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
The satellite-based augmentation system(SBAS)provides differential and integrity augmentation services for life safety fields of aviation and navigation.However,the signal structure of SBAS is public,which incurs a ri...The satellite-based augmentation system(SBAS)provides differential and integrity augmentation services for life safety fields of aviation and navigation.However,the signal structure of SBAS is public,which incurs a risk of spoofing attacks.To improve the anti-spoofing capability of the SBAS,European Union and the United States conduct research on navigation message authentication,and promote the standardization of SBAS message authentication.For the development of Beidou satellite-based augmentation system(BDSBAS),this paper proposes navigation message authentication based on the Chinese commercial cryptographic standards.Firstly,this paper expounds the architecture and principles of the SBAS message authentication,and then carries out the design of timed efficient streaming losstolerant authentication scheme(TESLA)and elliptic curve digital signature algorithm(ECDSA)authentication schemes based on Chinese commercial cryptographic standards,message arrangement and the design of over-the-air rekeying(OTAR)message.Finally,this paper conducts a theoretical analysis of the time between authentications(TBA)and maximum authentication latency(MAL)for L5 TESLA-I and L5 ECDSA-Q,and further simulates the reception time of OTAR message,TBA and MAL from the aspects of OTAR message weight and demodulation error rate.The simulation results can provide theoretical supports for the standardization of BDSBAS message authentication.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
Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from...Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.展开更多
To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC per...To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.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
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
In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and th...In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.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.
基金supported by National Natural Science Foundation of China:Space-based occultation detection with ground-based GNSS atmospheric horizontal gradient model(41904033).
文摘The satellite-based augmentation system(SBAS)provides differential and integrity augmentation services for life safety fields of aviation and navigation.However,the signal structure of SBAS is public,which incurs a risk of spoofing attacks.To improve the anti-spoofing capability of the SBAS,European Union and the United States conduct research on navigation message authentication,and promote the standardization of SBAS message authentication.For the development of Beidou satellite-based augmentation system(BDSBAS),this paper proposes navigation message authentication based on the Chinese commercial cryptographic standards.Firstly,this paper expounds the architecture and principles of the SBAS message authentication,and then carries out the design of timed efficient streaming losstolerant authentication scheme(TESLA)and elliptic curve digital signature algorithm(ECDSA)authentication schemes based on Chinese commercial cryptographic standards,message arrangement and the design of over-the-air rekeying(OTAR)message.Finally,this paper conducts a theoretical analysis of the time between authentications(TBA)and maximum authentication latency(MAL)for L5 TESLA-I and L5 ECDSA-Q,and further simulates the reception time of OTAR message,TBA and MAL from the aspects of OTAR message weight and demodulation error rate.The simulation results can provide theoretical supports for the standardization of BDSBAS message authentication.
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 Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)[RS-2021-II211341,Artificial Intelligence Graduate School Program(Chung-Ang University)],and by the Chung-Ang University Graduate Research Scholarship in 2024.
文摘Legal case classification involves the categorization of legal documents into predefined categories,which facilitates legal information retrieval and case management.However,real-world legal datasets often suffer from class imbalances due to the uneven distribution of case types across legal domains.This leads to biased model performance,in the form of high accuracy for overrepresented categories and underperformance for minority classes.To address this issue,in this study,we propose a data augmentation method that masks unimportant terms within a document selectively while preserving key terms fromthe perspective of the legal domain.This approach enhances data diversity and improves the generalization capability of conventional models.Our experiments demonstrate consistent improvements achieved by the proposed augmentation strategy in terms of accuracy and F1 score across all models,validating the effectiveness of the proposed method in legal case classification.
基金supported by the Jiangsu Engineering Research Center of the Key Technology for Intelligent Manufacturing Equipment and the Suqian Key Laboratory of Intelligent Manufacturing(Grant No.M202108).
文摘To address the issues of insufficient and imbalanced data samples in proton exchange membrane fuel cell(PEMFC)performance degradation prediction,this study proposes a data augmentation-based model to predict PEMFC performance degradation.Firstly,an improved generative adversarial network(IGAN)with adaptive gradient penalty coefficient is proposed to address the problems of excessively fast gradient descent and insufficient diversity of generated samples.Then,the IGANis used to generate datawith a distribution analogous to real data,therebymitigating the insufficiency and imbalance of original PEMFC samples and providing the predictionmodel with training data rich in feature information.Finally,a convolutional neural network-bidirectional long short-termmemory(CNN-BiLSTM)model is adopted to predict PEMFC performance degradation.Experimental results show that the data generated by the proposed IGAN exhibits higher quality than that generated by the original GAN,and can fully characterize and enrich the original data’s features.Using the augmented data,the prediction accuracy of the CNN-BiLSTM model is significantly improved,rendering it applicable to tasks of predicting PEMFC performance degradation.
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.
基金funded by the Bavarian State Ministry of Science,Research and Art(Grant number:H.2-F1116.WE/52/2)。
文摘In order to address the widespread data shortage problem in battery research,this paper proposes a generative adversarial network model that combines it with deep convolutional networks,the Wasserstein distance,and the gradient penalty to achieve data augmentation.To lower the threshold for implementing the proposed method,transfer learning is further introduced.The W-DC-GAN-GP-TL framework is thereby formed.This framework is evaluated on 3 different publicly available datasets to judge the quality of generated data.Through visual comparisons and the examination of two visualization methods(probability density function(PDF)and principal component analysis(PCA)),it is demonstrated that the generated data is hard to distinguish from the real data.The application of generated data for training a battery state model using transfer learning is further evaluated.Specifically,Bi-GRU-based and Transformer-based methods are implemented on 2 separate datasets for estimating state of health(SOH)and state of charge(SOC),respectively.The results indicate that the proposed framework demonstrates satisfactory performance in different scenarios:for the data replacement scenario,where real data are removed and replaced with generated data,the state estimator accuracy decreases only slightly;for the data enhancement scenario,the estimator accuracy is further improved.The estimation accuracy of SOH and SOC is as low as 0.69%and 0.58%root mean square error(RMSE)after applying the proposed framework.This framework provides a reliable method for enriching battery measurement data.It is a generalized framework capable of generating a variety of time series data.
