Delay tolerant lazy release consistency for distributed shared memory in opportunistic networks

Eary, Chance; Kumar, Mohan; Záruba, Gergely

doi:10.1016/j.pmcj.2015.03.001

Cited by 1 publication

(1 citation statement)

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“…Tests conducted in terms of both time and memory efficiency and were scalable for handling clique sizes that were beyond the capabilities of existing methods. Eary et al [13] had a unique approach called Delay Tolerant Lazy Release Consistency (DTLRC) to implement distributed shared memory in opportunistic networks. The results indicated that DTLRC was an effective technology in various scenarios involving opportunistic contacts among wireless devices.…”

Section: Related Workmentioning

confidence: 99%

Condition monitoring of electric vehicle motor testing machine’s Vital components using bagged trees and quadratic SVM: a comparative study

Ravi Kumar,

Syed Wahid Ali,

Pandian

et al. 2024

Eng. Res. Express

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The future of the automotive industry lies in the utilization of electric vehicles (EVs). A crucial component of these EVs is the drive motor. The verification of critical parameters for the electrical motor is considered to be of utmost importance, and this is achieved through the use of a motor testing machine. The motor testing machine is utilized frequently to determine characteristic curve points and the electromagnetic behavior of electric motors under various conditions. However, it has been observed that the presence of a fault in the helical gear setup of the testing machine leads to frequent breakdowns and suboptimal performance, thereby affecting the efficiency of the assembly line in the production of electric vehicles. In light of these observations, the main objective of this research endeavor is to improve the motor test bench apparatus by altering essential design parameters through the utilization of vibration signal analysis and machine learning techniques. Prior to extracting statistical features, vibration signals are obtained at different gear settings. These signals are subsequently categorized using classifiers such as Bagged Trees and Quadratic SVM. Machine learning techniques are utilized to classify the gathered signals as either normal or faulty, both with and without the inclusion of a 0.25 KW load for each condition. The performance of multiple algorithms is evaluated and scrutinized. The results have demonstrated that the Bagged Trees technique outperforms the other algorithm, achieving an impressive accuracy of 95.3%. Consequently, the proposed method presents an adept solution for augmenting the motor test bench’s capability by modifying crucial design parameters.

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Section: Related Workmentioning

confidence: 99%