Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image a...Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.展开更多
Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM ...Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.展开更多
Based on optimized forecast method of unascertained classifying,a unascer- tained measurement classifying model (UMC) to predict mining induced goaf collapse was established,The discriminated factors of the model are ...Based on optimized forecast method of unascertained classifying,a unascer- tained measurement classifying model (UMC) to predict mining induced goaf collapse was established,The discriminated factors of the model are influential factors including over- burden layer type,overburden layer thickness,the complex degree of geologic structure, the inclination angle of coal bed,volume rate of the cavity region,the vertical goaf depth from the surface and space superposition layer of the goaf region.Unascertained mea- surement (UM) function of each factor was calculated.The unascertained measurement to indicate the classification center and the grade of waiting forecast sample was determined by the UM distance between the synthesis index of waiting forecast samples and index of every classification.The training samples were tested by the established model,and the correct rate is 100%.Furthermore,the seven waiting forecast samples were predicted by the UMC model.The results show that the forecast results are fully consistent with the ac- tual situation.展开更多
The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incom...The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incomplete SCI or single radiculopathy are potential consequences of the blunt trauma over this region. The subaxial cervical spine accounts the vast majority of cervical injuries, making up two thirds of all cervical fractures （Alday, 1996）. Few classifications （Holdsworth, 1970; White et al., 1975; Mien et al., 1982; Denis, 1984; Vaccaro et al., 2007） have been proposed to describe injuries of the cervical spine for several reasons. First, to delineate the best treatment in each case; second, to determinate an accurate neurological prognosis, and third, to establish a standard way to communicate and describe specific characteristics of cervical injuries patterns. Classical systems are primarily descriptive and no single system has gained widespread use, largely because of restrictions in clinical relevance and its complexity.展开更多
During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more ...During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more discriminative, this paper builds an Interval Modified DEA (IMDEA) model based on MDEA. Furthermore, models of obtaining upper and lower bounds of the efficiency scores for each DMU are set up. Based on this, the DMUs are classified into three types. Next, a new order relation between intervals which can express the DM’s preference to the three types is proposed. As a result, a full and more convictive ranking is made on all the DMUs. Finally an example is given.展开更多
This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with ...This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples.展开更多
This paper demonstrates a Geographic Information Systems （GIS） procedure of classifying and mapping forest management category in Baihe Forestry Burea, Jilin Province, China. Within the study area, Baihe Forestry Bu...This paper demonstrates a Geographic Information Systems （GIS） procedure of classifying and mapping forest management category in Baihe Forestry Burea, Jilin Province, China. Within the study area, Baihe Forestry Bureau land was classified into a two-hierarchy system. The top-level class included the non-forest and forest. Over 96% of land area is forest in the study area, which was further divided into key ecological service forest （KES）, general ecological service forest （GES）, and commodity forest （COM）. COM covered 45.0% of the total land area and was the major forest management type in Baihe Forest Bureau. KES and GES accounted for 21.2% and 29.9% of the total land area, respectively. The forest management zones designed with GIS in this study were then compared with the forest management zones established using the hand draw by the local agency. There were obvious differences between the two products. It suggested that the differences had some to do with the data sources, basic unit and mapping procedures. It also suggested that the GIS method was a useful tool in integrating forest inventory data and other data for classifying and mapping forest zones to meet the needs of the classified forest management system.展开更多
According to the theory of the stochastic trajectory model of particle in the gas-solid two-phase flows, the two-phase turbulence model between the blades in the inner cavity of the FW-Φ150 horizontal turbo classifie...According to the theory of the stochastic trajectory model of particle in the gas-solid two-phase flows, the two-phase turbulence model between the blades in the inner cavity of the FW-Φ150 horizontal turbo classifier was established, and the commonly-used PHOENICS code was adopted to carried out the numerical simulation. It was achieved the flow characteristics under a certain condition as well as the motion trace of particles with different diameters entering from certain initial location and passing through the flow field between the blades under the correspondent condition. This research method quite directly demonstrates the motion of particles. An experiment was executed to prove the accuracy of the results of numerical simulation.展开更多
This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues(VAT and SAT)measured by magnetic resonance imaging(MRI),to analyze the relationsh...This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues(VAT and SAT)measured by magnetic resonance imaging(MRI),to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors(BSDs),and to develop a classifier to predict the fat distribution clusters using the BSDs.In the study,66 male and 54 female participants were scanned by MRI and a stereovision body imaging(SBI)to measure participants’abdominal VAT and SAT volumes and the BSDs.A fuzzy c-means algorithm was used to form the inherent grouping clusters of abdominal fat distributions.A support-vector-machine(SVM)classifier,with an embedded feature selection scheme,was employed to determine an optimal subset of the BSDs for predicting internal fat distributions.A fivefold cross-validation procedure was used to prevent over-fitting in the classification.The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry(DXA)measurements.Four clusters were identified for abdominal fat distributions:(1)low VAT and SAT,(2)elevated VAT and SAT,(3)higher SAT,and(4)higher VAT.The cross-validation accuracies of the traditional anthropometric,DXA and BSD measurements were 85.0%,87.5% and 90%,respectively.Compared to the traditional anthropometric and DXA measurements,the BSDs appeared to be effective and efficient in predicting abdominal fat distributions.展开更多
The nutritional status of rubber trees(Hevea brasiliensis)is inseparable from the production of natural rubber.Nitrogen(N)and potassium(K)levels in rubber leaves are 2 crucial criteria that reflect the nutritional sta...The nutritional status of rubber trees(Hevea brasiliensis)is inseparable from the production of natural rubber.Nitrogen(N)and potassium(K)levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubber tree.Advanced hyperspectral technology can evaluate N and K statuses in leaves rapidly.However,high bias and uncertain results will be generated when using a small size and imbalance dataset to train a spectral estimaion model.A typical solution of laborious long-term nutrient stress and high-intensive data collection deviates from rapid and flexible advantages of hyperspectral tech.Therefore,a less intensive and streamlined method,remining information from hyperspectral image data,was assessed.展开更多
In this paper we introduce the history and present situation of the computation of the cohomology rings of Kac-Moody groups,their flag manifolds and classifying spaces,and give some problems and conjectures that deser...In this paper we introduce the history and present situation of the computation of the cohomology rings of Kac-Moody groups,their flag manifolds and classifying spaces,and give some problems and conjectures that deserve further study.展开更多
The way to deal with flexible data from their stochastic presence point of view as output or input in the evaluation of efficiency of the decision-making units(DMUs)motivates new perspectives in modeling and solving d...The way to deal with flexible data from their stochastic presence point of view as output or input in the evaluation of efficiency of the decision-making units(DMUs)motivates new perspectives in modeling and solving data envelopment analysis(DEA)in the presence of flexible variables.Because the orientation of flexible data is not pre-determined,and because the number of DMUs is fixed and all the DMUs are independent,flexible data can be treated as random variable in terms of both input and output selection.As a result,the selection of flexible variable as input or output for n DMUs can be regarded as binary random variable.Assuming the randomness of choosing flexible data as input or output,we deal with DEA models in the presence of flexible data whose input or output orientation determines a binomial distribution function.This study provides a new insight to classify flexible variable and investigates the input or output status of a variable using a stochastic model.The proposed model obviates the problems caused by the use of the large M number and using its different values in previous models.In addition,it can obtain the most appropriate efficiency value for decision-making units by assigning the chance of choosing the orientation of flexible variable to the model itself.The proposed method is compared with other available methods by employing numerical and empirical examples.展开更多
The COVID-19 pandemic has profoundly impacted global health, with far-reaching consequences beyond respiratory complications. Increasing evidence highlights the link between COVID-19 and cardiovascular diseases (CVD),...The COVID-19 pandemic has profoundly impacted global health, with far-reaching consequences beyond respiratory complications. Increasing evidence highlights the link between COVID-19 and cardiovascular diseases (CVD), raising concerns about long-term health risks for those recovering from the virus. This study rigorously investigates the influence of COVID-19 on cardiovascular disease risk, focusing on conditions such as heart failure and myocardial infarction. Using a dataset of 52,683 individuals aged 30 to 80, including both COVID-19 survivors and those unaffected, the study employs machine learning models—logistic regression, decision trees, and random forests—to predict cardiovascular outcomes. The multifaceted approach allowed for a comprehensive evaluation of the model’s predictive capabilities, with logistic regression yielding the highest Binary F1 score of 0.94, effectively identifying cardiovascular risks in both the COVID-19 and non-COVID-19 groups. The correlation matrix revealed significant associations between COVID-19 and key symptoms of heart disease, emphasizing the need for early cardiovascular risk assessment. These findings underscore the importance of machine learning in enhancing early diagnosis and developing preventive strategies for COVID-19-related heart complications. Ultimately, this research contributes to a broader understanding of the pandemic’s lasting health effects, highlighting the critical role of cardiovascular care in post-COVID-19 recovery.展开更多
A method based on syntactic pattern recognition was presented to automatically classify whistles of bottlenose dolphin. Dolphin whistles have typically been characterized in terms of their instantaneous frequency as a...A method based on syntactic pattern recognition was presented to automatically classify whistles of bottlenose dolphin. Dolphin whistles have typically been characterized in terms of their instantaneous frequency as a function of time, which is also known as ＂whistle contour＂. The frequency variation features of a whistle were extracted according to its contour. Then, the frequency variation features were used for learning grammatical patterns. A whistle was classified according to grammatical pattern of its frequency variation features. The exper- imental results showed that the classification accuracy of the proposed method was 95%. The method can provide technical support for acoustic study of dolphins＇ biological behavior.展开更多
Cluster analysis is a method often used in pattern recognition. With the aid of the signal processing and the learning of the computer, disfferent samples can be classifeid and recognized in a dimension reduction spac...Cluster analysis is a method often used in pattern recognition. With the aid of the signal processing and the learning of the computer, disfferent samples can be classifeid and recognized in a dimension reduction space of the characteristics because of the differences of their character -istics. To realize dimension reduction transformation, a nonlinear mapping method was discussed in this paper. To prove that the cluster analysis is suitable for quite different fields of samples, in this paper some ship noises and some EEG as the samples belong to two different fields are classified and shown. And it is worthy to point out that an adaptive step size expression of adaptive iteration deduced here will also be effective if it is applied to speed adaptive algorithm convergence of general signal processing.展开更多
To reveal the historical urban development in large areas using satellite data such as Landsat MSS still need to overcome many challenges.One of them is the need for high-quality training samples.This study tested the...To reveal the historical urban development in large areas using satellite data such as Landsat MSS still need to overcome many challenges.One of them is the need for high-quality training samples.This study tested the feasibility of migrating training samples collected from Landsat MSS data across time and space.We migrated training samples collected for Washington,D.C.in 1979 to classify the city’s land covers in 1982 and 1984.The classifier trained with Washington,D.C.’s samples were used in classifying Boston’s and Tokyo’s land covers.The results showed that the overall accuracies achieved using migrated samples in 1982(66.67%)and 1984(65.67%)for Washington,D.C.were comparable to that of 1979(68.67%)using a random forest classifier.Migration of training samples between cities in the same urban ecoregion,i.e.Washington,D.C.,and Boston,achieved higher overall accuracy(59.33%)than cities in the different ecoregions(Tokyo,50.33%).We concluded that migrating training samples across time and space in the same urban ecoregion are feasible.Ourfindings can contribute to using Landsat MSS data to reveal the historical urbanization pattern on a global scale.展开更多
Indoor microorganisms impact asthma and allergic rhinitis(AR),but the associated microbial taxa often vary extensively due to climate and geographical variations.To provide more consistent environmental assessments,ne...Indoor microorganisms impact asthma and allergic rhinitis(AR),but the associated microbial taxa often vary extensively due to climate and geographical variations.To provide more consistent environmental assessments,new perspectives on microbial exposure for asthma and AR are needed.Home dust from 97 cases(32 asthma alone,37 AR alone,28 comorbidity)and 52 age-and gender-matched controls in Shanghai,China,were analyzed using high-throughput shotgun metagenomic sequencing and liquid chromatography-mass spectrometry.Homes of healthy children were enriched with environmental microbes,including Paracoccus,Pseudomonas,and Psychrobacter,and metabolites like keto acids,indoles,pyridines,and flavonoids(astragalin,hesperidin)(False Discovery Rate<0.05).A neural network co-occurrence probability analysis revealed that environmental microorganisms were involved in producing these keto acids,indoles,and pyridines.Conversely,homes of diseased children were enriched with mycotoxins and synthetic chemicals,including herbicides,insecticides,and food/cosmetic additives.Using a random forest model,characteristic metabolites and microorganisms in Shanghai homes were used to classify high and low prevalence of asthma/AR in an independent dataset in Malaysian schools(N=1290).Indoor metabolites achieved an average accuracy of 74.9%and 77.1%in differentiating schools with high and low prevalence of asthma and AR,respectively,whereas indoor microorganisms only achieved 51.0%and 59.5%,respectively.These results suggest that indoor metabolites and chemicals rather than indoor microbiome are potentially superior environmental indicators for childhood asthma and AR.This study extends the traditional risk assessment focusing on allergens or air pollutants in childhood asthma and AR,thereby revealing potential novel intervention strategies for these diseases.展开更多
First developed 30 years ago,the Compendium of Physical Activities(Compendium)was created to provide a standardized way of measuring and classifying specific physical activities(PAs),allowing researchers and health pr...First developed 30 years ago,the Compendium of Physical Activities(Compendium)was created to provide a standardized way of measuring and classifying specific physical activities(PAs),allowing researchers and health professionals to assess the energy expenditure and health benefits associated with different PA.1Since its inception,the Compendium has been widely utilized and recognized as a fundamental PA and health resource.展开更多
The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal.These IDS uses many methods of machine learn...The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal.These IDS uses many methods of machine learning(ML)to learn from pastexperience attack i.e.signatures based and identify the new ones.Even though these methods are effective,but they have to suffer from large computational costs due to considering all the traffic features,together.Moreover,emerging technologies like the Internet of Things(Io T),big data,etc.are getting advanced day by day;as a result,network traffics are also increasing rapidly.Therefore,the issue of computational cost needs to be addressed properly.Thus,in this research,firstly,the ML methods have been used with the feature selection technique(FST)to reduce the number of features by picking out only the important ones from NSL-KDD,CICIDS2017,and CIC-DDo S2019datasets later that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale network.The experimental result demonstrated that the proposed model i.e.Decision tree(DT)with Recursive feature elimination(RFE)performs better than other classifiers with RFE in terms of accuracy,specificity,precision,sensitivity,F1-score,and G-means on the investigated datasets.展开更多
As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The ...As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.展开更多
文摘Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.
基金The US National Science Foundation (No. CMMI-0408390,CMMI-0644552)the American Chemical Society Petroleum Research Foundation (No.PRF-44468-G9)+3 种基金the Research Fellowship for International Young Scientists (No.51050110143)the Fok Ying-Tong Education Foundation (No.114024)the Natural Science Foundation of Jiangsu Province (No.BK2009015)the Postdoctoral Science Foundation of Jiangsu Province (No.0901005C)
文摘Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.
基金the National Natural Science Foundation of China(50490274)Mittal Innovative and Enterprising Project at Center South University(07MX14)
文摘Based on optimized forecast method of unascertained classifying,a unascer- tained measurement classifying model (UMC) to predict mining induced goaf collapse was established,The discriminated factors of the model are influential factors including over- burden layer type,overburden layer thickness,the complex degree of geologic structure, the inclination angle of coal bed,volume rate of the cavity region,the vertical goaf depth from the surface and space superposition layer of the goaf region.Unascertained mea- surement (UM) function of each factor was calculated.The unascertained measurement to indicate the classification center and the grade of waiting forecast sample was determined by the UM distance between the synthesis index of waiting forecast samples and index of every classification.The training samples were tested by the established model,and the correct rate is 100%.Furthermore,the seven waiting forecast samples were predicted by the UMC model.The results show that the forecast results are fully consistent with the ac- tual situation.
文摘The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incomplete SCI or single radiculopathy are potential consequences of the blunt trauma over this region. The subaxial cervical spine accounts the vast majority of cervical injuries, making up two thirds of all cervical fractures （Alday, 1996）. Few classifications （Holdsworth, 1970; White et al., 1975; Mien et al., 1982; Denis, 1984; Vaccaro et al., 2007） have been proposed to describe injuries of the cervical spine for several reasons. First, to delineate the best treatment in each case; second, to determinate an accurate neurological prognosis, and third, to establish a standard way to communicate and describe specific characteristics of cervical injuries patterns. Classical systems are primarily descriptive and no single system has gained widespread use, largely because of restrictions in clinical relevance and its complexity.
文摘During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more discriminative, this paper builds an Interval Modified DEA (IMDEA) model based on MDEA. Furthermore, models of obtaining upper and lower bounds of the efficiency scores for each DMU are set up. Based on this, the DMUs are classified into three types. Next, a new order relation between intervals which can express the DM’s preference to the three types is proposed. As a result, a full and more convictive ranking is made on all the DMUs. Finally an example is given.
文摘This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples.
基金Foundation project： This research was jointly supported by the National Natural Science Foundation of China （70373044＆30470302）, China＇s Ministry of Science and Technology （04EFN216600328）, and Northeast Rejuvenation Program of the Chinese Academy of Sciences.
文摘This paper demonstrates a Geographic Information Systems （GIS） procedure of classifying and mapping forest management category in Baihe Forestry Burea, Jilin Province, China. Within the study area, Baihe Forestry Bureau land was classified into a two-hierarchy system. The top-level class included the non-forest and forest. Over 96% of land area is forest in the study area, which was further divided into key ecological service forest （KES）, general ecological service forest （GES）, and commodity forest （COM）. COM covered 45.0% of the total land area and was the major forest management type in Baihe Forest Bureau. KES and GES accounted for 21.2% and 29.9% of the total land area, respectively. The forest management zones designed with GIS in this study were then compared with the forest management zones established using the hand draw by the local agency. There were obvious differences between the two products. It suggested that the differences had some to do with the data sources, basic unit and mapping procedures. It also suggested that the GIS method was a useful tool in integrating forest inventory data and other data for classifying and mapping forest zones to meet the needs of the classified forest management system.
文摘According to the theory of the stochastic trajectory model of particle in the gas-solid two-phase flows, the two-phase turbulence model between the blades in the inner cavity of the FW-Φ150 horizontal turbo classifier was established, and the commonly-used PHOENICS code was adopted to carried out the numerical simulation. It was achieved the flow characteristics under a certain condition as well as the motion trace of particles with different diameters entering from certain initial location and passing through the flow field between the blades under the correspondent condition. This research method quite directly demonstrates the motion of particles. An experiment was executed to prove the accuracy of the results of numerical simulation.
文摘This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues(VAT and SAT)measured by magnetic resonance imaging(MRI),to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors(BSDs),and to develop a classifier to predict the fat distribution clusters using the BSDs.In the study,66 male and 54 female participants were scanned by MRI and a stereovision body imaging(SBI)to measure participants’abdominal VAT and SAT volumes and the BSDs.A fuzzy c-means algorithm was used to form the inherent grouping clusters of abdominal fat distributions.A support-vector-machine(SVM)classifier,with an embedded feature selection scheme,was employed to determine an optimal subset of the BSDs for predicting internal fat distributions.A fivefold cross-validation procedure was used to prevent over-fitting in the classification.The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry(DXA)measurements.Four clusters were identified for abdominal fat distributions:(1)low VAT and SAT,(2)elevated VAT and SAT,(3)higher SAT,and(4)higher VAT.The cross-validation accuracies of the traditional anthropometric,DXA and BSD measurements were 85.0%,87.5% and 90%,respectively.Compared to the traditional anthropometric and DXA measurements,the BSDs appeared to be effective and efficient in predicting abdominal fat distributions.
基金supported by the High-level Talent Project of Natural Science Foundation of Hainan Province(No.321RC468)the Key R&D project of Hainan Province(ZDYF2022GXJS008)+1 种基金the National Natural Science Foundation of China(No.32060413)the Innovation Research Team Project of Natural Science Foundation of Hainan Province(No.320CXTD431).
文摘The nutritional status of rubber trees(Hevea brasiliensis)is inseparable from the production of natural rubber.Nitrogen(N)and potassium(K)levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubber tree.Advanced hyperspectral technology can evaluate N and K statuses in leaves rapidly.However,high bias and uncertain results will be generated when using a small size and imbalance dataset to train a spectral estimaion model.A typical solution of laborious long-term nutrient stress and high-intensive data collection deviates from rapid and flexible advantages of hyperspectral tech.Therefore,a less intensive and streamlined method,remining information from hyperspectral image data,was assessed.
基金National Natural Science Foundation of China(Grant No.12071034).
文摘In this paper we introduce the history and present situation of the computation of the cohomology rings of Kac-Moody groups,their flag manifolds and classifying spaces,and give some problems and conjectures that deserve further study.
文摘The way to deal with flexible data from their stochastic presence point of view as output or input in the evaluation of efficiency of the decision-making units(DMUs)motivates new perspectives in modeling and solving data envelopment analysis(DEA)in the presence of flexible variables.Because the orientation of flexible data is not pre-determined,and because the number of DMUs is fixed and all the DMUs are independent,flexible data can be treated as random variable in terms of both input and output selection.As a result,the selection of flexible variable as input or output for n DMUs can be regarded as binary random variable.Assuming the randomness of choosing flexible data as input or output,we deal with DEA models in the presence of flexible data whose input or output orientation determines a binomial distribution function.This study provides a new insight to classify flexible variable and investigates the input or output status of a variable using a stochastic model.The proposed model obviates the problems caused by the use of the large M number and using its different values in previous models.In addition,it can obtain the most appropriate efficiency value for decision-making units by assigning the chance of choosing the orientation of flexible variable to the model itself.The proposed method is compared with other available methods by employing numerical and empirical examples.
文摘The COVID-19 pandemic has profoundly impacted global health, with far-reaching consequences beyond respiratory complications. Increasing evidence highlights the link between COVID-19 and cardiovascular diseases (CVD), raising concerns about long-term health risks for those recovering from the virus. This study rigorously investigates the influence of COVID-19 on cardiovascular disease risk, focusing on conditions such as heart failure and myocardial infarction. Using a dataset of 52,683 individuals aged 30 to 80, including both COVID-19 survivors and those unaffected, the study employs machine learning models—logistic regression, decision trees, and random forests—to predict cardiovascular outcomes. The multifaceted approach allowed for a comprehensive evaluation of the model’s predictive capabilities, with logistic regression yielding the highest Binary F1 score of 0.94, effectively identifying cardiovascular risks in both the COVID-19 and non-COVID-19 groups. The correlation matrix revealed significant associations between COVID-19 and key symptoms of heart disease, emphasizing the need for early cardiovascular risk assessment. These findings underscore the importance of machine learning in enhancing early diagnosis and developing preventive strategies for COVID-19-related heart complications. Ultimately, this research contributes to a broader understanding of the pandemic’s lasting health effects, highlighting the critical role of cardiovascular care in post-COVID-19 recovery.
文摘A method based on syntactic pattern recognition was presented to automatically classify whistles of bottlenose dolphin. Dolphin whistles have typically been characterized in terms of their instantaneous frequency as a function of time, which is also known as ＂whistle contour＂. The frequency variation features of a whistle were extracted according to its contour. Then, the frequency variation features were used for learning grammatical patterns. A whistle was classified according to grammatical pattern of its frequency variation features. The exper- imental results showed that the classification accuracy of the proposed method was 95%. The method can provide technical support for acoustic study of dolphins＇ biological behavior.
基金The project supported by National Natural Science Foundation of China
文摘Cluster analysis is a method often used in pattern recognition. With the aid of the signal processing and the learning of the computer, disfferent samples can be classifeid and recognized in a dimension reduction space of the characteristics because of the differences of their character -istics. To realize dimension reduction transformation, a nonlinear mapping method was discussed in this paper. To prove that the cluster analysis is suitable for quite different fields of samples, in this paper some ship noises and some EEG as the samples belong to two different fields are classified and shown. And it is worthy to point out that an adaptive step size expression of adaptive iteration deduced here will also be effective if it is applied to speed adaptive algorithm convergence of general signal processing.
基金supported by the National Key Research and Development Program of China[grant number 2019YFA0607201].
文摘To reveal the historical urban development in large areas using satellite data such as Landsat MSS still need to overcome many challenges.One of them is the need for high-quality training samples.This study tested the feasibility of migrating training samples collected from Landsat MSS data across time and space.We migrated training samples collected for Washington,D.C.in 1979 to classify the city’s land covers in 1982 and 1984.The classifier trained with Washington,D.C.’s samples were used in classifying Boston’s and Tokyo’s land covers.The results showed that the overall accuracies achieved using migrated samples in 1982(66.67%)and 1984(65.67%)for Washington,D.C.were comparable to that of 1979(68.67%)using a random forest classifier.Migration of training samples between cities in the same urban ecoregion,i.e.Washington,D.C.,and Boston,achieved higher overall accuracy(59.33%)than cities in the different ecoregions(Tokyo,50.33%).We concluded that migrating training samples across time and space in the same urban ecoregion are feasible.Ourfindings can contribute to using Landsat MSS data to reveal the historical urbanization pattern on a global scale.
基金The study was funded by the National Natural Science Foundation of China(No.81861138005)the Natural Science Foundation of Guangdong Province(Nos.2020A1515010845 and 2021A1515010492)+1 种基金the Science and Technology Program of Guangzhou(No.202102080362)Shanghai B&R Joint Laboratory(No.22230750300)and the Swedish Research Council(Vetenskapsrådet)project(No.2017-05845).
文摘Indoor microorganisms impact asthma and allergic rhinitis(AR),but the associated microbial taxa often vary extensively due to climate and geographical variations.To provide more consistent environmental assessments,new perspectives on microbial exposure for asthma and AR are needed.Home dust from 97 cases(32 asthma alone,37 AR alone,28 comorbidity)and 52 age-and gender-matched controls in Shanghai,China,were analyzed using high-throughput shotgun metagenomic sequencing and liquid chromatography-mass spectrometry.Homes of healthy children were enriched with environmental microbes,including Paracoccus,Pseudomonas,and Psychrobacter,and metabolites like keto acids,indoles,pyridines,and flavonoids(astragalin,hesperidin)(False Discovery Rate<0.05).A neural network co-occurrence probability analysis revealed that environmental microorganisms were involved in producing these keto acids,indoles,and pyridines.Conversely,homes of diseased children were enriched with mycotoxins and synthetic chemicals,including herbicides,insecticides,and food/cosmetic additives.Using a random forest model,characteristic metabolites and microorganisms in Shanghai homes were used to classify high and low prevalence of asthma/AR in an independent dataset in Malaysian schools(N=1290).Indoor metabolites achieved an average accuracy of 74.9%and 77.1%in differentiating schools with high and low prevalence of asthma and AR,respectively,whereas indoor microorganisms only achieved 51.0%and 59.5%,respectively.These results suggest that indoor metabolites and chemicals rather than indoor microbiome are potentially superior environmental indicators for childhood asthma and AR.This study extends the traditional risk assessment focusing on allergens or air pollutants in childhood asthma and AR,thereby revealing potential novel intervention strategies for these diseases.
文摘First developed 30 years ago,the Compendium of Physical Activities(Compendium)was created to provide a standardized way of measuring and classifying specific physical activities(PAs),allowing researchers and health professionals to assess the energy expenditure and health benefits associated with different PA.1Since its inception,the Compendium has been widely utilized and recognized as a fundamental PA and health resource.
文摘The key objective of intrusion detection systems(IDS)is to protect the particular host or network by investigating and predicting the network traffic as an attack or normal.These IDS uses many methods of machine learning(ML)to learn from pastexperience attack i.e.signatures based and identify the new ones.Even though these methods are effective,but they have to suffer from large computational costs due to considering all the traffic features,together.Moreover,emerging technologies like the Internet of Things(Io T),big data,etc.are getting advanced day by day;as a result,network traffics are also increasing rapidly.Therefore,the issue of computational cost needs to be addressed properly.Thus,in this research,firstly,the ML methods have been used with the feature selection technique(FST)to reduce the number of features by picking out only the important ones from NSL-KDD,CICIDS2017,and CIC-DDo S2019datasets later that helped to build IDSs with lower cost but with the higher performance which would be appropriate for vast scale network.The experimental result demonstrated that the proposed model i.e.Decision tree(DT)with Recursive feature elimination(RFE)performs better than other classifiers with RFE in terms of accuracy,specificity,precision,sensitivity,F1-score,and G-means on the investigated datasets.
基金supported by the Meteorological Soft Science Project(Grant No.2023ZZXM29)the Natural Science Fund Project of Tianjin,China(Grant No.21JCYBJC00740)the Key Research and Development-Social Development Program of Jiangsu Province,China(Grant No.BE2021685).
文摘As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.
