Scale-aware Progressive Optimization Network

2020 ◽  
Author(s):  
Ying Chen ◽  
Lifeng Huang ◽  
Chengying Gao ◽  
Ning Liu
Keyword(s):  
Progressive Optimization

scholarly journals A novel framework of deriving joint impoundment rules for large-scale reservoir system based on a classification-aggregation-decomposition approach

2020 ◽  
Author(s):  
Shaokun He ◽  
Shenglian Guo ◽  
Chong-Yu Xu ◽  
Kebing Chen ◽  
Zhen Liao ◽  
...  
Keyword(s):  
Flood Control ◽  
Large Scale ◽  
Geographical Location ◽  
Nsga Ii ◽  
Decomposition Approach ◽  
Flood Prevention ◽  
Reservoir System ◽  
Upper Yangtze River ◽  
The Upper Yangtze River ◽  
Progressive Optimization

Abstract. Joint and optimal impoundment operation of the large-scale reservoir system has become more crucial for modern water management. Since the existing techniques fail to optimize the large-scale multi-objective impoundment operation due to the complex inflow stochasticity and high dimensionality, we develop a novel combination of parameter simulation optimization and classification-aggregation-decomposition approach here to overcome these obstacles. There are four main steps involved in our proposed framework: (1) reservoirs classification based on geographical location and flood prevention targets; (2) assumption of a hypothetical single reservoir in the same pool; (3) the derivation of the initial impoundment policies by the non-dominated sorting genetic algorithm-II (NSGA-II); (4) further improvement of the impoundment policies via Parallel Progressive Optimization Algorithm (PPOA). The framework potential is performed on China's mixed 30-reservoir system in the upper Yangtze River. Results indicate that our method can provide a series of schemes to refer to different flood event scenarios. The best scheme outperforms the conventional operating rule, as it increases impoundment efficiency from 89.50 % to 94.16 % and hydropower generation by 7.70 billion kWh (or increase 3.79 %) while flood control risk is less than 0.06.

scholarly journals Design of a High-Performance Digital Slit-Lamp Microscope with Five-Switched Zoom

2020 ◽  
Vol 10 (8) ◽  
pp. 2757 ◽  
Author(s):  
Youping Huang ◽  
Xiaogang Chen ◽  
Hao Zhang ◽  
Shuyan Huang ◽  
Feng Lin
Keyword(s):  
Optical System ◽  
Modulation Transfer Function ◽  
High Performance ◽  
Tolerance Analysis ◽  
Slit Lamp ◽  
Modulation Transfer ◽  
Coaxial System ◽  
Progressive Optimization ◽  
Telescope System ◽  
Quality Imaging

In this study, we design and present a five-fold digital slit-lamp microscope (DSLM) with built-in photographic lens and CCD. The initial structures of the front objective, Galilean telescope system, and photographic lens are systematically investigated and discussed in the design. A progressive optimization process is employed in the non-coaxial system design after the coaxial system achieves high performance. The analysis of spot diagrams and the modulation transfer function (MTF) show that this DSLM optical system achieves quasi-diffraction-limited performance and enables high-quality imaging for ophthalmic examination. Furthermore, tolerance analysis of this optical system is also performed, which provides a theoretical basis for machining and assembly. This design provides an idea for the design of a digital-zoom microscope in biomedical imaging instruments.

scholarly journals Stable Portfolio Selection Strategy for Mean-Variance-CVaR Model under High-Dimensional Scenarios

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yu Shi ◽  
Xia Zhao ◽  
Fengwei Jiang ◽  
Yipin Zhu
Keyword(s):  
Optimization Problems ◽  
Short Selling ◽  
Computational Method ◽  
High Dimensional ◽  
Selection Strategy ◽  
Covariance Estimator ◽  
Out Of Sample ◽  
Sparse Portfolio ◽  
Mean Variance ◽  
Progressive Optimization

This paper aims to study stable portfolios with mean-variance-CVaR criteria for high-dimensional data. Combining different estimators of covariance matrix, computational methods of CVaR, and regularization methods, we construct five progressive optimization problems with short selling allowed. The impacts of different methods on out-of-sample performance of portfolios are compared. Results show that the optimization model with well-conditioned and sparse covariance estimator, quantile regression computational method for CVaR, and reweighted L1 norm performs best, which serves for stabilizing the out-of-sample performance of the solution and also encourages a sparse portfolio.

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