The Experts below are selected from a list of 162627 Experts worldwide ranked by ideXlab platform
Xingqin Lin - One of the best experts on this subject based on the ideXlab platform.
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WCNC - Rogue Drone Detection: A Machine Learning Approach
2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019Co-Authors: Henrik Ryden, Sakib Bin Redhwan, Xingqin LinAbstract:The emerging, practical and observed issue of how to detect rogue drones carrying terrestrial user equipment (UE) on mobile networks is addressed in this paper. This issue has drawn much attention since the rogue drones may generate excessive interference to mobile networks and may not be allowed by regulations in some regions. In this paper, we propose a novel Machine Learning Approach to identify the rogue drones in mobile networks based on radio measurements. We apply two classification Machine Learning models, Logistic Regression, and Decision Tree, using features from radio measurements to identify the rogue drones. Simulation results show that the proposed Machine Learning solutions can achieve high rogue drone detection rate for high altitudes while not mis-classifying regular ground based UEs as rogue drone UEs.
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Rogue Drone Detection: A Machine Learning Approach
arXiv: Networking and Internet Architecture, 2018Co-Authors: Henrik Ryden, Sakib Bin Redhwan, Xingqin LinAbstract:The emerging, practical and observed issue of how to detect rogue drones that carry terrestrial user equipment (UEs) on mobile networks is addressed in this paper. This issue has drawn much attention since the rogue drones may generate excessive interference to mobile networks and may not be allowed by regulations in some regions. In this paper, we propose a novel Machine Learning Approach to identify the rogue drones in mobile networks based on radio measurements. We apply two classification Machine Learning models, Logistic Regression, and Decision Tree, using features from radio measurements to identify the rogue drones. We find that for high altitudes the proposed Machine Learning solutions can yield high rogue drone detection rate while not mis-classifying regular ground based UEs as rogue drone UEs. The detection accuracy however degrades at low altitudes.
Joel M. Bowman - One of the best experts on this subject based on the ideXlab platform.
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A Machine Learning Approach for Prediction of Rate Constants.
The journal of physical chemistry letters, 2019Co-Authors: Paul L. Houston, Apurba Nandi, Joel M. BowmanAbstract:We report a Machine Learning Approach to train and predict bimolecular thermal rate constants over a large temperature range. The Approach uses Gaussian process (GP) regression to evaluate the diff...
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a Machine Learning Approach for prediction of rate constants
Journal of Physical Chemistry Letters, 2019Co-Authors: Paul L. Houston, Apurba Nandi, Joel M. BowmanAbstract:We report a Machine Learning Approach to train and predict bimolecular thermal rate constants over a large temperature range. The Approach uses Gaussian process (GP) regression to evaluate the difference between accurate quantum results and Eckart-corrected conventional transition state theory, mostly for collinear reactions. Training is done on a database of rate constants for 13 reaction/potential surface combinations, and testing is performed on a set of 39 reaction/potential surface combinations. Averaged over all test reactions, the GP method is within 80% of the accurate answer, whereas transition state theory (TST) is only within 330% and Eckart-corrected TST (ECK) is within 110%. In the tunneling region, GP is generally (with a few exceptions) more accurate and sometimes much more accurate. In the high-temperature recrossing region, GP is significantly more accurate than either TST or ECK, neither of which addresses the possibility of recrossing. The GP predictions for the 3D reactions O(3P) + H2,...
Henrik Ryden - One of the best experts on this subject based on the ideXlab platform.
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WCNC - Rogue Drone Detection: A Machine Learning Approach
2019 IEEE Wireless Communications and Networking Conference (WCNC), 2019Co-Authors: Henrik Ryden, Sakib Bin Redhwan, Xingqin LinAbstract:The emerging, practical and observed issue of how to detect rogue drones carrying terrestrial user equipment (UE) on mobile networks is addressed in this paper. This issue has drawn much attention since the rogue drones may generate excessive interference to mobile networks and may not be allowed by regulations in some regions. In this paper, we propose a novel Machine Learning Approach to identify the rogue drones in mobile networks based on radio measurements. We apply two classification Machine Learning models, Logistic Regression, and Decision Tree, using features from radio measurements to identify the rogue drones. Simulation results show that the proposed Machine Learning solutions can achieve high rogue drone detection rate for high altitudes while not mis-classifying regular ground based UEs as rogue drone UEs.
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Rogue Drone Detection: A Machine Learning Approach
arXiv: Networking and Internet Architecture, 2018Co-Authors: Henrik Ryden, Sakib Bin Redhwan, Xingqin LinAbstract:The emerging, practical and observed issue of how to detect rogue drones that carry terrestrial user equipment (UEs) on mobile networks is addressed in this paper. This issue has drawn much attention since the rogue drones may generate excessive interference to mobile networks and may not be allowed by regulations in some regions. In this paper, we propose a novel Machine Learning Approach to identify the rogue drones in mobile networks based on radio measurements. We apply two classification Machine Learning models, Logistic Regression, and Decision Tree, using features from radio measurements to identify the rogue drones. We find that for high altitudes the proposed Machine Learning solutions can yield high rogue drone detection rate while not mis-classifying regular ground based UEs as rogue drone UEs. The detection accuracy however degrades at low altitudes.
Paul L. Houston - One of the best experts on this subject based on the ideXlab platform.
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A Machine Learning Approach for Prediction of Rate Constants.
The journal of physical chemistry letters, 2019Co-Authors: Paul L. Houston, Apurba Nandi, Joel M. BowmanAbstract:We report a Machine Learning Approach to train and predict bimolecular thermal rate constants over a large temperature range. The Approach uses Gaussian process (GP) regression to evaluate the diff...
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a Machine Learning Approach for prediction of rate constants
Journal of Physical Chemistry Letters, 2019Co-Authors: Paul L. Houston, Apurba Nandi, Joel M. BowmanAbstract:We report a Machine Learning Approach to train and predict bimolecular thermal rate constants over a large temperature range. The Approach uses Gaussian process (GP) regression to evaluate the difference between accurate quantum results and Eckart-corrected conventional transition state theory, mostly for collinear reactions. Training is done on a database of rate constants for 13 reaction/potential surface combinations, and testing is performed on a set of 39 reaction/potential surface combinations. Averaged over all test reactions, the GP method is within 80% of the accurate answer, whereas transition state theory (TST) is only within 330% and Eckart-corrected TST (ECK) is within 110%. In the tunneling region, GP is generally (with a few exceptions) more accurate and sometimes much more accurate. In the high-temperature recrossing region, GP is significantly more accurate than either TST or ECK, neither of which addresses the possibility of recrossing. The GP predictions for the 3D reactions O(3P) + H2,...
Guang Yang - One of the best experts on this subject based on the ideXlab platform.
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IntelliSys (1) - A Machine Learning Approach to Shipping Box Design
Advances in Intelligent Systems and Computing, 2019Co-Authors: Guang YangAbstract:Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer’s online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely challenging operations task to strategically select the best combination of tens of box sizes from thousands of feasible ones to be responsible for hundreds of thousands of orders daily placed on millions of inventory products. In this paper, we present a Machine Learning Approach to tackle the task by formulating the box design problem prescriptively as a generalized version of weighted k-medoids clustering problem, where the parameters are estimated through a variety of descriptive analytics. We test this Machine Learning Approach on fulfillment data collected from Walmart U.S. eCommerce, and our Approach is shown to be capable of improving the box utilization rate by more than \(10\%\).
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A Machine Learning Approach to Shipping Box Design.
arXiv: Machine Learning, 2018Co-Authors: Guang YangAbstract:Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely challenging operations task to strategically select the best combination of tens of box sizes from thousands of feasible ones to be responsible for hundreds of thousands of orders daily placed on millions of inventory products. In this paper, we present a Machine Learning Approach to tackle the task by formulating the box design problem prescriptively as a generalized version of weighted $k$-medoids clustering problem, where the parameters are estimated through a variety of descriptive analytics. We test this Machine Learning Approach on fulfillment data collected from Walmart U.S. eCommerce, and our Approach is shown to be capable of improving the box utilization rate by more than $10\%$.
