Photo retouching enables photographers to invoke dramatic visual impressions by artistically enhancing their photos through stylistic color and tone adjustments. However, it is also a time-consuming and challenging task that requires advanced skills beyond the abilities of casual photographers. Using an automated algorithm is an appealing alternative to manual work but such an algorithm faces many hurdles. Many photographic styles rely on subtle adjustments that depend on the image content and even its semantics. Further, these adjustments are often spatially varying. Because of these characteristics, existing automatic algorithms are still limited and cover only a subset of these challenges. Recently, deep machine learning has shown unique abilities to address hard problems that resisted machine algorithms for long. This motivated us to explore the use of deep learning in the context of photo editing. In this paper, we explain how to formulate the automatic photo adjustment problem in a way suitable for this approach. We also introduce an image descriptor that accounts for the local semantics of an image. Our experiments demonstrate that our deep learning formulation applied using these descriptors successfully capture sophisticated photographic styles. In particular and unlike previous techniques, it can model local adjustments that depend on the image semantics. We show on several examples that this yields results that are qualitatively and quantitatively better than previous work.
Interest in low-temperature operation of solid oxide fuel cells is growing. Recent advances in perovskite phases have resulted in an efficient H + /O 2- /e - triple-conducting electrode BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ for low-temperature fuel cells. Here, we further develop BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ for electrolyte applications by taking advantage of its high ionic conduction while suppressing its electronic conduction through constructing a BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ -ZnO p-n heterostructure. With this approach, it has been demonstrated that BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ can be applied in a fuel cell with good electrolyte functionality, achieving attractive ionic conductivity and cell performance. Further investigation confirms the hybrid H + /O 2- conducting capability of BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ -ZnO. An energy band alignment mechanism based on a p-n heterojunction is proposed to explain the suppression of electronic conductivity and promotion of ionic conductivity in the heterostructure. Our findings demonstrate that BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ is not only a good electrode but also a highly promising electrolyte. The approach reveals insight for developing advanced low-temperature solid oxide fuel cell electrolytes.
We report a confined proton transportation in the CeO 2 /CeO 2−δ core−shell structure to build up proton shuttles, leading to a super proton conductivity of 0.16 S cm −1 for the electrolyte and advanced fuel cell performance, 697 mW cm −2 at 520 °C. The semiconductor nature of the CeO 2 (i-type) core and the CeO 2−δ (n-type) shell reveals a unique proton transport mechanism based on the charged layers formed at the interface of the CeO 2−δ /CeO 2 heterostructure. Two key factors of this structure confine proton transport to the particle surface. The first is the high concentration of oxygen vacancies in the surface layer, which benefits proton conduction. The second is a depletion region created by the core−shell interface that allows proton migration only on the surface layer rather than into the bulk CeO 2 . The constrained surface region of the CeO 2−δ builds up continuous proton shuttles. This work presents a new methodology and understanding for proton transport in general oxides and a new generation proton ceramic fuel cells.
This paper studies the weights stability and accuracy of the implicit fifth-order weighted essentially nonoscillatory finite difference scheme. It is observed that the weights of the Jiang-Shu weighted essentially nonoscillatory scheme oscillate even for smooth flows. An increased " value of 10 2 is suggested for the weighted essentially nonoscillatory smoothness factors, which removes the weights oscillation and significantly improves the accuracy of the weights and solution convergence. With the improved " value, the weights achieve the optimum value with minimum numerical dissipation in smooth regions and maintain the sensitivity to capture nonoscillatory shock profiles for the transonic flows. The theoretical justification of this treatment is given in the paper. The wall surface boundary condition uses a half-point mesh so that the conservative differencing can be enforced. A third-order accurate finite difference scheme is given to treat wall boundary conditions. The implicit time-marching method with unfactored Gauss-Seidel line relaxation is used with the high-order schemes to achieve a high convergence rate. Several transonic cases are calculated to demonstrate the robustness, efficiency, and accuracy of the methodology. Nomenclature C k = optimal weight IS k = smoothness estimator J = Jacobian of transformation M = Mach number Pr = Prandtl number Pr t = turbulent Prandtl number p = pressure/power used for weighted essentially nonoscillatory scheme q k = heat flux in Cartesian coordinates/third-order polynomial interpolation Re = Reynolds number t = time u, v, w = velocity components in x, y, and z direction x, y, z = Cartesian coordinates = ratio of specific heats U = difference of the conservative variables " = parameter introduced in weighted essentially nonoscillatory scheme = molecular viscosity t = turbulent viscosity , , = generalized coordinates = density ! k = weight Subscripts i, j, k = indices w = wall 1 = freestream Superscripts L, R = left and right sides of the interface n = time level = dimensionless variable
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