Failure Evaluation and Analysis of Mechatronics-Based Production Systems during Design Stage Using Structural Modeling

Loganathan, M. K.; Goswami, Priyom; Bhagawati, Bedabrat

doi:10.4028/www.scientific.net/amm.852.799

Cited by 18 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…People who are suffering from major mental diseases are more likely to develop chronic physical ailments. Those patients who have severe physical disorders are more prone to develop mental problems [ 16 , 17 ]. The connection among psychological and physical health is well established [ 18 ], While new technologies are being developed to observe numerous indices of physical health with wearable and wireless devices, such as pulse, blood pressure, and body temperature [ 19 , 20 ], the alternatives for monitoring cognitive burden are far more limited.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder

Murugesan

Ahmed

Shabaz

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Major depressive disorder (MDD) is a mood state that is not usually associated with vision problems. Recent research has found that the inhibitory neurotransmitter GABA levels in the occipital brain have dropped. Aim. The aim of the research is to evaluate mental workload by single channel electroencephalogram (EEG) approach through visual-motor activity and comparison of parameter among depressive disorder patient and in control group. Method. Two tests of a visual-motor task similar to reflect drawings were performed in this study to compare the visual information processing of patients with depression to that of a placebo group. The current study looks into the accuracy of monitoring cognitive burden with single-channel portable EEG equipment. Results. The alteration of frontal brain movement in reaction to fluctuations in cognitive burden stages generated through various vasomotor function was examined. By applying a computerised oculomotor activity analogous to reflector image diagram, we found that the complexity of the path to be drawn was more important than the real time required accomplishing the job in determining perceived difficulty in depressive disorder patients. The overall perceived difficulty of the exercise is positively linked with EEG activity measured from the motor cortex region at the start of every experiment test. The average rating for task completion for depression patients and in control group observed and no statistical significance association reported between rating scale and time spent on each trial ( p = 1.43 ) for control group while the normalised perceived difficulty rating had 0.512, 0.623, and 0.821 correlations with the length of the pathway, the integer of inclination in the pathway, and the time spent to complete every experiment test, respectively ( p < 0.0001 ) among depression patients. The findings imply that alterations in comparative cognitive burden levels during an oculomotor activity considerably modify frontal EEG spectrum. Conclusion. Patients with depression perceived the optical illusion in the arrays as weaker, resulting in a little bigger disparity than individuals who were not diagnosed with depression. This discovery provided light on the prospect of adopting a user-friendly mobile EEG technology to assess mental workload in everyday life.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder

Murugesan

Ahmed

Shabaz

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Despite its widespread application in evaluating such systems, few learning algorithms have been developed to explicitly optimize mean average precision (MAP) due to computational constraints. Existing MAP optimization approaches either fail to find a globally optimal solution or are wasteful in terms of computation [20,21].…”

Section: Introductionmentioning

confidence: 99%

A Global Optimization Algorithm for Intelligent Electromechanical Control System with Improved Filling Function

Zhang

Srividya

Kakaravada

et al. 2022

Scientific Programming

View full text Add to dashboard Cite

To improve the speed of global optimization algorithm, a class of global optimization algorithms for intelligent electromechanical control system with improved filling function is proposed. By attaching the intelligent managing system improving algorithm and the filling function procedure, the algorithm can stand out from the current particular optimal solution, avoid the phenomenon of falling into the local favorable solution in the process of algorithm iteration, make the algorithm find a better solution, and improve the efficiency of solving the multiextremum global improving problem. Multiextremum-seeking is an optimal control technique that works with unknown conditions while assuming that measurements of the plant’s input and output signals are accessible. The presented work is for an electromechanical system which will handle the low accuracy and untimely tendency of conventional systems which are used in various practical applications. Few learning algorithms have been developed to explicitly optimize mean average precision (MAP) due to computational constraints. The outcomes show that the convergence of the test functions F6 and F7 is not good when the MAPID algorithm is only used for optimization. The MAPID_FF algorithm not only ensures the convergence and optimization precision of the two test functions, but also reduces the optimization time compared with the filling function method. Compared with the filling function method, the improved algorithm has higher accuracy and faster speed, and it is not simple to fall into the local optimum, so the global optimal value is more accurate.

show abstract

“…With explosive growth of the social economy and the widespread adoption of automated technology, human demands for small -scale production and quality of products have increased dramatically in recent years. To keep up with the trends, several companies have integrated robots into a variety of automated production lines [1,2]. With china as the world's major industrial products producer, industrial robots have been broadly used in various fields of factories production.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Sorting Robot Control System Based on Deep Learning and Machine Vision

Chang-yong

Nie

Naveed

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

To enhance the control technology of coal gangue dry separation method which is replaced by the machine in coal washing plant and to explore the control effects of traditional PID and dynamic domain fuzzy self-tuning PID, which will aid in determining the ideal position and orientation for grasping an object as well as understanding physical and logistic data patterns, an optimal design of PID controller for sorting robot based on deep learning is initiated. The mathematical model of ball screw system driven by a single joint motor of the robot is introduced, the control effects of classical PID and variable domain fuzzy self-tuning PID are studied and imitated, respectively. The simulation outcome appears that the selection time is 0.001 s and simulation time is 8 s. The tracking error of variable domain fuzzy PID is minor than that of PID tracking at the starting point, and the convergence rate of error is quick than that of PID manage, the steady-state error is minor than PID, the control accuracy is higher, and the tracking performance is better. The advantages of variable domain fuzzy PID control method in position tracking control are verified, the variable domain fuzzy PID can modify the control framework online as per the different position mistake and mistake change rate, the design of the variable domain of input and output makes the fuzzy inference rules locally finer, the speed of adjustment is faster and the tracking accuracy is further improved, so it has finer tracking presentation than the traditional PID tracking management.

show abstract

Failure Evaluation and Analysis of Mechatronics-Based Production Systems during Design Stage Using Structural Modeling

Cited by 18 publications

References 11 publications

Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder

Assessment of Mental Workload by Visual Motor Activity among Control Group and Patient Suffering from Depressive Disorder

A Global Optimization Algorithm for Intelligent Electromechanical Control System with Improved Filling Function

Optimization of Sorting Robot Control System Based on Deep Learning and Machine Vision

Contact Info

Product

Resources

About