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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Islam, Shafiqul NCF-263 Xavier University of Louisiana Dept. of Dual-Degree Computer Science and Engineering New OrleansLA70125 United States

ISBN: (纸本)9798350336504

In this paper, we present adaptive sliding mode-based load frequency control for interconnected electric power systems networks with the presence of disturbances. The design comprises a state and sliding mode feedback vector with robust and adaptive control terms to learn and compensate for disturbances. The disturbances in power networks may appear from load variations, modelling errors and other external fault disturbances. The power networks may also face additional disturbance from the changes in the power system structure for bilateral contracts between distribution and transmission companies for the future open and liberal electricity trading policy. The design and analysis are developed by using the Lyapunov method. It is shown in our analysis shows that the states of the power networks are bounded and their bounds asymptotically converge to zero in the Lyapunov sense. Compared with existing results, the proposed method does not require the exact bounds of the disturbances. The design is simple and easy to implement as it does not require the exact model of the interconnected power systems networks. Evaluation results on three-area power systems with extreme conditions are presented to illustrate the effectiveness of the proposed method for real-time applications. © 2023 IEEE.

关键词： Electric power system interconnection

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A Mobile Application for the Crowdsensing of Road Pavements Conditions 22

A Mobile Application for the Crowdsensing of Road Pavements ...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Bartolucci, Nicola Mirri, Silvia Salomoni, Paola Prandi, Catia University of Bologna Dept. of Computer Science and Engineering Italy University of Bologna Bachelor Degree in Computer Science and Engineering Italy

ISBN: (纸本)9798331508050

Urban environments face significant challenges due to deteriorating road pavements, affecting all transportation modes' safety and comfort. Traditional methods of road assessment are costly and infrequent, but advancements in mobile technology and crowdsensing offer real-time, large-scale data collection solutions. This paper introduces 'ShakeSensing', a mobile application that uses Android device sensors to measure vibrations experienced by cyclists and scooter users, providing insights into road quality. The collected data, analyzed to assess road conditions, can benefit both users and municipal authorities, promoting safer and more comfortable travel. © 2025 IEEE.

关键词： Urban transportation

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Adaptive Sliding Mode Control for Interconnected Power Systems Networks with Disturbances

Adaptive Sliding Mode Control for Interconnected Power Syste...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Shafiqul Islam Dept. of Dual-Degree Computer Science and Engineering NCF-263 Xavier University of Louisiana New Orleans LA USA

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A Mobile Application for the Crowdsensing of Road Pavements Conditions

A Mobile Application for the Crowdsensing of Road Pavements ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Giovanni Delnevo Nicola Bartolucci Silvia Mirri Paola Salomoni Catia Prandi Dept. of Computer Science and Engineering University of Bologna Italy Bachelor Degree in Computer Science and Engineering University of Bologna Italy

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Urban environments face significant challenges due to deteriorating road pavements, affecting all transportation modes' safety and comfort. Traditional methods of road assessment are costly and infrequent, but advancements in mobile technology and crowdsensing offer real-time, large-scale data collection solutions. This paper introduces “ShakeSensing”, a mobile application that uses Android device sensors to measure vibrations experienced by cyclists and scooter users, providing insights into road quality. The collected data, analyzed to assess road conditions, can benefit both users and municipal authorities, promoting safer and more comfortable travel.

关键词： Vibrations Crowdsensing Roads Urban areas Transportation Vibration measurement Mobile applications Sensors Safety Sustainable development

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Artificial Intelligence Based Examination of the Field of Ophthalmology 10

Artificial Intelligence Based Examination of the Field of Op...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Bhargavi, V.Sesha Selvaraj, Krishnamoorthy Koti, Valli Madhavi Maheshwari, Rajeev Kumar Radhamahendran, S. Chandra, Sukumar G Narayanamma Institute of Technology & Science Department of Information Technology Shaikpet Hyderabad India Department of Erp Keller India Giet Degree College Department of Computer Science Andhra Pradesh Rajahmundry India R D Engineering College Computer Science&Engg. Dept. Ghaziabad India Department of Bioinformatics Chennai117 India Pingla Thana Mahavidyalaya Department of Physics West Bengal Medinipur India

ISBN: (纸本)9798350382471

Early and correct diagnosis is critical for optimal care and prevention of visual loss due to glaucoma, the leading cause of permanent blindness globally. New possibilities for better glaucoma identification and monitoring have emerged the progress that has been made in the field of AI and machine learning. Using a unique use of both SVM and CNN, or support vector machines and convolutional neural networks, to solve a problem., this study automates the examination of ophthalmological results relevant to glaucoma. In this paper, we introduce a hybrid SVM-CNN algorithm that draws from the most promising aspects of both existing these two approaches to machine learning. In order to lay a solid groundwork for further investigation, SVM is combined first- class classification and feature extraction. CNN is used to refine and improve classification accuracy since it is a potent deep learning architecture that can automatically learn complicated characteristics from raw data. The algorithm is educated on a large collection of ophthalmic pictures used in glaucoma diagnosis, such as fundus photos, examinations of the eye's visual acuity and OCT scans (optical coherence tomography). Noise is removed, contrast is increased, and the format is standardized in these preprocessed photos. The presence or absence of glaucoma is subsequently determined by feature extraction and classification using the SVM-CNN hybrid model. This study's findings support the use of the SVM-CNN hybrid algorithm for reliable detection of glaucoma-related pathology in ophthalmic pictures. The suggested AI-based methodology has various benefits over conventional manual diagnosis approaches, including increased efficiency, more consistency, and the possibility of earlier detection. It can also help ophthalmologists by offering automated preliminary assessments, so the specialists can devote their time and energy where it is most needed. This study concludes the promise of AI-based approaches, and in partic

关键词： Artificial Intelligence CNN (Convolutional Neural Networks) etc Glaucoma Ophthalmology SVM (Support Vector Machines)

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Reliable Data Transmission through Private CBRS Networks

arXiv

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arXiv 2023年

作者： Kuo, Hsun-Yu Liu, Szu-Yu Huang, Chin-Ya Chen, Yu-Chi Xie, Meng-Hua The Bachelor Degree Program of Applied Science and Technology National Taiwan University of Science and Technology Taiwan The Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan

We consider the use of a domain proxy assisted private citizen broadband radio service (CBRS) network and propose a Maximum Transmission Continuity (MTC) scheme to transmit Internet of Things (IoT) data reliably. MTC dynamically allocates available CBRS channels to sustain the continuity of data transmission without violating the channel access requirements. MTC allocates the granted CBRS channels according to the priority of each user, the instant channel access status, interference among users, and the fairness. The simulation results demonstrate the improvement in managing reliable IoT data transmission in the private CBRS network. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Monitor and Control Electricity for Home Appliance

Monitor and Control Electricity for Home Appliance

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Sharma, Pradosh Kumar Lakshmi, B. Prakash, A. Verma, Manish Kumar Sasank, V.V.S. Ashokkumar, N. Chinmaya Degree College Department of Physics Haridwar India Deparment of Computer Applications Vijayawada India Jain Deemed to Be University Department of Ise Fet Campus Bangalore India Lovely Professional University Department: Psychology Phagwara India Koneru Lakshmaiah Education Foundation Dept. of Computer Science and Engineering Vaddeswaram India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Ece Tirupati India

ISBN: (纸本)9798350317060

Electricity is a vital part of modern life;therefore, it is crucial to learn how to utilize it safely. Over time, current systems for domestic power regulation have been analyzed with an eye toward improvement. However, existing Power Monitoring and Switching (PMAS) methods have a greater error rate and cannot be remotely monitored. As a result, this research suggests a SMACS for monitoring and controlling domestic appliances. The relevance of the application is in its ability to monitor the power consumption of home appliances via the use of hardware and IoT techniques. An Arduino UNO, a liquid-crystal display (LCD), an ACS812 current sensor module, some relays, and some AC sources all go into making the prototype of the suggested system. The software library is mined for parts, and identical simulation and prototype results are identified. Due to its absence from the system, the ESP9266 WiFi module has been included in the design. Thing-speak is used to record the information in the cloud. Data is accessed using a mobile app (Virtuoso), so it may be visualized numerically and graphically. Through the use of mobile apps, this research offers consumers a simple mechanism for keeping tabs on and adjusting the power usage of standard home equipment. The results demonstrate that the present Power Monitoring and Switching (PMAS) system produces 8.8% of current errors for the hairdryer appliance, whereas the suggested system gives just 1.6% of existing faults. © 2023 IEEE.

关键词： Domestic appliances

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Predictive Model Based on Supervised Machine Learning for Heart Disease Diagnosis

Predictive Model Based on Supervised Machine Learning for He...

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2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society, TRIBES 2021

作者： Kumar, Vinod Lalotra, Gotam Singh Koneru Lakshmaiah Education Foundation Dept. Computer Science and Engineering Vaddeswaram India University of Jammu Govt. Degree College Basohli India

ISBN: (纸本)9781665433426

Nowadays, heart disease has become a very common disease in human beings. Due to the rapidly increasing number of heart disease cases, it has now become inevitable to have some efficient and accurate mechanism that can detect heart disease early in patients. Although various heart disease diagnosis methods are available in modern medical science, these methods are very costly and involve a lot of analysis of the test results obtained during the patients' health examinations. To ease the heart disease diagnosis process, there exist several supervised machine learning techniques which can predict heart disease in patients based on laboratory test data. Prediction technique that is the most efficient, accurate, and suitable over the heart disease dataset for prediction is still required to be explored. So, this research contribution focuses on identifying the potential predictive model based on supervised machine learning suitable in the prediction of heart disease over the heart disease dataset. The proposed work found Logistic Regression to be the promising predictive model with classification accuracy 85.3 %, F1- Score 80%, Precision 82.6%, and recall 85.3%. © 2021 IEEE.

关键词： Supervised learning

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids

Integrating Noise Classification and Speech Enhancement Mode...

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International SoC Design Conference, ISOCC

作者： Ying-Hsiu Hung Yen-Ching Chang Shin-Chi Lai Wen-Ho Juang Ming-Hwa Sheu Jeng-Dao Lee Dept. Electronics Engineering National Yunlin University of Science and Technology Douliu Taiwan Doctoral Degree Program in Smart Industry Technology Research and Development National Formosa University Huwei Taiwan Dept. Automation Engineering & Smart Machinery and Intelligent Manufacturing Research Center National Formosa University Huwei Taiwan Dept. Computer Science and Information Engineering National Formosa University Huwei Taiwan Dept. Automation Engineering National Formosa University Huwei Taiwan

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

关键词： Accuracy Noise Noise reduction Speech recognition Speech enhancement Hearing aids Classification algorithms Convolutional neural networks Human voice Older adults

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