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Automation, Robotics and Communications for Industry 4.0/5.0 2023年 2023卷 44-48页

作者： Lee, T.C. Su, Y. Department of Electrical Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan College of Computer and Data Science Fuzhou University Fuzhou350116 China

It is well-known that a digraph has a spanning tree if and only if its graph Laplacian (matrix) has a simple zero eigenvalue and all the other nonzero eigenvalues have positive real parts. Different from the approach of matrix and graph theory, this paper proposes a novel proof based on a LaSalle invariance principle. Firstly, it is shown that the max-min function considered in present literature is a weak Lyapunov function for the error dynamic based on the coordinate transformation with any convex combination of the agents’ states. Thus, the steady-state behavior can be characterized by the spanning tree condition. Secondly, the proof relates directly the steady-state studies to the graph property. Such an approach has a potential to extend similar results to more complex systems such as nonlinear systems or time-varying systems, and thus provides a new point of view in studying consensus problems of multi-agent systems. Both of them may provide a new point of view in studying consensus problems. © 2023, International Frequency Sensor Association (IFSA). All rights reserved.

关键词： Time varying systems

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Novel Power-Aware Optimization Methodology and Efficient Task Scheduling Algorithm

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computer Systems science & engineering 2022年第4期41卷 209-224页

作者： K.Sathis Kumar K.Paramasivam Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401TamilnaduIndia Department of Electrical and Electronics Engineering Kumaraguru College of TechnologyCoimbatore641049TamilnaduIndia

The performance of central processing units(CPUs)can be enhanced by integrating multiple cores into a single *** performance can be improved by allocating the tasks using intelligent *** Small tasks wait for long time or executes for long time,then CPU consumes more ***,the amount of power consumed by CPUs can be reduced without increasing the *** are used to connect cores,which are organized together to form a network called network on chips(NOCs).NOCs are mainly used in the design of ***,its performance can still be enhanced by reducing power *** main problem lies with task scheduling,which fully utilizes the ***,we propose a novel randomﬁt algorithm for NOCs based on power-aware *** this algorithm,tasks that are under the same application are mapped to the neighborhoods of the same application,whereas tasks belonging to different applications are mapped to the processor cores on the basis of a series of *** scheduling process is performed during the run *** results show that the proposed randomﬁt algorithm reduces the amount of power consumed and increases system performance based on effective scheduling.

关键词： Randomﬁt algorithm network on chips processor cores power-aware optimization

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Multi-variate Factors Assessment of Harmful Algal Blooms (HABs)

Multi-variate Factors Assessment of Harmful Algal Blooms (HA...

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2023 IEEE International Conference on Recent Trends in Electronics and Communication, ICRTEC 2023

作者： Sujatha, Meera Gopinath Patel, Devarshi Ranganathan, Prakash Korom, Scott School of Electrical Engineering and Computer Science University of North Dakota United States College of Engineering and Mines University of North Dakota United States

ISBN: (纸本)9798350396195

Harmful Algal Blooms (HABs) are becoming a serious concern in many water bodies. Predicting the formation of these blooms help waterbody stakeholders take appropriate actions and treatment plans for algae management. Many factors such as Alkalinity, Surface Temperature (ST), Precipitation, the effect of nutrients, etc., contribute to bloom formation and can vary based on geographical regions. This paper is focused on a region-based analysis and identifying factors contributing to the large concentration of cyanobacteria. The study includes several lakes from the states of Ohio, Indiana, and Kentucky. We investigate a multi-variate time series analysis using Vector Autoregressive (VAR) / Vector Error Correction Model (VECM) and ARIMAX models and evaluated their Root Mean Square Error(RMSE) and Mean Arctangent Absolute Percentage Error (MAAPE) scores. The preliminary results indicate that the Northern region dataset has a forecasting accuracy of 98%. VAR/VECM models outperform ARIMAX models. The Granger-causality test indicates that Surface Temperature (ST) played an important role in bloom formation in the Northern region lakes, while other regional results in conducive. © 2023 IEEE.

关键词： Lakes

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SCN-DRL: Scheduler for large-scale Critical Notification applications based on Deep Reinforcement Learning 9

SCN-DRL: Scheduler for large-scale Critical Notification app...

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9th International Conference on Future Internet of Things and Cloud, FiCloud 2022

作者： Vrbaski, Mira Bolic, Miodrag Majumdar, Shikharesh University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada Carleton University Dept of Systems and Computer Engineering Ottawa Canada

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

A multi-objective Scheduling approach for large-scale microservice Critical Notification system applications (SCN-DRL) based on Deep Reinforcement Learning is presented. This paper addresses optimization for three objectives: cloud service cost, cloud resource utilization, and system notification deadline. Recent advances in the deep reinforcement learning field inspired us to research how to build a solution that learns to manage the cloud container's resources directly from experience. SCN-DRL is an example solution that translates the containerized microservice cloud cluster scheduling problem into a learning problem. To the best of our knowledge, this is the first work dealing with multi-optimization objectives for critical microservice notification applications using deep reinforcement learning, where the notification load is variable and depends on the results of the other previously prior processed microservice. Our initial results show that SCNDRL performs better than state-of-The-Art heuristics and adapts to different workload conditions without a noticeable drop in performance. © 2022 IEEE.

关键词： Containers

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Design and Assessment of a Li-Fi Based Home Automation System: An Alternative to Wi-Fi and Bluetooth Communication System 2

Design and Assessment of a Li-Fi Based Home Automation Syste...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Imran, Mohammed Thushar Mohd Shams, Sharif Ahmed, Nayeem Refat, Rafiul Islam Rahman, Tareq Mostakim, Abdullah A.L. Brac University Dept. of Electrical Electronic Engineering Dhaka Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350325799

This research work presents a home automation system based on Li-Fi (Light Fidelity) technology as an alternative to the RF communication systems such as Wi-Fi or Bluetooth. Li-Fi uses visible light for transmitting signals and commands through communication channels. In this literature, the usage of several types of sensors to programmatically control home appliances incorporating Li-Fi technology is thoroughly discussed. Furthermore, a prototype is constructed to assess the feasibility of the proposed system, and a Proteus based software simulation is demonstrated in parallel to corroborate the operability of the prototype. Additionally, a Tinkercad based circuit connection diagram is illustrated for better visualization of the proposed system. The study further validates that Li-Fi offers the most affordable and secure way to create a communication channel for home automation when compared to other contemporary operational devices. A technical comparison on the Li-Fi, Wi-Fi, and Bluetooth-based home automation system has also provided that emphasizes the advantages of Li-Fi over other commonly used communication technologies. Lastly, since the proposed system uses Visible Light for signal transmission, it becomes the fastest in contrast to other RF technologies now in use. © 2023 IEEE.

关键词： Bluetooth

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Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 388-402页

作者： Xian Yang Jing Yan Changchun Hua Xinping Guan the Institute of Information Science and Engineering Yanshan University the Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao 066004China IEEE the Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects *** the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback ***,due to hardware technology and cost constraints,the velocity measurements are not always *** addition,the time-varying communication delay makes it challenging to achieve tracking *** paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication *** order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity *** the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be ***,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically *** the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking *** particular,the relationship between the controller design parameters and the allowable maximum delay values is ***,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.

关键词： Asymptotic stability bilateral teleoperation time-varying delay velocity observer

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Attention-Based Approach for Cassava Leaf Disease Classification in Agriculture

Attention-Based Approach for Cassava Leaf Disease Classifica...

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Khabusi, Simon Peter Pheroijam, Prishika Kshetrimayum, Satchidanand National Taipei University of Technology Department of Electrical Engineering Taipei10608 Taiwan National Taipei University of Technology Department of Electrical Engineering and Computer Science Taipei10608 Taiwan

ISBN: (纸本)9781665476010

Cassava is a food crop that is rich in carbohydrates. However, the crop is vulnerable to many diseases. Research has revealed that image recognition using machine learning and deep learning techniques can be applied in automatic identification of cassava leaf diseases. Therefore this study focuses on using strongly discriminative features of the leaf regions affected by disease and weakening regions of low interest to improve the classification accuracy. A convolutional block attention module (CBAM) is a common attention mechanism integrated in feed-forward convolutional neural networks. In this study, CBAM is added to the pretrained ResNet50 and VGG19 models to recognize the cassava leaf regions affected by disease. This is done by sequentially inferring attention maps along two dimensions, channel and spatial for every intermediate feature map. The attention maps are then multiplied to the input feature map for adaptive feature refinement. The performance of baseline models such as EfficientNet, ResNet50, Inceptionv3, and Xception is compared with the attention-based models trained, validated and tested on a public dataset from Makerere University AI laboratory. ResNet50+CBAM achieve the highest performance with accuracy of 97%, precision of 96%, recall of 94% and F-measure of 95%. Conclusively, attention-based models perform better than the baseline models with a performance improvement of over 1%. © 2023 IEEE.

关键词： Deep learning

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Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 1026-1036页

作者： Jingyang Mao Xiangyu Meng Derui Ding Department of Control Science and Engineering University of Shanghai for Science and TechnologyShanghai 200093China Division of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA70803 USA IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership ***,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy ***,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and *** the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation ***-time recursive algorithms are given to find the minimal ellipsoid containing the true ***,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

关键词： Affine model membership functions set-membership filtering stability Takagi-Sugeno fuzzy modeling

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Tuberculosis Disease Detection from Chest X-rays Using Deep Learning Techniques 26

Tuberculosis Disease Detection from Chest X-rays Using Deep ...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Rabby, Mehedi Hasan Islam, Oahidul Assaduzzaman, Md. Dutta, Monoronjon Daffodil International University Department of Computer Science & Engineering Dhaka Bangladesh Daffodil International University Department of Electrical & Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Millions of cases of tuberculosis (TB) are recorded each year, making it a significant worldwide health concern. Early and accurate TB detection is essential for the disease to be effectively treated and controlled. Deep learning methods have recently become effective tools for analyzing medical images, and they have a lot of potential for use in the field of TB detection. This study describes a revolutionary deep-learning method for detecting TB illness. We used a dataset of 3500 chest X-ray images from individuals with tuberculosis. There are two classes in the dataset: Tuberculosis and Normal. We used the highly regarded deep learning models VGG16, VGG19, MobileNetV2, and InceptionV3 to categorize such elements. Out of all of them, MobileNetV2 has obtained the highest accuracy, which is accurate in training of 99.99% and a test's reliability of 98.93%. Furthermore, in VGG16, VGG19, and Inception-V3, we achieved test accuracy of 98.90%, 99.14%, and 97.87%, respectively. © 2023 IEEE.

关键词： Chest InceptionV3 MobileNetV2 Tuberculosis Vgg16 Vgg19 X-rays

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ElectrodeNet - A Deep-Learning-Based Sound Coding Strategy for Cochlear Implants

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IEEE Transactions on Cognitive and Developmental Systems 2024年第1期16卷 346-357页

作者： Huang, Enoch Hsin-Ho Chao, Rong Tsao, Yu Wu, Chao-Min Department of Electrical Engineering National Central University Taoyuan320317 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan701401 Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan320314 Taiwan

ElectrodeNet, a deep-learning-based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the fast Fourier transformed bins and channel envelopes obtained from the processing of clean speech by the ACE strategy. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet using CI simulations. Sentence recognition tests for vocoded Mandarin speech were conducted with normal-hearing listeners. DNN, CNN, and LSTM-based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC), and Spearman's rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly higher average in STOI and sentence recognition compared to ACE. The methods and findings demonstrated the feasibility and potential of using deep learning in the CI coding strategy. © 2023 IEEE.

关键词： Cochlear implants

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