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A Methodology for the Energy Aware Clustering and Aggregate Node Rotation with Sink Relocation in MANET 1

2nd International Conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2023

作者： Bhargava, Neeraj Rathore, Pramod Singh Bhowmick, Apoorva School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India MDS University Ajmer India

ISBN: (数字)9789819919468

ISBN: (纸本)9789819919451

The word energy is frequently used in MANET in both daily life and the computer industry. Reducing energy consumption and extending node lifespan are critical components of the MANET. There are many nodes available between the path and destination as the data are transferred from the source to the destination nodes. Energy can be used by the intermediate nodes during data transfer, and some energy is lost during travel to the destination. The EAC-ASR protocol (Energy Aware Clustering Aggregate Node Rotation) with sink relocation approach is suggested to be utilised in order to get over these disadvantages. There are some processes that are present in this protocol are clustering, data aggregation, mobile node rotation by swapping algorithm and sink relocation. The theoretical and simulation analyses are done and the result will be shown in the EAC-ASR protocol reduces the energy consumption and increase the energy efficiency. Better system administration as far as conceded calls along regarded QoS imperatives. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MATLAB

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Power Sharing-Based Framework for Allocating Automatic Generation Control in Distributed Energy Resources

Power Sharing-Based Framework for Allocating Automatic Gener...

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2024 IEEE/PES Transmission and Distribution Conference and Exposition, T and D 2024

作者： Ali, Ahmad Cui, Hantao Wang, Wenbo Fang, Xin Oklahoma State University School of Electrical & Computer Engineering StillwaterOK United States Energy System Integration National Renewable Energy Laboratory GoldenCO United States Mississippi State University Dept. of Electrical & Computer Engineering StarkvilleMS United States

ISBN: (纸本)9798350316377

The recent proliferation of distributed energy resources (DERs) in the power network along with the retirement of conventional generators has made it challenging to regulate system frequency. This paper presents a centralized control framework to leverage the potential of DERs in the distribution network to provide secondary frequency control services to the grid. The proposed framework is based on network volt-watt sensitivity analysis and takes into account DER operational and network-imposed constraints to allocate the automatic generation control (AGC) request among the aggregated units. The proposed framework was implemented on the IEEE 8,500-node test feeder and the results were validated against a standard linear programming-based scheme. Numerical results indicate that the proposed framework can successfully utilize the available power production headroom of the network to meet the AGC request while maintaining nodal voltages within acceptable limits. © 2024 IEEE.

关键词： Virtual Power Plants

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning 8

Defect Screening on Nuclear Power Plant Concrete Structures:...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Huang, Wenlian Zhu, Guanming Huang, Qiang Chen, Zhuangzhuang Chen, Jie Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results. © 2023 IEEE.

关键词： Defects

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Data Mining with Comprehensive Oppositional Based Learning for Rainfall Prediction

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computers, Materials & Continua 2023年第2期74卷 2725-2738页

作者： Mohammad Alamgeer Amal Al-Rasheed Ahmad Alhindi Manar Ahmed Hamza Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemMecca24382Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Data mining process involves a number of steps fromdata collection to visualization to identify useful data from massive data *** same time,the recent advances of machine learning(ML)and deep learning(DL)models can be utilized for effectual rainfall *** this motivation,this article develops a novel comprehensive oppositionalmoth flame optimization with deep learning for rainfall prediction(COMFO-DLRP)*** proposed CMFO-DLRP model mainly intends to predict the rainfall and thereby determine the environmental ***,data pre-processing and correlation matrix(CM)based feature selection processes are carried *** addition,deep belief network(DBN)model is applied for the effective prediction of rainfall ***,COMFO algorithm was derived by integrating the concepts of comprehensive oppositional based learning(COBL)with traditional MFO ***,the COMFO algorithm is employed for the optimal hyperparameter selection of the DBN *** demonstrating the improved outcomes of the COMFO-DLRP approach,a sequence of simulations were carried out and the outcomes are assessed under distinct *** simulation outcome highlighted the enhanced outcomes of the COMFO-DLRP method on the other techniques.

关键词： Data mining rainfall prediction deep learning correlation matrix hyperparameter tuning metaheuristics

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Research on Image Captioning Based on Vision-language Pre-trained Models 9

Research on Image Captioning Based on Vision-language Pre-tr...

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9th International Conference on Big Data and Information Analytics, BigDIA 2023

作者： Zou, Shiwei Xie, Yuxiang Yan, Jie Wei, Yingmei Luan, Xidao National University of Defense Technology Department of System Engineering Hunan China National University of Defense Technology Key Laboratory of Big Data Department of System Engineering Changsha Hunan China Changsha University Department of Computer Science Hunan China

ISBN: (纸本)9798350330076

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively;2) a multi-modal encoder to align the two modalities;and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance. © 2023 IEEE.

关键词： Image enhancement

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Fuzzy Tool for Crowdfunding Readiness Level Assessment for Innovative Projects 18

Fuzzy Tool for Crowdfunding Readiness Level Assessment for I...

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18th Iberian Conference on Information systems and Technologies, CISTI 2023

作者： Cseminschi, Stanislav Ionica, Andreea Cristina Leba, Monica Florea, Vlad Alexandru University of Petrosani Department of Management and Industrial Engineering Petrosani Romania University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Mechanical Engineering Petrosani Romania

ISBN: (纸本)9789893347928

Stimulating creativity in technological entrepreneurial education leads to knowledge and connection to current trends in the development of innovative technological projects and the creation of innovative start-up and spin-off enterprises. Their success or failure depends on a number of factors such as the market, the team, the technological level of the product, but also on the funding methods, including crowdfunding. The paper proposes a Fuzzy tool for preliminary assessment of the opportunity of crowdfunding campaigns for innovative projects resulting from the stimulation of entrepreneurial creativity through gamification-based education. © 2023 ITMA.

关键词： engineering education

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Secure-by-Construction Synthesis for Control systems

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IEEE Transactions on Automatic Control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Examining the Role of Natural Language Processing in Generating Topics from Web Content

Examining the Role of Natural Language Processing in Generat...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Altarturi, Hamza H.M. Saadoon, Muntadher Anuar, Nor Badrul Universiti Malaya Department of Computer System and Technology Kuala Lumpur Malaysia Universiti Malaya Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350302684

Natural Language Processing (NLP) is a major subfield of machine learning that focuses on the interaction between humans and computers through several techniques, including topic modeling, which extracts and generates topics from large amounts of unstructured data on the web. Topic modeling uncovers hidden patterns and contextual meanings in web pages, allowing for the categorization, clustering, recommendation of relevant content, detection of emerging trends, and improved information retrieval. The benefit of topic models relies on their performance to detect and generate topics;however, the evaluation of applying topic modeling on web content data remains lacking in current studies. The absence of examining such performance evaluations hinders understanding performance variability across different web pages, the development of refined models, and the ability to keep pace with emerging patterns in the dynamic online environment. This study presents a comprehensive performance evaluation of the benchmark topic models when applied to web content data. It presents a comparison of topic models using four topic coherence metrics, providing an in-depth performance analysis. The evaluation is conducted on a public dataset consisting of 2 million web pages, providing a substantial basis for assessing the strengths and limitations of these models in the context of web data. Additionally, this study examines the utilization of NLP techniques for topic generation within the context of web content and sheds light on the challenges and issues associated with generating topics from web content data, highlighting potential research prospects and the need for future work in the field of web topic modeling using natural language processing. © 2023 IEEE.

关键词： Websites

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SpatialHD: Spatial Transformer Fused with Hyperdimensional Computing for AI Applications 5

SpatialHD: Spatial Transformer Fused with Hyperdimensional C...

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5th IEEE International Conference on Artificial Intelligence Circuits and systems, AICAS 2023

作者： Bettayeb, Meriem Hassan, Eman Mohammad, Baker Saleh, Hani Khalifa University of Science and Technology System on Chip Lab Electrical Engineering and Computer Science Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350332674

Brain-inspired computing methods have shown remarkable efficiency and robustness compared to deep neural networks (DNN). In particular, HyperDimensional Computing (HDC) and Vision Transformer (ViT) have demonstrated promising achievements in facilitating effective and reliable cognitive learning. This paper proposes SpatialHD, the first framework that combines spatial transformer networks (STN) and HDC. First, SpatialHD exploits the STN, which explicitly allows the spatial manipulation of data within the network. Then, it employs HDC to operate over STN output by mapping feature maps into high-dimensional space, learning abstracted information, and classifying data. In addition, the STN output is resized to generate a smaller input feature map. This further reduces computing complexity and memory storage compared to HDC alone. Finally, to test the model's functionality, we applied spatial HD for image classification, utilizing the MNIST and Fashion-MNIST datasets, using only 25% of the dataset for training. Our results show that SpatialHD improves accuracy by ≈ 8% and enhances efficiency by approximately 2.5x compared to base-HDC. © 2023 IEEE.

关键词： Image classification

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A Method Based on Phase-shifting Carrier for Reducing DC-link Current Harmonics in Dual Three-phase PMSM Drive under Open-phase Conditions 6

A Method Based on Phase-shifting Carrier for Reducing DC-lin...

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2022 6th International Conference on Electrical, Mechanical and computer engineering, ICEMCE 2022

作者： Wang, Qingzhe Du, Yanwei Yang, Guang Liu, Hongyang Computer & Electronic System Engineering Dept AVIC xi'An Flight Automatic Control Research Institute Shanxi Province Xi'an710076 China

The All-Electric of modern military equipment has become an important trend, in the field of aircraft the traditional servo control focus on the exact control of the motor. With the development of power electronics technology, a high-power motor has expanded into a multiphase kind which cast off the limitation of traditional three-phase motors. The multiphase motor has more control resources and fault-tolerant control ability under the fault condition, which has practical meaning for high safety and high-reliability applications in an airborne environment. The multiphase motor can be controlled by a series of phase-shift carriers, which can reduce DC current ripple more effectively than the traditional carrier-based pulse width modulation, but it needs to find the optimal phase shift angle. In this paper, the optimal carrier phase shift angle is analyzed mathematically which can be used for both under health and single open-phase fault of a dual three-phase motor, and the simulation result illustrates that the carrier phase shift method used in this paper has the DC current ripple optimization effect. © Published under licence by IOP Publishing Ltd.

关键词： Synchronous motors

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