检索结果-内蒙古大学图书馆

IoT-Aware Real-Time Healthcare Diagnostic Framework For Diabetes Using Wearable Sensors through Deep Reinforcement Learning

引用

IEEE Internet of Things Journal 2025年第11期12卷 18183-18194页

作者： Farman, Haleem Shahzad, Yasir Jan, Bilal Nasralla, Moustafa M. Sallam, Karam M. Munasinghe, Kumudu Jamalipour, Abbas Prince Sultan University Smart Systems Engineering Lab Department of Communications and Networks Engineering College of Engineering Riyadh Saudi Arabia University of Peshawar Department of Computer Science Pakistan FATA University Department of Computer Science Kohat Pakistan University of Sharjah Computer Science Department Sharjah United Arab Emirates University of Canberra School of Information Technology and Systems ACT2617 Australia The University of Sydney School of Electrical and Information Engineering NSW2006 Australia

Machine Learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this paper, a 5G aware real-time diabetes prediction framework is proposed using optimized Bidirectional Long Short-Term Memory (Bi-LSTM) with Deep Reinforcement Learning (DRL). Bi-LSTM can analyze time-series data in forward and backward passes on patient health metrics, such as Blood Glucose (BG) levels of a diabetic patient, to identify patterns and trends and make predictions about future health outcomes. A local dataset using a wearable sensor is collected of 10 Type-2 diabetic patients, encompassing daily BG levels at different times, alongside additional parameters such as blood pressure and body weight. The proposed framework leverages a dataset of 1,830 data points to forecast glucose levels for the following day. It also harnesses DRL to improve and optimize the model's future predictive performance. The obtained results are evaluated with other ML algorithms to validate the effectiveness of the proposed framework, which shows an improvement from 93.1% to 98.6% accuracy. The patient's diabetic condition is categorized using the Surveillance Error Grid (SEG) to increase the clinical impact of glucose prediction and make informed decisions. The results show that Bi-LSTM-DRL is an effective approach to predicting glucose levels in real-time and can adapt to health-related changes and optimize its predictions accordingly. © 2014 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

High-Capacity DWDM Transmission System for Free-Space Optical Network 6

High-Capacity DWDM Transmission System for Free-Space Optica...

引用

6th International Conference on electrical, control and Instrumentation engineering, ICECIE 2024

作者： Rahman, M.T. Jahid, M.A. Anha, S.Y. Rahman, Md. Shohanur Azmi Orpy, F. Bakibillah, A.S.M. Department of Computer Science and Engineering Bangladesh Tokyo Institute of Technology Department of Systems and Control Engineering Tokyo152-8552 Japan

ISBN: (纸本)9798350380040

This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. The system under consideration comprises 64 individual channels, each of which is capable of transferring data streams at a speed of 20 Gbps. A return-to-zero (RZ) line coding algorithm is incorporated to maximize performance across 2.0 km of FSO link. Important parameters that affect the system's performance, like beam divergence and atmosphere attenuation, are taken into account in the analysis. The signals' bit-error-rate (BER), quality factor (Q), and eye diagrams are calculated and examined to assess the system's performance. For future optical-wireless networks that require significant bandwidth capabilities, cost-effective implementation, and increased flexibility, this novel high-capacity design offers a promising solution. The results aid in the development of dependable and useful high-speed FSO communication systems. © 2024 IEEE.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Improving Spiking Neural Network Performance Using Astrocyte Feedback for Farsi Digit Recognition 31

Improving Spiking Neural Network Performance Using Astrocyte...

引用

31st International Conference on electrical engineering, ICEE 2023

作者： Nazari, Malihe Bahrami, Fariba Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Advanced Control Systems Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Advanced Control Systems Lab Tehran Iran

ISBN: (纸本)9798350312560

The third generation of neural networks is called spiking neural networks. Spiking neural networks can not only answer all the problems that can be solved by common neural networks, they can also be computationally more powerful than them. Also, these networks are closer to the biological reality of neurons. According to these reasons, spiking neural networks have received much attention in recent years. In addition to neurons, other elements are also used in spiking neural networks, whose connection with neurons has been proven biologically. These elements are called astrocytes. In this study, we have used astrocyte local feedback in a spiking neural network to improve the accuracy of the network in a digit recognition process. To reach this goal, we use the network proposed by Diehl et al in 2015, then we added astrocyte local feedback to this network. We run the network in both states. The classification accuracy of the network in the absence of astrocytes was 88.48% on Farsi handwritten digits. After adding astrocyte local feedback, it is observed that astrocytes control the activity of neurons and modified the weight of connections based on neuron activity. In this state classification accuracy reached 90.26%. The idea was to create a neural network for learning, where, inspired by the physiological role of astrocytes at the tripartite synapse improves the performance of the neural network and we demonstrate that systems that include neurons and astrocytes perform better than systems incorporating only neurons. © 2023 IEEE.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Robust Stability Analysis of Smith Predictor Based Interval Fractional-Order control systems:A Case Study in Level control Process

引用

IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 762-780页

作者： Majid Ghorbani Mahsan Tavakoli-Kakhki Aleksei Tepljakov Eduard Petlenkov Department of Computer Systems Tallinn University of TechnologyTallinn 12618Estonia Faculty of Electrical Engineering K.N.Toosi University of TechnologyTehran ***Iran IEEE

The robust stability study of the classic Smith predictor-based control system for uncertain fractional-order plants with interval time delays and interval coefficients is the emphasis of this *** uncertainties are a type of parametric uncertainties that cannot be avoided when modeling real-world ***,in the considered Smith predictor control structure it is supposed that the controller is a fractional-order proportional integral derivative(FOPID)*** the best of the authors'knowledge,no method has been developed until now to analyze the robust stability of a Smith predictor based fractional-order control system in the presence of the simultaneous uncertainties in gain,time-constants,and time *** three primary contributions of this study are as follows:ⅰ)a set of necessary and sufficient conditions is constructed using a graphical method to examine the robust stability of a Smith predictor-based fractionalorder control system—the proposed method explicitly determines whether or not the FOPID controller can robustly stabilize the Smith predictor-based fractional-order control system;ⅱ)an auxiliary function as a robust stability testing function is presented to reduce the computational complexity of the robust stability analysis;andⅲ)two auxiliary functions are proposed to achieve the control requirements on the disturbance rejection and the noise ***,four numerical examples and an experimental verification are presented in this study to demonstrate the efficacy and significance of the suggested technique.

关键词： Interval uncertainty FOPID controller fractional-order systems robust stability analysis smith predictor

来源：评论

学校读者我要写书评

暂无评论

Neural Reactivity to Haptics: Virtual Tasks Versus Physical Tasks

引用

IEEE Sensors Journal 2024年第7期24卷 11817-11828页

作者： Liu, Yi-Hung Vaitheeshwari, R. Yeh, Shih-Ching Wu, Eric Hsiao-Kuang Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan

This study investigates the effectiveness of haptic feedback in hand rehabilitation exercises, within both virtual reality (VR) and real-world settings, to enhance upper limb functionality in post-stroke recovery. We developed a VR system that incorporates haptic feedback into pinch simulations and compared its efficacy with similar real-world pinch training exercises, primarily focusing on the rehabilitation of the paretic hand. Our main objective was to ascertain the similarity of the simulated haptic feedback in the VR environment to actual tactile feedback in the real world. This was achieved by analyzing electroencephalogram (EEG) features and psychometric data collected from 35 healthy subjects. A key aspect of our approach was the use of machine learning (ML) techniques to classify the EEG signal features as originating from VR or Real tasks. This classification was based on data from the top-n electrode channels, enabling us to assess the degree of similarity in brain responses to tactile stimulation in both environments. The study's results revealed that the brain activity patterns elicited by haptic feedback were comparable in both the VR and real-world setups. Additionally, the VR feedback system was positively received by users, scoring high on experience and satisfaction metrics. This research provides a replicable methodology for assessing tactile feedback in existing rehabilitation systems and lays the groundwork for the development of novel therapeutic applications. Our findings are crucial for designing future rehabilitation interventions that effectively utilize haptic feedback, potentially leading to improved treatment outcomes for stroke patients. © 2001-2012 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for Smart Agriculture: A Comprehensive Survey

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2568-2588页

作者： Mahmood, M. Rezwanul Matin, Mohammad Abdul Goudos, Sotirios K. Karagiannidis, George North South University Department of Electrical and Computer Engineering Dhaka1229 Bangladesh Aristotle University of Thessaloniki ELEDIA@AUTH Department of Physics Thessaloniki54124 Greece Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Cyber Security Systems and Applied AI Research Center Beirut1102 2801 Lebanon

As communication technologies and equipment evolve, smart assets become smarter. The agricultural industry is also evolving in line with the implementation of modern communication protocols, intelligent sensors, and equipment. This evolution is enabling large-scale agricultural production processes to operate independently, thus, securing the food supply chain for an ever-growing population. Data processing for such a system with multiple heterogeneous sources requires proper management for effective agricultural operations. Recognizing the advantages of machine learning (ML) in performing large-scale data processing, researchers are investigating the implementation of ML to design an effective intelligent agricultural architecture. The aim of this article is to provide a thorough analysis of the state-of-the-art in smart agriculture, open challenges, and guidelines for the development of further enhanced smart agriculture systems. Specifically, we describe how ML is used to create intelligent agricultural systems supported by state-of-the-art technology. © 2020 IEEE.

关键词： Weed control

来源：评论

学校读者我要写书评

暂无评论

A learning artificial visual system for motion direction detection

引用

Neural Computing and Applications 2024年第27期36卷 17181-17197页

作者： Chen, Tianqi Kobayashi, Yuki Todo, Yuki Tang, Zheng Faculty of Electrical and Computer Engineering Kanazawa University Kakuma Ishikawa Kanazawa9201162 Japan Department of Intelligence Information Systems University of Toyama Toyama9308555 Japan

Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Neurons

来源：评论

学校读者我要写书评

暂无评论

Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

引用

Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

来源：评论

学校读者我要写书评

暂无评论

Design an optimal FOPD controller for electric vehicle speed tracking control 7

Design an optimal FOPD controller for electric vehicle speed...

引用

7th International Conference on Vocational Education and electrical engineering, ICVEE 2024

作者： Abood, Layla Hattim Abood, May H. Action, Dalal Hammood University of Technology Department of Control and Systems Engineering Baghdad Iraq Al-Iraqia University Department of Computer Engineering Baghdad Iraq Mtu Edu Department of Computer Engineering Baghdad Iraq

ISBN: (纸本)9798331505103

Electric Vehicles (EVs) become very important issue and gained attention due to many reasons like its economic price, saving environment and more reliable. In this study, controlling speed for EV is utilized by tracking the desired speed using Fractional Order Proportional Derivative (FOPD) controller with a smart tuning algorithm named Sunflower Optimization (SFO) algorithm to satisfy robust and efficient controlled speed. The numerical analysis is obtained using Matlab program in two steps, controlling the system to reach the desired value in best and stable response then apply two undesired signals (disturbance signals) to test system robustness and finally compare the suggested controller with different conventional controllers(Normal PD controller-Optimal PD controller). © 2024 IEEE.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning

Leveraging Large Language Models for Wireless Symbol Detecti...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Abbas, Momin Kar, Koushik Chen, Tianyi Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States

ISBN: (纸本)9798350351255

Deep neural networks (DNNs) have made significant strides in tackling challenging tasks in wireless systems, especially when an accurate wireless model is not available. However, when available data is limited, traditional DNNs often yield subpar results due to underfitting. At the same time, large language models (LLMs) exemplified by GPT-3, have remarkably showcased their capabilities across a broad range of natural language processing *** whether and how LLMs can benefit challenging non-language tasks in wireless systems is *** this work, we propose to leverage the in-context learning ability (a.k.a. prompting) of LLMs to solve wireless tasks in the low data regime without any training or fine-tuning, unlike DNNs which require training. We further demonstrate that the performance of LLMs varies significantly when employed with different prompt templates. To solve this issue, we employ the latest LLM calibration methods. Our results reveal that using LLMs via ICL methods generally outperforms traditional DNNs on the symbol demodulation task and yields highly confident predictions when coupled with calibration techniques. © 2024 IEEE.

关键词： Contrastive Learning

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：