the stability of production processes. Analysis and forecasting of dough parameters will improve the quality of produced bread or other bakery products. Management based also on the predicted values of technological indicators of the predough will allow the enterprise to avoid unnecessary costs of materials and energy and increase economic efficiency. As part of the study, seven machine learning models were created and reviewed, including three linear and four varieties of binary decision trees. An analysis of the best model - a regression decision tree - was carried out, confirming its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important indicators of predough with a high degree of reliability, which indicates its importance in practical applications in production. The created model can be used to monitor and control product quality in real time, allowing operators to quickly identify and correct any deviations in process performance. Also, by analyzing changes in the variables of the predough production process, it is possible to identify precursors of possible production accidents or deviations in product quality, which will allow timely measures to be taken to avoid them.

关键词： Force Process control Production Machine learning Quality control Humidity Predictive models Stability analysis Monitoring Thermal stability

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Brain Stroke Detection from CT Images using Transfer Learning Method

Brain Stroke Detection from CT Images using Transfer Learnin...

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Necip Çınar Buket Kaya Mehmet Kaya Department of Computer Engineering Dicle University Diyabakır Turkey Department of Electronics and Automation Fırat University Elazığ Turkey Dept. of Computer Engineering Fırat University Elazığ Turkey

Recently, with the production of high-capacity computers, artificial intelligence methods have been used in many areas. In particular, in the field of health, artificial intelligence methods are used to detect diseases and determine the treatments to be applied. Deep learning methods, a sub-branch of artificial intelligence, show a high success in diagnosing many diseases thanks to its deep CNN networks. In this study, firstly, it was tried to determine which deep learning methods are more successful for the detection of brain stroke from computerized tomography images. This study is of great importance in terms of determining which deep learning architectures we will focus on in our future studies on brain stroke. In this study, brain stroke disease was detected from CT images by using the five most common used models in the field of image processing, one of the deep learning methods. The pre-trained ResNetl01, VGG19, EfficientNet-B0, MobileNet-V2 and GoogleNet models were run with the same dataset and same parameters. As a result, the EfficientNet-B0 architecture showed the highest performance with 97.93% accuracy. ResNetl01, VGG19, MobileNet-V2 and GoogleNet models showed 94.32%, 97.28%, 92.30% and 91.61% accuracy rates, respectively. With this study, it was concluded that the EfficientNet architecture is more suitable for the detection of brain stroke disease from CT images.

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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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A controller for reaching and unveiling a partially occluded object of interest with an eye-in-hand robot

A controller for reaching and unveiling a partially occluded...

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IEEE-RAS International Conference on Humanoid Robots

作者： Dimitrios Papageorgiou Leonidas Koutras Zoe Doulgeri Dept. of Electrical & Computer Engineering Automation & Robotics Lab Aristotle University of Thessaloniki Greece

ISBN: (纸本)9798350309805

In this work, a control scheme for approaching and unveiling a partially occluded object of interest is proposed. The control scheme is based only on the classified point cloud obtained by the in-hand camera attached to the robot's end effector. It is shown that the proposed controller reaches in the vicinity of the object progressively unveiling the neighborhood of each visible point of the object of interest. It can therefore potentially achieve the complete unveiling of the object. The proposed control scheme is evaluated through simulations and experiments with a UR5e robot with an in-hand RealSense camera on a mock-up vine setup for unveiling the stem of a grape cluster.

关键词： Point cloud compression Robot vision systems Pipelines Humanoid robots Cameras End effectors

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Steel Defect Detection in Bridges Using Deep Encoder-Decoder Networks 13

Steel Defect Detection in Bridges Using Deep Encoder-Decoder...

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13th International Workshop on Structural Health Monitoring: Enabling Next-Generation SHM for Cyber-Physical Systems, IWSHM 2021

作者： Ahmed, Habib La, Hung Manh Advanced Robotics and Automation Laboratory Dept. of Computer Science and Engineering University of Nevada Reno RenoNV89527 United States

ISBN: (纸本)9781605956879

Recent major accidents related to bridges have emphasized the need for developing effective technological solutions for defect detection, which can minimize the possibility of bridge-related accidents in the future. In this respect, this research will focus towards development of automated system for the detection of defective regions within different steel parts of bridges. At present, there is no open-source image dataset, which can be used for this purpose. Consequently, the training dataset has been developed by using images acquired from bridges in Vietnam and validation was performed using images acquired from Lovelock bridge situated at Highway-80, Lovelock, NV, USA. A total of 5,500 (4,000 images for training and 1,500 for validation) images of different dimensions have been used the original dimensions of the steel bridge images have been modified 572 × 572 pixels, which have been used for the training and evaluation of the dataset on different Deep Encoder-Decoder networks. The use of diverse data from different bridges will allow the development of a robust Deep Encoder-Decoder network with considerable implications for practical systems in the future. This study will employ state-of-the-art Deep Encoder-Decoder network, which have been recently developed for other applications. However, no such study has been developed for defect detection in steel bridges. A comparative evaluation of different Deep Encoder-Decoder networks will be examined. At the same time, the performance of the system will be compared with recent advanced approaches. The results reveal the considerable potential of Deep Encoder-Decoder towards defect detection of steel bridges, which will be further exploited in the future studies. © 2021 Structural Health Monitoring 2021: Enabling Next-Generation SHM for Cyber-Physical Systems - Proceedings of the 13th International Workshop on Structural Health Monitoring, IWSHM 2021. All rights reserved.

关键词： Embedded systems

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Implementation of Industrial automation Sustainable Training in Savannakhet University, Laos 8

Implementation of Industrial Automation Sustainable Training...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Georgieva, Tsvetelina Bencheva, Nina Daskalov, Plamen Yahoui, Hamed Khemmarath, Sitha Norasane, Khonesavanh Chanthanouvong, Lamnguen Leaunglith, Youlananh Keovorabouth, Vinai University of Ruse Angel Kanchev Automation and Mechatronics Department Ruse Bulgaria University of Ruse Angel Kanchev Telecommunications Department Ruse Bulgaria Automation &processing Claude Bernard University Lyon 1 Department of Mechanical Engineering Lyon France Savannakhet University Faculty of Engineering Savannakhet Laos Savannakhet University Faculty of Information Technology Technology Computer Network Dept. Savannakhet Laos Savannakhet University Faculty of Information Technology Savannakhet Laos

ISBN: (纸本)9798350396546

The paper presents part of the work fulfilled under the Asean Factori 4.0 Erasmus+ project focused on the implementation of industrial automation in the education in 6 universities from 3 countries in South-East Asia: Thailand, Laos and Cambodia. The implementation of the project tasks, related to increasing the capacity and more precisely to the training of the professors from the Asian Universities is presented. According to the planned activities, professors from the University of Ruse conduct training for teachers from two universities: Chulalongkorn University, Thailand and Savannakhet University, Laos. The phases of training included in the project activities and how many of them have already been implemented are briefly presented. The program and the tasks of the teachers practical training for building the necessary teaching capacity in Savannakhet University and implementation of sustainable training in the field of industrial automation were presented and analyzed. Conclusions and future training of the staff of Asian universities planned under the project are presented in the paper. © 2023 IEEE.

关键词： Personnel training

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A New Robust Hybrid Active Noise Control System

A New Robust Hybrid Active Noise Control System

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Z. Wang Y. Xiao Y. Ma L. Ma K. Khorasani Dept. of Management & Information Systems Prefectural University of Hiroshima Hiroshima Japan Institute of Automation Jiangnan University Wuxi China Dept. of Electrical & Computer Engineering Concordia University Montreal Canada

The conventional hybrid active noise control (ANC, HANC) may significantly degrade if the reference signal for the feedforward ANC (FFANC) subcontroller consists of not only a broadband component but also a low-frequency sinusoidal component that is uncorrelated or partially correlated with the narrowband noise component that is attenuated by the feedback ANC (FBANC) subcontroller. In this paper, a new robust HANC system is proposed to mitigate the performance degradation resulting from the low-frequency sinusoidal component. A band-pass filter bank (BPFB) is applied to the FFANC reference signal to separate the low-frequency sinusoidal component from the broadband one and each of them is fed to an FFANC subcontroller that solely focuses on a single noise component. A supporting filter takes the extracted broadband component and the residual error as its input and desired signal, respectively. The same BPFB is then applied to the supporting filter error in order to separate the remaining low-frequency sinusoidal component from the narrowband component that persists in the residual error. The use of the two BPFBs and the supporting filter allows the three HANC subcontrollers to operate practically independently, each taking care of one of the pre-processed noise components which are uncorrelated with each other irrespective of the relationship between the two original noise sources. Extensive simulations are provided to demonstrate the improved effectiveness and robust capability of the proposed HANC as compared to its counterpart, even in a case that the low-frequency sinusoidal component in the FFANC reference signal is partially corrected with the primary narrowband noise component that is targeted by the FBANC.

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Analysis of Air-Gap Field Modulation in Asymmetric Stator Tooth Bilateral-Excitation Flux Modulation Machine

Analysis of Air-Gap Field Modulation in Asymmetric Stator To...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Fei Zhao Kuang Yang Ruixiang Liu Jincheng Yu S M Sajjad Hossain Rafin Qasim Ali School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Dept. of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University USA Department of Electrical Engineering Sukkur IBA University Pakistan

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

In view of the complexity of the flux density harmonic components of bilateral-excitation flux modulation (BFM) machines, it is not easy to analyze the role of air-gap flux density harmonics on the electromagnetic torque of BFM machines. In this digest, Taking the novel asymmetric stator tooth (AST) BFM machine as the research object, the flux modulation phenomena of the PM magneto motive-force (MMF) and armature winding MMF are analyzed in detail. Meanwhile, based on the air-gap field modulation theory and Maxwell stress tensor equation, the contribution of air-gap flux density harmonics to the electromagnetic torque of the BFM machine is obtained.

关键词： Magnetic flux Torque Tensors Magnetomechanical effects Air gaps Windings Modulation

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