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

共 10552 条记录，以下是881-890 订阅

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Implementation of the Inter-Band Carrier Aggregation Method to Solve Congestion network Conditions in Urban Areas 7

Implementation of the Inter-Band Carrier Aggregation Method ...

引用

7th International conference on Computer and Communication systems, ICCCS 2022

作者： Fadhila, Pinasthika Aulia Nashiruddin, Muhammad Imam Adriansyah, Nachwan Mufti Telkom University Centre of Regulation and Management of Telecommunication School of Electrical Engineering Bandung Indonesia Indonesia

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

The use of the internet today is inseparable from daily activities, such as communicating with other people, seeking the latest information, working, and others. Because of this, users expect that there will be an increase in the quality of the data network. However, network congestion problems arise due to current technological advancements and rising users. The inter-band carrier aggregation approach is used in this study to arrange for more frequency to alleviate network congestion. This method combines a frequency of 2.1 GHz and a frequency of 1.8 GHz as additional frequencies for LTE Advanced in North Jakarta with Key performance Indicator (KPI) as the parameter focused on in this research. In order to cover the whole region adequately, this research found that 123 locations of sites were required through the use of a 0.474 km radius, with an average RSRP value of -69.42 dBm and an average SINR of 2.61 dB. The network quality obtained by the user is excellent, with 30,926.13 kbps average throughput and service with QPSK 1/2 modulation. © 2022 IEEE.

关键词： Benchmarking

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A Unified Approach to Enforce Non-Negativity Constraint in Neural network Approximation for Optimal Voltage Regulation 58

A Unified Approach to Enforce Non-Negativity Constraint in N...

引用

58th Hawaii International conference on System Sciences, HICSS 2025

作者： Wu, Jiaqi Yuan, Jingyi Weng, Yang Wang, Guangwen Arizona State University United States

ISBN: (纸本)9780998133188

Power system voltage regulation is crucial to maintain power quality while integrating intermittent renewable resources in distribution grids. However, the system model on the grid edge is often unknown, making it difficult to model physical equations for optimal control. Therefore, previous work proposes structured data-driven methods like input convex neural networks (ICNN) for "optimal" control without relying on a physical model. While ICNNs offer theoretical guarantees based on restrictive assumptions of non-negative neural network (NN) parameters, can one improve the approximation power with an extra step on negative duplication of inputs? We show that such added mirroring step fails to improve accuracy, as a linear combination of the original input and duplicated input is equivalent to a linear operation of ICNN's input without duplication. While this design can not improve performance, we propose a unified approach to embed the non-negativity constraint as a regularized optimization of NN, contrary to the existing methods, which added a loosely integrated second step for post-processing on parameter negation. Our integration directly ties back-propagation to simultaneously minimizing the approximation error while enforcing the convexity constraints. Numerical experiments validate the issues of the mirroring method and show that our integrated objective can avoid problems such as unstable training and non-convergence existing in other methods for optimal control. © 2025 IEEE Computer Society. All rights reserved.

关键词： Numerical methods

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Applications of Artificial Intelligence-Based Communication systems in Rail Transit

Applications of Artificial Intelligence-Based Communication ...

引用

internet of Things, Automation and Artificial Intelligence (IoTAAI), International conference on

作者： Yuming Fu Lanzhou Institute of Technology Lanzhou China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

This paper explores the integration of Artificial Intelligence (AI) into communication systems within urban rail transit, emphasizing the development of a Train-to-Train (TT) communication-based train control system that deviates from traditional centralized control methods. By utilizing the capabilities of Deep Reinforcement Learning (DRL) and neural network architectures, the proposed system enhances operational efficiency, safety, and reliability of train operations by allowing autonomous and distributed decision-making directly on trains. The application of a multi-layer fully connected neural network enables sophisticated data processing and decision-making through deep learning techniques, addressing the challenges of resource allocation in dense urban transit scenarios. Simulation results validate the effectiveness of the AI-driven approach, demonstrating significant improvements in the quality of service (QoS), spectral efficiency, and system throughput under varying train densities. The study confirms that AI can play a crucial role in advancing the intelligence and efficiency of rail transit systems, offering a scalable solution for future developments in urban transportation.

关键词： Rails Spectral efficiency Neural networks Decision making Transportation quality of service Data processing Safety internet of Things Resource management

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Proceedings - 10th International conference on Wireless networks and Mobile Communications, WINCOM 2023

Proceedings - 10th International Conference on Wireless Netw...

引用

10th International conference on Wireless networks and Mobile Communications, WINCOM 2023

ISBN: (纸本)9798350329674

The proceedings contain 146 papers. The topics discussed include: MIMO-NOMA with OSTBC for B5G cellular networks with enhanced quality of service;power optimization for joint large intelligent surface - amplify-and-forward relay in full-duplex communications;an approach for intrusion detection using machine learning algorithms;internet of Things systems security based on the physical objects: an overview;basketball free throw analysis using object detection techniques;neighborhood feature extraction and Haralick attributes for medical image analysis: application to breast cancer mammography image;DSP: a deep neural network approach for serving cell positioning in mobile networks;enhancement of a user authentication scheme for big data collection in IoT-based intelligent transportation system;and fuzzy logic control of four-phase interleaved boost converter for PEM fuel cell.

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network Cluster Reliability with Enhanced Security and Privacy of IoT Data for Anomaly Detection Using a Deep Learning Model 3

Network Cluster Reliability with Enhanced Security and Priva...

引用

3rd International conference on Intelligent Computing, Instrumentation and control Technologies, ICICICT 2022

作者： Sagar, Maloth Vanmathi, C. Vellore Institute of Technology School of Information Technology and Engineering Tamilnadu Vellore India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Cyber Physical systems (CPS), which contain devices to aid with physical infrastructure activities, comprise sensors, actuators, control units, and physical objects. CPS sends messages to physical devices to carry out computational operations. CPS mainly deals with the interplay among cyber and physical environments. The real-time network data acquired and collected in physical space is stored there, and the connection becomes sophisticated. CPS incorporates cyber and physical technologies at all phases. Cyber Physical systems are a crucial component of internet of Things (IoT) technology. The CPS is a traditional concept that brings together the physical and digital worlds inhabit. Nevertheless, CPS has several difficulties that are likely to jeopardise our lives immediately, while the CPS's numerous levels are all tied to an immediate threat, therefore necessitating a look at CPS security. Due to the inclusion of IoT devices in a wide variety of applications, the security and privacy of users are key considerations. The rising level of cyber threats has left current security and privacy procedures insufficient. As a result, hackers can treat every person on the internet as a product. Deep Learning (DL) methods are therefore utilised to provide accurate outputs from big complex databases where the outputs generated can be used to forecast and discover vulnerabilities in IoT systems that handles medical data. Cyber-physical systems need anomaly detection to be secure. However, the rising sophistication of CPSs and more complex attacks means that typical anomaly detection approaches are unsuitable for addressing these difficulties since they are simply overwhelmed by the volume of data and the necessity for domain-specific knowledge. The various attacks like DoS, DDoS need to be avoided that impact the network performance. In this paper, an effective network Cluster Reliability Model with enhanced security and privacy levels for the data in IoT for Anomaly Detection

关键词： internet of things

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Revolutionizing Indoor Air quality: An Intelligent IoT Solution for Pollution Detection

Revolutionizing Indoor Air Quality: An Intelligent IoT Solut...

引用

High performance Big Data and Intelligent systems (HPBD&IS)

作者： Weiqi Hong Karim H. Moussa School of Internet of Things Xi’ an Jiaotong-liverpool University Suzhou China

Indoor air pollution is a persistent and significant issue with detrimental effects on human health, including respiratory problems, allergies, carcinogenic risks, and impacts on the nervous and cardiovascular systems. The presence of harmful gases resulting from human activities further exacerbates these health concerns, leading to reduced productivity, increased morbidity, and economic losses. This paper aims to address the current challenges of indoor air pollution by proposing a practical internet of Things (IoT)-based online monitoring system. The system comprises three key components: (1) the design and implementation of sensor-based data collection for indoor air quality using Raspberry Pi, (2) the development of network protocols and data storage utilizing the Ali cloud platform, and (3) the creation of user-friendly web visualization and VI design. By integrating IoT technology, the poposed air quality control system can achieve enhanced intelligence and performance, making it a valuable tool for improving indoor air quality.

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Enhanced network Anomaly Detection Using Autoencoders: A Deep Learning Approach for Proactive Cybersecurity

Enhanced Network Anomaly Detection Using Autoencoders: A Dee...

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Intelligent Cyber Physical systems and internet of Things (ICoICI), International conference on

作者： Judy Simon Nellore Kapileswar Rajamanickam Vani Narala Madhukara Reddy Dudekula Moulali Anki Reddy Narayana Reddy Dept. of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Anomaly detection in network traffic is core part of modern network management. It plays vital role in finding the security threats and performance issues. Finding the threats using the machine learning (ML) method provides an efficient solution to dynamically lean normal network behaviour and detect deviation indicative of anomalies. This study proposed a Support vector machine with Autoencoder for detecting the anomalies in network traffic. Implementing ML methods for threat identification improves network security by enabling rapid response to potential threats and issues. Hence this method also reducing downtime and improving overall network reliability. This study indicates the benefits of ML driven anomaly detection, highlighting its capability to provide a proactive security posture, rapid identification and resolution of anomalies ensuring an efficient network infrastructure.

关键词： Support vector machines performance evaluation Deep learning Telecommunication traffic network security Traffic control Vectors Reliability internet of Things Anomaly detection

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Comparative analysis of RPL performance under different wireless network topologies 3

Comparative analysis of RPL performance under different wire...

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3rd International conference on Power, Energy, control and Transmission systems, ICPECTS 2022

作者： Shabana, Parveen M. Bhuvaneswari, P.T.V. Sri Sairam Engineering College Dept. of Electronics and Communication Engineering Chennai India Madras Institute of Technology Dept. of ElectronicsEngineering Chennai India

ISBN: (纸本)9781665462754

IPV6 Routing Protocol for Low Power Lossy networks (RPL) is one of the routing protocols that can be used for internet of Things networks based applications. In this study, the performance of RPL with linear and ellipse topologies is examined. Two scenarios are considered case 1, all nodes are assumed to be static, while case 2, some nodes are made dynamic. In terms of Packet Delivery Ratio (PDR), control overhead, convergence time and energy usage, RPL's performance on these topologies is assessed. The analysis is performed in a COOJA simulator environment. The topology with improved energy efficiency and reliability is chosen. The outcomes demonstrate that elliptical topology performs admirably in both situations. © 2022 IEEE.

关键词： Topology

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Super Twisting Fuzzy High-Order Sliding Mode control of Variable-Speed Wind Turbine 6th

Super Twisting Fuzzy High-Order Sliding Mode Control of Vari...

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6th International conference on Artificial Intelligence in Renewable Energetic systems, IC-AIRES 2022

作者： Saihi, Lakhdar Ferroudji, Fateh Roummani, Khayra Bakou, Youcef Koussa, Khaled Boura, Mohammed Unité de Recherche en Energies Renouvelables en Milieu Saharien URERMS Centre de Développement des Energies Renouvelables CDER Adrar01000 Algeria Department of Science and Technology University of Tahri Mohammed Bechar P.O.B. 417 Bechar08000 Algeria

ISBN: (纸本)9783031212154

This study proposes a novel super twisting fuzzy high-order sliding mode control of doubly-fed induction generator (DFIG) associated with a horizontal axis wind turbine, the generator fed directly in the stator by the network and supplied by converter side rotor (CSR) in the rotor. In the first part, we illustrate the model of the mechanical and electrical parts of the wind turbine system (turbine + DFIG). In the next section, we propose a novel approach by using a hybrid technique between a super twisting algorithm and fuzzy logic control for regulating the stator powers (active/reactive), in order to reduce the chattering issue and enhance the power quality of the grid compared with a classical high-order sliding mode control. The proposed strategy (STF-HOSMC) offers higher performance and robustness (reduce the chattering effect and ameliorate the quality of energy). The results of the simulations are given and analysed for the wind system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Stators

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Few-shot Learning for quality Detection of Logistical Parcels 11

Few-shot Learning for Quality Detection of Logistical Parcel...

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11th International conference on Information systems and Computing Technology, ISCTech 2023

作者： Chen, Zhi Guan, Quanlong Duan, Xiuliang Zhong, Hongbin Li, Zhefu Lv, Shuanghuan Li, Jian Zhou, Yuyu Jinan University College of Information Science and Technology Guangzhou China Jinan University Guangdong Institute of Smart Education Guangzhou China Jinan University Network and Education Technology Center Guangzhou China

ISBN: (纸本)9798350342406

With advancements in science and technology and the ongoing enhancements made to distribution systems, the volume of logistical parcels being handled has increased significantly. However, the quality control process for logistical parcels still relies on manual inspections, which proves to be inefficient, especially when attempting to flag damaged parcels, an issue that requires immediate attention. Moreover, as the percentage of damaged parcels within the overall volume is relatively low, this presents a challenge that can be addressed through few-shot learning techniques. In this study, we present an application of few-shot learning to the recognition of logistical parcels. We compare three distinct few-shot learning methods - Model-Agnostic MetaLearning (MAML), Siamese Neural network, and DAFT, and identify the optimal approach through the evaluation of our self-designed parcel dataset. Results from our experiments reveal that the MAML method is not effective for parcel recognition. The Siamese Neural network method attained a high accuracy of 70.31% on small-scale parcel datasets while the DAFT method achieved the best accuracy of 83.33% in identifying damaged parcels. Consequently, the DAFT approach provides an excellent recognition solution for logistical parcels and empowers automated damage parcel identification with improved performance. © 2023 IEEE.

关键词： Learning systems

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