In this paper, the implementations of two reinforcement learnings namely, Q learning and deep Q network (DQN) on the Gazebo model of a self balancing robot have been discussed. The goal of the experiments is to make the robot model learn the best actions for staying balanced in an environment. The more time it can remain within a specified limit, the more reward it accumulates and hence more balanced it is. We did various tests with many hyperparameters and demonstrated the performance curves.Electronic supplementary materialThe online version of this article (10.1186/s40638-018-0091-9) contains supplementary material, which is available to authorized users.
Pipelines are one of the most widely used means for oil/gas and water transportation worldwide. These pipelines are often subject to failures like erosion, sabotage and theft, causing high financial, environmental and health risks. Therefore, detecting leakages, estimating its size and location is very important. Current pipeline monitoring systems needs to be more automated, efficient and accurate methods for continuous inspection/reporting about faults. For this purpose, several pattern recognition and data mining techniques have been brought into the research community. In light of the issues of low efficiency and high false alarm rates in traditional pipeline condition monitoring, in this paper, we have used negative pressure wave (NPW) coupled with intelligent machine learning techniques integrated in distributed wireless sensor network (WSN) to identify specific events beased on raw data gathered by individual sensor nodes. This collaborative approach reduces communication overhead to minimum by processing raw data on sensor nodes directly and reporting the detected events only. We apply the methods of support vector machine (SVM), K-nearest neighbor (KNN) and Gaussian mixture model (GMM) in multidimensional feature space. The suggested technique is validated using a serial publication of experimentation on a field deployed test bed, with regard to performance of detection of leakages in pipelines.
Keywords-Wireless sensor network (WSN), Negative pressure wave(NPW), Support Vector Machine (SVM), Gaussian Mixture Model (GMM), K-nearest neighbor (KNN)
Bursts and leakages have turned out to be one of the most frequent malfunctions in liquid pipeline distribution systems. In recent years, the issue has gained a lot of attention in research community due to associated financial costs, environmental hazards, and safety considerations. Wireless sensor network (WSN) based leakage detection and localization can provide an exceptional level of operational efficiency, safety assurance, and real-time parametric view of the entire pipeline network. In this paper, we propose a transient pressure wave based technique coupled with wavelet analysis to achieve reliable detection and localization of abrupt bursts and leakages. The presented technique uses the information carried in the transient pressure signal. A specific pattern is induced on the pressure traces within the pipeline due to leak; we use wavelet analysis to detect these local singularities. The proposed algorithm is distributed in nature and run on low power sensor nodes. The algorithm is deployed in field on a custom pipeline test bed and performance results are documented for various testing scenarios. A comparison of proposed wavelet technique with other widely used methods has been carried out. The technique provides more than 90% accuracy in a number of deployment scenarios for high noise generating long pipeline networks.
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