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检索条件"机构=Process Control and Instrumentation in the Engineering Technology Department"

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Continuous Temperature Monitoring of Substation Switchgear Using PassiveWireless RFID technology

Continuous Temperature Monitoring of Substation Switchgear U...

引用

2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Arif, Kamran Nallathambi, Ashok Kumar Chakaravarthi, Geetha Department of Instrumentation and Control Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India Department of Mechanical Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India

ISBN: (纸本)9798350379693

This work presents the design and development of an Ultra High Frequency (UHF) RFID sensor tag for continuous temperature monitoring in switchgear substations. The tag antenna of dimension, 69 x 15 mm2 designed on a 1.52 mm thick FR-4 substrate using EM simulation software HFSS®, Ansys Inc., USA, achieved a gain of 1.8 dBi at 915 MHz. Simulation and measurement results demonstrated a reflection coefficient of - 13.76 dB and -12.26 dB, respectively, with a maximum read rate of 1.5 m. Real-time temperature measurement under actual switchgear conditions validated the sensor's suitability for condition monitoring applications. © 2024 IEEE.

关键词： Antenna condition monitoring RFID Switchgear Temperature Monitoring

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Diagnosis of Chronic Kidney Diseases Using Machine Learning 1

引用

19th control instrumentation System Conference, CISCON 2022

作者： Kumar, Chandan Dhole, Sugat B. Chauhan, Bhanu Pratap Singh Pahuja, Roop Department of Instrumentation and Control Engineering Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India

ISBN: (数字)9789819995547

ISBN: (纸本)9789819995530

Chronic Kidney Disease has been affecting more than 10% of the world's population, and millions of people die each year due to the inaccessibility of affordable treatment. Chronic kidney disease is more common in older people, women, racial minorities, and people with diabetes and hypertension. According to data estimates, kidney disease affected nearly 843.6 million people worldwide in 2017. Early detection of CKD is important for preserving millions of lives. Many researchers have achieved substantial progress in this field by using various machine learning (ML) techniques to diagnose CKD at an early stage. However, more lightweight and cost-effective ML models are still mostly required. We provide a systematic and thorough strategy for dealing with difficulties, as well as a performance-optimized light-weighted medical data model. In this article, we used Random Value Imputation to replace missing values using the chi2 (chi-squared) technique to select features. Decision Tree Classifier, K-Nearest Neighbor, and Random Forest Classifier are the classification algorithms used in this study. Finally, the Random Forest Classifier can identify CKD with a 98% accuracy rate and no data leakage. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Multi-Agent Swarm Robotics for Accurate Position Detection in Disaster Scenarios 6

Multi-Agent Swarm Robotics for Accurate Position Detection i...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Ingale, Kiran Deshmukh, Amogh Deshpande, Amogh Deshmukh, Shravani Deshmukh, Mahesh Bhise, Suhas Department of Electronics and Telecommunication Engineering Vishwakarma Institute of Technology Pune India Department of Instrumentation and Control Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350398496

Swarm Robotics is a specialized area in robotics that focuses on developing and implementing systems of multiple robots that collaborate and coordinate with each other to achieve a specific task. This approach is inspired by the behavior of social insects such as ants and bees, which work collectively to achieve a common goal. One of the most promising applications of swarm robotics is in the human evacuation during natural disasters. During such events, large numbers of people may become lost or trapped, making it challenging for rescue teams to locate and extract them. By deploying position-detecting swarm robots equipped with sensors and communication capabilities, rescue teams can quickly locate individuals and guide them to safety. These robots can also communicate with each other to coordinate their efforts and avoid duplication of work. This research study is prominently focused on this application and analyzing its potential use in the fieldof Swarm robotics. © 2023 IEEE.

关键词： Swarm intelligence

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Gender-Based Diagnosis of Frontotemporal Dementia Using Deep Learning 15

Gender-Based Diagnosis of Frontotemporal Dementia Using Deep...

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

作者： Sahu, Pankaj Kumar Department of Instrumentation and Control Engineering Dr B.R. Ambedkar National Institute of Technology Punjab Jalandhar144008 India

ISBN: (纸本)9798350370249

A clinical illness called dementia causes a continuous decline in cognitive and emotional capacities severe enough to affect day-to-day activities. Frontotemporal dementia (FtD) is characterised by frontal and anterior temporal brain atrophy, primarily impairing personality and social abilities. The fast development of electroencephalogram (EEG) sensors has made the EEG a practical, precise, and robust biomarker for detecting neurodegenerative disorders. The Rudiment Densely-Coupled Convolutional Gated Recurrent Unit (RDCGRU) deep-learning model is proposed for gender-based diagnosing FtD. Two brain regions were considered when selecting the EEG electrodes to detect FtD. The frontal-temporal (F3, Fz, F4, T3, and T4) area is the first, and the parietal-occipital (O1, O2, P3, Pz, and P4) part is the second. Three gender-based data file groups have been created to train the deep learning model: male group-01, female group-02, and mixed-gender group-03. Only five critical EEG channels were considered in the data files of two brain regions, and extra electrodes were eliminated. The parietal-occipital electrodes are more critical than the frontal-temporal electrodes in diagnosing FtD. The proposed RDCGRU model achieved the greatest f1-score, 71.00%, for the female group-02 with frontal-temporal electrodes. In addition, the model attended the highest f1-score (92.57%) for the female group-02 with the parietal-occipital electrodes. The gender-based categorisation is more accurate in identifying Frontotemporal Dementia (FtD) than the mixed-gender classification. In the examination of FtD, the parietal-occipital brain region is more critical than the frontal-temporal region. © 2024 IEEE.

关键词： Electroencephalography

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Practical Realization of Only Active Floating Lossless Inductance Simulator 10

Practical Realization of Only Active Floating Lossless Induc...

引用

10th International Conference on engineering, Applied Sciences, and technology, ICEAST 2024

作者： Roongmuanpha, Natchanai Unhavanich, Sumalee Pukkalanun, Tattaya Tangsrirat, Worapong School Of Engineering Department Of Computer Engineering Bangkok10520 Thailand Faculty Of Digital Technology Bangkok10300 Thailand School Of Engineering Department Of Instrumentation And Control Engineering Bangkok10520 Thailand

ISBN: (纸本)9798350385946

This paper describes an electronically controllable floating lossless inductance simulator circuit implemented with only off-the-shelf available integrated circuits, namely LT1228 and operational amplifier (OA). The proposed design consists solely of two LT1228s and a single OA. By using an active element named LT1228, the equivalent inductance can be tuned electronically with depended on the externally supplied current applied to LT1228. The proposed simulator imposed just one component matching restriction that required the equality of two transconductances. It is easily attainable with a simple current mirror. The worthiness of the proposed floating inductance simulator has been proven and applied to the construction of the 2nd-order RLC highpass/bandpass filter as an application. PSPICE simulations are used to investigate the characteristics of the proposed simulator and its filter realization. © 2024 IEEE.

关键词： Operational amplifiers

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A Novel Error Minimization Approach for Real-Time MPPT of Solar PV System 3

A Novel Error Minimization Approach for Real-Time MPPT of So...

引用

3rd IEEE International Conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Bisht, Rahul Sikander, Afzal Dr. B. R. Ambedkar National Institute of Technology Instrumentation and Control Engineering Department Jalandhar India

ISBN: (纸本)9798350387193

The solar PV system's performance is greatly affected by environmental uncertainties, making its conversion efficiency very low. Therefore, a novel error minimization approach based on sanitized teacher learning based optimization (s-TLBO) algorithm is proposed for the maximum power point tracking (MPPT) of solar PV system. The real-time setup is developed using 42.13 W polycrystalline solar panel, Arduino UNO, arduino IDE software, digital storage oscilloscope (DSO), and digital multimeter (DMM). The proposed algorithm is employed and tested in real-time and simulation environments, considering varying irradiances. Upon conducting a thorough analysis, it is evident that the proposed algorithm demonstrates reduced settling time, minimized oscillations around the maximum power point (MPP), and decreased overshoot across different solar irradiance levels. Finally, a comparative analysis is performed between the results obtained in real-time and MATLAB environments, revealing a relative difference close to -3.158 %. © 2024 IEEE.

关键词： Solar irradiance

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The Deep Learning Model For The Examination Of Brain Tumor 2

The Deep Learning Model For The Examination Of Brain Tumor

引用

2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Sahu, Pankaj Kumar Jain, Karan Dr B.R. Ambedkar National Institute of Technology Department of Instrumentation and Control Engineering Punjab Jalandhar144008 India

ISBN: (纸本)9798350360523

Radiologists can locate the tumor location using the experimental medical imaging technique known as magnetic reasoning imaging (MRI). However, manually testing the MRI pictures is time-consuming and requires experience. The development of Computer-assisted Diagnosis (CAD), and deep learning in particular, now enable radiologists to diagnose brain tumors more accurately. Deep learning techniques can be developed to produce accurate classification results without requiring manual feature extraction. The research article suggests the deep learning model for classifying multi-class (pituitary, glioma, meningioma, and no-tumor) and binary-class (tumor, no-tumor) brain tumor datasets. Two datasets were used to test the proposed model: Dataset-1 contains 3264 photos, while Dataset-2 contains 3000 images. The model consists of three convolution blocks whose kernel size is 10, 8, and 6, respectively. The model has three max-pooling blocks. The model also contains one flattened block and two fully connected blocks. Finally, the model included softmax and sigmoid activation functions for the multi-class and binary-class brain tumor image classification. The created deep learning model achieved 94.22% classification accuracy with multi-class brain tumor dataset-1. The same model gained 97.78% classification accuracy for binary-class brain tumor dataset-2. The created deep learning model performs similarly to VGG19 and works well with a smaller dataset than CNN. © 2024 IEEE.

关键词： Tumors

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Efficient Tool Condition Monitoring Using SOM-Hebb Classifier on FPGA 6

Efficient Tool Condition Monitoring Using SOM-Hebb Classifie...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Akanksha, A. Somani Kokate, R.D. Abhilasha, Mishra Maharashtra Institute of Technology Department of Electronics & Computer Engineering Aurangabad India College of Engineering Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798350304480

Tool condition monitoring is vital for enhancing productivity, reducing costs, and improving product quality in manufacturing industries. Existing approaches, such as threshold-based methods, sensor-based methods, and machine learning-based techniques, have limitations in terms of accuracy, adaptability, interpretability, and computational costs. This research proposes a tool condition monitoring system based on the Self-Organizing Map (SOM) algorithm and the Hebbian learning algorithm. The system is implemented on a Field-Programmable Gate Array (FPGA) platform for real-time processing in manufacturing environments. A dataset is acquired using sensors, pre-processed, and divided into training and testing sets. The SOM-Hebb classifier is designed with appropriate parameters, and the tool condition monitoring algorithm involves projection histograms, discrete Fourier transform (DFT), and the SOM-Hebb classifier. The FPGA implementation achieves high-speed processing and meets timing constraints. Simulation results demonstrate the system's functionality and accuracy, with a classification accuracy of 99.22%. The system exhibits a processing time of 35 clock cycles. This research provides a promising approach for efficient and accurate tool condition monitoring in manufacturing industries. © 2023 IEEE.

关键词： Field-Programmable Gate Array (FPGA) Hebbian learning algorithm Real-time processing Self-Organizing Map (SOM) Tool condition monitoring

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Corrosion Detection using Deep Learning and Custom Object Detection 2

Corrosion Detection using Deep Learning and Custom Object De...

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2nd Asian Conference on Innovation in technology, ASIANCON 2022

作者： Mundada, Kapil Kulkarni, Mihir Sagar, Pranjali Asegaonkar, Rohit Mulgir, Nikhil Vishwakarma Institute of Technology Department of Instrumentation and Control Engineering Pune India Vishwakarma Institute of Technology Instrumentation and Control Engineering Pune India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Corrosion is the major problem faced in many industrial types of equipment in the oil, gas, and petrochemical industries. If such a problem cannot be detected in a particular period, it directly affects the whole industrial process loop's reliability and performance. So for accurate detection of Corrosion, we are designing a deep learning algorithm that will detect the Corrosion in a particular metal and avoid further issues. Here we implement the custom object detection method to detect Corrosion in copper and iron metal. Accordingly, how much area of that metal has got rusted or corroded is identified. © 2022 IEEE.

关键词： Deep learning

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Modular Design of a Fused Deposition Modelling Based Device for Constructing Structures on Mars 12

Modular Design of a Fused Deposition Modelling Based Device ...

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12th RSI International Conference on Robotics and Mechatronics, ICRoM 2024

作者： Sudhakar, Dhanush Mohamed Abdulla, Afeef Ahmed Kotilingesh, R. Shasica Ramaraj, N.P. National Institute of Technology Tiruchirappalli Department of Instrumentation and Control Engineering Tiruchirappalli India National Institute of Technology Tiruchirappalli Department of Electronics and Communication Engineering Tiruchirappalli India National Institute of Technology Tiruchirappalli Department of Mechanical Engineering Tiruchirappalli India

ISBN: (纸本)9798331529734

In recent years, the significant investments made in space exploration programs globally have elevated the conversation about populating Mars to the fore. However, it poses major difficulties, particularly given the enormous payload needed for sustained missions. It would be expensive and impractical to transport all necessary supplies from Earth, including large construction machinery like cranes. This paper introduces the design of a device that autonomously constructs structures on Mars utilizing local regolith as feedstock. The solution aims to remove the need of heavy machinery transportation by additively manufacturing structures using smaller wheeled mobile units. © 2024 IEEE.

关键词： Space research

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