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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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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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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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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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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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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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Vector control design and analysis for PMSM using real-Time HIL simulation 4

Vector control design and analysis for PMSM using real-Time ...

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4th International Conference on Smart Grid Metrology, SMAGRIMET 2023

作者： Knol, Kristian Bensic, Tin Barukcic, Marinko Osijek Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Department of Electromechanical Engineering Osijek Croatia

ISBN: (纸本)9798350323597

During the development of control strategies for permanent magnet synchronous machines (PMSM) used in wind turbines, usage of wide range of simulation allows for more detailed, more safe and less expensive development. This paper presents type of HIL simulation, where instead of using real controller and real-Time simulator, two real-Time simulators are used and form a co-simulation environment where vector control of PMSM is conducted. The whole process of designing and simulating is a part of model-based design (MBD). The PMSM is described and controller for vector control is derived. Created co-simulation environment with its advantages and problems is described. An experiment with different scenarios is conducted to test created co-simulation. © 2023 IEEE.

关键词： Vectors

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An empirical study of LLaMA3 quantization: from LLMs to MLLMs

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Visual Intelligence 2024年第1期2卷 1-13页

作者： Huang, Wei Zheng, Xingyu Ma, Xudong Qin, Haotong Lv, Chengtao Chen, Hong Luo, Jie Qi, Xiaojuan Liu, Xianglong Magno, Michele Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Road 999077 Hong Kong School of Computer Science and Engineering Beihang University Xueyuan Road Beijing 100191 China Department of Information Technology and Electrical Engineering ETH Zurich Sternwartstrasse 7 Zürich Switzerland

The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3’s capabilities when quantized to low bit-width. This exploration can potentially provide new insights and challenges for the low-bit quantization of LLaMA3 and other future LLMs, especially in addressing performance degradation issues that suffer in LLM compression. Specifically, we comprehensively evaluate the 10 existing post-training quantization and LoRA fine-tuning (LoRA-FT) methods of LLaMA3 on 1-8 bits and various datasets to reveal the low-bit quantization performance of LLaMA3. To uncover the capabilities of low-bit quantized MLLM, we assessed the performance of the LLaMA3-based LLaVA-Next-8B model under 2-4 ultra-low bits with post-training quantization methods. Our experimental results indicate that LLaMA3 still suffers from non-negligible degradation in linguistic and visual contexts, particularly under ultra-low bit widths. This highlights the significant performance gap at low bit-width that needs to be addressed in future developments. We expect that this empirical study will prove valuable in advancing future models, driving LLMs and MLLMs to achieve higher accuracy at lower bit to enhance practicality. © The Author(s) 2024.

关键词： Deep learning Large language model Model quantization Multi-modal

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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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