检索结果-内蒙古大学图书馆

Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Ahmed A. J. Al-Hchaimi Abdul Hadi M. Alaidi Yousif Raad Muhsen Mohammed F. Alomari Nasri Bin Sulaiman Mamika Ujianita Romdhini Dept. of Electromechanical Systems Engineering Southern Technical University Basra Iraq Dept. of Computer Science and Information Technology Universiti Tenaga National Kuala Lumpur Malaysia Dept. of Computer Science Universiti Putra Malaysia Kuala Lumpur Selangor Malaysia Dept. of Electrical and Electronic Engineering Universiti Putra Malaysia Kuala Lumpur Malaysia Dept. of Mathematics University of Mataram Mataram Indonesia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

This paper proposes a new algorithm called Dynamic Optimization of Energy and QoS in VANETs (DOEQ-VANET), designed to maximize the energy efficiency and Quality of Service (QoS) of the Vehicular Ad-hoc Networks (VANETs). Through exploiting the heterogeneity of Multiprocessor System-on-Chip (MPSoC) employing an approximate computation, DOEQ-VANET is capable of dynamically adjusting the computational accuracy for non-critical applications which results in energy conservation without the risk of propagating the misinformation. Task assignment and scheduling are continuously dynamically adjusted as they respond to network fluctuation, vehicle dynamics and energy availability. The simulation result and validation proved the power of the proposed algorithm, which would mark a large advancement toward more green and smart vehicular networks within the field of Intelligent Transport systems (ITS). This algorithm is crucial for improving the sustainability and efficiency of future transportation systems.

关键词： Heuristic algorithms Green products Vehicular ad hoc networks Transportation Quality of service Approximation algorithms Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

An IoT-based Parameter Extraction Platform for Powder Metallurgy Sintering Furnace

An IoT-based Parameter Extraction Platform for Powder Metall...

引用

International Conference on Internet of Things: systems, Management and Security

作者： Shih-Man Chang Hao Pu Lin Chin-Chuan Han Yu-Chi Wu Dept. of Computer Science and Information Engineering National United University Miao-Li Taiwan Program in Material and Chemical Engineering National United University MiaoLi Taiwan Dept. of Electrical Engineering National United University Miao-Li Taiwan

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

Internet of Things (IoT) technology has widely used in industrial production. Most cases focused on designing new equipment or processes. However, small and medium enterprises especially the powder metallurgy manufacturers in Taiwan would not replace existing production equipment due to the finical problems. In this study, an IoT-based platform is conducted to extract the sintering parameters for technology to augment sintering furnaces by installing sensors and data transmission modules, setting a sintering parameter extraction platform for powder metallurgy sintering furnace. Long-term monitoring and remote control are the main technologies for solving three problems during the sintering process, i.e., temperature, atmosphere, and time. Monitoring targets include checking whether the heaters are damaged, ensuring stable air flow meter readings, and verifying the normal speed of the conveyor belt. Additionally, remote control technology has been developed to execute cooling and reheating processes on the sintering furnace. The staying time on-site for personnel is reduced and the sintering time is increased for production increase during working hours.

关键词： Temperature sensors Powders Furnaces Sintering Process control Metallurgy Internet of Things Remote control Monitoring Parameter extraction

来源：评论

学校读者我要写书评

暂无评论

Sustainable Cultural Tourism Strategy and Promotion: An ICT Management Platform

Sustainable Cultural Tourism Strategy and Promotion: An ICT ...

引用

International Conference on Application of Information and Communication Technologies (AICT)

作者： Kashyap Raiyani Shabnam Pasandideh Sajib Ahmed Pedro Pereira João Martins Dept. Electrical and Computer Engineering Center of Technology and Systems-UNINOVA NOVA School of Science and Technology NOVA University Lisbon Caparica Portugal

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Cultural Tourism (CT) is a significant element of today’s economy, accounting for around 37% of the total tourist industry and expanding at a pace of over 15% each year. This function and economic impact can benefit some EU and non-EU locations and areas with high cultural, social, and environmental potential. Other synergistic variables such as know-how, Information Communication Technologies (ICTs), gastronomy, identity, local culture, values, intangible legacy, or other characteristics also contribute to this influence. The work presented in this paper is part of the Social Innovation and TEchnologies for Sustainable Growth through Participative Cultural TOURism (TExTOUR) project, which brings together partners from the quintuple social innovation helix (knowledge, business, society, government, and entrepreneurs) to co-design, validate, and scale up policies and strategies that have a positive impact on socioeconomic territorial development based on cultural tourism. TExTOUR collaborates with eight CT Pilots in lesser-known destinations to develop collaborative work methodologies for developing CT strategies for local sites, utilizing ICTs and social innovation tools. The CT-Labs assist stakeholders in putting CT ad hoc strategies and action plans into action, monitoring them, and validating them. As a result, a technological platform (ICT tool) is presented in this paper, with its components outlined.

关键词： Industries Technological innovation Federated learning Government Information and communication technology Cultural differences Global communication Stakeholders Socioeconomics Monitoring

来源：评论

学校读者我要写书评

暂无评论

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics 46

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Mod...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Siviero, Ilaria Helmhold, Florian Ray, Andreas M. Bibián, Carlos Menegaz, Gloria Murguialday, Ander Ramos Storti, Silvia F. Dept. of Computer Science University of Verona Verona Italy Inst. of Medical Psychology and Behavioral Neurobiology University of Tübingen Tübingen Germany Gtc of Neuroscience Imprs for Cognitive and Systems Neuroscience Tübingen Germany Dept. of Engineering for Innovation Medicine University of Verona Verona Italy Tecnalia Basque Research and Technology Alliance San Sebastian Spain Dept. of Neurology and Stroke University of Tübingen Tübingen Germany Athenea Neuroclinics San Sebastian Spain

ISBN: (纸本)9798350371499

Technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like electromyographic (EMG) and inertial measurement unit (IMU) data are crucial for decoding continuous human actions. Classical modeling methods, such as linear, adaptive, or static filters, lack the capacity to capture complex relationships between surface EMG and kinematics, as well as generalizability across subjects. We propose a deep learning (DL) model for the continuous decoding of hand motion. Custom-made wearable devices acquired EMG-IMU data from 27 healthy subjects performing a wrist flexion/extension task. Two regression models were compared with a hybrid convolutional neural network and a gated recurrent unit. To address inter-subject variability, a leave-one-subject-out cross-validation approach was implemented. The DL model showed a mean R2 increase of 0.18 compared to the polynomial regressor. Our approach enhances wrist kinematics decoding providing a generalized model based on data captured with wearable devices. The findings hold potential for innovative home-based telemedicine solutions in motor rehabilitation. © 2024 IEEE.

关键词： Adaptive filters

来源：评论

学校读者我要写书评

暂无评论

Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications

Resource Efficient Federated Learning for Deep Anomaly Detec...

引用

International Conference on Digital Signal Processing (DSP)

作者： Alexandros Gkillas Aris Lalos Athena Research Center Industrial Systems Institute Greece Dept. of Computer Engineering & Informatics University of Patras

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Orchestrating Microservice-based SDN Controllers: the MSN Realistic Use Case

Orchestrating Microservice-based SDN Controllers: the MSN Re...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Juan Luis Herrera Domenico Scotece Jaime Galán-Jiménez Javier Berrocal Giuseppe Di Modica Paolo Bellavista Luca Foschini Dipartamento di Informatica Scienza e Ingegneria University of Bologna Italy Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The Software-Defined Networking (SDN) paradigm disaggregates the data plane, embodied by switches that only forward data, from the control plane, embodied by SDN controllers that communicate with said switches. SDN also proposes a third, application layer, which implements various functions such as firewalls or service discovery by communicating with the controllers through the northbound interface. However, while state-of-the-art works propose the deployment of multiple, distributed SDN controllers, the software architecture of these controllers is still monolithic, requiring not only the controller runtime but also all the network-level applications to be deployed across all SDN controller hardware. On the other hand, state-of-the-art SDN controllers such as MSN allow treating network-level applications as microservices, which comes with the challenge of orchestrating the microservices across the network. In this paper, we present Grex, a framework to orchestrate network-level applications across microservice-based SDN controllers. We test and validate the optimization model of Grex by performing experiments in a realistic network testbed using the MSN controller.

关键词： Hands Runtime Software architecture Optimization models Microservice architectures Quality of service Control systems Hardware Global communication Software defined networking

来源：评论

学校读者我要写书评

暂无评论

PointNetPGAP-SLC: A 3D LiDAR-based Place Recognition Approach with Segment-level Consistency Training for Mobile Robots in Horticulture

arXiv

引用

arXiv 2024年

作者： Barros, T. Garrote, L. Conde, P. Coombes, M.J. Liu, C. Premebida, C. Nunes, U.J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Dept. of Aeronautical and Automotive Engineering LUCAS Lab Loughborough University United Kingdom

3D LiDAR-based place recognition remains largely underexplored in horticultural environments, which present unique challenges due to their semi-permeable nature to laser beams. This characteristic often results in highly similar LiDAR scans from adjacent rows, leading to descriptor ambiguity and, consequently, compromised retrieval performance. In this work, we address the challenges of 3D LiDAR place recognition in horticultural environments, particularly focusing on inter-row ambiguity by introducing three key contributions: (i) a novel model, PointNetPGAP, which combines the outputs of two statistically-inspired aggregators into a single descriptor;(ii) a Segment-Level Consistency (SLC) model, used exclusively during training to enhance descriptor robustness;and (iii) the HORTO-3DLM dataset, comprising LiDAR sequences from orchards and strawberry fields. Experimental evaluations conducted on the HORTO-3DLM and KITTI Odometry datasets demonstrate that PointNetPGAP outperforms state-of-the-art models, including OverlapTransformer and PointNetVLAD, particularly when the SLC model is applied. These results underscore the model’s superiority, especially in horticultural environments, by significantly improving retrieval performance in segments with higher ambiguity. The dataset and the code will be made publicly available at https://***/Cybonic/*** © 2024, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Computation for Latency Minimization in SDN Microservice Architectures

Evolutionary Computation for Latency Minimization in SDN Mic...

引用

IEEE International Conference on Communications (ICC)

作者： José Gómez-delaHiz Juan Luis Herrera Domenico Scotece Jaime Galán-Jiménez Javier Berrocal Giuseppe Di Modica Luca Foschini Dept. of Computer Systems and Telematics Engineering University of Extremadura Spain Dipartamento di Informatica Scienza e Ingegneria University of Bologna Italy

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In recent years, Software-Defined Networking (SDN) research literature has proposed the integration of multiple SDN controllers into the same network, improving the scalability and reliability of the network. However, while this evolution has focused on control plane hardware, the architecture of SDN controller software is still monolithic, and its communication with the application plane through the northbound interface is done by the integration of the network-level applications' codebase with the controller software. The proposal of SDN Mi-croservices Architectures (SDN MSAs) is aimed at transforming the application plane, from a monolithic architecture to a set of independently deployable modules named SDN microservices. However, the promising paradigm of SDN MSAs also increases the complexity of network management, as these microservices must be placed through the SDN controllers. This placement is especially complex due to its NP-hard nature. In this work, we present Genetic Algorithm for SDN MSA (GASM), an evolutionary computation-based heuristic to solve this issue in tractable times. Experimental results show that GASM represents an average speed-up of 846.33 × compared to optimal solvers.

关键词： Scalability Microservice architectures computer architecture Minimization Software Hardware Software reliability

来源：评论

学校读者我要写书评

暂无评论

Asymptotic Stability Preservation of Input Delayed Nonlinear systems under Sampled-Data Feedback

Asymptotic Stability Preservation of Input Delayed Nonlinear...

引用

IEEE Conference on Decision and Control

作者： Xin Yu Wei Lin School of Electrical Engineering and Automation Jiangsu Normal University Xuzhou China Dept. of Electrical Systems and Computer Engineering Case Western Reserve University Cleveland Ohio

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We study the problem of global asymptotic stability preservation (GASP) for $C^systems$ non-smoothly stabilizable systems with input delay under sampled-data feedback. With the aid of Halanay inequality and the notion of homogeneity, the following sampled-data control results are established under a fast sampling: 1) GAS is preservable if the nonlinear system is homogeneous of degree zero and globally asymptotically stabilizable by homogeneous feedback; 2) As a consequence, GAS via sampled-data feedback is achieved for a class of non-smoothly stabilizable systems with input delay in a lower-triangular or upper-triangular form.

关键词： Asymptotic stability State feedback Simulation Aerospace electronics Delays Nonlinear systems

来源：评论

学校读者我要写书评

暂无评论

The Effect of Balanced and Imbalanced Dataset on Voice Pathology Detection Using Online Sequential Extreme Learning Machine (OSELM)

The Effect of Balanced and Imbalanced Dataset on Voice Patho...

引用

Advanced Telecommunication and Networking Technologies (ATNT), IEEE International Conference on

作者： N. F. S. Md Sazihan N. M. Abdul Latiff N. N. N. Abd Malik S. Mashohor K. Alaghbari F. T. AL-Dhief Faculty of Electrical Engineering Universiti Teknolgi Malaysia UTM Johor Bahru Malaysia Dept. of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia

ISBN: (数字)9798350353501

ISBN: (纸本)9798350353518

Voice pathology detection is crucial for early diagnosis and treatment of vocal disorders. This research studies the effect of balanced and imbalanced datasets on the accuracy of voice pathology detection using Online Sequential Extreme Learning Machine (OSELM) on the Saarbruecken Voice Database (SVD). The dataset's balance has been identified as having a significant influence on the performance of the machine learning models. In this work, the voice signals from vowel /a/ of pathology and non-pathology classes are taken from SVD. The dataset is divided into two category which are balanced and imbalanced dataset. Then, the features of the voice signals are then extracted using Mel-Frequency Cepstral Coefficient (MFCC) and fed into classifier called OSELM. The result of balanced and imbalanced dataset is assessed for its accuracy. The results showed that the model achieved an accuracy of 66.25% with the balanced dataset compared to 48% for the imbalanced dataset. These findings highlight the importance of addressing dataset balance to enhance the reliability and effectiveness of voice pathology detection systems.

关键词： Pathology Accuracy Extreme learning machines Databases Machine learning Feature extraction Communications technology Telecommunication network reliability Mel frequency cepstral coefficient

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：