L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.

In recent years, deep learning has ushered in significant development in medical image registration, and the method of non-rigid registration using deep neural networks to generate a deformation field has higher accuracy. However, unlike monomodal medical image registration, multimodal medical image registration is a more complex and challenging task. This paper proposes a new linear-to-nonlinear framework (L2NLF) for multimodal medical image registration. The first linear stage is essentially image conversion, which can reduce the difference between two images without changing the authenticity of medical images, thus transforming multimodal registration into monomodal registration. The second nonlinear stage is essentially unsupervised deformable registration based on the deep neural network. In this paper, a brand-new registration network, CrossMorph, is designed, a deep neural network similar to the U-net structure. As the backbone of the encoder, the volume CrossFormer block can better extract local and global information. Booster promotes the reduction of more deep features and shallow features. The qualitative and quantitative experimental results on T1 and T2 data of 240 patients' brains show that L2NLF can achieve excellent registration effect in the image conversion part with very low computation, and it will not change the authenticity of the converted image at all. Compared with the current state-of-the-art registration method, CrossMorph can effectively reduce average surface distance, improve dice score, and improve the deformation field's smoothness. The proposed methods have potential value in clinical application.

Efficacy and Safety of Transarterial Chemoembolization Plus Donafenib with or without Immune Checkpoint Inhibitors as the First-Line Treatment for Unresectable Hepatocellular Carcinoma: A Propensity Score Matching Analysis.

Purpose: To compare the efficacy and safety of transarterial chemoembolization (TACE) plus donafenib with immune checkpoint inhibitors (ICIs) (T+D+I) versus TACE plus donafenib (T+D) as the first-line treatment for patients with unresectable hepatocellular carcinoma (HCC). Methods: This retrospective study included patients with unresectable HCC who received T+D+I or T+D between June 2021 and February 2023. The tumor response was analyzed according to the modified Response Evaluation Criteria in Solid Tumors. The objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and treatment-related adverse events (TRAEs) in the two groups were compared before and after propensity score matching (PSM). Cox's proportional-hazards regression model was used to analyze factors affecting PFS and OS. Results: This study included 69 patients: 41 patients in the T+D group and 28 patients in the T+D+I group. After PSM, 26 patients in each group were analyzed. Patients in the T+D+I group had a higher DCR (96.2% vs 73.1%, P = 0.021), longer median PFS (13.1 vs 7.2 months, P = 0.017), and longer median OS (23.1 vs 14.7 months, P = 0.021) than those in the T+D group. The ORR in the two groups was similar (53.8% vs 50.0%, P = 0.781). Multivariate analyses revealed that T+D+I treatment and total bilirubin levels of <20 µmol/L were independent prognostic factors for long PFS. T+D+I treatment, Child-Pugh class A, and single-lobe tumor distribution were independent prognostic factors for long OS. The incidence of TRAEs in the two groups was similar (P > 0.05). Conclusion: In comparison with TACE plus donafenib, TACE plus donafenib with ICIs could significantly improve DCR, PFS, and OS as a potential first-line treatment for unresectable HCC with an acceptable safety profile.

Deep learning-based 3D brain multimodal medical image registration.

Medical image registration is a critical preprocessing step in medical image analysis. While traditional medical image registration techniques have matured, their registration speed and accuracy still fall short of clinical requirements. In this paper, we propose an improved VoxelMorph network incorporating ResNet modules and CBAM (RCV-Net), for 3D multimodal unsupervised registration. Unlike popular convolution-based U-shaped registration networks like VoxelMorph, RCV-Net incorporates the convolutional block attention module (CBAM) during the convolution process. This inclusion enhances the feature map information extraction capabilities during training and effectively prevents information loss. Additionally, we introduce a lightweight and residual network module at the network's base, which enhances learning ability without significantly increasing training parameters. To evaluate the superiority of our registration model, we utilize evaluation metrics such as structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and mean square error (MSE). Experimental results demonstrate that our proposed network structure outperforms current state-of-the-art methods, yielding better performance in multimodal registration tasks. Furthermore, generalization testing on databases outside of the training set has confirmed the optimal registration effectiveness of our model.

Exploring native and non-native English speaker teachers' perceptions of English teacher qualities and their students' responses.

Due to globalization, English has gradually become a lingua franca, leading to a rising demand for proficient English teachers all over the globe. In China, more EFL teachers are being recruited, particularly at the tertiary level, with a greater preference for so-called "native English speaking teachers (NESTs)" over "non-native English-speaking teachers (NNESTs)" due to the impacts of native-speakerism. Research has shown NESTs, NNESTs, and students are often misaligned in terms of beliefs about language learning and teaching which affect teaching effectiveness as well as student achievement. Recognizing this issue, this study investigated NESTs', NNESTs', and Chinese English-major students' perceptions of characteristics of effective EFL teachers at four mid-tier universities across China. Findings from semi-structured interviews with 16 students suggest that NNESTs and Chinese English-major students had similar views on language learning and teaching. Both groups valued prerequisite qualities such as having expert knowledge, language skills, teaching skills, and professionalism. NESTs, however, valued qualities such as caring, patience, flexibility, engagement, and awareness of students' learning needs. These differences are likely the result of these two groups of teachers' linguistic, cultural, and educational background differences. The highly uniform views of the two groups of teachers suggest that they tended to emphasize certain qualities while disregarding others. These findings suggest the need to raise teachers' and students' awareness of the benefits of different types of teacher qualities so that curriculum design and lesson planning can be implemented for better instructional alignment to ultimately improve teaching effectiveness.

Improving performance of medical image alignment through super-resolution.

Medical image alignment is an important tool for tracking patient conditions, but the quality of alignment is influenced by the effectiveness of low-dose Cone-beam CT (CBCT) imaging and patient characteristics. To address these two issues, we propose an unsupervised alignment method that incorporates a preprocessing super-resolution process. We constructed the model based on a private clinical dataset and validated the enhancement of the super-resolution on alignment using clinical and public data. Through all three experiments, we demonstrate that higher resolution data yields better results in the alignment process. To fully constrain similarity and structure, a new loss function is proposed; Pearson correlation coefficient combined with regional mutual information. In all test samples, the newly proposed loss function obtains higher results than the common loss function and improve alignment accuracy. Subsequent experiments verified that, combined with the newly proposed loss function, the super-resolution processed data boosts alignment, can reaching up to 9.58%. Moreover, this boost is not limited to a single model, but is effective in different alignment models. These experiments demonstrate that the unsupervised alignment method with super-resolution preprocessing proposed in this study effectively improved alignment and plays an important role in tracking different patient conditions over time.

Semi-supervised lane detection for continuous traffic scenes.

OBJECTIVE: This article aims to upgrade the lane detection algorithm from image to video level in order to advance automatic driving technology. The objective is to propose a cost-efficient algorithm that can handle complex traffic scenes and different driving speeds using continuous image inputs. METHODS: To achieve this objective, we introduce the Multi-ERFNet-ConvLSTM network framework, which combines Efficient Residual Factorized ConvNet (ERFNet) and Convolution Long Short Term Memory (ConvLSTM). Additionally, we incorporate the Pyramidally Attended Feature Extraction (PAFE) Module into our network design to effectively handle multi-scale lane objects. The algorithm is evaluated using a divided dataset and comprehensive assessments are conducted across multiple dimensions. RESULTS: In the testing phase, the Multi-ERFNet-ConvLSTM algorithm surpasses the primary baselines and demonstrates superior performance in terms of Accuracy, Precision, and F1-score metrics. It exhibits excellent detection results in various complex traffic scenes and performs well at different driving speeds. CONCLUSIONS: The proposed Multi-ERFNet-ConvLSTM algorithm provides a robust solution for video-level lane detection in advanced automatic driving. By utilizing continuous image inputs and incorporating the PAFE Module, the algorithm achieves high performance while reducing labeling costs. Its exceptional accuracy, precision, and F1-score metrics highlight its effectiveness in complex traffic scenarios. Moreover, its adaptability to different driving speeds makes it suitable for real-world applications in autonomous driving systems.

A deformable patch-based transformer for 3D medical image registration.

PURPOSE: Medical image registration is of great importance in clinical medicine. However, medical image registration algorithms are still in the development stage due to the challenges posed by the related complex physiological structures. The objective of this study was to design a 3D medical image registration algorithm that satisfies the need for high accuracy and speed of complex physiological structures. METHODS: We present a new unsupervised learning algorithm, "DIT-IVNet," for 3D medical image registration. Unlike the more popular convolution-based U-shaped registration network architectures like VoxelMorph, DIT-IVNet uses a combined convolution and transformer network architecture. To better extract image information features and reduce the heavy training parameters, we improved the 2D_Depatch module to a 3D_Depatch module, thus replacing the patch embedding in the original Vision Transformer which adaptively performs patch embedding based on 3D image structure information. We also designed inception blocks in the down-sampling part of the network to help coordinate feature learning from images to different scales. RESULTS: Dice score, Negative Jacobian determinant, Hausdorff distance, and Structural Similarity evaluation metrics were used to evaluate the registration effects. The results showed that our proposed network had the best metric results compared with some state-of-the-art methods. Moreover, our network obtained the highest Dice score in the generalization experiments which indicated better generalizability of our model. CONCLUSION: We proposed an unsupervised registration network and evaluated its performance in deformable medical image registration. The results of the evaluation metrics showed that the network structure outperformed state-of-the-art methods for the registration of brain datasets.

Synthetic CT generation from CBCT using double-chain-CycleGAN.

PURPOSE: Cone-beam CT (CBCT) has the advantage of being less expensive, lower radiation dose, less harm to patients, and higher spatial resolution. However, noticeable noise and defects, such as bone and metal artifacts, limit its clinical application in adaptive radiotherapy. To explore the potential application value of CBCT in adaptive radiotherapy, In this study, we improve the cycle-GAN's backbone network structure to generate higher quality synthetic CT (sCT) from CBCT. METHOD: An auxiliary chain containing a Diversity Branch Block (DBB) module is added to CycleGAN's generator to obtain low-resolution supplementary semantic information. Moreover, an adaptive learning rate adjustment strategy (Alras) function is used to improve stability in training. Furthermore, Total Variation Loss (TV loss) is added to generator loss to improve image smoothness and reduce noise. RESULTS: Compared to CBCT images, the Root Mean Square Error (RMSE) dropped by 27.97 from 158.49. The Mean Absolute Error (MAE) of the sCT generated by our model improved from 43.2 to 32.05. The Peak Signal-to-Noise Ratio (PSNR) increased by 1.61 from 26.19. The Structural Similarity Index Measure (SSIM) improved from 0.948 to 0.963, and the Gradient Magnitude Similarity Deviation (GMSD) improved from 12.98 to 9.33. The generalization experiments show that our model performance is still superior to CycleGAN and respath-CycleGAN.

Quantification of epicardial fat volume using low-dose cardiac scan in patients with low calcium score.

Background: We investigated the accuracy of quantifying epicardial adipose tissue volume (EATV) using low-dose cardiac scan (EATVcardiac scan) and evaluated its clinical utility in predicting coronary heart disease in patients with low or mild calcification. Methods: In total, 204 patients with clinical symptoms of coronary heart disease and coronary artery calcium score (CACS) of <100 AU were enrolled in this retrospective study. After obtaining EATVcardiac scan and EATV measured using computed tomography angiography (EATVCTA), the agreement between the two measurements was evaluated using Pearson correlation coefficient and Bland-Altman analysis. Multivariate logistic regression was used to analyze the utility of EATV in predicting plaque and vulnerable plaque. Receiver operating characteristic curves were constructed. Results: The mean EATVcardiac scan (101.51±41.57 cm3) and EATVCTA (104.57±41.34 cm3) of all patients were similar, and the two measurements were strongly correlated (r=0.9596, P<0.001). The difference between EATVcardiac scan and EATVCTA was -3.0549, with only 4.9% (10/204) of patients having values outside the 95% confidence interval (CI) range (-26.15 to 20.04; P for agreement =0.0003). Further, a significant agreement was observed between EATVcardiac scan and EATVCTA in 126 patients with plaques, with an estimated difference of -3.354, and 6.35% (8/126) of patients had values outside the 95% CI range (-31.37 to 24.66; P for agreement =0.0095). After adjustment for age and sex, EATVcardiac scan and EATVCTA were significantly associated with plaque (all P values <0.001), and the areas under the curve (AUCs) were 0.662 and 0.670 (P=0.4331), respectively. In contrast, EATVcardiac scan and EATVCTA were not associated with vulnerable plaque (P>0.05), with AUCs of 0.550 and 0.530, respectively (P=0.2157). Conclusions: The study results indicate that EATVcardiac scan and EATVCTA are equivalent. In addition, both methods provide comparable values for predicting coronary arteriosclerosis in patients with low-to-mild calcification (CACS of <100 AU).

Study of visual SLAM methods in minimally invasive surgery.

In recent years, minimally invasive surgery has developed rapidly in the clinical practice of surgery and has gradually become one of the critical surgical techniques. Compared with traditional surgery, the advantages of minimally invasive surgery include small incisions and less pain during the operation, and the patients recover faster after surgery. With the expansion of minimally invasive surgery in several medical fields, traditional minimally invasive techniques have bottlenecks in clinical practice, such as the inability of the endoscope to determine the depth information of the lesion area from the two-dimensional images obtained, the difficulty in locating the endoscopic position information and the inability to get a complete view of the overall situation in the cavity. This paper uses a visual simultaneous localization and mapping (SLAM) approach to achieve endoscope localization and reconstruction of the surgical region in a minimally invasive surgical environment. Firstly, the K-Means algorithm combined with the Super point algorithm is used to extract the feature information of the image in the lumen environment. Compared with Super points, the logarithm of successful matching points increased by 32.69%, the proportion of effective points increased by 25.28%, the error matching rate decreased by 0.64%, and the extraction time decreased by 1.98%. Then the iterative closest point method is used to estimate the position and attitude information of the endoscope. Finally, the disparity map is obtained by the stereo matching method, and the point cloud image of the surgical area is finally recovered.

Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm.

In order to enhance cone-beam computed tomography (CBCT) image information and improve the registration accuracy for image-guided radiation therapy, we propose a super-resolution (SR) image enhancement method. This method uses super-resolution techniques to pre-process the CBCT prior to registration. Three rigid registration methods (rigid transformation, affine transformation, and similarity transformation) and a deep learning deformed registration (DLDR) method with and without SR were compared. The five evaluation indices, the mean squared error (MSE), mutual information, Pearson correlation coefficient (PCC), structural similarity index (SSIM), and PCC + SSIM, were used to validate the results of registration with SR. Moreover, the proposed method SR-DLDR was also compared with the VoxelMorph (VM) method. In rigid registration with SR, the registration accuracy improved by up to 6% in the PCC metric. In DLDR with SR, the registration accuracy was improved by up to 5% in PCC + SSIM. When taking the MSE as the loss function, the accuracy of SR-DLDR is equivalent to that of the VM method. In addition, when taking the SSIM as the loss function, the registration accuracy of SR-DLDR is 6% higher than that of VM. SR is a feasible method to be used in medical image registration for planning CT (pCT) and CBCT. The experimental results show that the SR algorithm can improve the accuracy and efficiency of CBCT image alignment regardless of which alignment algorithm is used.

Particle Swarm Optimization and Salp Swarm Algorithm for the Segmentation of Diabetic Retinal Blood Vessel Images.

In recent years, the incidence of diabetes has been increasing year by year. Since most of the fundus lesions are located near blood vessels, the image information is complex, and the end vessels are difficult to identify. So, a new segmentation method of diabetic retinal vessel images based on particle swarm optimization and salp swarm algorithm is proposed. This paper uses a Gaussian filter to enhance the main blood vessels, and a top-bot hat transform is used to strengthen the end vessels. The preprocessing process is completed by combining and reconstructing the two images through a normalization operation. The improved particle swarm optimization and salp swarm algorithms perform multi-threshold segmentation on the preprocessed vessel images. The best fit value, Structural Similarity Index Measure, Peak Signal to Noise Rati, feature similarity index measure, sensitivity, accuracy, regional consistency, Dice coefficient, Jaccard similarity, and Shannon entropy are selected for comprehensive evaluation and analysis. The results showed that this paper's improved particle swarm-salp swarm algorithm for segmenting diabetic retinal blood vessel images is more efficient, and the threshold is better. The vascular segmentation method in this paper is applied in medical image processing, which improves the accuracy of medical image processing and reduces the computational effort.

Post-disaster building damage assessment based on improved U-Net.

When a severe natural disaster occurs, the extraction of post-disaster building damage information is one of the methods to quickly obtain disaster information. The increasingly mature high-resolution remote sensing technology provides a solid foundation for obtaining information about building damage. To address the issues with inaccurate building positioning in existing building damage assessment methods, as well as poor classification due to similar minor and major damage characteristics in building damage classification. Based on U-Net, we designed a two-stage building damage assessment network. The first stage is an independent U-Net focused on building segmentation, followed by a Siamese U-Net focused on building damage classification. The Extra Skip Connection and Asymmetric Convolution Block were used for enhancing the network's ability to segment buildings on different scales; Shuffle Attention directed the network's attention to the correlation of buildings before and after the disaster. The xBD dataset was used for training and testing in the study, and the overall performance was evaluated using a balanced F-score (F1). The improved network had an F1 of 0.8741 for localization and F1 of 0.7536 for classification. When compared to other methods, it achieved better overall performance for building damage assessment and was able to generalize to multiple disasters.

Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network.

In order to improve the accuracy of network security situation prediction and the convergence speed of prediction algorithm, this paper proposes a combined prediction model (EMD-ELPSO-BiGRU) based on empirical mode decomposition (EMD) and improved particle swarm optimization (ELPSO) to optimize BiGRU neural network. Firstly, the network security situation data sequence is decomposed into a series of intrinsic mode function by EMD. Then, a particle swarm optimization algorithm (ELPSO) based on cooperative update of evolutionary state judgment and learning strategy is proposed to optimize the hyper-parameters of BiGRU neural network. Finally, a network security situation prediction model based on EMD-ELPSO-BiGRU is constructed to predict each intrinsic mode function, respectively, and the prediction results are superimposed to obtain the final network security situation prediction value. Simulation results show that ELPSO has better optimization performance, and EMD-ELPSO-BiGRU model has higher prediction accuracy and significantly improved convergence speed compared with other traditional prediction methods.

Robust 3D lane detection in complex traffic scenes using Att-Gen-LaneNet.

Robust 3D lane detection is the key to advanced autonomous driving technologies. However, complex traffic scenes such as bad weather and variable terrain are the main factors affecting the robustness of lane detection algorithms. In this paper, a generalized two-stage network called Att-Gen-LaneNet was proposed to achieve robust 3D lane detection in complex traffic scenes. The Efficient Channel Attention (ECA) module and the Convolutional Block Attention Module (CBAM) were combined in this network. In the first stage of the network, we improved the semantic segmentation network ENet and proposed the weighted cross-entropy loss function to solve the problem of ambiguous distant lane segmentation. This method improved Pixel Accuracy to 99.7% and MIoU to 89.5%. In the second stage of the network, we introduced the interpolation loss function to achieve accurate lane fitting. This method outperformed existing detection methods by 6% in F-score and Average Precision on the Apollo Synthetic dataset. The proposed method achieved better overall performance in 3D lane detection and was applicable to broader and more complex traffic scenes.

Improving cone-beam CT quality using a cycle-residual connection with a dilated convolution-consistent generative adversarial network.

Objective.Cone-Beam CT (CBCT) often results in severe image artifacts and inaccurate HU values, meaning poor quality CBCT images cannot be directly applied to dose calculation in radiotherapy. To overcome this, we propose a cycle-residual connection with a dilated convolution-consistent generative adversarial network (Cycle-RCDC-GAN).Approach.The cycle-consistent generative adversarial network (Cycle-GAN) was modified using a dilated convolution with different expansion rates to extract richer semantic features from input images. Thirty pelvic patients were used to investigate the effect of synthetic CT (sCT) from CBCT, and 55 head and neck patients were used to explore the generalizability of the model. Three generalizability experiments were performed and compared: the pelvis trained model was applied to the head and neck; the head and neck trained model was applied to the pelvis, and the two datasets were trained together.Main results.The mean absolute error (MAE), the root mean square error (RMSE), peak signal to noise ratio (PSNR), the structural similarity index (SSIM), and spatial nonuniformity (SNU) assessed the quality of the sCT generated from CBCT. Compared with CBCT images, the MAE improved from 28.81 to 18.48, RMSE from 85.66 to 69.50, SNU from 0.34 to 0.30, and PSNR from 31.61 to 33.07, while SSIM improved from 0.981 to 0.989. The sCT objective indicators of Cycle-RCDC-GAN were better than Cycle-GAN's. The objective metrics for generalizability were also better than Cycle-GAN's.Significance.Cycle-RCDC-GAN enhances CBCT image quality and has better generalizability than Cycle-GAN, which further promotes the application of CBCT in radiotherapy.

Deepfake Video Detection Based on EfficientNet-V2 Network.

As technology advances and society evolves, deep learning is becoming easier to operate. Many unscrupulous people are using deep learning technology to create fake pictures and fake videos that seriously endanger the stability of the country and society. Examples include faking politicians to make inappropriate statements, using face-swapping technology to spread false information, and creating fake videos to obtain money. In view of this social problem, based on the original fake face detection system, this paper proposes using a new network of EfficientNet-V2 to distinguish the authenticity of pictures and videos. Moreover, our method was used to deal with two current mainstream large-scale fake face datasets, and EfficientNet-V2 highlighted the superior performance of the new network by comparing the existing detection network with the actual training and testing results. Finally, based on improving the accuracy of the detection system in distinguishing real and fake faces, the actual pictures and videos are detected, and an excellent visualization effect is achieved.

Synthetic CT generation based on CBCT using respath-cycleGAN.

PURPOSE: Cone-beam computed tomography (CBCT) plays an important role in radiotherapy, but the presence of a large number of artifacts limits its application. The purpose of this study was to use respath-cycleGAN to synthesize CT (sCT) similar to planning CT (pCT) from CBCT for future clinical practice. METHODS: The method integrates the respath concept into the original cycleGAN, called respath-cycleGAN, to map CBCT to pCT. Thirty patients were used for training and 15 for testing. RESULTS: The mean absolute error (MAE), root mean square error (RMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and spatial nonuniformity (SNU) were calculated to assess the quality of sCT generated from CBCT. Compared with CBCT images, the MAE improved from 197.72 to 140.7, RMSE from 339.17 to 266.51, and PSNR from 22.07 to 24.44, while SSIM increased from 0.948 to 0.964. Both visually and quantitatively, sCT with respath is superior to sCT without respath. We also performed a generalization test of the head-and-neck (H&N) model on a pelvic data set. The results again showed that our model was superior. CONCLUSION: We developed a respath-cycleGAN method to synthesize CT with good quality from CBCT. In future clinical practice, this method may be used to develop radiotherapy plans.

Biomechanical effect of metal augment and bone graft on cup stability for acetabular reconstruction of total hip arthroplasty in hip dysplasia: a finite element analysis.

BACKGROUND: Different methods of acetabular reconstruction with total hip arthroplasty (THA) for Crowe II and III of adult developmental dysplasia of the hip (DDH) acetabular bone defect have been implemented clinically. However, the biomechanical effect of different augmented materials for acetabular reconstruction in THA on shell stability has never been discussed. METHODS: In the present study, autologous bone graft (BG)and metal (Ti6Al4V) augment (MA) were simulated with several acetabular bone defect models of DDH in THA. The contact pressure and micromotion between the shell and host bone were measured for evaluating the shell stability using a finite element method. RESULTS: The peak contact stress between shell and host bone was higher in the MA situation (12.45 vs 8.71 MPa). And the load transfer path was different, for BG models, the high local contact stresses were found at the junction of bone graft and host bone while for MA models the concentrated contact stresses were at the surface of MA. The peak relative micromotion between shell and host bone was higher in the MA situation (12.61 vs 11.13 µm). However, the peak micromotion decreased in the contact interface of MA and cup compared to the BG models. CONCLUSIONS: The higher micromotion was found in MA models, however, enough for bone ingrowth, and direct stronger fixation was achieved in the MA-cup interface. Thus, we recommended the MA can be used as an option, even for Crowe III, however, the decision should be made from clinical follow-up results.

Design of Insulation Tape Tension Control System of Transformer Winding Machine Based on Fuzzy PID.

With the rapid development of science and technology as well as the comprehensive societal progress, the demand for electricity in all walks of life is also increasing. As is known to all, the mechanical structure and tension control of a transformer winding machine is the key to improving the quality of coil winding, due to coil winding being generally considered the core technology of transformer manufacturing. Aiming at the synchronous winding control problem of the conductor and insulating layer of the transformer winding machine, this paper presents a mechanical structure and tension control scheme of a new type of transformer winding machine. Based on the dynamic analysis and modeling of the mechanical structure of the winding machine, the speed control of the main speed roller by the fuzzy PID control rate is implemented initially. Combined with the actual demand of the project, the feasibility and effectiveness of the control target with different tension are verified by the simulation experiment and further compared with the traditional PID control method. The simulation results show that the proposed fuzzy PID control rate can realize the automatic and efficient winding of the transformer winding machine, showing that it is superior to the traditional PID control rate in overcoming the disturbance and controlling effect.