Experimental Accuracy Research Articles

Application of NotebookLM, a large language model with retrieval-augmented generation, for lung cancer staging.

In radiology, large language models (LLMs), including ChatGPT, have recently gained attention, and their utility is being rapidly evaluated. However, concerns have emerged regarding their reliability in clinical applications due to limitations such as hallucinations and insufficient referencing. To address these issues, we focus on the latest technology, retrieval-augmented generation (RAG), which enables LLMs to reference reliable external knowledge (REK). Specifically, this study examines the utility and reliability of a recently released RAG-equipped LLM (RAG-LLM), NotebookLM, for staging lung cancer. We summarized the current lung cancer staging guideline in Japan and provided this as REK to NotebookLM. We then tasked NotebookLM with staging 100 fictional lung cancer cases based on CT findings and evaluated its accuracy. For comparison, we performed the same task using a gold-standard LLM, GPT-4 Omni (GPT-4o), both with and without the REK. For GPT-4o, the REK was provided directly within the prompt rather than through RAG. NotebookLM achieved 86% diagnostic accuracy in the lung cancer staging experiment, outperforming GPT-4o, which recorded 39% accuracy with the REK and 25% without it. Moreover, NotebookLM demonstrated 95% accuracy in searching reference locations within the REK. NotebookLM, a RAG-LLM, successfully performed lung cancer staging by utilizing the REK, demonstrating superior performance compared to GPT-4o (without RAG). Additionally, it provided highly accurate reference locations within the REK, allowing radiologists to efficiently evaluate the reliability of NotebookLM's responses and detect possible hallucinations. Overall, this study highlights the potential of NotebookLM, a RAG-LLM, in image diagnosis.

Slope Surface Deformation Monitoring Based on Close-Range Photogrammetry: Laboratory Insights and Field Applications

Slope surface deformation monitoring plays an important role in landslide risk assessment and early warning. Currently, the mainstream GNSS, as a point-measurement technique, is expensive to deploy, resulting in information on only a few points of displacement being obtained on a target slope in practical applications. In contrast, optical images can contain more information on slope displacement at a much lower cost. Therefore, a low-cost, high-spatial-resolution and easy-to-implement landslide surface deformation monitoring system based on close-range photogrammetry is developed in this paper. The proposed system leverages multiple image processing methods and monocular visual localization, combined with machine learning, to ensure accurate monitoring under time series. The results of several laboratory landslide experiments show that the proposed system achieved millimeter-level monitoring accuracy in laboratory landslide experiments. Moreover, the proposed system could capture slow displacement precursors of 5 mm to 10 mm before significant landslide failure occurred, which provides favorable surface deformation evidence for landslide monitoring and early warning. In addition, the system was deployed on a natural slope in Lanzhou, yielding preliminary effective monitoring results. The laboratory experimental results demonstrated the system’s effectiveness and high accuracy in monitoring landslide surface deformation, particularly its significant application value in early warning. The field deployment results indicated that the system could also effectively provide data support in natural environments, offering practical evidence for landslide monitoring and warning.

Miniaturized on-chip optical differentiator based on 2F-structured metasurfaces.

Analog optical computing based on Fourier optics has attracted ever-growing attention, offering unprecedented low power consumption and high parallelism computation at the speed of light. Typically, classical optical 4F systems have been widely employed as one of the most common approaches for analog optical computing. However, most existing schemes replicate the original architecture relying on two Fourier transforms and one spatial-frequency filtering, resulting in bulky size and complex structure. Here, we propose a novel, to the best of our knowledge, on-chip 2F structure that achieves ultra-miniaturized optical analog computing. Taking advantage of the exceptional design flexibility of metasurfaces, we reduce the optical path length through a combination of phase compensation and complex amplitude modulation, thereby significantly simplifying the system structure without sacrificing accuracy compared to the traditional 4F system. As a proof-of-concept demonstration, we design and fabricate an on-chip optical differentiator on a silicon-on-insulator platform, achieving 84.01% and 79.81% differentiation accuracy in simulation and experiment, respectively.

Highly Calibrated Relationship Between Bleomycin Concentrations and Facets of the Active Phase Fibrosis in Classical Mouse Bleomycin Model.

The mouse bleomycin model is useful in pre-clinical IPF research to understand pathophysiological mechanisms and pharmacological interventions. In the present study, we systematically investigated the effects of bleomycin at a 60-fold dose range on experimental features of lung fibrosis in the mouse bleomycin model. We analyzed the effect of intratracheal (i.t.) dosing of 0.05-3 U/kg bleomycin on disease phenotypes, including weight loss, morbidity and mortality, pulmonary inflammation, lung collagen content, various BALF biomarkers, and histology in a 14-day mouse model when the animals are in the active phase of fibrosis. In mice, challenge with 1-2 U/kg bleomycin doses induced significant and saturated responses on fibrotic endpoints, confirmed by collagen content, BALF biomarker levels, and marked weight loss compared to the normal control (NC). We observed 100% mortality in 3 U/kg of bleomycin-treated mice. In contrast, 0.05-0.5 U/kg bleomycin doses induced a dose-dependent fibrotic phenotype. The mice challenged with doses of 0.25-0.5 U/kg bleomycin showed optimum body weight loss, a significant increase in pulmonary inflammation, and the fibrotic phenotype compared to NC. Furthermore, we showed 0.25-0.5 U/kg bleomycin increases expression levels of (pro-) fibrotic cytokines, which are the mediators involved in the activation of myofibroblast during fibrogenesis (TGF-β1, IL-13, IL-6, WISP-1, VEGF), angiogenesis (VEGF), matrix remodeling (TIMP-1), and non-invasive lung function biomarker (CRP) compared to NC. A modified Ashcroft scale quantified that the fibrotic changes in the lungs were significantly higher in the lung of mice dosed at 0.25-0.5 U/kg > 0.1 U/kg bleomycin and non-significant in mice lung dosed at 0.05 U/kg bleomycin compared to NC. We demonstrated that the changes due to 0.25-0.5 U/kg i.t. bleomycin on protein biomarkers are enough to drive robust and detectable fibrotic pathology without mortality. The 0.1 U/kg has a moderate phenotype, and 0.05 U/kg had no detectable phenotype. The Goodness of Fit (r2) and Pearson correlation coefficient (r) analyses revealed a positive linear association between change evaluated in all experimental features of fibrosis and bleomycin concentrations (0.05-0.5 U/kg). Here, we provide an examination of a highly calibrated relationship between 60-fold bleomycin concentrations and a set of in vivo readouts that covers various facets of experimental fibrosis. Our study shows that there is a dose-dependent effect of bleomycin on the features of experimental fibrosis at <1 U/kg, whereas saturated responses are achieved at >1 U/kg. Our careful experimental observations, accuracy, and comprehensive data set provided meaningful insights into the effect of bleomycin dose(s) on the fibrotic phenotype, which is valuable in preclinical drug development and lung fibrosis research. In addition, we have presented a set of reproducible frameworks of endpoints that can be used for reliable assessment of the fibrotic phenotype, and in vivo therapeutic intervention(s) with improved accuracy.

The Genus Commiphora: An Overview of Its Traditional Uses, Phytochemistry, Pharmacology, and Quality Control.

Myrrh is the resinous substance secreted by plants of the genus Commiphora. In traditional Chinese medicine, Ayurvedic medicine, and traditional Arabic medicine, myrrh is regarded as an important medicinal material, widely used in the treatment of trauma, arthritis, hyperlipidemia, and other diseases. This review explores the evolving scientific understanding of the genus Commiphora, covering facets of ethnopharmacology, phytochemistry, pharmacology, artificial cultivation, and quality control. In particular, the chemical constituents and pharmacological research are reviewed. More than 300 types of secondary metabolites have been identified through phytochemical studies of this genus. Guggulsterone is a bioactive steroid isolated mainly from Commiphora mukul. The two isomers, Z- and E-guggulsterone, have shown a wide range of in vitro and in vivo pharmacological effects, including anti-proliferation, antioxidant, anti-inflammatory, and antibacterial. However, the current scientific research on quality control of medicinal materials and identification of original plants is insufficient, which limits the reproducibility and accuracy of biological activity evaluation experiments. Therefore, the establishment of analytical protocols and standardization of extracts is an important step before biological evaluation. At the same time, in order to find more bioactive substances, it is necessary to strengthen the research on the stems, barks, and leaves of this genus. The sources used in this study include PubMed, CNKI, Web of Science, Google Scholar, and other databases, as well as multinational pharmacopoeias, ancient books of traditional medicine, herbal classics, and modern monographs.

Model-Based Sequential Design of Experiments with Machine Learning for Aerospace Systems

Traditional experimental design methods often face challenges in handling complex aerospace systems due to the high dimensionality and nonlinear behavior of such systems, resulting in nonoptimal experimental designs. To address these challenges, machine learning techniques can be used to further increase the application areas of modern Bayesian Optimal Experimental Design (BOED) approaches, enhancing their efficiency and accuracy. The proposed method leverages neural networks as surrogate models to approximate the underlying physical processes, thereby reducing computational costs and allowing for full differentiability. Additionally, the use of reinforcement learning enables the optimization of sequential designs and essential real-time capability. Our framework is validated by optimizing experimental designs that are used for the efficient characterization of turbopumps for liquid propellant rocket engines. The reinforcement learning approach yields superior results in terms of the expected information gain related to a sequence of 15 experiments, exhibiting mean performance increases of 9.07% compared to random designs and 6.47% compared to state-of-the-art approaches. Therefore, the results demonstrate significant improvements in experimental efficiency and accuracy compared to conventional methods. This work provides a robust framework for the application of advanced BOED methods in aerospace testing, with implications for broader engineering applications.

Microdroplet pull-out testing: Significance of fiber fracture results

Fiber reinforced composites are used in structural materials that required light weight and stiffness. The properties of the fibers or matrix are important, but the interfacial properties have a significant impact on the properties of fiber reinforced composite. In this study, the interfacial shear strength (IFSS) was measured using acrylic resin and epoxy resin as base materials. The chemical composition of acrylic and epoxy matrix materials was analyzed to predict the effects on IFSS. Additionally, IFSS was measured through a microdroplet pull-out test. The reliability of the experimental results was enhanced by applying a statistical analysis to IFSS results. In the case of epoxy, GF/epoxy exhibited higher IFSS to twice and half times than CF/epoxy specimens. It means that the surface treatment of the fibers has a significant impact on the interface. In the case of acrylic, IFSS could be measured for GF. But in the case of CF, IFSS was too low to get accurate results of IFSS. Through this research, methods to improve the accuracy of composite interfacial strength measurement experiments were examined, and the study suggested the need for standardized criteria to evaluate composite interfacial adhesion.

Correction to Density Functional Theory Calculations and Machine Learning Interatomic Potentials for Molten Salts to Achieve Experimental Accuracy

Correction to Density Functional Theory Calculations and Machine Learning Interatomic Potentials for Molten Salts to Achieve Experimental Accuracy

Cross-condition fault diagnosis of chillers based on an ensemble approach with adaptive weight allocation

The Heating, Ventilation and Air Conditioning (HVAC) systems are complex and prone to failures during operation, often leading to significant energy waste. Timely and accurate Fault Detection and Diagnosis (FDD) can enhance energy efficiency. The HVAC system operates under diverse conditions, data-driven models trained under existing conditions may experience performance degradation when faced with new conditions. Transfer learning offers an effective solution to this issue. This study proposes a novel transfer learning ensemble model based on adaptive weights, leveraging different transfer learning strategies to improve diagnosis performance under new conditions. Multiple cross-condition transfer learning tasks were implemented to test the proposed method, and its effectiveness was validated through multiple experiments to minimize the impact of randomness. Results showed that, compared to fine-tuning (FT), domain-adversarial neural network (DANN), and baseline models, the proposed method outperforms the other models. The average accuracy of multiple experiments improved by 0.21 % to 2.34 % compared to FT. Additionally, modifying DANN to utilize a small amount of labeled information from the target domain has led to greater overlap between the feature distributions of the source and target domains, resulting in improved performance that is close to that of FT. Finally, we analyzed the impact of target domain data volume on the performance of the four methods. The performance of the baseline model improved significantly with the increase in data volume, while the other models showed less improvement. Meanwhile, the diagnostic results of the baseline model were significantly influenced by experimental randomness when there is less training data, whereas the FT diagnostic results were relatively more stable.

Methodology for comparative assessment of battery technologies: Experimental design, modeling, performance indicators and validation with four technologies

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

Batch channel normalized-CWGAN with Swin Transformer for imbalanced data fault diagnosis of rotating machinery

Abstract In real scenarios, rotating machinery is mainly operated in optimal condition, leading to fault data scarce and difficult to collect. This issue results in imbalanced data, significantly limiting the effectiveness of intelligent fault diagnosis methods. To address this issue, a novel fault diagnosis method for rotating machinery is proposed in this paper, which combines the batch channel normalized conditional wasserstein generative adversarial network (BCN-CWGAN) with Swin Transformer. Firstly, the one-dimensional vibration signal is preprocessed into two-dimensional feature images using a symmetrized dot pattern (SDP). Subsequently, self-attention mechanism and deep feature learning module constructed by DenseNet are integrated into the generator of GAN to acquire more discriminative feature information. Meanwhile, the discriminator of GAN is combined with batch channel normalization strategy, which further enhances the generalization ability. Besides, a two time-scale update rule strategy enhances training stability and convergence speed by updating model parameters at different time scales. Then, the data augmentation capability of BCN-CWGAN is used to generate high-quality fault samples to augment the imbalanced dataset. Finally, Swin Transformer is combined to achieve accurate fault diagnosis. The performance enhancement of the proposed method is verified through comparison and diagnosis results of two engineering experiments, demonstrating its substantial value for research in engineering practice. With the proposed data augmentation method, the average accuracy of A1, B1, C1, and D1 datasets in experiment 1 reached 99.24%, 98.85%, 96.78%, and 96.04%, respectively. Meanwhile, the proposed method achieved the best accuracy in experiment 2.

A dual emission ratiometric fluorescent probe constructed based on MOF-embedded dye for sequential detection of Al3+ and PPA

The ratiometric fluorescence probe has a self-calibration system that greatly reduces interference from the external environment, it has high sensitivity and accuracy in qualitative and quantitative experiments. In this work, a novel ratio fluorescence probe (RhB@1) was constructed by enclosing Rhodamine B (RhB) in the channel of [Cd2(H2L)2(1,4-bib)2]•DMA (H2L=2-hydroxyterephthalic acid, 1,4-bib=1, 4-bis (imidazol-1-ylmethyl) benzene). The probe can display dual emission characteristics under a single excitation. Al3+ ion was detected by RhB@1. The results showed that the fluorescence of 1 in RhB@1 was increased, the fluorescence of RhB is unchanged, and limit of detection (LOD) of Al3+ is as low as 20 .9nM. Interestingly, when PPA (phenylpyruvic acid) is added to the mixture of RhB@1 and Al3+, PPA competes with RhB@1 for Al3+ result in achieving continuous ratio sensing of Al3+ and PPA, and LOD of PPA is 3 .21nM. More importantly, the response of RhB@1 to Al3+ in tap water and PPA in artificial urine was quantitatively determined by label recovery method. The results showed that the recovery rate were 97.706 %–99.060 % (Al3+) and 94.732 %–98.100 % (PPA). In addition, we prepared a portable test paper modified with carboxymethyl cellulose. This test paper has obvious color change when detecting Al3+ under ultraviolet light, which is easy to detect Al3+ by naked eye.

Multi-plane light conversion (MPLC) LP mode multiplexer based on grayscale maskless lithography.

Multi-plane light conversion (MPLC) is a technique that uses multiple phase plates to modulate a light beam step-by-step. This technique has attracted widespread attention in the field of mode-division multiplexing (MDM) communications due to its high flexibility. MPLC device requires precisely controlled fabrication accuracy in experiments, but conventional multi-etching processes will accumulate alignment errors. Here, the fabrication of the MPLC device using maskless grayscale lithography was proposed, which requires only a single-exposure process. Through single-exposure lithography, the continuous phase of the digital mask ranging from 0 to 2π on MPLC is discretized into 128 steps. The digital masks of the MPLC with more steps of phase can reduce the insertion loss and mode crosstalk of LP modes. By using the fabricated MPLC, we experimentally demonstrate the MDM of LP01, LP11a, LP11b, and LP21 modes with mode crosstalk less than -22 dB, and the insertion loss less than 4 dB. In high-speed optical communications, each LP mode carries a 10 Gbit/s on-off keying (OOK) signals, and the experimentally measured bit error rates (BER) curves power penalty is less than -7 dB. The experiment demonstrated that maskless grayscale lithography can efficiently and accurately fabricate MPLC mode multiplexers.

A robust approach for bond strength prediction of mortar using machine learning with SHAP interpretability

Application of machine learning (ML) in predicting mortars bond strength contributes to low experimental cost and high accuracy. This study explores the performance of four ML models—Back Propagation Neural Network (BPNN), Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—using 304 experimental data points for training and 76 results for testing. Among these, the XGBoost model demonstrated the highest predictive accuracy, with R², MAE, and RMSE values of 0.95, 0.06, and 0.13 in the training set, and 0.94, 0.08, and 0.16 in the testing set, respectively. Compared with traditional models, ML approaches, particularly XGBoost, were more effective in providing reliable predictions of bond strength. SHapley Additive exPlanation (SHAP) show that substrate type, ethylene-vinyl acetate (EVA) content and water to binder ratio W/C have the most remarkable effect on bond strength, while the type of cement and admixture shows the least effect. These findings highlight the potential of ML models in optimizing mortar formulations and improving prediction reliability.

Optimization of Decoder Priors for Accurate Quantum Error Correction.

Accurate decoding of quantum error-correcting codes is a crucial ingredient in protecting quantum information from decoherence. It requires characterizing the error channels corrupting the logical quantum state and providing this information as a prior to the decoder. We introduce a reinforcement learning inspired method for calibrating these priors that aims to minimize the logical error rate. Our method significantly improves the decoding accuracy in repetition and surface code memory experiments executed on Google's Sycamore processor, outperforming the leading decoder-agnostic method by 16% and 3.3%, respectively. This calibration approach will serve as an important tool for maximizing the performance of both near-term and future error-corrected quantum devices.

Fast Hyperspectral Image Classification with Strong Noise Robustness Based on Minimum Noise Fraction

A fast hyperspectral image classification algorithm with strong noise robustness is proposed in this paper, aiming at the hyperspectral image classification problems under noise interference. Based on the Fast 3D Convolutional Neural Network (Fast-3DCNN), this algorithm enables the classification model to have good tolerance for various types of noise by using a Minimum Noise Fraction (MNF) as dimensionality reduction module for hyperspectral image input data. In addition, by introducing lightweight hybrid attention modules with the spatial and the channel information, the deep features extracted by the Convolutional Neural Network are further refined, ensuring that the model has high classification accuracy. Public dataset experiments have shown that compared to traditional methods, the MNF in this algorithm reduces the dimensionality of input spectral data, preserves information with higher signal-to-noise ratio(SNR) in the spectral bands, and aggregates spectral features into class feature vectors, greatly improving the noise robustness of the model. At the same time, based on a lightweight spectral–spatial hybrid attention mechanism, combined with fewer spectral dimensions, the model effectively avoids overfitting. With less loss in model training speed, it achieved better classification accuracy in small-scale training sample experiments, fully demonstrating the good generalization ability of this algorithm.

Validation of non-destructive morphology-based selection of cerebral cortical organoids by paired morphological and single-cell RNA-seq analyses

Organoids, self-organized cell aggregates, contribute significantly to developing disease models and cell-based therapies. Organoid-to-organoid variations, however, are inevitable despite the use of the latest differentiation protocols. Here, we focused on the morphology of organoids formed in a cerebral organoid differentiation culture and assessed their cellular compositions by single-cell RNA sequencing analysis. The data revealed that organoids primarily composed of non-neuronal cells, such as those from the neural crest and choroid plexus, showed unique morphological features. Moreover, we demonstrate that non-destructive morphological analysis can accurately distinguish organoids composed of cerebral cortical tissues from other cerebral tissues, thus enhancing experimental accuracy and reliability to ensure the safety of cell-based therapies.

Deep learning with photonic neural cellular automata.

Rapid advancements in deep learning over the past decade have fueled an insatiable demand for efficient and scalable hardware. Photonics offers a promising solution by leveraging the unique properties of light. However, conventional neural network architectures, which typically require dense programmable connections, pose several practical challenges for photonic realizations. To overcome these limitations, we propose and experimentally demonstrate Photonic Neural Cellular Automata (PNCA) for photonic deep learning with sparse connectivity. PNCA harnesses the speed and interconnectivity of photonics, as well as the self-organizing nature of cellular automata through local interactions to achieve robust, reliable, and efficient processing. We utilize linear light interference and parametric nonlinear optics for all-optical computations in a time-multiplexed photonic network to experimentally perform self-organized image classification. We demonstrate binary (two-class) classification of images using as few as 3 programmable photonic parameters, achieving high experimental accuracy with the ability to also recognize out-of-distribution data. The proposed PNCA approach can be adapted to a wide range of existing photonic hardware and provides a compelling alternative to conventional photonic neural networks by maximizing the advantages of light-based computing whilst mitigating their practical challenges. Our results showcase the potential of PNCA in advancing photonic deep learning and highlights a path for next-generation photonic computers.

Orbital angular momentum superimposed mode recognition based on multi-label image classification.

Orbital angular momentum (OAM) multiplexing technology has great potential in high capacity optical communication. OAM superimposed mode can extend communication channels and thus enhance the capacity, and accurate recognition of multi-OAM superimposed mode at the receiver is very crucial. However, traditional methods are inefficient and complex for the recognition task. Machine learning and deep learning can offer fast, accurate and adaptable recognition, but they also face challenges. At present, the OAM mode recognition mainly focus on single OAM mode and ±l superimposed dual-OAM mode, while few researches on multi-OAM superimposed mode, due to the limitations of single-object image classification techniques and the diversity of features to recognize. To this end, we develop a recognition method combined with multi-label image classification to accurately recognize multi-OAM superimposed mode vortex beams. Firstly, we create datasets of intensity distribution map of three-OAM and four-OAM superimposed mode vortex beams based on numerical simulations and experimental acqusitions. Then we design a progressive channel-spatial attention (PCSA) model, which incorporates a progressive training strategy and two weighted attention modules. For the numerical simulation datasets, our model achieves the highest average recognition accuracy of 94.9% and 91.2% for three-OAM and four-OAM superimposed mode vortex beams with different transmission distances and noise strengths respectively. The highest experimental average recognition accuracy for three-OAM superimposed mode achieves 92.7%, which agrees with the numerical result very well. Furthermore, our model significantly outperforms in most metrics compared with ConvNeXt, and all experiments are within the affordable range of computational cost.

DMATCH: A novel integrated testing method for floating wind turbine dynamics – Potential error and its impact

With floating offshore wind turbines (FOWT) steadily advancing towards large-scale deployments in deep water, the traditional wave basin model testing method shows its shortage to fulfill the testing requirement. To enhance the effectiveness of model tests for FOWT, this paper introduces a distributed model testing method, dMATCH (distributed-Mooring-Aerodynamics-Testing-Control-Hydrodynamics). The dMATCH method separates the experiment into aerodynamic testing in the wind tunnel and hydrodynamic testing in the wave basin, achieving integrated coupling through real-time data transmission using a data exchange system. Errors in sensor acquisition and actuator actuation during data communication may impact the accuracy of experiments. To investigate the potential error and its impact within dMATCH, an integrated model testing system in the wave basin was constructed. Four load cases were established, introducing varying magnitudes of normally distributed random errors to simulate acquisition and actuation inaccuracies. The investigation revealed a positive correlation between errors in experimental results and those in acquisition and actuation. As error increased, result fluctuations became more pronounced. Within the current experimental framework, acquisition error had a greater influence on results compared to actuation error, with aerodynamic thrust being more sensitive to error than the pitch and surge motions of platform. When acquisition and actuation errors were controlled below 5%, the relative RMSE of the results remained below 5%. The dMATCH method proposed in this paper offers a new approach for integrated model testing of FOWT, and the study of potential error impact is crucial to ensuring the reliability of this innovative testing method.