Mismatch Problem Research Articles

Design and Implementation of a Width-Adjustable Asynchronous FIFO for Cross-Clock Domain Data Transmission

Existing research shows that asynchronous FIFO has been widely used in a variety of complex system designs, such as FPGA-based image processing systems, USB communication systems, and so on. To make the designed asynchronous FIFO more programmable and flexible, so that it can adapt to various application scenarios more quickly, this paper studies and designs an asynchronous FIFO with adjustable input and output bit width. In this study, according to the relationship between 24-bit input data and 32-bit storage space, four 24-bit data are selected to fill three RAM storage spaces at one time. According to the multiple relationships between 32-bit storage space and 128-bit output data, the reading pointer is enlarged by 4 times to realize the function of reading four 32-bit data at one time. This whole process realizes the conversion of data bit width through data splicing. Finally, the 24-bit RGB888 image data is successfully converted into 128-bit data and transmitted to the bus, which successfully solves the problem of data width mismatch.

Structure preservation constraints for unsupervised domain adaptation intracranial vessel segmentation.

Unsupervised domain adaptation (UDA) has received interest as a means to alleviate the burden of data annotation. Nevertheless, existing UDA segmentation methods exhibit performance degradation in fine intracranial vessel segmentation tasks due to the problem of structure mismatch in the image synthesis procedure. To improve the image synthesis quality and the segmentation performance, a novel UDA segmentation method with structure preservation approaches, named StruP-Net, is proposed. The StruP-Net employs adversarial learning for image synthesis and utilizes two domain-specific segmentation networks to enhance the semantic consistency between real images and synthesized images. Additionally, two distinct structure preservation approaches, feature-level structure preservation (F-SP) and image-level structure preservation (I-SP), are proposed to alleviate the problem of structure mismatch in the image synthesis procedure. The F-SP, composed of two domain-specific graph convolutional networks (GCN), focuses on providing feature-level constraints to enhance the structural similarity between real images and synthesized images. Meanwhile, the I-SP imposes constraints on structure similarity based on perceptual loss. The cross-modality experimental results from magnetic resonance angiography (MRA) images to computed tomography angiography (CTA) images indicate that StruP-Net achieves better segmentation performance compared with other state-of-the-art methods. Furthermore, high inference efficiency demonstrates the clinical application potential of StruP-Net. The code is available at https://github.com/Mayoiuta/StruP-Net .

Temporal-enhanced Radar and Camera Fusion for Object Detection

Recently, object detection methods based on multimodal fusion have gained widespread adoption in autonomous driving, proving to be valuable for detecting objects in dynamic environments. Among them, millimetre wave (mmWave) radar is commonly utilized as an effective complement to cameras, as it is almost unaffected by harsh weather conditions. However, current approaches that fuse mmWave radar and camera often overlook the correlation between the two modalities, failing to fully exploit their complementary features. To address this, we propose a temporal-enhanced radar and camera fusion network to explore the correlation between these two modalities and learn a comprehensive representation for object detection. In our model, a temporal fusion model is introduced to fuse mmWave radar features from different moments, thus mitigating the problem of mmWave radar point-object mismatch due to object movement. Moreover, a new correlation-based fusion strategy using the dedicated mask cross attention is proposed to fuse mmWave radar and vision features more effectively. Finally, we design a gate feature pyramid network that selects shallow texture information based on deep semantic information to obtain more representative features. The experimental results on the nuScenes benchmark demonstrate the effectiveness of our proposed method.

Thermal cycling and steam corrosion behavior of Yb3Al5O12‐based multilayered environmental barrier coatings for SiCf/SiC

AbstractTo alleviate thermal mismatch problem of environmental barrier coatings (EBCs) on SiC fiber‐reinforced SiC ceramic matrix composites (SiCf/SiC CMCs) surface and improve their high‐temperature durability for aircraft engines, by fully utilizing the appropriate thermal expansion coefficient, low oxygen transmittance, low thermal conductivity, and other advantages of ytterbium aluminum garnet (Yb3Al5O12), the double ceramic layered Yb3Al5O12/Yb2Si2O7 (DCL‐Yb3Al5O12) and the triple ceramic layered Yb3Al5O12/Yb2SiO5/Yb2Si2O7 (TCL‐Yb3Al5O12) EBC systems were prepared on SiCf/SiC CMCs by atmospheric plasma spraying (APS). Their phase composition and microstructures were investigated comparatively. The thermal cycling and water vapor/oxygen corrosion behavior of these coating systems were compared at 1300°C. The results showed that the thermal cycling life of the DCL‐Yb3Al5O12 and TCL‐Yb3Al5O12 EBC systems were 295 and 320 times, respectively. The failure of both the coating systems occurred between the Yb2Si2O7 layer and Si bond coat. The strength retention rate of DCL‐Yb3Al5O12 and TCL‐Yb3Al5O12 EBC systems after water vapor/oxygen corrosion for 70 h was 12.8% and 23.1%, respectively, and the fracture modes of both the systems exhibited “pseudoplastic” characteristic. TCL‐Yb3Al5O12 EBC systems with a gradient structure of more layers exhibit more excellent high‐temperature durability than DCL‐Yb3Al5O12 EBCs.

Deep-time origin of tympanic hearing in crown reptiles.

The invasion of terrestrial ecosystems by tetrapods (c. 375 million years [Ma]) represents one of the major evolutionary transitions in the history of life on Earth. The success of tetrapods on land is linked to evolutionary novelties. Among these, the evolution of a tympanic ear contributed to mitigating the problem of an impedance mismatch between the air and the fluid embedding sound-detecting hair cells in the inner ear.1,2,3 Pioneering studies advocated that similarities in the tympanic ear of tetrapods could only result from a single origin of this structure in the group,4,5 an idea later challenged by paleontological and developmental data.4,6,7,8 Current evidence suggests that this sensory structure evolved independently in amphibians, mammals, and reptiles,1,6 but it remains uncertain how many times tympanic hearing originated in crown reptiles.9,10 We combine developmental information with paleontological data to evaluate the evolution of the tympanic ear in reptiles from two complementary perspectives. Phylogenetically informed ancestral reconstruction analyses of a taxonomically broad sample of early reptiles point to the presence of a tympanic membrane as the ancestral condition of the crown group. Consistently, comparative analyses using embryos of lizards and crocodylians reveal similarities, including the formation of the tympanic membrane within the second pharyngeal arch, which has been previously reported for birds. Therefore, both our developmental and paleontological data suggest a single origin for the tympanic middle ear in the group, challenging the current paradigm of multiple acquisitions of tympanic hearing in living reptiles.

Active damping control based bus voltage resonance suppression in electrolytic capacitorless PMSM application

Abstract The structure of electrolytic capacitorless in automotive electronic applications has the advantages of lower cost and better reliability. However, motor drive systems using this structure may experience unstable bus voltage. To this end, this paper proposes an active damping control strategy based on virtual impedance. By modelling the electric drive system and conducting stability analysis, the cause of bus voltage resonance due to the use of small capacitors is revealed. Based on the extracted DC bus voltage harmonic signal, additional voltage vectors are added in the d-axis and q-axis voltage vectors, eliminating the impedance mismatch problem in the drive system caused by the reduction of bus capacitance, effectively suppressing the voltage oscillation on the machine side of the drive system. Simulation data shows that under the condition of using small-capacity ceramic capacitors, the method proposed in this paper achieves a harmonic suppression capability similar to that of large-capacity electrolytic capacitors, effectively ensuring the application safety of automotive drivers with electrolytic capacitorless architecture.

Review on hard particle reinforced laser cladding high-entropy alloy coatings

Since their advent in 2004, high-entropy alloys have allured the field of materials science and engineering. Laser cladding technology, with its advantages of a small heat-affected zone and rapid heating and cooling, has become a popular technique for preparing high-entropy alloy coatings. Traditional high-entropy alloys have the problem of strength-plasticity mismatch, which limits the further application of laser cladding high-entropy alloys in industry. As a method to solve these challenges, hard particle reinforced alloy coating technology can effectively control the comprehensive properties of high-entropy alloy coatings. This paper first explores and analyzes the five strengthening mechanisms and four main influencing factors of hard particle reinforced laser cladding alloy coatings. Then, from the perspective of introducing particle types, the research status of ceramic particles, rare earth particles and other types of hard particle reinforced laser cladding high entropy alloy coatings is introduced. Finally, the characteristics of hard particle reinforced laser cladding high-entropy alloy coatings are summarized, and future recommendations for hard particle reinforced alloy coatings technology and its application in laser cladding high-entropy alloys are proposed, which will be helpful for researchers and producers in this field.

Improved maize leaf area index inversion combining plant height corrected resampling size and random forest model using UAV images at fine scale

ContextAccurate monitoring of leaf area index (LAI) is conducive to timely and targeted management measures. Unmanned aerial vehicle (UAV) remote sensing provides an important way for non-destructive monitoring of crop leaf area index. ObjectiveIn this study, visible light (RGB) and multispectral remote sensing data from the UAV and ground-measured LAI data from the Plant Canopy Analyzer LAI-2200 C were used to conduct inversion of maize LAI on a fine scale. MethodsTo address the problem of spatial scale mismatch between the spatial resolution of UAV images and the ground-measured LAI, the scale difference between UAV image data and ground-measured data was reduced by removing the outermost ring data measured by the LAI-2200 C instrument, calculating the spatial resolution of the UAV images after resampling based on the height of the plant, and the resampling method based on the circle. Finally, through the above method to resample the UAV images, we extract the vegetation index and canopy height features as the input variables of the random forest model to build the maize LAI inversion model in vegetative stages and reproductive stages respectively, which is referred to as the Vis_H+RF method. Results and conclusionsThe Vis_H+RF method of Tongliao experimental station has an R2 of 0.96 in the vegetative stages and a R2 of 0.61 in the reproductive stages, both of which perform well and have certain migration capabilities. SignificanceThe LAI inversion model constructed based on the method in this study is basically consistent with the actual situation and can provide data support for maize growth monitoring.

Fabrication of MXene-encapsulated Co@C nanoparticles for efficient microwave absorption in the X-band

Two-dimensional MXene has structural advantages in electromagnetic wave scattering due to its layered structure, but MXene materials can lead to impedance mismatch problems due to their high dielectric constants, so it is still a challenge to design highly efficient wave-absorbing materials based on MXene with low reflection loss, thin thickness, and wide absorption frequency. In this study, composite wave-absorbing materials were fabricated from Co–Co PBA precursors and MXene using liquid nitrogen flash freezing and freeze-drying techniques. By treating MXene and the PBA precursor at a high temperature of 750 °C, a rich heterogeneous interface was formed between Co@C and MXene (CCM7), and the impedance matching was optimized to improve the reflection loss capability. The optimized sample has an effective absorption bandwidth of 4.1 GHz at 2.5 mm covering the entire X-band with a minimum reflection loss of −61.42 dB. It is also demonstrated that CCM7 is satisfactory for Radar Cross-Section of flat panels and unmanned aerial vehicles by CST calculations, and this work provides a fresh perspective on the use of effective MXene composites for microwave absorption.

High temperature flexible 2D metasurface based on optimal design for thermal insulation and electromagnetic wave absorption skin

The development of radar stealth skin with long term stable service and flexible deformability is the premise and foundation of improving the battlefield survivability of deformable aircraft. Aiming at the bottleneck problem of mismatch between the flexible configuration and the electromagnetic configuration of the existing stealth composite materials, a new type of SiO2 fiber paper (SFP) with flame retardant, thermal insulation, low dielectric constant and super-hydrophobic was prepared by combining the papermaking process with surface modification. The effects of solid content and high temperature environment on the mechanical properties, thermal insulation properties, dielectric properties and hydrophobic properties of SFP materials were studied in detail. Based on this, the optimal design and construction method of radar stealth metasurface suitable for flexible deformation are proposed, and the high temperature resistant flexible stealth skin metasurface is obtained. The metasurface has been demonstrated to achieve an effective wave absorption of −8 dB in the 10.9–16.4 GHz range at 400 μm thickness, making it an ideal candidate for flame retardant, flexible, thermal insulation and radar stealth skin under extreme conditions. It provides theoretical basis and technical support for the innovative development of high-speed deformable stealth aircraft.

A fuzzy compensation - Koopman MPC design for pressure regulation in PEM electrolyzer

Proton exchange membrane (PEM) electrolyzer have attracted increasing attention from the industrial and researchers in recent years due to its excellent hydrogen production performance. Developing accurate models to predict their performance is crucial for promoting and accelerating the design and optimization of electrolysis systems. This work developed a Koopman MPC method incorporating fuzzy compensation for regulating the anode and cathode pressures in a PEM electrolyzer. A PEM electrolyzer is then built to study pressure control and provide experimental data for the identification of the Koopman linear predictor. The identified linear predictors are used to design the Koopman MPC. In addition, the developed fuzzy compensator can effectively solve the Koopman MPC model mismatch problem. The effectiveness of the proposed method is verified through the hydrogen production process in PEM simulation.

Enhanced WiFi/Pedestrian Dead Reckoning Indoor Localization Using Artemisinin Optimization-Particle Swarm Optimization-Particle Filter

WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, this paper proposes the fusion algorithm combining WiFi and pedestrian dead reckoning (PDR). Firstly, the particle swarm optimization (PSO) model is utilized to optimize the weighted k-nearest neighbors (WKNN) in the WiFi part. Additionally, the artemisinin optimization (AO) algorithm is used to optimize the particle filter (PF) to improve the fusion effect of the WiFi and PDR. Finally, to thoroughly validate the localization performance of the proposed algorithm, we designed experiments involving two scenarios with four smartphone gestures: calling, dangling, handheld, and pocketed. The experimental results unequivocally indicate that the positioning error of AO-PSO-PF algorithm is lower than that of other algorithms including PDR, WiFi, PF, APF, and FPF. The average positioning errors for the two experiments are 0.95 m and 1.42 m, respectively.

Highly matched electrode for all-solid-state sodium-ion fiber hybrid supercapacitor with wide temperature and high energy density

In order to solve the kinetic mismatch problem of hybrid supercapacitors, a simple and effective assembly technology for wide temperature all-solid-state sodium-ion fiber hybrid supercapacitor is proposed based on anode and cathode with optimal volume capacity ratio and complementary potential windows. Ti3C2Tx fiber anode is produced by wet spinning in acetic acid bath. It reveals an excellent volume capacity of 406 F cm−3, good cycle stability of 90 % retention in 1 M NaClO4 electrolyte, high conductivity of 3303 S cm−1, and tensile strength of 80 MPa. NaV3O8/rGO (NVO/rGO) fiber cathode is obtained by wet spinning and hydrothermal treating, exhibiting high matched volumetric capacitance and improved rate performance. Moreover, a wide temperature all-solid-state sodium-ion fiber hybrid supercapacitor (PVA EGHG NVO/rGO//Ti3C2Tx AFSIC) is assembled using polyvinyl alcohol ethylene glycol hydrogel (PVA EGHG) as separator and electrolyte. The AFSIC shows an outstanding volumetric specific capacitance of 76 F cm−3 at 0.1 A cm−3, and a maximum volumetric energy density of 30.6 mWh cm−3 at a power density of 83.2 mW cm−3. In addition, it can be bend to diverse degrees without significant change in its electrochemical performance. In addition, the AFSIC exhibites exceptional capacitance and outstanding flexibility in a wide temperature range of −40 to 60 °C, showing that the PVA EGHG NVO/rGO//Ti3C2Tx AFSIC has wide prospect in the area of wearable electronic components.

OBCTeacher: Resisting labeled data scarcity in oracle bone character detection by semi-supervised learning

Oracle bone characters (OBCs) are ancient ideographs for divination and memorization, as well as first-hand evidence of ancient Chinese culture. The detection of OBC is the premise of advanced studies and was mainly done by authoritative experts in the past. Deep learning techniques have great potential to facilitate OBC detection, but the high annotation cost of OBC brings the scarcity of labeled data, hindering its application. This paper proposes a novel OBC detection framework called OBCTeacher based on semi-supervised learning (SSL) to resist labeled data scarcity. We first construct a large-scale OBC detection dataset. Through investigation, we find that spatial mismatching and class imbalance problems lead to decreased positive anchors and biased predictions, affecting the quality of pseudo labels and the performance of OBC detection. To mitigate the spatial mismatching problem, we introduce a geometric-priori-based anchor assignment strategy and a heatmap polishing procedure to increase positive anchors and improve the quality of pseudo labels. As for the class imbalance problem, we propose a re-weighting method based on estimated class information and a contrastive anchor loss to achieve prioritized learning on different OBC classes and better class boundaries. We evaluate our method by using only a small portion of labeled data while using the remaining data as unlabeled and all labeled data with extra unlabeled data. The results demonstrate the effectiveness of our method compared with other state-of-the-art methods by superior performance and significant improvements of an average of 11.97 in AP50:95 against the only supervised baseline. In addition, our method achieves comparable performance using only 20% of labeled data to the fully-supervised baseline using 100% of labeled data, demonstrating that our method significantly reduces the dependence on labeled data for OBC detection.

Constructing Metal-Organic Framework Films with Adjustable Electronic Properties on Hematite Photoanode for Boosting Photogenerated Carrier Transport.

Hematite (α-Fe2O3) has become a research hotspot in the field of photoelectrochemical water splitting (PEC-WS), but the low photogenerated carrier separation efficiency limits further application. The electronic structure regulation, such as element doping and organic functional groups with different electrical properties, is applied to alleviate the problems of poor electrical conductivity, interface defects, and band mismatch. Herein, α-Fe2O3 photoanodes are modified to regulate their electric structures and improve photogenerated carrier transport by the bimetallic metal-organic frameworks (MOFs), which are constructed with Fe/Ni and terephthalate (BDC) with 2-substitution of different organic functional groups (─H, ─Br, ─NO2 and ─NH2). The α-Fe2O3 photoanode loaded with FeNi-NH2BDC MOF catalyst exhibits the optimal photocurrent density (2mAcm-2) at 1.23VRHE, which is 2.33 times that of the pure α-Fe2O3 photoanode. The detailed PEC analyses demonstrate that the bimetallic synergistic effect between Fe and Ni can improve the conductivity and inhibit the photogenerated carrier recombination of α-Fe2O3 photoanodes. The ─NH2 group as an electron-donor group can effectively regulate the electron distribution and band structure of α-Fe2O3 photoanodes to prolong the lifetime of photogenerated holes, which facilitates photogenerated carrier transport and further enhances the PEC-WS performance of α-Fe2O3 photoanode.

Cloud computing load prediction method based on CNN-BiLSTM model under low-carbon background

With the establishment of the "double carbon” goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM ), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} , 50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, 34%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} and 45%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} respectively.

Ceramic Heads With 12/14 Titanium Sleeves Used on Manufacturer-Non-Compatible Retained Femoral Components Do Not Lead to Implant Failure in Revision Hip Arthroplasty

BackgroundCeramic femoral heads with titanium sleeves are commonly used in revision total hip arthroplasty (rTHA). Companies advise against combination with a retained femoral component from another manufacturer. However, no data are available. The aim of this study was to evaluate and compare the implant failure and revision rates of ceramic heads with a 12/14 titanium sleeve used on manufacturer-compatible versus noncompatible retained femoral components. MethodsA retrospective single-center cohort analysis was performed using a prospectively maintained institutional arthroplasty registry. We identified 439 patients who received a titanium 12/14 ceramic head during rTHA between January 1, 2007, and December 31, 2022. There were 229 manufacturer-compatible and 210 manufacturer-noncompatible retained femoral stems, according to the company's official product compatibility list. Implant failure and rerevision rates were evaluated. ResultsAfter a median follow-up of 6.6 years (IQR (interquartile range): 4.5 to 9.3), there was no significant difference (P = 0.770) in the rerevision rate between the manufacturer-compatible group (17.0%) and the noncompatible group (18.1%). Revision-free survival after rTHA was 81.2% in the manufacturer-compatible group and 78.9% in the manufacturer-noncompatible group after 15 years (P = 0.653). Most rerevisions occurred in the first year after rTHA, with 29 of 229 (12.7%) in the manufacturer-compatible group and 24 of 210 (11.4%) in the manufacturer-noncompatible group (P = 0.705). We observed only one implant failure in the manufacturer-noncompatible group, but this was not related to a mismatch problem. ConclusionsAlthough legal uncertainties remain, this study showed no increased risk of implant failure or revision rates when a ceramic femoral head, with a 12/14 titanium sleeve, was used on a noncompatible femoral stem from a manufacturer.

The Social and Spatial Mismatch of Women Returning From Jail Contexts: An Intersectional Mixed-Methods Analysis

Seminal work on spatial mismatch has shown that geographic isolation from job-rich areas resulted in unemployment for low-income women of color. Though social capital is a critical component of the job search for justice-involved individuals, justice-involved women of color may have unique barriers to accessing social capital related to spatial mismatch. This concurrent mixed-methods study addresses the interconnections of race, economic opportunity, social capital, and employment for a sample of 56 women returning from jail contexts. Results reveal racialized differences in the mobilization of social capital to address the spatial mismatch problem, identifying a source of inequity for women of color leaving carceral settings.

A semantic segmentation model for road cracks combining channel-space convolution and frequency feature aggregation

In transportation, roads sometimes have cracks due to overloading and other reasons, which seriously affect driving safety, and it is crucial to identify and fill road cracks in time. Aiming at the defects of existing semantic segmentation models that have degraded the segmentation performance of road crack images and the standard convolution makes it challenging to capture the spatial and channel coupling relationship between pixels. It is difficult to differentiate crack pixels from background pixels in complex backgrounds; this paper proposes a semantic segmentation model for road cracks that combines channel-spatial convolution with the aggregation of frequency features. A new convolutional block is proposed to accurately identify cracked pixels by grouping spatial displacements and convolutional kernel weight dynamization while modeling pixel spatial relationships linked to channel features. To enhance the contrast of crack edges, a frequency domain feature aggregation module is proposed, which uses a simple windowing strategy to solve the problem of mismatch of frequency domain inputs and, at the same time, takes into account the effect of the frequency imaginary part on the features to model the deep frequency features effectively. Finally, a feature refinement module is designed to refine the semantic features to improve the segmentation accuracy. Many experiments have proved that the model proposed in this paper has better performance and more application potential than the current popular general model.

Research on path planning for clear aligner flexible manufacturing

In recent years, clear aligner can enhance individual appearance with dental defects, so it used more and more widely. However, in manufacturing process, there are still some problems, such as low degree of automation and high equipment cost. The problem of coordinate system mismatch between gingival curve point cloud and dental CAD model is faced to. The PCA-ICP registration algorithm is proposed, which includes coarse match algorithm and improve-ICP registration algorithm. The principal component analysis (PCA) based method can roughly find the posture relationship between the two point clouds. Using z-level dynamic hierarchical, the ICP registration can accurately find the posture between these two clouds. The final registration maximum distance error is 0.03 mm, which is smaller than robot machining error. Secondly, the clear aligner machining process is conducted to verify the registration effectiveness. Before machining, the path is generated based on the well registered gingival curve. After full registration, the tool path is calculated by establishing a local coordinate system between the workpiece and the tool to avoid interference. This path is calculated and generated as an executable program for ABB industrial robots. Finally, the robot was used for flexible cutting of clear aligners and was able to extract products, ensuring the effectiveness of the proposed research. This method can effectively solve the limitations of traditional milling path planning under such complex conditions.