Large Errors Research Articles

Application of image processing technology based on field programmable gate array in mechanical part inspection

Introduction: In the field of industrial manufacturing, accurate inspection of mechanical components such as gears and bearings is of Paramount importance. However, the traditional mechanical testing methods are often disturbed by human factors, which not only affects the stability of the test results, but also leads to low efficiency and large error. In order to solve these problems, this research focuses on developing a new edge detection model.Methods: A novel edge detection model based on field-programmable gate array image processing technology was used in this study. The model uses adaptive threshold multi-directional edge detection technology to identify the edge features of mechanical gears and bearings, aiming at improving the precision of detection.Results and Discussion: After performance verification, the running time of the model was controlled within 11 s, and the detection error was limited to less than 9%. Compared with the control group and the experimental group, their performance was superior. Further analysis data show that the detection accuracy of this model is as high as 0.9004, its internal resource utilization rate is 88%, and the detection rate is as high as 91%, which are better than the comparison model.Conclusion: The proposed test model not only significantly improves the efficiency and accuracy of the test, but also fully meets the requirements of the test. This new edge detection model has potential application value in industrial manufacturing field, and provides a new solution for industrial manufacturing quality inspection.

Design and analysis of tracking differentiator based on SO(3)

Motivated by the issue of insufficient dynamic performance and tracking accuracy in SO(3)-based attitude tracking differentiators during large-angle maneuvers and complex trajectory tracking, a novel design approach for a three-degree-of-freedom attitude tracking differentiator within the SO(3) framework is proposed by incorporating second-order system theory and Lie group theory and improving the classical tracking differentiator. The kinematics model and error dynamics model of a rigid body on SO(3) are derived, and a reasonable virtual control input on SO(3) is constructed subsequently in order to achieve better dynamic response and tracking performance. Simulation and experimental results validate that the designed tracking differentiator could realize rapid and smooth convergence during large-angle maneuvers, and the initial large tracking error rapidly drops to near zero in a short period of time; additionally, it can also track expected time-varying curves well in complex trajectory tracking, with initial errors rapidly decreasing and maintaining at normal levels, demonstrating excellent tracking and control capabilities. There are strong application prospects for this new approach in addition to its theoretical significance.

Hydrodynamic simulations of cool stellar atmospheres with MANCHA

Three-dimensional time-dependent simulations of stellar atmospheres are essential to study the surface of stars other than the Sun. These simulations require the opacity binning method to reduce the computational cost of solving the radiative transfer equation down to viable limits. The method depends on a series of free parameters, among which the location and number of bins are key to set the accuracy of the resulting opacity. Our aim is to test how different binning strategies previously studied in one-dimensional models perform in three-dimensional radiative hydrodynamic simulations of stellar atmospheres. Realistic box-in-a-star simulations of the near-surface convection and photosphere of three spectral types (G2V, K0V, and M2V) were run with the MANCHA $-bins. These rates were compared with the ones computed with opacity distribution functions. Then, stellar simulations were run with grey, four-bin, and 18-bin opacities to see the impact of the opacity setup on the mean stratification of the temperature and its gradient after time evolution. The simulations of main sequence cool stars with the MANCHA code are consistent with those in the literature. For the three stars, the radiative energy exchange rates computed with 18 bins are remarkably close to the ones computed with the opacity distribution functions. The rates computed with four bins are similar to the rates computed with 18 bins, and present a significant improvement with respect to the rates computed with the Rosseland opacity, especially above the stellar surface. The Rosseland mean can reproduce the proper rates in sub-surface layers, but produces large errors for the atmospheric layers of the G2V and K0V stars. In the case of the M2V star, the Rosseland mean fails even in sub-surface layers, owing to the importance of the contribution from molecular lines in the opacity, underestimated by the harmonic mean. Similar conclusions are reached studying the mean stratification of the temperature and its gradient after time evolution.

An approach for estimating microbial growth and chlorinated ethenes biotransformation in groundwater based on discrete observations

The bioremediation of chlorinated ethenes (CEs) contaminated groundwater is attracting increasingly attention in practical remediation projects. However, modelling of microbial metabolic processes under the constraints of substrate and environmental factors is inadequate. This study developed a new kinetic model, which incorporated the logistic model and Dual-Monod kinetic to represent the interaction between the controlled microbial growth and the bioavailable substrates in CE-contaminated groundwater. The proposed model was based on discrete observations to simulate microbial growth under the constraints of substrate and environmental conditions, reducing the amount of observational data required for the model. Meanwhile, the proposed model introduced two new kinetic parameters, the effective specific growth rate μeff and the real self-limiting coefficient of microbial growth keff,sl, to simplified the number of independent parameters. A parameter estimation method based on the quasi-Newton's algorithm for the proposed model was also developed. The model was validated based on the hypothetical data, experimental results, and a published dataset, demonstrated the successful simulation of microbial growth and the sequential biodegradation of PCE in groundwater systems (*E < 0.3). The monitoring duration and the sampling schedule have significant impacts on estimating the biological parameters, and large errors would be induced when the data from the periods of extremely low substrate concentration or microbial growth decline were involved in parameter estimation. The research suggested that the proposed kinetic model provided a new insight to express the limitation of microbial population growth due to the available substrates and environmental factors, and is hoping to be applied in actual CE-contaminated sites.

Electrocortical theta activity may reflect sensory prediction errors during adaptation to a gradual gait perturbation

Locomotor adaptation to abrupt and gradual perturbations are likely driven by fundamentally different neural processes. The aim of this study was to quantify brain dynamics associated with gait adaptation to a gradually introduced gait perturbation, which typically results in smaller behavioral errors relative to an abrupt perturbation. Loss of balance during standing and walking elicits transient increases in midfrontal theta oscillations that have been shown to scale with perturbation intensity. We hypothesized there would be no significant change in anterior cingulate theta power (4–7 Hz) with respect to pre-adaptation when a gait perturbation is introduced gradually because the gradual perturbation acceleration and stepping kinematic errors are small relative to an abrupt perturbation. Using mobile electroencephalography (EEG), we measured gait-related spectral changes near the anterior cingulate, posterior cingulate, sensorimotor, and posterior parietal cortices as young, neurotypical adults (n = 30) adapted their gait to an incremental split-belt treadmill perturbation. Most cortical clusters we examined (&gt;70%) did not exhibit changes in electrocortical activity between 2–50 Hz. However, we did observe gait-related theta synchronization near the left anterior cingulate cortex during strides with the largest errors, as measured by step length asymmetry. These results suggest gradual adaptation with small gait asymmetry and perturbation magnitude may not require significant cortical resources beyond normal treadmill walking. Nevertheless, the anterior cingulate may remain actively engaged in error monitoring, transmitting sensory prediction error information via theta oscillations.

Improvements on the discretisation of boundary conditions to the momentum balance for glacial ice

Abstract The flow of glacial ice is typically approximated as a nonNewtonian viscous fluid, with the momentum balance described by (an approximation to) the Stokes equations, and the nonlinear rheology described by a flow law. The most commonly used rheological law for glacial ice, Glen's flow law, yields infinite viscosity in the case of zero deformation, which can be the case at the ice surface. This poses a problem when solving the momentum balance numerically. We show that two commonly-used discretisation schemes for the boundary conditions at the ice surface and base, which yield proper numerical convergence when applied to simpler problems, produce poor numerical convergence and large errors, when used to solve the momentum balance with Glen's flow law. We show that a discretisation scheme based on the concept of ghost nodes, which substitutes the boundary conditions directly into the momentum balance equations, yields second-order numerical convergence and errors that can be up to four orders of magnitude smaller. These results are robust across different momentum balance approximations. We show that the improved boundary conditions are particularly useful for solving the 3-D higher-order Blatter-Pattyn Approximation (BPA). In general, this work underlines the importance of thoroughly verifying the numerical solvers used in ice-sheet models, before applying them to future projections of ice-sheet mass loss.

High-precision imaging of small voids in tunnel lining based on reverse time migration

Abstract Diseases such as voids behind the initial support of the tunnel will lead to problems such as lining rupture and concrete damage in the tunnel structure, which seriously affects the driving safety in the tunnel. In tunnel construction, ground penetrating radar is frequently employed as a method for detecting hidden defects. However, when the cavity size is small, there will be a large error when the original radar data is directly interpreted, which cannot meet the needs of practical engineering. To enhance the precision in tunnel detection, the Finite-Difference Time-Domain method is used to simulate various small cavities of different shapes located behind the primary support lining of the tunnel, and the study involves examining the electromagnetic response characteristics of different kinds of holes. The migration techniques of KIRCHHOFF, F-K, and reverse time are employed to reconstruct the hole target. Furthermore, digital morphology is employed to enhance the clarity of the image. Finally, the edge detection technology is used to further extract the hole features. After that, small cavities of different shapes are buried in the established outdoor concrete model box and radar detection is carried out. The detection position of the reconstructed radar image and the actual position of the cavity in the model box are verified. The results show that compared with KIRCHHOFF migration and F-K migration, the image processed by reverse time migration is clearer and more intuitive, and can restore the contour of small holes well. The research findings can serve as a reference for the interpretation of radar data of the cavity behind the initial support of the tunnel.

A New Grid Zenith Tropospheric Delay Model Considering Time-Varying Vertical Adjustment and Diurnal Variation over China

Improving the accuracy of zenith tropospheric delay (ZTD) models is an important task. However, the existing ZTD models still have limitations, such as a lack of appropriate vertical adjustment function and being unsuitable for China, which has a complex climate and great undulating terrain. A new approach that considers the time-varying vertical adjustment and delicate diurnal variations of ZTD was introduced to develop a new grid ZTD model (NGZTD). The NGZTD model employed the Gaussian function and considered the seasonal variations of Gaussian coefficients to express the vertical variations of ZTD. The effectiveness of vertical interpolation for the vertical adjustment model (NGZTD-H) was validated. The root mean squared errors (RMSE) of the NGZTD-H model improved by 58% and 22% compared to the global pressure and temperature 3 (GPT3) model using ERA5 and radiosonde data, respectively. The NGZTD model’s effectiveness for directly estimating the ZTD was validated. The NGZTD model improved by 22% and 31% compared to the GPT3 model using GNSS-derived ZTD and layered ZTD at radiosonde stations, respectively. Seasonal variations in Gaussian coefficients need to be considered. Using constant Gaussian coefficients will generate large errors. The NGZTD model exhibited outstanding advantages in capturing diurnal variations and adapting to undulating terrain. We analyzed and discussed the main error sources of the NGZTD model using validation of spatial interpolation accuracy. This new ZTD model has potential applications in enhancing the reliability of navigation, positioning, and interferometric synthetic aperture radar (InSAR) measurements and is recommended to promote the development of space geodesy techniques.

Shallow beds are not plug flow reactors – Analysis of kinetic data in the presence of axial dispersion effects

Laboratory-scale shallow beds filled with catalyst particles are frequently used in catalyst screening, activity tests and kinetic measurements. Often the experimental data are interpreted with plug flow or differential reactor models. Residence time measurements have shown large deviations from ideal plug flow behavior in shallow beds, indication the presence of axial dispersion effects, i.e. is backmixing and diffusion in the bed. The behavior of laboratory-scale bed reactors was investigated by theoretical considerations of the axial dispersion model and numerical simulations in the Damköhler-Péclet space. The results revealed that large errors in kinetic parameters can appear when the dispersion model is replaced by the plug flow model. This key effect was quantified using the ratio (F) of two Damköhler numbers: the real one, and the one wrongly estimated on experimental data affected by dispersion problems and interpreted with ideal plug-flow approach. Thus, the effect of back-mixing is evaluated varying a single parameter of the kinetic model, namely the rate constant. The conclusions are valid for power-law, Langmuir-Hinshelwood and Eley-Rideal kinetics.

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

Impact of Pressure-Dependent Interfacial Tension and Contact Angle on Capillary Heterogeneity Trapping of CO2 in Storage Aquifers

Summary Carbon dioxide (CO2) capillary trapping increases the total amount of CO2 that can be effectively immobilized in storage aquifers. This trapping, manifesting itself as accumulated CO2 columns at a continuum scale, is because of capillary threshold effects that occur below low-permeability barriers. Considering that capillary pressure is dictated by heterogeneous pore throat size, the trapped CO2 column height and associated CO2 saturation will vary spatially within a storage aquifer. This variation will be influenced by two pressure-dependent interfacial parameters—CO2/brine interfacial tension (IFT) and CO2/brine/rock contact angle. Our objective is to understand how the pressure dependence of these two parameters affects the heterogeneity of capillary trapped CO2 at a continuum scale. Our conceptual model is a 1D two-zone system with the upper zone being a flow barrier (low permeability) and the lower zone being a flow path (high permeability). The inputs to this model include microfacies-dependent capillary pressure vs. saturation curves and permeability values. The input capillary pressure curves were collected in the literature that represents carbonate microfacies (e.g., dolograinstone) in a prevalent formation in the Permian Basin. We then used the Leverett j-function to scale the capillary pressure curve for the two zones that are assigned with the same or different microfacies. During scaling, we considered the influence of pressure on both the IFT and contact angle of CO2/brine/dolomite systems. We varied the zone permeability contrast ratio from 2 to 50. We then assumed capillary gravity equilibriums and calculated the CO2 saturation buildup corresponding to various trapped CO2 column heights. The CO2 saturation buildup is defined as the CO2 saturation in the lower layer minus that in the upper one. We found that the saturation buildup can be doubled when varying pressure in a storage aquifer, after considering pressure-dependent IFT and contact angles. Thus, assuming these two parameters to be constant across such aquifers would cause large errors in the quantification of capillary trapping of CO2. The whole study demonstrates the importance of considering pressure-dependent interfacial properties in predicting the vertical distribution of capillary trapped CO2. It has important implications in developing a better understanding of leakage risks and consequent storage safety.

Forecasting of tropical cyclones ASANI (2022) and MOCHA (2023) over the Bay of Bengal - real time challenges to forecasters

This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones (TC) ASANI and MOCHA. The analysis of various Numerical Weather Prediction (NWP) model forecasts [ECMWF (European Centre for Medium range Weather Forecast), NCEP (National Centers for Environmental Prediction), NCUM (National Centre for Medium Range Weather Forecast-Unified Model), IMD (India Meteorological Department), HWRF (Hurricane Weather Research and Forecasting)], MME (Multi-model Ensemble), SCIP (Statistical Cyclone Intensity Prediction) model, and OFCL (Official) forecasts shows that intensity forecasts of ASANI and track forecasts of MOCHA were reasonably good, but there were large errors and wide variation in track forecasts of ASANI and in intensity forecasts of MOCHA. Among all model forecasts, the track forecast errors of IMD model and MME were least in general for ASANI and MOCHA respectively. Also, the landfall point forecast errors of IMD were least for ASANI, and the MME and OFCL forecast errors were least for MOCHA. No model is found to be consistently better for landfall time forecast for ASANI, and the errors of ECMWF, IMD and HWRF were least and of same order for MOCHA. The intensity forecast errors of OFCL and SCIP were least for ASANI, and the forecast errors of HWRF, IMD, NCEP, SCIP and OFCL were comparable and least for MOCHA up to 48 h forecast and HWRF errors were least thereafter in general. The ECMWF model forecast errors for intensity were found to be highest for both the TCs. The results also show that although there is significant improvement of track forecasts and limited or no improvement of intensity forecast in previous decades but challenges still persists in real time forecasting of both track and intensity due to wide variation and inconsistency of model forecasts for different TC cases.

Study on the hydraulic turbine modeling method based on IGSA-NN revision strategy

Abstract With the further increase of renewable energy such as wind power and photovoltaic in the power grid, the hydraulic turbine regulating system (HTRS) faces enormous challenges. As simulation modeling is the basis for studying the optimal control of HTRS, it is crucial to establish a high-precision model of HTRS. As the core component of the HTRS, the hydro-turbine contains extremely complex nonlinearity, which model accuracy directly affects the accuracy of the HTRS simulation platform. However, current modeling methods have certain disadvantages such as large modeling errors and making the structure of the HTRS simulation platform more complex. Therefore, this paper proposes a data modification strategy (IGSA-NN) based on the BP neural network (BPNN) and an improved gravity search algorithm (IGSA) and then achieves high-precision modeling of the hydro-turbine combining the BPNN and actual operation data. Finally, the accuracy of the hydro-turbine model is verified by the actual operation data, and the method to build the simulation platform of the HTRS which contains both real and virtual components is given. The research results have a certain guiding significance for hydro-turbine modeling.

Study on Cavitation Determination of Pump Turbine Model Test

Abstract At present, it is difficult to determine whether cavitation has occurred in the pump turbine model test, with a large human error and no more accurate judgment guidelines, this paper proposes an intelligent algorithm based on image recognition to determine when cavitation has occurred. The method collects images of water bodies in the recognition area, extracts and processes the features of fixed parts, and compares them with database image data to finally realize the recognition of cavitation phenomena. The results show that the method captures the cavitation phenomenon more accurately and efficiently, and achieves the intelligent judgment of the cavitation phenomenon.

Foreseeing of Next-day Large Forecast Error of Solar Irradiance by Parameter Ensemble Forecasting using WRF

Foreseeing of Next-day Large Forecast Error of Solar Irradiance by Parameter Ensemble Forecasting using WRF

Understanding the xenon primary scintillation yield for cutting-edge rare event experiments

Xenon scintillation has been widely used in rare event detection experiments, such as in neutrinoless double beta decay, double electron captures and dark matter searches. Nonetheless, experimental values for primary scintillation yield in gaseous xenon (GXe) remain scarce and dispersed. The mean energy required to produce a scintillation photon, w sc, in GXe in the absence of recombination has been measured to be in the range of 34–111 eV. Lower w sc-values were reported for α-particles when compared to electrons produced by γ- or x-rays. Since w sc is expected to be similar for x-, γ-rays or electrons and almost equal to that obtained for α-particles, the above difference can not be understood. In addition, at present one may also pose the question of a dependence of w sc on photon energy. We carried out a systematic study on the absolute primary scintillation yield in GXe under reduced electric fields in the 70–300 V cm-1 bar-1 range and near atmospheric pressure, 1.2 bar, supported by a robust geometrical efficiency simulation model. We were able to clear-out the above standing problems: w sc was determined for x/γ-rays in the 5.9–60 keV energy range as well as for α-particles in the 1.5–2.5 MeV range, and no significant dependency neither on radiation type nor on energy has been observed. Our experimental w sc-values agree well with both up-to-date simulations and literature data obtained for α-particles. The discrepancy between our results and the experimental values found in the literature for x/γ-rays is discussed in this work and attributed to unaddressed large systematic errors in those previous studies. These findings can be extrapolated to other gases, and have impact on experiments such as double beta decay, double electron capture and directional dark matter searches while also on potential future detection systems such as DUNE-Gas. Only assuming the VUV emission band as is the case of most of the literature values, a mean w sc-value of 38.7 ± 0.6 (sta.)+7.7 -7.2 (sys.) eV was obtained. If the UV-VIS emission band is to be considered, the average energy to produce a photon was determined to be w 2nd = 43.5 ± 0.7 (sta.)+8.7 -8.1 (sys.) eV and w 3rd = 483 ± 7 (sta.+110 -105 (sys.) eV, in the VUV and UV-VIS bands, respectively.

Cost‐sensitive classification with time constraint on incomplete data

AbstractMissing values are common, but dealing with them by inappropriate method may lead to large classification errors. Empirical evidences show that the tree‐based classification algorithms such as classification and regression tree (CART) can benefit from imputation, especially multiple imputation. Nevertheless, less attention has been paid to incorporating multiple imputation into cost‐sensitive decision tree induction. This study focuses on the treatment of missing data based on a time‐constrained minimal‐cost tree algorithm. We introduce various approaches to handle incomplete data into the algorithm including complete‐case analysis, missing‐value branch, single imputation, feature acquisition, and multiple imputation. A simulation study under different scenarios examines the predictive performances of the proposed strategies. The simulation results show that the combination of the algorithm with multiple imputation can assure classification accuracy under the budget. A real medical data example provides insights into the problem of missing values in cost‐sensitive learning and the advantages of the proposed methods.

Promptable foundation model for automatic whole body RECIST measurement.

e13643 Background: RECIST 1.1 is the gold standard for tumor evaluation in clinical trials and patient care. However, it faces challenges due to the subjective selection and measurement of lesions, notably because of inter-observer variability in the 20-30% range (1), which can potentially result in inaccuracies in therapeutic response classification (2). The main objective of this study is to assess the added value of a radiological foundation model to assist the radiologist to measure RECIST across different lesion topographies, using visual prompting. Methods: We use a promptable segmentation algorithm, based on a visual foundation model, pre-trained on various CT-scans and then trained on a subset of the Medical Segmentation Decathlon (MSD) dataset, covering 538 patients with pancreas, liver, colon and lung lesions. It outputs a 3D segmentation mask of the lesion, using a visual prompt, i.e. a 3D bounding box (BBox) as input. We then measure the capacity of the model to measure RECIST on the MSD validation set and on the KiTS validation set (99 samples), which features a new lesion type (kidney). To simulate inter-reader variability of up to 23%, we provide as inputs BBoxes centered on the lesions but with a varying error (15% variability: +10 pixels, 23% variability: +15 pixels). Results: The model is able to correct variability in BBox input: with all BBox error ranges, it beats the input variability (15% and 23% thresholds in each column) across many organ types, except in the pancreas when using large error BBoxes. Moreover, the model is able to generalize to new lesion types not seen during training, on an external dataset (KiTS - Kidney). Conclusions: Unlike existing supervised machine learning models dedicated to lesion detections in specific organs, our approach is organ and lesion agnostic and offers a more reliable, precise tumor assessment. This is a first step before allowing the longitudinal evaluation of tumors while safeguarding the clinical intuition necessary for selecting the right target lesions. Moreover, we believe that these methods will be crucial in advancing beyond RECIST 1.1, facilitating the identification of new prognostic biomarkers and proxies for tumor burden derived from previously underexplored radiomics features, ultimately refining the efficacy of clinical trial assessments and elevating the standard of patient care. 1. Yoon, S et al. 2015. 2. Kuhl et al. 2018. [Table: see text]

Short-Term and Imminent Rainfall Prediction Model Based on ConvLSTM and SmaAT-UNet.

Short-term precipitation forecasting methods are mainly divided into statistical forecasting, numerical model-based forecasting, and radar image extrapolation techniques. The two methods based on statistical prediction and numerical model have the disadvantages of being unstable and generating large errors. Therefore, this study proposes the use of deep learning for radar image extrapolation for precipitation forecasting, in particular by developing algorithms for ConvLSTM and SmaAT-UNet. The ConvLSTM model is a fusion of a CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory network), which solves the challenge of processing spatial sequence data, which is a task that traditional LSTM models cannot accomplish. At the same time, SmaAT-UNet enhances the traditional UNet structure by incorporating the CBAM (Convolutional Block Attention Module) attention mechanism and replacing the standard convolutional layer with depthwise separable convolution. This innovative approach aims to improve the efficiency and accuracy of short-term precipitation forecasting by improving feature extraction and data processing techniques. Evaluation and analysis of experimental data show that both models exhibit good predictive ability, with the SmaAT-UNet model outperforming ConvLSTM in terms of accuracy. The results show that the performance indicators of precipitation prediction, especially detection probability (POD) and the Critical Success index (CSI), show a downward trend with the extension of the prediction time. This trend highlights the inherent challenges of maintaining predictive accuracy over longer periods of time and highlights the superior performance and resilience of the SmaAT-UNet model under these conditions. Compared with the statistical forecasting method and numerical model forecasting method, its accuracy in short-term rainfall forecasting is improved.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols.

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.