Automatic Extraction Method of 3D Feature Guidelines for Complex Cultural Relic Surfaces Based on Point Cloud

Cultural relics line graphic serves as a crucial form of traditional artifact information documentation,which is a simple and intuitive product with low cost of displaying compared with 3D models.Dimensionality reduction is undoubtedly necessary for line drawings.However,most existing methods for artifact drawing rely on the principles of orthographic projection that always cannot avoid angle occlusion and data overlapping while the surface of cultural relics is complex.Therefore,conformal mapping was introduced as a dimensionality reduction way to compensate for the limitation of orthographic projection.Based on the given criteria for assessing surface complexity,this paper proposed a three-dimensional feature guideline extraction method for complex cultural relic surfaces.A 2D and 3D combined factor that measured the importance of points on describing surface features,vertex weight,was designed.Then the selection threshold for feature guideline extraction was determined based on the differences between vertex weight and shape index distributions.The feasibility and stability were verified through experiments conducted on real cultural relic surface data.Results demonstrated the ability of the method to address the challenges associated with the automatic generation of line drawings for complex surfaces.The extraction method and the obtained results will be useful for line graphic drawing,displaying and propaganda of cultural relics.

Preferentially selective extraction of lithium from spent LiCoO_(2)cathodes by medium-temperature carbon reduction roasting

Lithium recovery from spent lithium-ion batteries(LIBs)have attracted extensive attention due to the skyrocketing price of lithium.The medium-temperature carbon reduction roasting was proposed to preferential selective extraction of lithium from spent Li-CoO_(2)(LCO)cathodes to overcome the incomplete recovery and loss of lithium during the recycling process.The LCO layered structure was destroyed and lithium was completely converted into water-soluble Li2CO_(3)under a suitable temperature to control the reduced state of the cobalt oxide.The Co metal agglomerates generated during medium-temperature carbon reduction roasting were broken by wet grinding and ultrasonic crushing to release the entrained lithium.The results showed that 99.10%of the whole lithium could be recovered as Li2CO_(3)with a purity of 99.55%.This work provided a new perspective on the preferentially selective extraction of lithium from spent lithium batteries.

Effects of conductive agent type on lithium extraction from salt lake brine with LiFePO_(4) electrodes

Electrochemical lithium extraction from salt lakes is an effective strategy for obtaining lithium at a low cost.Nevertheless,the elevated Mg:Li ratio and the presence of numerous coexisting ions in salt lake brines give rise to challenges,such as prolonged lithium extraction periods,diminished lithium extraction efficiency,and considerable environmental pollution.In this work,Li FePO4(LFP)served as the electrode material for electrochemical lithium extraction.The conductive network in the LFP electrode was optimized by adjusting the type of conductive agent.This approach resulted in high lithium extraction efficiency and extended cycle life.When the single conductive agent of acetylene black(AB)or multiwalled carbon nanotubes(MWCNTs)was replaced with the mixed conductive agent of AB/MWCNTs,the average diffusion coefficient of Li+in the electrode increased from 2.35×10^(-9)or 1.77×10^(-9)to 4.21×10^(-9)cm^(2)·s^(-1).At the current density of 20 mA·g^(-1),the average lithium extraction capacity per gram of LFP electrode increased from 30.36 mg with the single conductive agent(AB)to 35.62 mg with the mixed conductive agent(AB/MWCNTs).When the mixed conductive agent was used,the capacity retention of the electrode after 30 cycles reached 82.9%,which was considerably higher than the capacity retention of 65.8%obtained when the single AB was utilized.Meanwhile,the electrode with mixed conductive agent of AB/MWCNTs provided good cycling performance.When the conductive agent content decreased or the loading capacity increased,the electrode containing the mixed conductive agent continued to show excellent electrochemical performance.Furthermore,a self-designed,highly efficient,continuous lithium extraction device was constructed.The electrode utilizing the AB/MWCNT mixed conductive agent maintained excellent adsorption capacity and cycling performance in this device.This work provides a new perspective for the electrochemical extraction of lithium using LFP electrodes.

Development of a Simple Extraction Method for Tetracycline Analogues from Bovine Milk

Tetracycline and analogues are among the most used antibiotics in the dairy industry. Besides the therapeutic uses, tetracyclines are often incorporated into livestock feed as growth promoters. A considerable amount of antibiotics is released unaltered through milk from dairy animals. The presence of antibiotic residues in milk and their subsequent consumption can lead to potential health impacts, including cancer, hypersensitivity reactions, and the development of antibiotic resistance. Thus, it is important to monitor residual levels of tetracyclines in milk. The purpose of this study is to develop a quick and simple method for simultaneously extracting five tetracycline analogues from bovine milk. Specifically, five tetracycline analogues: Chlortetracycline (CTC), demeclocycline (DEM), doxycycline (DC), minocycline (MC), and tetracycline (TC) were simultaneously extracted from milk using trifluoroacetic acid. Subsequently, the extracted analogues were separated by reverse-phase high-performance liquid chromatography (RP-HPLC) and detected at 355 nm using UV/Vis. Calibration curves for all five tetracycline analogues show excellent linearity (r2 value > 0.99). Percent recovery for MC, TC, DEM, CTC, and DC were: 31.88%, 96.91%, 151.29, 99.20%, and 85.58% respectively. The developed extraction method has good precision (RSD < 9.9% for 4 of the 5 analogues). The developed method with minimal sample preparation and pretreatment has the potential to serve as an initial screening test.

A Joint Entity Relation Extraction Model Based on Relation Semantic Template Automatically Constructed

The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities,and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation.However,this method has some problems,such as relying on expert experience and poor portability.Inspired by the rule-based entity relation extraction method,this paper proposes a joint entity relation extraction model based on a relation semantic template automatically constructed,which is abbreviated as RSTAC.This model refines the extraction rules of relation semantic templates from relation corpus through dependency parsing and realizes the automatic construction of relation semantic templates.Based on the relation semantic template,the process of relation classification and triplet extraction is constrained,and finally,the entity relation triplet is obtained.The experimental results on the three major Chinese datasets of DuIE,SanWen,and FinRE showthat the RSTAC model successfully obtains rich deep semantics of relation,improves the extraction effect of entity relation triples,and the F1 scores are increased by an average of 0.96% compared with classical joint extraction models such as CasRel,TPLinker,and RFBFN.

Development of Extraction and Detection Method for a Chemotherapeutic Drug with Phenytoin in Biological Samples

Dexamethasone is classified as a corticosteroid and is commonly used among cancer patients to decrease the amount of swelling around the tumor. Among patients with cancer, in particular brain tumors, seizures can become a daily routine in their everyday lives. To counteract the seizures, an antiepileptic drug such as phenytoin is administered to act as an anticonvulsant. Phenytoin and dexamethasone are frequently administrated concurrently to brain cancer patients. A previous study has shown that phenytoin serum concentration decreases when administrated concurrently with dexamethasone. Thus, it is important to monitor the concentration of these two drugs in biological samples to ensure that the proper dosages are administrated to the patients. This study aims to develop an effective extraction and detection method for dexamethasone and phenytoin. A reverse-phase high-performance liquid chromatography (HPLC) method with UV/Vis detection has been developed to separate phenytoin and dexamethasone at 219 nm and 241 nm respectively from urine samples. The mobile phase consists of a mixture of 0.01 M KH2PO4, acetonitrile, and methanol adjusted to pH 5.6 (48:32:20) and is pumped at a flow rate of 1.0 mL/min. Calibration curves were prepared for phenytoin and dexamethasone (r2 > 0.99). An efficient solid-phase extraction (SPE) method for the extraction of dexamethasone and phenytoin from urine samples was developed with the use of C-18 cartridges. The percent recovery for phenytoin and dexamethasone is 95.4% (RSD = 1.15%) and 81.1% (RSD = 3.56%) respectively.

Highly selective extraction of aromatics from aliphatics by using metal chloride-based ionic liquids

The separation of aromatics from aliphatics is essential for achieving maximum exploitation of oil resources in the petrochemical industry.In this study,a series of metal chloride-based ionic liquids were prepared and their performances in the separation of 1,2,3,4-tetrahydronaphthalene(tetralin)/dodecane and tetralin/decalin systems were studied.Among these ionic liquids,1-ethyl-3-methylimidazolium tetrachloroferrate([EMIM][FeCl_(4)])with the highest selectivity was used as the extractant.Density functional theory calculations showed that[EMIM][FeCl_(4)]interacted more strongly with tetralin than with dodecane and decalin.Energy decomposition analysis of[EMIM][FeCl_(4)]-tetralin indicated that electrostatics and dispersion played essential roles,and induction cannot be neglected.The van der Waals forces was a main effect in[EMIM][FeCl_(4)]-tetralin by independent gradient model analysis.The tetralin distribution coefficient and selectivity were 0.8 and 110,respectively,with 10%(mol)tetralin in the initial tetralin/dodecane system,and 0.67 and 19.5,respectively,with 10%(mol)tetralin in the initial tetralin/decalin system.The selectivity increased with decreasing alkyl chain length of the extractant.The influence of the extraction temperature,extractant dosage,and initial concentrations of the system components on the separation performance were studied.Recycling experiments showed that the regenerated[EMIM][FeCl_(4)]could be used repeatedly.

Influence of extraction voltage on electron and ion behavior characteristics

The characteristics of the extracted ion current have a significant impact on the design and testing of ion source performance.In this paper,a 2D in space and 3D in velocity space particle in cell(2D3V PIC)method is utilized to simulate plasma motion and ion extraction characteristics under various initial plasma velocity distributions and extraction voltages in a Cartesian coordinate system.The plasma density is of the order of 10^(15)m^(-3)-10^(16)m^(-3)and the extraction voltage is of the order of 100 V-1000 V.The study investigates the impact of various extraction voltages on the velocity and density distributions of electrons and positive ions,and analyzes the influence of different initial plasma velocity distributions on the extraction current.The simulation results reveal that the main reason for the variation of extraction current is the spacecharge force formed by the relative aggregation of positive and negative net charges.This lays the foundation for a deeper understanding of extraction beam characteristics.

Feature extraction and learning approaches for cancellable biometrics:A survey

Biometric recognition is a widely used technology for user authentication.In the application of this technology,biometric security and recognition accuracy are two important issues that should be considered.In terms of biometric security,cancellable biometrics is an effective technique for protecting biometric data.Regarding recognition accuracy,feature representation plays a significant role in the performance and reliability of cancellable biometric systems.How to design good feature representations for cancellable biometrics is a challenging topic that has attracted a great deal of attention from the computer vision community,especially from researchers of cancellable biometrics.Feature extraction and learning in cancellable biometrics is to find suitable feature representations with a view to achieving satisfactory recognition performance,while the privacy of biometric data is protected.This survey informs the progress,trend and challenges of feature extraction and learning for cancellable biometrics,thus shedding light on the latest developments and future research of this area.

A bulk extraction method to determine the stable isotope ratios of iron,nickel,copper,zinc,and cadmium in seawater using multi-collector inductively coupled plasma mass spectrometry

The oceanic trace metals iron(Fe),nickel(Ni),copper(Cu),zinc(Zn),and cadmium(Cd)are crucial to marine phytoplankton growth and global carbon cycle,and the analysis of their stable isotopes can provide valuable insights into their biogeochemical cycles within the ocean.However,the simultaneous isotopic analysis of multiple elements present in seawater is challenging because of their low concentrations,limited volumes of the test samples,and high salt matrix.In this study,we present the novel method developed for the simultaneous analysis of five isotope systems by 1 L seawater sample.In the developed method,the NOBIAS Chelate-PA1 resin was used to extract metals from seawater,the AG MP-1M anion-exchange resin to purify Cu,Fe,Zn,Cd,and the NOBIAS Chelate-PA1 resin to further extract Ni from the matrix elements.Finally,a multi-collector inductively coupled plasma mass spectroscope(MC-ICPMS)was employed for the isotopic measurements using a doublespike technique or sample-standard bracketing combined with internal normalization.This method exhibited low total procedural blanks(0.04 pg,0.04 pg,0.21 pg,0.15 pg,and 3 pg for Ni,Cu,Fe,Zn,and Cd,respectively)and high extraction efficiencies(100.5%±0.3%,100.2%±0.5%,97.8%±1.4%,99.9%±0.8%,and 100.1%±0.2%for Ni,Cu,Fe,Zn,and Cd,respectively).The external errors and external precisions of this method could be considered negligible.The proposed method was further tested on the seawater samples obtained from the whole vertical profile of a water column during the Chinese GEOTRACES GP09 cruise in the Northwest Pacific,and the results showed good agreement with previous related data.This innovative method will contribute to the advancement of isotope research and enhance our understanding of the marine biogeochemical cycling of Fe,Ni,Cu,Zn,and Cd.

Extraction of Acoustic Normal Mode Depth Functions Using Range-Difference Method with Vertical Linear Array Data

Data-derived normal mode extraction is an effective method for extracting normal mode depth functions in the absence of marine environmental data.However,when the corresponding singular vectors become nonunique when two or more singular values obtained from the cross-spectral density matrix diagonalization are nearly equal,this results in unsatisfactory extraction outcomes for the normal mode depth functions.To address this issue,we introduced in this paper a range-difference singular value decomposition method for the extraction of normal mode depth functions.We performed the mode extraction by conducting singular value decomposition on the individual frequency components of the signal's cross-spectral density matrix.This was achieved by using pressure and its range-difference matrices constructed from vertical line array data.The proposed method was validated using simulated data.In addition,modes were successfully extracted from ambient noise.

Insight into the experiment and extraction mechanism for separating carbazole from anthracene oil with quaternary ammonium-based deep eutectic solvents

Carbazole is an irreplaceable basic organic chemical raw material and intermediate in industry.The separation of carbazole from anthracene oil by environmental benign solvents is important but still a challenge in chemical engineering.Deep eutectic solvents (DESs) as a sustainable green separation solvent have been proposed for the separation of carbazole from model anthracene oil.In this research,three quaternary ammonium-based DESs were prepared using ethylene glycol (EG) as hydrogen bond donor and tetrabutylammonium chloride (TBAC),tetrabutylammonium bromide or choline chloride as hydrogen bond acceptors.To explore their extraction performance of carbazole,the conductor-like screening model for real solvents (COSMO-RS) model was used to predict the activity coefficient at infinite dilution (γ^(∞)) of carbazole in DESs,and the result indicated TBAC:EG (1:2) had the stronger extraction ability for carbazole due to the higher capacity at infinite dilution (C^(∞)) value.Then,the separation performance of these three DESs was evaluated by experiments,and the experimental results were in good agreement with the COSMO-RS prediction results.The TBAC:EG (1:2) was determined as the most promising solvent.Additionally,the extraction conditions of TBAC:EG (1:2) were optimized,and the extraction efficiency,distribution coefficient and selectivity of carbazole could reach up to 85.74%,30.18 and 66.10%,respectively.Moreover,the TBAC:EG (1:2) could be recycled by using environmentally friendly water as antisolvent.In addition,the separation performance of TBAC:EG (1:2) was also evaluated by real crude anthracene,the carbazole was obtained with purity and yield of 85.32%,60.27%,respectively.Lastly,the extraction mechanism was elucidated byσ-profiles and interaction energy analysis.Theoretical calculation results showed that the main driving force for the extraction process was the hydrogen bonding ((N–H...Cl) and van der Waals interactions (C–H...O and C–H...π),which corresponding to the blue and green isosurfaces in IGMH analysis.This work presented a novel method for separating carbazole from crude anthracene oil,and will provide an important reference for the separation of other high value-added products from coal tar.

Feature extraction for machine learning-based intrusion detection in IoT networks

A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems(NIDSs).Consequently,network interruptions and loss of sensitive data have occurred,which led to an active research area for improving NIDS technologies.In an analysis of related works,it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction(FR)and Machine Learning(ML)techniques on NIDS datasets.However,these datasets are different in feature sets,attack types,and network design.Therefore,this paper aims to discover whether these techniques can be generalised across various datasets.Six ML models are utilised:a Deep Feed Forward(DFF),Convolutional Neural Network(CNN),Recurrent Neural Network(RNN),Decision Tree(DT),Logistic Regression(LR),and Naive Bayes(NB).The accuracy of three Feature Extraction(FE)algorithms is detected;Principal Component Analysis(PCA),Auto-encoder(AE),and Linear Discriminant Analysis(LDA),are evaluated using three benchmark datasets:UNSW-NB15,ToN-IoT and CSE-CIC-IDS2018.Although PCA and AE algorithms have been widely used,the determination of their optimal number of extracted dimensions has been overlooked.The results indicate that no clear FE method or ML model can achieve the best scores for all datasets.The optimal number of extracted dimensions has been identified for each dataset,and LDA degrades the performance of the ML models on two datasets.The variance is used to analyse the extracted dimensions of LDA and PCA.Finally,this paper concludes that the choice of datasets significantly alters the performance of the applied techniques.We believe that a universal(benchmark)feature set is needed to facilitate further advancement and progress of research in this field.

Extraction,chemical components,bioactive functions and adulteration identification of walnut oils:A review

Walnut oil is a functional wood oil known to researchers that may potentially be a large source of Chinese edible oils.There are various extraction methods for walnut oil,including traditional(pressing,solvent-and enzymeassisted extraction)and novel methods(microwave,ultrasound,supercritical CO_(2),subcritical and other extraction technologies).Walnut oil is rich in nutrients,including phytosterols,tocopherols,polyphenols,squalene and minerals.It provides many health benefits,such as antioxidant,antitumor,anti-inflammatory,antidiabetic and lipid metabolism-related functions.In addition,the authentication of walnut oil has received much research attention.The present review provides detailed research on walnut oil extraction,composition,health benefits and adulteration identification methods.The path toward further walnut oil improvement in the context of the market value of walnut oil is also discussed.

Ten-year observation of corneal densitometry and associated factors following small incision lenticule extraction

●AIM:To investigate the long-term changes of corneal densitometry(CD)and its contributing elements after small incision lenticule extraction(SMILE).●METHODS:Totally 31 eyes of 31 patients with mean spherical equivalent of-6.46±1.50 D and mean age 28.23±7.38y were enrolled.Full-scale examinations were conducted on all patients preoperatively and during followup.Visual acuity,manifest refraction,axial length,corneal thickness,corneal higher-order aberrations,and CD were evaluated.●RESULTS:All surgeries were completed successfully without complications or adverse events.Ten-year safety index was 1.17±0.20 and efficacy 1.04±0.28.CD value of 0–6 mm zones in central layer was statistically significantly lower 10y postoperatively,compared with preoperative values(0–2 mmΔ=-1.62,2–6 mmΔ=-1.24,P<0.01).There were no correlations between CD values and factors evaluated.●CONCLUSION:SMILE is a safe and efficient procedure for myopia on a long-term basis.CD values get lower 10y postoperatively,whose mechanism is to be further discussed.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A Review on Sources,Extractions and Analysis Methods of a Sustainable Biomaterial:Tannins

Condensed and hydrolysable tannins are non-toxic natural polyphenols that are a commercial commodity industrialized for tanning hides to obtain leather and for a growing number of other industrial applications mainly to substitute petroleum-based products.They are a definite class of sustainable materials of the forestry industry.They have been in operation for hundreds of years to manufacture leather and now for a growing number of applications in a variety of other industries,such as wood adhesives,metal coating,pharmaceutical/medical applications and several others.This review presents the main sources,either already or potentially commercial of this forestry by-materials,their industrial and laboratory extraction systems,their systems of analysis with their advantages and drawbacks,be these methods so simple to even appear primitive but nonetheless of proven effectiveness,or very modern and instrumental.It constitutes a basic but essential summary of what is necessary to know of these sustainable materials.In doing so,the review highlights some of the main challenges that remain to be addressed to deliver the quality and economics of tannin supply necessary to fulfill the industrial production requirements for some materials-based uses.

Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

Study on the green extraction of corncob xylan by deep eutectic solvent

Corn as one of the world's major food crops,its by-product corn cob is also rich in resources.However,the unreasonable utilization of corn cob often causes the environmental pollution,waste of resources and other problems.As one of the most abundant polymers in nature,xylan is widely used in food,medicine,materials and other fields.Corn cob is rich in xylan,which is an ideal raw material for extracting xylan.However,the intractable lignin is covalently linked to xylan,which increases the difficulty of xylan extraction.It has been reported that the deep eutectic solvent(DES)could preferentially dissolve lignin in biomass,thereby dissolving the xylan.Then,the xylan in the extract was separated by ethanol precipitation method.The xylan precipitate was obtained after centrifugation,while the supernatant was retained.The components of the supernatant after ethanol precipitation were separated by the rotary evaporator.The ethanol,water and DES were collected for the subsequent extraction of corn cob xylan.In this study,a novel way was provided for the green production of corn cob xylan.The DES was used to extract xylan from corn cob which was used as the raw material.The effects of solid-liquid ratio,reaction time,reaction temperature and water content of DES on the extraction rate of corn cob xylan were investigated by the single factor test.Furthermore,the orthogonal test was designed to optimize the xylan extraction process.The structure of corn cob xylan was analyzed and verified.The results showed that the optimum extraction conditions of corn cob xylan were as follows:the ratio of corn cob to DES was 1:15(g:mL),the extraction time was 3 h,the extraction temperature was 60℃,and the water content of DES was 70%.Under these conditions,the extraction rate of xylan was 16.46%.The extracted corn cob xylan was distinctive triple helix of polysaccharide,which was similar to the structure of commercially available xylan.Xylan was effectively and workably extracted from corn cob by the DES method.This study provided a new approach for high value conversion of corn cob and the clean production of xylan.

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.