To enhance the properties of bio-based polyesters,enabling them to more closely mimic the characteristics of terephthalate-based materials,a series of aliphatic-aromatic copolyesters(P_(1)–P_(4))were synthesized via ...To enhance the properties of bio-based polyesters,enabling them to more closely mimic the characteristics of terephthalate-based materials,a series of aliphatic-aromatic copolyesters(P_(1)–P_(4))were synthesized via melt polycondensation.Diester monomers M and N were synthesized via the Williamson reaction,using lignin-derived 2-methoxyhydroquinone,methyl 4-chloromethylbenzoate,and methyl chloroacetate as starting materials.Hydroquinone bis(2-hydroxyethyl)ether(HQEE)and 1,4-cyclohexanedimethanol(CHDM)were employed as cyclic segments,while 1,4-butanediol(BDO)and 1,6-hexanediol(HDO)served as alkyl segments within the copolymer structures.The novel copolyesters exhibited molecular weights(Mw)in the range of 5.25×10^(4)–5.87×10^(4) g/mol,with polydispersity indices spanning from 2.50–2.66.Evaluation of the structural and thermomechanical properties indicated that the inclusion of alkyl segments induced a reduction in both crystallinity and molecular weight,while significantly improving the flexibility,whereas cyclic segments enhanced the processability of the copolyesters.Copolyesters P_(1) and P_(2),due to the presence of rigid segments(HQEE and CHDM),displayed relatively high glass transition temperatures(Tg>80℃)and melting temperatures(Tm>170℃).Notably,P_(2),incorporating CHDM,exhibited superior elongation properties(272%),attributed to the enhanced chain mobility resulting from its trans-conformation,while P_(1) was found to be likely brittle owing to excessive chain stiffness.Biodegradability assessment using earthworms as bioindicators revealed that the copolyesters demonstrated moderate degradation profiles,with P_(2) exhibiting a degradation rate of 4.82%,followed by P_(4) at 4.07%,P_(3) at 3.65%,and P_(1) at 3.17%.The higher degradation rate of P_(2) was attributed to its relatively larger d-spacing and lower toxicity,which facilitated enzymatic hydrolytic attack by microorganisms.These findings highlight the significance of optimizing the structural chain segments within aliphatic-aromatic copolyesters.By doing so,it is possible to significantly enhance their properties and performance,offering viable bio-based alternatives to petroleum-based polyesters such as polyethylene terephthalate(PET).展开更多
Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pre...Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pressure signal. The field-sampled water hammer signal is often disturbed by noise interference. Noise interference exists in various pumping stages during water hammer diagnostics, with significantly different frequency range and energy distribution. Clarifying the differences in frequency range and energy distribution between effective water hammer signals and noise is the basis of setting specific filtering parameters, including filtering frequency range and energy thresholds. Filtering specifically could separate the effective signal and noise, which is the key to ensuring the accuracy of water hammer diagnosis. As an emerging technique, there is a lack of research on the frequency range and energy distribution of effective signals in water hammer diagnostics. In this paper, the frequency range and energy distribution characteristics of field-sampled water hammer signals were clarified quantitatively and qualitatively for the first time by a newly proposed comprehensive water hammer segmentation-energy analysis method. The water hammer signals were preprocessed and divided into three segments, including pre-shut-in, water hammer oscillation, and leak-off segment. Then, the three segments were analyzed by energy analysis and correlation analysis. The results indicated that, one aspect, the frequency range of water hammer oscillation spans from 0 to 0.65 Hz, considered as effective water hammer signal. The pre-shut-in and leak-off segment ranges from 0 to 0.35 Hz and 0-0.2 Hz respectively. Meanwhile, odd harmonics were manifested in water hammer oscillation segment, with the harmonic frequencies ranging approximately from 0.07 to 0.75 Hz. Whereas integer harmonics were observed in pre-shut-in segment, ranging from 6 to 40 Hz. The other aspect, the energy distribution of water hammer signals was analyzed in different frequency ranges. In 0-1 Hz, an exponential decay was observed in all three segments. In 1-100 Hz, a periodical energy distribution was observed in pre-shut-in segment, an exponential decay was observed in water hammer oscillation, and an even energy distribution was observed in leak-off segment. In 100-500 Hz, an even energy distribution was observed in those three segments, yet the highest magnitude was noted in leak-off segment. In this study, the effective frequency range and energy distribution characteristics of the field-sampled water hammer signals in different segments were sufficiently elucidated quantitatively and qualitatively for the first time, laying the groundwork for optimizing the filtering parameters of the field filtering models and advancing the accuracy of identifying downhole event locations.展开更多
Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pathogen responsible for coronavirus disease 2019(COVID-19),continues to evolve,giving rise to more variants and global reinfections.Previous research ha...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pathogen responsible for coronavirus disease 2019(COVID-19),continues to evolve,giving rise to more variants and global reinfections.Previous research has demonstrated that barcode segments can effectively and cost-efficiently identify specific species within closely related populations.In this study,we designed and tested RNA barcode segments based on genetic evolutionary relationships to facilitate the efficient and accurate identification of SARS-CoV-2 from extensive virus samples,including human coronaviruses(HCoVs)and SARSr-CoV-2 lineages.Nucleotide sequences sourced from NCBI and GISAID were meticulously selected and curated to construct training sets,encompassing 1733 complete genome sequences of HCoVs and SARSr-CoV-2 lineages.Through genetic-level species testing,we validated the accuracy and reliability of the barcode segments for identifying SARS-CoV-2.Subsequently,75 main and subordinate species-specific barcode segments for SARS-CoV-2,located in ORF1ab,S,E,ORF7a,and N coding sequences,were intercepted and screened based on single-nucleotide polymorphism sites and weighted scores.Post-testing,these segments exhibited high recall rates(nearly 100%),specificity(almost 30%at the nucleotide level),and precision(100%)performance on identification.They were eventually visualized using one and two-dimensional combined barcodes and deposited in an online database(http://virusbarcodedatabase.top/).The successful integration of barcoding technology in SARS-CoV-2 identification provides valuable insights for future studies involving complete genome sequence polymorphism analysis.Moreover,this cost-effective and efficient identification approach also provides valuable reference for future research endeavors related to virus surveillance.展开更多
Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
Retinal aging has been recognized as a significant risk factor for various retinal disorders,including diabetic retinopathy,age-related macular degeneration,and glaucoma,following a growing understanding of the molecu...Retinal aging has been recognized as a significant risk factor for various retinal disorders,including diabetic retinopathy,age-related macular degeneration,and glaucoma,following a growing understanding of the molecular underpinnings of their development.This comprehensive review explores the mechanisms of retinal aging and investigates potential neuroprotective approaches,focusing on the activation of transcription factor EB.Recent meta-analyses have demonstrated promising outcomes of transcription factor EB-targeted strategies,such as exercise,calorie restriction,rapamycin,and metformin,in patients and animal models of these common retinal diseases.The review critically assesses the role of transcription factor EB in retinal biology during aging,its neuroprotective effects,and its therapeutic potential for retinal disorders.The impact of transcription factor EB on retinal aging is cell-specific,influencing metabolic reprogramming and energy homeostasis in retinal neurons through the regulation of mitochondrial quality control and nutrient-sensing pathways.In vascular endothelial cells,transcription factor EB controls important processes,including endothelial cell proliferation,endothelial tube formation,and nitric oxide levels,thereby influencing the inner blood-retinal barrier,angiogenesis,and retinal microvasculature.Additionally,transcription factor EB affects vascular smooth muscle cells,inhibiting vascular calcification and atherogenesis.In retinal pigment epithelial cells,transcription factor EB modulates functions such as autophagy,lysosomal dynamics,and clearance of the aging pigment lipofuscin,thereby promoting photoreceptor survival and regulating vascular endothelial growth factor A expression involved in neovascularization.These cell-specific functions of transcription factor EB significantly impact retinal aging mechanisms encompassing proteostasis,neuronal synapse plasticity,energy metabolism,microvasculature,and inflammation,ultimately offering protection against retinal aging and diseases.The review emphasizes transcription factor EB as a potential therapeutic target for retinal diseases.Therefore,it is imperative to obtain well-controlled direct experimental evidence to confirm the efficacy of transcription factor EB modulation in retinal diseases while minimizing its risk of adverse effects.展开更多
AIM: To investigate the efficacy of Ferrara rings (FR) implantation in the treatment of keratoconus.METHODS: It was a retrospective case series descriptive study. The sample was comprised of 50 patients 79 eyes diagno...AIM: To investigate the efficacy of Ferrara rings (FR) implantation in the treatment of keratoconus.METHODS: It was a retrospective case series descriptive study. The sample was comprised of 50 patients 79 eyes diagnosed with progressive keratoconus. This included 24 (48%) males and 26 (52%) females between the age of 13 and 44 years. All participants underwent surgical implantation of FR in the period between January 2009 and September 2010 at Jordan University Hospital. Thorough ophthalmologic examinations were applied to measure vital variables for each pathological condition before and after surgery. RESULTS: Findings indicated an overall significant postoperative improvement in both uncorrected visual acuity (UCVA) and best spectacle corrected visual acuity (BSCVA) throughout follow up visits. Moreover, results illustrated a significant decrease in spherical equivalent (SE) and keratometric readings (lower, higher and the average). CONCLUSION: Surgical intervention strategies are being frequently developed to meet the needs of patients with keratoconus. The implantation of Ferrara rings has proven to be a safe and feasible alternative procedure for the treatment of mild-moderate keratoconus especially for patients with contact lenses intolerance. We have found that this procedure has improved visual outcomes in all eyes studied. Nevertheless, further research is needed to investigate long term outcomes.展开更多
In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology play...The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.展开更多
Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progr...Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.展开更多
Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satell...Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.展开更多
Waterproof performance of gaskets between segments is the focus of shield tunnels.This paper proposed an analytical method for determining seepage characteristics at tunnel-gasketed joints based on the hydraulic fract...Waterproof performance of gaskets between segments is the focus of shield tunnels.This paper proposed an analytical method for determining seepage characteristics at tunnel-gasketed joints based on the hydraulic fracturing theories.First,the mathematical model was established,and the seepage governing equation and boundary conditions were obtained.Second,three dimensionless parameters were introduced for simplifying the expressions,and the seepage governing equations were normalized.Third,analytical expressions were derived for the interface opening and liquid pressure.Moreover,the influencing factors of seepage process at the gasketed interface were analyzed.Parametric analyses revealed that,in the normalized criterion of liquid viscosity,the liquid tip coordinate was influenced by the degree of negative pressure in the liquid lag region,which was related to the initial contact stress.The coordinate of the liquid tip affected the liquid pressure distribution and the interface opening,which were analyzed under different liquid tip coordinate conditions.Finally,under two limit states,comparative analysis showed that the results of the variation trend of the proposed method agree well with those of previous research.Overall,the proposed analytical method provides a novel solution for the design of the waterproof in shield tunnels.展开更多
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventi...Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.展开更多
Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiv...Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.展开更多
The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structur...The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structure in the upper crust and seismogenic tectonics in the Hefei segment of this fault, we collected phase velocity dispersion data of fundamental mode Rayleigh waves from ambient noise cross-correlation functions of ~400 temporal seismographs in an area of approximately 80 × 70 km along the fault zone. The period band of the dispersion data was ~0.5–10 s. We inverted for the upper crustal three-dimensional(3-D) shear velocity model with azimuthal anisotropy from the surface to 10 km depth by using a 3-D direct azimuthal anisotropy inversion method. The inversion result shows the spatial distribution characteristics of the tectonic units in the upper crust. Additionally, the deformation of the Tan-Lu Fault Zone and its conjugated fault systems could be inferred from the anisotropy model. In particular, the faults that have remained active from the early and middle Pleistocene control the anisotropic characteristics of the upper crustal structure in this area. The direction of fast axes near the fault zone area in the upper crust is consistent with the strike of the faults, whereas for the region far away from the fault zone, the direction of fast axes is consistent with the direction of the regional principal stress caused by plate movement. Combined with the azimuthal anisotropy models in the deep crust and uppermost mantle from the surface wave and Pn wave, the different anisotropic patterns caused by the Tan-Lu Fault Zone and its conjugated fault system nearby are shown in the upper and lower crust. Furthermore,by using the double-difference method, we relocated the Lujiang earthquake series, which contained 32 earthquakes with a depth shallower than 10 km. Both the Vs model and earthquake relocation results indicate that earthquakes mostly occurred in the vicinity of structural boundaries with fractured media, with high-level development of cracks and small-scale faults jammed between more rigid areas.展开更多
A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance m...A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance method.Firstly,an altitude-energy profile is designed,and the bank angle is derived analytically as the initial iteration value for the predictor-corrector method.The predictor-corrector guidance method has been improved by deriving an analytical form for predicting the range-to-go error,which greatly accelerates the iterative speed.Then,a segmented guidance algorithm is proposed.The above analytically predictor-corrector guidance method is adopted when the energy exceeds an energy threshold.When the energy is less than the threshold,the equidistant test method is used to calculate the bank angle command,which ensures guidance accuracy as well as computational efficiency.Additionally,an adaptive guidance cycle strategy is applied to reduce the computational time of the reentry guidance trajectory.Finally,the accuracy and robustness of the proposed method are verified through a series of simulations and Monte-Carlo experiments.Compared with the traditional integral method,the proposed method requires 75%less computation time on average and achieves a lower landing error.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
基金financially supported by State Administration of Foreign Experts Affairs(SAFEA)through the High-End Foreign Expert Program(No.BG2021227001)postdoctoral funding from Wuhan University of Science and Technology(No.105008701)。
文摘To enhance the properties of bio-based polyesters,enabling them to more closely mimic the characteristics of terephthalate-based materials,a series of aliphatic-aromatic copolyesters(P_(1)–P_(4))were synthesized via melt polycondensation.Diester monomers M and N were synthesized via the Williamson reaction,using lignin-derived 2-methoxyhydroquinone,methyl 4-chloromethylbenzoate,and methyl chloroacetate as starting materials.Hydroquinone bis(2-hydroxyethyl)ether(HQEE)and 1,4-cyclohexanedimethanol(CHDM)were employed as cyclic segments,while 1,4-butanediol(BDO)and 1,6-hexanediol(HDO)served as alkyl segments within the copolymer structures.The novel copolyesters exhibited molecular weights(Mw)in the range of 5.25×10^(4)–5.87×10^(4) g/mol,with polydispersity indices spanning from 2.50–2.66.Evaluation of the structural and thermomechanical properties indicated that the inclusion of alkyl segments induced a reduction in both crystallinity and molecular weight,while significantly improving the flexibility,whereas cyclic segments enhanced the processability of the copolyesters.Copolyesters P_(1) and P_(2),due to the presence of rigid segments(HQEE and CHDM),displayed relatively high glass transition temperatures(Tg>80℃)and melting temperatures(Tm>170℃).Notably,P_(2),incorporating CHDM,exhibited superior elongation properties(272%),attributed to the enhanced chain mobility resulting from its trans-conformation,while P_(1) was found to be likely brittle owing to excessive chain stiffness.Biodegradability assessment using earthworms as bioindicators revealed that the copolyesters demonstrated moderate degradation profiles,with P_(2) exhibiting a degradation rate of 4.82%,followed by P_(4) at 4.07%,P_(3) at 3.65%,and P_(1) at 3.17%.The higher degradation rate of P_(2) was attributed to its relatively larger d-spacing and lower toxicity,which facilitated enzymatic hydrolytic attack by microorganisms.These findings highlight the significance of optimizing the structural chain segments within aliphatic-aromatic copolyesters.By doing so,it is possible to significantly enhance their properties and performance,offering viable bio-based alternatives to petroleum-based polyesters such as polyethylene terephthalate(PET).
基金support from the National Natural Science Foundation of China(No.52374019).
文摘Water hammer diagnostics is an important fracturing diagnosis technique to evaluate fracture locations and other downhole events in fracturing. The evaluation results are obtained by analyzing shut-in water hammer pressure signal. The field-sampled water hammer signal is often disturbed by noise interference. Noise interference exists in various pumping stages during water hammer diagnostics, with significantly different frequency range and energy distribution. Clarifying the differences in frequency range and energy distribution between effective water hammer signals and noise is the basis of setting specific filtering parameters, including filtering frequency range and energy thresholds. Filtering specifically could separate the effective signal and noise, which is the key to ensuring the accuracy of water hammer diagnosis. As an emerging technique, there is a lack of research on the frequency range and energy distribution of effective signals in water hammer diagnostics. In this paper, the frequency range and energy distribution characteristics of field-sampled water hammer signals were clarified quantitatively and qualitatively for the first time by a newly proposed comprehensive water hammer segmentation-energy analysis method. The water hammer signals were preprocessed and divided into three segments, including pre-shut-in, water hammer oscillation, and leak-off segment. Then, the three segments were analyzed by energy analysis and correlation analysis. The results indicated that, one aspect, the frequency range of water hammer oscillation spans from 0 to 0.65 Hz, considered as effective water hammer signal. The pre-shut-in and leak-off segment ranges from 0 to 0.35 Hz and 0-0.2 Hz respectively. Meanwhile, odd harmonics were manifested in water hammer oscillation segment, with the harmonic frequencies ranging approximately from 0.07 to 0.75 Hz. Whereas integer harmonics were observed in pre-shut-in segment, ranging from 6 to 40 Hz. The other aspect, the energy distribution of water hammer signals was analyzed in different frequency ranges. In 0-1 Hz, an exponential decay was observed in all three segments. In 1-100 Hz, a periodical energy distribution was observed in pre-shut-in segment, an exponential decay was observed in water hammer oscillation, and an even energy distribution was observed in leak-off segment. In 100-500 Hz, an even energy distribution was observed in those three segments, yet the highest magnitude was noted in leak-off segment. In this study, the effective frequency range and energy distribution characteristics of the field-sampled water hammer signals in different segments were sufficiently elucidated quantitatively and qualitatively for the first time, laying the groundwork for optimizing the filtering parameters of the field filtering models and advancing the accuracy of identifying downhole event locations.
基金supported by grants from Key Research&Development Project of Nanhua Biomedical Co.,Ltd.(No.H202191490139)National Natural Science Foundation of China(No.31872866)+1 种基金China Postdoctoral Science Foundation(Nos.2021M701160 and 2022M721101)Funds of Hunan university(521119400156).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pathogen responsible for coronavirus disease 2019(COVID-19),continues to evolve,giving rise to more variants and global reinfections.Previous research has demonstrated that barcode segments can effectively and cost-efficiently identify specific species within closely related populations.In this study,we designed and tested RNA barcode segments based on genetic evolutionary relationships to facilitate the efficient and accurate identification of SARS-CoV-2 from extensive virus samples,including human coronaviruses(HCoVs)and SARSr-CoV-2 lineages.Nucleotide sequences sourced from NCBI and GISAID were meticulously selected and curated to construct training sets,encompassing 1733 complete genome sequences of HCoVs and SARSr-CoV-2 lineages.Through genetic-level species testing,we validated the accuracy and reliability of the barcode segments for identifying SARS-CoV-2.Subsequently,75 main and subordinate species-specific barcode segments for SARS-CoV-2,located in ORF1ab,S,E,ORF7a,and N coding sequences,were intercepted and screened based on single-nucleotide polymorphism sites and weighted scores.Post-testing,these segments exhibited high recall rates(nearly 100%),specificity(almost 30%at the nucleotide level),and precision(100%)performance on identification.They were eventually visualized using one and two-dimensional combined barcodes and deposited in an online database(http://virusbarcodedatabase.top/).The successful integration of barcoding technology in SARS-CoV-2 identification provides valuable insights for future studies involving complete genome sequence polymorphism analysis.Moreover,this cost-effective and efficient identification approach also provides valuable reference for future research endeavors related to virus surveillance.
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
基金supported by the Start-up Fund for new faculty from the Hong Kong Polytechnic University(PolyU)(A0043215)(to SA)the General Research Fund and Research Impact Fund from the Hong Kong Research Grants Council(15106018,R5032-18)(to DYT)+1 种基金the Research Center for SHARP Vision in PolyU(P0045843)(to SA)the InnoHK scheme from the Hong Kong Special Administrative Region Government(to DYT).
文摘Retinal aging has been recognized as a significant risk factor for various retinal disorders,including diabetic retinopathy,age-related macular degeneration,and glaucoma,following a growing understanding of the molecular underpinnings of their development.This comprehensive review explores the mechanisms of retinal aging and investigates potential neuroprotective approaches,focusing on the activation of transcription factor EB.Recent meta-analyses have demonstrated promising outcomes of transcription factor EB-targeted strategies,such as exercise,calorie restriction,rapamycin,and metformin,in patients and animal models of these common retinal diseases.The review critically assesses the role of transcription factor EB in retinal biology during aging,its neuroprotective effects,and its therapeutic potential for retinal disorders.The impact of transcription factor EB on retinal aging is cell-specific,influencing metabolic reprogramming and energy homeostasis in retinal neurons through the regulation of mitochondrial quality control and nutrient-sensing pathways.In vascular endothelial cells,transcription factor EB controls important processes,including endothelial cell proliferation,endothelial tube formation,and nitric oxide levels,thereby influencing the inner blood-retinal barrier,angiogenesis,and retinal microvasculature.Additionally,transcription factor EB affects vascular smooth muscle cells,inhibiting vascular calcification and atherogenesis.In retinal pigment epithelial cells,transcription factor EB modulates functions such as autophagy,lysosomal dynamics,and clearance of the aging pigment lipofuscin,thereby promoting photoreceptor survival and regulating vascular endothelial growth factor A expression involved in neovascularization.These cell-specific functions of transcription factor EB significantly impact retinal aging mechanisms encompassing proteostasis,neuronal synapse plasticity,energy metabolism,microvasculature,and inflammation,ultimately offering protection against retinal aging and diseases.The review emphasizes transcription factor EB as a potential therapeutic target for retinal diseases.Therefore,it is imperative to obtain well-controlled direct experimental evidence to confirm the efficacy of transcription factor EB modulation in retinal diseases while minimizing its risk of adverse effects.
文摘AIM: To investigate the efficacy of Ferrara rings (FR) implantation in the treatment of keratoconus.METHODS: It was a retrospective case series descriptive study. The sample was comprised of 50 patients 79 eyes diagnosed with progressive keratoconus. This included 24 (48%) males and 26 (52%) females between the age of 13 and 44 years. All participants underwent surgical implantation of FR in the period between January 2009 and September 2010 at Jordan University Hospital. Thorough ophthalmologic examinations were applied to measure vital variables for each pathological condition before and after surgery. RESULTS: Findings indicated an overall significant postoperative improvement in both uncorrected visual acuity (UCVA) and best spectacle corrected visual acuity (BSCVA) throughout follow up visits. Moreover, results illustrated a significant decrease in spherical equivalent (SE) and keratometric readings (lower, higher and the average). CONCLUSION: Surgical intervention strategies are being frequently developed to meet the needs of patients with keratoconus. The implantation of Ferrara rings has proven to be a safe and feasible alternative procedure for the treatment of mild-moderate keratoconus especially for patients with contact lenses intolerance. We have found that this procedure has improved visual outcomes in all eyes studied. Nevertheless, further research is needed to investigate long term outcomes.
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
基金funded by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(grant number 22KJD440001)Changzhou Science&Technology Program(grant number CJ20220232).
文摘The Internet of Vehicles (IoV) has become an important direction in the field of intelligent transportation, in which vehicle positioning is a crucial part. SLAM (Simultaneous Localization and Mapping) technology plays a crucial role in vehicle localization and navigation. Traditional Simultaneous Localization and Mapping (SLAM) systems are designed for use in static environments, and they can result in poor performance in terms of accuracy and robustness when used in dynamic environments where objects are in constant movement. To address this issue, a new real-time visual SLAM system called MG-SLAM has been developed. Based on ORB-SLAM2, MG-SLAM incorporates a dynamic target detection process that enables the detection of both known and unknown moving objects. In this process, a separate semantic segmentation thread is required to segment dynamic target instances, and the Mask R-CNN algorithm is applied on the Graphics Processing Unit (GPU) to accelerate segmentation. To reduce computational cost, only key frames are segmented to identify known dynamic objects. Additionally, a multi-view geometry method is adopted to detect unknown moving objects. The results demonstrate that MG-SLAM achieves higher precision, with an improvement from 0.2730 m to 0.0135 m in precision. Moreover, the processing time required by MG-SLAM is significantly reduced compared to other dynamic scene SLAM algorithms, which illustrates its efficacy in locating objects in dynamic scenes.
文摘Brain tumors present significant challenges in medical diagnosis and treatment,where early detection is crucial for reducing morbidity and mortality rates.This research introduces a novel deep learning model,the Progressive Layered U-Net(PLU-Net),designed to improve brain tumor segmentation accuracy from Magnetic Resonance Imaging(MRI)scans.The PLU-Net extends the standard U-Net architecture by incorporating progressive layering,attention mechanisms,and multi-scale data augmentation.The progressive layering involves a cascaded structure that refines segmentation masks across multiple stages,allowing the model to capture features at different scales and resolutions.Attention gates within the convolutional layers selectively focus on relevant features while suppressing irrelevant ones,enhancing the model's ability to delineate tumor boundaries.Additionally,multi-scale data augmentation techniques increase the diversity of training data and boost the model's generalization capabilities.Evaluated on the BraTS 2021 dataset,the PLU-Net achieved state-of-the-art performance with a dice coefficient of 0.91,specificity of 0.92,sensitivity of 0.89,Hausdorff95 of 2.5,outperforming other modified U-Net architectures in segmentation accuracy.These results underscore the effectiveness of the PLU-Net in improving brain tumor segmentation from MRI scans,supporting clinicians in early diagnosis,treatment planning,and the development of new therapies.
文摘Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.
基金Project(52278421)supported by the National Natural Science Foundation of ChinaProject(2024ZZTS0754)supported by the Fundamental Research Funds for the Central Universities of Central South University,China+2 种基金Project(2023CXQD067)supported by the Central South University Innovation-Driven Research Programme,ChinaProject(2022QNRC001)supported by Young Elite Scientists Sponsorship Program by CASTProject(2023TJ-N24)supported by the Youth Talent Program by China Railway Society and the Hunan Provincial Science and Technology Promotion Talent Project。
文摘Waterproof performance of gaskets between segments is the focus of shield tunnels.This paper proposed an analytical method for determining seepage characteristics at tunnel-gasketed joints based on the hydraulic fracturing theories.First,the mathematical model was established,and the seepage governing equation and boundary conditions were obtained.Second,three dimensionless parameters were introduced for simplifying the expressions,and the seepage governing equations were normalized.Third,analytical expressions were derived for the interface opening and liquid pressure.Moreover,the influencing factors of seepage process at the gasketed interface were analyzed.Parametric analyses revealed that,in the normalized criterion of liquid viscosity,the liquid tip coordinate was influenced by the degree of negative pressure in the liquid lag region,which was related to the initial contact stress.The coordinate of the liquid tip affected the liquid pressure distribution and the interface opening,which were analyzed under different liquid tip coordinate conditions.Finally,under two limit states,comparative analysis showed that the results of the variation trend of the proposed method agree well with those of previous research.Overall,the proposed analytical method provides a novel solution for the design of the waterproof in shield tunnels.
基金the National Natural Science Foundation of China(42472194,42302153,and 42002144)the Fundamental Research Funds for the Central Univer-sities(22CX06002A).
文摘Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.
基金founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06)Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).
文摘Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.
基金financially supported by the National Key Research and Development Program of China (2022YFC3005600)the Foundation of the Anhui Educational Commission (2023AH051198)+1 种基金the National Natural Science Foundation of China (42125401 and 42104063)the Joint Open Fund of Mengcheng National Geophysical Observatory (MENGO-202201)。
文摘The Tan-Lu Fault Zone is a large NNE-trending fault zone that has a substantial effect on the development of eastern China and its earthquake disaster prevention efforts. Aiming at the azimuthally anisotropic structure in the upper crust and seismogenic tectonics in the Hefei segment of this fault, we collected phase velocity dispersion data of fundamental mode Rayleigh waves from ambient noise cross-correlation functions of ~400 temporal seismographs in an area of approximately 80 × 70 km along the fault zone. The period band of the dispersion data was ~0.5–10 s. We inverted for the upper crustal three-dimensional(3-D) shear velocity model with azimuthal anisotropy from the surface to 10 km depth by using a 3-D direct azimuthal anisotropy inversion method. The inversion result shows the spatial distribution characteristics of the tectonic units in the upper crust. Additionally, the deformation of the Tan-Lu Fault Zone and its conjugated fault systems could be inferred from the anisotropy model. In particular, the faults that have remained active from the early and middle Pleistocene control the anisotropic characteristics of the upper crustal structure in this area. The direction of fast axes near the fault zone area in the upper crust is consistent with the strike of the faults, whereas for the region far away from the fault zone, the direction of fast axes is consistent with the direction of the regional principal stress caused by plate movement. Combined with the azimuthal anisotropy models in the deep crust and uppermost mantle from the surface wave and Pn wave, the different anisotropic patterns caused by the Tan-Lu Fault Zone and its conjugated fault system nearby are shown in the upper and lower crust. Furthermore,by using the double-difference method, we relocated the Lujiang earthquake series, which contained 32 earthquakes with a depth shallower than 10 km. Both the Vs model and earthquake relocation results indicate that earthquakes mostly occurred in the vicinity of structural boundaries with fractured media, with high-level development of cracks and small-scale faults jammed between more rigid areas.
基金National Natural Science Foundation of China(Nos.61773387 and 62022061).
文摘A segmented predictor-corrector method is proposed for hypersonic glide vehicles to address the issue of the slow computational speed of obtaining guidance commands using the traditional predictor-corrector guidance method.Firstly,an altitude-energy profile is designed,and the bank angle is derived analytically as the initial iteration value for the predictor-corrector method.The predictor-corrector guidance method has been improved by deriving an analytical form for predicting the range-to-go error,which greatly accelerates the iterative speed.Then,a segmented guidance algorithm is proposed.The above analytically predictor-corrector guidance method is adopted when the energy exceeds an energy threshold.When the energy is less than the threshold,the equidistant test method is used to calculate the bank angle command,which ensures guidance accuracy as well as computational efficiency.Additionally,an adaptive guidance cycle strategy is applied to reduce the computational time of the reentry guidance trajectory.Finally,the accuracy and robustness of the proposed method are verified through a series of simulations and Monte-Carlo experiments.Compared with the traditional integral method,the proposed method requires 75%less computation time on average and achieves a lower landing error.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
