首页|Researchers’ Work from VIT University Focuses on Machine Learning (A Novel Custom One-Dimensional Time-Series DenseNet for Water Pipeline Leak Detection and Localization Using Acousto- Optic Sensor)
Researchers’ Work from VIT University Focuses on Machine Learning (A Novel Custom One-Dimensional Time-Series DenseNet for Water Pipeline Leak Detection and Localization Using Acousto- Optic Sensor)
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New study results on artificial intelligence have been published. According to news originating from Tamil Nadu, India, by NewsRx editors, the research stated, “A crucial component within any structural health monitoring system is a pipeline leak detection mechanism, vital for preventing avoidable water loss.” Funders for this research include Universiti Tunku Abdul Rahman, Sungai Long Campus, Kajang, Selangor, Malaysia, Through The Trans-disciplinary Research Grant Scheme (Trgs) Project. The news journalists obtained a quote from the research from VIT University: “Contemporary literature employs machine learning and deep learning for detecting pipeline leaks and cross-correlation for leak localization. The major drawbacks in the existing methodologies are that machine learning and deep learning methods need two different architectures for leak detection and localization, and the cross-correlation needs two sensors with a denoising technique. The primary objective of this paper is to deploy a unified architecture capable of executing both the detection and localization of a leak without any denoising technique and with a single sensor. The proposed technique utilizes the data collected using an Acousto-optic sensor with two different pressures. This paper proposes a novel custom one-dimensional time-series DenseNet for leak detection and localization. The proposed method gives better accuracies compared with the existing one-dimensional DenseNet-121, three different one-dimensional convolutional neural networks (1DCNN), and cross-correlation for two different pressure datasets.”
VIT UniversityTamil NaduIndiaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
IEEE
