648 research outputs found
Water Pipeline Leakage Detection Based on Machine Learning and Wireless Sensor Networks
The detection of water pipeline leakage is important to ensure that water supply networks can operate safely and conserve water resources. To address the lack of intelligent and the low efficiency of conventional leakage detection methods, this paper designs a leakage detection method based on machine learning and wireless sensor networks (WSNs). The system employs wireless sensors installed on pipelines to collect data and utilizes the 4G network to perform remote data transmission. A leakage triggered networking method is proposed to reduce the wireless sensor network’s energy consumption and prolong the system life cycle effectively. To enhance the precision and intelligence of leakage detection, we propose a leakage identification method that employs the intrinsic mode function, approximate entropy, and principal component analysis to construct a signal feature set and that uses a support vector machine (SVM) as a classifier to perform leakage detection. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify the water pipeline leakage and has lower energy consumption than the networking methods used in conventional wireless sensor networks
Linear Maximum Margin Classifier for Learning from Uncertain Data
In this paper, we propose a maximum margin classifier that deals with
uncertainty in data input. More specifically, we reformulate the SVM framework
such that each training example can be modeled by a multi-dimensional Gaussian
distribution described by its mean vector and its covariance matrix -- the
latter modeling the uncertainty. We address the classification problem and
define a cost function that is the expected value of the classical SVM cost
when data samples are drawn from the multi-dimensional Gaussian distributions
that form the set of the training examples. Our formulation approximates the
classical SVM formulation when the training examples are isotropic Gaussians
with variance tending to zero. We arrive at a convex optimization problem,
which we solve efficiently in the primal form using a stochastic gradient
descent approach. The resulting classifier, which we name SVM with Gaussian
Sample Uncertainty (SVM-GSU), is tested on synthetic data and five publicly
available and popular datasets; namely, the MNIST, WDBC, DEAP, TV News Channel
Commercial Detection, and TRECVID MED datasets. Experimental results verify the
effectiveness of the proposed method.Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence. (c)
2017 IEEE. DOI: 10.1109/TPAMI.2017.2772235 Author's accepted version. The
final publication is available at
http://ieeexplore.ieee.org/document/8103808
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