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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bajpai, Chhavi Gaur, Manish Saravanan, B. Raja, G.V. Malikl, Neeru Sucharitha, M. Centre for Advanced Studies Department of Computer Science & Engineering Uttar Pradesh Lucknow India Institute of Engineering and Technology Department of Computer Science & Engineering Uttar Pradesh Lucknow India Sri Eshwar College of Engineering Department of Cse Kondampatti Kinathukadavu Tamil Nadu Coimbatore India Sri Sairam College of Engineering Department of Ece Karnataka Bangalore India Pimpri Chinchwad University Department of Engineering and Technology Maharashtra India Andhra Pradesh Amaravati India

ISBN: (纸本)9798350377972

Plant diseases often lead to significant agricultural losses, causing economic difficulties and food shortages. Conventional detection approaches are inefficient, challenging, and must have expert engagement, resulting in decreasing scalability. Recent developments, including machine learning and IoT-integrated systems, have enhanced real-time monitoring;However, they encounter obstacles such as restricted generalization to varied environmental circumstances, data imbalance, and insufficient adaptability to uncharacteristic diseases. This research tackles these issues by introducing an IoT-Enhanced Convolutional Neural Network (CNN) integrated with a One-Class Support Vector Machine (SVM). The model utilizes IoT for real-time data collection, CNN for effective feature extraction, and One-Class SVM for anomaly identification. Experimental results indicate the system's exceptional performance, attaining 95.6% accuracy, a 3.4% lower rate of false positives, and 97.2% precision, offering a scalable, automated solution for early disease detection, minimizing crop losses, and enhancing agricultural sustainability. © 2024 IEEE.

关键词： Convolutional neural networks

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Parametric Investigation of Injection Moulding for LDPE using Capuchin Search Algorithm and Honey Badger Algorithm 2nd

Parametric Investigation of Injection Moulding for LDPE usin...

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2nd International Conference on Functional Material, Manufacturing and Performances, ICFMMP 2021

作者： Jeet, Siddharth Barua, Abhishek Bagal, Dilip Kumar Pradhan, Swastik Panda, Surya Narayan Mahapatra, Siba Sankar Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Odisha Rourkela India Department of Mechanical Engineering Government College of Engineering Odisha Kalahandi Bhawanipatna India School of Mechanical Engineering Lovely Professional University Punjab Phagwara India Department of Production Engineering Birsa Institute of Technology Jharkhand Sindri Dhanbad India Department of Mechanical Engineering National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9789811941467

In our rapidly expanding world, there is a rising demand for low-cost, long-lasting, and less dangerous medicinal products. Medical items ranging from intravenous fluid containers to medical syringes are created to satisfy their demands using a range of thermoplastics and Plastic Injection Moulding (PIM). However, even advanced profile mouldings can suffer from dimensional inaccuracies. The current study advances our understanding of thermoplastics, specifically Low-Density Polyethylene (LDPE) material moulding for medical syringe plungers using injection moulding equipment. Eight injection moulding input factors were investigated in order to decrease the depth sink marks and weight created during the injection moulding of thermoplastic LDPE material. The 27 trials were piloted in accord with Taguchi's Design of Experiment, and the variables were optimised using the newly developed Honey Badger and Capuchin Search Algorithms, as well as analysis of variance, for determining the most dominating parameter. The cooling time and melt temperature of the plastic injection moulded part are the most significant factors influencing the sink-mark depth and weight of the part, respectively, according to the Analysis of Variance (ANOVA) test. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Injection molding

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Design and Analysis of Gudgeon Pin to Minimize Stress Concentration by Selecting Different Material Through FEA 5th

Design and Analysis of Gudgeon Pin to Minimize Stress Concen...

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5th International Conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Kumaraswamy, J. Siva Ramakrishna, Ch. Ramasubramanian, S. Mishra, Rahul Sakhare, D.T. Karthik, K. Baskar, S. Pandey, Krishna Kant Department of Mechanical Engineering R. L. Jalappa Institute of Technology Karnataka Doddaballapur561203 India Department of Mechanical Engineering Vignan’s Institute of Information Technology Andhra Pradesh Visakhapatnam530049 India Department of Automobile Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai600117 India Department of Mechanical Engineering IES Institute of Technology & Management IES University Madhya Pradesh Bhopal462044 India Department of Chemistry Shivaji Art’s Commerce & Science College Kannad Maharashtra Aurangabad431103 India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Mechatronics Engineering Manipal University Jaipur Rajasthan Jaipur303007 India

ISBN: (纸本)9789819767311

The gudgeon pin is one of the engine's most stressed components. Gudgeon pins are used in automotive engines to link the piston and connecting rod. Stress concentration is a critical concern in the design of mechanical components, as it can lead to premature fatigue failure and, consequently, catastrophic consequences. To mitigate this risk, engineers continually seek ways to optimize the design of components like the gudgeon pin, and one avenue of exploration is the judicious choice of materials. This study looks at the detailed design and analysis of existing gudgeon pins in order to detect the pin's stress concentration and then minimize it by making necessary changes to the pin's design and mounting. The current work involved constructing a gudgeon pin and then using finite element analysis (FEA) software to examine and minimize stresses on the pin. This comprehensive study delves into the intricate world of gudgeon pin design and analysis, with a specific emphasis on minimizing stress concentration by selecting different materials through FEA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stress concentration

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Hybrid Power Generation and Storage System for the Application of EV Using IoT and Big Data

Hybrid Power Generation and Storage System for the Applicati...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sahana S. K. Sasikala Department of Electrical and Electronics Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS)

In recent years, the installation of renewable energy facilities has grown rapidly due to the movement to reduce greenhouse gas emissions and preserve fossil fuels. Hence, the use of Electric vehicles (EV) are becoming more prevalent, making it necessary to improve and expand charging infrastructure. Hybrid renewable energy source (HRES) is needed for EV charging stations because the stored energy in an EV battery is insufficient for long distance travel. In this work, the concept of EV charging stations that are capable of being integrated with renewable energy sources (RES) and energy storage devices is proposed. Since the output from fuel cell and Photovoltaic (PV) system are intermittent due to environmental conditions, DC-DC converter is required to enhance voltage. Hence, Boost and improved Cuk converter is employed to improve voltage attained from fuel cell and PV system respectively. A single phase voltage source inverter (1 VSI) assists in the conversion of the DC link voltage to AC voltage, which is subsequently delivered to the EV battery for further usage. The AC power is then supplied to the grid. Internet of Things (IoT) is employed for remote monitoring and control purposes by sending vital data to cloud, in which the parameters such as DC link voltage and State of Charge (SOC) of battery is monitored. The MATLAB Simulink software is used to run all of the components and produce the appropriate results. It is clear from these results that the new approach is 97% more efficient than the traditional approaches.

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Investigation on the effects of electrical discharge machining on mild steel surface texture and its corrosion behaviour in NaCl solutions

Investigation on the effects of electrical discharge machini...

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作者： Kumar Tiwari, Alok Kumar Jha, Vinit Kumar Singh, Rabesh Ji, Gopal Centre for Advanced Studies U.P Lucknow-226031 India Department of Mechanical Engineering R.R. Institute of Technology Bakshi Ka Talab U.P Lucknow226201 India

Electrical Discharge machining (EDM) is an emerging solution for the precise machining of the commercial metals and alloys. In EDM, machining is performed by the heat input with the successive electrical spark between the electrode and work piece. Hence surface texture of the materials changes after the machining process due to this too much heat input. Thus, surface texture depends on the variation of heat input during the process. In present work, galvanized mild steel (GMS) is machined by EDM at different current values (5 A, 10 A, 15 A and 20 A) for studying the effect of heat input on the surface texture. In addition, corrosion behaviors of GMS samples machined by EDM at different currents have been studied in 0.5 M NaCl by open circuit potentials (OCP), polarization curves and electrochemical Impedance Spectroscopy (EIS). Scanning Electron Microscopy (SEM) is used to monitor the effects of corrosion and machining surface texture of GMS. The SEM, surface roughness measurements and corrosion tests confirm that the surface is becoming rougher and corrosion behavior is worsening with the increase in current. The roughness of the surface can be correlated to thickness of recast layer (measured in SEM) that is increasing with the increase in current (heat input). © 2023

关键词： Scanning electron microscopy

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AI-Enabled Predictive Maintenance for Industrial Equipment in the Era of IoT 9

AI-Enabled Predictive Maintenance for Industrial Equipment i...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Subbiah, Muthu Raman Devi, Kabirdoss Dhanamalar, M. Shareef, Ashraf Mohammed Ai-Shaikhli, Taha Raad Nidamanuri, Sudheer University of Technology And Applied Sciences Mie Section Department of Engineering Muscat Oman School of Management Studies Vels Institute-of Science Technology And Advanced Studies Chennai Tamil Nadu Pallavaram600117 India Kristu Jayanti College Department of Computer Science Karnataka Bengaluru India College of Pharmacy National University of Science And Technology DhiQar Iraq AL-Nisour University College Department of Medical Laboratories Technology Baghdad Iraq Madanapalle Institute of Technology And Science Computer Science And Technology Andhra Pradesh Madanapalle517325 India

ISBN: (纸本)9798350365092

Modern digital technology breakthroughs include machine learning. It's used in medical, image processing, manufacturing, aviation, autonomics, and more. Our main goal is to find an automated system that can accurately rate production machines at any given moment. The critical energy meter characteristics needed to create power reports in automation system to solve production difficulties at defined intervals were also not captured. We apply machine learning to estimate manufacturing machine accuracy in this study. We employed supervised machine learning of binary decision tree using CART method and power report, obtained data using RS232 to RS485 convertor through Modbus communication protocol, to overcome these challenges. This study presents issue formulation, energy meter data analysis and retrieval, and ML approaches used to estimate production machine accuracy. After retrieving the parameters, we provide power reports for each machine and a graphical warning of deteriorating performance. © 2024 IEEE.

关键词： Internet of things

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Biosynthesis, therapeutic characteristics, origin and strategies to improve the yield of vasicine in plants

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Vegetos 2025年 1-15页

作者： Padmanabhan, Deepthi Manimekalai, Ramaswamy Senthil-Nathan, Sengottayan Suganthi, Muthusamy Palanisamy, Senthilkumar Department of Genetic Engineering School of Bioengineering SRM Institute of Science and Technology Kattankulathur 603203 India Molecular Biology and Bioinformatics Sugarcane Breeding Institute Tamil Nadu Coimbatore 641 007 India Division of Biopesticides and Environmental Toxicology Sri Paramakalyani Centre for Excellence in Environmental Sciences Manonmaniam Sundaranar University Tamil Nadu Alwarkurichi India Department of Biotechnology Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai 600 117 India

From the year 2016 to 2019, a total of 196 botanical drugs were registered for clinical trials, out of which 81 drugs were in phase II or III laid an environment for further trials and marketing of the drugs. Vasicine, a pyrroquinazoline alkaloidal compound found in some medicinal plants such as Justicia adhatoda and Sida cordifolia to name a few. Due to its broad range of health-promoting properties, the compound has piqued the attention of nutritionists and medicinal chemists. The compound’s recent pharmacological applications include antiviral activity against SARS-Co-V2, antimicrobial activity against E. aerogenes, S. epidermidis and P. aeruginosa, cytotoxic effect against lung adenocarcinoma and free radical scavenging activity, implying a potential role as an antioxidant. Vasicine was also discovered to have a possible anti-inflammatory property in carrageenan-induced inflammation, as well as neuroprotective efficacy in in vitro experiments on neuroblastoma cells by blocking cholinesterases in Alzheimer’s disease and improving memory and cognition. The integration of a multi-omics approach to elucidate the biosynthetic pathway and key regulatory enzymes laid the basis for metabolically engineering the entire pathway in microbial systems, as well as polyculture of microbes for robust biosynthesis strategies to overcome the limitations of traditional monoculture techniques for bioactive production. Genome editing with CRISPR Cas technologies plays a major role in the transformation of vasicine metabolite engineering to alter the process of biosynthesis and increase large-scale production in plants. © The Author(s) under exclusive licence to Society for Plant Research 2025.

关键词： Medicinal plants Natural products Novel drugs Secondary metabolites

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Design and Implementation of a Damping Controller in Microgrids

Design and Implementation of a Damping Controller in Microgr...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Harshini Panda Pradeep Kumar Sivakumar Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

The power generation and distribution sectors observe high-level penetration of Renewable Energy Sources (RESs) and Distributed Generators (DGs) due to several factors such as perpetual energy demand, nature of sources and load. Nevertheless, more specifically, the direct current (DC) micro grid is getting more attention over the conventional alternating current (AC) Micro rid for several benefits; for example, higher efficiency and reliability. This research work focus on three aspects the first one to design phase shift full bridge DC-DC converter with and discuss with various optimization techniques for implementing the Micro Grid. The second aspects to validate to implemented the Enthalpy Sigmoid Neuro-Fuzzy crowbar control using DFIG for power oscillation damping. The third aspects to analysis and implementation of a damping controller with renewable energy sources. The advantages and disadvantages of different protection and planning approaches are explained in detail. The effectiveness for the proposed strategy is proved.

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Drilling Simulation of AA7075 T351 Graded Aluminium Alloy Using Deform-3D 2nd

Drilling Simulation of AA7075 T351 Graded Aluminium Alloy Us...

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2nd International Conference on Functional Material, Manufacturing and Performances, ICFMMP 2021

作者： Bagal, Dilip Kumar Jeet, Siddharth Barua, Abhishek Pradhan, Swastik Rath, Arati Panda, Surya Narayan Department of Mechanical Engineering Government College of Engineering Odisha Kalahandi Bhawanipatna India Department of Mechanical Engineering Centre for Advanced Post Graduate Studies BPUT Odisha Rourkela India School of Mechanical Engineering Lovely Professional University Punjab Phagwara India School of Computer Engineering Kalinga Institute of Industrial Technology Odisha Bhubaneswar India Department of Production Engineering Birsa Institute of Technology Jharkhand Sindri Dhanbad India

ISBN: (纸本)9789811941467

The article develops a three-dimensional drilling model using DEFORM-3D software based on the Usui Tool Wear model, the Johnson and Cook model and the Lagrangian approach. Thermal, structural and chip morphology analyses were performed in this section. The effect of output responses is examined through the lens of their simulation results. The workpiece is made of AA7570 T351 Aluminium alloy with machining type and the drill bit is made of tungsten carbide (WC) with a diameter of 10 mm. Due to its high strength and resistance to corrosion, this alloy is widely used in the aerospace and biomedical equipment manufacturing industries. Thermal analysis reveals that the tool's maximum temperature is 254.8 °C, while the highest temperature is 273.1 °C in the case of AA7075 T351 graded alloy workpiece. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Corrosion resistance

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Analysis Of Raw 3D Images Of Stages Of Alzheimer’s Disease Using Deep Learning

Analysis Of Raw 3D Images Of Stages Of Alzheimer’s Disease ...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Tr Thamizhvani Rj Hemalatha V Rachel Cynthia S Swetha Department of Biomedical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Alzheimer’s disease is defined as a brain ailment that progressively impairs thinking, cognitive skills, and the capacity to complete most basic tasks. Memory loss, cognitive changes, and other neuronal brain disorders are all symptoms of AD, a degenerative condition. Since risk awareness encourages patients to take preventative measures even before the onset of irreversible brain damage, an absolute diagnosis of Alzheimer’s disease is vital. Total brain atrophy and hippocampal atrophy are considered to be the main diagnostic tests for the condition. For this condition, early identification is important, and automatic system design is required. Computer-assisted methods are implemented for the analysis of AD in several types of research and the outcomes are constrained due to the congenital findings. Early stages of AD can be diagnosed but not predicted because prediction is only useful before the disease manifests itself. Deep learning (DI) techniques are used to analyze the raw MRI 3D images to identify AD and its progressive stages. The efficiency of the deep learning networks is defined to be less, which is indefinite for diagnosing AD stages. Therefore, processing of the images is necessary for the detection and to predict the progression of AD.

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