Results 31 to 40 of about 1,590,381 (357)
Factors modulating 99mTc‐MAA planar lung dosimetry for 90Y radioembolization
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Purpose To investigate the accuracy and biases of predicted lung shunt fraction (LSF) and lung dose (LD) calculations via 99mTc‐macro‐aggregated albumin (99mTc‐MAA) planar imaging for treatment planning of 90Y‐microsphere radioembolization.
Benjamin P. Lopez +4 more
wiley +1 more source
IEEE Access, 2021 Particle swarm optimization (PSO) is a swarm intelligence-based metaheuristic algorithm inspired by the natural behavior of birds flocking or fish schooling.
Rehan Ali Khan +4 more
doaj +1 more source
Global Optimality in Low-Rank Matrix Optimization [PDF]
IEEE Transactions on Signal Processing, 2017 This paper considers the minimization of a general objective function $f(X)$ over the set of rectangular $n\times m$ matrices that have rank at most $r$. To reduce the computational burden, we factorize the variable $X$ into a product of two smaller matrices and optimize over these two matrices instead of $X$. Despite the resulting nonconvexity, recent
Zhihui Zhu +3 more
openaire +3 more sources
Semi‐supervised classification of fundus images combined with CNN and GCN
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Purpose Diabetic retinopathy (DR) is one of the most serious complications of diabetes, which is a kind of fundus lesion with specific changes. Early diagnosis of DR can effectively reduce the visual damage caused by DR. Due to the variety and different morphology of DR lesions, automatic classification of fundus images in mass screening can ...
Sixu Duan +8 more
wiley +1 more source
New global optimality conditions for nonsmooth DC optimization problems [PDF]
Journal of Global Optimization. 76:1 (2020) 25-55, 2018 In this article we propose a new approach to an analysis of DC optimization problems. This approach was largely inspired by codifferential calculus and the method of codifferential descent and is based on the use of a so-called affine support set of a convex function instead of the Frenchel conjugate function.
arxiv +1 more source
A Direct Search Algorithm for Global Optimization
Algorithms, 2016 A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations.
Enrique Baeyens +2 more
doaj +1 more source
Sequential optimization of strip bending process using multiquadric radial basis function surrogate models [PDF]
, 2013 Surrogate models are used within the sequential optimization strategy for forming processes. A sequential improvement (SI) scheme is used to refine the surrogate model in the optimal region. One of the popular surrogate modeling methods for SI is Kriging.
Boogaard, A.H. van den +2 more
core +4 more sources
Global Optimization of Robotic Grasps
Robotics: Science and Systems VII, 2011 This paper presents a procedure to optimize the quality of robotic grasps for objects that need to be held and manipulated in a specific way, characterized by a number of tight contact constraints. The main difficulties of the problem include that the set of feasible grasps is a manifold implicitly defined by a system of non-linear equations, the high ...
Rosales Gallegos, Carlos +2 more
openaire +3 more sources
Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022 Abstract Positron emission tomography with x‐ray computed tomography (PET/CT) is increasingly being utilized for radiation treatment planning (RTP). Accurate delivery of RT therefore depends on quality PET/CT data. This study covers quality control (QC) procedures required for PET/CT for diagnostic imaging and incremental QC required for RTP.
Ran Klein +7 more
wiley +1 more source
Enhanced Global Optimization with Parallel Global and Local Structures [PDF]
arXiv, 2022 In practice, objective functions of real-time control systems can have multiple local minimums or can dramatically change over the function space, making them hard to optimize. To efficiently optimize such systems, in this paper, we develop a parallel global optimization framework that combines direct search methods with Bayesian parallel optimization.
arxiv