Nelder-Mead Research Articles

Simultaneous estimation of local intersecting slopes

The local slope of a seismic event is one of the most important attributes in seismic data processing and interpretation. In simple cases, only one slope value corresponds to a data location; however, in some complex geologic environments, such as unconformities or faults, two or more seismic events may intersect and yield several slopes. Although there are well-established methods for computing slopes in simple cases, there is no satisfactory algorithm in complex situations. We first detail the multimodality of coherence of intersecting events in the slope domain, and then propose a novel multimodal optimization method to capture their local slopes simultaneously. In the method, we use the neighborhood-based crowding differential evolution (NCDE) multimodal optimization algorithm cascaded by the hill-valley niche detection function and the Nelder-Mead simplex local search algorithm. Using this approach, we can obtain all of the local intersecting slopes simultaneously and avoid the need for a fine search when fully adopting the NCDE algorithm. The results of our numerical analysis indicate that the proposed method is robust, efficient, and accurate. Furthermore, the application of the proposed method to the dip-steering median filter for random noise attenuation shows better results than the iterative [Formula: see text] method in the case of complex wavefields.

An automated approach to the alignment of compound refractive lenses.

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are used to focus the beam or image the sample in many beamlines at X-ray facilities. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam, often requiring regular readjustments. Some groups circumvent the full realignment procedure by using engineering controls (e.g. mounting optics) that sacrifice some of the beam's focusing precision, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. This approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder-Mead (SNM) simplex method. This method is outlined and its efficacy is demonstrated with numerical simulation that is tested in real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

Hybrid optimization of driving frequency for crack signature enhancement in nonlinear ultrasonic nondestructive testing

Nonlinear ultrasonic nondestructive testing methods have largely improved early crack detection in solid materials. However, the nonlinear signature of the crack can be difficult to measure. In this paper, to overcome this drawback, we present a parametric optimization to automatically find the best driving frequency which maximizes the nonlinear signature. The optimization process is carried out in two steps to maximize a cost function corresponding to the nonlinear-linear ratio (NLR). From the signal received, NLR assesses the energy of the second harmonic over the energy of the fundamental component. This optimization is achieved by the hybridization of two algorithms: the genetic algorithm and the Nelder–Mead algorithm. The method is applied experimentally on a duraluminium sample including a crack. The optimal frequency is obtained after less than 14 iterations only. Moreover, the gain of the nonlinear-linear ratio reaches up to 12 dB in comparison with that obtained by the usual frequency setting.

The Critical Diameter for Continuous Evaporation Is between 3 and 4 nm for Hydrophilic Nanopores.

Evaporation studies of water using classical molecular dynamics simulations are largely limited due to their high computational expense. This study addresses that issue by developing coarse-grained molecular dynamics models based on Morse potential. Models are optimized based on multi-temperature and at room temperature using machine learning techniques like Genetic Algorithm, Nelder-Mead algorithm, and Strength Pareto Evolutionary Algorithm. The multi-temperature-based model named as Morse-D is found to be more accurate than the single temperature model in representing the water properties at higher temperatures. Using this Morse-D water model, evaporation from hydrophilic nanopores with pore diameter varying from 2 to 5 nm is studied. Our results show that the critical diameter to initiate continuous evaporation at nanopores lies between 3 and 4 nm. A maximum heat flux of 21.3 kW/cm2 is observed for a pore diameter of 4.5 nm and a maximum mass flow rate of 16.2 ng/s for a pore diameter of 5 nm. The observed heat flux is an order of magnitude times larger than the currently reported values from experiments in the literature for water, which indicates that we need to focus on nanoscale evaporation to enhance the critical heat flux.

Design modification and CFD formulation of 1D3D mechanical cyclone for optimal particle collection efficiency

Standard 1D3D mechanical cyclone with standard inlet velocity of 16m/s and cylinder diameter of 0.1524m was modified using Downhill simplex method to develop four modified cyclone designs. Computational Fluid Dynamics (CFD) formulation involving model design, meshing, CFD simulation and postprocessing was carried out on the mechanical cyclones. ANSYS Fluent software was used for the CFD formulation using fine mesh of the default minimum mesh size of the CFD mesher. Cornstarch was used as inlet particulate matter. Reynolds Stress Turbulence Model (RSTM) was used to model the swirling turbulent flow while Discrete Phase Model (DPM) was used to track about 10,000 particles through the simulated cyclones. The DPM result of each mechanical cyclone was used to calculate it’s CFD, particle collection efficiency result. Standard 1D3D mechanical cyclone obtained particle collection efficiency result of 87.38% using cornstarch and default minimum mesh size for CFD simulation while modified 1D3D mechanical cyclone one (1) recorded the optimal CFD particle collection efficiency result among modified 1D3D mechanical cyclones with 94.00%. Its convergence iteration point also showed improvement in simulation time compared to the standard 1D3D mechanical cyclone. Consequently, it was concluded that, design modification in line with CFD formulation offers an alternative and powerful approach to modeling 1D3D mechanical cyclones performance.

Subject-specific optimization of background suppression for arterial spin labeling magnetic resonance imaging using a feedback loop on the scanner.

Background suppression (BGS) in arterial spin labeling (ASL) magnetic resonance imaging leads to a higher temporal signal‐to‐noise ratio (tSNR) of the perfusion images compared with ASL without BGS. The performance of the BGS, however, depends on the tissue relaxation times and on inhomogeneities of the scanner's magnetic fields, which differ between subjects and are unknown at the moment of scanning. Therefore, we developed a feedback loop (FBL) mechanism that optimizes the BGS for each subject in the scanner during acquisition. We implemented the FBL for 2D pseudo‐continuous ASL scans with an echo‐planar imaging readout. After each dynamic scan, the acquired ASL images were automatically sent to an external computer and processed with a Python processing tool. Inversion times were optimized on the fly using 80 iterations of the Nelder–Mead method, by minimizing the signal intensity in the label image while maximizing the signal intensity in the perfusion image. The performance of this method was first tested in a four‐component phantom. The regularization parameter was then tuned in six healthy subjects (three males, three females, age 24–62 years) and set as λ = 4 for all other experiments. The resulting ASL images, perfusion images, and tSNR maps obtained from the last 20 iterations of the FBL scan were compared with those obtained without BGS and with standard BGS in 12 healthy volunteers (five males, seven females, age 24–62 years) (including the six volunteers used for tuning of λ). The FBL resulted in perfusion images with a statistically significantly higher tSNR (2.20) compared with standard BGS (1.96) ( p<5x10−3, two‐sided paired t‐test). Minimizing signal in the label image furthermore resulted in control images, from which approximate changes in perfusion signal can directly be appreciated. This could be relevant to ASL applications that require a high temporal resolution. Future work is needed to minimize the number of initial acquisitions during which the performance of BGS is reduced compared with standard BGS, and to extend the technique to 3D ASL.

Comparison of Interior Permanent Magnet and Synchronous Homopolar Motors for a Mining Dump Truck Traction Drive Operated in Wide Constant Power Speed Range

Synchronous homopolar motors (SHMs) with an excitation winding located at the stator and a toothed salient pole rotor are a good alternative to motors traditionally used in traction applications such as induction motors or interior permanent magnet synchronous motors (IPMSM). This study presents the results of a theoretical comparison between an IPMSM and an SHM in a traction application with a constant power speed range of 1:10, which is specific to the mining truck drives, and with a rated power of 370 kW. The considered IPMSM and SHM have the same number of phases, poles and stator slots, and the same outer diameter of the stator lamination. The IPMSM design is optimized using the Nelder–Mead method. The main objectives of optimization are to minimize the average losses in the operating cycle and to limit the required power of the semiconductor inverter. The performance of the optimized IPMSM is compared with the previously obtained performance of the SHM optimized by the same method. Although the average losses in the operating cycle in the compared motors are approximately equal, the losses at high speed for the IPMSM are about two times greater than at low speed with maximum torque, which means that there is a need to intensify the IPMSM cooling system and there is deterioration of reliability. The advantage of the IPMSM is the reduction in the length of the active part by 30%. The advantage of the SHM is that there is 4.6 times lower cost of active materials. In addition, the SHM is more reliable than the IPMSM, as there is no risk of overheating, demagnetization or degradation of permanent magnets over time.

Experimental and Theoretical Investigation of the Acoustic Absorbers Fabricated from Natural Kenaf Fibres and Rice Husk

ABSTRACT Fabrication of acoustic absorbers from a combination of natural waste materials is a novel approach in the field of noise control. The aim of this study was to investigate the acoustic absorption performance of several sample sound absorbers made from natural kenaf fibers and rice husk. A new method of fabricating these sample sound absorbers was also employed to study the usability as well as performance improvement via the approach fibro-granular sound absorbers (FGSA). The values of acoustic absorption performance, porosity, and airflow resistivity of the samples were used in the experimental design. The Dunn and Davern (D-D) theoretical model as well as the reversed D-D model with Nelder–Mead Simplex were applied to evaluate the sound absorption as well. According to the obtained values, it was found that at low- and mid-frequency range, the sound absorption value of FGSA is higher than other acoustic materials. The FGSA (with a density of 150 kg/m3) increased noise reduction coefficient value by 31.25% compared to samples fabricated to the rice husk and also by 10.53% compared to kenaf. Based on comparison of the theoretical and experimental results, the model optimized with the Nelder–Mead method had higher accuracy and fitting in predicting the sound absorption values than the D-D model. Finally, it was found that improvement in the sound absorption performance of the natural acoustic absorbers might be achieved through adopting the approach of combining natural materials and fabrication of FGSA.

Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

The inherent multimodal and nonlinear characteristics of solar photovoltaic (PV) systems make it challenging to accurately extract PV parameters. Therefore, this study proposes an efficient and accurate optimization technique, improved Hunger Games Search, to identify unknown parameters in PV systems named IHGS. IHGS makes full use of the population information generated by the differential vectors while incorporating the quantum rotation gate strategy and the Nelder-Mead simplex method (NMs) are fused to further increase the population diversity while rotating the corresponding positions of the search agents, thus efficiently exploring the neighborhood of the optimal solution in the decision space and improving the stability of the algorithm in developing the global optimal solution. The first part of the experiments is to simply verify the effectiveness and the stable merit-seeking ability of the IHGS to handle complex functions by putting the proposed IHGS to test on the IEEE CEC 2017 benchmark test suite. The efficiency of IHGS is verified based on Wilcoxon singed-rank test and Friedman tests. Next, we apply IHGS to three different PV module models (single-diode, double-diode, and PV module) for PV parameter extraction experiments to verify the efficiency and accuracy of IHGS in extracting PV parameters. The root mean square error produced by the IHGS proposed in this study is 9.8602E-04, 9.8248E-04, and 2.4251E-03 for SDM, DDM, and PV modules, respectively. Examples show that IHGS outperforms HGS and other advanced algorithms in terms of convergence speed, data fitting, consistency, and stability. Finally, we perform the PV module parameter extraction problem on three commercial PV models (thin-film ST40, monocrystalline SM55, and multicrystalline KC200GT). We observe that IHGS can still maintain high accuracy when dealing with commercial PV models under complex environments such as different irradiation and temperature. The results show that IHGS is a valuable optimization technique for solar PV parameter estimation based on multi-model prediction.

Optimizing geometric parameters for the rotor of a traction synchronous reluctance motor assisted by partitioned permanent magnets

This paper considers partitioning parameters and the mutual arrangement of magnets in the rotor of the traction synchronous-jet engine with permanent partitioned magnets. The synthesis of geometrical parameters for the rotor of a synchronous reluctance motor with partitioned permanent magnets was proposed on the basis of solving the problem of conditional optimization. To solve the synthesis problem, a mathematical model has been built to determine the electromagnetic momentum of a synchronous reluctance motor with partitioned permanent magnets. It is based on the calculation of the electromagnetic momentum of the engine employing the results of a finite-element analysis of the magnetic field in the flat-parallel statement of the problem. The model is implemented in the finite-element analysis FEMM environment and makes it possible to determine the electromagnetic momentum of the engine with a variety of partitioning of permanent magnets. As an analysis problem, it is proposed to use a mathematical model of the magnetic field of the engine. The problem of conditional optimization of the rotor of a synchronous reluctance motor was stated according to the geometric criteria of the rotor. Restrictions are set on geometric, strength indicators, as well as on the level of electromagnetic momentum. The chosen optimization method is the Nelder-Mead method. Based on the results of solving the problem of synthesizing parameters for the partitioned rotor of the traction motor of trolleybus wheels, it was established that the volume of permanent magnets was reduced by 2.27 times compared to the base structure; their optimal geometric dimensions were determined (5 mm, 5.2 mm, and 5 mm), as well as the distance between them, 17.8 mm and 15.3 mm, and the engine load angle, which is 121.12 electrical degrees. Based on the results of solving the problem of synthesizing parameters for the partitioned rotor of a trolleybus traction synchronous reluctance motor, its optimal geometric parameters have been determined

Extremum seeking control for optimization of an open-loop grinding mill using grind curves

A semi-autogenous grinding mill is simulated with gradient and non-gradient based extremum seeking controllers to maximize the mill performance using grind curves. Grind curves map the essential performance measures of a grinding mill to the mill load and rotational speed. The curves vary with the changes in the feed ore characteristic but show generic parabolic features with extremums. The extremum seeking controllers search along the unknown input–output map to steer the process towards an unknown optimum. In this study, a classical perturbation-based method, a time-varying parameter estimation-based method and the Nelder–Mead simplex method are employed as extremum seeking control (ESC) methods to search along the grind curves to either optimize the mill throughput or grind by means of manipulating the mill feed or rotational speed. The proposed extremum seeking controller could reduce the need for a plant operator to manually select the optimal operating conditions that maximize the performance measures of a grinding mill. Since the controller is agnostic to the process model, the grinding mill can be optimized without the need for a detailed process model. The simulated results show that the extremum seeking controllers steer the mill operating conditions toward the steady-state optimum and can be used to satisfy operational objectives. However, the slow grinding mill dynamics result in a long convergence rate when the initial conditions are far from the optimal operating conditions.

Empirical scatter correction: CBCT scatter artifact reduction without prior information.

The image quality of cone beam CT (CBCT) scans severely suffers from scattered radiation if no countermeasures are taken. Scatter artifacts may induce cupping and streak artifacts and lead to a reduced image contrast and wrong CT values of the reconstructed volumes. Established software-based approaches for a correction of scattered radiation typically rely on prior knowledge of the CT system, scan parameters, the scanned object, or all of theaforementioned. This study proposes a simple and effective postprocessing software-based correction method of scatter artifacts in CBCT scans without specific priorknowledge. We propose the empirical scatter correction (ESC), which generates scatter-like basis images from each projection image by convolution operations. A linear combination of these basis images is subtracted from the original projection image. The logarithm is taken and an FDK reconstruction is performed. The coefficients needed for the linear combination are determined automatically by a downhill simplex algorithm such that the resulting reconstructed images show no scatter artifacts. We demonstrate the potential of ESC by correcting simulated volumes with Monte Carlo scatter artifacts, a head phantom scan performed on our table-top CBCT, and a pelvis scan from a Varian Edge CBCTscanner. ESC is able to improve the image quality of CBCT scans, which is shown on the basis of our simulations and on measured data. For a simulated head CT, the CT value difference to the scatter-free reference image was as low as -6 HU after using ESC, whereas the uncorrected data deviated by more than -200 HU from the reference data. Simulations of thorax and abdomen CT scans show that although scatter artifacts are not fully removed, anatomical features which were hard to discover prior to the correction become clearly visible and better segmentable with ESC. Similar results are obtained in the phantom measurement, where a comparison to a slit scan of our head phantom shows only small differences. The CT values in soft tissue are improved in this measurement, as well. In soft tissue areas with severe scatter artifacts, the CT values agree well with those of the slit scan (difference to slit scan: 35HU corrected and -289 HU uncorrected). Scatter artifacts in measured patient data can also be reduced using the proposed ESC. The results are comparable to those achieved with designated correction algorithms installed on the Varian Edge CBCT system. ESC allows to reduce artifacts caused by patient scatter solely based on the projectiondata.

Comparison study between hybrid Nelder-Mead particle swarm optimization and open circuit voltage—Recursive least square for the battery parameters estimation

Recently, the whole world has turned to the rechargeable battery as the primary source of electric vehicles (EVs). Meanwhile, the majority of the automobile manufacturers quantify lithium as a key material for the development of battery chemistry. Currently, Lithium-ion (Li-ion) batteries are the dominant technology for battery electric vehicles (BEVs). The real value of Lithium is that they deliver high and fast energy densities in a small volume or configuration. Thus, the description, identification and modeling of the battery, during its operation, are essential actors for the success of EVs development. However, modeling Li-ion-based energy storage devices has not yet been easy enough due to the complexity of chemical reactions in real time. In this work, the modeling of Li-ion cells is performed with dynamic equivalent electrical circuit model (ECM). This study aims to exploit the advantages of two hybrid estimation methods for the identification of Li-ion battery model parameters. The first method is hybrid optimization algorithm PSO-NM which is composed of a particle swarm optimization algorithm (PSO) and a Nelder–Mead algorithm (NM). On the other hand, the second hybrid model, namely the OCV-RLS algorithm, benefits from the advantages of direct measurement of open circuit voltage (OCV) and of a Least Square Recursive (RLS) to obtain an optimum overall performance estimate. The validation results support the reliability of these proposed hybrid methods to identify the Li-ion battery model. In addition, they constitute a reference for the comparison of the two algorithms PSO-NM and OCV-RLS to solve the problem of Li-ion battery SOC estimating.

Enhanced Brain Storm Optimization Algorithm Based on Modified Nelder–Mead and Elite Learning Mechanism

Brain storm optimization algorithm (BSO) is a popular swarm intelligence algorithm. A significant part of BSO is to divide the population into different clusters with the clustering strategy, and the blind disturbance operator is used to generate offspring. However, this mechanism is easy to lead to premature convergence due to lacking effective direction information. In this paper, an enhanced BSO algorithm based on modified Nelder–Mead and elite learning mechanism (BSONME) is proposed to improve the performance of BSO. In the proposed BSONEM algorithm, the modified Nelder–Mead method is used to explore the effective evolutionary direction. The elite learning mechanism is used to guide the population to exploit the promising region, and the reinitialization strategy is used to alleviate the population stagnation caused by individual homogenization. CEC2014 benchmark problems and two engineering management prediction problems are used to assess the performance of the proposed BSONEM algorithm. Experimental results and statistical analyses show that the proposed BSONEM algorithm is competitive compared with several popular improved BSO algorithms.

RETRACTED: Method of Four Moments Mixture-A new approach for parametric estimation of Weibull Probability Distribution for wind potential estimation applications

Cutting down the reliance on fossil fuels and utilization of wind energy as green energy source requires detailed resource exploration using some probability distribution. In contrast to literature methods which are based on first and second moment of Weibull Probability Distribution (WPD) for its parametric estimation, the new method proposed in this study (called Method of Four Moments Mixture, MFMM) combines the effect of first four moments of WPD. This model is based on the squared deviation (deviation of sample from population) of first four moments; minimization of which using Nelder-Mead algorithm estimates parameters of WPD. In order to assess its comprehensive effectiveness, this method has been validated using large dataset i.e. five years wind data measured at 50 m height for thirty-six stations (in Pakistan) for parametric estimation and it has been compared with six past methods using MAPE, RMSE and R2. To rank all seven methods, Global Performance Indicator (GPI) was evaluated and it was found that MFMM is the best method for all stations. Therefore, it can be effectively used in wind resource assessment of various geographical regions in the world.

An efficient combined local and global search strategy for optimization of parallel kinematic mechanisms with joint limits and collision constraints

The optimization of parallel kinematic manipulators (PKM) involve several constraints that are difficult to formalize, thus making optimal synthesis problem highly challenging. The presence of passive joint limits as well as the singularities and self-collisions lead to a complicated relation between the input and output parameters. In this article, a novel optimization methodology is proposed by combining a local search, Nelder–Mead algorithm, with global search methodologies such as low discrepancy distribution for faster and more efficient exploration of the optimization space. The effect of the dimension of the optimization problem and the different constraints are discussed to highlight the complexities of closed-loop kinematic chain optimization. The work also presents the approaches used to consider constraints for passive joint boundaries as well as singularities to avoid internal collisions in such mechanisms. The proposed algorithm can also optimize the length of the prismatic actuators and the constraints can be added in modular fashion, allowing to understand the impact of given criteria on the final result. The application of the presented approach is used to optimize two PKMs of different degrees of freedom.

Natural frequency and buckling optimization considering weight saving for hybrid graphite/epoxy-sitka spruce and graphite-flax/epoxy laminated composite plates using stochastic methods

In recent years, the use of hybrid composites, in which natural fibers combine with synthetic fibers, in place of composite structures consisting only of synthetic fibers has become increasingly popular due to growing environmental concerns and the need to develop sustainable, more lightweight, recyclable materials required for engineering applications. In this regard, the present paper is an attempt to show the usage of sitka spruce as an alternative to synthetic E-glass fiber and natural flax fiber in design of interply hybrid composite structures in terms of fundamental frequency, buckling resistance and weight. The single-objective and multi-objective approaches are considered to acquire optimum design for non-hybrid and hybrid structures. The modified versions of the optimization methods Differential Evolution (MDE), Nelder Mead (MNM) and Simulated Annealing (MSA) algorithms are considered to solve problems. The results show that the optimum design of the hybrid graphite/epoxy-sitka spruce laminated plate to obtain the structure having a more lightweight (e.g., 53%), higher frequency (e.g., 65%) and buckling resistance (e.g., 90%) was computed and compared with those of the hybrid graphite-flax/epoxy, neat sitka spruce, neat flax/epoxy and neat glass/epoxy laminated composite plates.

A Bias-Corrected Method for Fractional Linear Parameter Varying Systems

This paper proposes an identification algorithm for the fractional Linear Parameter Varying (LPV) system considering noisy scheduling and output measurements. A bias correction technique is provided in order to compensate for the bias caused by the least squares algorithm. This approach was created to estimate either coefficients or fractional-order differentiation, and it has been proven to produce unbiased and reliable results. The suggested method’s performance is assessed by the identification of two fractional models and was compared with Nelder–Mead Simplex method.

Employing the MCMC technique to compute the projection depth in high dimensions

Projection depth (PD), one of the prevailing location depth notions, is a powerful and favored tool for multivariate nonparametric analysis. It permits the extension of the univariate median and weighted means to a multivariate setting. The multidimensional projection depth median (PM) and depth weighted means, including the Stahel–Donoho (SD) estimator are highly robust and affine equivariant. PM has the highest finite sample breakdown point robustness among affine equivariant location estimators.However, the computation of PD remains a challenge because its exact computation is only feasible for a data set with a dimension that is theoretically no higher than eight but practically no higher than three. Approximate algorithms such as random direction procedure or simulated annealing (SA) algorithm, are time-consuming in high-dimensional cases. Here, we present an efficient SA algorithm and its extension for the computation of PD. Simulated and real data examples indicate that the proposed algorithms outperform their competitors, including the Nelder–Mead method, and the SA algorithm, in high-dimensional cases and can obtain highly accurate results compared with those of the exact algorithm in low-dimensional cases.

Evaluation of a proprietary software application for motion monitoring during stereotactic paraspinal treatment.

PurposeStereotactic paraspinal treatment has become increasingly popular due to its favorable clinical outcome. An often‐overlooked factor that compromises the effectiveness of such treatment is the patients’ involuntary intrafractional motion. This work introduces and validates a proprietary software application that quantifies such motion for accurate patient monitoring during treatment.MethodsThe software uses a separate full‐trajectory cone‐beam computed tomography (CBCT) after daily patient setup to establish reference projections. Once treatment starts, the software grabs the intrafraction motion review (IMR) image acquired by TrueBeam via the Varian iTools Capture software and compares it against the corresponding reference projection to instantly determine the 2D shifts of the vertebrae being monitored using the classical downhill simplex optimization method. To evaluate its performance, an anthropomorphic phantom was shifted 0, 0.6, 1.2, 1.8, 2.4, 3.0, and 5 mm in three orthogonal directions, immediately after the full‐trajectory CBCT but prior to treatment. Depending on the scenario of shift, a nine‐field fixed gantry intensity‐modulated radiation therapy (IMRT) plan and/or a four partial‐posterior‐arcs volume‐modulated radiation therapy (VMAT) plan were delivered. For the IMRT plan, three IMR images were acquired sequentially every 200 monitor units (MU) at each treatment angle. For the VMAT plan, one IMR image was acquired every 15° of each arc. For each IMR image, the software‐reported 2D shift was compared with the ground truth. Certain tests were repeated with 1°, 2°, and 3° of rotation, pitch, and roll, respectively. Some of these tests were also repeated independently on separate days.ResultsBased on the group of tests that involved only the IMRT delivery, the maximum standard deviation of the software‐reported shifts for each set of three IMR images was 0.16 mm, with 95th percentile at 0.02 mm. For translational shift, the maximum registration error was 0.44 mm, with 95th percentile at 0.23 mm. Left unaccounted for, rotation and pitch degraded the registration accuracy mainly in the longitudinal direction, while roll degraded it mainly in the lateral direction. The degradation of registration accuracy is positively related to the degree of rotation, pitch, and roll. The maximum registration errors under 3° rotation, pitch, and roll were 2.97, 1.44, 2.72 mm, respectively. Based on the group of tests that compared IMRT delivery with VMAT delivery, the registration errors slightly increased as magnitude of shifts increased; however, they were well under the 0.5‐mm threshold. No significant differences in registration errors were observed between IMRT and VMAT deliveries. In addition, the variation in registration errors among different days was limited for both IMRT and VMAT deliveries.ConclusionsOur proprietary software has high repeatability, both intrafractionally and interfractionally, and high accuracy in registering IMR images with the reference projections for motion monitoring, regardless of the magnitude of shifts or treatment delivery technique. Rotation, pitch, and roll degrade registration accuracy and need to be accounted for in the future work.