105 research outputs found
Precise large deviations of some net loss processes in three nonstandard renewal risk models
For three nonstandard renewal risk models, in which claim sizes are
identically distributed random variables with consistently varying tails, using
two key probability inequalities for wide dependent random variables, we study
precise large deviations of proportional net loss process and
excess-of-net-loss process under the consideration of income factor. Then we
apply the above results to obtain asymptotic estimate of mean of stop-net-loss
reinsurance treat and random-time ruin probability. In addition, we have
improved and generalized some known related results, none of which involve the
income factor
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization
Reinforcement learning (RL) has achieved promising results on most robotic
control tasks. Safety of learning-based controllers is an essential notion of
ensuring the effectiveness of the controllers. Current methods adopt whole
consistency constraints during the training, thus resulting in inefficient
exploration in the early stage. In this paper, we propose an algorithm named
Constrained Policy Optimization with Extra Safety Budget (ESB-CPO) to strike a
balance between the exploration efficiency and the constraints satisfaction. In
the early stage, our method loosens the practical constraints of unsafe
transitions (adding extra safety budget) with the aid of a new metric we
propose. With the training process, the constraints in our optimization problem
become tighter. Meanwhile, theoretical analysis and practical experiments
demonstrate that our method gradually meets the cost limit's demand in the
final training stage. When evaluated on Safety-Gym and Bullet-Safety-Gym
benchmarks, our method has shown its advantages over baseline algorithms in
terms of safety and optimality. Remarkably, our method gains remarkable
performance improvement under the same cost limit compared with baselines.Comment: 7 pages, 8 figure
Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images
White matter hyperintensities (WMH) are commonly found in the brains of
healthy elderly individuals and have been associated with various neurological
and geriatric disorders. In this paper, we present a study using deep fully
convolutional network and ensemble models to automatically detect such WMH
using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance
(MR) scans. The algorithm was evaluated and ranked 1 st in the WMH Segmentation
Challenge at MICCAI 2017. In the evaluation stage, the implementation of the
algorithm was submitted to the challenge organizers, who then independently
tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score,
precision and robust Hausdorff distance obtained on held-out test datasets were
80%, 84% and 6.30mm respectively. These were the highest achieved in the
challenge, suggesting the proposed method is the state-of-the-art. In this
paper, we provide detailed descriptions and quantitative analysis on key
components of the system. Furthermore, a study of cross-scanner evaluation is
presented to discuss how the combination of modalities and data augmentation
affect the generalization capability of the system. The adaptability of the
system to different scanners and protocols is also investigated. A quantitative
study is further presented to test the effect of ensemble size. Additionally,
software and models of our method are made publicly available. The
effectiveness and generalization capability of the proposed system show its
potential for real-world clinical practice.Comment: final version in NeuroImag
Robust Fault-Tolerant Tracking Control for Nonlinear Networked Control System: Asynchronous Switched Polytopic Approach
This paper is concerned with the robust fault-tolerant tracking control problem for networked control system (NCS). Firstly, considering the locally overlapped switching law widely existed in engineering applications, the NCS is modeled as a locally overlapped switched polytopic system to reduce designing conservatism and solving complexity. Then, switched parameter dependent fault-tolerant tracking controllers are constructed to deal with the asynchronous switching phenomenon caused by the updating delays of the switching signals and weighted coefficients. Additionally, the global uniform asymptotic stability in the mean (GUAS-M) and desired weighted l2 performance are guaranteed by combining the switched parameter dependent Lyapunov functional method with the average dwell time (ADT) method, and the feasible conditions for the fault-tolerant tracking controllers are obtained in the form of linear matrix inequalities (LMIs). Finally, the performance of the proposed approach is verified on a highly maneuverable technology (HiMAT) vehicle’s tracking control problem. Simulation results show the effectiveness of the proposed method
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