Error Model Research Articles

A Spin-Optical Quantum Computing Architecture

We introduce an adaptable and modular hybrid architecture designed for fault-tolerant quantum computing. It combines quantum emitters and linear-optical entangling gates to leverage the strength of both matter-based and photonic-based approaches. A key feature of the architecture is its practicality, grounded in the utilisation of experimentally proven optical components. Our framework enables the execution of any quantum error correcting code, but in particular maintains scalability for low-density parity check codes by exploiting built-in non-local connectivity through distant optical links. To gauge its efficiency, we evaluated the architecture using a physically motivated error model. It exhibits loss tolerance comparable to existing all-photonic architecture but without the need for intricate linear-optical resource-state-generation modules that conventionally rely on resource-intensive multiplexing. The versatility of the architecture also offers uncharted avenues for further advancing performance standards.

An improved transmission efficiency prediction method for nonlinear characteristics of the cycloid reducer

This study aims to accurately predict the nonlinear characteristics of transmission efficiency of cycloid reducers under different operating conditions. Firstly, equivalent modeling of the multi-source errors (MSEs) in the designed cycloid reducer is conducted. Force analysis algorithms considering MSEs are proposed for the cycloid drive mechanism, the output mechanism, and the bearings. Secondly, mathematical models are established for the load-dependent power losses, while an equivalent test is used for modeling load-independent power losses. Subsequently, an improved transmission efficiency prediction (TEP) method for cycloid reducers is proposed, which is then applied to the performance prediction of a prototype under different operating conditions. The advantages of the improved TEP method over the conventional method are discussed, and the influences of MSEs and load-independent power losses on the nonlinear characteristics of transmission efficiency are summarized. Finally, tests are conducted for the reducer prototype, and the test results are found to be in good agreement with the results obtained by the proposed TEP method. The main contribution of this study is to establish a solid algorithmic and modeling foundation for the optimal design of nonlinear transmission efficiency in cycloid reducers and provide reliable guidance for their engineering applications.

Residual stresses prediction in transition metal nitrides sputtered coatings using artificial neural network and experimental evaluation of surface morphology

In the production of transition metal nitride hard coatings under specific deposition conditions, the resulting residual stresses play a critical role in determining the mechanical properties, adhesion, and overall performance of the coatings. In this work, an artificial neural network (ANN) that utilizes feed-forward architecture is used to predict the average residual stress in titanium nitride (TiN) coatings with different coating thickness. These coatings are manufactured under varying bias voltages and temperature on substrate. The impact of variations in coating thickness, bias voltage, and temperature on development of internal residual stresses and changes in microstructure within sputtered coatings have been gathered from existing literature. The data collected is within the context of fibrous (zone I) and mix columnar and fibrous structure (combined zone) as defined by an existing structure zone model. The model is trained with experimental data from literature. The model is trained with experimental data from literature. The validation of the ANN model is carried out by comparing the predictions with experimental results. X-ray diffraction is used to analyze the residual stresses, preferred orientation, and the structure (zone I and combined zone) of produced coatings under different sputtering parameters. The regression and performance curves are used to assess the efficacy of ANN model. The coefficient of determination of trained model and mean square error are 0.9918 and 0.35, respectively. The predicted results show good agreement with experimental and subsequently AFM grain morphology analysis captures the growth mechanisms involved in the residual stresses evolution.

Spatial dependence and poverty factors: A study of Pantura Regions in East Java Province, Indonesia

Poverty is still a problem in various countries in the world, so eradicating poverty and hunger by 2030 is the backbone of the goals of the sustainable development agenda. Poverty, which is the main goal of the Millennium Development Goals (MDGs), is again the main goal in the Sustainable Development Goals (SDGs). This research aims to determine the spatial dependence of poverty in the Pantura Region of East Java Province and to determine the influence of economic growth, education, unemployment, natural resources, health and accessibility on poverty in the Pantura Region of East Java Province. This research uses a quantitative approach, with data analysis methods using Moran’s index analysis, and spatial panel regression by selecting the best model from four models, namely the spatial autoregressive model (SAR), the spatial error model (SEM), the spatial Durbin model (SDM), and spatial autoregressive combined (SAC) model. The research results show that there is spatial interaction between coastal areas through endogenous and exogenous interactions between independent variables. Based on the selection of the best model, the spatial Durbin model (SDM) was selected for the Pantura Region. Economic growth has a negative effect on poverty. Education has a negative effect on poverty. Unemployment has no effect on poverty. Natural resources have a positive effect on poverty. Health has no effect. Accessibility has a negative effect on poverty in the Pantura Region. This research provides a new and important contribution in understanding the spatial patterns of poverty and the factors that influence it in the Pantura Region, which is a strategic and vulnerable area in East Java. This research also faces several limitations and challenges, such as data quality, model assumptions, and spatial validity. Future research could expand this analysis using more complete data, more flexible models, and more sophisticated techniques.

A Comprehensive Evaluation of Black Carbon in Snow and Its Radiative Forcing in CMIP5 and CMIP6 Models Based on Global Field Observations

AbstractBlack carbon in snow (BCS) is a crucial parameter in Earth System modeling, as it influences global radiative balance. Here, simulated BCS from Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) that provided BCS as a model output were evaluated. In comparison with global BCS observations, CMIP5/6 models successfully reproduced long‐term historical trends linked to human activities, but struggled capturing decadal variability caused by natural climate variability. CMIP6 models NorESM2‐MM, NorESM2‐LM, and TaiESM1 yielded the most accurate simulations of BCS concentration with modest overestimation of <50%, while the four CESM2 models underestimated concentrations by up to ∼80%. These errors effectively balanced for the CMIP6 multi‐model ensemble mean (MME), which had a relative error (RE) of −37%. However, the CMIP5 MME was less reliable due to extreme overestimation by up to 8,000% in the three MIROC models. The significant BCS concentration errors in the MIROC and CESM2 models were linked mainly to errors in handling of BC in snow processes. Conversely, marked improvements in NorESM, the only common to both CMIP5 and CMIP6, were due to improved simulation of black carbon deposition. BCS errors significantly impacted radiative forcing estimates, particularly at the poles, where model errors reached several thousandfold. CMIP6 exhibited superior results compared to CMIP5, achieving global MME RE of −33% in radiative forcing estimates. However, it's worth noting BCS output is currently limited, with only seven models available for each of CMIP5 and CMIP6 here. Additional models simulating BCS are desirable in the next CMIP generations.

Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot

A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair. With its lightweight design, high load-bearing capacity, and smooth surface, the coupled-drive joint is particularly well suited for these robots. However, the coupled nature of the joint disrupts the direct linear relationship between the input and output torques, posing challenges for dynamic modeling and practical applications. This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics. Building on this foundation, the Newton-Euler method was used to develop a dynamic model for the entire robotic arm. A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero. An experimental method was designed to compensate for gravity, inertia, and modeling errors to identify the parameters of the friction model. This method establishes a mapping relationship between the friction force and motor current. In addition, a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm. Trajectory tracking experiments were conducted during the experimental validation phase, demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm. This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms, thereby establishing a foundation for motion control in humanoid nursing robots.

Research on stochastic error suppression method of FOG based on quasi-proportional information

Abstract In order to improve the precision of fiber optic gyro (FOG), it is necessary to suppress the stochastic error effectively. Therefore, the stochastic error suppression of FOG is studied. Firstly, the data of FOG at room temperature and variable temperature are analyzed. Then, a general form of FOG random error model is derived, and an adaptive filtering method based on quasi-proportional information is proposed to estimate the process noise covariance effectively. At last, the room temperature experiment, the variable temperature experiment and the dynamic experiment are carried out. Compared with the recently proposed adaptive filter algorithm, the variance reduction of the proposed algorithm at the average time of 33 s, 54 s and 55 s is more than 70%, and the suppression effect of the algorithm on the random walk coefficient is improved by more than 2%, showing strong robustness.

MOB-Net: Limb-modularized uncertainty torque learning of humanoids for sensorless external torque estimation

Momentum observer (MOB) can estimate external joint torque without requiring additional sensors, such as force/torque or joint torque sensors. However, the estimation performance of MOB deteriorates due to the model uncertainty which encompasses the modeling errors and the joint friction. Moreover, the estimation error is significant when MOB is applied to high-dimensional floating-base humanoids, which prevents the estimated external joint torque from being used for force control or collision detection in the real humanoid robot. In this paper, the pure external joint torque estimation method named MOB-Net, is proposed for humanoids. MOB-Net learns the model uncertainty torque and calibrates the estimated signal of MOB, substantially reducing the estimation errors of MOB. The external joint torque can be estimated in the generalized coordinate including whole-body and virtual joints of the floating-base robot with only internal sensors (an IMU on the pelvis and encoders in the joints). Furthermore, MOB-Net shows more robust performance for the unseen data compared to the end-to-end learning method, and the robustness of MOB-Net is validated through extensive simulations, real robot experiments, and ablation studies. Finally, various collision handling scenarios are presented to show the versatility of MOB-Net: contact wrench feedback control for locomotion, collision detection, and collision reaction for safety.

Simultaneous compensation of geometric and compliance errors for robotics with consideration of variable payload effects

In order to satisfy the high accuracy requirements of robotic applications, it is necessary to consider not only the geometric errors but also the compliance errors which are caused by the self-gravity of the link and the external payloads. A general error model is developed based on the modified Denavit-Hartenberg (DH) model. For the parameters in the error model, there is a coupling between the compliance coefficients and the link parameters, making it difficult to use only the compliance coefficients to compute the compliance errors due to external payloads. As the external payload for the robot manipulator varies, the parameter identification of the model should be conducted again. In this paper, a novel algorithm using a variable payload method is proposed to first identify the compliance coefficients using different payloads and end effector position information. Second, the kinematic parameter errors and link parameters are identified with the given compliance coefficients. Then, the algorithm generates a modified trajectory using the calibrated DH tables for the precision compensation. Simulation and experimental results demonstrate that the positioning accuracy can be improved by 80 % to 90 % even under different payloads. The root mean square, mean, maximum, and standard deviation of the residual errors by using the proposed algorithm could outperform the conventional kinematic algorithm.

Learning from models: high-dimensional analyses on the performance of machine learning interatomic potentials

Machine learning interatomic potential (MLIP) has been widely adopted for atomistic simulations. While errors and discrepancies for MLIPs have been reported, a comprehensive examination of the MLIPs’ performance over a broad spectrum of material properties has been lacking. This study introduces an analysis process comprising model sampling, benchmarking, error evaluations, and multi-dimensional statistical analyses on an ensemble of MLIPs for prediction errors over a diverse range of properties. By carrying out this analysis on 2300 MLIP models based on six different MLIP types, several properties that pose challenges for the MLIPs to achieve small errors are identified. The Pareto front analyses on two or more properties reveal the trade-offs in different properties of MLIPs, underscoring the difficulties of achieving low errors for a large number of properties simultaneously. Furthermore, we propose correlation graph analyses to characterize the error performances of MLIPs and to select the representative properties for predicting other property errors. This analysis process on a large dataset of MLIP models sheds light on the underlying complexities of MLIP performance, offering crucial guidance for the future development of MLIPs with improved predictive accuracy across an array of material properties.

Short-Term Photovoltaic Power Prediction Based on a Digital Twin Model

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation.

Predicting Structural Temperature of Ancient City Walls: A Case Study Using Ambient Temperature and Deep Learning

ABSTRACT Accurate prediction of the structural temperature field is required for the performance assessment of engineering structures and for maintaining structural safety. However, there are complex time-lag effects between ambient temperature and structural temperature. It is difficult to accurately describe and predict the structural temperature using traditional linear and nonlinear fitting methods. Hence, this study uses the advantages of gated recurrent unit (GRU) neural networks in dealing with complex nonlinear mapping issues, further considering solar radiation and developing a structural temperature prediction framework using ambient temperature and the deep learning model. The framework is applied in structural temperature prediction for an ancient city wall in China. The solar altitude angle at the location of the city wall is calculated, and the input and output structures of the deep learning neural network model are optimized. In the long-term prediction of the structural temperature of the city wall, the root mean square error (RMSE) of the GRU model is less than 0.6°C, which solves the problem of low accuracy caused by time-lag effects.

Ensemble-based sensitivity analysis of track forecasts of typhoon In-fa (2021) without and with model errors in the ECMWF, NCEP, and CMA ensemble prediction systems

Ensemble-based sensitivity analysis (ESA) has been widely applied to identifying and investigating the sources of forecast uncertainty in tropical cyclone (TC) track. The standard ESA used in most preceding studies involves calculating the time-lagged covariance of ensemble perturbations by removing the ensemble mean. This method primarily focuses on the influence of initial errors. However, such studies ignore two critical dimensions of ESA. One is how ensemble sensitivity is influenced by the varying forecast performance across different ensemble prediction systems (EPSs). Secondly, the impact of model errors on forecast uncertainties remains unaddressed. An in-depth examination of these two aspects provides a more comprehensive understanding of the factors contributing to TC track uncertainties.This study employed the standard ESA to analyze and compare the sources of uncertainty in the track forecast of Typhoon In-fa (2021) in three representative operational EPSs. Our findings reveal that the EPS's specific performance in ensemble spread markedly influence ensemble sensitivity. We identified that variations in the shape and location of key synoptic systems, such as the western Pacific subtropical high and monsoon trough, across ensemble members were notably distinct. These variations played a significant role in shaping the uncertainty for In-fa's track forecast within each system. Furthermore, we introduced a modified ESA to better account for the influence of model errors on TC track uncertainties. The modified ESA, when applied to ensembles with substantial systematic deviations, predominantly reflects the impact of model errors on track forecast inaccuracies, offering a notably different perspective compared to the standard ESA. Significance statementEnsemble-based sensitivity analysis (ESA) serves as an effective method for identifying the origins of forecast uncertainty in tropical cyclone (TC) tracks. Most preceding studies based on the standard ESA have predominantly concentrated on examining key physical processes within individual ensemble prediction system (EPS) and the impact of initial condition uncertainties. They often overlook the dependence of the ensemble sensitivity on the varying forecast performance of EPSs, as well as the impact of model errors on sensitivity assessment. This study revealed that the ensemble forecasts across different EPSs exhibit different performance in terms of ensemble spread in the shape and location of primary weather systems. This variability significantly contributes to the uncertainty in TC track forecasts through diverse processes. Furthermore, this research introduced a modified ESA, which effectively identifies the impact of model systematic deviations on TC track forecast errors. This approach along with the standard ESA provide a more comprehensive and nuanced analysis of the forecast error sensitivity associated with both initial condition and model related uncertainties.

Dynamic performance of a polygonal thermoelectric generator using sickle-shaped fins for automotive application

About 40 % of automotive fuel energy is lost in the form of heat through exhaust gas, the utilization of thermoelectric power generation technology presents a direct conversion of thermal to electric energy. A thermoelectric generator (TEG) system was designed in this study, comprising a polygonal heat exchanger using sickle-shaped fins to enhance waste heat recycling. Additionally, a transient Computational Fluid Dynamics (CFD)-analysis model was introduced to investigate its dynamic response utilizing the instantaneous variations of exhaust gas and coolant under the China Light-Duty Vehicle Test Cycle (CLTC) as transient inputs. The TEG system exhibits an average power output of 24.48 W and a conversion efficiency of 1.12 % throughout the CLTC. After heating from ambient temperature to normal operating temperature, the power rises to 34.48 W and efficiency to 1.57 %, showing enhanced output performance. A comparison of the transient and steady-state analysis results indicates that the transient analysis reflects the hysteresis effect of heat transfer more accurately. Through experimental verification, the mean absolute percentage errors of the transient model are calculated to be 5.57 % and 8.28 % respectively, proving the credibility of the simulated results. The proposed dynamic model allows for evaluating the actual behavior of the TEG system under intricate driving cycles and offers a robust reference for TEG system optimization and its automotive applications in future.

Bifurcation detection in intravascular optical coherence tomography using vision transformer based deep learning

Objective. Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a significant role in guiding optimal revascularization strategies for percutaneous coronary intervention (PCI). We propose a bifurcation detection method using vision transformer (ViT) based deep learning in IVOCT. Approach. Instead of relying on lumen segmentation, the proposed method identifies the bifurcation image using a ViT-based classification model and then estimate bifurcation ostium points by a ViT-based landmark detection model. Main results. By processing 8640 clinical images, the Accuracy and F1-score of bifurcation identification by the proposed ViT-based model are 2.54% and 16.08% higher than that of traditional non-deep learning methods, are similar to the best performance of convolutional neural networks (CNNs) based methods, respectively. The ostium distance error of the ViT-based model is 0.305 mm, which is reduced 68.5% compared with the traditional non-deep learning method and reduced 24.81% compared with the best performance of CNNs based methods. The results also show that the proposed ViT-based method achieves the highest success detection rate are 11.3% and 29.2% higher than the non-deep learning method, and 4.6% and 2.5% higher than the best performance of CNNs based methods when the distance section is 0.1 and 0.2 mm, respectively. Significance. The proposed ViT-based method enhances the performance of bifurcation detection of IVOCT images, which maintains a high correlation and consistency between the automatic detection results and the expert manual results. It is of great significance in guiding the selection of PCI treatment strategies.

Bayesian inversion for in-situ thermal characterisation of walls in the presence of thermal anomalies

This paper develops a Bayesian inverse modelling framework for the in-situ characterisation of walls' thermal performance in the presence of thermal anomalies subject to uncertainty. For any given wall, the proposed framework uses in-situ contact measurements of temperature and heat flux in order to approximate the (posterior) distribution of a set of parameters which are inputs of a 3D heat transfer model of the wall that accounts for the presence of unknown anomalies. The set of inputs that we infer include (i) the geometry of the thermal anomaly as well was its material properties, (ii) the as-built thermophysical properties of the clear part (i.e. without anomaly) of the wall (iii) surface resistances and (iv) modelling errors that arise from unaccounted sources of uncertainty in the boundary conditions. The geometry of the unknown thermal anomaly is parameterised with a level-set function that is inferred within the proposed Bayesian approach. In order to incorporate prior statistical information of the thermal anomaly, a Gaussian process conditioned on measurements from thermal images is employed to build a prior on the level-set function. The posterior distribution of inferred inputs is approximated via the implementation of an Ensemble Kalman Inversion algorithm that provides stable and accurate approximations of the posterior. This paper also proposes a methodology that uses the inferred 3D model to derive a thermally equivalent 1D model of the wall that is suitable for its integration with existing building energy performance software. The proposed inverse modelling technique is experimentally validated by characterising the thermal performance of an insulation panel in the presence of a thermal anomaly. Our computations show that the proposed approach produces a 3D model that accurately describes thermal performance, and a derived equivalent 1D model that effectively reproduces the dynamic thermal performance of the insulation panel in the presence of the thermal anomaly.

Using metal oxide gas sensors to estimate the emission rates and locations of methane leaks in an industrial site: assessment with controlled methane releases

Abstract. Fugitive methane (CH4) emissions occur in the whole chain of oil and gas production, including from extraction, transportation, storage, and distribution. Such emissions are usually detected and quantified by conducting surveys as close as possible to the source location. However, these surveys are labour-intensive, are costly, and fail to not provide continuous emissions monitoring. The deployment of permanent sensor networks in the vicinity of industrial CH4 emitting facilities would overcome the limitations of surveys by providing accurate emission estimates, thanks to continuous sampling of emission plumes. Yet high-precision instruments are too costly to deploy in such networks. Low-cost sensors using a metal oxide semiconductor (MOS) are presented as a cheap alternative for such deployments due to their compact dimensions and to their sensitivity to CH4. In this study, we demonstrate the ability of two types of MOS sensors (TGS 2611-C00 and TGS 2611-E00) manufactured by Figaro® to reconstruct a CH4 signal, as measured by a high-precision reference gas analyser, during a 7 d controlled release campaign conducted by TotalEnergies® in autumn 2019 near Pau, France. We propose a baseline voltage correction linked to atmospheric CH4 background variations per instrument based on an iterative comparison of neighbouring observations, i.e. data points. Two CH4 mole fraction reconstruction models were compared: multilayer perceptron (MLP) and second-degree polynomial. Emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model. Despite obtaining emission estimates comparable with those obtained using high-precision instruments (average emission rate error of 25 % and average location error of 9.5 m), the application of these emission estimates is limited to adequate environmental conditions. Emission estimates are also influenced by model errors in the inversion process.

Study on Key Properties and Model Establishment of Innovative Recycled Aggregate Pervious Concrete.

In order to meet the needs of low-impact development and sustainable development, there is an urgent desire to develop an innovative recycled aggregate pervious concrete (I-RAPC) that is of high strength and permeability. In this study, I-RAPC was prepared based on response surface methodology (RSM) using recycled aggregate, river sand, and different types of pipes as the materials, and the effects of different pipe parameters (number, diameter, material, and distribution form) on the performance of I-RAPC were investigated. In addition, the calculation model of the compressive strength and the permeability coefficient of I-RAPC were proposed. The results showed that the frontal- and lateral-compressive strengths of I-RAPC were 39.8 MPa and 42.5 MPa, respectively, when the pipe material was acrylic, the position was 1EM, and the diameter was 10 mm-at which time the permeability coefficient was 3.02 mm/s, which was the highest in this study. The maximum relative errors of the compressive strength calculation model and the permeability coefficient calculation model were only 7.52% and 4.42%, respectively, as shown by the post hoc test. Therefore, I-RAPC has the advantages of high strength and permeability and is expected to be applied in low-impact development in cities with heavy surface sediment content and rainfall.

Verification and validation of a coupled CFD–FEM approach with overset structured grid through free decay and regular wave tests

This paper presents a comprehensive verification and validation study applied to the hydrodynamic analysis of a semi-submersible Floating Offshore Wind Turbine (FOWT) platform. The numerical simulation is conducted using a coupled Computational Fluid Dynamics-Finite Element Method (CFD–FEM) approach. The CFD program incorporates an overset grid interpolation method, facilitating a more systematic grid refinement in the estimation of discretization uncertainties. A mooring analysis program, based on the FEM scheme, is integrated with the CFD program to simulate the dynamic responses of mooring system. The necessity of the nonlinear mooring model is demonstrated by comparing solutions with a comparative test using a linear mooring model. The verification study, based on a least-squares-based extrapolation method, quantifies the spatial and temporal discretization errors of the numerical model. The numerical solutions based on the baseline model are further validated against the model test measurements. A pitch free decay model test and a regular wave model test conducted within the OC5 project are utilized for the verification and validation study. Results from both tests demonstrate the reliability and accuracy of the coupled method.

Radio Astrometry at Different Frequencies

The very long baseline interferometry technique allows us to determine the positions of thousands of radio sources using the absolute astrometry approach. I have investigated the impacts of a selection of observing frequencies in a range from 2 to 43 GHz in single-band, dual-band, and quad-band observing modes on astrometric results. I processed seven data sets in a range of 72,000 to 6.9 million observations, estimated source positions, and compared them. I found that source positions derived from dual-band, quad-band, and 23.6 GHz single-band data agree at a level below 0.2 mas. Comparison of independent data sets allowed me to assess the error levels of individual catalogs: 0.05–0.07 mas per position component. Further comparison showed that individual catalogs have systematic errors at the same level. The positions from 23.6 GHz single-band data show systematic errors related to the residual ionosphere contribution. Analysis of source position differences revealed systematic errors along jet directions at a level of 0.09 mas. Network-related systematic errors affect all the data, regardless of frequency. Comparison of position estimates allowed me to derive the stochastic error model that closes the error budget. Based on the collected evidence, I have made a conclusion that the development of frequency-dependent reference frames of the entire sky is not warranted. In most cases dual-band, quad-band, and single-band data at a frequency of 22 GHz and higher can be used interchangeably, which allows us to exploit the strength of a specific frequency setup for given objects. Mixing observations at different frequencies causes errors not exceeding 0.07 mas.