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Efficient Resource Allocation Using Service Function Chain in Agricultural Consumer Electronics

IEEE Transactions on Consumer Electronics 2025年

作者： Yao, Jiamin Qiao, Sibo Xie, Yu Kumari, Saru Lin, Qiao Khan, Fazlullah Shanghai Maritime University College of Information Engineering Shanghai201306 China Tiangong University School of Software Tianjin300387 China Chaudhary Charan Singh University Department of Mathematics Meerut India University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China

As the level of intelligence in agricultural consumer electronics continues to advance, data-driven devices are often faced with challenges such as resource shortages, high real-time requirements, and complex data processing. In particular, the diversity and variability of massive agricultural consumer electronics in the agricultural Internet of Things (IoT) complicate the resource allocation process. Traditional centralized cloud computing architectures often struggle to meet these demands. To address these issues, this paper proposes a resource collaborative allocation method for agricultural consumer electronics based on Service Function Chain (SFC). First, we formalize the resource requirements of various agricultural consumer electronics applications based on SFC in a resource mapping model. Subsequently, we develop a multidimensional resource balancing model with computing resources, storage resources, and bandwidth resources requirements of agricultural consumer electronics. We also devise a fine-grained end-to-end latency model to comprehensively consider the effective utilization of multidimensional resources and latency guarantees for agricultural consumer electronics. Finally, we employ a deep reinforcement learning algorithm with experience replay to optimize the resource allocation strategy in real-time, thereby enhancing the agricultural consumer electronics system's responsiveness and resource utilization efficiency. Experimental results demonstrate that our proposed strategy outperforms existing methods in terms of improving resource utilization and reducing latency. © 1975-2011 IEEE.

关键词： Chains

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Transmission Line Age Determination with Statistical Health Index and Failure Probability Curve Fitting Technique

Transmission Line Age Determination with Statistical Health ...

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International Conference on Condition Monitoring and Diagnosis, CMD

作者： Cattareeya Suwanasri Ittiphong Yongyee Thanapong Suwanasri Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mechatronics Engineering Faculty of Engineering and Architecture Rajamangala University of Technology Suvarnabhumi Nonthaburi Thailand Electrical and Software Systems Engineering The Sirindhorn International Thai-German Graduate School of Engineering (TGGS) King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9788986510225

ISBN: (纸本)9798350353877

The deterioration of transmission lines has a profound effect on the reliability and safety of the power grid. Accurate estimation of their age is critical for effective maintenance and investment planning. This paper proposes a method that combines the percentage statistical health index (%SHI) with failure probability curve-fitting (FPCF) to estimate the ages of overhead transmission lines. %SHI assesses equipment condition through scoring and weighting, while FPCF correlates SHI with the probability of failure. Evaluation of 924 towers situated in various terrains across Thailand reveals discrepancies between estimated and actual ages, influenced by environmental factors. To enhance reliability, proactive maintenance strategies such as regular inspections and advanced monitoring are recommended.

关键词： Power transmission lines Poles and towers Probability Inspection Maintenance Safety Planning Curve fitting Monitoring Investment

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Neural Networks and Deep Learning: Innovations and Applications in Soft Computing

Neural Networks and Deep Learning: Innovations and Applicati...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Jahagirdar Imran Abdulkadar Chinnem Rama Mohan P. Vijaya Bharati Shamik Palit Gunda Srinivasa Rao Pankaj Mudholkar Computer Application Savitribai Phule Pune University Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Department of CSE Vignans Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computing Science and Software Engineering University of Stirling RAK Campus UAE School of Management and Commerce Mallareddy University Kompally Faculty of Computer Applications Marwadi University Rajkot Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing.

关键词： Deep learning Technological innovation Data privacy Privacy Natural languages Process control Real-time systems Informatics Biological neural networks Robots

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Multi-batch Nuclear-norm Adversarial Network for Unsupervised Domain Adaptation

Multi-batch Nuclear-norm Adversarial Network for Unsupervise...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Pei Wang Yun Yang Zhenyu Yu School of Information Science and Engineering Yunnan University Kunming China National Pilot School of Software Yunnan University Kunming China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China Guangzhou University Guangzhou China CISiP Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Adversarial learning has achieved great success for unsupervised domain adaptation (UDA). Existing adversarial UDA methods leverage the predicted discriminative information with Nuclear-norm Wasserstein discrepancy for feature alignment. However, the limited memory space makes it very difficult to accurately calculate the Nuclear-norm, which hinders domain adaptation. To address this challenge, we propose a multi-batch Nuclear-norm adversarial network, termed as MBAN. Specifically, we build a dynamic queue to cache features, which encourages to generate a large and consistent output matrix, enabling accurate calculation of the Nuclear-norm. Then, the multi-batch Nuclear-norm discrepancy is proposed, which can effectively improve the transferability and discriminability of the learned features. Experimental results show that MBAN could achieve significant performance improvement, especially when the number of categories is quite large. Code is available at https://***/peiwang0518/Multi-BAN.

关键词： Codes Accuracy Adversarial machine learning Noise measurement

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Fast Division Algorithm for Finite Real Number 16

Fast Division Algorithm for Finite Real Number

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Chen, Ting Li, Fenfen Lin, Jinbao National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation Huai'an China

ISBN: (纸本)9781665405539

The data representation and division for finite real number in computers has always been a hot topic in the fields of scientific research and engineering technology. With the research of existing real number division algorithm, design, improvement and adjustment for data structure, algorithm and programming language, and realize the fast division for finite real number. The algorithm can apply to the engineering requirement of finite real number of 506 bits. It replaces the commonly used trial division method with a comparison method, reducing the time complexity of the algorithm. The experimental results demonstrate the correctness and efficiency of the algorithm. The division of the algorithm uses comparison instead of trial division, which reduces the time complexity of the algorithm from O(n2) to O(n). The experimental results show that the algorithm is correct and efficient. © 2021 IEEE.

关键词： Computational complexity

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Study on Potability Water Quality Classification Based on Integrated Learning 16

Study on Potability Water Quality Classification Based on In...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Fen, Li Fen Lei, Zhou Ting, Chen Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaian China

ISBN: (纸本)9781665405539

Aiming at the problem of low accuracy of simple classification model, based on the drinking water data set of kaggle official website, integrated learning models is proposed, which improves the precision and accuracy of binary classification of drinking water quality. K-fold cross validation was performed to measure model consistency. The effect of set classifier is higher than that of simple classifier. © 2021 IEEE.

关键词： Water quality

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Lightweight Deep Network for the Classification of Breast Cancer Histopathological Images 15

Lightweight Deep Network for the Classification of Breast Ca...

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15th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2022

作者： Waqas, Muhammad Maul, Tomas Liao, Iman Yi Ahmed, Amr School of Computer Science Faculty of Science and Engineering University of Nottingham Malaysia Campus Selangor Dar-ul-Ehsan Semenyih43500 Malaysia Department of Software Engineering University of Malakand Lower Dir Khyber Pakhtunkhwa Chakdara18800 Pakistan School of Computer Science Faculty of Science University of Nottingham NottinghamNG8 1BB United Kingdom Department of Computer Science Edge Hill University LancashireL39 4QP United Kingdom

ISBN: (数字)9781665488877

ISBN: (纸本)9781665488877

Out of all cancers among female patients, breast cancer is on top in terms of prevalence. Many deep learning approaches have been investigated for the classification of histopathological images of breast cancer, however most tend to be either too complex or too large to adopt at a clinical level. In this study we propose a lightweight network, based on knowledge distillation, which performs almost the same as the teacher network but with significantly fewer parameters. Our student network consists of inverted residual blocks of MobileNetV2 and the ghost module. We performed our experiments on BreakHis, BACH and Kaggle breast cancer histopathological imaging datasets. The results show that different versions of our proposed lightweight student architecture perform with similar accuracy levels compared with the teacher network, while using significantly fewer parameters. © 2022 IEEE.

关键词： Distillation

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Choosing the Discipline of Restoring computer Systems with Acceptable Degradation with Consolidation of Node Resources Saved After Failures 6

Choosing the Discipline of Restoring Computer Systems with A...

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6th International Scientific Conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Bogatyrev, Vladimir A. Bogatyrev, Stanislav V. Bogatyrev, Anatoly V. Security Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Saint Petersburg Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia Yadro Cloud Storage Development Center Saint Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

An approach to substantiating the choice of a discipline for the maintenance of a redundant computer system, with the possible use of node resources saved after failures, is considered. The choice is aimed at improving the reliability and profitability of the system, taking into account the operational costs of restoring nodes. Models of reliability of systems with service disciplines are proposed, providing both the possibility of immediate recovery of nodes after failures, and allowing degradation of the system when using node resources stored after failures in it. The models take into account the conditions of the admissibility or inadmissibility of the loss of information accumulated during the operation of the system. The operating costs are determined, taking into account the costs of restoring nodes for the system maintenance disciplines under consideration © 2022 IEEE.

关键词： Fault tolerance

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Control of Multipath Transmissions in the Nodes of Switching Segments of Reserved Paths 6

Control of Multipath Transmissions in the Nodes of Switching...

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6th International Scientific Conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Bogatyrev, Vladimir A. Bogatyrev, Stanislav V. Bogatyrev, Anatoly V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia Yadro Cloud Storage Development Center Saint Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

The options for ensuring timely delivery of packages to the addressee in networks with duplication of communication paths with the addressee for some subscribers are analyzed. Connecting subscribers to two communication routes with the addressee allows you to load-balance routes and carry out duplicated transmissions aimed at increasing the probability of delivering at least one copy of the transmitted packet in the required time. The possibilities of using route switch nodes for traffic management and replication of transmitted packets are analyzed. At the same time, two variants of duplicated transmissions are considered. For the first option, new replicas and ways of their transmission to the addressee are formed in one of the nodes connecting the existing paths by the first accepted replica. The replica coming in second is destroyed. For the second option, changing the transmission paths of replicas generated from the source in the nodes linking the paths is not carried out. The area of effectiveness of the proposed variants of duplicated transmissions is shown. The influence of the location of route switches on the timeliness of package delivery from subscribers connected to one or two communication paths with the addressee is investigated. The existence of an optimal arrangement of route switches is shown. © 2022 IEEE.

关键词： Real time control

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Imitation Learning from Purified Demonstrations

arXiv

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arXiv 2023年

作者： Wang, Yunke Dong, Minjing Zhao, Yukun Du, Bo Xu, Chang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong China School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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