On the Convergence of Decomposition Algorithms in Many-Objective Problems

2019 ◽  
pp. 39-50
Author(s):  
Ricardo H. C. Takahashi
Keyword(s):  
Decomposition Algorithms

Decomposition of tasks in a group of robots using cloud computing technologies

2017 ◽  
Vol 12 (1) ◽  
pp. 83-88
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Cloud Computing ◽  
Experimental Verification ◽  
Decomposition Algorithms ◽  
Complex Task ◽  
Large Problem ◽  
Working Capacity ◽  
Linear Distribution ◽  
Effective Teams ◽  
Computing Technologies

In this paper, various variants of decomposition of tasks in a group of robots using cloud computing technologies are considered. The specifics of the field of application (teams of robots) and solved problems are taken into account. In the process of decomposition, the solution of one large problem is divided into a solution of a series of smaller, simpler problems. Three ways of decomposition based on linear distribution, swarm interaction and synthesis of solutions are proposed. The results of experimental verification of the developed decomposition algorithms are presented, the working capacity of methods for planning trajectories in the cloud is shown. The resulting solution is a component of the complex task of building effective teams of robots.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals Robust Template Decomposition without Weight Restriction for Cellular Neural Networks Implementing Arbitrary Boolean Functions Using Support Vector Classifiers

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Yih-Lon Lin ◽  
Jer-Guang Hsieh ◽  
Jyh-Horng Jeng
Keyword(s):  
Neural Networks ◽  
Boolean Function ◽  
Past Research ◽  
Cellular Neural Network ◽  
Decomposition Methods ◽  
Cellular Neural Networks ◽  
Support Vector ◽  
Decomposition Algorithms ◽  
Weight Restriction ◽  
The Given

If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.

Flow Control Around a SUV Simplified Vehicle

2018 ◽  
Author(s):  
Stéphie Edwige ◽  
Philippe Gilotte ◽  
Iraj Mortazavi ◽  
Yoann Eulalie ◽  
David Holst ◽  
...  
Keyword(s):  
Drag Reduction ◽  
Flow Control ◽  
Time Scale ◽  
Pressure Coefficient ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Decomposition Algorithms ◽  
Reference Case ◽  
Control Techniques ◽  
Run Time

The research on the external aerodynamics of ground vehicles can nowadays be related to sustainable development strategies, confirmed by the worldwide CO2 regulation target. Automotive manufacturers estimate that a drag reduction of 30% contributes to 10g/km of CO2 reduction. However, this drag reduction should be obtained without constraints on the design, the safety, comfort and habitability of the passengers. Thus, it is interesting to find flow control solutions, which will remove or remote recirculation zones due to separation edges with appropriate control techniques. In automotive sales, the SUV, 4x4 and compact cars represent a large part of the market share and the study of control approaches for this geometry is practically useful. In this work, appropriate control techniques are designed to decrease the drag forces around a reduced scale SUV car benchmark called POSUV. The control techniques are based on the DMD (Dynamic Mode Decomposition) algorithms generating an optimized drag reduction procedure. It involves independent transient inflow boundary conditions for flow control actuation in the vicinity of the separation zones and time resolved pressure sensor output signals on the rear end surface of the mockup. This study, that exploits dominant flow features behind the tailgate and the rear bumper, is performed using Large Eddy Simulations on a sufficient run time scale, in order to minimize a cost function dealing with the time and space average pressure coefficient. The resulting dynamic modal decomposition obtained by these LES simulations and by wind tunnel measurements has been compared for the reference case, in order to select the most appropriate run time scale. Analysis of the numerical results shows a significant pressure increase on the tailgate, for independent flow control frequencies. Similar decomposition performed in the wake with and without numerical flow control help understanding the flow modifications in the detachment zones.

Sign in / Sign up

Export Citation Format

Share Document