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2023 International Symposium on Electromagnetic Compatibility - EMC Europe, EMC Europe 2023

作者： Svistunou, Aliaksandr Mordachev, Vladimir Sinkevich, Eugene Ye, Ming Dubovik, Arthur Belarusian State University of Informatics and Radioelectronics EMC RandD Laboratory Minsk Belarus Huawei Technologies Sweden AB RandD Engineering Lab Stockholm Sweden

ISBN: (纸本)9798350324006

The analysis of EMC between equipment of wireless systems (4G/5G user equipment of cellular communications as well as access points of radio local area network) and medical narrowband Internet-of-things (NB IoT) devices (utilizing LTE carrier in 452.5-457.5 MHz range for uplink and 462.5-467.5 MHz range for downlink) operating inside a hospital building is made. Computer simulation with the use of multipath radiowave propagation model and a 3D model of hospital premises is performed. The integrated interference margin involved as a criterion of EMC is calculated as a result of the analysis. It was concluded that the equipment of considered wireless systems can interfere with medical NB IoT devices (as well as NB IoT devices can interfere with receivers of these wireless systems) if emitters and receptors are located within the same room or in contiguous rooms. In order to reduce the levels of electromagnetic interference, recommendations are given. © 2023 IEEE.

关键词： 3D modeling

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Predicting Fish Diseases Using Machine Learning Based on Water Quality Parameters 15

Predicting Fish Diseases Using Machine Learning Based on Wat...

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

作者： Rajkumar, R. Rahul, S.G. Balpunuri, Saketh Reddy Nallagatla, Vishnu Sai Akula, Lokesh Nandhini Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350370249

In aquaculture, detecting fish diseases early is crucial for farmers to prevent outbreaks and financial losses. These diseases, often caused by viruses and bacteria, disrupt water quality parameters, leading to fish mortality. Inspired by recent successes in machine learning, this study utilizes cutting-edge algorithms to swiftly and accurately detect and predict water quality degradation. This research proposes the use of machine learning techniques to enable early identification of diseases in fish populations by predicting deteriorating water quality conditions. The aim is to provide stakeholders with a system that allows them to take preventive actions before disease outbreaks occur. The study evaluated the performance of several machine learning models, including Logistic Regression, Support Vector Machines (SVM), Multilayer Perceptrons (MLP), and Random Forest classifiers, in predicting disease occurrences. Among these techniques, the Random Forest algorithm demonstrated the highest predictive accuracy, achieving 98.97% correct classification of diseased and non-diseased cases. This integration of machine learning enhances aquaculture management, safeguarding against economic losses and fostering sector resilience. A web application utilizing machine learning predicts fish disease outbreaks based on real-Time water quality data, providing early warnings to aquaculture farmers. It offers proactive measures to mitigate risks and promotes sustainable aquaculture practices. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Impacts of Various Masks on Human Health using IoT Device 5

Analysis of Impacts of Various Masks on Human Health using I...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Vijayalakshmi, S. Paramasivam, A. Khanal, Riddhi Kamar, Ridakordor Lyngdoh, Lawanbha Banu, N.M. Masoodhu Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Biomedical Engineering Chennai India

ISBN: (纸本)9781665474672

In general, the protective masks are used to lower the chances of the virus entering our respiratory system. Nowadays, wearing face masks is highly recommended in crowded places. People have been wearing different types of masks such as N95, surgical masks, cloth masks etc. to protect themselves from airborne foreign bodies. In this work, an Internet of Things (IoT) based human vital sign monitoring system is designed and developed. Further, the impacts of various masks such as cloth masks, surgical masks and N95 masks on human health are analyzed. Results demonstrate that the wearing of cloth mask impacts more on human SpO2 levels when compared to the other masks namely surgical masks and N95 masks. Also, it is seen that the wearing of various masks doesn't affect the human heart beat rate. © 2023 IEEE.

关键词： Internet of things

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Fenugreek Spinach Growth through Controlled Hydroponics and Real-Time Monitoring 2

Fenugreek Spinach Growth through Controlled Hydroponics and ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Rahul, S.G. Rajkumar, R. Gadde, Mukesh Narayana Rupa Sree Reddy, B. Afrin Sultana, S.K. Harshavardhan Reddy, S. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350327533

This study showcases the innovation in hydroponic cultivation, specifically focusing on Fenugreek Spinach, a valuable leafy vegetable. The unique aspect of this research lies in the integration of a hydroponic system with real-time monitoring through ThingSpeak, enabling precise control over essential growth parameters like pH levels, temperature, water level, and moisture content. Maintaining the pH within the ideal range of 6.0 to 7.0 was crucial for optimal nutrient availability, fostering robust plant growth. A controlled temperature range of 26°C to 31°C provided an environment conducive to healthy plant development. Effective management of water level and moisture, facilitated by ThingSpeak, played a pivotal role in averting waterlogging and maintaining appropriate moisture levels in the growth medium. The successful cultivation of Fenugreek Spinach in this controlled hydroponic environment underscores the potential for resource-efficient utilization, reduced water consumption, and consistent year-round cultivation, independent of external climate conditions. This study establishes a foundation for future research and advancements in hydroponic methodologies across various crops, promoting sustainable agricultural practices and addressing constraints linked to traditional soil-based farming. Through technological integration and growth condition optimization, hydroponics emerges as a promising solution for sustainable food production, significantly bolstering global food security. © 2024 IEEE.

关键词： Arduino (ATMega328P) Hydroponics Sensors Think and speak

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Design of Multiple-Output Flyback Converter with Independently Controlled Outputs for TV Power Supply 18

Design of Multiple-Output Flyback Converter with Independent...

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18th Conference on Electrical Machines, Drives and Power Systems, ELMA 2023

作者： Duzgun, Ramazan Parlak, Mehmet Yilmazlar, Ismail Electrical and Electronics Engineering Ozyegin University Istanbul Turkey Vestel Electronics RandD Department Manisa Turkey

ISBN: (纸本)9798350311273

This paper presents the design of a multiple-output flyback converter (MOFC) for a television power supply system. The proposed converter is capable of independently controlling the output voltage of multiple power rails, allowing for efficient and precise power delivery to various subsystems within the TV. The backlight LEDs' current and system supply voltage is controlled independently and the backlight LEDs are fed from the same flyback transformer directly, so there is no need for a buck or boost converter stage. The system voltage of 12V is regulated by the extra N-MOSFET. By eliminating the need for an additional converter stage and adopting smaller transformer sizes, this design not only reduces card sizes and cost but also leads to significant improvements in peak efficiency and power density. The proposed MOFC is explained in details together with the simulation and measurement results. © 2023 IEEE.

关键词： Light emitting diodes

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Analyzing the downforce generated by rear spoiler of hatchback vehicle and creating an aeromapp using CFD analysis

Analyzing the downforce generated by rear spoiler of hatchba...

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4th International Conference on Smart and Sustainable Developments in Materials, Manufacturing and Energy engineering, SME 2022

作者： Amala Justus Selvam, M. Rajesh Kumar, M. Padmanabhan, S. Hitesh Sri Subramanyam, K. Navneeth Reddy, M. Department of Automobile Engineering Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Chennai India

Aeromapping is a chart which shows the changes in a performance metric such as Coefficient of Lift, Coefficient of Drag and balance as a function of the car attitudes. A generic four-wheeler vehicle body was designed for commercial purposes. In this study, the working principle was based on Bernoulli's principle of Fluid Flow. Laminar flow was tested on the model of the vehicle under static conditions. From the New Car Assessment Program (NCAP) standards, boundary conditions were fixed, so that the experiment can be as realistic as possible. Using ANSYS Fluent multiple simulations were done varying the experimental parameters, such as Fluid velocity, Yaw Angle of the Vehicle and varying the Fluid Pressure. All of these experiments were studied using the flow visualization, pressure graphs, graphs between independent variables etc. Successful results were achieved and the aero map was created and a significant downforce of 33% was achieved. Copyright © 2023 Elsevier Ltd. All rights reserved.

关键词： Drag

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A recommendation system for energy saving and user engagement in existing buildings

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2022年第1期176卷 1-11页

作者： Pruvost, Hervé Calleja-Rodríguez, Gloria Enge-Rosenblatt, Olaf Jiménez-Redondo, Noemi Peralta-Escalante, Juan Jacobo Division Engineering of Adaptive Systems Fraunhofer Institute for Integrated Circuits Dresden Germany RandD Division Cemosa Málaga Spain

Ranked as the major energy-consuming sector, the building industry continuously introduces enhanced energy-management systems towards more energy efficiency. Despite technological progress, high amounts of resources are still wasted worldwide due to the wrong usage of energy systems in existing building stock. As a response, this paper proposes a recommendation system that is based on information and communication technologies and that provides building users with hands-on executable recommendations to reduce their energy consumption. Indeed, because of their lack of energy awareness and knowledge, people may need guidance in their daily usage of a building to change their energy behaviour and to save energy. For that purpose, the recommendation system analyses building operation data in real time to identify energy wastes as well as suitable energy conservation measures that shall help in saving energy. One main challenge is to provide a highly scalable solution that can be easily applied in a wider range of buildings, thus enabling resource saving at a large scale or even worldwide. For that purpose, the system relies on a model-based expert system for easy adaptation in any building regardless of its type, size and usage. © 2023 Published with permission by the ICE under the CC-BY 4.0 license. (.

关键词： Buildings

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Machine Learning Algorithm for Predicting Soil Classification using Smart Agriculture 3

Machine Learning Algorithm for Predicting Soil Classificatio...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Ponkumar, D. David Neels Kumar, Kolipaka Pushpa Krupa Himesh Ramesh, S. Kumar, J. Arun Nallasivan, G. Jayachandra, A. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chengalpattu India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396157

The advent of Internet of Things (IoT) technology and data analytics has brought about a significant transformation in contemporary agricultural practices, sometimes referred to as smart agriculture. One of the fundamental uses of this technology is the prediction of soil classification, a critical factor in the optimization of crop management strategies and the allocation of resources. The present work investigates the use of machine learning techniques to achieve precise and efficient soil categorization in the context of smart agriculture. The study utilizes a comprehensive dataset consisting of several soil characteristics, including pH levels, moisture content, texture, and nutrient composition. These attributes were obtained via the use of Internet of Things (IoT) sensors and Unmanned Aerial Vehicles (UAVs). Several machine learning techniques, such as Random Forest, Support Vector Machine, and Neural Networks, are assessed in terms of the classification accuracy in identifying soil types using the given characteristics. The findings indicate that machine learning models has the capability to accurately forecast soil categorization, hence enabling the use of precision agricultural techniques. These models assist farmers in making informed choices based on data analysis pertaining to crop selection, irrigation, and fertilization, resulting in enhanced agricultural productivity and the adoption of sustainable resource management practices. Additionally, this research investigates the interpretability of the chosen machine learning models, with the aim of ensuring that farmers are able to grasp and place faith in the forecasts. The proper implementation of smart agricultural systems also involves addressing ethical problems related to data privacy and security. This study makes a valuable contribution to the progress of sustainable agriculture via the use of machine learning and Internet of Things (IoT) technology. By leveraging these technologies, the research a

关键词： Soils

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An Adaptive Modelling of Neuro-Fuzzy and Craziness PSO Algorithm for DC Fault Protection System

An Adaptive Modelling of Neuro-Fuzzy and Craziness PSO Algor...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Chandrashekhar, M. Dhal, P.K. Vel Tech Rangarajan Dr.Sagunthala RandD Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9781665474511

This study makes use of PSO and CRPSO-based two unique optimization methodologies. The process of well portions of the DC system must not be suggestively disrupted by a DC fault on a single division. Around DC circuit breakers (DCCBs), like power-driven types, have low costs and power losses, but because of their continued opening times, they have been careful unsuitable for DC fault separation in the system. The combination of switching processes and changing capacitor voltages of the sub-modules in a modular multilevel converter (MMC) source second and other even-order harmonics in the circulating currents. Additional power loss increased current strain on power components, and even changeableness during transients will all result from these harmonic currents. Various case studies that involve both DC and AC problems are established. The strategic control approach is validated by simulation results on a three-phase grid-connected MMC high voltage DC system, which used sophisticated algorithms (ANFIS+CRPSO) to compensate for physical faults at the grid side. © 2023 IEEE.

关键词： Electric circuit breakers

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Optimization Algorithms in Diabetes Prediction: A Comparative Work 3

Optimization Algorithms in Diabetes Prediction: A Comparativ...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Kannan, E. Carmel Mary Belinda, M.J. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Accurately predicting chronic conditions such as diabetes is essential for prompt treatment and improved patient outcomes. Using the UCI diabetes dataset, this work assesses four optimization algorithms: Adam, RMSprop, Nadam, and Stochastic Gradient Descent (SGD). The objective is to evaluate their efficacy in creating precise prediction models by empirical means. These algorithms have a significant impact on the stability and speed of convergence of deep learning models during training. With patient data, machine learning and deep learning models can be used to assess the risk of diabetes. Diabetes is a complex metabolic disease that requires early detection to prevent complications;however, the choice of optimizer has a significant impact on the models' efficacy. As the optimizer with the highest accuracy, precision, and F1 Score, Nadam stands out. SGD does exceptionally well in recall, while Adam and RMSprop do well as well. The choice of optimizer should take trade-offs between measures into account and be in line with specific therapeutic goals. They can be more effectively tuned as hyperparameters in diabetes prediction models. This work improves patient outcomes in the management of diabetes and healthcare risk assessment. © 2024 IEEE.

关键词： Forecasting

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