Piotr Skworcow

Couch-based motion compensation: modelling, simulation and real-time experiments

The paper presents a couch-based active motion compensation strategy evaluated in simulation and validated experimentally using both a research and a clinical Elekta Precise Table™. The control strategy combines a Kalman filter to predict the surrogate motion used as a reference by a linear model predictive controller with the control action calculation based on estimated position and velocity feedback provided by an observer as well as predicted couch position and velocity using a linearized state space model. An inversion technique is used to compensate for the dead-zone nonlinearity. New generic couch models are presented and applied to model the Elekta Precise Table™ dynamics and nonlinearities including dead zone. Couch deflection was measured for different manufacturers and found to be up to 25 mm. A feed-forward approach is proposed to compensate for such couch deflection. Simultaneous motion compensation for longitudinal, lateral and vertical motions was evaluated using arbitrary trajectories generated from sensors or loaded from files. Tracking errors were between 0.5 and 2 mm RMS. A dosimetric evaluation of the motion compensation was done using a sinusoidal waveform. No notable differences were observed between films obtained for a fixed- or motion-compensated target. Further dosimetric improvement could be made by combining gating, based on tracking error together with beam on/off time, and PSS compensation.

Combined Energy and Pressure Management in Water Distribution Systems

In this paper a method is proposed for combined energy and pressure management via integration and coordination of pump scheduling with pressure control aspects. The proposed solution involves: formulation of an optimisation problem with the cost function being the total cost of water treatment and pumps energy usage, utilisation of an hydraulic model of the network with pressure dependent leakage, and inclusion of a PRV model with the PRV set-points included as a set of decision variables. Such problem formulation led to the optimizer attempting to reduce both energy usage and leakage. The developed algorithm has been integrated into a modelling, simulation and optimisation environment called FINESSE. The case study selected is a major water supply network, being part of Yorkshire Water Services, with a total average demand of 400 l/s.

Model Predictive Control for Real-Time Tumor Motion Compensation in Adaptive Radiotherapy

This paper presents the development and real-time implementation of a control system to automatically adjust the patient support system (PSS) position, thereby compensating for tumor motion caused by respiration and patient movements during radiotherapy treatment. The control scheme utilizes an observer to estimate the PSS state feedback, and a tumor position prediction algorithm to provide the reference for a model predictive controller. The real-time control algorithm was implemented using the Matlab and Simulink environments, with the communication with the clinical PSS performed through the dSPACE real-time system. The controller was shown to be able to position the PSS accurately and was able to track and compensate for organ motion with an accuracy of less than 1 mm in terms of root mean square error, giving rise to dose distributions indistinguishable from a static beam on a fixed target. From a clinical perspective, the increased targeting accuracy will enable an increased dose to the tumor without compromising the surrounding healthy tissues.

An optimal sequence of tasks for autonomous learning systems

In this paper, we consider an optimal sequence of tasks for systems that improve their performances due to autonomous learning (learning-by-doing). In particular, we focus on a problem of determining sequence of performed tasks for the autonomous learning systems to minimize the total weighted completion times of tasks. Fundamental for the presented approach is that schedule (a sequence of tasks) allows to efficiently utilize learning abilities of the system to optimize its objective, but it does not affect the system itself. To solve the problem, we prove an eliminating property that is used to construct a branch and bound algorithm and present some fast heuristic and metaheuristic methods. An extensive analysis of the efficiency of the proposed algorithms is also provided.

Optimization of task processing on parallel processors with learning abilities

In this paper, we analyse the two identical parallel processor makespan minimization problem with the learning effect, which is modelled by position dependent job/task processing times. We construct an exact pseudopolynomial time algorithm for the considered problem that takes into consideration the learning ability of the processors. Moreover, we analyse the efficiency of the exact algorithm dedicated for the classical problem with constant job/task processing times, if it is used to provide a schedule of jobs/tasks for the learning system.