Quantification of tolerance limits of engineering system using uncertainty modeling for sustainable energy

Datta, Debabrata; Mishra, Sangeeta; Rajest, S. Suman

doi:10.1016/j.ijin.2020.05.006

Cited by 69 publications

(13 citation statements)

References 15 publications

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“…Depends on the selection of these independent variable models [26]. When voltages are defined as independent variables, the corresponding currents are dependent, and the voltage-to-current matrix is in the input form, as shown in figure 1.…”

Section: Design Methodology Of Proposed Techniques Selection Of Bus Smentioning

confidence: 99%

Bus Allocation for Voltage Stabilization in Multi-Machine Power System Using GWO

AL-Zubaydi¹,

Jasim²

2018

EAI Endorsed Transactions on Energy Web

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Section: Design Methodology Of Proposed Techniques Selection Of Bus Smentioning

confidence: 99%

Bus Allocation for Voltage Stabilization in Multi-Machine Power System Using GWO

AL-Zubaydi¹,

Jasim²

2018

EAI Endorsed Transactions on Energy Web

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“…They utilized the parallelizable lattice Boltzmann method (LBM) to solve the level set equation(LSE). This technique is quicker since it is unravelled in histogram space instead of pixel domain [77][78][79][80][81]. The time problem is extensively diminished since the quantity of grey levels is commonly a lot littler than the size of the picture.…”

Section: Eai Endorsed Transactions On Pervasive Health and Technologymentioning

confidence: 99%

A Review of Medical Image Segmentation Algorithms

Ramesh¹,

Kumar²,

Swapna³

et al. 2021

EAI Endorsed Trans Perv Health Tech

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136

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INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM. OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation. METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM). CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.

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“…Rachhpal Singh [10] proposed a hybrid MPG optimization algorithm with PSO and GA to feature gene expression. With this method, data dimension and duplication among the classified sets have been reduced significantly [47][48][49][50][51]. Nature-inspired optimization algorithm based on PSO is used for E-health services in Job scheduling on mobile competitive cloud distributed systems [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Analysis of Fractional Earthworm Optimization Algorithm for Optimal Routing in Wireless Sensor Networks

Pasupuleti¹,

Balaswamy²

2018

ICST Transactions on Scalable Information Systems

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In Wireless Sensor Networks (WSNs), the data transmission from the sensing node to the sink node consumes a lot of energy as the number of communications increases, so the battery life of nodes is limited, and the network also has a limited lifetime. Recent studies show that the bio-inspired meta-heuristic algorithms for solving engineering problems such as energy reduction in autonomous networks in the multidisciplinary areas of WSN, Internet of Things (IoT) and Machine learning models. Hence to increase Network lifetime, optimized clustering and energy-efficient routing techniques are required. In all applications of WSN, not only energy-efficient but also delay and throughput of the network are important for efficient transmission of data to the destination. This paper analyses optimized clustering by selecting cluster heads based on fractional calculus earthworm optimization algorithm (FEWA). The route from cluster heads to sink node is selected based on the fit factor. This paper's main intention is to provide an extensive comparative study of the FEWA with all standard optimization-based clustering and routing techniques. This method's performance is compared with existing optimized clustering methods like GA, PSO, ACO, DE and EWO in terms of the number of energy, delay, and throughput. At the end of 1000 iterations, the analysis shows that the FEWA outperforms existing methods with maximum average remaining energy of the nodes as 0.216J, the minimum average delay of 0.208 sec and maximum average throughput of 88.57% for 100 nodes.

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Quantification of tolerance limits of engineering system using uncertainty modeling for sustainable energy

Cited by 69 publications

References 15 publications

Bus Allocation for Voltage Stabilization in Multi-Machine Power System Using GWO

Bus Allocation for Voltage Stabilization in Multi-Machine Power System Using GWO

A Review of Medical Image Segmentation Algorithms

Performance Analysis of Fractional Earthworm Optimization Algorithm for Optimal Routing in Wireless Sensor Networks

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