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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Girdhar, Mehull Soni, Umang DPS-Modern Indian School Al Wakrah Qatar Netaji Subhas University of Technology Division of manufacturing process and automation engineering New Delhi India

ISBN: (纸本)9798350368413

Parkinson's Disease (PD) is a rare, progressive disease that gets worse over time, and subsequently has no cure. It causes symptoms such as stiffness, and shaking, and leads to a loss of balance and coordination in day-to-day activities. Mobility challenges and speech difficulties exclusive to PD often complicate treatment for the disease. Timely detection of PD is of the highest importance, enabling patients to make the most of their remaining lives. The aging global population highlights the urgent need for accurate remote detection models. This research study analyzes various machine learning algorithms for determining whether a person is suffering from Parkinson's disease or not. This paper deals with the implementation of 4 machine learning techniques, which include Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression - trained on the MDVP audio dataset obtained from UCIML, UC Irvine's Machine Learning Repository. The results of the models are put up against each other based on precision, F score, recall, and accuracy as evaluation metrics. The model demonstrating the highest accuracy was the Random Forest classifier, achieving an accuracy of 94.87% with perfect sensitivity (recall) of 1.0. Key features contributing to its efficacy include vocal fundamental frequency metrics such as PPE, MDVP (Hz), and spread1, which highlight the vital role of these features in disease detection. These findings support the integration of machine learning in telemedicine, offering solutions that promise to constructively contribute to the lives of patients suffering from this ailment. © 2024 IEEE.

关键词： Random forests

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Artificial Intelligence in Healthcare for Early Parkinson’s Disease Diagnosis

Artificial Intelligence in Healthcare for Early Parkinson’s...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Mehull Girdhar Umang Soni 12th Grade DPS-Modern Indian School Al Wakrah Qatar Division of manufacturing process and automation engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Parkinson’s Disease (PD) is a rare, progressive disease that gets worse over time, and subsequently has no cure. It causes symptoms such as stiffness, and shaking, and leads to a loss of balance and coordination in day-to-day activities. Mobility challenges and speech difficulties exclusive to PD often complicate treatment for the disease. Timely detection of PD is of the highest importance, enabling patients to make the most of their remaining lives. The aging global population highlights the urgent need for accurate remote detection models. This research study analyzes various machine learning algorithms for determining whether a person is suffering from Parkinson’s disease or not. This paper deals with the implementation of 4 machine learning techniques, which include Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression—trained on the MDVP audio dataset obtained from UCIML, UC Irvine’s Machine Learning Repository. The results of the models are put up against each other based on precision, F score, recall, and accuracy as evaluation metrics. The model demonstrating the highest accuracy was the Random Forest classifier, achieving an accuracy of 94.87% with perfect sensitivity (recall) of $\mathbf{1. 0}$. Key features contributing to its efficacy include vocal fundamental frequency metrics such as PPE, MDVP (Hz), and spread1, which highlight the vital role of these features in disease detection. These findings support the integration of machine learning in telemedicine, offering solutions that promise to constructively contribute to the lives of patients suffering from this ailment.

关键词： Support vector machines Analytical models Accuracy Machine learning algorithms Biological system modeling Nearest neighbor methods Feature extraction Ensemble learning Random forests Diseases

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Fault Detection and Correction in Omni Bundle Robot Using EKF

Fault Detection and Correction in Omni Bundle Robot Using EK...

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International Conference on manufacturing, Advanced Computing, Renewable Energy and Communication, MARC 2018

作者： Rana, Rohit Agarwal, Vijyant Gaur, Prerna Parthasarathy, Harish Instrumentation and Control Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India Manufacturing Process and Automation Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India Electronics and Communication Engineering Division Netaji Subhas Institute of Technology Sec-03 Dwarka110078 India

ISBN: (纸本)9789811367717

In this paper, the entire focus is on fault detection and correction using extended Kalman filter (EKF) on industrially used omni bundle robot. Here, the authors considered a problem where a robot does monotonous job and repetitively traverse the same trajectory every time. Due to wear and tear or any fluctuation in input signals, the robot may not track the desired trajectory. Faults may arise at various levels in robots, for example, in actuator, encoder, and dynamical parameters. Actuator fault detection using encoder sensor will be the main focus in this research paper. The dynamics of robotic arm link is modeled using a DC motor attached with the link, and the encoder attached with the motor will provide position feedback. The fault is detected using EKF state estimation, and the fault is corrected using a PID controller feedback into the motor input torque. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Extended Kalman filters

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Effect of Particulate Filler on Mechanical Properties of Polyester based Composites

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Materials Today: Proceedings 2017年第9期4卷 9893-9897页

作者： Vijay Chaudhary Akash Kumar Rajput Pramendra Kumar Bajpai Division of Manufacturing Process and Automation Engineering Netaji Subhas Institute of Technology Sec-3 Dwarka New Delhi India

Fiber reinforced plastics (FRPs) are acquiring its place in almost every application area where conventional materials are used. The present study describes the mechanical characterization (impact strength and flexural strength) of glass, carbon and glass-carbon hybrid fiber reinforced polyester hybrid composites with and without particulate filler. The composites were developed using conventional hand lay-up technique. Attempts have been made to explore the possible use of SiC as filler material in these composites. Effect of reinforcing different percentage of silicon carbide particles on mechanical properties of the developed hybrid composites has been studied. The results have been compared with and without filler materials. The study concludes that reinforcing filler material has strong effect on the mechanical performance of the developed composites.

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Surface Quality Modification Using Powder Metallurgy processed CuW Electrode During Electric Discharge Machining of Inconel 718

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Procedia Materials Science 2014年 5卷 2629-2634页

作者： Naveen Beri Sachin Maheshwari Chitra Sharma Anil Kumar Department of Mechanical Engineering Beant College of Engineering and Technology Gurdaspur 143521 Punjab India Division of Manufacturing Process & Automation Engineering Netaji Subhas Institute of Technology Dwarka New Delhi110078. India Department of Mechanical and Automation Engineering IGIT Kashmere Gate Delhi 110403. India

Use of powder metallurgy processed (PM) electrode in electrical discharge machining (EDM) has emerged as an alternate tooling option with a view to impart improved surface properties to the work surface. Preliminary investigation with CuW PM electrode using Taguchi method was done on Inconel 718 to obtain best parameter settings for higher the better surface roughness. In the present work surface micro hardness study and X-ray diffraction (XRD) analysis was performed on the machined surface at the best parameter setting and comparison was done with the surface machined using conventional copper electrode. It was observed that the micro hardness of the machined surface was improved from 380.9 HV of the base metal to 496.7 HV of the surface machined with powder metallurgy processed electrode. XRD analysis revealed the formation of Fe2W3C6 phase on the machined surface causing remarkable improvement in the surface micro hardness.

关键词： Powder metallurgy (PM) electrical discharge machining (EDM) X-ray diffraction (XRD) micro hardness recast layers electrode.

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Evaluation of surface quality during electrical discharge machining of Inconel 718 with powder metallurgy electrodes

Evaluation of surface quality during electrical discharge ma...

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20th International Symposium on processing and Fabrication of Advanced Materials, PFAM XX

作者： Beri, Naveen Maheshwari, Sachin Sharma, Chitra Department of Mechanical Engineering Beant College of Engineering and Technology Gurdaspur Punjab India University School of Engineering and Technology GGSIPU Delhi India Division of Manufacturing Process and Automation Engineering NetajiSubhas Institute of Technology Dwarka New Delhi India Department of Mechanical and Automation Engineering IGIT Kashmere Gate Delhi India

ISBN: (纸本)9783037853160

In recent years, researchers have reported powder metallurgy processed electrodes as alternative tooling for electrical discharge machining (EDM). The present experimental study evaluates the quality of machined surface during electrical discharge machining (EDM) of Inconel 718 alloy steel with powder metallurgy (PM) processed electrodes. The investigated process parameters were polarity, electrode type, peek current, pulse on time, duty cycle, gap voltage, retract distance and flushing pressure. The surface quality was measured in terms of surface roughness (Ra). An orthogonal array L36 (2 1× 37) based on Taguchi methodology was applied to plan and design experiments. Experimental data was statistically analyzed using analysis of variance (ANOVA) and optimum condition was achieved for evaluation criteria. It was concluded that polarity, electrode type, peek current, have significant effect on surface quality and minimum Ra is obtained with CuW2080 electrode at minimum current and negative polarity. Deposition of tungsten with CuW2080 (80%W 20%Cu) electrode was confirmed by energy dispersion spectrum (EDS) of the machined surface.

关键词： Surface roughness

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Effect of cryogenically treated electrode during AEDM of nickel based super alloy 718 for surface quality improvement

Effect of cryogenically treated electrode during AEDM of nic...

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20th International Symposium on processing and Fabrication of Advanced Materials, PFAM XX

作者： Kumar, Anil Maheshwari, Sachin Sharma, Chitra Department of Mechanical Engineering Beant College of Engineering and Technology Gurdaspur Punjab India University School of Engineering and Technology GGSIPU Delhi India Division of Manufacturing Process and Automation Engineering Netaji Subhas Institute of Technology Dwarka New Delhi India Department of Mechanical and Automation Engineering IGIT Kashmere Gate Delhi India

ISBN: (纸本)9783037853160

In this experimental study attempt has been made to realize potential of cryogenic treatment in enhancing surface quality with fine aluminum and graphite additives powders in AEDM of nickel based super alloy Inconel 718. L 36 (23×36) Orthogonal Array has been selected to conduct and analyze experiments based on Taguchi methodology. Polarity, peak current, pulse on time, duty cycle, gap voltage, retract distance, concentration of fine aluminum and graphite powder added into the dielectric fluid are chosen as input process variables to study performance in terms of surface roughness (SR) using copper and deep cryogenically treated copper electrode. It is found experimentally that deep cryogenically treated copper electrode with addition of 6g/l graphite powder improves surface roughness appreciably. The same has been supported by SEM analysis of the machined surface.

关键词： Surface roughness

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Optimisation of electrical discharge machining process with CuW powder metallurgy electrode using grey relation theory

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International Journal of Machining and Machinability of Materials 2011年第1-2期9卷 103-115页

作者： Beri, Naveen Kumar, Anil Maheshwari, S. Sharma, C. Department of Mechanical Engineering Beant College of Engineering and Technology Gurdaspur Punjab Pin 143 521 India Division of Manufacturing Process and Automation Engineering Netaji Subash Institute of Technology Dwarka New Delhi Pin 110 078 India Department of Mechanical and Automation Engineering IGIT Kashmiri Gate Delhi Pin 110 006 India

In this paper, an attempt has been made to correlate the usefulness of powder metallurgy (PM) electrodes in electrical discharge machining (EDM). This paper explains the same with the help of experimentation on AISID2 steel in kerosene with CuW (25% Cu and 75% W) PM electrode. An L18 orthogonal array was used to identify the effect of process parameters (viz. electrode material, duty cycle, flushing pressure and current) on the performance characteristics i.e., material removal rate surface roughness and surface hardness. A grey relation grade obtained from the grey relation analysis is used to solve the EDM process with multiple performance characteristics. Experimental results have shown that electric discharge machining process performance can be improved efficiently through this approach. It is found that copper tungsten PM electrode gives better multi-objective performance than conventional copper electrode. Copyright © 2011 Inderscience Enterprises Ltd.

关键词： Powder metallurgy

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Multivariable Constrained Predictive Control (With Application to High Performance Distillation)

Multivariable Constrained Predictive Control (With Applicati...

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American Control Conference (ACC)

作者： D.J. Wilkinson M.T. Tham A.J. Morris Automation & Control Group Manufacturing Services Division Shell International Petroleum Company Limited The Hague Netherlands Department of Chemical and Process Engineering University of Newcastle Newcastle-Upon-Tyne UK

A multivariable constrained predictive controller is briefly described. Its objective function is solved via Singular Value Decomposition (SVD). Apart from being a numerically robust solution technique, the approach also provides useful on-line information which can be used to enhance the stability of the closed loop system. The performance characteristics of the algorithm are illustrated by applications to nonlinear simulations of two high purity distillation columns.

关键词： Predictive control manufacturing automation Robust stability Cost function Automatic control Petroleum Chemical processes Chemical engineering Feedback control PROM

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RECENT DEVELOPMENTS IN automation OF CEMENT PLANTS

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IEEE TRANSACTIONS ON INDUSTRY AND GENERAL APPLICATIONS 1971年第4期IGA7卷 458-&页

作者： GAUTIER, EH HURLBUT, MR RICH, EAE Manufacturing and Process Automation Business Division General Electric Company Limited West Lynn MA USA Manufacturing and Process Automation Business Division General Electric Company Industry Sales and Engineering Operation General Electric Company Limited Schenectady NY USA

Over a decade has passed since process computers were first applied to control parts of the cement manufacturing process. The path from there to the present has successes-and failures-along its way. Over 30 cement plants have installed such computers as part of their efforts to keep profit margins from shrinking. Progress in using these process control techniques has been largely evolutionary. Certain factors can now be identified more certainly as essential ingredients for success. Among these are the following. 1) "People factors" of the cement manufacturer stand as first in importance. These include management support, process know-how, training and supervision of operators, and an inner confidence and determination that "we can make it work." 2) Well-done interface jobs of adapting control room design and operators to each other, automation components with the process and its machinery, and plant design to fit automatic control fundamentals. 3) Designing the process to really be controllable. 4) Control hardware and software which fit the nature of this industry. Each of the foregoing factors is expanded with emphasis on how recent developments of better understanding, control functions, hardware, software, and of process and plant design are merging to help shape the future of automation in the cement industry.

关键词： Automatic control Job design manufacturing automation Management training Design automation process design Hardware Shape control Computer aided manufacturing manufacturing processes

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