Image Restoration using Total Variation Regularized Deep Image Prior

In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity. However, increasingly the field is moving towards trainable models, inspired from deep learning. Deep image prior (DIP) is a recent regularization framework that uses a convolutional neural network (CNN) architecture without data-driven training. This paper extends the DIP framework by combining it with the traditional TV regularization. We show that the inclusion of TV leads to considerable performance gains when tested on several traditional restoration tasks such as image denoising and deblurring

A new family of fourth-order methods for multiple roots of nonlinear equations

Recently, some optimal fourth-order iterative methods for multiple roots of nonlinear equations are presented when the multiplicity m of the root is known. Different from these optimal iterative methods known already, this paper presents a new family of iterative methods using the modified Newton’s method as its first step. The new family, requiring one evaluation of the function and two evaluations of its first derivative, is of optimal order. Numerical examples are given to suggest that the new family can be competitive with other fourth-order methods and the modified Newton’s method for multiple roots

Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactly-relevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.Comment: Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed equally to this wor

Multi-point and multi-objective optimization of a centrifugal compressor impeller based on genetic algorithm

The design of high efficiency, high pressure ratio, and wide flow range centrifugal impellers is a challenging task. The paper describes the application of a multiobjective, multipoint optimization methodology to the redesign of a transonic compressor impeller for this purpose. The aerodynamic optimization method integrates an improved nondominated sorting genetic algorithm II (NSGA-II), blade geometry parameterization based on NURBS, a 3D RANS solver, a self-organization map (SOM) based data mining technique, and a time series based surge detection method. The optimization results indicate a considerable improvement to the total pressure ratio and isentropic efficiency of the compressor over the whole design speed line and by 5.3% and 1.9% at design point, respectively. Meanwhile, surge margin and choke mass flow increase by 6.8% and 1.4%, respectively. The mechanism behind the performance improvement is further extracted by combining the geometry changes with detailed flow analysis

Surface Energy and Mass Balance Model for Greenland Ice Sheet and Future Projections

The Greenland Ice Sheet contains nearly 3 million cubic kilometers of glacial ice. If the entire ice sheet completely melted, sea level would raise by nearly 7 meters. There is thus considerable interest in monitoring the mass balance of the Greenland Ice Sheet. Each year, the ice sheet gains ice from snowfall and loses ice through iceberg calving and surface melting. In this thesis, we develop, validate and apply a physics based numerical model to estimate current and future surface mass balance of the Greenland Ice Sheet. The numerical model consists of a coupled surface energy balance and englacial model that is simple enough that it can be used for long time scale model runs, but unlike previous empirical parameterizations, has a physical basis. The surface energy balance model predicts ice sheet surface temperature and melt production. The englacial model predicts the evolution of temperature and meltwater within the ice sheet. These two models can be combined with estimates of precipitation (snowfall) to estimate the mass balance over the Greenland Ice Sheet. We first compare model performance with in-situ observations to demonstrate that the model works well. We next evaluate how predictions are degraded when we statistically downscale global climate data. We find that a simple, nearest neighbor interpolation scheme with a lapse rate correction is able to adequately reproduce melt patterns on the Greenland Ice Sheet. These results are comparable to those obtained using empirical Positive Degree Day (PDD) methods. Having validated the model, we next drove the ice sheet model using the suite of atmospheric model runs available through the CMIP5 atmospheric model inter-comparison, which in turn built upon the RCP 8.5 (business as usual) scenarios. From this exercise we predict how much surface melt production will increase in the coming century. This results in 4--10 cm sea level equivalent, depending on the CMIP5 models. Finally, we try to bound melt water production from CMIP5 data with the model by assuming that the Greenland Ice Sheet is covered in black carbon (lowering the albedo) and perpetually covered by optically thick clouds (increasing long wave radiation). This upper bound roughly triples surface meltwater production, resulting in 30 cm of sea level rise by 2100. These model estimates, combined with prior research suggesting an additional 40--100 cm of sea level rise associated with dynamical discharge, suggest that the Greenland Ice Sheet is poised to contribute significantly to sea level rise in the coming century.PHDAtmospheric, Oceanic & Space ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137047/1/xjliu_1.pd

Measuring Hydrometeors with a Precipitation Microphysical Characteristics Sensor: Calibration and Field Measurements

Aiming at the simultaneous measurement of the size, shape, and fall velocity of precipitation particles in the natural environment, we present here a new ground-based precipitation microphysical characteristics sensor (PMCS) based on the particle imaging velocimetry technology. The PMCS can capture autocorrelated images of precipitation particles by double-exposure in one frame, by which the size, axis ratio, and fall velocity of precipitation particles can be calculated. The PMCS is calibrated by a series of glass balls with certain diameters under varying light conditions, and a self-adaptive threshold method is proposed. The shape, axis ratio, and fall velocity of raindrops were calculated and discussed based on the field measurement results of PMCS. The typical shape of large raindrop is an oblate ellipsoid, the axis ratio of raindrops decreases linearly with the diameter, the fall velocity of raindrops approaches its asymptote, and the above observed results are in good agreement with the empirical models; the synchronous observation of a PMCS, an OTT PARSIVEL disdrometer, and a rain gauge shows that the PMCS is able to measure the rain intensity, accumulated rainfall, and drop size distribution with high accuracy. These results have validated the performance of PMCS

A Strategy Optimization Approach for Mission Deployment in Distributed Systems

In order to increase operational efficiency, reduce delays, and/or maximize profit, almost all the organizations have split their mission into several tasks which are deployed in distributed system. However, due to distributivity, the mission is prone to be vulnerable to kinds of cyberattacks. In this paper, we propose a mission deployment scheme to optimize mission payoff in the face of different attack strategies. Using this scheme, defenders can achieve “appropriate security” and force attackers to jointly safeguard the mission situation