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Innovation in technology (ASIANCON), Asian Conference on

作者： Madona.B. Sahaai M. Akash Tharasvi M. Jeevan Raj Department of Electronic and Communication Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Mobile ad hoc networks, often known as MANETs, are network modes that operate without being dependant on any type of network structure and central access. The quick swift mode of networking makes MANET’s capable of executing all of the complex tasks. Nevertheless, open channels and changing topology present inherent security risks. We demonstrated explicitly that the AAS is resilient against sophisticated attacks, route spoofing using the BAN sense, finicky forwarding, bogus routing, and violent bumps mixing in MANET. If there are any particularly sharp bumps in the network, the AAS might raise the packet’s sending speed around to 40, on a regular basis. There are numerous active routing protocols that it works *** nasty nodes strike in simulated trials ,AAS resilience is demonstrated by its ability to sustain 79.2 percent of the network's performance. Nearly 1.6 times as many connections occur on average on the network as with the Cap-OLSR protocol, which prevents conspiracy attacks.

关键词： Technological innovation Prevention and mitigation Systems architecture Routing Ad hoc networks Routing protocols Topology Security Mobile computing Resilience

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AI-Based Temperature Automation and Location Tracking in Ship Container Monitoring System (TALT)

AI-Based Temperature Automation and Location Tracking in Shi...

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Advancement in technology (ICONAT), International Conference for

作者： V Hari Haran R Maiyappan P Vijayalakshmi Department of Electronic and Communication Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

In a world increasingly reliant on technology, the need for innovative solutions in various aspects of daily life is paramount. This project presents an advanced system that integrates artificial intelligence (AI) and temperature control technology to address the critical domain of food storage. To achieve food identification, a deep learning model, specifically a Convolutional Neural Network (CNN), is trained on a diverse dataset of food images. When a new item is introduced into the container, a Pi camera captures an image, and the AI model instantly identifies the food type. Simultaneously, the system employs a Proportional Integral Derivative (PID) controller to regulate the temperature using a Peltier device. Temperature set points for each food category are predefined or learned over time, ensuring precise temperature control. Incorporating GPS location tracking further enhances the system’s utility. By integrating a GPS module, the container’s location can be accurately determined and monitored in real time. In previous work, it has a monitoring system using IOT-based technology.

关键词： Temperature sensors Temperature measurement Software algorithms Containers Software Temperature control Convolutional neural networks Artificial intelligence Monitoring Global Positioning System

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Machinability investigation and optimizing process parameters in ECM of stainless steel −12X18H10T for minimizing surface roughness

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Materials Today: Proceedings 2023年 81卷 443-448页

作者： A Jerin K Karunakaran Research Scholar Department of Mechanical Engineering School of Engineering Vels Institute of Science Technology and Advanced Studies (Deemed to be University) Vels University Chennai TN India Research Supervisor & Associate Professor Department of Mechanical Engineering School of Engineering Vels Institute of Science Technology and Advanced Studies (Deemed to be University) Vels University Chennai TN India

The tool and die applications are preferred the unconventional machining processes as they are popular in machining the difficult to cut type complex contour/ profile. The complex profile machined on hard material. Some special materials like Stainless Steel 12X18H10T withstand its inherent properties even it exposes at higher temperature. This investigation aims to expand its application by investigating its machinability in Electro Chemical machining process for surface finish. The process parameters like gap between work material and tool, discharge current, concentration of electrolyte and applied voltage at three levels. The Taguchi approach is used for optimizing the process parameters and confirmation runs also conducted for developing the mathematical modelling. For optimizing number of experiment the Taguchi L9 is utilized for this investigation.

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Numerical analysis of hemodynamic parameters in stenosed arteries under pulsatile flow conditions

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Medicine in Novel technology and Devices 2023年第4期20卷 32-50页

作者： Priyambada Praharaj Chandrakant Sonawane Anand Pandey Vikas Kumar Arundhati Warke Hitesh Panchal R.Ibrahim Chander Prakash Jayawant Shikshan Prasarak Mandal(JSPM)’s Rajarshi Shahu College of Engineering PuneIndia Symbiosis Institute of Technology Symbiosis International Deemed UniversityPuneIndia Centre for Development of Advanced Computing(CDAC) PuneIndia Department of Mechanical Engineering Government Engineering College patanGujaratIndia Department of medical instruments engineering techniques Al-farahidi UniversityBaghdadIraq Research&Developemnt SVKM’S Narsee Monjee Institute of Management StudiesMumbaiIndia Mukesh Patel School of Technology Management and Engineering SVKM’S Narsee Monjee Institute of Management StudiesMumbaiIndia

This research studies the changes in flow patterns and hemodynamic parameters of diverse shapes and sizes of *** different shapes and sizes of stenosis are constructed to investigate the variations in hemodynamics as the morphology *** in shape(trapezoidal and bell-shaped)and sizes of stenosis change the stresses on the walls and their flow *** and OSI results specify that trapezoidal stenosis exerts greater stress than bell-shaped ***,as the length of the trapezoidal stenosis increases,the TAWSS increases,whereas the trend is the opposite for bell-shaped ***,this paper also studies different degrees of stenosis extracted from real *** in velocity flow patterns,wall shear stress(WSS),Time-averaged wall shear stress(TAWSS)and Oscillatory shear index(OSI)have been studied for these *** illustrate that the peak velocity rises drastically as the stenosis percentage *** velocity is seen close to the artery's walls,indicating flow *** flow separation region is seen throughout the cycle except in the accelerating flow *** increase in stenosis also increases WSS and TAWSS *** WSS is seen downstream of stenosis,indicating flow *** negative WSS in the blood vessels also promotes endothelial *** values greater than 0.2 are seen near the stenosis region,indicating atherosclerosis *** of high OSI and low TAWSS are also identified,indicating probable regions of plaque development.

关键词： Cardiovascular Stenosis morphology Real geometry CFD simulation Artificial compressibility method HLLC-AC Pulsatile velocity

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A Three-Step Procedural Paradigm for Domain-Specific Social Media Slang Analytics 2

A Three-Step Procedural Paradigm for Domain-Specific Social ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sundaram, Aishwarya Subramaniam, Hema Ab Hamid, Siti Hafizah Nor, Azmawaty Mohamad Universiti Malaya Institute for Advanced Studies Kuala Lumpur Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia Universiti Malaya Faculty of Education Department of Educational Psychology and Counselling Kuala Lumpur Malaysia

ISBN: (纸本)9798350384277

Social media is a crucial aspect of modern society, shaping global communication and information exchange. Social media slang encompasses informal language expressions, abbreviations, and unconventional words on these platforms, serving purposes such as concise communication and identity establishment. Data analytics from social media slang plays a vital role in providing real-time trends, enhancing sentiment analysis, and improving the accuracy of predictive models by capturing evolving linguistic trends. This study aims to address gaps in existing social media slang analytics by proposing a three-step process for extracting and validating domain-specific social media slang terms, specifically focusing on the domain of anxiety prediction in the context of mental health. The proposed approach involves the identification of relevant users, extraction of social media slang terms, and validation by subject matter experts. A pilot study is conducted in the domain of anxiety prediction, employing a self-prepared questionnaire and involving participants aged 13-14 from schools in Selangor, Malaysia. The pilot study identifies ten social media slang terms associated with anxiety, validated by subject matter experts who are licensed mental health practitioners. The slang terms exhibit varying prevalence levels among adolescents, with some terms absent from popular slang dictionaries like Urban Dictionary. The findings highlight the need for extracting slang terms from pertinent users and subject matter expert validation in incorporating social media slang into more appropriate predictive analytics. © 2024 IEEE.

关键词： Data Analytics

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Bird Impact Analysis of a Typical Transport Aircraft Wing 4th

Bird Impact Analysis of a Typical Transport Aircraft Wing

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4th International Conference on advanced Materials and Modern Manufacturing, ICAMMM 2021

作者： Sivabalan, S. Sridhar, R. Pugazhenthi, R. Kumar, Eriki Ananda Sathish Kumar, G. Department of Mechanical Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India Department of Mechanical Engineering GCET Andhra Pradesh Chennur Kadapa India

ISBN: (纸本)9789811902437

Bird striking event is one of the most dangerous risks to the safety of aircrafts. Although most of the bird strike events involve relied small birds which do not cause risk, there is a possibility of severe damage to critical aircraft components such as shield of the wind, leading edges, empennages, engine structure and front, rear blades. In this aspect, a numerical study can help in identifying the parameters which influence the behaviour of the aircraft structure during bird impacts. The current project involves impact analysis carried out at the leading edge of the wing of a large passenger aircraft model. The project work focuses on developing a model to simulate a bird impact analysis on the leading edge of the wing using the explicit dynamics method in Ansys 15.0 workbench. Explicit dynamics simulates the response of structures to loadings involving short duration and severe loading which cause large material deformation. Explicit dynamics can be classified into Eulerian, Lagrangian and smooth particle hydrodynamics method. Lagrangian method is associated with the material of the structure and therefore each node of the mesh follows the material under motion and deformation. Smooth particle hydrodynamics method is a meshless Lagrangian technique where the bird model deformation is covered fully without the distortion of mesh by the interfacing particles are occurred independently. A large-scale passenger aircraft wing is used as a reference wing required for this analysis. The wing and the bird are modelled using unigraphics NX 10 software. Numerical simulations were performed in the commercially available explicit solver Ansys AUTODYN. The important parameters of the bird model include mass, structure, velocity of bird, angle of impact, and impact location. The bird model was assigned to impact the wing at three different velocities. The deformation, stress and strain values for different velocities are compared. Results of impact simulation give an underst

关键词： Aircraft wing Bird wing Formatting Insert Style Styling

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Neural Network Modeling for Process Parameter Optimization During Machining of EN24 STEEL 8th

Neural Network Modeling for Process Parameter Optimization D...

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8th International and 29th All India Manufacturing technology, Design and Research Conference , AIMTDR 2021

作者： Kumar, Aswani S. Priyadarshini, B. Geetha Krishnaraj, V. Amrishraj, D. Nampoothiri, Jayakrishnan Nanotech Research Innovation and Incubation Centre PSG Institute of Advanced Studies Tamil Nadu Coimbatore India Department of Production Engineering PSG College of Technology TamilNadu Coimbatore641004 India Department of Mechanical Engineering V.S.B Engineering college Karur India

ISBN: (纸本)9789811945557

In recent years, there has been a growing interest in tool wear prediction by machining characteristics such as interface temperature, friction, and chip thickness that use a data-driven approach to transform data into models, which are further applied to the fine-turning of process parameters. The paper presents the effects of process parameters like depth of cut, cutting speed, and feed rate upon the responses like cutting force, chip thickness, and temperature during machining with workpiece EN24 and tool (KCU25). The experiments were performed as per the response surface methodology (RSM) approach. This establishes the percentage contribution of each factor in determining the variability (variance) of data. The neural network from the regression equation with higher value determination coefficients and lower MSE values proved the convergence of the artificial neural network (ANN) results with experimental data. The proposed models in ANN are used to predict the responses for a set of cutting parameters, which show an average error of less than 1.5%. A comparison of the ANN and experimental results shows similar outputs, thereby confirming the accuracy of the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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Mechanical Properties of Nanometal Matrix Composites Produced by Duralcan Process 4th

Mechanical Properties of Nanometal Matrix Composites Produce...

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4th International Conference on advanced Materials and Modern Manufacturing, ICAMMM 2021

作者： Suyamburajan, Vijay Ananth Jayaseelan, V. Chandramohan, P. Kumar, R. Vels Institute of Science Technology and Advanced Studies Chennai India Prathyusha Engineering College Chennai India Misrimal Navajee Munoth Jain Engineering College Chennai India Mai-Nefhi College of Engineering and Technology Abardae Eritrea

ISBN: (纸本)9789811902437

The Duralcan process was used to create aluminium metal matrix nanocomposites with aluminium nanoparticles. It is a type of stir casting process in which metal and ceramics were placed layer by layer and composites were prepared through a heating and stirring process. The composite was made with nano-Al2O3 in weight percentages of 1, 1.5 and 2.5. The hardness composite properties were tested with and without heat treatment and the tensile strength was also discovered. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Al2O3 Metal matrix composite Nanoparticles

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Deep Learning PNN Based Fault Monitoring System for Three Phase Industrial Drive System 7

Deep Learning PNN Based Fault Monitoring System for Three Ph...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chempavathy, B. Raju, K. David Mani, P.K. Vijayalakshmi, S. Janaki, N. Karthikeyan, D. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Malla Reddy Engineering College for Women Department of Electrical and Electronics Engineering Hyderabad India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electrical and Electronics Engineering Chennai India K. Ramakrishnan College of Technology Department of Electrical and Electronics Engineering Tiruchirappalli Tamil Nadu Samayapuram India Technology and Advanced Studies Vels Institute of Science Department of Electrical and Electronics Engineering Tamilnadu Chennai India SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350372816

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Application of Random Search Methods in the Determination of Learning Rate for Training Container Dwell Time Data Using Artificial Neural Networks

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Intelligent Control and Automation 2024年第4期15卷 109-124页

作者： Justice Awosonviri Akodia Clement K. Dzidonu David King Boison Philip Kisembe Accra Institute of Technology Advanced School of Systems and Data Studies (ASSDAS) Accra Ghana Accra Institute of Technology Accra Ghana Knowledge Web Centre Accra Ghana Department of Computer Engineering Ghana Communication Technology University Accra Ghana

Purpose: This study aimed to enhance the prediction of container dwell time, a crucial factor for optimizing port operations, resource allocation, and supply chain efficiency. Determining an optimal learning rate for training Artificial Neural Networks (ANNs) has remained a challenging task due to the diverse sizes, complexity, and types of data involved. Design/Method/Approach: This research used a RandomizedSearchCV algorithm, a random search approach, to bridge this knowledge gap. The algorithm was applied to container dwell time data from the TOS system of the Port of Tema, which included 307,594 container records from 2014 to 2022. Findings: The RandomizedSearchCV method outperformed standard training methods both in terms of reducing training time and improving prediction accuracy, highlighting the significant role of the constant learning rate as a hyperparameter. Research Limitations and Implications: Although the study provides promising outcomes, the results are limited to the data extracted from the Port of Tema and may differ in other contexts. Further research is needed to generalize these findings across various port systems. Originality/Value: This research underscores the potential of RandomizedSearchCV as a valuable tool for optimizing ANN training in container dwell time prediction. It also accentuates the significance of automated learning rate selection, offering novel insights into the optimization of container dwell time prediction, with implications for improving port efficiency and supply chain operations.

关键词： Container Dwell Time Prediction Artificial Neural Networks (ANNs) Learning Rate Optimization RandomizedSearchCV Algorithm and Port Operations Efficiency

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