scholarly journals Smart grid initialization reduces the computational complexity of multi-objective image registration based on a dual-dynamic transformation model to account for large anatomical differences

2016 ◽  
Author(s):  
Peter A. N. Bosman ◽  
Tanja Alderliesten
Keyword(s):  
Computational Complexity ◽  
Image Registration ◽  
Smart Grid ◽  
Transformation Model ◽  
Multi Objective ◽  
Dynamic Transformation

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

A multi-objective market-driven framework for power matching in the smart grid

2018 ◽  
Vol 70 ◽  
pp. 199-215 ◽  
Author(s):  
Armin Ghasem Azar ◽  
Mohsen Afsharchi ◽  
Mansoor Davoodi ◽  
Bahram Sadeghi Bigham
Keyword(s):  
Smart Grid ◽  
Multi Objective ◽  
Power Matching ◽  
Market Driven

scholarly journals Weight Feedback Adaptive Multi Objective Cooperative Scheduling in Smart Grid Application

2019 ◽  
Vol 8 (2) ◽  
pp. 607-611
Keyword(s):  
Smart Grid ◽  
Power Supply ◽  
Smart Grids ◽  
Grid Integration ◽  
Multi Objective ◽  
Monitoring Model ◽  
Power Scheduling ◽  
Grid Interface ◽  
Feedback Scheduling ◽  
Cooperative Scheduling

Smart grid integration needs a highly accurate power scheduling to minimizes the losses and efficiently utilize the power supply to minimize the loss. Scheduling of a smart grid interface is monitored based on single or multiple objectives scheduler, where the smart grids are scheduled based on the measured parameters of power dispatch and the consumption model. Wherein, multi objective scheduling results in prominent result, the system is a linear monitoring model, where no previous observations are considered in making present decision. This constraint the accuracy of scheduling. In this paper, a new feedback scheduling operation based on feedback operation is proposed. the approach significantly feedbacks the past parameter variation and leads to an optimal power supply in smart grid interface. The experimental results obtained signifies a optimal improvement in the decision delay and power compensation.

scholarly journals A Robust False Matching Points Detection Method for Remote Sensing Image Registration

2015 ◽  
Vol XL-7/W3 ◽  
pp. 699-702
Author(s):  
X. J. Shan ◽  
P. Tang
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Detection Method ◽  
Graph Transformation ◽  
Remote Sensing Image ◽  
Transformation Model ◽  
Superior Performance ◽  
Geometric Distortion ◽  
Remote Sensing Images ◽  
Accuracy Result

Given the influences of illumination, imaging angle, and geometric distortion, among others, false matching points still occur in all image registration algorithms. Therefore, false matching points detection is an important step in remote sensing image registration. Random Sample Consensus (RANSAC) is typically used to detect false matching points. However, RANSAC method cannot detect all false matching points in some remote sensing images. Therefore, a robust false matching points detection method based on Knearest- neighbour (K-NN) graph (KGD) is proposed in this method to obtain robust and high accuracy result. The KGD method starts with the construction of the K-NN graph in one image. K-NN graph can be first generated for each matching points and its K nearest matching points. Local transformation model for each matching point is then obtained by using its K nearest matching points. The error of each matching point is computed by using its transformation model. Last, L matching points with largest error are identified false matching points and removed. This process is iterative until all errors are smaller than the given threshold. In addition, KGD method can be used in combination with other methods, such as RANSAC. Several remote sensing images with different resolutions and terrains are used in the experiment. We evaluate the performance of KGD method, RANSAC + KGD method, RANSAC, and Graph Transformation Matching (GTM). The experimental results demonstrate the superior performance of the KGD and RANSAC + KGD methods.

An Interactive Neural Network for Constrained Multi-Objective Optimization with Application to the Design of Digital Filters

2012 ◽  
Vol 433-440 ◽  
pp. 2808-2816
Author(s):  
Jian Jin Zheng ◽  
You Shen Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Complexity ◽  
Digital Filters ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

This paper presents a new interactive neural network for solving constrained multi-objective optimization problems. The constrained multi-objective optimization problem is reformulated into two constrained single objective optimization problems and two neural networks are designed to obtain the optimal weight and the optimal solution of the two optimization problems respectively. The proposed algorithm has a low computational complexity and is easy to be implemented. Moreover, the proposed algorithm is well applied to the design of digital filters. Computed results illustrate the good performance of the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document