TH-A-116-09: A Novel Prior-Knowledge-Based Optimization Algorithm for Automatic Treatment Planning and Adaptive Radiotherapy Re-Planning

2013 ◽  
Vol 40 (6Part32) ◽  
pp. 530-530 ◽  
Author(s):  
M Zarepisheh ◽  
T Long ◽  
N Li ◽  
E Romeijn ◽  
X Jia ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Prior Knowledge ◽  
Optimization Algorithm ◽  
Adaptive Radiotherapy ◽  
Knowledge Based ◽  
Automatic Treatment

scholarly journals A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

2014 ◽  
Vol 41 (6Part1) ◽  
pp. 061711 ◽  
Author(s):  
Masoud Zarepisheh ◽  
Troy Long ◽  
Nan Li ◽  
Zhen Tian ◽  
H. Edwin Romeijn ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Optimization Algorithm ◽  
Adaptive Radiotherapy ◽  
Automatic Treatment

TH-C-137-10: Development of a GPU Research Platform for Automatic Treatment Planning and Adaptive Radiotherapy Re-Planning

2013 ◽  
Vol 40 (6Part32) ◽  
pp. 534-534 ◽  
Author(s):  
Q Gautier ◽  
Z Tian ◽  
Y Graves ◽  
N Li ◽  
M Zarepisheh ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Adaptive Radiotherapy ◽  
Research Platform ◽  
Automatic Treatment

SU-E-T-652: GPU-Based Automatic Treatment Planning Using Previously Delivered Treatment Plans as Prior Knowledge

2013 ◽  
Vol 40 (6Part21) ◽  
pp. 356-356
Author(s):  
N Li ◽  
Q Gautier ◽  
M Zarepisheh ◽  
Y Graves ◽  
Z Tian ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Prior Knowledge ◽  
Treatment Plans ◽  
Automatic Treatment

SU-E-J-71: Spatially Preserving Prior Knowledge-Based Treatment Planning

2015 ◽  
Vol 42 (6Part8) ◽  
pp. 3280-3280
Author(s):  
H Wang ◽  
L Xing
Keyword(s):  
Treatment Planning ◽  
Prior Knowledge ◽  
Knowledge Based

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

scholarly journals Automatic treatment planning for VMAT‐based total body irradiation using Eclipse scripting

2021 ◽  
Vol 22 (3) ◽  
pp. 119-130
Author(s):  
Jose R. Teruel ◽  
Sameer Taneja ◽  
Paulina E. Galavis ◽  
K. Sunshine Osterman ◽  
Allison McCarthy ◽  
...  
Keyword(s):  
Treatment Planning ◽  
Total Body Irradiation ◽  
Total Body ◽  
Automatic Treatment

scholarly journals Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

2017 ◽  
Vol 221 ◽  
pp. 427-436 ◽  
Author(s):  
Anthony L. Schroeder ◽  
Dalma Martinović-Weigelt ◽  
Gerald T. Ankley ◽  
Kathy E. Lee ◽  
Natalia Garcia-Reyero ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
River Basin ◽  
Biological Effects ◽  
Knowledge Based

A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

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