Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficu...The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.展开更多
When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes...When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets.展开更多
Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus o...Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively.展开更多
Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
Climate change significantly affects environment,ecosystems,communities,and economies.These impacts often result in quick and gradual changes in water resources,environmental conditions,and weather patterns.A geograph...Climate change significantly affects environment,ecosystems,communities,and economies.These impacts often result in quick and gradual changes in water resources,environmental conditions,and weather patterns.A geographical study was conducted in Arizona State,USA,to examine monthly precipi-tation concentration rates over time.This analysis used a high-resolution 0.50×0.50 grid for monthly precip-itation data from 1961 to 2022,Provided by the Climatic Research Unit.The study aimed to analyze climatic changes affected the first and last five years of each decade,as well as the entire decade,during the specified period.GIS was used to meet the objectives of this study.Arizona experienced 51–568 mm,67–560 mm,63–622 mm,and 52–590 mm of rainfall in the sixth,seventh,eighth,and ninth decades of the second millennium,respectively.Both the first and second five year periods of each decade showed accept-able rainfall amounts despite fluctuations.However,rainfall decreased in the first and second decades of the third millennium.and in the first two years of the third decade.Rainfall amounts dropped to 42–472 mm,55–469 mm,and 74–498 mm,respectively,indicating a downward trend in precipitation.The central part of the state received the highest rainfall,while the eastern and western regions(spanning north to south)had significantly less.Over the decades of the third millennium,the average annual rainfall every five years was relatively low,showing a declining trend due to severe climate changes,generally ranging between 35 mm and 498 mm.The central regions consistently received more rainfall than the eastern and western outskirts.Arizona is currently experiencing a decrease in rainfall due to climate change,a situation that could deterio-rate further.This highlights the need to optimize the use of existing rainfall and explore alternative water sources.展开更多
This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,...This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures.展开更多
This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to...This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.展开更多
Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive activities.However,because phishing websites look benign and not all website visitors ha...Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive activities.However,because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites,they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware.It is impossible to stop attackers fromcreating phishingwebsites,which is one of the core challenges in combating them.However,this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handing over sensitive information.In this study,five machine learning(ML)and DL algorithms—cat-boost(CATB),gradient boost(GB),random forest(RF),multilayer perceptron(MLP),and deep neural network(DNN)—were tested with three different reputable datasets and two useful feature selection techniques,to assess the scalability and consistency of each classifier’s performance on varied dataset sizes.The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.9%,95.73%,and 98.83%.The GB classifier achieved the second-best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.16%,95.18%,and 98.58%.MLP achieved the best computational time across all datasets(DS-1,DS-2,and DS-3)with respective values of 2,7,and 3 seconds despite scoring the lowest accuracy across all datasets.展开更多
This paper proposed a method to generate semi-experimental biomedical datasets based on full-wave simulation software.The system noise such as antenna port couplings is fully considered in the proposed datasets,which ...This paper proposed a method to generate semi-experimental biomedical datasets based on full-wave simulation software.The system noise such as antenna port couplings is fully considered in the proposed datasets,which is more realistic than synthetical datasets.In this paper,datasets containing different shapes are constructed based on the relative permittivities of human tissues.Then,a back-propagation scheme is used to obtain the rough reconstructions,which will be fed into a U-net convolutional neural network(CNN)to recover the high-resolution images.Numerical results show that the network trained on the datasets generated by the proposed method can obtain satisfying reconstruction results and is promising to be applied in real-time biomedical imaging.展开更多
The CRA-Interim trial production of the global atmospheric reanalysis for 10 years from 2007 to 2016 was carried out by the China Meteorological Administration in 2017. The structural characteristics of the horizontal...The CRA-Interim trial production of the global atmospheric reanalysis for 10 years from 2007 to 2016 was carried out by the China Meteorological Administration in 2017. The structural characteristics of the horizontal shear line over the Tibetan Plateau (TPHSL) based on the CRA-Interim datasets are examined by objectively identifying the shear line, and are compared with the analysis results of the European Centre for Medium-Range Weather Forecasts reanalysis data (ERA-Interim). The case occurred at 18UTC on July 5, 2016. The results show that both of the ERA-Interim and CRA-Interim datasets can well reveal the circulation background and the dynamic and thermal structure characteristics of TPHSL, and they have shown some similar features. The middle and high latitudes at 500 hPa are characterized by the circulation situation of"two troughs and two ridges", and at 200 hPa, the TPHSL is located in the northeast quadrant of the South Asian High Pressure (SAHP). The TPHSL locates in the positive vorticity zone and passes through the positive vorticity center corresponding to the ascending motion. Near the TPHSL, the contours of pseudo-equivalent potential temperature (θse) tend to be intensive, with a high-value center on the south side of the TPHSL. The TPHSL can extend to460 hPa and vertically inclines northward. There is a positive vorticity zone near the TPHSL which is also characterized by the northward inclination with the height, the ascending motion near the TPHSL can extend to 300 hPa, and the atmospheric layer above the TPHSL is stable. However, the intensities of the TPHSL’s structure characteristics analyzed with the two datasets are different, revealing the relatively strong intensity of geopotential height field, vertical velocity field, vorticity field and divergence field from the CRA-Interim datasets. In addition, the vertical profiles of the dynamic and water vapor thermal physical quantities of the two datasets are also consistent in the east and west part of the TPHSL. In summary, the reliable and usable CRA-Interim datasets show excellent properties in the analysis on the structural characteristics of a horizontal shear line over the Tibetan Plateau.展开更多
Land use/cover change is an important parameter in the climate and ecological simulations. Although they had been widely used in the community, SAGE dataset and HYDE dataset, the two representative global historical l...Land use/cover change is an important parameter in the climate and ecological simulations. Although they had been widely used in the community, SAGE dataset and HYDE dataset, the two representative global historical land use datasets, were little assessed about their accuracies in regional scale. Here, we carried out some assessments for the traditional cultivated region of China （TCRC） over last 300 years, by comparing SAGE2010 and HYDE （v3.1） with Chinese Historical Cropland Dataset （CHCD）. The comparisons were performed at three spatial scales： entire study area, provincial area and 60 km by 60 km grid cell. The results show that （1） the cropland area from SAGE2010 was much more than that from CHCD moreover, the growth at a rate of 0.51% from 1700 to 1950 and -0.34% after 1950 were also inconsistent with that from CHCD. （2） HYDE dataset （v3.1） was closer to CHCD dataset than SAGE dataset on entire study area. However, the large biases could be detected at provincial scale and 60 km by 60 km grid cell scale. The percent of grid cells having biases greater than 70% （〈-70% or 〉70%） and 90% （〈-90% or 〉90%） accounted for 56%-63% and 40%-45% of the total grid cells respectively while those having biases range from -10% to 10% and from -30% to 30% account for only 5%-6% and 17% of the total grid cells respectively. （3） Using local historical archives to reconstruct historical dataset with high accuracy would be a valu- able way to improve the accuracy of climate and ecological simulation.展开更多
Based on C-LSAT2.0,using high-and low-frequency components reconstruction methods,combined with observation constraint masking,a reconstructed C-LSAT2.0 with 756 ensemble members from the 1850s to 2018 has been develo...Based on C-LSAT2.0,using high-and low-frequency components reconstruction methods,combined with observation constraint masking,a reconstructed C-LSAT2.0 with 756 ensemble members from the 1850s to 2018 has been developed.These ensemble versions have been merged with the ERSSTv5 ensemble dataset,and an upgraded version of the CMSTInterim dataset with 5°×5°resolution has been developed.The CMST-Interim dataset has significantly improved the coverage rate of global surface temperature data.After reconstruction,the data coverage before 1950 increased from 78%−81%of the original CMST to 81%−89%.The total coverage after 1955 reached about 93%,including more than 98%in the Northern Hemisphere and 81%−89%in the Southern Hemisphere.Through the reconstruction ensemble experiments with different parameters,a good basis is provided for more systematic uncertainty assessment of C-LSAT2.0 and CMSTInterim.In comparison with the original CMST,the global mean surface temperatures are estimated to be cooler in the second half of 19th century and warmer during the 21st century,which shows that the global warming trend is further amplified.The global warming trends are updated from 0.085±0.004℃(10 yr)^(–1)and 0.128±0.006℃(10 yr)^(–1)to 0.089±0.004℃(10 yr)^(–1)and 0.137±0.007℃(10 yr)^(–1),respectively,since the start and the second half of 20th century.展开更多
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau,High Asia,incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere ...We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau,High Asia,incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS),Global Land cover mapping at 30 m resolution (GlobeLand30),MODIS Land Cover Type product (MCD12Q1),Climate Change Initiative Land Cover (CCI-LC),Global Land Cover 2000 (GLC2000),University of Maryland (UMD),and GlobCover 2009 (Glob- Cover).We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources.We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation.The results of this study reveal that distinct differences,even inverse time series trends,in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015,with the exception of category areal discordance between the seven datasets.We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns,that is,high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions.The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%,and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts,yielding maximum overall accuracy (OA) values of 77.39% and 61.43%,respectively.Finally,5.63% of this area is characterized by both high assessment and accuracy (HH) values,mainly located in central and eastern regions of the Qiangtang Plateau,while most low accuracy regions are found in northern,northeastern,and western regions.展开更多
Comparisons of the west Pacific subtropical high with the South Asia High are made using the NCEP/NCAR and ECMWF 500 hPa and 100 hPa monthly boreal geopotential height fields for the period 1961-2000. Discrepancies ar...Comparisons of the west Pacific subtropical high with the South Asia High are made using the NCEP/NCAR and ECMWF 500 hPa and 100 hPa monthly boreal geopotential height fields for the period 1961-2000. Discrepancies are found for the time prior to 1980. The west Pacific subtropical high in the NCEP/NCAR data is less intense than in ECMWF data before 1980. The range and strength of the west Pacific subtropical high variation described by the NCEP/NCAR data are larger than those depicted by ECMWF data. The same situation appears in the 100-hPa geopotential field. These discoveries suggest that the interdecadal variation of the two systems as shown by the NCEP/NCAR data may not be true. Besides, the South Asia High center in the NCEP/NCAR data is obviously stronger than in the ECMWF data during the periods 1969, 1979-1991 and 1992-1995. Furthermore, the range is larger from 1992 to 1995.展开更多
In this study,through experimental research and an investigation on large datasets of the durability parameters in ocean engineering,the values,ranges,and types of distribution of the durability parameters employed fo...In this study,through experimental research and an investigation on large datasets of the durability parameters in ocean engineering,the values,ranges,and types of distribution of the durability parameters employed for the durability design in ocean engineering in northern China were confirmed.Based on a modified theoretical model of chloride diffusion and the reliability theory,the service lives of concrete structures exposed to the splash,tidal,and underwater zones were calculated.Mixed concrete proportions meeting the requirement of a service life of 100 or 120 years were designed,and a cover thickness requirement was proposed.In addition,the effects of the different time-varying relationships of the boundary condition(Cs)and diffusion coefficient(Df)on the service life were compared;the results showed that the time-varying relationships used in this study(i.e.,Cscontinuously increased and then remained stable,and Dfcontinuously decreased and then remained stable)were beneficial for the durability design of concrete structures in marine environment.展开更多
In this study, the upper ocean heat content （OHC） variations in the South China Sea （SCS） during 1993- 2006 were investigated by examining ocean temperatures in seven datasets, including World Ocean Atlas 2009 （W...In this study, the upper ocean heat content （OHC） variations in the South China Sea （SCS） during 1993- 2006 were investigated by examining ocean temperatures in seven datasets, including World Ocean Atlas 2009 （WOA09） （climatology）, Ishii datasets, Ocean General Circulation ModeI for the Earth Simulator （OFES）, Simple Ocean Data Assimilation system （SODA）, Global Ocean Data Assimilation System （GODAS）, China Oceanic ReAnalysis system （CORA） , and an ocean reanalysis dataset for the joining area of Asia and Indian-Pacific Ocean （AIPO1.0）. Among these datasets, two were independent of any numerical model, four relied on data assimilation, and one was generated without any data assimilation. The annual cycles revealed by the seven datasets were similar, but the interannual variations were different. Vertical structures of temperatures along the 18~N, 12.75~N, and 120~E sections were compared with data collected during open cruises in 1998 and 2005-08. The results indicated that Ishii, OFES, CORA, and AIPO1.0 were more consistent with the observations. Through systematic shortcomings and advantages in presenting the upper comparisons, we found that each dataset had its own OHC in the SCS.展开更多
This is a study to compare three selected tropical cyclone datasets separately compiled by CMA Shanghai Typhoon Institute （CMA SHI）, the Joint Typhoon Warning Center （JTWC）, and the Japan Meteorological Agency （...This is a study to compare three selected tropical cyclone datasets separately compiled by CMA Shanghai Typhoon Institute （CMA SHI）, the Joint Typhoon Warning Center （JTWC）, and the Japan Meteorological Agency （JMA）. The annual fi＇equencies, observation times and destructive power index as the characteristic quantities are investigated of the tropical cyclones over the western North Pacific. The comparative study has resulted in the following findings： 1） Statistical gaps between the datasets compared are narrowing down as the intensity of tropical cyclones increases. 2） In the context of interdecadal distribution, there is for the 1950s a relatively large gap between the datasets, as compared with a narrowed gap for the period from the mid 1970s to the 1980s, and a recurring widened gap for the mid and late 1990s. Additionally, an approach is proposed in the paper to correct the wind speed data in the TC Yearbook.展开更多
Version 4(v4) of the Extended Reconstructed Sea Surface Temperature(ERSST) dataset is compared with its precedent, the widely used version 3b(v3b). The essential upgrades applied to v4 lead to remarkable differences i...Version 4(v4) of the Extended Reconstructed Sea Surface Temperature(ERSST) dataset is compared with its precedent, the widely used version 3b(v3b). The essential upgrades applied to v4 lead to remarkable differences in the characteristics of the sea surface temperature(SST) anomaly(SSTa) in both the temporal and spatial domains. First, the largest discrepancy of the global mean SSTa values around the 1940 s is due to ship-observation corrections made to reconcile observations from buckets and engine intake thermometers. Second, differences in global and regional mean SSTa values between v4 and v3b exhibit a downward trend(around-0.032℃ per decade) before the 1940s, an upward trend(around 0.014℃ per decade) during the period of 1950–2015, interdecadal oscillation with one peak around the 1980s, and two troughs during the 1960s and 2000s, respectively. This does not derive from treatments of the polar or the other data-void regions, since the difference of the SSTa does not share the common features. Third, the spatial pattern of the ENSO-related variability of v4 exhibits a wider but weaker cold tongue in the tropical region of the Pacific Ocean compared with that of v3b, which could be attributed to differences in gap-filling assumptions since the latter features satellite observations whereas the former features in situ ones. This intercomparison confirms that the structural uncertainty arising from underlying assumptions on the treatment of diverse SST observations even in the same SST product family is the main source of significant SST differences in the temporal domain. Why this uncertainty introduces artificial decadal oscillations remains unknown.展开更多
Computed tomography(CT) reconstruction with a well-registered priori magnetic resonance imaging(MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar st...Computed tomography(CT) reconstruction with a well-registered priori magnetic resonance imaging(MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.展开更多
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
文摘The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.
基金supported by the Natural Science Basic Research Program of Shaanxi(Program No.2024JC-YBMS-026).
文摘When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets.
基金supported by the National Key R&D Program of China(2022YFD1401600)the National Science Foundation for Distinguished Young Scholars of Zhejang Province,China(LR23C140001)supported by the Key Area Research and Development Program of Guangdong Province,China(2018B020205003 and 2020B0202090001).
文摘Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively.
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
文摘Climate change significantly affects environment,ecosystems,communities,and economies.These impacts often result in quick and gradual changes in water resources,environmental conditions,and weather patterns.A geographical study was conducted in Arizona State,USA,to examine monthly precipi-tation concentration rates over time.This analysis used a high-resolution 0.50×0.50 grid for monthly precip-itation data from 1961 to 2022,Provided by the Climatic Research Unit.The study aimed to analyze climatic changes affected the first and last five years of each decade,as well as the entire decade,during the specified period.GIS was used to meet the objectives of this study.Arizona experienced 51–568 mm,67–560 mm,63–622 mm,and 52–590 mm of rainfall in the sixth,seventh,eighth,and ninth decades of the second millennium,respectively.Both the first and second five year periods of each decade showed accept-able rainfall amounts despite fluctuations.However,rainfall decreased in the first and second decades of the third millennium.and in the first two years of the third decade.Rainfall amounts dropped to 42–472 mm,55–469 mm,and 74–498 mm,respectively,indicating a downward trend in precipitation.The central part of the state received the highest rainfall,while the eastern and western regions(spanning north to south)had significantly less.Over the decades of the third millennium,the average annual rainfall every five years was relatively low,showing a declining trend due to severe climate changes,generally ranging between 35 mm and 498 mm.The central regions consistently received more rainfall than the eastern and western outskirts.Arizona is currently experiencing a decrease in rainfall due to climate change,a situation that could deterio-rate further.This highlights the need to optimize the use of existing rainfall and explore alternative water sources.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413).
文摘This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures.
文摘This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.
文摘Onemust interact with a specific webpage or website in order to use the Internet for communication,teamwork,and other productive activities.However,because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites,they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware.It is impossible to stop attackers fromcreating phishingwebsites,which is one of the core challenges in combating them.However,this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handing over sensitive information.In this study,five machine learning(ML)and DL algorithms—cat-boost(CATB),gradient boost(GB),random forest(RF),multilayer perceptron(MLP),and deep neural network(DNN)—were tested with three different reputable datasets and two useful feature selection techniques,to assess the scalability and consistency of each classifier’s performance on varied dataset sizes.The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.9%,95.73%,and 98.83%.The GB classifier achieved the second-best accuracy across all datasets(DS-1,DS-2,and DS-3)with respective values of 97.16%,95.18%,and 98.58%.MLP achieved the best computational time across all datasets(DS-1,DS-2,and DS-3)with respective values of 2,7,and 3 seconds despite scoring the lowest accuracy across all datasets.
基金National Natural Science Foundation of China(No.61971036)Fundamental Research Funds for the Central Universities(No.2023CX01011)Beijing Nova Program(No.20230484361)。
文摘This paper proposed a method to generate semi-experimental biomedical datasets based on full-wave simulation software.The system noise such as antenna port couplings is fully considered in the proposed datasets,which is more realistic than synthetical datasets.In this paper,datasets containing different shapes are constructed based on the relative permittivities of human tissues.Then,a back-propagation scheme is used to obtain the rough reconstructions,which will be fed into a U-net convolutional neural network(CNN)to recover the high-resolution images.Numerical results show that the network trained on the datasets generated by the proposed method can obtain satisfying reconstruction results and is promising to be applied in real-time biomedical imaging.
基金National Science Foundation of China (42030611,91937301)The Second Tibetan Plateau Scientific Expedition and Research (STEP) Program (2019QZKK0105)。
文摘The CRA-Interim trial production of the global atmospheric reanalysis for 10 years from 2007 to 2016 was carried out by the China Meteorological Administration in 2017. The structural characteristics of the horizontal shear line over the Tibetan Plateau (TPHSL) based on the CRA-Interim datasets are examined by objectively identifying the shear line, and are compared with the analysis results of the European Centre for Medium-Range Weather Forecasts reanalysis data (ERA-Interim). The case occurred at 18UTC on July 5, 2016. The results show that both of the ERA-Interim and CRA-Interim datasets can well reveal the circulation background and the dynamic and thermal structure characteristics of TPHSL, and they have shown some similar features. The middle and high latitudes at 500 hPa are characterized by the circulation situation of"two troughs and two ridges", and at 200 hPa, the TPHSL is located in the northeast quadrant of the South Asian High Pressure (SAHP). The TPHSL locates in the positive vorticity zone and passes through the positive vorticity center corresponding to the ascending motion. Near the TPHSL, the contours of pseudo-equivalent potential temperature (θse) tend to be intensive, with a high-value center on the south side of the TPHSL. The TPHSL can extend to460 hPa and vertically inclines northward. There is a positive vorticity zone near the TPHSL which is also characterized by the northward inclination with the height, the ascending motion near the TPHSL can extend to 300 hPa, and the atmospheric layer above the TPHSL is stable. However, the intensities of the TPHSL’s structure characteristics analyzed with the two datasets are different, revealing the relatively strong intensity of geopotential height field, vertical velocity field, vorticity field and divergence field from the CRA-Interim datasets. In addition, the vertical profiles of the dynamic and water vapor thermal physical quantities of the two datasets are also consistent in the east and west part of the TPHSL. In summary, the reliable and usable CRA-Interim datasets show excellent properties in the analysis on the structural characteristics of a horizontal shear line over the Tibetan Plateau.
基金China Global Change Research Program, No.2010CB950901 National Natural Science Foundation of China, No.41271227 No.41001122
文摘Land use/cover change is an important parameter in the climate and ecological simulations. Although they had been widely used in the community, SAGE dataset and HYDE dataset, the two representative global historical land use datasets, were little assessed about their accuracies in regional scale. Here, we carried out some assessments for the traditional cultivated region of China （TCRC） over last 300 years, by comparing SAGE2010 and HYDE （v3.1） with Chinese Historical Cropland Dataset （CHCD）. The comparisons were performed at three spatial scales： entire study area, provincial area and 60 km by 60 km grid cell. The results show that （1） the cropland area from SAGE2010 was much more than that from CHCD moreover, the growth at a rate of 0.51% from 1700 to 1950 and -0.34% after 1950 were also inconsistent with that from CHCD. （2） HYDE dataset （v3.1） was closer to CHCD dataset than SAGE dataset on entire study area. However, the large biases could be detected at provincial scale and 60 km by 60 km grid cell scale. The percent of grid cells having biases greater than 70% （〈-70% or 〉70%） and 90% （〈-90% or 〉90%） accounted for 56%-63% and 40%-45% of the total grid cells respectively while those having biases range from -10% to 10% and from -30% to 30% account for only 5%-6% and 17% of the total grid cells respectively. （3） Using local historical archives to reconstruct historical dataset with high accuracy would be a valu- able way to improve the accuracy of climate and ecological simulation.
文摘Based on C-LSAT2.0,using high-and low-frequency components reconstruction methods,combined with observation constraint masking,a reconstructed C-LSAT2.0 with 756 ensemble members from the 1850s to 2018 has been developed.These ensemble versions have been merged with the ERSSTv5 ensemble dataset,and an upgraded version of the CMSTInterim dataset with 5°×5°resolution has been developed.The CMST-Interim dataset has significantly improved the coverage rate of global surface temperature data.After reconstruction,the data coverage before 1950 increased from 78%−81%of the original CMST to 81%−89%.The total coverage after 1955 reached about 93%,including more than 98%in the Northern Hemisphere and 81%−89%in the Southern Hemisphere.Through the reconstruction ensemble experiments with different parameters,a good basis is provided for more systematic uncertainty assessment of C-LSAT2.0 and CMSTInterim.In comparison with the original CMST,the global mean surface temperatures are estimated to be cooler in the second half of 19th century and warmer during the 21st century,which shows that the global warming trend is further amplified.The global warming trends are updated from 0.085±0.004℃(10 yr)^(–1)and 0.128±0.006℃(10 yr)^(–1)to 0.089±0.004℃(10 yr)^(–1)and 0.137±0.007℃(10 yr)^(–1),respectively,since the start and the second half of 20th century.
基金The Strategic Priority Research Program of the Chinese Academy of Sciences,Nos.XDA20040200,XDB03030500Key Foundation Project of Basic Work of the Ministry of Science and Technology of China,No.2012FY111400National Key Technologies R&D Program,No.2012BC06B00
文摘We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau,High Asia,incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS),Global Land cover mapping at 30 m resolution (GlobeLand30),MODIS Land Cover Type product (MCD12Q1),Climate Change Initiative Land Cover (CCI-LC),Global Land Cover 2000 (GLC2000),University of Maryland (UMD),and GlobCover 2009 (Glob- Cover).We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources.We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation.The results of this study reveal that distinct differences,even inverse time series trends,in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015,with the exception of category areal discordance between the seven datasets.We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns,that is,high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions.The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%,and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts,yielding maximum overall accuracy (OA) values of 77.39% and 61.43%,respectively.Finally,5.63% of this area is characterized by both high assessment and accuracy (HH) values,mainly located in central and eastern regions of the Qiangtang Plateau,while most low accuracy regions are found in northern,northeastern,and western regions.
基金Key Laboratory on Natural Disasters for Jiangsu Province (KLME050210)
文摘Comparisons of the west Pacific subtropical high with the South Asia High are made using the NCEP/NCAR and ECMWF 500 hPa and 100 hPa monthly boreal geopotential height fields for the period 1961-2000. Discrepancies are found for the time prior to 1980. The west Pacific subtropical high in the NCEP/NCAR data is less intense than in ECMWF data before 1980. The range and strength of the west Pacific subtropical high variation described by the NCEP/NCAR data are larger than those depicted by ECMWF data. The same situation appears in the 100-hPa geopotential field. These discoveries suggest that the interdecadal variation of the two systems as shown by the NCEP/NCAR data may not be true. Besides, the South Asia High center in the NCEP/NCAR data is obviously stronger than in the ECMWF data during the periods 1969, 1979-1991 and 1992-1995. Furthermore, the range is larger from 1992 to 1995.
基金financial support provided by the National Natural Science Foundation of China(51508272,11832013,51878350,and 51678304)。
文摘In this study,through experimental research and an investigation on large datasets of the durability parameters in ocean engineering,the values,ranges,and types of distribution of the durability parameters employed for the durability design in ocean engineering in northern China were confirmed.Based on a modified theoretical model of chloride diffusion and the reliability theory,the service lives of concrete structures exposed to the splash,tidal,and underwater zones were calculated.Mixed concrete proportions meeting the requirement of a service life of 100 or 120 years were designed,and a cover thickness requirement was proposed.In addition,the effects of the different time-varying relationships of the boundary condition(Cs)and diffusion coefficient(Df)on the service life were compared;the results showed that the time-varying relationships used in this study(i.e.,Cscontinuously increased and then remained stable,and Dfcontinuously decreased and then remained stable)were beneficial for the durability design of concrete structures in marine environment.
基金supported by the National Basic Research Program of China (Grant Nos. 2010CB950400 and 2013CB430301)the National Natural Science Foundation of China (Grant Nos. 41276025 and 41176023)+2 种基金the R&D Special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY201106036)The OFES simulation was conducted on the Earth Simulator under the support of JAMSTECsupported by the Data Sharing Infrastructure of Earth System Science-Data Sharing Service Center of the South China Sea and adjacent regions
文摘In this study, the upper ocean heat content （OHC） variations in the South China Sea （SCS） during 1993- 2006 were investigated by examining ocean temperatures in seven datasets, including World Ocean Atlas 2009 （WOA09） （climatology）, Ishii datasets, Ocean General Circulation ModeI for the Earth Simulator （OFES）, Simple Ocean Data Assimilation system （SODA）, Global Ocean Data Assimilation System （GODAS）, China Oceanic ReAnalysis system （CORA） , and an ocean reanalysis dataset for the joining area of Asia and Indian-Pacific Ocean （AIPO1.0）. Among these datasets, two were independent of any numerical model, four relied on data assimilation, and one was generated without any data assimilation. The annual cycles revealed by the seven datasets were similar, but the interannual variations were different. Vertical structures of temperatures along the 18~N, 12.75~N, and 120~E sections were compared with data collected during open cruises in 1998 and 2005-08. The results indicated that Ishii, OFES, CORA, and AIPO1.0 were more consistent with the observations. Through systematic shortcomings and advantages in presenting the upper comparisons, we found that each dataset had its own OHC in the SCS.
基金Monitoring and Detection of Upperair Climate Change in China (GYHY200906014)
文摘This is a study to compare three selected tropical cyclone datasets separately compiled by CMA Shanghai Typhoon Institute （CMA SHI）, the Joint Typhoon Warning Center （JTWC）, and the Japan Meteorological Agency （JMA）. The annual fi＇equencies, observation times and destructive power index as the characteristic quantities are investigated of the tropical cyclones over the western North Pacific. The comparative study has resulted in the following findings： 1） Statistical gaps between the datasets compared are narrowing down as the intensity of tropical cyclones increases. 2） In the context of interdecadal distribution, there is for the 1950s a relatively large gap between the datasets, as compared with a narrowed gap for the period from the mid 1970s to the 1980s, and a recurring widened gap for the mid and late 1990s. Additionally, an approach is proposed in the paper to correct the wind speed data in the TC Yearbook.
基金supported by the National Key Basic Research and Development Plan (No.2015CB953900)the Natural Science Foundation of China (Nos.41330960 and 41776032)
文摘Version 4(v4) of the Extended Reconstructed Sea Surface Temperature(ERSST) dataset is compared with its precedent, the widely used version 3b(v3b). The essential upgrades applied to v4 lead to remarkable differences in the characteristics of the sea surface temperature(SST) anomaly(SSTa) in both the temporal and spatial domains. First, the largest discrepancy of the global mean SSTa values around the 1940 s is due to ship-observation corrections made to reconcile observations from buckets and engine intake thermometers. Second, differences in global and regional mean SSTa values between v4 and v3b exhibit a downward trend(around-0.032℃ per decade) before the 1940s, an upward trend(around 0.014℃ per decade) during the period of 1950–2015, interdecadal oscillation with one peak around the 1980s, and two troughs during the 1960s and 2000s, respectively. This does not derive from treatments of the polar or the other data-void regions, since the difference of the SSTa does not share the common features. Third, the spatial pattern of the ENSO-related variability of v4 exhibits a wider but weaker cold tongue in the tropical region of the Pacific Ocean compared with that of v3b, which could be attributed to differences in gap-filling assumptions since the latter features satellite observations whereas the former features in situ ones. This intercomparison confirms that the structural uncertainty arising from underlying assumptions on the treatment of diverse SST observations even in the same SST product family is the main source of significant SST differences in the temporal domain. Why this uncertainty introduces artificial decadal oscillations remains unknown.
基金the National Natural Science Foundation of China(No.813716234)the National Basic Research Program(973)of China(No.2010CB834302)Shanghai Jiao Tong University Medical Engineering Cross Research Funds(Nos.YG2013MS30 and YG2014ZD05)
文摘Computed tomography(CT) reconstruction with a well-registered priori magnetic resonance imaging(MRI) image can improve reconstruction results with low-dose CT, because well-registered CT and MRI images have similar structures. However, in clinical settings, the CT image of patients does not always match the priori MRI image because of breathing and movement of patients during CT scanning. To improve the image quality in this case, multi-group datasets expansion is proposed in this paper. In our method, multi-group CT-MRI datasets are formed by expanding CT-MRI datasets. These expanded datasets can also be used by most existing CT-MRI algorithms and improve the reconstructed image quality when the CT image of a patient is not registered with the priori MRI image. In the experiments, we evaluate the performance of the algorithm by using multi-group CT-MRI datasets in several unregistered situations. Experiments show that when the CT and priori MRI images are not registered, the reconstruction results of using multi-group dataset expansion are better than those obtained without using the expansion.
