This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

Computational geometry

Annual Energy Outlook

Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO2 from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.

https://science.sciencemag.org/content/sci/early/2018/06/20/science.aar7204.full.pdf

Assessment of methane emissions from the U.S. oil and gas supply chain

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Computational Fluid Mechanics And Heat Transfer

In this paper, we propose a quantization approach, as an alternative of sparsification, to curb the growth of the radial basis function structure in kernel adaptive filtering. The basic idea behind this method is to quantize and hence compress the input (or feature) space. Different from sparsification, the new approach uses the “redundant” data to update the coefficient of the closest center. In particular, a quantized kernel least mean square (QKLMS) algorithm is developed, which is based on a simple online vector quantization method. The analytical study of the mean square convergence has been carried out. The energy conservation relation for QKLMS is established, and on this basis we arrive at a sufficient condition for mean square convergence, and a lower and upper bound on the theoretical value of the steady-state excess mean square error. Static function estimation and short-term chaotic time-series prediction examples are presented to demonstrate the excellent performance.

Quantized Kernel Least Mean Square Algorithm

In a recent paper, we developed a novel quantized kernel least mean square algorithm, in which the input space is quantized (partitioned into smaller regions) and the network size is upper bounded by the quantization codebook size (number of the regions). In this paper, we propose the quantized kernel least squares regression, and derive the optimal solution. By incorporating a simple online vector quantization method, we derive a recursive algorithm to update the solution, namely the quantized kernel recursive least squares algorithm. The good performance of the new algorithm is demonstrated by Monte Carlo simulations.

Quantized Kernel Recursive Least Squares Algorithm

This paper presents an automatic target recognition (ATR) approach for sonar onboard unmanned underwater vehicles (UUVs). In this approach, target features are extracted by a convolutional neural network (CNN) operating on sonar images, and then classified by a support vector machine (SMV) that is trained based on manually labeled data. The proposed approach is tested on a set of sonar images obtained by a UUV equipped with side-scan sonar. Automatic target recognition is achieved through the use of matched filters, while target classification is achieved with the trained SVM classifier based on features extracted by the CNN. The results show that deep learning feature extraction provide better performance compared to using other feature extraction techniques such as histogram of oriented gradients (HOG) and local binary pattern (LBP). By processing images autonomously, the proposed approach can be combined with onboard planning and control systems to develop autonomous UUVs able to search for underwater targets without human intervention.

Deep learning feature extraction for target recognition and classification in underwater sonar images

This paper addresses the need for surveillance of fugitive methane emissions over broad geographical regions. Most existing techniques suffer from being either extensive (but qualitative) or quantitative (but intensive with poor scalability). A total of two novel advancements are made here. First, a recursive Bayesian method is presented for probabilistically characterizing fugitive point-sources from mobile sensor data. This approach is made possible by a new cross-plume integrated dispersion formulation that overcomes much of the need for time-averaging concentration data. The method is tested here against a limited data set of controlled methane release and shown to perform well. We then present an information-theoretic approach to plan the paths of the sensor-equipped vehicle, where the path is chosen so as to maximize expected reduction in integrated target source rate uncertainty in the region, subject to given starting and ending positions and prevailing meteorological conditions. The information-driven sensor path planning algorithm is tested and shown to provide robust results across a wide range of conditions. An overall system concept is presented for optionally piggybacking of these techniques onto normal industry maintenance operations using sensor-equipped work trucks.

Pingping Zhu

Papers

Quantized Kernel Least Mean Square Algorithm

Quantized Kernel Recursive Least Squares Algorithm

Deep learning feature extraction for target recognition and classification in underwater sonar images

A Mobile Sensing Approach for Regional Surveillance of Fugitive Methane Emissions in Oil and Gas Production

Fixed budget quantized kernel least-mean-square algorithm