With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown ma...With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.展开更多
Dual-frequency multi-constellation(DFMC) satellitebased augmentation system(SBAS) does not broadcast fast correction, which is important in reducing range error in L1-only SBAS.Meanwhile, the integrity bound of a sate...Dual-frequency multi-constellation(DFMC) satellitebased augmentation system(SBAS) does not broadcast fast correction, which is important in reducing range error in L1-only SBAS.Meanwhile, the integrity bound of a satellite at low elevation is so loose that the service availability is decreased near the boundary of the service area. Therefore, the computation of satellite clockephemeris(SCE) augmentation parameters needs improvement.We propose a method introducing SCE prediction to eliminate most of the SCE error resulting from global navigation satellite system GNSS broadcast message. Compared with the signal-inspace(SIS) after applying augmentation parameters broadcast by the wide area augmentation system(WAAS), SIS accuracy after applying augmentation parameters computed by the proposed algorithm is improved and SIS integrity is ensured. With global positioning system(GPS) only, the availability of category-I(CAT-I)with a vertical alert level of 15 m in continental United States is about 90%, while the availability in the other part of the WAAS service area is markedly improved. With measurements made by the stations from the crustal movement observation network of China,users in some part of China can obtain CAT-I(vertical alert limit is 15 m) service with GPS and global navigation satellite system(GLONASS).展开更多
This paper presents the design of stability augmentation system (SAS) for the airship, which is robust with respect to parametric plant uncertainties. A robust pole placement approach is adopted in the design, which u...This paper presents the design of stability augmentation system (SAS) for the airship, which is robust with respect to parametric plant uncertainties. A robust pole placement approach is adopted in the design, which uses genetic algorithm (GA) as the optimization tool to derive the most robust solution of the state-feedback gain matrix K. The method can guarantee the resulting closed-loop poles to remain in a specified allocation region despite plant parameter uncertainty. Thus, the longitudinal stability of the airship is augmented by robustly assigning the closed-loop poles in a prescribed region of the left half s-plane.展开更多
As the deployment of large Low Earth Orbiters(LEO)communication constellations,navigation from the LEO satellites becomes an emerging opportunity to enhance the existing satellite navigation systems.The LEO navigation...As the deployment of large Low Earth Orbiters(LEO)communication constellations,navigation from the LEO satellites becomes an emerging opportunity to enhance the existing satellite navigation systems.The LEO navigation augmentation(LEO-NA)systems require a centimeter to decimeter accuracy broadcast ephemeris to support high accuracy positioning applications.Thus,how to design the broadcast ephemeris becomes the key issue for the LEO-NA systems.In this paper,the temporal variation characteristics of the LEO orbit elements were analyzed via a spectrum analysis.A non-singular element set for orbit fitting was introduced to overcome the potential singularity problem of the LEO orbits.Based on the orbit characteristics,a few new parameters were introduced into the classical 16 parameter ephemeris set to improve the LEO orbit fitting accuracy.In order to identify the optimal parameter set,different parameter sets were tested and compared and the 21 parameters data set was recommended to make an optimal balance between the orbit accuracy and the bandwidth requirements.Considering the real-time broadcast ephemeris generation procedure,the performance of the LEO ephemeris based on the predicted orbit is also investigated.The performance of the proposed ephemeris set was evaluated with four in-orbit LEO satellites and the results indicate the proposed 21 parameter schemes improve the fitting accuracy by 87.4%subject to the 16 parameters scheme.The accuracy for the predicted LEO ephemeris is strongly dependent on the orbit altitude.For these LEO satellites operating higher than 500 km,10 cm signal-in-space ranging error(SISRE)is achievable for over 20 min prediction.展开更多
Low Earth Orbit(LEO)satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System(GNSS)outage,or as an enhancement by means of traditional GNSS positioning algori...Low Earth Orbit(LEO)satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System(GNSS)outage,or as an enhancement by means of traditional GNSS positioning algorithms.No matter which service mode is used,signal acquisition is a prerequisite for providing enhanced LEO navigation services.Compared with the medium orbit satellite,the transit time of the LEO satellite is shorter.Thus,it is of great significance to expand the successful acquisition time range of the LEO signal.Previous studies on LEO signal acquisition are based on simulation data.However,signal acquisition research based on real data is crucial.In this work,the signal characteristics of LEO satellites:power space density in free space and the Doppler shift of LEO satellites are individually studied.The unified symbolic definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given.To verify these algorithms for LEO signal acquisition,a Software Defined Receiver(SDR)is developed.The performance of these integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite.The experimental results show that the integration strategy can expand the successful acquisition time range,and it will not expand indefinitely with the integration duration.The performance of the coherent integration and differential integration algorithms is better than the other two integration algorithms,so the two algorithms are recommended for LEO signal acquisition and a 20 ms integration duration is preferred.The detection threshold of 2.5 is not suitable for all integration algorithms and various integration durations,especially for the Maximum-to-Mean Ratio indicator.展开更多
Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of ...Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of which are resistant to medical treatment.This study investigates the effect and outcome of magnetic sphincter augmentation(MSA),a minimally invasive treatment for GERD,in this population.Methods:A thorough search of the PubMed,Cochrane,Scopus,Web of Science,and Google Scholar databases from inception until June 6,2024 was performed to retrieve relevant studies that evaluated the effects of MSA on the GERD health-related quality of life(GERD-HRQL)score and the reduction in proton pump inhibitor(PPI)use in patients who underwent bariatric surgery.The“meta”package in RStudio version 2023.12.0 t 369 was used.Results:A total of eight studies were included in the systematic review and seven studies were included in the meta-analysis.MSA significantly reduced the GERD-HRQL score(MD?27.55[95%CI:30.99 to24.11],p<0.01)and PPI use(RR?0.23[95%CI:0.16 to 0.33],p<0.01).Conclusion:MSA is a viable treatment option for patients with GERD symptoms who undergo bariatric surgery.This approach showed promising results in terms of reducing the GERD-HRQL score and reducing the use of PPI.展开更多
Satellite integrity monitoring is vital to satellite-based augmentation systems,and can provide the confdence of the diferential corrections for each monitored satellite satisfying the stringent safety-of-life require...Satellite integrity monitoring is vital to satellite-based augmentation systems,and can provide the confdence of the diferential corrections for each monitored satellite satisfying the stringent safety-of-life requirements.Satellite integrity information includes the user diferential range error and the clock-ephemeris covariance which are used to deduce integrity probability.However,the existing direct statistic methods sufer from a low integrity bounding percentage.To address this problem,we develop an improved covariance-based method to determine satellite integrity information and evaluate its performance in the range domain and position domain.Compared with the direct statistic method,the integrity bounding percentage is improved by 24.91%and the availability by 5.63%.Compared with the covariance-based method,the convergence rate for the user diferential range error is improved by 8.04%.The proposed method is useful for the satellite integrity monitoring of a satellite-based augmentation system.展开更多
AIM To present the long-term results of complex knee injuries,treated early using the Ligament Augmentation and Reconstruction System(LARS)artificial ligament to reconstruct posterior cruciate ligament(PCL).METHODS Fr...AIM To present the long-term results of complex knee injuries,treated early using the Ligament Augmentation and Reconstruction System(LARS)artificial ligament to reconstruct posterior cruciate ligament(PCL).METHODS From September 1997 to June 2010,thirty-eight complex knee injuries were treated,where early arthroscopic PCL reconstructions were undergone,using the LARS(Surgical Implants and Devices,Arc-sur-Tille,France)artificial ligament.Exclusion criteria were:Late(>4 wk)reconstruction,open technique,isolated PCL reconstruction,knee degenerative disease,combinedfracture or vascular injury and use of allograft or autograft for PCL reconstruction.Clinical and functional outcomes were assessed with IKDC Subjective Knee Form,KOS-ADLS questionnaire,Lysholm scale and SF-12 Health Survey.Posterior displacement(PD)was measured with the Telos Stress Device.RESULTS Seven patients were excluded;two because of coexisting knee osteoarthritis and the remaining five because of failure to attend the final follow-up.The sample consisted of 31 patients with mean age at the time of reconstruction 33.2±12.5 years(range 17-61).The postoperative follow-up was on average 9.27±4.27 years(range 5-18).The mean average IKDC and KOS scores were 79.32±17.1 and 88.1±12.47%respectively.Average PD was 3.61±2.15 mm compared to 0.91±1.17 mm in the uninjured knees(one with grade 1+and two with grade 2+).Dial test was found positive in one patient,whereas the quadriceps active drawer test was positive in three patients.None was tested positive on the reverse-pivot shift test.The range of motion(ROM)was normal in thirty knees,in comparison with the contralateral one.There was no extension deficit.Osteoarthritic changes were found in three knees(9.6%).CONCLUSION Early treatment of complex knee injuries,using LARS artificial ligament for PCL reconstruction sufficiently reduces posterior tibia displacement and provides satisfactory long-term functional outcomes.展开更多
Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequentl...Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequently,various studies have been conducted on deep learning techniques related to the detection of parcel damage.This study proposes a deep learning-based damage detectionmethod for various types of parcels.Themethod is intended to be part of a parcel information-recognition systemthat identifies the volume and shipping information of parcels,and determines whether they are damaged;this method is intended for use in the actual parcel-transportation process.For this purpose,1)the study acquired image data in an environment simulating the actual parcel-transportation process,and 2)the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation.Additionally,3)a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage.Finally,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was confirmed.This model can improve customer satisfaction and reduce return costs for parcel delivery companies.展开更多
AIM: To investigate the effectiveness of mesenchymal stem cells(MSCs) in maxillary sinus augmentation(MSA), with various scaffold materials.METHODS: MEDLINE, EMBASE and SCOPUS were searched using keywords such as sinu...AIM: To investigate the effectiveness of mesenchymal stem cells(MSCs) in maxillary sinus augmentation(MSA), with various scaffold materials.METHODS: MEDLINE, EMBASE and SCOPUS were searched using keywords such as sinus graft, MSA, maxillary sinus lift, sinus floor elevation, MSC and cellbased, in different combinations. The searches included full text articles written in English, published over a 10-year period(2004-2014). Inclusion criteria were clinical/radiographic and histologic/ histomorphometric studies in humans and animals, on the use of MSCs in MSA. Meta-analysis was performed only for experimental studies(randomized controlled trials and controlled trials) involving MSA, with an outcome measurement of histologic evaluation with histomorphometric analysis reported. Mean and standard deviation values of newly formed bone from each study were used, and weighted mean values were assessed to account for the difference in the number of subjects among the different studies. To compare the results between the test and the control groups, the differences of regenerated bone in mean and 95% confidence intervals were calculated.RESULTS: Thirty-nine studies(18 animal studies and 21 human studies) published over a 10-year period(between 2004 and 2014) were considered to be eligible for inclusion in the present literature review. These studies demonstrated considerable variation with respect to study type, study design, follow-up, and results. Metaanalysis was performed on 9 studies(7 animal studies and 2 human studies). The weighted mean difference estimate from a random-effect model was 9.5%(95%CI: 3.6%-15.4%), suggesting a positive effect of stem cells on bone regeneration. Heterogeneity was measured by the I2 index. The formal test confirmed the presence of substantial heterogeneity(I2 = 83%, P < 0.0001). In attempt to explain the substantial heterogeneity observed, we considered a meta-regression model with publication year, support type(animal vs humans) andfollow-up length(8 or 12 wk) as covariates. After adding publication year, support type and follow-up length to the meta-regression model, heterogeneity was no longer significant(I2 = 33%, P = 0.25).CONCLUSION: Several studies have demonstrated the potential for cell-based approaches in MSA; further clinical trials are needed to confirm these results.展开更多
The Chicago Area Waterway System(CAWS)is a 133.9 km branching network of navigable waterways controlled by hydraulic structures,in which the majority of the flow is treated wastewater effluent and there are periods of...The Chicago Area Waterway System(CAWS)is a 133.9 km branching network of navigable waterways controlled by hydraulic structures,in which the majority of the flow is treated wastewater effluent and there are periods of substantial combined sewer overflows.The CAWS comprises a network of effluent dominated streams.More stringent dissolved oxygen(DO)standards and a reduced flow augmentation allowance have been recently applied to the CAWS.Therefore,a carefully calibrated and verified one-dimensional flow and water quality model was applied to the CAWS to determine emission-based real-time control guidelines for the operation of flow augmentation and aeration stations.The goal of these guidelines was to attain DO standards at least 95%of the time.The“optimal”guidelines were tested for representative normal,dry,and wet years.The finally proposed guidelines were found in the simulations to attain the 95%target for nearly all locations in the CAWS for the three test years.The developed operational guidelines have been applied since 2018 and have shown improved attainment of the DO standards throughout the CAWS while at the same time achieving similar energy use at the aeration stations on the Calumet River system,greatly lowered energy use on the Chicago River system,and greatly lowered discretionary diversion from Lake Michigan,meeting the recently enacted lower amount of allowed annual discretionary diversion.This case study indicates that emission-based real-time control developed from a well calibrated model holds potential to help many receiving water bodies achieve high attainment of water quality standards.展开更多
It is an effective method to broadcast the augmentation information of satellite navigation system using GEO technology.However,it becomes difficult to receive GEO signal in some special situation,for example in citie...It is an effective method to broadcast the augmentation information of satellite navigation system using GEO technology.However,it becomes difficult to receive GEO signal in some special situation,for example in cities or canyons,in which the signal will be sheltered by big buildings or mountains.In order to solve this problem,an Internet-based broadcast network has been proposed to utilize the infrastructure of the Internet to broadcast the augmentation information of satellite navigation system,which is based on application-layer multicast protocols.In this paper,a topology and position aware overlay network construction protocol is proposed to build the network for augmentation information of satellite navigation system.Simulation results show that the new algorithm is able to achieve better performance in terms of delay,depth and degree utilization.展开更多
BACKGROUND Patellar tendon rupture is a rare disease,and reports regarding patellar tendon reconstruction with ligament augmentation reconstruction system(LARS)ligaments are limited,with only three reports available i...BACKGROUND Patellar tendon rupture is a rare disease,and reports regarding patellar tendon reconstruction with ligament augmentation reconstruction system(LARS)ligaments are limited,with only three reports available in the literature.LARS ligaments are made of polyethylene terephthalate and have been certified as a more favorable option than other tendon transplants.To our knowledge,this is the first report of patellar tendon reconstruction with LARS for suture fixation due to poor quality of the tendon after multiple operations to enable early mobilization and quick rehabilitation.CASE SUMMARY A 65-year-old woman had limited ability in extending her leg and an inability to perform a straight leg raise after multiple operations due to patella fracture.The patient underwent patellar tendon reconstruction with LARS artificial ligaments.After 12 mo of follow-up,the patient was able to perform a straight leg raise,and the incision healed well without complications.The Lysholmscore was 95 and the range of motion of the knee was 0-130°.CONCLUSION This study revealed that patellar tendon reconstruction with LARS artificial ligaments is possible in a patient with a patellar tendon rupture who required rapid postoperative recovery.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect s...An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.展开更多
Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmenta...Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.展开更多
Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on opt...Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on optimizing machine learning(ML)algorithms,very limited attention has been paid to enhancing data quality for improved predictive performance.This study employs strategic data augmentation(DA)techniques to enhance the accuracy of shallow landslide prediction.Using five DA methods including singular spectrum analysis(SSA),moving averages(MA),wavelet denoising(WD),variational mode decomposition(VMD),and linear interpolation(LI),we utilize strategies such as smoothing,denoising,trend decomposition,and synthetic data generation to improve the training dataset.Four machine learning algorithms,i.e.artificial neural network(ANN),recurrent neural network(RNN),one-dimensional convolutional neural network(CNN1D),and long short-term memory(LSTM),are used to forecast landslide displacement.The case study of a landslide in southwest China shows the effectiveness of our approach in predicting landslide displacements,despite the inherent limitations of the monitoring dataset.VMD proves the most effective for smoothing and denoising,improving R^(2),RMSE,and MAPE by 172.16%,71.82%,and 98.9%,respectively.SSA addresses missing data,while LI is effective with limited data samples,improving metrics by 21.6%,52.59%,and 47.87%,respectively.This study demonstrates the potential of DA techniques to mitigate the impact of data defects on landslide prediction accuracy,with implications for similar cases.展开更多
Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research.Additionally,performing edge computing on lo...Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research.Additionally,performing edge computing on low-level devices using small neural networks can be an important research direction.In this paper,we use the EfficientNetV2B0 model for bird species classification,applying transfer learning on a dataset of 525 bird species.We also employ the BiRefNet model to remove backgrounds from images in the training set.The generated background-removed images are mixed with the original training set as a form of data augmentation.We aim for these background-removed images to help the model focus on key features,and by combining data augmentation with transfer learning,we trained a highly accurate and efficient bird species classification model.The training process is divided into a transfer learning stage and a fine-tuning stage.In the transfer learning stage,only the newly added custom layers are trained;while in the fine-tuning stage,all pre-trained layers except for the batch normalization layers are fine-tuned.According to the experimental results,the proposed model not only has an advantage in size compared to other models but also outperforms them in various metrics.The training results show that the proposed model achieved an accuracy of 99.54%and a precision of 99.62%,demonstrating that it achieves both lightweight design and high accuracy.To confirm the credibility of the results,we use heatmaps to interpret the model.The heatmaps show that our model can clearly highlight the image feature area.In addition,we also perform the 10-fold cross-validation on the model to verify its credibility.Finally,this paper proposes a model with low training cost and high accuracy,making it suitable for deployment on edge computing devices to provide lighter and more convenient services.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aim...Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aims to determine the composition of bioactive compounds in chikwangue, including biosurfactant-like molecules and proteins content. Antibacterial activities were investigated through the preliminary emulsification index of chikwangue and fermented paste. Antibacterial assay, 16S rRNA, cytK, hblD, nheB and entFM PCR amplifications, DNA sequence analysis, NCBI homology analysis, and phylogenic tree were performed using NGPhylogeny. fr and iTOL (interactive of live). Fermented cassava paste and chikwangue contain biosurfactants with an emulsification index of 50%. The total protein concentration in fermented cassava paste was 4 g/ml and the chikwangue was 2.5 g/mL Further sequence analysis showed that isolates shared a homology of up to 99.9% with Bacillus cereus PQ432941.1, B. licheniformis PQ432758.1, B. altitudinis PQ432754.1, B. subtilis PQ432759.1, B. mojavensis PQ432755.1, B. tequilensis MT994788.1, B. subtilis MT994789.1, Paenibacillus polymyxa PQ452544.1, B. velezensis PQ452545.1, B. thuringiensis PQ432763.1, B. pumilus PQ432762.1, B. subtilis MT994787.1, B. mycoides PQ432890.1, B. thuringiensis PQ432766.1, B. subtilis PQ432757.1 and B. amyloliquefaciens PQ432756.1. Importantly, the emulsification index (E24) ranged from 60 to 100% and the crude biosurfactant for the Bacillus strains mentioned above could easily inhibit the growth for pathogen Gram-negative bacteria (S. enterica, S. flexneri, E. coli, Klebsiella sp. and P. aeruginosa) with diameters ranging from 2.3 ± 0.1 cm to 5.5 ± 0.4 cm. On the other hand, the diameters of Gram-positive pathogenic bacteria (B. cereus and S. aureus) varied between 1.5 ± 0.5 cm and 4.0 ± 0.2 cm. These findings involve the promise purpose of Bacillus isolated from retted cassava, and this study systematically uncovered the biodiversity and distribution characteristics of retted paste cassava and chikwangue.展开更多
基金supported in part by the National Key Research and Development Program of China(No.2022YFB4500800)the National Science Foundation of China(No.42071431).
文摘With the emergence of new attack techniques,traffic classifiers usually fail to maintain the expected performance in real-world network environments.In order to have sufficient generalizability to deal with unknown malicious samples,they require a large number of new samples for retraining.Considering the cost of data collection and labeling,data augmentation is an ideal solution.We propose an optimized noise-based traffic data augmentation system,ONTDAS.The system uses a gradient-based searching algorithm and an improved Bayesian optimizer to obtain optimized noise.The noise is injected into the original samples for data augmentation.Then,an improved bagging algorithm is used to integrate all the base traffic classifiers trained on noised datasets.The experiments verify ONTDAS on 6 types of base classifiers and 4 publicly available datasets respectively.The results show that ONTDAS can effectively enhance the traffic classifiers’performance and significantly improve their generalizability on unknown malicious samples.The system can also alleviate dataset imbalance.Moreover,the performance of ONTDAS is significantly superior to the existing data augmentation methods mentioned.
文摘Dual-frequency multi-constellation(DFMC) satellitebased augmentation system(SBAS) does not broadcast fast correction, which is important in reducing range error in L1-only SBAS.Meanwhile, the integrity bound of a satellite at low elevation is so loose that the service availability is decreased near the boundary of the service area. Therefore, the computation of satellite clockephemeris(SCE) augmentation parameters needs improvement.We propose a method introducing SCE prediction to eliminate most of the SCE error resulting from global navigation satellite system GNSS broadcast message. Compared with the signal-inspace(SIS) after applying augmentation parameters broadcast by the wide area augmentation system(WAAS), SIS accuracy after applying augmentation parameters computed by the proposed algorithm is improved and SIS integrity is ensured. With global positioning system(GPS) only, the availability of category-I(CAT-I)with a vertical alert level of 15 m in continental United States is about 90%, while the availability in the other part of the WAAS service area is markedly improved. With measurements made by the stations from the crustal movement observation network of China,users in some part of China can obtain CAT-I(vertical alert limit is 15 m) service with GPS and global navigation satellite system(GLONASS).
文摘This paper presents the design of stability augmentation system (SAS) for the airship, which is robust with respect to parametric plant uncertainties. A robust pole placement approach is adopted in the design, which uses genetic algorithm (GA) as the optimization tool to derive the most robust solution of the state-feedback gain matrix K. The method can guarantee the resulting closed-loop poles to remain in a specified allocation region despite plant parameter uncertainty. Thus, the longitudinal stability of the airship is augmented by robustly assigning the closed-loop poles in a prescribed region of the left half s-plane.
基金the National Natural Science Foundation of China[grant number 42074036]the Fundamental Research Funds for the Central Universities.
文摘As the deployment of large Low Earth Orbiters(LEO)communication constellations,navigation from the LEO satellites becomes an emerging opportunity to enhance the existing satellite navigation systems.The LEO navigation augmentation(LEO-NA)systems require a centimeter to decimeter accuracy broadcast ephemeris to support high accuracy positioning applications.Thus,how to design the broadcast ephemeris becomes the key issue for the LEO-NA systems.In this paper,the temporal variation characteristics of the LEO orbit elements were analyzed via a spectrum analysis.A non-singular element set for orbit fitting was introduced to overcome the potential singularity problem of the LEO orbits.Based on the orbit characteristics,a few new parameters were introduced into the classical 16 parameter ephemeris set to improve the LEO orbit fitting accuracy.In order to identify the optimal parameter set,different parameter sets were tested and compared and the 21 parameters data set was recommended to make an optimal balance between the orbit accuracy and the bandwidth requirements.Considering the real-time broadcast ephemeris generation procedure,the performance of the LEO ephemeris based on the predicted orbit is also investigated.The performance of the proposed ephemeris set was evaluated with four in-orbit LEO satellites and the results indicate the proposed 21 parameter schemes improve the fitting accuracy by 87.4%subject to the 16 parameters scheme.The accuracy for the predicted LEO ephemeris is strongly dependent on the orbit altitude.For these LEO satellites operating higher than 500 km,10 cm signal-in-space ranging error(SISRE)is achievable for over 20 min prediction.
基金the National Key R&D Program of China[grant number 2018YFB0505400]the Natural Science Fund of Hubei Province with Project[grant number 2018CFA007]National Key Research and Development Program of China[2018YFB0505400]。
文摘Low Earth Orbit(LEO)satellite navigation signal can be used as an opportunity signal in the case of a Global Navigation Satellite System(GNSS)outage,or as an enhancement by means of traditional GNSS positioning algorithms.No matter which service mode is used,signal acquisition is a prerequisite for providing enhanced LEO navigation services.Compared with the medium orbit satellite,the transit time of the LEO satellite is shorter.Thus,it is of great significance to expand the successful acquisition time range of the LEO signal.Previous studies on LEO signal acquisition are based on simulation data.However,signal acquisition research based on real data is crucial.In this work,the signal characteristics of LEO satellites:power space density in free space and the Doppler shift of LEO satellites are individually studied.The unified symbolic definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given.To verify these algorithms for LEO signal acquisition,a Software Defined Receiver(SDR)is developed.The performance of these integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite.The experimental results show that the integration strategy can expand the successful acquisition time range,and it will not expand indefinitely with the integration duration.The performance of the coherent integration and differential integration algorithms is better than the other two integration algorithms,so the two algorithms are recommended for LEO signal acquisition and a 20 ms integration duration is preferred.The detection threshold of 2.5 is not suitable for all integration algorithms and various integration durations,especially for the Maximum-to-Mean Ratio indicator.
文摘Objective:Although bariatric surgeries are widely performed around the world,patients frequently experience the recurrence of pre-existing gastroesophageal reflux disease(GERD)symptoms or develop new symptoms,some of which are resistant to medical treatment.This study investigates the effect and outcome of magnetic sphincter augmentation(MSA),a minimally invasive treatment for GERD,in this population.Methods:A thorough search of the PubMed,Cochrane,Scopus,Web of Science,and Google Scholar databases from inception until June 6,2024 was performed to retrieve relevant studies that evaluated the effects of MSA on the GERD health-related quality of life(GERD-HRQL)score and the reduction in proton pump inhibitor(PPI)use in patients who underwent bariatric surgery.The“meta”package in RStudio version 2023.12.0 t 369 was used.Results:A total of eight studies were included in the systematic review and seven studies were included in the meta-analysis.MSA significantly reduced the GERD-HRQL score(MD?27.55[95%CI:30.99 to24.11],p<0.01)and PPI use(RR?0.23[95%CI:0.16 to 0.33],p<0.01).Conclusion:MSA is a viable treatment option for patients with GERD symptoms who undergo bariatric surgery.This approach showed promising results in terms of reducing the GERD-HRQL score and reducing the use of PPI.
基金supported by the Research Startup Funds from Tianjin University of Technology under Grant 01002101.
文摘Satellite integrity monitoring is vital to satellite-based augmentation systems,and can provide the confdence of the diferential corrections for each monitored satellite satisfying the stringent safety-of-life requirements.Satellite integrity information includes the user diferential range error and the clock-ephemeris covariance which are used to deduce integrity probability.However,the existing direct statistic methods sufer from a low integrity bounding percentage.To address this problem,we develop an improved covariance-based method to determine satellite integrity information and evaluate its performance in the range domain and position domain.Compared with the direct statistic method,the integrity bounding percentage is improved by 24.91%and the availability by 5.63%.Compared with the covariance-based method,the convergence rate for the user diferential range error is improved by 8.04%.The proposed method is useful for the satellite integrity monitoring of a satellite-based augmentation system.
文摘AIM To present the long-term results of complex knee injuries,treated early using the Ligament Augmentation and Reconstruction System(LARS)artificial ligament to reconstruct posterior cruciate ligament(PCL).METHODS From September 1997 to June 2010,thirty-eight complex knee injuries were treated,where early arthroscopic PCL reconstructions were undergone,using the LARS(Surgical Implants and Devices,Arc-sur-Tille,France)artificial ligament.Exclusion criteria were:Late(>4 wk)reconstruction,open technique,isolated PCL reconstruction,knee degenerative disease,combinedfracture or vascular injury and use of allograft or autograft for PCL reconstruction.Clinical and functional outcomes were assessed with IKDC Subjective Knee Form,KOS-ADLS questionnaire,Lysholm scale and SF-12 Health Survey.Posterior displacement(PD)was measured with the Telos Stress Device.RESULTS Seven patients were excluded;two because of coexisting knee osteoarthritis and the remaining five because of failure to attend the final follow-up.The sample consisted of 31 patients with mean age at the time of reconstruction 33.2±12.5 years(range 17-61).The postoperative follow-up was on average 9.27±4.27 years(range 5-18).The mean average IKDC and KOS scores were 79.32±17.1 and 88.1±12.47%respectively.Average PD was 3.61±2.15 mm compared to 0.91±1.17 mm in the uninjured knees(one with grade 1+and two with grade 2+).Dial test was found positive in one patient,whereas the quadriceps active drawer test was positive in three patients.None was tested positive on the reverse-pivot shift test.The range of motion(ROM)was normal in thirty knees,in comparison with the contralateral one.There was no extension deficit.Osteoarthritic changes were found in three knees(9.6%).CONCLUSION Early treatment of complex knee injuries,using LARS artificial ligament for PCL reconstruction sufficiently reduces posterior tibia displacement and provides satisfactory long-term functional outcomes.
基金supported by a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 1615013176)(https://www.kaia.re.kr/eng/main.do,accessed on 01/06/2024)supported by a Korea Evaluation Institute of Industrial Technology(KEIT)grant funded by the Korean Government(MOTIE)(141518499)(https://www.keit.re.kr/index.es?sid=a2,accessed on 01/06/2024).
文摘Damage to parcels reduces customer satisfactionwith delivery services and increases return-logistics costs.This can be prevented by detecting and addressing the damage before the parcels reach the customer.Consequently,various studies have been conducted on deep learning techniques related to the detection of parcel damage.This study proposes a deep learning-based damage detectionmethod for various types of parcels.Themethod is intended to be part of a parcel information-recognition systemthat identifies the volume and shipping information of parcels,and determines whether they are damaged;this method is intended for use in the actual parcel-transportation process.For this purpose,1)the study acquired image data in an environment simulating the actual parcel-transportation process,and 2)the training dataset was expanded based on StyleGAN3 with adaptive discriminator augmentation.Additionally,3)a preliminary distinction was made between the appearance of parcels and their damage status to enhance the performance of the parcel damage detection model and analyze the causes of parcel damage.Finally,using the dataset constructed based on the proposed method,a damage type detection model was trained,and its mean average precision was confirmed.This model can improve customer satisfaction and reduce return costs for parcel delivery companies.
文摘AIM: To investigate the effectiveness of mesenchymal stem cells(MSCs) in maxillary sinus augmentation(MSA), with various scaffold materials.METHODS: MEDLINE, EMBASE and SCOPUS were searched using keywords such as sinus graft, MSA, maxillary sinus lift, sinus floor elevation, MSC and cellbased, in different combinations. The searches included full text articles written in English, published over a 10-year period(2004-2014). Inclusion criteria were clinical/radiographic and histologic/ histomorphometric studies in humans and animals, on the use of MSCs in MSA. Meta-analysis was performed only for experimental studies(randomized controlled trials and controlled trials) involving MSA, with an outcome measurement of histologic evaluation with histomorphometric analysis reported. Mean and standard deviation values of newly formed bone from each study were used, and weighted mean values were assessed to account for the difference in the number of subjects among the different studies. To compare the results between the test and the control groups, the differences of regenerated bone in mean and 95% confidence intervals were calculated.RESULTS: Thirty-nine studies(18 animal studies and 21 human studies) published over a 10-year period(between 2004 and 2014) were considered to be eligible for inclusion in the present literature review. These studies demonstrated considerable variation with respect to study type, study design, follow-up, and results. Metaanalysis was performed on 9 studies(7 animal studies and 2 human studies). The weighted mean difference estimate from a random-effect model was 9.5%(95%CI: 3.6%-15.4%), suggesting a positive effect of stem cells on bone regeneration. Heterogeneity was measured by the I2 index. The formal test confirmed the presence of substantial heterogeneity(I2 = 83%, P < 0.0001). In attempt to explain the substantial heterogeneity observed, we considered a meta-regression model with publication year, support type(animal vs humans) andfollow-up length(8 or 12 wk) as covariates. After adding publication year, support type and follow-up length to the meta-regression model, heterogeneity was no longer significant(I2 = 33%, P = 0.25).CONCLUSION: Several studies have demonstrated the potential for cell-based approaches in MSA; further clinical trials are needed to confirm these results.
基金supported by the Metropolitan Water Reclamation District of Greater Chicago(Requisition No.1449764).
文摘The Chicago Area Waterway System(CAWS)is a 133.9 km branching network of navigable waterways controlled by hydraulic structures,in which the majority of the flow is treated wastewater effluent and there are periods of substantial combined sewer overflows.The CAWS comprises a network of effluent dominated streams.More stringent dissolved oxygen(DO)standards and a reduced flow augmentation allowance have been recently applied to the CAWS.Therefore,a carefully calibrated and verified one-dimensional flow and water quality model was applied to the CAWS to determine emission-based real-time control guidelines for the operation of flow augmentation and aeration stations.The goal of these guidelines was to attain DO standards at least 95%of the time.The“optimal”guidelines were tested for representative normal,dry,and wet years.The finally proposed guidelines were found in the simulations to attain the 95%target for nearly all locations in the CAWS for the three test years.The developed operational guidelines have been applied since 2018 and have shown improved attainment of the DO standards throughout the CAWS while at the same time achieving similar energy use at the aeration stations on the Calumet River system,greatly lowered energy use on the Chicago River system,and greatly lowered discretionary diversion from Lake Michigan,meeting the recently enacted lower amount of allowed annual discretionary diversion.This case study indicates that emission-based real-time control developed from a well calibrated model holds potential to help many receiving water bodies achieve high attainment of water quality standards.
基金supported by National High Technical Research and Development Program of China (863 Program) under Grant No. 2009AA12Z322
文摘It is an effective method to broadcast the augmentation information of satellite navigation system using GEO technology.However,it becomes difficult to receive GEO signal in some special situation,for example in cities or canyons,in which the signal will be sheltered by big buildings or mountains.In order to solve this problem,an Internet-based broadcast network has been proposed to utilize the infrastructure of the Internet to broadcast the augmentation information of satellite navigation system,which is based on application-layer multicast protocols.In this paper,a topology and position aware overlay network construction protocol is proposed to build the network for augmentation information of satellite navigation system.Simulation results show that the new algorithm is able to achieve better performance in terms of delay,depth and degree utilization.
基金Supported by National Natural Science Foundation of China,No.81871814Natural Science Foundation of Shandong Province,No.ZR2017MH119
文摘BACKGROUND Patellar tendon rupture is a rare disease,and reports regarding patellar tendon reconstruction with ligament augmentation reconstruction system(LARS)ligaments are limited,with only three reports available in the literature.LARS ligaments are made of polyethylene terephthalate and have been certified as a more favorable option than other tendon transplants.To our knowledge,this is the first report of patellar tendon reconstruction with LARS for suture fixation due to poor quality of the tendon after multiple operations to enable early mobilization and quick rehabilitation.CASE SUMMARY A 65-year-old woman had limited ability in extending her leg and an inability to perform a straight leg raise after multiple operations due to patella fracture.The patient underwent patellar tendon reconstruction with LARS artificial ligaments.After 12 mo of follow-up,the patient was able to perform a straight leg raise,and the incision healed well without complications.The Lysholmscore was 95 and the range of motion of the knee was 0-130°.CONCLUSION This study revealed that patellar tendon reconstruction with LARS artificial ligaments is possible in a patient with a patellar tendon rupture who required rapid postoperative recovery.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
基金supported by the Jiangsu Province IUR Cooperation Project (No.BY2021258)the Wuxi Science and Technology Development Fund Project (No.G20212028)。
文摘An improved cycle-consistent generative adversarial network(CycleGAN) method for defect data augmentation based on feature fusion and self attention residual module is proposed to address the insufficiency of defect sample data for light guide plate(LGP) in production,as well as the problem of minor defects.Two optimizations are made to the generator of CycleGAN:fusion of low resolution features obtained from partial up-sampling and down-sampling with high-resolution features,combination of self attention mechanism with residual network structure to replace the original residual module.Qualitative and quantitative experiments were conducted to compare different data augmentation methods,and the results show that the defect images of the LGP generated by the improved network were more realistic,and the accuracy of the you only look once version 5(YOLOv5) detection network for the LGP was improved by 5.6%,proving the effectiveness and accuracy of the proposed method.
基金supported by National Natural Science Foundation of China(61673186 and 61871196)Beijing Normal University Education Reform Project(jx2024040)Guangdong Undergraduate Universities Teaching Quality and Reform Project(jx2024309).
文摘Data augmentation plays an important role in boosting the performance of 3D models,while very few studies handle the 3D point cloud data with this technique.Global augmentation and cut-paste are commonly used augmentation techniques for point clouds,where global augmentation is applied to the entire point cloud of the scene,and cut-paste samples objects from other frames into the current frame.Both types of data augmentation can improve performance,but the cut-paste technique cannot effectively deal with the occlusion relationship between the foreground object and the background scene and the rationality of object sampling,which may be counterproductive and may hurt the overall performance.In addition,LiDAR is susceptible to signal loss,external occlusion,extreme weather and other factors,which can easily cause object shape changes,while global augmentation and cut-paste cannot effectively enhance the robustness of the model.To this end,we propose Syn-Aug,a synchronous data augmentation framework for LiDAR-based 3D object detection.Specifically,we first propose a novel rendering-based object augmentation technique(Ren-Aug)to enrich training data while enhancing scene realism.Second,we propose a local augmentation technique(Local-Aug)to generate local noise by rotating and scaling objects in the scene while avoiding collisions,which can improve generalisation performance.Finally,we make full use of the structural information of 3D labels to make the model more robust by randomly changing the geometry of objects in the training frames.We verify the proposed framework with four different types of 3D object detectors.Experimental results show that our proposed Syn-Aug significantly improves the performance of various 3D object detectors in the KITTI and nuScenes datasets,proving the effectiveness and generality of Syn-Aug.On KITTI,four different types of baseline models using Syn-Aug improved mAP by 0.89%,1.35%,1.61%and 1.14%respectively.On nuScenes,four different types of baseline models using Syn-Aug improved mAP by 14.93%,10.42%,8.47%and 6.81%respectively.The code is available at https://github.com/liuhuaijjin/Syn-Aug.
基金supported by the National Natural Science Foundation of China(Grant No.42101089)Sichuan Science and Technology Program(2022YFS0586)the Open Fund of Key Laboratory of Mountain Hazards and Earth Surface Processes Chinese Academy of Sciences.
文摘Rainfall-induced shallow landslides pose one of significant geological hazards,necessitating precise monitoring and prediction for effective disaster mitigation.Most studies on landslide prediction have focused on optimizing machine learning(ML)algorithms,very limited attention has been paid to enhancing data quality for improved predictive performance.This study employs strategic data augmentation(DA)techniques to enhance the accuracy of shallow landslide prediction.Using five DA methods including singular spectrum analysis(SSA),moving averages(MA),wavelet denoising(WD),variational mode decomposition(VMD),and linear interpolation(LI),we utilize strategies such as smoothing,denoising,trend decomposition,and synthetic data generation to improve the training dataset.Four machine learning algorithms,i.e.artificial neural network(ANN),recurrent neural network(RNN),one-dimensional convolutional neural network(CNN1D),and long short-term memory(LSTM),are used to forecast landslide displacement.The case study of a landslide in southwest China shows the effectiveness of our approach in predicting landslide displacements,despite the inherent limitations of the monitoring dataset.VMD proves the most effective for smoothing and denoising,improving R^(2),RMSE,and MAPE by 172.16%,71.82%,and 98.9%,respectively.SSA addresses missing data,while LI is effective with limited data samples,improving metrics by 21.6%,52.59%,and 47.87%,respectively.This study demonstrates the potential of DA techniques to mitigate the impact of data defects on landslide prediction accuracy,with implications for similar cases.
文摘Bird species classification is not only a challenging topic in artificial intelligence but also a domain closely related to environmental protection and ecological research.Additionally,performing edge computing on low-level devices using small neural networks can be an important research direction.In this paper,we use the EfficientNetV2B0 model for bird species classification,applying transfer learning on a dataset of 525 bird species.We also employ the BiRefNet model to remove backgrounds from images in the training set.The generated background-removed images are mixed with the original training set as a form of data augmentation.We aim for these background-removed images to help the model focus on key features,and by combining data augmentation with transfer learning,we trained a highly accurate and efficient bird species classification model.The training process is divided into a transfer learning stage and a fine-tuning stage.In the transfer learning stage,only the newly added custom layers are trained;while in the fine-tuning stage,all pre-trained layers except for the batch normalization layers are fine-tuned.According to the experimental results,the proposed model not only has an advantage in size compared to other models but also outperforms them in various metrics.The training results show that the proposed model achieved an accuracy of 99.54%and a precision of 99.62%,demonstrating that it achieves both lightweight design and high accuracy.To confirm the credibility of the results,we use heatmaps to interpret the model.The heatmaps show that our model can clearly highlight the image feature area.In addition,we also perform the 10-fold cross-validation on the model to verify its credibility.Finally,this paper proposes a model with low training cost and high accuracy,making it suitable for deployment on edge computing devices to provide lighter and more convenient services.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
文摘Cassava is the most widely distributed food crop in Central Africa. Chikwangue, also known as kwanga in the Republic of Congo, is a starchy fermented cassava product that is a staple food in the country. This work aims to determine the composition of bioactive compounds in chikwangue, including biosurfactant-like molecules and proteins content. Antibacterial activities were investigated through the preliminary emulsification index of chikwangue and fermented paste. Antibacterial assay, 16S rRNA, cytK, hblD, nheB and entFM PCR amplifications, DNA sequence analysis, NCBI homology analysis, and phylogenic tree were performed using NGPhylogeny. fr and iTOL (interactive of live). Fermented cassava paste and chikwangue contain biosurfactants with an emulsification index of 50%. The total protein concentration in fermented cassava paste was 4 g/ml and the chikwangue was 2.5 g/mL Further sequence analysis showed that isolates shared a homology of up to 99.9% with Bacillus cereus PQ432941.1, B. licheniformis PQ432758.1, B. altitudinis PQ432754.1, B. subtilis PQ432759.1, B. mojavensis PQ432755.1, B. tequilensis MT994788.1, B. subtilis MT994789.1, Paenibacillus polymyxa PQ452544.1, B. velezensis PQ452545.1, B. thuringiensis PQ432763.1, B. pumilus PQ432762.1, B. subtilis MT994787.1, B. mycoides PQ432890.1, B. thuringiensis PQ432766.1, B. subtilis PQ432757.1 and B. amyloliquefaciens PQ432756.1. Importantly, the emulsification index (E24) ranged from 60 to 100% and the crude biosurfactant for the Bacillus strains mentioned above could easily inhibit the growth for pathogen Gram-negative bacteria (S. enterica, S. flexneri, E. coli, Klebsiella sp. and P. aeruginosa) with diameters ranging from 2.3 ± 0.1 cm to 5.5 ± 0.4 cm. On the other hand, the diameters of Gram-positive pathogenic bacteria (B. cereus and S. aureus) varied between 1.5 ± 0.5 cm and 4.0 ± 0.2 cm. These findings involve the promise purpose of Bacillus isolated from retted cassava, and this study systematically uncovered the biodiversity and distribution characteristics of retted paste cassava and chikwangue.
