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检索条件"任意字段=Conference on Internet Quality and Performance and Control of Network Systems"

共 10532 条记录，以下是121-130 订阅

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Proceedings of the 10th International conference on internet of Things, Big Data and Security, IoTBDS 2025

Proceedings of the 10th International Conference on Internet...

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10th International conference on internet of Things, Big Data and Security, IoTBDS 2025

ISBN: (纸本)9789897587504

The proceedings contain 55 papers. The topics discussed include: dynamic obfuscation for secure and efficient multi-cloud business processes;bridging the cost gap: a comprehensive analysis of CAPEX and OPEX for smart home transition from a provider's perspective;Sockpuppet detection in Wikipedia using machine learning and voting classifiers;PatSimBoosting: enhancing patient representations for disease prediction through similarity analysis;real-time manufacturing data quality: leveraging data profiling and quality metrics;toward a more realistic energy consumption model for IoT nodes in extreme-edge computing environments;data network game: enabling collaboration via data mesh;and approach to deploying batch file data products in a big data environment.

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Toward Multi-Layer networking for Satellite network Operations

Toward Multi-Layer Networking for Satellite Network Operatio...

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2024 International conference on Wireless for Space and Extreme Environments

作者： Hu, Peng Univ Manitoba Dept Elect & Comp Engn Winnipeg MB Canada Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9798350351118;9798350351125

Recent advancements in low-Earth-orbit (LEO) satellites aim to bring resilience, ubiquitous, and high-quality service to future internet infrastructure. However, the soaring number of space assets, increasing dynamics of LEO satellites and expanding dimensions of network threats call for an enhanced approach to efficient satellite operations. To address these pressing challenges, we propose an approach for satellite network operations based on multi-layer satellite networking (MLSN), called "SatNetOps". Two SatNetOps schemes are proposed, referred to as LEO-LEO MLSN (LLM) and GEO-LEO MLSN (GLM). The performance of the proposed schemes is evaluated in 24-hr satellite scenarios with typical payload setups in simulations, where the key metrics such as latency and reliability are discussed with the consideration of the Consultative Committee for Space Data systems (CCSDS) standard-compliant telemetry and telecommand missions. Although the SatNetOps approach is promising, we analyze the factors affecting the performance of the LLM and GLM schemes. The discussions on the results and conclusive remarks are made in the end.

关键词： Satellites Telecommand Telemetry Data Link network Operations Multiple Orbits Multi-Layer networks

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Optimizing Comfort and Energy Efficiency: The Impact of Model Accuracy on Multi-Objective MPC 6

Optimizing Comfort and Energy Efficiency: The Impact of Mode...

引用

6th Global Power, Energy and Communication conference (GPECOM)

作者： Micheneau, Kevin Boodi, Abhinandana CESI LINEACT UR 7527 CESI Brest Campus Brest France

ISBN: (纸本)9798350351088;9798350351095

Buildings are responsible for similar to 30% of primary energy consumption, mainly because of Heating, Ventilation, and Air Conditioning (HVAC) systems. The usual ON/OFF controller tends to react to occupancy presence, causing discomfort and energy waste. Furthermore, these controllers usually focus on thermal comfort and disregard other comforts, such as air quality, visual, etc. due to their inability to handle complex multi-objective problems. In this context, Model Predictive controller (MPC) presents a promising alternative for dynamic control of HVAC systems. However, the accuracy of the building's thermal and air quality models greatly influences the MPC performance. This paper proposes simple models to develop a multi-objective MPC to minimize energy consumption while maintaining occupancy thermal and air quality comfort. Each models are developed using the limited data available. Since these models largely depend on occupancy information, a further study is conducted to analyze the impact of occupancy estimation accuracy on MPC performance. It is found that occupancy estimation models reach 98% of the optimum performance with a 90% accuracy.

关键词： Building energy CO2 model MPC occupancy impact thermal network model

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Hierarchical Multi-Scale Synergistic Fusion network for Pathological Myopia ATN Classification 24

Hierarchical Multi-Scale Synergistic Fusion Network for Path...

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4th International conference on Computer, internet of Things and control Engineering

作者： Jiang, Han Zhao, Xiujuan Zhang, Wei Dong, Zhicheng Yu, Shanshan Song, Huiying Qin, Haonan Tibet Univ Lhasa Xizang Peoples R China Guangdong Prov Clin Res Ctr Ocular Dis Guangzhou Guangdong Peoples R China ShenZhen Technol Univ Coll Big Data & Internet Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798400711848

Deep learning has achieved groundbreaking progress in the field of image recognition, yet existing networks have not fully explored the potential connections between network architecture and fundus image features. To address this issue, this paper proposes the Hierarchical Multi-Scale Synergistic Fusion network (HMSFNet), aimed at resolving the problems of feature collapse in CNNs and the performance limitations of ensemble learning fusion mechanisms. HMSFNet is based on a dual-branch integrated architecture that analyzes key features in fundus images from multiple dimensions and embeds a feature synergy stability module to promote mutual learning between the two branches. Additionally, this study designs a novel and more efficient attention mechanism focused on multi-scale feature recognition of lesion areas. The study constructed a dataset comprising 11,495 high-quality fundus images. In benchmark tests, HMSFNet achieved optimal performance in diagnosing atrophic lesions (A), tractional lesions (T), and neovascular lesions (N), with accuracies of 94.9%, 95.82%, and 99.4%, respectively, significantly outperforming existing models. This achievement not only demonstrates the effectiveness of HMSFNet in diagnosing pathological myopic maculopathy (ATN) but also provides a powerful auxiliary tool for clinical applications.

关键词： Deep learning Multi-modal fusion Attention mechanism Pathologic myopia Medical Image Analysis

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A multi-objective distribution network partitioned voltage active-reactive coordination control strategy based on Synchronous ADMM 4

A multi-objective distribution network partitioned voltage a...

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4th International conference on Smart Grid and Energy internet, SGEI 2024

作者： Wang, Huifeng Zou, Bowen Song, Zhigang Song, Lei Gao, Yue Wang, Miaomiao Wang, Xin State Grid LongNan Power Supply Company LongNan China School of Electrical and Electronic Information Xihua University Chengdu China

ISBN: (纸本)9798350368314

In the face of the increasingly intricate and fluid landscape of contemporary power systems, the effective administration of distribution networks has emerged as a pivotal issue for electricity companies. In light of this, this paper proposes a multi-objective distribution network partitioned voltage active-reactive coordination control strategy based on Synchronous ADMM. Firstly, a holistic electrical distance metric, regional coupling intensity, and regional reactive power equilibrium indicator have been formulated for the distribution network. The PSO technique is employed for the network partitioning process. Secondly, based on the regional partitioning, a regional voltage autonomous model is constructed with the objective function of minimizing the sum of photovoltaic active power reduction, node voltage deviation, and network loss. Finally, network is partitioned and decoupled, and the synchronous ADMM algorithm is utilized to achieve coordinated optimization control of voltage levels based on inter-regional information exchange. The simulations performed on the improved IEEE 33-node distribution network validate the effectiveness of the proposed regional voltage control strategy. This method is demonstrated to markedly enhance the general voltage quality and boost the safety and stability of the distribution network. ©2024 IEEE.

关键词： Power distribution networks

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Quantifying the Impact of Physical internet systems Under Decentralized control 3rd

Quantifying the Impact of Physical Internet Systems Under De...

引用

3rd International conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2023 and the 7th Asian conference of Management Science and Applications, ACMSA 2023

作者： Suzuki, Sadami Kraiwuttianant, Ornida Yokohama National University Kanagawa Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9789819701933

The logistics crisis spread worldwide due to the imbalance between the supply shortage and the rise of fluctuating demands. Physical internet (PI), the analogous of digital internet described as an open logistics network sharing assets and flow consolidation, is introduced to solve the inefficiency of transportation by providing high interconnectivity, standardization protocol, and decentralized management. This research comparatively investigates the performance of a supply chain under different network frameworks, namely, traditional supply network (TR) and PI system by using multi-agent simulation approach. Regarding to PI in particular, assuming the initial phase of PI implementation, decentralized control system has been adopted in which each PI participant pursues their own profitability like TR even though PIS participants share the resources such as warehouses and trucks in a supply chain. Simulation results showed that PI maintain significantly lower number of opportunity losses than TR, even in the system under decentralized control that are assumed to be in the initial phase of PI implementation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decentralized control

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Research on vehicle autonomous driving based on convolutional neural network and embedded system in computer vision 4

Research on vehicle autonomous driving based on convolutiona...

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4th International conference on Green Communication, network, and internet of Things, CNIoT 2024

作者： Huang, David New York University IndianaPA15701 United States

ISBN: (纸本)9781510685710

With the rapid development of autonomous driving technology, the autonomous driving ability of vehicles has become an important research direction in intelligent transportation systems. Convolutional Neural network (CNN) has become one of the core technologies in autonomous driving vision systems due to its excellent performance in image recognition and classification. Embedded systems also play an important role in the realization of autonomous vehicle driving due to their high efficiency and real-time nature. In this study, a vehicle autonomous driving scheme based on Convolutional Neural network (CNN) and embedded system is proposed. Firstly, the convolutional neural network was used to perform real-time image processing and feature extraction on the road scene. Specifically, through the superposition of multi-layer convolutional layers and pooling layers, features such as edges, textures, and objects in the image are extracted layer by layer, so as to achieve efficient recognition of complex road environments. In this process, the convolutional layer is used to extract local features, while the pooling layer is used to reduce dimensionality and prevent overfitting to ensure the robustness and efficiency of the model. Secondly, an embedded system was designed and optimized, and the trained CNN model was deployed on the system to ensure real-time processing power and efficient energy consumption management. The design of the embedded system focuses on the optimal allocation of hardware resources and the effective control of energy consumption to meet the real-time operation needs of vehicles under different road conditions. Specifically, the embedded system uses high-performance processors and low-power hardware modules to ensure fast inference and real-time decision-making capabilities of CNN models. In addition, the overall performance and reliability of the system are further improved through the co-design of software and hardware. Combining the above two techn

关键词： Convolutional neural networks

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Framework for Securing quality control Process in Engineering Using IOT based Machine Learning Models

Framework for Securing Quality Control Process in Engineerin...

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2024 International conference on Intelligent and Innovative Practices in Engineering and Management, IIPEM 2024

作者： Nigam, Manoj Kumar Khatkale, Pravin B Diwan, Tarun Dhar Roy, Sandeep Badholia, Abhishek Ghige, Onkar Kalinga University Department of Electrical Engineering Naya Raipur India Sanjivani University Maharashtra Kopargaon India Atal Bihari Vajpayee University Bilaspur India Shri Shankaracharya Institute of Professional Management and Technology Department of Data Science Raipur India Sanjivani College of Engineering Department of Elecrical Engineering MH Kopargaon India

ISBN: (纸本)9798350390049

The study aims to investigate how combining many technologies may improve the scalability, adaptability, and security of network engineering systems. Some of the technologies that come under this category include the internet of Things (IoT), network Function Virtualization (NFV), Machine Learning (ML), and Software Defined networking (SDN). Examining potential effective merger strategies for these technologies is one of the study's objectives. Our study has shown that our approach improves organizational effectiveness, defect finding, and engineering productivity. This is accomplished via the use of machine learning (ML) and the internet of Things (IoT) to real-time data collecting, predictive analytics, and quality control assurance. By making use of these two technologies, this assignment may be successfully completed. The case studies demonstrate the potential of our suggested machine learning models to decrease training time and improve engineering quality control assessment accuracy. The fact that our models can do both of these tasks in comparison to earlier machine learning models serves as evidence for this. © 2024 IEEE.

关键词： Model predictive control

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internet of Things Massive Data Analysis System Based on Association Rules Algorithm 7

Internet of Things Massive Data Analysis System Based on Ass...

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7th International conference on Advanced Algorithms and control Engineering, ICAACE 2024

作者： He, Chao Fushun Vocational Technology Institute Liaoning Fushun113122 China

ISBN: (纸本)9798350361445

With the vigorous development of the social economy and the continuous improvement of the level of science and technology, people's requirements for the quality of life are gradually improving. internet of Things technology, as a rapidly developing field, has also made great progress. Based on the existing data mining methods, this paper presents a network retrieval algorithm based on association rules. The characteristics of the algorithm include simple, practical and easy to maintain operation, which can provide timely, accurate and reliable information for decision makers, and has good scalability and visualization degree. In this paper, the association rules algorithm is used to design the massive data analysis system of the internet of Things, and the performance of the system is tested and studied. The test results show that every time the data size increases by 10 times, the data processing time also increases, showing an approximate linear relationship. In the mass data analysis system, processing larger data will lead to longer processing time, that is, the data processing time is proportional to the data scale. By meeting user needs, improving system performance and reducing cost, the algorithm can effectively use resources. © 2024 IEEE.

关键词： internet of things

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The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and control Optimization in IoT Environments 3

The Fusion of Deep Reinforcement Learning and Edge Computing...

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3rd International conference on Energy and Power Engineering, control Engineering (EPECE)

作者： Xu, Jingyu Wan, Weixiang Pan, Linying Sun, Wenjian Liu, Yuxiang No Arizona Univ 1900 S Knoles Dr Flagstaff AZ 86011 USA Univ Elect Sci & Technol China Chengdu Peoples R China Trine Univ Phoenix AZ USA Yantai Univ Tokyo Japan Northwestern Univ Atlanta GA USA

ISBN: (纸本)9798350366105;9798350366099

In response to the demand for real-time performance and control quality in industrial internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of edge computing resources, achieving global optimization. Results demonstrate that this approach reduces cloud-edge communication latency, accelerates response to abnormal situations, reduces system failure rates, extends average equipment operating time, and saves costs for manual maintenance and replacement. This ensures real-time and stable control.

关键词： Deep reinforcement learning Edge computing Industrial internet of Things Lightweight policy network Dynamic resource allocation

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