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

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Signal Optimization and Noise Reduction Based on Frequency control in Acoustic Imaging Instruments 7

Signal Optimization and Noise Reduction Based on Frequency C...

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7th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2024

作者： Cao, Zuyang Zhang, Xin Bao, Junkang Hangzhou Crysound Electronics Co. Ltd. Zhejiang Hangzhou China

ISBN: (纸本)9798350350760

Papermaking machinery noise is the noise generated during the production process of a papermaking enterprise, which includes mechanical vibration and low-frequency noise. In the field test of paper-making enterprises, the noise range is 100-120 dB. According to the industry noise standard, the value of the noise can not be greater than 85 dB under the working condition of 8 hours. Therefore, to control the source of the noise, it is necessary to control the noise according to the noise mechanism of the equipment itself and block its propagation path in order to effectively reduce the noise of the factory. Visual acoustic imagers can quickly identify the sound source and introduce corresponding noise reduction measures in line with environmental protection. The aim of this study is to investigate the frequency control based signal optimization and noise reduction methods in acoustic imagers. Using the Visual Acoustic Imager of CRY2600 series, the authors conducted experiments in mechanical air noise and collision vibration noise environments to evaluate the effects of different noise environments, filter types, dynamic frequency tuning strategies and signal processing algorithms on signal optimization and noise reduction. The results show that both low-pass and high-pass filters are effective in improving the signal-to-noise ratio in a specific frequency range, and the dynamic frequency tuning strategy performs better in improving the signal quality compared to the static tuning strategy. Both wavelet threshold denoising and generative adversarial network denoising algorithms perform well in different noise environments (wavelet threshold denoising improves the signal strength from 50dB to 70dB in a mechanical air noise environment, with a 20dB improvement in the signal-to-noise ratio), significantly improving the signal strength. The research results show that combining multiple noise reduction strategies can effectively improve the overall performance of the acoust

关键词： Noise abatement

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Short-Term network Traffic Prediction with Multilayer Perceptron 6

Short-Term Network Traffic Prediction with Multilayer Percep...

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6th SLAAI International conference on Artificial Intelligence, SLAAI-ICAI-2022

作者： Ganowicz, Agnieszka Starosta, Bartosz Knapinska, Aleksandra Walkowiak, Krzysztof Wroclaw University of Science and Technology Department of Systems and Computer Networks Wroclaw Poland

ISBN: (纸本)9781665476072

The constantly increasing internet traffic and rising network requirements trigger fast development and implementation of new networking architectures and technologies. Predictability of network traffic can bring significant benefits in many areas, such as network planning, network security, dynamic bandwidth allocation, and predictive congestion control. This paper studies the problem of short-term traffic forecasting in application-aware backbone optical networks. The proposed method is based on the Multilayer Perceptron (mlp). Multiple neural network architectures are evaluated using three datasets with diverse characteristics, representing different types of internet traffic in a real-world backbone network. An extensive examination is performed to find the best neural network architecture for each traffic type. The proposed method revealed high prediction quality, achieving the mean absolute percentage errors between 2% and 10%, depending on the traffic type. The proposed neural networks outperform the baseline regression model in all considered types of traffic. © 2022 IEEE.

关键词： Machine learning

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Artificial Intelligence Algorithms in Sports Rehabilitation control Management System 2

Artificial Intelligence Algorithms in Sports Rehabilitation ...

引用

2nd International conference on Data Science and Information System, ICDSIS 2024

作者： Xu, Jian Xu, Zhan China Academy Of Art Department Of General Physical Education Zhejiang Hangzhou China Zhejiang SCI-TECH University Department Of Pe Zhejiang Hangzhou China

ISBN: (纸本)9798350381665

The aim of this study is to explore and propose the application of artificial intelligence algorithms in sports rehabilitation control management systems, in order to solve the problems existing in traditional research. By introducing advanced artificial intelligence technologies, including machine learning, deep learning, data mining, personalized rehabilitation plans can be achieved, and data analysis efficiency can be improved. Real-time monitoring and feedback can also be achieved, and intelligent decision support can be provided to promote more intelligent and personalized rehabilitation treatment, thus improving the quality of life and rehabilitation outcomes of patients. The performances of long short-term memory (LSTM) and traditional recurrent neural network (RNN) in terms of rehabilitation action recognition accuracy are compared through experiments. The experimental results show that the average accuracy of LSTM reaches 75%, significantly higher than the 60% of traditional RNN. In terms of recall ratio for rehabilitation actions, LSTM achieves 84%, while traditional RNN only achieves 69%. These clear numerical differences clearly demonstrate the excellent performance of LSTM in rehabilitation action recognition. This study provides empirical support for the application of artificial intelligence algorithms in sports rehabilitation, revealing the enormous potential of LSTM in the field of rehabilitation. © 2024 IEEE.

关键词： Long short-term memory

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A Highly Nonlinear Survival network for Hospital Readmission Prediction of Cardiac Patients 10

A Highly Nonlinear Survival Network for Hospital Readmission...

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

作者： Zhai, Yuejing Li, Yiping He, Lihua Luo, Wuman Macao Polytechnic University China Macau University of Science and Technology China

ISBN: (纸本)9789897587504

Hospital readmission prediction of cardiac patients is an increasingly important survival analysis problem these days. So far, three groups of methods for cardiac readmission have been proposed: statistical-based, machine learning-based and deep learning-based. However, the assumptions of the statistical-based methods limit their practicality in real-world applications. The traditional machine learning-based methods suffer from the problem of over-reliance on feature engineering. Deep learning-based methods can be further classified into two groups in terms of how they deal with first hitting times: discrete strategy-based and continuous strategy-based. It is nontrivial for the discrete strategy-based methods to find the optimal granularity of output time intervals. The continuous strategy-based methods assume nonlinear proportional hazards condition, which often limits the model performance in practical applications. Besides, existing deep learning-based methods still have room for improvement in calculating the mean value of fitted dropout models. To address these issues, in this paper, we propose a highly nonlinear survival network called Environment-Aware Maxout Deep Survival Neural network (EMaxSurv) to predict the risk value of hospital readmission of cardiac patients. EMaxSurv is based on a key observation that environmental conditions have a significant impact on the health of cardiac patients. The basic idea of EMaxSurv is to adopt maxout deep networks combined with environmental information to better capture the relationship between covariates and the distribution of the first-hitting times. To evaluate the proposed model, we conduct extensive experiments on three real world datasets. The experimental results show that EMaxSurv outperforms the other baselines in all three datasets. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Deep neural networks

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An intelligent routing algorithm based on energy-efficient progressive-recursive clusters for wireless sensor networks 6

An intelligent routing algorithm based on energy-efficient p...

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6th International conference on Advanced Communication Technologies and networking, CommNet 2023

作者： Hilmani, Adil Koundi, Mohammed Aouad, Siham Sabri, Yassine Maizate, Abderrahim Ayoub, Fouad Mohamed v University City of Rabat Superior School of Technologies of Sale Morocco Ibn Tofail University Ensa Kenitra Morocco Mohamed v University Smart Systems Laboratory ENSIAS Rabat Morocco Ibn Zohr University Laboratory of Metrology and Information Processing - Lmti FacuIty of Sciences Agadir Morocco University Hassan Ii RITM-ESTC/CED-ENSEM Casablanca Morocco Eratic Team LaREAMI Lab CRMEF-K Kenitra Morocco

ISBN: (纸本)9798350329391

The implementation of wireless sensor networks (WSNs) is central to the operation of smart parking in modern cities. These networks are responsible for monitoring and tracking the occupancy of each parking space in real-time. The WSNs consist of sensor nodes that detect, process, and transmit data to the base station at regular intervals. However, power and communication limitations in these nodes can impact the performance and quality of the network. Early energy depletion, for instance, can shorten the network's lifespan and necessitate the replacement of sensor nodes. To address these challenges, this paper proposes a WSN routing protocol that employs clustering to build clusters and facilitate multi-hop communication to the base station. This approach offers a robust and efficient algorithm that reduces power dissipation and extends the lifespan of both sensor nodes and the WSN network. Simulation results indicate that this proposed protocol is more energy-efficient, reliable in data transmission, and longer-lasting compared to other protocols commonly used in smart parking management. © 2023 IEEE.

关键词： internet of things

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Pragmatic Analysis of IoMT network Modelling Techniques from a Statistical Perspective

Pragmatic Analysis of IoMT Network Modelling Techniques from...

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2022 International conference on Electronics and Renewable systems, ICEARS 2022

作者： Fuke, Radhika P. Mahajan, R.P. Sage University Department of Information Technology Phd Scholar Indore India Sage University Department of Computer Science Engineering and It Professor Indore India

ISBN: (纸本)9781665484251

internet of Medical Things (IoMT) networks are high precision wireless interfaces that require design of accurate sensing, actuation, and processing devices. These devices include wireless electrocardiograph (ECG) sensors, electroencephalograph (EEG) sensors, wireless blood pressure monitoring devices, etc. Due to direct patient interface, these devices are required to have superior performance in terms of sensing accuracy, processing efficiency, and communication quality of service (QoS) parameters. A wide variety of models are proposed by IoMT researchers to perform this task, but each of these models vary in terms of network size, deployment complexity, cost of deployment, processing delay, etc. These models include machine learning routing techniques, blockchain based security methods, privacy preservation methods, high-precision sensor design methods, high-performance communication interfaces, etc. Due to such a wide variation in performance, IoMT network design requires continuous validation, which increases time-to-market, thereby increasing deployment cost. In order to reduce number of validations, a statistical survey of models for IoMT network design is discussed in this text. This discussion is focussed towards evaluating various characteristics, advantages, limitations, and future research scopes in existing models. readers would be able to identify best performing model(s) for a given IoMT application. It is followed by a statistical analysis of the reviewed IoMT network design models in terms of end-to-end delay, communication QoS, network security, scalability, computational complexity, cost of deployment, and application of use. This statistical performance evaluation will further assist readers to statistically compare the reviewed methods, and identify best performing model(s) their combinations for context-specific network deployments. Due to this, readers would be able to reduce network design validation delay, which will further assist in reduci

关键词： Scalability

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Estimation of quality of Service in Tactile internet, Augmented Reality and internet of Things 21st

Estimation of Quality of Service in Tactile Internet, Augmen...

引用

21st International conference on Next Generation Teletraffic and Wired/Wireless Advanced networks and systems, NEW2AN 2021 and 14th conference on internet of Things and Smart Spaces, ruSMART 2021

作者： Alzaghir, Abbas Paramonov, Alexander Koucheryav, Andrey The Bonch-Bruevich Saint-Petersburg State University of Telecommunications St. Petersburg193232 Russia

ISBN: (纸本)9783030977764

In this paper the researchers provide a model to analyse the traffic streams of Tactile internet and Augmented Reality with the concept internet of Things. In addition, the directions in traffic served by communication networks, construct a real time interactive system between human and machine and present a novel evolution in human to machine communication. The key requirement in the implementation of augmented reality and Tactile internet is how to achieve a low latency transmission. In this work the effect of internet of Things, augmented reality and tactile internet traffic, is investigated, formed by surveillance and dispatching control systems, when the properties of this traffic are qualified by regular data stream properties. The researchers evaluate the effect of such traffic stream on key QoS indicators that represent the probability of packet loss and time delivery delay. The queuing system (QS) is used with a combined service discipline as a constructed model of a communication network. The simulation results of the analytical model turned satisfying. © 2022, Springer Nature Switzerland AG.

关键词： internet of things

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Advanced Battery Energy Storage System Employing Cloud-based Platform 8

Advanced Battery Energy Storage System Employing Cloud-based...

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8th Southern Power Electronics conference and the 17th Brazilian Power Electronics conference, SPEC / COBEP 2023

作者： Gonçalves, Diuary Soares Callegari, João Marcus De Barros, Rodrigo Cássio Cupertino, Allan Fagner Pereira, Heverton Augusto CEFET-MG Department of Electrical Engineering Belo Horizonte Brazil Universidde Federal de Minas Gerais Department of Electrical Engineering Belo Horizonte Brazil Universidade Federal Do Recôncavo da Bahia Department of Electrical Engineering Cruz das Almas Brazil Universidade Federal de Juiz de Fora Department of Electrical Energy Juiz de Fora Brazil Universidade Federal de Viçosa Department of Electrical Engineering Viçosa Brazil

ISBN: (纸本)9798350321128

This work proposes an upgrade from a conventional Battery Energy Storage System (BESS) to an advanced BESS. The employed hierarchical three-level control architecture improves the system capabilities, allowing the advanced BESS to execute different ancillary services. The internet of Things (IoT)-based network also allows remote commands by external agents. The connection between the BESS and the cloud ensures operational reliability and enables advanced cloud-based features. Experimental tests showed suitable communication with the broker platform under the BESS charging procedure. Additionally, a second experiment was conducted over long periods of time to demonstrate satisfactory performance of the IoT network. © 2023 IEEE.

关键词： Auxiliary systems Battery Energy Storage System internet of Things Low-Cost Smart systems

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Comprehensive Power quality Management for Low-Voltage Distributed Photovoltaic System Design

Comprehensive Power Quality Management for Low-Voltage Distr...

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Smart Grid and Artificial Intelligence (SGAI), International conference on

作者： Jichuan Zhang Hongquan Li Linli Zhang Chuanyu Tan State Grid Shanghai Energy Internet Research Institute Beijing China Shandong Electric Group Co. Ltd. Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

This paper investigates the power quality issues arising from the grid integration of low-voltage distributed photovoltaic (PV) systems, such as reverse heavy overload and voltage over-limit conditions. It evaluates the limitations of current mitigation strategies and proposes a design for a power quality comprehensive treatment device. This device is capable of real-time power quality monitoring and can interact with intelligent substations via remote communication. It achieves functions including active power regulation, reactive power compensation, and harmonic control, thereby effectively addressing voltage over-limit problems and enhancing the safe operational performance of the power grid.

关键词： Photovoltaic systems performance evaluation Low voltage Reactive power Voltage fluctuations Substations Power quality Smart grids Voltage control Monitoring

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Artificial Neural network-Based Multilevel Inverter for Renewable Energy Applications 3rd

Artificial Neural Network-Based Multilevel Inverter for Rene...

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3rd International conference on Emerging Trends in Expert Applications and Security, ICE-TEAS 2024

作者： Priya, R. Karpaga Absar, R. Udesh, S. Department of Electrical and Electronic Engineering Saveetha Engineering College Chennai India

ISBN: (纸本)9789819737444

In contemporary times, utilization will be multilevel inverter in high-power applications is becoming increasingly prominent. These inverters are being favored for those abilities for provide pure-quality result voltage of minimal distortion, along with the advantage of requiring semiconductor switches with lower blocking voltage, as comparing to traditional voltage sources inverters. In the project, we introduce a novel configuration known as the dual-T-type five-level CMI, designed specifically for renewable energy applications. This innovation aims to address these challenges without comparing those desirable voltage-boosting capabilities. The core concept revolves around the integration of a half bridge and an inductor to facilitate the gradual charging of a capacitor connected in series with the DC source. The control strategy for this proposed topology is implemented using an Artificial Neural network (ANN) Algorithm. Furthermore, uniform, and coordinated operation can be achieved when extending this configuration in a cascaded manner. The operational principle of this innovative topology is thoroughly analyzed and elucidated. To validate its performance, simulation results from a prototype are presented using the MATLAB Simulink platform. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial neural network (ANN) Converter Load current Multilevel inverters Power switches Source current

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