Evaluation of the Clinical Utility of an IrisTM Collimator Combined with a Sequential Optimization Algorithm for Robotic Radiosurgery

Deterministic optimal designs that are obtained without taking into account uncertainty/variation are usually unreliable. Although reliability-based design optimization accounts for variation, it assumes that statistical information is available in the form of fully defined probabilistic distributions. This is not true for a variety of engineering problems where uncertainty is usually given in terms of interval ranges. In this case, interval analysis or possibility theory can be used instead of probability theory. This paper shows how possibility theory can be used in design and presents a computationally efficient sequential optimization algorithm. After, the fundamentals of possibility theory and fuzzy measures are described, a double-loop, possibility-based design optimization algorithm is presented where all design constraints are expressed possibilistically. The algorithm handles problems with only uncertain or a combination of random and uncertain design variables and parameters. In order to reduce the high computational cost, a sequential algorithm for possibility-based design optimization is presented. It consists of a sequence of cycles composed of a deterministic design optimization followed by a set of worst-case reliability evaluation loops. Two examples demonstrate the accuracy and efficiency of the proposed sequential algorithm.

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Assessment of the Effect of Energy Consumption on Trajectory Improvement for a Car-like Robot

Robotica ◽

10.1017/s0263574719000407 ◽

2019 ◽

Vol 37 (11) ◽

pp. 1998-2009 ◽

Cited By ~ 4

Author(s):

Francisco Valero ◽

Francisco Rubio ◽

Carlos Llopis-Albert

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Mobile Robot ◽

Optimization Algorithm ◽

Random Distribution ◽

Lower Cost ◽

Maximum Velocity ◽

Optimization Techniques ◽

Sequential Optimization ◽

Inertial Parameters

SummaryReducing the energy consumed by a car-like mobile robot makes it possible to move at a lower cost, yet it takes more working time. This paper proposes an optimization algorithm for trajectories with optimal times and analyzes the consequences of restricting the energy consumed on the trajectory obtained for a car-like robot. When modeling the dynamic behavior of the vehicle, it is necessary to consider its inertial parameters, the behavior of the motor, and the basic properties of the tire in its interaction with the ground. To obtain collision-free, minimum-time trajectories quadratic sequential optimization techniques are used, where the objective function is the time taken by the robot to move between two given configurations. This is subject to constraints relating to the vehicle and tires as well as the energy consumed, which is the basis for this paper. We work with a real random distribution of consumed energy values following a normal Gaussian distribution in order to analyze its influence on the trajectories obtained by the vehicle. The energy consumed, the time taken, the maximum velocity reached, and the distance traveled are analyzed in order to characterize the properties of the trajectories obtained. The proposed algorithm has been applied to 101 examples, showing that the computational times needed to obtain the solutions are always lower than those required to realize the trajectories. The results obtained allow us to reach conclusions about the energy efficiency of the trajectories.

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Novel inverse planning optimization algorithm for robotic radiosurgery: First clinical implementation and dosimetric evaluation

Physica Medica ◽

10.1016/j.ejmp.2019.07.020 ◽

2019 ◽

Vol 64 ◽

pp. 230-237 ◽

Cited By ~ 3

Author(s):

Michele Zeverino ◽

Maud Marguet ◽

Cedric Zulliger ◽

André Durham ◽

Raphael Jumeau ◽

...

Keyword(s):

Optimization Algorithm ◽

Clinical Implementation ◽

Inverse Planning ◽

Robotic Radiosurgery ◽

Planning Optimization

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A Sequential Algorithm for Possibility-Based Design Optimization

Journal of Mechanical Design ◽

10.1115/1.2803250 ◽

2007 ◽

Vol 130 (1) ◽

Cited By ~ 20

Author(s):

Jun Zhou ◽

Zissimos P. Mourelatos

Keyword(s):

Design Optimization ◽

Optimization Algorithm ◽

Computational Cost ◽

Possibility Theory ◽

Sequential Optimization ◽

Sequential Algorithm ◽

Computationally Efficient ◽

Worst Case ◽

Reliability Based Design ◽

Design Variables

Deterministic optimal designs that are obtained without taking into account uncertainty/variation are usually unreliable. Although reliability-based design optimization accounts for variation, it assumes that statistical information is available in the form of fully defined probabilistic distributions. This is not true for a variety of engineering problems where uncertainty is usually given in terms of interval ranges. In this case, interval analysis or possibility theory can be used instead of probability theory. This paper shows how possibility theory can be used in design and presents a computationally efficient sequential optimization algorithm. After the fundamentals of possibility theory and fuzzy measures are described, a double-loop, possibility-based design optimization algorithm is presented where all design constraints are expressed possibilistically. The algorithm handles problems with only uncertain or a combination of random and uncertain design variables and parameters. In order to reduce the high computational cost, a sequential algorithm for possibility-based design optimization is presented. It consists of a sequence of cycles composed of a deterministic design optimization followed by a set of worst-case reliability evaluation loops. Two examples demonstrate the accuracy and efficiency of the proposed sequential algorithm.

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Deployment the Spoke of a Large-Sized Transformable Refl ector Using a Sequential Optimization Algorithm

Mekhatronika Avtomatizatsiya Upravlenie ◽

10.17587/mau.22.433-441 ◽

2021 ◽

Vol 22 (8) ◽

pp. 433-441

Author(s):

S. A. Kabanov ◽

D. S. Kabanov

Keyword(s):

Optimization Algorithm ◽

Simulation Modeling ◽

Spreading Rate ◽

Terminal Condition ◽

Sequential Optimization ◽

Bending Vibrations ◽

The Maximum Principle ◽

Composed Functions ◽

Comparison Of The Results ◽

Weight Coefficients

The use of large-sized opening reflectors close-packed in spacecraft is associated with spreading the spokes at a given angle, extending the fragments of the spokes and adjusting the shape of the radio-reflecting mesh. The problem of optimizing these processes with automatic output of the reflector to the deployed working state is urgent. The optimal control problem of spreading spokes of a large-sized space-based reflector with respect to bending vibrations is investigated in the article. The optimization process is complicated by ensuring convergence of iterative procedure for control finding. The bending vibrations of the spokes complicate the task of spreading. That makes it difficult to fix spokes when reaching the stops. In this paper the mathematical model of spoke dynamics is improved with respect to spoke’s bend change in length and in time, the model takes into account the presence of stop and retainer devices and an actuator. It is proposed to consider a hierarchy of two target composed functions and develop an algorithm for sequential optimization for a smooth exit to the stops. It is suggested to include the terminal condition for the angular spreading rate in the first criterion. A study was carried out using mathematical simulation for the process of turning the spoke by a given angle at small values of the angular velocity at the final moment of time taking into account bending vibrations. The exact values of the weight coefficients included in the target composed functions are found. Weight coefficients influence on transient processes is investigated. The performance of the algorithm was checked when the value of the optimization interval was changed. The comparison of the results of simulation modeling with control options using the PID controller, application of an algorithm with a predictive model and an algorithm with optimal correction of the control structure, revealed by means of the maximum principle, was carried out. The results of simulation modeling foe spokes spreading process using the sequential optimization algorithm demonstrate the achievement of the required accuracy with permissible tolerance residual vibrations. The developed algorithm of sequential optimization forms control in a real time and it is recommended to use it in more complex solutions under random disturbances using measurement process and optimization of observation intervals.

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Sequential Optimization Scripts to Facilitate Treatment Planning for Robotic Radiosurgery Clinical Studies for Prostate and Lung Cancers

IFMBE Proceedings - World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany ◽

10.1007/978-3-642-03474-9_290 ◽

2009 ◽

pp. 1031-1034 ◽

Cited By ~ 1

Author(s):

E. Lessard ◽

W. Kilby ◽

J. Dooley ◽

C. Sims ◽

A. Schlaefer ◽

...

Keyword(s):

Treatment Planning ◽

Clinical Studies ◽

Sequential Optimization ◽

Lung Cancers ◽

Robotic Radiosurgery

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Simultaneous and Sequential Estimation of Optimal Placement and Controls of Wells With a Covariance Matrix Adaptation Algorithm

SPE Journal ◽

10.2118/173256-pa ◽

2016 ◽

Vol 21 (02) ◽

pp. 501-521 ◽

Cited By ~ 21

Author(s):

Fahim Forouzanfar ◽

Walter E. Poquioma ◽

Albert C. Reynolds

Keyword(s):

Life Cycle ◽

Covariance Matrix ◽

Optimization Algorithm ◽

Optimization Problem ◽

Temporal Correlation ◽

Production Optimization ◽

Sequential Optimization ◽

Joint Optimization ◽

Well Placement ◽

Covariance Matrix Adaptation

Summary In this paper, we present both simultaneous and sequential algorithms for the joint optimization of well trajectories and their life-cycle controls. The trajectory of a well is parameterized in terms of six variables that define a straight line in three dimensions. In the simultaneous joint optimization algorithm, the set of controls of a well throughout the life cycle of the reservoir is constructed as a linear combination of the left singular vectors that correspond to the largest singular values of a specified temporal covariance matrix. This covariance matrix is used to impose a temporal correlation on the controls at each well. In this approach, well controls are parameterized in terms of a few optimization parameters to reduce the dimension of the joint optimization problem. Moreover, the imposed smoothness on the well controls will result in temporally smooth well controls. We use an implementation of the covariance matrix adaptation–evolution strategy (CMA-ES) optimization algorithm to solve the defined optimization problem. In the sequential optimization algorithm, first, the trajectories of the wells are optimized with the CMA-ES optimization algorithm whereas the controls of the wells are prespecified. After the optimum trajectories of the wells are obtained, the life-cycle production optimization step is performed to find the optimal well controls for the specified well trajectories. For the production optimization step, we compare the performance of three optimization algorithms that are the standard ensemble-based optimization algorithm (EnOpt), the standard CMA-ES algorithm, and a variant of the CMA-ES algorithm in which we set the initial covariance matrix equal to a prespecified covariance that imposes a temporal correlation on the controls of each well. The performance of the proposed algorithms is tested for the joint optimization of well trajectories and controls of injectors and producers for the PUNQ reservoir model. The proposed simultaneous well placement/well control optimization algorithm obtained better results than did the sequential optimization framework. The CMA-ES algorithm performed well for both well placement and production optimization purposes. Moreover, the CMA algorithm with a prespecified covariance that imposes a temporal correlation on the well controls obtained a higher net present value compared with EnOpt for the life-cycle production optimization step of the sequential framework.

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