We propose a non-photorealistic rendering method for generating ripple images from gray-scale photographic images. Ripple images represent photographic images with wavy lines in a certain direction. Our method is executed by an iterative process with two smoothing filers using different regions in the window. Our method has two features. The first feature is that ripple patterns can be automatically generated according to the density and contour of photographic images. The second feature is that the interval and direction of ripple patterns can be changed by changing the values of the parameters of our method. In order to verify the effectiveness of our method, we conducted experiments using various photographic images. Experimental results show that our method can realize the two features.
This paper proposes an optimization method of demand resource capacities to manage the voltage in the distribution network with photovoltaic generations (PV). The proposed method is within the cooperative voltage management system based on the cooperative game theory in which an imputation in a non-empty core is guaranteed. In the proposed method, the optimization problem is formulated as a bi-level programming problem in which a leader, who is a distribution network operator, optimizes the demand resource capacities of consumers and followers, who are consumers in the distribution network, determine the amount of controlled resources under the optimized resource capacities. In order to solve the bi-level programming problem by a commercial solver efficiently, it is converted to a mixed-integer linear programming problem. The imputation in the core is computed based on a dual solution of the lower level problem. The proposed method is validated by computational experiments using a large scale distribution network model with multiple feeders and PVs.
It is expected to grasp how the lung deform by the deaeration during surgery compared to the inflated lung. In this study we propose a method to estimate deformation of the deflated lung from the inflated one based on the relative position of some landmarks using dog lungs. The kernel method was employed for the estimation as a machine learning technique. We achieved mean local positional error of 2.96 mm for test data where the volume reduction by the deaeration was 40 %.