Fitting Capability Research Articles

Novel Life Prediction Method of PMMA for Cultural Relics Protection Based on the BP Neural Network.

Poly(methyl methacrylate) (PMMA) is widely used in the preservation and exhibition of cultural relics in museums. Accurately predicting its service life can help avoid many negative effects caused by PMMA aging. To study the change in the yellowing index of PMMA after aging in a UV light environment, an aging experiment was conducted. A prediction model for the service life of PMMA was established using nonlinear curve fitting and a back propagation (BP) neural network. By comparing the goodness of fit, simulation and modeling capabilities of the initial data, and the predictive ability for new data, it was found that the BP neural network prediction model outperformed the nonlinear curve fitting prediction model. In this study, the service life of newly produced PMMA samples was calculated as 7.83, 8.47, and 8.42 years, based on the yellowing index of retired PMMA as a benchmark and using the output data from the BP neural network prediction model. At this time, the performance and exhibition effect of the PMMA are poor, and the batch of PMMA needs to be updated.

Multi-view spatiotemporal learning for traffic forecasting

The spatiotemporal learning has been one of the most burning research topics in traffic data analytics for traffic forecasting, which is instrumental in developing the intelligent transportation systems. Capturing accurately and efficiently complex spatiotemporal dependencies of the road network is a critical prerequisite of the traffic spatiotemporal forecasting. This study proposes a multi-view spatiotemporal learning (MVSTL) framework, which combines the fast parallel learning (FPL) and the serial learning (SL) to juggle the receptive field with the fitting capability of the model. The FPL is proposed to capture the spatiotemporal correlations synchronously and efficiently by reducing the number of parameters significantly. As a supplement to the FPL, the SL, which extracts the spatial and temporal features serially, can expand the spatial/temporal receptive fields and avoid information overwriting. The evaluation of the MVSTL is carried out by employing four sets of traffic data from the LA freeway system, and the experiment results reveal that the MVSTL is able to perform better than the advanced methods in prediction accuracy.

PMNN: Physical model-driven neural network for solving time-fractional differential equations

In this paper, an innovative Physical Model-driven Neural Network (PMNN) method is proposed to solve time-fractional differential equations. It establishes a temporal iteration scheme based on physical model-driven neural networks which effectively combines deep neural networks (DNNs) with interpolation approximation of fractional derivatives. Specifically, once the fractional differential operator is discretized, DNNs are employed as a bridge to integrate interpolation approximation techniques with differential equations. On the basis of this integration, we construct a neural-based iteration scheme. Subsequently, by training DNNs to learn this temporal iteration scheme, approximate solutions to the differential equations can be obtained. The proposed method aims to preserve the intrinsic physical information within the equations as far as possible. It fully utilizes the powerful fitting capability of neural networks while maintaining the efficiency of the difference schemes for fractional differential equations. The experimental results show that the PMNN maintains precision comparable to traditional methods while exhibiting superior computational efficiency. This implies the potential of PMNN in addressing large-scale problems. Moreover, when considering both error and convergence rate, PMNN consistently outperforms fPINN. Additionally, the performance of PMNN on L2−1σ surpasses that on L1 in an overall comparison. The data and code can be found at https://github.com/DouMiao1226/PMNN.

Development and Validation of a New Score to Assess the Risk of Posttransplantation Diabetes Mellitus in Kidney Transplant Recipients.

Posttransplantation diabetes mellitus (PTDM) is a serious complication of solid organ transplantation. It is associated with major adverse cardiovascular events, which are a leading cause of morbidity and mortality in transplant patients. This study aimed to develop and validate a score to predict the risk of PTDM in kidney transplant recipients. A single-center retrospective cohort study was conducted in a tertiary care hospital in Medellín, Colombia, between 2005 and 2019. Data from 727 kidney transplant recipients were used to develop a risk prediction model. Significant predictors with competing risks were identified using time-dependent Cox proportional hazard regression models. To build the prediction model, the score for each variable was weighted using calculated regression coefficients. External validation was performed using independent data, including 198 kidney transplant recipients from Tübingen, Germany. Among the 727 kidney transplant recipients, 122 developed PTDM. The predictive model was based on 5 predictors (age, gender, body mass index, tacrolimus therapy, and transient posttransplantation hyperglycemia) and exhibited good predictive performance (C-index: 0.7 [95% confidence interval, 0.65-0.76]). The risk score, which included 33 patients with PTDM, was used as a validation data set. The results showed good discrimination (C-index: 0.72 [95% confidence interval, 0.62-0.84]). The Brier score and calibration plot demonstrated an acceptable fit capability in external validation. We proposed and validated a prognostic model to predict the risk of PTDM, which performed well in discrimination and calibration, and is a simple score for use and implementation by means of a nomogram for routine clinical application.

Forest Single-Frame Remote Sensing Image Super-Resolution Using GANs

Generative Adversarial Networks (GANs) possess remarkable fitting capabilities and play a crucial role in the field of computer vision. Super-resolution restoration is the process of converting low-resolution images into high-resolution ones, providing more detail and information. This is of paramount importance for monitoring and managing forest resources, enabling the surveillance of vegetation, wildlife, and potential disruptive factors in forest ecosystems. In this study, we propose an image super-resolution model based on Generative Adversarial Networks. We incorporate Multi-Scale Residual Blocks (MSRB) as the core feature extraction component to obtain image features at different scales, enhancing feature extraction capabilities. We introduce a novel attention mechanism, GAM Attention, which is added to the VGG network to capture more accurate feature dependencies in both spatial and channel domains. We also employ the adaptive activation function Meta ACONC and Ghost convolution to optimize training efficiency and reduce network parameters. Our model is trained on the DIV2K and LOVEDA datasets, and experimental results indicate improvements in evaluation metrics compared to SRGAN, with a PSNR increase of 0.709/2.213 dB, SSIM increase of 0.032/0.142, and LPIPS reduction of 0.03/0.013. The model performs on par with Real-ESRGAN but offers significantly improved speed. Our model efficiently restores single-frame remote sensing images of forests while achieving results comparable to state-of-the-art methods. It overcomes issues related to image distortion and texture details, producing forest remote sensing images that closely resemble high-resolution real images and align more closely with human perception. This research has significant implications on a global scale for ecological conservation, resource management, climate change research, risk management, and decision-making processes.

Anomaly Detection via Progressive Reconstruction and Hierarchical Feature Fusion.

The main challenges in reconstruction-based anomaly detection include the breakdown of the generalization gap due to improved fitting capabilities and the overfitting problem arising from simulated defects. To overcome this, we propose a new method called PRFF-AD, which utilizes progressive reconstruction and hierarchical feature fusion. It consists of a reconstructive sub-network and a discriminative sub-network. The former achieves anomaly-free reconstruction while maintaining nominal patterns, and the latter locates defects based on pre- and post-reconstruction information. Given defective samples, we find that adopting a progressive reconstruction approach leads to higher-quality reconstructions without compromising the assumption of a generalization gap. Meanwhile, to alleviate the network's overfitting of synthetic defects and address the issue of reconstruction errors, we fuse hierarchical features as guidance for discriminating defects. Moreover, with the help of an attention mechanism, the network achieves higher classification and localization accuracy. In addition, we construct a large dataset for packaging chips, named GTanoIC, with 1750 real non-defective samples and 470 real defective samples, and we provide their pixel-level annotations. Evaluation results demonstrate that our method outperforms other reconstruction-based methods on two challenging datasets: MVTec AD and GTanoIC.

A data‐driven constitutive model for soft biological tissues

AbstractConstitutive modeling of soft biological tissues is crucial for biomedical simulations and a deep understanding of tissue mechanics. Classical constitutive models have a fixed mathematical expression, however, the microstructure and the corresponding macro‐mechanical response of different tissue types can vary significantly. A model that works for one tissue may not work for another kind of tissue, and choosing the right model may become challenging. To solve this issue, this study introduces a new data‐driven approach to creating a unified model for predicting the mechanical response of various tissue classes. The proposed model is based on B‐Spline approximations and assumes the strain energy function can be divided into volumetric, isotropic, and anisotropic components. The B‐Spline ansatz replaces partial derivatives of free energy energy function with respect to invariants with control points and polynomial degree, and allows the use of existing dispersion models. The model adapts its control point values to reduce the error between data and prediction until a threshold is reached, and is thermodynamically consistent through the use of optimization constraints. The model is demonstrated on various biological tissues, showing excellent fitting capabilities with a minimal number of control points. The outcome is a generic framework that can model any tissue given the data from experiments and imaging techniques.

An improved physics-free genetic impedance fitting technique for precise high-frequency modeling of two-terminal electrical systems

There is an increasing industrial demand of using impedance fitting techniques to enable an equivalent circuit to approximate the electrical behavior of an electrical system. However, due to the high dependence on the prior physical information of the electrical system, previous impedance fitting techniques have long suffered from unacceptable implementation complexity and unsatisfactory fitting precision. To realize easy, cheap, and precise impedance fitting, a Physics-free genetic impedance fitting technique (PFGIFT) is proposed in this paper. Firstly, PFGIFT utilizes a five-component RLC-parallel (FCRLCP) equivalent circuit, which gets rid of expensive physics-based equivalent circuit modeling, to approximate the impedance of a two-terminal electrical system. Then, a novel bounded gaussian mixture model based genetic algorithm (BGMMBGA) is proposed to fully leverage the fitting capability of the FCRLCP equivalent circuit by embedding parameter tuning expertise into the evolutionary process. Under the combined effects of FCRLCP equivalent circuit and BGMMBGA, PFGIFT achieves precise impedance fitting performance without the use of expensive knowledge about physical information. The effectiveness of PFGIFT is verified separately on a human-designed impedance fitting task and an industrial application of fitting the common-mode impedance of a motor drive system.

A novel deep-learning-based pressure distribution prediction approach of airfoils

Pressure distribution is a crucial flow characteristic and a key consideration in supercritical airfoil design. Traditionally, obtaining the pressure distribution involves time-consuming and computationally expensive wind tunnel experiments and computational fluid dynamics calculations. This study proposes a deep-learning-based approach to directly map input geometric information to the pressure distribution output, thereby avoiding costly wind tunnel experiments and iterative computational fluid dynamics simulations based on Navier–Stokes equations to address these challenges. Conventional surrogate models typically focus on predicting simple force factors, such as lift and drag coefficients, or require the conversion of airfoil data into images for model training. The novel approach utilizes a Variational Autoencoder for pressure distribution characteristic extraction and reconstruction from feature variables. Unlike conventional models, this approach avoids image conversion and employs a radial basis function neural network for effective mapping. The model exhibits good fitting and generalization capabilities on both training and test datasets, offering a promising solution for rapid pressure distribution prediction in airfoil design. This novel deep-learning-based approach advances airfoil design methodologies, offering significant advantages in computational efficiency and performance prediction. By directly mapping geometric information to pressure distribution, it provides an innovative and promising tool for airfoil design optimization.

Kinetic, artificial neural network, and statistical modeling to optimize the parameters of the air flotation process to remove latex suspended solids in ABS wastewater

As China's contemporary industrial production undergoes continuous enhancement and economic development, there is a remarkable surge in the consumption of acrylonitrile–butadiene–styrene (ABS) materials. The extensive application of dissolved air flotation (DAF) has been adopted to effectively eliminate a substantial quantity of suspended solids present in ABS wastewater. Attaining precise control over the DAF process stands as the pivotal factor in enhancing the removal efficiency. In this study, we had constructed an air flotation apparatus and developed an open-structure software within the master computer. We innovatively compared the response surface methodology (RSM), artificial neural network (ANN), and adsorption kinetic model (AKM) with ABS wastewater DAF treatment process by incorporating relevant parameters. The results demonstrated that the ANN exhibited superior fitting capability in comparison to both the RSM and AKM. Drawing from the comparative results, it was evident that, in the context of COD removal, the ANN surpassed the RSM by margins of 5.06 %, 80 %, and 4.44 % in terms of R-squared (R2), Average Absolute Relative Error (AARE), and Root Mean Square Error (RMSE), respectively. The ANN outperformed the AKM by percentages of 35.71 %, 95.65 %, and 74.66 % concerning R2, AARE, and RMSE, respectively. Similar conclusions were drawn for suspended solids (SS) removal. This study provided valuable support for the adaptive adjustment of successive reaction conditions and the prediction of outlet quality, thus possessing commendable applicative significance and practical utility.

Ensemble learning approach for advanced metering infrastructure in future smart grids.

Typically, load forecasting models are trained in an offline setting and then used to generate predictions in an online setting. However, this approach, known as batch learning, is limited in its ability to integrate new load information that becomes available in real-time. On the other hand, online learning methods enable load forecasting models to adapt efficiently to new incoming data. Electricity Load and Price Forecasting (ELPF) is critical to maintaining energy grid stability in smart grids. Existing forecasting methods cannot handle the available large amount of data, which are limited by different issues like non-linearity, un-adjusted high variance and high dimensions. A compact and improved algorithm is needed to synchronize with the diverse procedure in ELPF. Our model ELPF framework comprises high/low consumer data separation, handling missing and unstandardized data and preprocessing method, which includes selecting relevant features and removing redundant features. Finally, it implements the ELPF using an improved method Residual Network (ResNet-152) and the machine-improved Support Vector Machine (SVM) based forecasting engine to forecast the ELP accurately. We proposed two main distinct mechanisms, regularization, base learner selection and hyperparameter tuning, to improve the performance of the existing version of ResNet-152 and SVM. Furthermore, it reduces the time complexity and the overfitting model issue to handle more complex consumer data. Furthermore, numerous structures of ResNet-152 and SVM are also explored to improve the regularization function, base learners and compatible selection of the parameter values with respect to fitting capabilities for the final forecasting. Simulated results from the real-world load and price data confirm that the proposed method outperforms 8% of the existing schemes in performance measures and can also be used in industry-based applications.

Constructing a Model of Poplus spp. Growth Rate Based on the Model Fusion and Analysis of Its Growth Rate Differences and Distribution Characteristics under Different Classes of Environmental Indicators

Poplar (Poplus spp.) is an important forest species widely distributed in China of great significance in identifying factors that clearly influence its growth rate in order to achieve effective control of poplar growth. In this study, we selected 16 factors, including tree size, competition, climate, location, topography, and soil characteristics, to construct linear regression (LR), multilayer perceptron (MLP), k-nearest neighbor regression (KNN), gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN) models based on the poplar growth rate. Using model fusion methods, the fitting accuracy and estimation capability were improved. The relative importance of each variable in estimating the poplar growth rate was analyzed using the permutation importance evaluation. The results showed the following: (1) the model fusion approach significantly improved the estimation accuracy of the poplar growth rate model with an R2 of 0.893; (2) the temperature and precipitation exhibited the highest importance in poplar growth; (3) the forest stand density, precipitation, elevation, and temperature had significant variations in growth rates among different-sized poplar trees within different ranges; (4) low-forest stand density, high-precipitation, low-elevation, and high-temperature environments significantly increased the poplar growth rate and had a larger proportion of large-sized individuals with high growth rates. In conclusion, environmental factors significantly influence poplar growth, and corresponding planting and protection measures should be tailored to different growth environments to effectively enhance the growth of poplar plantations.

Imperfect Debugging-Based Generalized Exponential Testing Effort Estimation for Software Fault Detection and Removal

In the current technological era, the reliability growth model depicts the failure in software that is employed for estimating the reliability of software. Conventional software reliability growth model (SRGMs) usually presume that faults in software are immediately corrected once a software fault is found and no new faults are introduced. This consideration can be impractical. However, introduction of several new faults is possible throughout debugging, which is called imperfect debugging. During such a process of debugging and introduction of new faults, consumption of testing effort also plays an important role. Hence, in this paper, two models are proposed that incorporate testing effort along with imperfect debugging during fault correction. Additionally, the combined testing effort is the addition of two efforts, specifically detection effort as well as correction effort. The formulated models are validated for real data set and contrasted with additional famous SRGMs. These inferences indicate that Model 2 performs best in terms of fitting and prediction capability.

Process-structure multi-objective inverse optimisation for additive manufacturing of lattice structures using a physics-enhanced data-driven method

ABSTRACT Additive manufacturing (AM) has become a practical solution for fabricating lightweight and high-strength metallic lattice structures. The inverse optimisation of process-structure parameters to achieve high performance and minimised trial-and-error experiments has presented a persistent challenge. To address this problem, an inverse optimisation methodology has been proposed for coping with multiple conflicting performance objectives, consisting of mechanical properties and lightweight extent under AM-constraints. In the pursuit of greater accuracy, a physics-enhanced data-driven algorithm, i.e. encoding-stiffness-analysis multi-task Gaussian process regression, has been developed. This empowers us to precisely analyse how process-structure parameters impact the properties of AM-formed lattice structures. As an emerging machine learning method for AM, the physics-enhanced data-driven algorithm exhibits strong fitting capability and extrapolation performance, due to the interpretability provided by physical information. It has been applied as a surrogate model within the multi-objective genetic algorithm, facilitating the efficient design of parameters and the expansion of objective space. Notably, a deviation of less than 15% has been observed between predictive and experimental results, providing solid confirmation of our methodology's reliability. This confluence of physical insights and data-driven modelling holds substantial promise for accelerating the development of highly efficient designs.

Physics-constrained sequence learning with attention mechanism for multi-horizon production forecasting

Production forecasting of oil and gas energy is a crucial task for reservoir management as it provides a fundamental basis for optimizing development plans and determining investment decisions. Recently, machine learning-based production forecasting methods are proliferating due to their powerful non-linear fitting capability. However, most existing methods lack direct consideration of the engineering physical processes, which may lead to production forecasts inconsistent with engineering science. In this paper, we propose a novel physics-constrained sequence learning method with attention mechanism for multi-horizon production forecasting. The proposed method is built upon on a backbone sequence-to-sequence neural network composed of gated recurrent unit (GRU) cells, which aims to learn the underlying decline behavior from historical production. An attention mechanism is introduced on the backbone network to improve the information utilization of historical production. Furthermore, known engineering parameters are incorporated into the decoder as physical constraints, enabling the network to take into consideration artificially controllable variables in addition to natural decline tendency. The proposed network is trained with a scheduled sampling strategy, which randomly allows the GRU decoder to access the true production from the previous step, thereby improving the robustness of the model to prediction errors. The proposed model is evaluated on the Volve public dataset. Experimental results validate the significant advantage of our method over ten baselines in multi-horizon production forecasting. In addition, ablation study from multiple aspects verifies the contributions of key components of our model. Our code is released at https://github.com/changji95/PAPF for academic use.

RDD-YOLOv5: Road Defect Detection Algorithm with Self-Attention Based on Unmanned Aerial Vehicle Inspection.

Road defect detection is a crucial aspect of road maintenance projects, but traditional manual methods are time-consuming, labor-intensive, and lack accuracy. Leveraging deep learning frameworks for object detection offers a promising solution to these challenges. However, the complexity of backgrounds, low resolution, and similarity of cracks make detecting road cracks with high accuracy challenging. To address these issues, a novel road crack detection algorithm, termed Road Defect Detection YOLOv5 (RDD-YOLOv5), was proposed. Firstly, a model was proposed to integrate the transformer structure and explicit vision center to capture the long-distance dependency and aggregate key characteristics. Additionally, the Sigmoid-weighted linear activations in YOLOv5 were replaced with the Gaussian Error Linear Units to enhance the model's nonlinear fitting capability. To evaluate the algorithm's performance, a UAV flight platform was constructed, and experimental freebies were provided to boost inspection efficiency. The experimental results demonstrate the effectiveness of RDD-YOLOv5, achieving a mean average precision of 91.48%, surpassing the original YOLOv5 by 2.5%. The proposed model proves its ability to accurately identify road cracks, even under challenging and complex traffic backgrounds. This advancement in road crack detection technology has significant implications for improving road maintenance and safety.

Design and Optimization of Sugarcane Spiral Fertilizer Applicator Based on Response Surface Methodology and Artificial Neural Networks

To address the challenges of poor fluidity and low uniformity in conventional sugarcane fertilizer applicators, a novel dual-directional spiral fertilizer applicator has been developed. The working principle of the applicator is explained, and, after analyzing the agronomic requirements for sugarcane, the parameter range for key components of the applicator is determined. The spiral blade’s diameter, pitch, and rotational velocity are chosen as the experimental factors, with the average fertilizer discharge uniformity as the evaluation criterion. Virtual simulation experiments are conducted using the discrete element method and a quadratic regression orthogonal rotating combined design. Regression models for the evaluation criterion and various experimental factors are obtained. Additionally, a dataset created from these experiments was then used to construct an artificial neural network (ANN) prediction model. Response surface methodology (RSM) and the ANN were both used to analyze and predict the outcomes. The results indicate that the artificial neural network outperforms response surface methodology in terms of better fitting capability and higher prediction accuracy. The determination coefficient, mean squared error, and root mean square error are 0.99629, 0.99163, 0.07763, 0.17498, 0.27862, and 0.41831, respectively. When comparing the two models, the optimal parameter combination is determined to be a diameter of 90.1669 mm, a pitch of 59.7407 mm, and a rotational speed of 53.8944 r/min, resulting in an average fertilizer discharge uniformity of 92.0670%. An experiment with these parameters confirmed the simulated findings, revealing a maximum discrepancy of 2.4%. This study offers valuable insights into optimizing spiral fertilizer applicators.

Intelligent Detection of Lightweight “Yuluxiang” Pear in Non-Structural Environment Based on YOLO-GEW

To detect quickly and accurately “Yuluxiang” pear fruits in non-structural environments, a lightweight YOLO-GEW detection model is proposed to address issues such as similar fruit color to leaves, fruit bagging, and complex environments. This model improves upon YOLOv8s by using GhostNet as its backbone for extracting features of the “Yuluxiang” pears. Additionally, an EMA attention mechanism was added before fusing each feature in the neck section to make the model focus more on the target information of “Yuluxiang” pear fruits, thereby improving target recognition ability and localization accuracy. Furthermore, the CIoU Loss was replaced with the WIoUv3 Loss as the loss function, which enhances the capability of bounding box fitting and improves model performance without increasing its size. Experimental results demonstrated that the enhanced YOLO-GEW achieves an F1 score of 84.47% and an AP of 88.83%, while only occupying 65.50% of the size of YOLOv8s. Compared to lightweight algorithms such as YOLOv8s, YOLOv7-Tiny, YOLOv6s, YOLOv5s, YOLOv4-Tiny, and YOLOv3-Tiny; there are improvements in AP by 2.32%, 1.51%, 2.95%, 2.06%, 2.92%, and 5.38% respectively. This improved model can efficiently detect “Yuluxiang” pears in non-structural environments in real-time and provides a theoretical basis for recognition systems used by picking robots.

Simple Bayesian reconstruction and forecasting of stream water temperature for ecologists-A tool using air temperature, optionally flow, in a time series decomposition approach.

Mitigating the impacts of global warming on wildlife entails four practical steps. First, we need to study how processes of interest vary with temperature. Second, we need to build good temperature scenarios. Third, processes can be forecast accordingly. Only then can we perform the fourth step, testing mitigating measures. While having good temperature data is essential, this is not straightforward for stream ecologists and managers. Water temperature (WT) data are often short and incomplete and future projections are currently not routinely available. There is a need for generic models which address this data gap with good resolution and current models are partly lacking. Here, we expand a previously published hierarchical Bayesian model that was driven by air temperature (AT) and flow (Q) as a second covariate. The new model can hindcast and forecast WT time series at a daily time step. It also allows a better appraisal of real uncertainties in the warming of water temperatures in rivers compared to the previous version, stemming from its hybrid structure between time series decomposition and regression. This model decomposes all-time series using seasonal sinusoidal periodic signals and time varying means and amplitudes. It then links the contrasted frequency signals of WT (daily and six month) through regressions to that of AT and optionally Q for better resolution. We apply this model to two contrasting case study rivers. For one case study, AT only is available as a covariate. This expanded model further improves the already good fitting and predictive capabilities of its earlier version while additionally highlighting warming uncertainties. The code is available online and can easily be run for other temperate rivers.

KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections.

Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of lung cancer is essential in diagnosing and treating lung lesions. Methods: This paper aims to collect histopathological section images of lung tumor surgical specimens to construct a clinical dataset for researching and addressing the classification problem of specific subtypes of lung tumors. Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. Meanwhile, Swin Transformer provides soft labels containing information about the distribution of images in various categories, making the model focused more on the information carried by types with smaller sample sizes while training. Results: This work has designed many experiments on a clinical lung tumor image dataset, and the KD_ConvNeXt achieved a superior classification accuracy of 85.64% and an F1-score of 0.7717 compared with other advanced image classification methods.