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Enhancing Electromechanical Coupling: A Modified Model for Studying Uterine Myometrial Contraction 2

Enhancing Electromechanical Coupling: A Modified Model for S...

引用

2nd international conference on automation, Robotics and computer engineering, ICARCE 2023

作者： Wang, Mengying Ruan, Luyi Lin, Yiwei Chen, Zhenqin Xu, Jinshan Zhejiang University of Technology College of Computer Science Hangzhou China

ISBN: (纸本)9798350308341

The mechanism of uterine electromyographic signals and mechanical contractions is a significant research topic. In this study, we propose a novel electromechanical model that utilizes the FitzHugh-Nagumo (FHN) model to simulate the enhanced excitability of uterine cells influenced by hormones, leading to the successful generation of pacemaker behavior upon the introduction of small perturbations. Furthermore, we observe that as pregnancy progresses, enhanced gap junction, anchoring junction, and electromechanical coupling activity contributes to global mechanical contraction synchronization. Our research results also demonstrate a considerable level of synchronization between electrical and mechanical signals, providing robust theoretical support for the practical application of electrical signals. These findings deepen our understanding of electromechanical coupling effects and offer new perspectives for research and applications in related fields. © 2023 IEEE.

关键词： Electromechanical coupling

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Determination of Brain Tumors with Neural Networks and CT Scan Imagery 2

Determination of Brain Tumors with Neural Networks and CT Sc...

引用

2nd international conference on Recent Trends in Microelectronics, automation, Computing, and Communications Systems, ICMACC 2024

作者： Ragam, Prashanth Gorantla, Bhavitha Sonakshi Eshtamsetti, Yasaswini Maji, Sayanatika Pavithra, Danaveni School of Computer Science and Engineering VIT-AP University Andhrapradesh India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350366570

The process of brain tumor detection is paramount in the field of medical diagnostics. Early iden- tification of a brain tumor significantly enhances the chances of a patient restoring from it. In recent years, Artificial Intel- ligence has emerged as a promising tool that has efficiently assisted brain tumor detection. This research paper intro-duces an Artificial Intelligence system specifically designed to detect brain tumors based on medical imaging data, like Computed Tomography scans. It is harnessing the power of advanced machine learning algorithms, including deep learning and accuracy of brain tumor detection, facilitating timely medical action and proper treatment planning for patients. We have taken models Support Vector Machine (SVM), Radial Basis Function Neural Network (RBFNN), and Back Propagation Neural Network (BPNN) to train on the Computed Tomography images of the brain collected from the Kaggle and achieved an accuracy of 88.88% using RBFNN and BPNN models. © 2024 IEEE.

关键词： Radial basis function networks

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Analysis of the Power Curve and Development of A Graphic Interface for the Operation of the Synchronous Generator in the Machu Hydroelectric Plant 5

Analysis of the Power Curve and Development of A Graphic Int...

引用

5th international conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Cardenas, Kenyi Mendoza Rodriguez, Gabriel Osiris Cairampoma Universidad Continental School of Electrical Engineering Faculty of Engineering Huancayo Peru

ISBN: (纸本)9798350355253

The objective of the research was to develop a computational interface that allows the automatic drawing of the power chart and is compatible with current automation process equipment such as the HMI and SCADA for the H.C. Machu. The research approach is quantitative, the method is deductive-descriptive, the type of research carried out is applied, a descriptive level of research and the design was non-experimental and longitudinal. Obtaining as a result from the mathematical review of the construction of the power curves of the synchronous generators for both smooth poles and salient poles, the operating restrictions in operability limits, conditions of maximum exciter current, maximum stator current, maximum primary motor power, minimum starting power, were absorbed into Cartesian and polar expressions, in such a way that this allowed the delivery of the algorithms to be processed computationally. Concluding that it was possible to develop a graphical interface in the MATLAB software, which traces the power curve of the synchronous generators, the location of the operating points and control of the operability limits through the current of the excitation field applicable to the Machu Hydroelectric Power Plant. © 2024 IEEE.

关键词： MATLAB

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Dynamic Street Lighting based on Adaptive Learning

Dynamic Street Lighting based on Adaptive Learning

引用

2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Biswas, Suvrojyoti Sinha, Mourani Techno India University Department of Computer Science and Engineering West Bengal Kolkata India Techno India University Department of Mathematics West Bengal Kolkata India

ISBN: (纸本)9798331513894

Smart street light solution utilizing Long Range Wide Area Network (LoRaWAN) is one of the highly interesting topics in the field of Internet of Things (IoT) automation engineering. It is seen that many prototypes and modelling were served with IoT based sensors in this existing research idea globally and in fact Narrow Band IoT is used in some cases for data sharing along with secure communication channels. But some factors other than sensory technology associated with it will become more crucial to consider as those are emerging now like street light controlling as per traffic loads, adjustment of illumination power dynamically, and some changes of road condition if possible to distribute the data under a fault-tolerant architecture. In this paper, there is proposed an adaptive algorithm for street light control with decision-making logic based on Reinforcement Learning in some of the road cases as discernment. And with it traffic pattern observation to feed the dataset further using a deep learning technique. Also, simulate a Deep Q-learning algorithm that is necessary to be shown here and the most enthusiastic thing of implementation is to observe the road patterns and vehicular movements and note them clearly. © 2025 IEEE.

关键词： Adaptive algorithms

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Sperm Manipulation and Selection Platform Using Optoelectronic Tweezers∗

Sperm Manipulation and Selection Platform Using Optoelectron...

引用

2024 IEEE international conference on Robotics and Biomimetics, ROBIO 2024

作者： Huang, Shunxiao Zhac, Jiawei Gan, Chunyuan Xiong, Hongyi Ye, Jingwen Sun, Hongyan Niu, Wenyan Wang, Ao Chen, Bo Zeng, Zijin Li, Chan Feng, Lin Beihang University School of Mechanical Engineering Automation Beijing China Beihang University Beijing Advanced Innovation Center for Biomedical Engineering School of Mechanical Engineering Automation Beijing100083 China

ISBN: (纸本)9781665481090

A sperm manipulation and selection platform using optoelectronic tweezers is proposed in this paper, fully leveraging the high throughput, damage-free, and contactless advantages of optoelectronic tweezers, combined with advanced computer vision techniques, to achieve precise manipulation and selection of sperm. Initially, through simulation, the force conditions of sperm under the optoelectronic tweezers are studied, followed by the development of a sperm detection algorithm based on YOLOv8, ByteTrack, and average speed strategies. This leads to the successful construction of a sperm manipulation and selection system encompassing detection, tracking, speed measurement, and manipulation. Experimental results demonstrate that the system can achieve high-precision manipulation of individual sperm, rapidly and orderly ar-range multiple sperms, and effectively select sperm based on morphology, speed, and vitality. Additionally, the system can significantly increase sperm concentration in a short period and effectively transport groups of sperm. With the increasing problem of infertility, particularly the decline in male sperm quality, this research provides an effective technical means for the development of efficient sperm manipulation and selection technology. © 2024 IEEE.

关键词： computer vision

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IoT Based Energy Management System

IoT Based Energy Management System

引用

2025 international conference on Control, automation, and Instrumentation, IC2AI 2025

作者： Nehme, Bechara El Murr, Peter Youness, Elias ABOU Serhan, Hayssam Electrical Telecommunication and Computer Engineering department Jounieh Lebanon

ISBN: (纸本)9798331529604

The need for efficient energy management solutions has never been more pressing in an era of rising energy consumption and increased environmental awareness. By creating a cutting-edge Energy Management System (EMS) that not only allows users to lower their energy costs but also promotes a healthier environment, this project aims to address this urgent problem. This system's foundation is an Internet of Things (IoT) infrastructure that makes use of Wi-Fi and PIC microcontrollers. This IoT system makes it possible to seamlessly control a variety of endpoints, ensuring optimal energy usage. To balance usage with demand and availability, devices are prioritized according to user preferences and the availability of energy sources. Additionally, by developing a user interface, this project aims to improve the user experience. Users can interact with the energy management system, which offers real-time insights into energy consumption and device control. Users can easily monitor, control, and tailor their preferences for energy use. which encourages greater engagement and control. © 2025 IEEE.

关键词： Energy management

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Recent Trends in Sign Language Detection System Using Machine Learning Algorithms 2

Recent Trends in Sign Language Detection System Using Machin...

引用

2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Sharma, Keshav Jain, Naman Upadhyay, Adarsh Mishra, Annu Chaturvedi, Ravi Prakash Pruthi, Jyoti Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India Sharda School of Engineering & Technology Department of Computer Science & Applications Greater Noida India

ISBN: (纸本)9798350368109

This study summarises current advances in sign language recognition systems, emphasising trends, problems, and prospects. Twenty key research publications are analysed, spanning a wide range of sign language recognition topics, from low-power bio-inspired armband solutions to sophisticated neural network structures. These technologies' incorporation into human-robot interactions, as well as their potential to improve communication accessibility, are highlighted. The communication gap limits social integration, access to services, information, and education for individuals who rely on sign language. To address this issue, our research aims to develop an innovative method for real-time translation of sign language into spoken and written languages. We propose a comprehensive system called "SignText"that utilizes Large Language Models (LLMs) and Python programming to interpret and translate sign language into subtitles. Through this approach, we aim to bridge the communication gap and enhance the lives of individuals who use sign language. Initial results demonstrate promising accuracy, with an average recognition rate of over 90% for basic signs and gestures, and up to 85% and 70% for more complex and similar signs, respectively. These results show significant progress toward fostering greater inclusion and accessibility in social interactions and daily life. © 2024 IEEE.

关键词： Human robot interaction

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Fault diagnosis of pumping unit based on lightweight convolutional neural network 8

Fault diagnosis of pumping unit based on lightweight convolu...

引用

8th international conference on Electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Jiang, Zhengfeng Li, Jianguo Wang, Hua Lanzhou Jiaotong University School of Automation and Electrical Engineering Gansu Lanzhou China Sichuan Polytechnic Technician College Department of Mechanical and Electrical Engineering Sichuan Chengdu China

ISBN: (纸本)9798331506230

In order to ensure the efficient operation of the pumping unit system and monitor its status in real time, rapid and accurate fault diagnosis and identification of the oil well operating curve (work diagram) is a key step to evaluate the working efficiency of the pumping unit. In response to this demand, a lightweight model based on the MobileNetV3 model is proposed, which integrates the lightweight convolutional block attention module (MLCA) and improves the accuracy of dynamometer diagnosis without increasing the computational load of the model. Experiments Expanded on a data set containing 7 types of different working conditions and a total of 9464 work map images, the results show that the model still achieves an average recognition accuracy of up to 98.44%, surpassing the performance of baseline models such as ShuffleNetV2, EfficientNetB0, and MobileNetV3. These data strongly prove that the improved MLCA-MobileNetV3 model performs well on the dynamometer recognition task and has significant practical value and efficiency advantages. © 2024 IEEE.

关键词： Oil well pumping

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Heart Disease Diagnosis using Deep Learning 2

Heart Disease Diagnosis using Deep Learning

引用

2nd IEEE international conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kumar, Ashish Jain, Dhyanendra Singh, Prashant Das, Sanjoy Abes Engineering College Department of CSE-AIML Ghaziabad India Sunder Deep Engineering College Department of Computer Science and Engineering Ghaziabad India Indira Gandhi National Tribal University Department of Computer Science Engineering Manipur India

ISBN: (纸本)9798350369960

Developments for automation and advanced computing in the area of medical data processing has outcome with different new learning techniques. Deep learning has evolved as an advanced approach in machine learning applied to different old and new area of applications. Deep learning approaches have evolved as supervised, semi-supervised and un-supervised mode applied for different real time applications. The approach has shown a significant usage for image processing, computer vision, medical diagnosis, robotic and control operation application. Among various usage of machine learning approaches for automation, medical diagnosis has been observed as a new upcoming application. The criticality of data processing, response time, and accuracy in decision, tends the learning system more complex in usage for medical diagnosis. This paper outlines the developments made in the area of medical diagnosis and deep learning application for heart disease diagnosis. The application, database and the learning system used in the automation process is reviewed and outlined the evolution of deep learning approach for medical data analysis. © 2023 IEEE.

关键词： Deep learning approach Diagnosis and decision Heart disease diagnosis

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Robust Fuzzy controller design for respiratory systems 10

Robust Fuzzy controller design for respiratory systems

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10th international conference on Control, Instrumentation and automation, ICCIA 2024

作者： Hossein, Noor Alhuda Farahani, Shoorangiz Shams Shamsabad Barzamini, Mehrshad Dorrimoghaddam, Farahnaz Arabian, Mohammad Barzamini, Roohollah Islamic Azad University Science and Research Branch Department of Electrical Engineering Tehran Iran Islamic Azad University Islamshahr Branch Department of Electrical Engineering Islamshahr Iran Sharif University of Technology Department of Computer Engineering Tehran Iran Department of Electrical Engineering Mashhad Iran Islamic Azad University Tehran Central Branch Department of Electrical Engineering Tehran Iran City University of London Department of Engineering London United Kingdom

ISBN: (纸本)9798331509972

A respiratory system combines a blower-hose-patient setup with a single lung system featuring nonlinear lung compliance. This paper explores the optimal design of resilient fuzzy control for such systems by integrating a fuzzy algorithm with a sliding mode control approach. The robust fuzzy controller, as proposed, effectively handles the system's dynamics and remains stable amidst nonlinear changes. It ensures airway flow and maintains peak pressure below critical levels despite uncertainties like hose leakage and varying respiratory effort. The findings show superior performance in reaction time, overshoot, and tracking errors compared to traditional methods. The control mechanism's parameters rise time, peak value, heave, and settlement values are independently computed, revealing an over 18% improvement in rise time and a more than 44% reduction in settling time. Simulations indicate the robust fuzzy controller outperforms the PID controller, demonstrating its efficacy for respiratory system control. © 2024 IEEE.

关键词： Hose

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