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Chaotic and Transfer Function-Assisted Grey Wolf Optimizer for IoT Task Scheduling in Cloud-of Things

Chaotic and Transfer Function-Assisted Grey Wolf Optimizer f...

引用

2024 ASU international conference in emerging Technologies for Sustainability and Intelligent Systems, ICeTSIS 2024

作者： Qasim, Mohammad Sajid, Mohammad Department of Computer Science Aligarh Muslim University Aligarh India

ISBN: (纸本)9798350372229

The amalgamation of Cloud computing and Internet-of-Things (IoT), i.e., Cloud-of-Things (CoT), has emerged as one of the indispensable technologies in the IT and business world. The success of CoT depends on the efficient services provided by cloud computing to the requests generated by IoT devices. The efficient management of IoT services on cloud virtualized resources, an NP-hard problem, is the major component in developing trust and reliability among IoT users. This research proposes a novel chaotic and transfer functionassisted modified grey wolf optimizer (MGWO) algorithm to schedule IoT requests on virtual machines in cloud computing. The proposed MGWO algorithm utilizes a transfer function to convert the wolf into the solution and a chaotic circle map to improve the initial population's diversity, which leads to faster convergence and better exploitation and exploration. The MGWO algorithm's performance is superior to the generic version of the GWO algorithm and its variants. © 2024 ieee.

关键词： Chaotic Map Cloud computing Grey Wolf Optimizer Task Scheduling Transfer Function

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Dual-Scale Cellular Traffic Prediction Based on FastDTW for Graph Neural Network 16

Dual-Scale Cellular Traffic Prediction Based on FastDTW for ...

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16th international conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Jia, Junrong Zhang, Haixia Zhou, Xiaotian School of Control Science and Engineering Shandong University Jinan250061 China Shandong University Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Jinan250061 China

ISBN: (纸本)9798350390643

Cellular traffic prediction is crucial to the network efficiency as it provides prior knowledge to guide the wireless resource allocation. The artificial intelligence (AI) based traffic prediction can provide better prediction performance and has drawn increasing interests. Nevertheless, most existing works utilized euclidean structure data, which can provide traffic prediction to only certain grid area rather than specific spot, such as each base station (BS). To give more precise BS level prediction, in this work we propose a dual-scale spatiotemporal prediction framework based on fast dynamic time warping (FastDTW) algorithm. The proposed framework can capture both the overall neighborhood relationship of historical traffic and the local neighborhood relationship graph to improve the prediction accuracy. More importantly, due to the utilization of FastDTW, it solves the disadvantage of the high complexity of the dynamic time warping (DTW) algorithm and completes the prediction at the BS level more efficiently. Simulation results confirms the generality and performance of the proposed method, which outperforms the benchmark algorithms. © 2024 ieee.

关键词： Prediction models

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Stability Impact of Frequency Measurement Accuracy on Decoupling Control of GCI under Weak grids

Stability Impact of Frequency Measurement Accuracy on Decoup...

引用

ieee international Instrumentation and Measurement Technology conference (I2MTC) - Rising Above Covid-19

作者： Lin, Xianfu Zhu, Liang Huang, Chang Wen, He Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China State Grid Jiangxi Elect Power Co Power Supply Serv Ctr Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781665453837

Due to the inherited coupling between d channel and q channel, the decoupling control is often introduced into the control of three phase grid-connected inverter (GCI) in dq-frame. Therein, the system's fundamental frequency needs to be adopted in the decoupling control. The published literature often uses the nominal frequency.1 or the estimated *** via a Phase-Locked Loop (PLL). However, it has not yet been revealed what is different between employing.1 *** for the decoupling control. In this article, a detailed discussion of the decoupling control with the two frequencies, i. e.,.1 ***, is provided. Initially, the small-signal impedance models of GCI considering the decoupling control with.1 *** are proposed. Afterward, the comparisons of the system's stability and decoupling precision with these two frequencies are given. The results demonstrate that 1) Frequency measurement accuracy has little impact on the system. 2) The control precision is approximately equal in the quasi-steady-state using the two decoupling frequencies even in the scenarios of frequency deviation (+/- 2Hz). 3) The system will be unstable *** is utilized, and then a lower PLL bandwidth should be adopted. Lastly, the theoretical analysis is verified by simulation results.

关键词： PLL GCI Frequency Measurement

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A Deep Unsupervised Representation Learning Architecture using Coefficient of Variation in Hidden Layers 9

A Deep Unsupervised Representation Learning Architecture usi...

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9th ieee international conference on Cloud computing and Intelligence Systems, CCIS 2023

作者： Ding, Xuemei Chu, Jielei Hu, Jiangtao Yu, Hua Li, Tianrui Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Chengdu Customs District Technological Center Chengdu China China E-port Data Center Chengdu Branch Chengdu China

ISBN: (纸本)9798350304428

The aim of this paper is to present a novel cov-RBM model on the basis of Restricted Boltzmann Machine (RBM) and the coefficient of variation (CoV), where the learning process is instructed by the CoV eigenvalues with respect to the hidden layer. Further, a deep unsupervised representation learning architecture (DURLA) is proposed to explore the capacity for representation learning with continuous excitation of CoV eigenvalues. We subject the proposed DURLA to extensive experimentation on nine real-valued datasets. The final experimental results show that our methods have a better effect than traditional and state-of-The-Art ways. © 2023 ieee.

关键词： eigenvalues and eigenfunctions

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computing for Mobile Ad Hoc Network in IoT Fuelled Smart environments: Computer Integration in Smart environment 2

Computing for Mobile Ad Hoc Network in IoT Fuelled Smart Env...

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2nd ieee international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Taneja, Dev Gupta, Pooja Taneja, Kavita Manipal University Jaipur Department of Computer and Communication Engineering India Panjab University Chandigarh Department of Computer Science and Applications India

ISBN: (纸本)9798350369960

Present Information Technology scenario is engulfed with exponential rise in mobile computing devices mostly embedded in diverse equipments of our daily life. This has led to evolutionary demand of new solutions to mobile computing for smart environments equipped with Internet of Things (IoT). A plethora of challenges are emerging to ensure effective internet connectivity for such mobile devices. These devices are mostly equipped with sensors and demand continuous computing and data transfers. The globe is evolving to humancentred society, i.e., Society 5.0 loaded with innovations of Big Data, IoT, Artificial Intelligence and much more to make social life comfortable and sustainable. Hence, this trend is the need of the hour but has brought a range of new challenges to MANeTs for mobile computing in smart environments. This paper explores the MANeT-IoT computing trends and provides a detailed analysis of the challenges for computing in the smart environment having MANeT-IoT as backbone for mobile communications. © 2023 ieee.

关键词： Internet of Things (IoT) Mobile Ad Hoc Network (MANeT) Mobile computing Society 5.0

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Cascaded based Adaptive R-CNN Network for Insulator Defect Detection 23

Cascaded based Adaptive R-CNN Network for Insulator Defect D...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data science and Systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yao, Nan Shan, Guangrui Pan, Jianya Wang, Zhen Zhu, Xueqiong State Grid Jiangsu Electric Power Co. Ltd. Research Institute Nanjing210000 China

ISBN: (纸本)9781665494571

Target detection is a popular direction in computer vision and digital image processing. It is widely used in robot navigation, intelligent video surveillance, industrial detection, aerospace, and many other fields. It has important practical significance to reduce the consumption of human capital through computer vision. Due to the widespread use of deep learning, target detection algorithms have developed relatively rapidly. In this paper, we propose a method for detecting insulator defects Based on Cascaded Fast R-CNN. We use RoIAlign instead of RoIPooling to get a better positioning effect and add a mask branch on the basis of Faster R-CNN to complete the pixel-level segmentation task, which can detect insulator defects in real-time with high detection accuracy. © 2021 ieee.

关键词： Image segmentation Computer vision Service robots Navigation Failure analysis Object detection Insulators

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Image Super-Resolution Reconstruction Based on Holistic Attention Dual Regression 2

Image Super-Resolution Reconstruction Based on Holistic Atte...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Liu, JunChen You, Yuan Zhang, TianYou Li, ShiYu Shenyang Ligong University School of Information Science and Engineering Shenyang China College of Foreign Studies Liaoning University Shenyang China North Huajin Chemical Industries Group Corporation Panjin China State Grid Corporation of China Huludao China

ISBN: (纸本)9798350368888

When addressing the challenges of an excessively large solution space and the difficulties in channel attention mechanisms to restore texture details during the generation of High-Resolution (HR) images from Low-Resolution (LR) images, an image super-resolution reconstruction algorithm based on the Holistic Residual Attention Dual Network (HRADN) is proposed. The use of a Dual Regression Network (DRN) with additional mappings is employed to estimate the downsampling kernel and impose constraints on the LR images, thereby reducing the solution space of functions. The incorporation of the Layer Attention Module (LAM) and Channel-Spatial Attention Module (CSAM) explores the correlations between different modules in the network and uncovers deeper information within the training data. experimental evaluations conducted on five benchmark datasets have shown that the proposed algorithm outperforms several prevalent models in terms of Peak Signal-to-Noise Ratio (PSNR), indicating its superior performance in super-resolution reconstruction. It significantly improves the clarity of details and reconstructs sharper high-frequency textures in subjective visual effects. © 2024 ieee.

关键词： Image reconstruction

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Impact of Mobile Coding Apps on Student Autonomous Learning in Tertiary education: A Case Study 6

Impact of Mobile Coding Apps on Student Autonomous Learning ...

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6th international conference on Computer science and Technologies in education, CSTe 2024

作者： Malimata, Charlyn A. Miranda, John Paul P. Mababa, Jonilo C. Canlas, Joel D. Hernandez, Hilene e. Yambao, Jaymark A. Pelayo, earl Lawrence B. National University Philippines Computing and Information Technology Department Manila Philippines Don Honorio Ventura State University Mexico Campus Mexico Philippines College of Computer Studies Angeles University Foundation Angeles Philippines College of Computing Studies Don Honorio Ventura State University Bacolor Philippines

ISBN: (纸本)9798350351804

This study looks into the impact of mobile coding apps on autonomous learning among tertiary students through a cross-sectional survey. Analyzing data from 377 students, primarily from the BS Information Technology program, the research reveals a potential gender gap in app usage and a reliance on smartphones for coding due to limited access to traditional computing devices. It highlights the widespread use of mobile coding applications, valued for their accessibility and hands-on experience, resulting in improved coding skills, critical thinking, and problem-solving abilities. While preferences leaned towards C++, a growing interest in Python was noted. These findings underscore the pivotal role of these apps in education, especially in resource-constrained environments, emphasizing the need for educational adaptation and policy changes to fully harness the transformative potential of technology in IT education, addressing disparities in access linked to income and gender while promoting autonomous learning. © 2024 ieee.

关键词： Application programs

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Stackelberg Game Model for Optimal Dispatching of electricity Consumption for Community Microgrid Considering Demand Response Mechanism 8

Stackelberg Game Model for Optimal Dispatching of Electricit...

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8th international conference on Power and Renewable energy, ICPRe 2023

作者： Tan, Di Fang, Xiaohan Fan, Yuan Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Hefei230601 China

ISBN: (纸本)9798350328813

Community microgrid is a kind of new power system that acts as an intermediary between community consumers and the grid, playing an important role in realising distributed autonomy and improving the power supply level. To achieve optimal power dispatch in community microgrid, this paper establishes a Stackelberg game model with the community microgrid operator (CMO) as the leader and the consumers aggregator as the follower, and combines the internal pricing method with the demand response mechanism. The paper uses a combination of genetic algorithm and solver to solve the Stackelberg game model to maximise the interests of all parties. Simulation results show that the model can secure the interests of each game subject, balance power supply and demand, and promote peak shaving and valley filling of electrical loads. © 2023 ieee.

关键词： Community microgrid Demand response mechanism Genetic algorithm Internal prices Stackelberg game model

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Predicting Cloud Applications Failures from Infrastructure Level Data 53

Predicting Cloud Applications Failures from Infrastructure L...

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53rd Annual ieee/IFIP international conference on Dependable Systems and Networks (DSN)

作者： Domingos, Jomar Cerveira, Frederico Barbosa, Raul Madeira, Henrique Univ Coimbra CISUC DEI Coimbra Portugal

ISBN: (纸本)9798350325430

Cloud computing assumes a crucial role in the internet applications and services landscape, serving as the default infrastructure for deploying and delivering web applications. To harness the cloud's technical and financial benefits, one must address the challenges of the increased consequences of software faults. Online failure prediction has emerged as a solution allowing for actions to be taken before the failures. However, conventionally, each target application requires a specific online failure predictor to be trained from application-specific data, which is impractical. In this paper we explore and assess the possibility to achieve online failure prediction for cloud applications from infrastructure-level data, i.e., data from the hypervisor. For this purpose, we present a method to accomplish this type of failure prediction, through fault injection on the hypervisor and machine learning techniques to create failure prediction models. From our experiments we find that it is possible to predict application failures from low-level infrastructural data, i.e., the infrastructure data presented enough application failure predictive power (with an accuracy of 96%, precision of 95% and a recall of 64%), showing the great potential that infrastructural data has to contribute to this task. Additionally, our study also opens interesting future research directions, such as multilevel failure prediction or generic failure prediction models.

关键词： failure prediction machine learning feature engineering time series forecast dependability cloud computing

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