Improving the age estimation accuracy by a hybrid optimization scheme

2017 ◽  
Vol 77 (2) ◽  
pp. 2543-2559 ◽  
Author(s):  
Reza Syahroel Ghufran ◽  
Jenq-Shiou Leu ◽  
Setya Widyawan Prakosa
Keyword(s):  
Age Estimation ◽  
Hybrid Optimization ◽  
Estimation Accuracy ◽  
Optimization Scheme

Multi-channel Attention-Fusion Neural Network for Brain Age Estimation: Accuracy, Generality, and Interpretation with 16,705 Healthy MRIs across Lifespan

2021 ◽  
pp. 102091
Author(s):  
Sheng He ◽  
Diana Pereira ◽  
Juan David Perez ◽  
Randy L. Gollub ◽  
Shawn N. Murphy ◽  
...  
Keyword(s):  
Neural Network ◽  
Age Estimation ◽  
Estimation Accuracy ◽  
Brain Age

scholarly journals Multifeature Extreme Ordinal Ranking Machine for Facial Age Estimation

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Zhao ◽  
Han Wang ◽  
Guang-Bin Huang
Keyword(s):  
Age Estimation ◽  
Optimization Problems ◽  
State Of The Art ◽  
Mathematical Optimization ◽  
The State ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Training Process ◽  
Ordinal Ranking ◽  
Facial Age

Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation. Experimental results clearly demonstrate that the proposed approach can sharply reduce the runtime (even up to nearly one hundred times faster) while achieving comparable or better estimation performances than the state-of-the-art approaches. The inner properties of MFEORM are further explored with more advantages.

scholarly journals Multiphase Trajectory Optimization of a Lunar Return Mission to an LEO Space Station

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Luyi Yang ◽  
Haiyang Li ◽  
Jin Zhang ◽  
Yazhong Luo
Keyword(s):  
Trajectory Optimization ◽  
Space Station ◽  
Low Earth Orbit ◽  
Hybrid Optimization ◽  
Target Space ◽  
Mission Design ◽  
Optimization Scheme ◽  
Initial State ◽  
Bank Angle ◽  
Entire Trajectory

Lunar exploration architecture can be made more flexible and reliable with the support of a low-Earth orbit (LEO) space station. This study therefore evaluated a proposed hybrid optimization scheme to design the entire trajectory of a reusable spacecraft starting from trans-Earth injection (EI) at the perilune and ending at an LEO space station. As such a trajectory has multiple constraints and multiple dynamical models, it is divided into the trans-Earth phase, aerocapture phase, and postatmospheric phase. The optimization scheme is performed at two levels: sublevel and top level. At the sublevel, two novel pseudo rules are proposed to optimize the trans-Earth trajectory so that it satisfies the coplanar constraints of the space station. Then, in the aerocapture phase, the bank angle is optimized to satisfy the mission constraints, and in the atmospheric phase, the one-impulsive maneuver is performed and optimized to insert the spacecraft into the target space station orbit. The multiple phases are connected to each other by boundary conditions where the terminal state of the previous phase is transformed into the initial state of the following phase. At the top level, the vacuum perigee height is selected as the mission design variable based on problem characteristics analysis and a hybrid optimization scheme is conducted to minimize the total velocity increment. The simulation results demonstrate that the proposed hybrid optimization method is effective for the design of an entire trajectory with acceptable velocity cost which is less than that in the previous study. The coplanar constraints of the space station and other mission constraints in each phase are also satisfied. Furthermore, the proposed trajectory design method is shown to be applicable to a reusable spacecraft returning to an LEO space station parked in any arbitrary orbital plane.

An elitist control scheme for power flow management in smart grid system: a hybrid optimization scheme

2021 ◽  
pp. 1-22
Author(s):  
Ganapathia Pillai Kannayeram ◽  
Rathinam Muniraj ◽  
Nattanmai Balasubramanian Prakash ◽  
Thankaswamy Jarin ◽  
Sivadhas Rosejanet Boselin Prabhu​
Keyword(s):  
Smart Grid ◽  
Power Flow ◽  
Hybrid Optimization ◽  
Grid System ◽  
Optimization Scheme ◽  
Flow Management ◽  
System A ◽  
Control Scheme ◽  
Smart Grid System
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