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Global Asymptotic Stabilty Analysis of DC-DC Buck Converters 1

Global Asymptotic Stabilty Analysis of DC-DC Buck Converters

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Kannabiran, Kanimozhi Mohamed Rabi, B. Raja Jayapalan, Booma Krismadinata Lakshmanan, Thangapalani Rajalakshmi, K. Npr College of Engineering & Technology Dept. of Electrical and Electronics Engg. Natham India Dept. of Mechanical Engineering Rathinam Technical Campus Coimbatore India Psna College of Engineering and Technology Dept. of Electronics and Communication Engg. Dindigul India Universitas Negeri Centre for Energy and Power Electronics Research Pedang Indonesia Sri Sairam Institute of Technology Dept. of Computer Science and Engineering Chennai India Npr College of Engineering & Technology Dept. of Computer Science and Engineering Natham India

ISBN: (纸本)9798350348637

Stability of power electronic converters is one of the most important problems related to their dynamics. The problems related to global asymptotic stability is discussed here. The design example used is DC-DC buck converter with Adaptive sliding mode control. An averaged small signal model is derived and transfer functions are obtained. Stability is also verified using conventional techniques by MATLAB simulation. Simulation results and mathematical proof shows that closed loop system designed is stable and a fast response is obtained. Simulated results show that the simulated converter system acquires the excellent robustness feature of achieving a stable steady-state and fast transient response under variable operating points. A prototype model has been developed and simulated results are compared with experimental results. The results validate the stability analysis of Dc-Dc buck converter © 2024 IEEE.

关键词： Buck converter

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Nutrition Estimation and Dietary Assessment in Indian Food through Deep Learning

Nutrition Estimation and Dietary Assessment in Indian Food t...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Nandanwar, Amit Kumar Kushwaha, Mohit Vishwatej, Kondabathula Singh, Dhirendra Pratap Choudhary, Jaytrilok Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering M.P Bhopal India

ISBN: (纸本)9798350348460

Food Nutrition prediction is an interesting area of work before 10 years. There was a active research went on. But after that there was a break of research on this area. Now after corona some active work is going on. As the people are interested to know the intake of their food nutrition. And in this paper proposing the nutrition prediction in the Indian food. In this paper followed the deep learning approach to predict the nutrition in the food. The person who is interested in finding the nutrition intake will capture the image of the food before intake and can get the nutrition values before going to consume. So, it can help the people to get an idea and track of the nutrition they are *** this we are using a deep learning approach to find the nutrition in the image. User will capture the food image then that image is sent through CNN model. then by the CNN model will find what food item is captured. And before capturing the image will ask user information also like name, age, height and weight. So will calculate the BMI and the nutrition required for the person. And will store the nutrition content user is consuming in every meal. So, at end will show the BMI of the user, and present nutrition user is consuming from the meal and total nutrition user has to consume in a day. Our model for food classification achieved an accuracy of 91.87% © 2024 IEEE.

关键词： BMI (Body Mass Index) Convolutional Neural Networks Data collection Deep Learning Dietary management Keras Machine Learning Pretrained models TensorFlow Transfer Learning

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Real-Time Detection of Sea Urchins Using Convolutional Neural Networks

Real-Time Detection of Sea Urchins Using Convolutional Neura...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nair, Vismaya V Bolem, Srinivas Nanthini, N. Parveen Akhther, A. Sathyabama University Dept. of Computer Science Engineering Chennai India National Institute of Ocean Technology Chennai India

ISBN: (纸本)9798331540821

The marine ecosystem contributes significantly to our planet's ecological balance. It supports a wide range of organisms, from microscopic plankton to giant marine mammals. Each species plays a unique role in the proper functioning of the ecosystem. One such species that significantly contributes to the biodiversity of the marine environment is the Sea urchins. The Sea urchins are known as the' indicator species' of the marine ecosystem and their presence provides valuable insights on the health and overall condition of the marine environment. Monitoring, understanding, and analyzing the population growth of sea urchins is vital for research and conservation. There has been multiple research that focus on identification and classification of other marine species but only a few have ventured into the domain of sea urchin detection. This paper presents an approachto automate the detection of sea urchins on the ocean floor using Convolutional Neural Networks (CNNs), computer vision and image processing. By harnessing CNN, we aim to simplify the labor-intensive task of manually identifying sea urchins, which are still largely carried out by divers. The proposed approach aims to provide a robust solution for the detection of sea urchins, which in turn would help in enhancing marine research and conservation techniques. Additionally, the model is loaded into Raspberry Pi, which is placed on a surveillance vehicle or aplatform above the surface of the water, it processes the input received from the camera that is deployed underwater in a watertight casing. The results are then transmitted to the user via GSM model of communication to any device within its vicinity. Our model shows an accuracy rate of 95.31%. By automating the process, this model facilitates the study of sea urchins and the surrounding marine environment, making it more accessible. This method helps to close the gap between marine biology, computer vision and deep learning. © 2024 IEEE.

关键词： Convolutional neural networks

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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches 4

An Efficient Model for Early Detection of Maize Leaf Disease...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. K.L.E.Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K.L.E.Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, Data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Speech Synthesis: An Empirical Analysis of Various Techniques in Text to Speech Generation 7

Speech Synthesis: An Empirical Analysis of Various Technique...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Ghorpade, Tushar H. Shinde, Subhash K. Ramrao Adik Institute of Technology University of Mumbai Dept. of Computer Engineering Navi Mumbai India Lokmanya Tilak College of Engineering University of Mumbai Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350304268

Speech is the most important resource of communication and a part of human development. Speech Synthesis gives an opportunity to communicate with deaf and dumb people. The state of the art in speech synthesis is automatically generate speech from text. In the digital era, a text-to-speech synthesizer system is able to read any text clearly. This is a primary difference between any other talking machine and a text-to-speech synthesizer. The recent task of speech synthesis is to produce speech in the form of acoustic waveforms from text input. It is used in conversational and nonconversational applications also. In conversational applications like speech recognizers, telephone-based dialogues with people, current chatbots, etc., and in non-conversational applications like video games, children's toys, devices, or machines which are helpful for blind people to read out the text or messages loud. The purpose of this article is to analyze generation-wise growth in speech synthesis techniques. In the comparative analysis, we have done the analysis of speech synthesis methods and a few evaluation parameters such as corpus size, the complexity of each technique, intelligibility, the naturalness of sound, advantages, and limitations. We have highlighted a few research gaps in those techniques. As a contribution proposed a framework to produce speech from the text as well as images using the Lj speech dataset. We have adopted the Tacotron model to generate speech from the text in the English language. For an evaluation generated speech is evaluated with a Mean Opinion Scale parameter from 30 listeners. © 2023 IEEE.

关键词： Articulatory Concate-native Formant Satistical and Tacotron model Speech Synthesis

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Advancing Image Security Through Deep Learning and Cryptography in Healthcare and Industry 4

Advancing Image Security Through Deep Learning and Cryptogra...

引用

4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kumar, Prajwal Laroia, Ankur Kumar, Mehul Laroia, Avi Upreti, Kamal Parashar, Jyoti Maharshi Dayanand University Computer Science Engineering Haryana India University of Texas at Dallas Ms in Business Analytics TX United States Amity University Computer Science Engineering Uttar Pradesh India Birla Institute of Technology Civil Engineering Pilani India Christ University Dept. of Computer Science Delhi NCR Campus India Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350353266

Securing electronic health records (EHRs) in the Internet of Medical Things (IoMT) ecosystem is a key concern in healthcare due to the sector's differed environment. As the evolution of technology continues, ensuring the confidentiality, integrity, and accessibility of EHRs becomes more and more challenging. To enhance the confidentiality of healthcare picture data, this study explores the combined use of deep learning and cryptography methods. Through the utilization of weight analysis for improving encryption strength and the combination of chaotic systems to generate undetectable encryption patterns, it explores how deep neural networks can be modified for use in encryption. It also provides a survey of the present scenario of deep learning-based image detection of anomalies methods in working environments, such as network typologies, supervision levels, and assessment norms. Techniques in cryptography provide an effective means to protect confidential medical picture data while it's being transmitted and stored. Deep learning, on the other hand, has the ability to entirely change cryptography by providing robust encryption, resolution augmentation, and detection capabilities for medical image security. The paper outlines future research approaches to overcome these problems and tackles the opportunities and obstacles in medical image cryptography and industrial picture anomaly detection. Through this work, picture privacy in the healthcare and industrial sectors is advanced, opening the door to enhanced privacy, integrity, and availability of vital image data by overcoming the gap between deep learning and cryptography. © 2024 IEEE.

关键词： Electronic health record

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A Comparative Analysis of ML Algorithms to Improve Crop Productivity Prediction

A Comparative Analysis of ML Algorithms to Improve Crop Prod...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Nigam, Anushree Jain, Divyanshu Talan, Manish Singh Singh, S.K. Umrao, Sandhya Galgotias College of Engineering and Technology Dept. of Information Technology Greater Noida India Noida Institute of Engineering and Technology Dept. of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350323887

According to numerous calculations, the global food output must significantly increase by 2050. Additionally, water levels have been declining, and there is a shortage of usable agricultural land. The first step to introducing smart agriculture in India is good analysis and implementation of machine learning. This research strives to present the best-fit machine learning algorithm to predict crop production in a specific area, state or season. The aim of this research is to make data-driven farming possible. By comparing various regression models namely decision tree, linear regression, XGB regression, and random forest, authors found the most accurate and fast algorithm to process the large volume of agricultural data. The data was visualized to find the top 5 crops produced in India and then the top 3 of them were visualized area-wise, state-wise, and season-wise to give a detailed analysis of crop distribution and production in India. © 2023 IEEE.

关键词： Crops

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Real-Time Helmet Detection System with Vehicle Number Extraction for Two-Wheeler Vehicles using YOLOv8 2

Real-Time Helmet Detection System with Vehicle Number Extrac...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Kumar, Abhash Guru Prasad, M.S. Soni, Dheeraj Rani, Vibha Arora, Kanika Ganesh, D.R. Dept. of Computer Science & Engineering Dehradun India REVA University School of Computing and Information Technology Bengaluru India

ISBN: (纸本)9798350371314

In the face of increasing two-wheeler usage and the persistent issue of riders neglecting helmet compliance, this paper proposes a groundbreaking Real-Time Helmet Detection System with Vehicle Number Extraction. The existing literature on helmet detection systems is reviewed, revealing a range of methodologies, accuracies, and limitations in current approaches. Building upon this foundation, the three-stage detector combines the efficiency of YOLO (You Only Look Once) for object detection with a specialized number plate extraction system. The system aims to provide real-time detection of two-wheeler riders, classification of helmet usage, and extraction of vehicle numbers. The proposed methodology, utilizing a pre-trained YOLO model for object detection and EasyOCR library, demonstrates an accuracy rate of 64% for vehicle detection, 78% for helmet detection, and 92% for number plate extraction. The practical implementation of this system showcases its potential for enhancing road safety, automating traffic management, and promoting regulatory enforcement. While showing potential, the ongoing research and development face challenges such as limitation of computational resources and privacy considerations. The Real-Time Helmet Detection System stands as a promising solution for improving road safety, with the potential for broader use in traffic management and safety enforcement. Continuous improvement and adaptation will guarantee its effectiveness in contributing to a safer and more controlled traffic environment. © 2024 IEEE.

关键词： Accident prevention

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Fault Detection in Solar Panel Images using a novel feed-forward based CNN model 15

Fault Detection in Solar Panel Images using a novel feed-for...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Karuppasamy, ArunaDevi Venugopal, Dhanalakshmi Balaji, P.S. Selvakumar, Karthik Ramachandran, Brintha Eliyan, Greeshma Cherian, Anju Mathew Pillai, Indu Govinda Gulf College Dept. of Computing Sciences Muscat Oman Sultan Qaboos University Dept. of Electrical and Computer Engineering Muscat Oman National Institute of Technology Dept. of Mechanical Engineering Rourkela India Pondicherry University Dept of Green Energy Technology Pondicherry India Saradha Gangadharan College Dept of Computer Applications Pondicherry India

ISBN: (纸本)9798350370249

The increasing dependence on renewable energy highlights the critical need for efficient and reliable maintenance strategies for solar panel installations. A key aspect of maintenance is early detection of faults in individual solar panels, which results in reduced energy production and increased maintenance costs. In this paper, we proposed a novel approach for fault detection in solar panel images. The proposed novel Feed-forward based CNN architecture utilizes the ability to automatically extract features from images to accurately identify faults in solar panels. The proposed CNN model trained on a small, annotated dataset of solar panel images, which includes clean and faulty panels. The Experimental results show the superiority of Feed-forward based CNN model in fault-detection compared to traditional CNN methods, achieving higher precision and recall rate in a wide range of fault types. The proposed approach not only significantly reduces the time and resources required for manual inspection, but also enhances the effectiveness of identifying faults within the solar energy systems. In conclusion, this paper introduces a novel Feed-forward based CNN for fault detection in solar panel images, which provides a solution to enhance the efficiency of solar power installation. © 2024 IEEE.

关键词： Convolutional Neural Network Fault Detection Filter Learning Logistic Regression Solar-Panel Images

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GANs for Synthetic Environmental Data Generation (Air Quality) for Low-Resource Regions 2

GANs for Synthetic Environmental Data Generation (Air Qualit...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Verma, Seema Jain, Neha Mahapatra, Madhumita Setia, Sonia Sachdeva, Vrinda Kumar, Manoj Dept. of Computer Science & Engineering Delhi Technical campus Uttar Pradesh Greater Noida India Dept. of Computer Science & Engineering Sharda University Uttar Pradesh Greater Noida India Dept. of Computer Science & Engineering G.L Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331541217

Due to the significant expenses and logistical difficulties involved in deploying monitoring equipment, many areas, especially those in developing nations, lack adequate environmental data. This work presents a novel approach that uses Generative Adversarial Networks (GANs) for creating artificial environmental data. The model of GAN is trained for generating synthetic data for under-monitored or unmonitored areas by utilizing high-resolution environmental data from well-monitored regions. The paper discusses the generation of synthetic data and its comparison with real data based on statistical properties. The analysis reveals that the characteristics of the created data closely align with the actual data. With the help of this synthetic data, researchers and policymakers will be able to make more informed decisions by having precise estimations of important environmental indicators like air quality. © 2024 IEEE.

关键词： Generative adversarial networks

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