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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. 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 to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Development of Software for Modeling the Drying Process of Hygroscopic Materials Using Cellular Automata

Development of Software for Modeling the Drying Process of H...

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International computer Sciences and information Technologies (CSIT)

作者： Yaroslav Sokolovskyy Oleksiy Sinkevych Roman Voliansky Oleksandr Storozhuk Department of Computer-Aided Design Systems Lviv Polytechnic National University Lviv Ukraine Department of Computer Science Ukrainian National Forestry University Lviv Ukraine Department of Electromechanical Systems Automation and Electric Drives National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine Department of Information Systems and Computer Modeling Ukrainian National Forestry University Lviv Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

In this work, software is developed for modeling the drying process of hygroscopic materials using cellular automata. The modeling process using cellular automata is described, and the developed transition rules that consider heat and mass transfer not only within the material but also between stacks are provided. The developed software uses a modern multi-tier client-server architecture, including a database for handling large volumes of computational data. Additionally, the results of the modeling, including their validation based on existing experimental data, are presented and analyzed.

关键词： computer science Accuracy Databases Computational modeling Automata computer architecture Software Data models information technology Heat transfer

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Domain-adaptive Person Re-identification without Cross-camera Paired Samples

arXiv

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arXiv 2023年

作者： Li, Huafeng Mao, Yanmei Zhang, Yafei Qi, Guanqiu Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Computer Information Systems Department State University of New York at Buffalo State BuffaloNY14222 United States

Existing person re-identification (re-ID) research mainly focuses on pedestrian identity matching across cameras in adjacent areas. However, in reality, it is inevitable to face the problem of pedestrian identity matching across long-distance scenes. The cross-camera pedestrian samples collected from long-distance scenes often have no positive samples. It is extremely challenging to use cross-camera negative samples to achieve cross-region pedestrian identity matching. Therefore, a novel domain-adaptive person re-ID method that focuses on cross-camera consistent discriminative feature learning under the supervision of unpaired samples is proposed. This method mainly includes category synergy co-promotion module (CSCM) and cross-camera consistent feature learning module (CCFLM). In CSCM, a task-specific feature recombination (FRT) mechanism is proposed. This mechanism first groups features according to their contributions to specific tasks. Then an interactive promotion learning (IPL) scheme between feature groups is developed and embedded in this mechanism to enhance feature discriminability. Since the control parameters of the specific task model are reduced after division by task, the generalization ability of the model is improved. In CCFLM, instance-level feature distribution alignment and cross-camera identity consistent learning methods are constructed. Therefore, the supervised model training is achieved under the style supervision of the target domain by exchanging styles between source-domain samples and target-domain samples, and the challenges caused by the lack of cross-camera paired samples are solved by utilizing cross-camera similar samples. In experiments, three challenging datasets are used as target domains, and the effectiveness of the proposed method is demonstrated through four experimental settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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A Fully Convolutional Neural Network-based Regression Approach for Effective Chemical Composition Analysis Using Near-infrared Spectroscopy in Cloud

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Journal of Artificial Intelligence and Technology 2021年第1期1卷 74-82页

作者： Daiyu Jiang Gang Hu Guanqiu Qi Neal Mazur College of Automation Chongqing University of Posts and TelecommunicationsChongqing 400065China Computer Information Systems Department State University of New York at Buffalo StateBuffaloNY 14222 USA

As one chemical composition,nicotine content has an important influence on the quality of tobacco *** and nondestructive quantitative analysis of nicotine is an important task in the tobacco ***-infrared(NIR)spectroscopy as an effective chemical composition analysis technique has been widely *** this paper,we propose a one-dimensional fully convolutional network(1D-FCN)model to quantitatively analyze the nicotine composition of tobacco leaves using NIR spectroscopy data in a cloud *** 1D-FCN model uses one-dimensional convolution layers to directly extract the complex features from sequential spectroscopy *** consists of five convolutional layers and two full connection layers with the max-pooling layer replaced by a convolutional layer to avoid information *** computing techniques are used to solve the increasing requests of large-size data analysis and implement data sharing and *** results show that the proposed 1D-FCN model can effectively extract the complex characteristics inside the spectrum and more accurately predict the nicotine volumes in tobacco leaves than other *** research provides a deep learning foundation for quantitative analysis of NIR spectral data in the tobacco industry.

关键词： nicotine tobacco leaves near-infrared spectroscopy fully convolutional network cloud computing

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Coronavirus Infection Segmentation Detection Using UNET Deep Learning Architecture 18

Coronavirus Infection Segmentation Detection Using UNET Deep...

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18th IEEE India Council International Conference, INDICON 2021

作者： Saxena, Naman Chahal, Ekam Singh Sinha, Adwitiya Chand, Satish Indian Institute of Science Department of Computer Science Automation Bengaluru India Jaypee Institute of Information Technology Department of Computer Science Eng. and Information Technology UP Noida India Jawaharlal Nehru University School of Computer Systems Sciences New Delhi India

ISBN: (纸本)9781665441759

The high potency and spread of the coronavirus pandemic has rapidly swept over a global scale, causing a large number of deaths and devastation. Its mutants have exaggerated the situation further, which has become a serious concern and a challenge for scientists, especially medical practitioners, to devise some advanced remedial actions. This paper intends to address this by developing a model based on deep learning for segmenting the affected regions in the lungs using CT-scan images. We propose a novel segmentation model based on UNet using Xception-Net in the encoder stage to detect covid-19 infection in CT-scans with two main aspects. It combines the local residual connections in the decoder unit of UNet with the typical global residual connections that lead to better performance. Also, the encoder component uses a pre-trained state-of-the-art feature extraction model that helps the system converge to the optimal value precisely due to the pre-trained weights. We apply a contrast-limited version of the adaptive histogram equalization in the data preparation stage to make the frequency of image pixels uniformly distributed. This decreases the biasedness in the model towards specific sections of CT-scans images. Our proposed model outperformed some existing counterparts, including TV-UNet, Inf-Net, ED-CNN. © 2021 IEEE.

关键词： Coronavirus

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Change Bit-Depth Procedure for Data Measured Preprocessing before Compression

Change Bit-Depth Procedure for Data Measured Preprocessing b...

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2022 IEEE International Multi-Conference on Engineering, computer and information Sciences, SIBIRCON 2022

作者： Levenets, Alexey V. Bondarenko, Vladislav V. Bahenov, Ruslan I. Belozerov, Oleg I. Belozerova, Svetlana I. Bogatenkov, Sergey A. Pacific National University Dept. Automation and System Engineering Khabarovsk Russia Sholom-Aleichem Priamursky State University Dept. Information Systems and Mathematics Birobidzhan Russia Far Eastern State Transport University Dept. Computer Technology and Computer Graphics Khabarovsk Russia South Ural State University Dept. Information Technology in Economics Chelyabinsk Russia

ISBN: (纸本)9781665464802

The paper proposes to change the bit-depth of the original data sequence as a preprocessing of the measuring data, which is carried out before the compression procedure. In this case the digit capacity conversion is performed without accounting for the primary data sample boundaries. The simulation was carried out in the Matlab software environment. The findings of the authors' research are presented. They prove to be a promising approach for some types of the studied data at least and several digit capacity values. The analysis of autocorrelation function values showing the data correlation revealed by the conversion is used as an assessment of perspective of the active conversion. In this article the authors review the values of secondary maximums of autocorrelation function. Their occurrence shows the demonstration of the implied correlative correspondence. Such correlations can potentially allow the converted data to be compressed more effectively than the original data using the known compression methods. © 2022 IEEE.

关键词： Data compression

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Transfer Learning for Russian Handwriting Recognition 46

Transfer Learning for Russian Handwriting Recognition

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Levkov, Andrey Kaplun, Dmitry Safonova, Anastasiia St. Petersburg Electrotechnical University "leti" Depart. of Computer Engineering St. Petersburg Russia St. Petersburg Electrotechnical University "leti" Depart. of Automation and Control Processes St. Petersburg Russia St. Petersburg Electrotechnical University "leti" Depart. of Information Systems St. Petersburg Russia

ISBN: (纸本)9798350303964

Handwriting recognition is one of the most important tasks in computer vision. Despite the variety of methods and algorithms used for text recognition in various languages, the recognition of handwritten Russian words is still a completely unsolved problem, which keeps the relevance of this topic. This paper proposes the application of a convolutional neural network (CNN) to the task of recognizing the 100 most frequently used Russian words, trained on a new unique dataset. A unique algorithm for extracting Russian handwritten words from forms was applied to obtain a new dataset. The paper also presents the results of a comparison of such powerful models as VGG-16, VGG-19, ResNet-50, and ResNet-101, which were trained using transfer learning technology. The best results were shown by VGG-16 with 98.7% accuracy, and the worst results were shown by ResNet-50 with 80% accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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An Adaptive-Gain Recurrent Neural Network for Solving the Joint-Angle Drift Issues of Redundant Manipulators

An Adaptive-Gain Recurrent Neural Network for Solving the Jo...

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2023 International Annual Conference on Complex systems and Intelligent Science, CSIS-IAC 2023

作者： Zheng, Boyu Li, Chunquan Zhang, Zhijun Yu, Junzhi Liu, P.X. School of Information Engineering Nanchang University Nanchang330031 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Engineering Peking University Beijing100871 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

ISBN: (纸本)9798350309003

This paper proposes a new adaptive-gain recurrent neural network (AG-RNN) to effectively cope with the joint-angle drift issues in redundant manipulators. Specifically, a joint-angle drift-free with the feedback control of the velocity layer motion equation (JADF-FC) is proposed via an optimization criterion for synchronously optimizing linear terms and quadratic. Then, the JADF-FC is reasonably formulated into a standard quadratic programming (QP) issue. Different from the previous recurrent neural networks (RNNs), the AG-RNN proposed in this paper constructs an error-based differential equation with a new adaptive-gain. It should be noted that the proposed adaptive-gain does not gradually approach infinity as time increases, which is more in line with actual hardware implementation requirements than the existing time-variant-gain. The adaptive-gain can reduce the joint-angle drift errors of the redundant manipulator. Therefore, the proposed AG-RNN can solve the QP problem of the manipulator more effectively and quickly. To validate the performance of the proposed AG-RNN, it is compared with representative RNNs. The experimental results indicate that smaller joint-angle drift errors can be get by the proposed AG-RNN solving JADF-FC scheme than the other solutions when solving the joint-angle drift issues. © 2023 IEEE.

关键词： Quadratic programming

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Integration of ANN for Accurate Estimation and Control in Wastewater Treatment 28

Integration of ANN for Accurate Estimation and Control in Wa...

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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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The Concept of Automated Phonetic Analysis of a Speech with Asymptotic Adaptation to the Specifics of Phonation of Language Units 12

The Concept of Automated Phonetic Analysis of a Speech with ...

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12th International Conference on Advanced computer information Technologies, ACIT 2022

作者： Bisikalo, Oleg Kovtun, Oksana Kovtun, Viacheslav Vinnytsia National Technical University Department of Automation and Intelligent Information Technologies Vinnytsia Ukraine Vasyl' Stus Donetsk National University Department of Theory and Practice of Translation Vinnytsia Ukraine Vinnytsia National Technical University Department of Computer Control Systems Vinnytsia Ukraine

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

A new concept of automated phonetic analysis of a speech with asymptotic adaptation to the specifics of phonation of language units is proposed. Unlike analogues, the concept is formalized in the paradigm of information theory with the criterion of analysis of the speech signal based on its relative entropy. The obtained mathematical apparatus allows for analysing the studied process both in statics and dynamics. The quality of the analysis is characterized by errors in the first (incorrect recognition of the language unit) and the second (variability of phonation of the language unit within the corresponding cluster) kind. The derived result is an analytically substantiated possibility of estimating the phonetic saturation of speech. The formulated concept can also be used to assess the degree of information saturation of the cluster of the language unit. Such an assessment is important for the task of creating universal background models, which are an essential element of current recognition systems for both language and speaker. © 2022 IEEE.

关键词： Speech

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