Multi-criteria decision making (MCDM) for the selection of Li-ion batteries used in electric vehicles (EVs)

Loganathan, M. K.; Mishra, Bikash; Tan, Cher Ming; Kongsvik, Trond; Naresh, Ram

doi:10.1016/j.matpr.2020.07.179

Cited by 41 publications

(22 citation statements)

References 27 publications

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“…The human cognitive system is of interest to researchers working on computational applications for a variety of reasons across many fields. For more than three decades, researchers have been seeking to overcome this challenge to understand human cognitive capability [ 1 , 2 ]. In major depressive disorder, abnormalities in motor activity are linked to other characteristics such as a lack of interest in daily chores (MDD).…”

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

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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.

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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

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“…To sum up, the concept of deep learning is very popular at present. It provides high accuracy and high-speed calculation for speech recognition and creates new opportunities for intelligent speech interaction, so it has been widely recognized by academia and industry [ 24 , 25 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm

Meng

Kuppuswamy

Upadhyay

et al. 2022

Computational Intelligence and Neuroscience

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As a result of the fast rise of globalization, people in China are learning English at a rapid pace. However, there is a severe shortage of English teachers in the region, which is a major hindrance. To address these concerns, a deep learning-based algorithm is proposed that can not only check English pronunciation but also help learners distinguish between phonemic and quality phonemic while listening and differentiating, as well as correct phonemic errors, thereby increasing their language learning capacity. In order to study the application of nonlinear network identification technology in English learning, this paper evaluates the English pronunciation quality through the deep learning algorithm of deep learning combined with the related contents of neural network data model, and the experimental results of speech recognition structure are analyzed and discussed in detail. The concordance between machine and manual intonation evaluation is 80%, the concordance rate of adjacent intonation evaluation is 98.33%, and the Pearson correlation coefficient is 0.627 that shows the technique is reliable. The method of English pronunciation and speech identification model is sensible and dependable, which can give beginners a punctual, exact and impartial judgment and response guidance, assist learners to get on the differences between their phonemic and standard phonemic, and correct phonemic mistakes, in order to enhance the ability of oral English learning.

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“…Loganathan et al [30] EV infrastructure selection model Using the MCDM method which was based on 8 criteria and 4 evaluation methods, an EV infrastructure selection model was built.…”

Section: Pamucar Et Al [29]mentioning

confidence: 99%

Battery Electric Bus Selection Based on Entropy Weight Method and Road Operation Test: Using Nanjing Bus Company as an Example

2022

Mathematical Problems in Engineering

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Before putting battery electric buses into actual daily operations, the selection of buses is of great importance to the bus companies and the improvement of energy efficiency. Based on multi-dimension big data provided by the Nanjing Bus Company and by adopting the entropy weight method and road operation test, this paper presented an objective method for the selection of battery electric bus. Each of these four enterprises, BYD, Kaiwo, Yinlong, and Jiankang which were commonly seen in the Chinese market, provided two types of battery electric buses. Four different routes were selected for the road test and collected over 50 days of daily operation test data. In order to reflect all aspects of the vehicle performance, eight indexes which had been considered and designed comprehensively were classified into three categories: reliability, economy, and security. Together with the road operation data, an evaluation system based on the bus’s daily operation performance was established through the entropy weight method. Assessing results showed that battery electric bus with better performance usually had a total evaluation score over 0.69. Generally, electric buses with high scores on one route also had higher scores on all the other routes, such as brand D. But, on unimpeded routes, results may differ, for example, in route 134 W, 10-meter buses of brand B ranked first. In contrast to earlier studies, the selection result obtained by this method was more objective and intuitional, which was also consistent with the actual scenario. The selection process indicates that this method is highly practical and can be easily carried out. Therefore, we can assume that the proposed method can evaluate the actual operation state of battery electric buses and provides a new selection way.

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Multi-criteria decision making (MCDM) for the selection of Li-ion batteries used in electric vehicles (EVs)

Cited by 41 publications

References 27 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

Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm

Battery Electric Bus Selection Based on Entropy Weight Method and Road Operation Test: Using Nanjing Bus Company as an Example

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