The development process of complex equipment involves multi-stage business processes,multi-level product architecture,and multi-disciplinary physical processes.The relationship between its system model and various dis...The development process of complex equipment involves multi-stage business processes,multi-level product architecture,and multi-disciplinary physical processes.The relationship between its system model and various disciplinary models is extremely complicated.In the modeling and integration process,extensive customized development is needed to realize model integration and interoperability in different business scenarios.Meanwhile,the differences in modeling and interaction between different modeling tools make it difficult to support the consistent representation of models in complex scenarios.To improve the efficiency of system modeling and integration in complex business scenarios,a system modeling and integration method was proposed.This method took the Sys ML language kernel as the core and system model function integration as the main line.Through the technical means of model view separation,abstract operation interface,and model view configuration,the model modeling and integration of multi-user,multi-model,multi-view,and different business logic in complex business scenarios were realized.展开更多
Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and i...Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.展开更多
The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often sim...The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.展开更多
The data association problem of multiple extended target tracking is very challenging because each target may generate multiple measurements.Recently,the belief propagation based multiple target tracking algorithms wi...The data association problem of multiple extended target tracking is very challenging because each target may generate multiple measurements.Recently,the belief propagation based multiple target tracking algorithms with high efficiency have been a research focus.Different from the belief propagation based Extended Target tracking based on Belief Propagation(ET-BP)algorithm proposed in our previous work,a new graphical model formulation of data association for multiple extended target tracking is proposed in this paper.The proposed formulation can be solved by the Loopy Belief Propagation(LBP)algorithm.Furthermore,the simplified measurement set in the ET-BP algorithm is modified to improve tracking accuracy.Finally,experiment results show that the proposed algorithm has better performance than the ET-BP and joint probabilistic data association based on the simplified measurement set algorithms in terms of accuracy and efficiency.Additionally,the convergence of the proposed algorithm is verified in the simulations.展开更多
A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and control...A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.展开更多
Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected ...Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.展开更多
Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constr...Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constructed a template of the relation between haloanhydrite mineralogy （anhydrite, salt, mudstone, and pore water） and wave velocities. We used the relation between the P-wave rnoduli ratio and porosity as constraint and constructed a graphical model （petrophysical template） for the relation between wave velocity, mineral content and porosity. We tested the graphical model using rock core and well logging data.展开更多
Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty...Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.展开更多
BACKGROUND Recently,research has linked Helicobacter pylori(H.pylori)stomach infection to colonic inflammation,mediated by toxin production,potentially impacting colorectal cancer occurrence.AIM To investigate the ris...BACKGROUND Recently,research has linked Helicobacter pylori(H.pylori)stomach infection to colonic inflammation,mediated by toxin production,potentially impacting colorectal cancer occurrence.AIM To investigate the risk factors for post-colon polyp surgery,H.pylori infection,and its correlation with pathologic type.METHODS Eighty patients who underwent colon polypectomy in our hospital between January 2019 and January 2023 were retrospectively chosen.They were then randomly split into modeling(n=56)and model validation(n=24)sets using R.The modeling cohort was divided into an H.pylori-infected group(n=37)and an H.pylori-uninfected group(n=19).Binary logistic regression analysis was used to analyze the factors influencing the occurrence of H.pylori infection after colon polyp surgery.A roadmap prediction model was established and validated.Finally,the correlation between the different pathological types of colon polyps and the occurrence of H.pylori infection was analyzed after colon polyp surgery.RESULTS Univariate results showed that age,body mass index(BMI),literacy,alcohol consumption,polyp pathology type,high-risk adenomas,and heavy diet were all influential factors in the development of H.pylori infection after intestinal polypectomy.Binary multifactorial logistic regression analysis showed that age,BMI,and type of polyp pathology were independent predictors of the occurrence of H.pylori infection after intestinal polypectomy.The area under the receiver operating characteristic curve was 0.969[95%confidence interval(95%CI):0.928–1.000]and 0.898(95%CI:0.773–1.000)in the modeling and validation sets,respectively.The slope of the calibration curve of the graph was close to 1,and the goodness-of-fit test was P>0.05 in the two sets.The decision analysis curve showed a high rate of return in both sets.The results of the correlation analysis between different pathological types and the occurrence of H.pylori infection after colon polyp surgery showed that hyperplastic polyps,inflammatory polyps,and the occurrence of H.pylori infection were not significantly correlated.In contrast,adenomatous polyps showed a significant positive correlation with the occurrence of H.pylori infection.CONCLUSION Age,BMI,and polyps of the adenomatous type were independent predictors of H.pylori infection after intestinal polypectomy.Moreover,the further constructed column-line graph prediction model of H.pylori infection after intestinal polypectomy showed good predictive ability.展开更多
Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq(scRNA-seq)data.Compared with the commonly used variance-based methods,by mimicking the human maker selection in the ...Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq(scRNA-seq)data.Compared with the commonly used variance-based methods,by mimicking the human maker selection in the 2D visualization of cells,a new feature selection method called HRG(Highly Regional Genes)is proposed to find the informative genes,which show regional expression patterns in the cell-cell similarity network.We mathematically find the optimal expression patterns that can maximize the proposed scoring function.In comparison with several unsupervised methods,HRG shows high accuracy and robustness,and can increase the performance of downstream cell clustering and gene correlation analysis.Also,it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.展开更多
Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points o...Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure.However,CV methods produce significantly more measurement errors.Thus,computer vision-based structural health monitoring(CVSHM)requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data.In this paper a complete CVSHM framework is proposed,and three damage assessment methods are tested.The first is the augmented inverse estimate(AIE),proposed by Peng et al.in 2021.This method is designed to work with highly contaminated measurement data,but it fails with a large noise provided by CV measurement.The second method,as proposed in this paper,is based on the AIE,but it introduces a weighting matrix that enhances the conditioning of the problem.The third method,also proposed in this paper,introduces additional constraints in the optimization process;these constraints ensure that the stiffness of structural elements can only decrease.Both proposed methods perform better than the original AIE.The latter of the two proposed methods gives the best results,and it is robust with respect to the selected coefficients,as required by the algorithm.展开更多
Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the I...Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.展开更多
The explosive growth of mobile data demand is becoming an increasing burden on current cellular network.To address this issue,we propose a solution of opportunistic data offloading for alleviating overloaded cellular ...The explosive growth of mobile data demand is becoming an increasing burden on current cellular network.To address this issue,we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic.The principle behind it is to select a few important users as seeds for data sharing.The three critical steps are detailed as follows.We first explore individual interests of users by the construction of user profiles,on which an interest graph is built by Gaussian graphical modeling.We then apply the extreme value theory to threshold the encounter duration of user pairs.So,a contact graph is generated to indicate the social relationships of users.Moreover,a contact-interest graph is developed on the basis of the social ties and individual interests of users.Corresponding on different graphs,three strategies are finally proposed for seed selection in an aim to maximize overloaded cellular data.We evaluate the performance of our algorithms by the trace data of real-word mobility.It demonstrates the effectiveness of the strategy of taking social relationships and individual interests into account.展开更多
Magnetic resonance imaging(MRI)is a clinically relevant,real-time imaging modality that is frequently utilized to assess stroke type and severity.However,specific MRI biomarkers that can be used to predict long-term f...Magnetic resonance imaging(MRI)is a clinically relevant,real-time imaging modality that is frequently utilized to assess stroke type and severity.However,specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need.Consequently,the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke.Stroke was induced via permanent middle cerebral artery occlusion.At 24 hours post-stroke,MRI analysis revealed focal ischemic lesions,decreased diffusivity,hemispheric swelling,and white matter degradation.Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke.Gaussian graphical models identified specific MRI outputs and functional recovery variables,including white matter integrity and gait performance,that exhibited strong conditional dependencies.Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance.Consequently,these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities(e.g.,white matter composition)that have proven to be critical in ischemic stroke pathophysiology.The study was approved by the University of Georgia(UGA)Institutional Animal Care and Use Committee(IACUC;Protocol Number:A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5)on November 22,2017.展开更多
In this article,we study different molecular structures such as Polythiophene network,PLY(n)for n=1,2,and 3,Orthosilicate(Nesosilicate)SiO4,Pyrosilicates(Sorosilicates)Si2O7,Chain silicates(Pyroxenes)(SiO3)n,and Cycli...In this article,we study different molecular structures such as Polythiophene network,PLY(n)for n=1,2,and 3,Orthosilicate(Nesosilicate)SiO4,Pyrosilicates(Sorosilicates)Si2O7,Chain silicates(Pyroxenes)(SiO3)n,and Cyclic silicates(Ring Silicates)Si3O9 for their cardinalities,chromatic numbers,graph variations,eigenvalues obtained from the adjacency matrices which are square matrices in order and their corresponding characteristics polynomials.We convert the general structures of these chemical networks in to mathematical graphical structures.We transform the molecular structures of these chemical networks which are mentioned above,into a simple and undirected planar graph and sketch them with various techniques of mathematics.The matrices obtained from these simple undirected graphs are symmetric.We also label the molecular structures by assigning different colors.Their graphs have also been studied for analysis.For a connected graph,the eigenvalue that shows its peak point(largest value)obtained from the adjacency matrix has multiplicity 1.Therefore,the gap between the largest and its smallest eigenvalues is interlinked with some form of“connectivity measurement of the structural graph”.We also note that the chemical structures,Orthosilicate(Nesosilicate)SiO4,Pyrosilicates(Sorosilicates)Si2O7,Chain silicates(Pyroxenes)(SiO3)n,and Cyclic silicates(Ring Silicates)Si3O9 generally have two same eigenvalues.Adjacency matrices have great importance in the field of computer science.展开更多
We propose a novel conditional graphical model -- spaceMap -- to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation o...We propose a novel conditional graphical model -- spaceMap -- to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations （CNAs） on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network, spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast （n = 77） and ovarian （n = 174） cancer studies. Each cancer exhibits disruption of＇ion transmembrane transport＇ and ＇regulation from RNA polymerase lI promoter＇ by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers.展开更多
In this article,we calculate various topological invariants such as symmetric division degree index,redefined Zagreb index,VL index,first and second exponential Zagreb index,first and second multiplicative exponential...In this article,we calculate various topological invariants such as symmetric division degree index,redefined Zagreb index,VL index,first and second exponential Zagreb index,first and second multiplicative exponential Zagreb indices,symmetric division degree entropy,redefined Zagreb entropy,VL entropy,first and second exponential Zagreb entropies,multiplicative exponential Zagreb entropy.We take the chemical compound named Proanthocyanidins,which is a very useful polyphenol in human’s diet.They are very beneficial for one’s health.These chemical compounds are extracted from grape seeds.They are tremendously anti-inflammatory.A subdivision formof this compound is presented in this article.The compound named subdivided grape seed Proanthocyanidins is abbreviated as SGSP_(3).This network SGSP_(3),is converted and modeled into its mathematical graphical formation with the support of the latest mathematical tools.We have also developed many closed formulas for the measurement of entropy for the general chemical structure of the subdivided grape seed Proanthocyanidins network.The achieved outcomes can be correlated with the chemical version of SGSP_(3) to get a better understanding of its biological as well as physical features.展开更多
Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network t...Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well.展开更多
Transcription factors(TFs)regulate cellular activities by controlling gene expression,but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking.We construct a universal human ge...Transcription factors(TFs)regulate cellular activities by controlling gene expression,but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking.We construct a universal human gene expression predictor named EXPLICIT-Human and utilize it to decode transcriptional regulation.Using the expression of 1613 TFs,the predictor reconstitutes highly accurate transcriptomes for samples derived from a wide range of tissues and conditions.The broad applicability of the predictor indicates that it recapitulates the quantitative relationships between TFs and target genes ubiquitous across tissues.Significant interacting TF-target gene pairs are extracted from the predictor and enable downstream inference of TF regulators for diverse pathways involved in development,immunity,metabolism,and stress response.A detailed analysis of the hematopoiesis process reveals an atlas of key TFs regulating the development of different hematopoietic cell lineages,and a portion of these TFs are conserved between humans and mice.The results demonstrate that our method is capable of delineating the TFs responsible for fate determination.Compared to other existing tools,EXPLICIT-Human shows a better performance in recovering the correct TF regulators.展开更多
Text summarization creates subset that represents the most important or relevant information in the original content,which effectively reduce information redundancy.Recently neural network method has achieved good res...Text summarization creates subset that represents the most important or relevant information in the original content,which effectively reduce information redundancy.Recently neural network method has achieved good results in the task of text summarization both in Chinese and English,but the research of text summarization in low-resource languages is still in the exploratory stage,especially in Tibetan.What’s more,there is no large-scale annotated corpus for text summarization.The lack of dataset severely limits the development of low-resource text summarization.In this case,unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data.In this paper,we propose an unsupervised graph-based Tibetan multi-document summarization method,which divides a large number of Tibetan news documents into topics and extracts the summarization of each topic.Summarization obtained by using traditional graph-based methods have high redundancy and the division of documents topics are not detailed enough.In terms of topic division,we adopt two level clustering methods converting original document into document-level and sentence-level graph,next we take both linguistic and deep representation into account and integrate external corpus into graph to obtain the sentence semantic clustering.Improve the shortcomings of the traditional K-Means clustering method and perform more detailed clustering of documents.Then model sentence clusters into graphs,finally remeasure sentence nodes based on the topic semantic information and the impact of topic features on sentences,higher topic relevance summary is extracted.In order to promote the development of Tibetan text summarization,and to meet the needs of relevant researchers for high-quality Tibetan text summarization datasets,this paper manually constructs a Tibetan summarization dataset and carries out relevant experiments.The experiment results show that our method can effectively improve the quality of summarization and our method is competitive to previous unsupervised methods.展开更多
文摘The development process of complex equipment involves multi-stage business processes,multi-level product architecture,and multi-disciplinary physical processes.The relationship between its system model and various disciplinary models is extremely complicated.In the modeling and integration process,extensive customized development is needed to realize model integration and interoperability in different business scenarios.Meanwhile,the differences in modeling and interaction between different modeling tools make it difficult to support the consistent representation of models in complex scenarios.To improve the efficiency of system modeling and integration in complex business scenarios,a system modeling and integration method was proposed.This method took the Sys ML language kernel as the core and system model function integration as the main line.Through the technical means of model view separation,abstract operation interface,and model view configuration,the model modeling and integration of multi-user,multi-model,multi-view,and different business logic in complex business scenarios were realized.
基金supported by the National Natural Science Foundation of China(Nos.61050005 and 61273330)Research Foundation for the Doctoral Program of China Ministry of Education(No.20120002110037)+1 种基金the 2014 Teaching Reform Project of Shandong Normal UniversityDevelopment Project of China Guangdong Nuclear Power Group(No.CNPRI-ST10P005)
文摘Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations.However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied "chaining" inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.
基金supported by the Science and Technology Project of China South Power Grid Co.,Ltd.,Grant Nos.036000KK52222044,GDKJXM20222430。
文摘The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.
基金supported by the National Natural Science Foundation of China(No.61871301)National Natural Science Foundation of Shaanxi Province,China(No.2018JQ6059)Postdoctoral Science Foundation of China(No.2018M633470)。
文摘The data association problem of multiple extended target tracking is very challenging because each target may generate multiple measurements.Recently,the belief propagation based multiple target tracking algorithms with high efficiency have been a research focus.Different from the belief propagation based Extended Target tracking based on Belief Propagation(ET-BP)algorithm proposed in our previous work,a new graphical model formulation of data association for multiple extended target tracking is proposed in this paper.The proposed formulation can be solved by the Loopy Belief Propagation(LBP)algorithm.Furthermore,the simplified measurement set in the ET-BP algorithm is modified to improve tracking accuracy.Finally,experiment results show that the proposed algorithm has better performance than the ET-BP and joint probabilistic data association based on the simplified measurement set algorithms in terms of accuracy and efficiency.Additionally,the convergence of the proposed algorithm is verified in the simulations.
基金supported by the National Basic Research Program of China(2015CB351806)the National Natural Science Foundation of China(61806011,61825101,61425025,and U1611461)+4 种基金the National Postdoctoral Program for Innovative Talents(BX20180005)the China Postdoctoral Science Foundation(2018M630036)the International Talent Exchange Program of Beijing Municipal Commission of Science and Technology(Z181100001018026)the Zhejiang Lab(2019KC0AB03 and 2019KC0AD02)the Royal Society Newton Advanced Fellowship(NAF-R1-191082).
文摘A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.
基金Financial support for this research was provided in part by the US Army Corps of Engineers through a subaward from the University of California,San Diego,USA。
文摘Computer vision-based inspection methods show promise for automating post-earthquake building inspections.These methods survey a building with unmanned aerial vehicles and automatically detect damage in the collected images.Nevertheless,assessing the damage′s impact on structural safety requires localizing damage to specific building components with known design and function.This paper proposes a BIM-based automated inspection framework to provide context for visual surveys.A deep learning-based semantic segmentation algorithm is trained to automatically identify damage in images.The BIM automatically associates any identified damage with specific building components.Then,components are classified into damage states consistent with component fragility models for integration with a structural analysis.To demonstrate the framework,methods are developed to photorealistically simulate severe structural damage in a synthetic computer graphics environment.A graphics model of a real building in Urbana,Illinois,is generated to test the framework;the model is integrated with a structural analysis to apply earthquake damage in a physically realistic manner.A simulated UAV survey is flown of the graphics model and the framework is applied.The method achieves high accuracy in assigning damage states to visible structural components.This assignment enables integration with a performance-based earthquake assessment to classify building safety.
基金supported by the National Major Scientific and Technological Special Project(No.2011ZX05029-003)the project of the Research Institute of Petroleum Exploration&Development(No.2012Y-058)
文摘Wave velocities in haloanhydrites are difficult to determine and significantly depend on the mineralogy. We used petrophysical parameters to study the wave velocity in haloanhydrites in the Amur Darya Basin and constructed a template of the relation between haloanhydrite mineralogy （anhydrite, salt, mudstone, and pore water） and wave velocities. We used the relation between the P-wave rnoduli ratio and porosity as constraint and constructed a graphical model （petrophysical template） for the relation between wave velocity, mineral content and porosity. We tested the graphical model using rock core and well logging data.
基金Supported by the National High Technology Research and Development Program of China(No.2012AA011005)
文摘Topic models such as Latent Dirichlet Allocation(LDA) have been successfully applied to many text mining tasks for extracting topics embedded in corpora. However, existing topic models generally cannot discover bursty topics that experience a sudden increase during a period of time. In this paper, we propose a new topic model named Burst-LDA, which simultaneously discovers topics and reveals their burstiness through explicitly modeling each topic's burst states with a first order Markov chain and using the chain to generate the topic proportion of documents in a Logistic Normal fashion. A Gibbs sampling algorithm is developed for the posterior inference of the proposed model. Experimental results on a news data set show our model can efficiently discover bursty topics, outperforming the state-of-the-art method.
文摘BACKGROUND Recently,research has linked Helicobacter pylori(H.pylori)stomach infection to colonic inflammation,mediated by toxin production,potentially impacting colorectal cancer occurrence.AIM To investigate the risk factors for post-colon polyp surgery,H.pylori infection,and its correlation with pathologic type.METHODS Eighty patients who underwent colon polypectomy in our hospital between January 2019 and January 2023 were retrospectively chosen.They were then randomly split into modeling(n=56)and model validation(n=24)sets using R.The modeling cohort was divided into an H.pylori-infected group(n=37)and an H.pylori-uninfected group(n=19).Binary logistic regression analysis was used to analyze the factors influencing the occurrence of H.pylori infection after colon polyp surgery.A roadmap prediction model was established and validated.Finally,the correlation between the different pathological types of colon polyps and the occurrence of H.pylori infection was analyzed after colon polyp surgery.RESULTS Univariate results showed that age,body mass index(BMI),literacy,alcohol consumption,polyp pathology type,high-risk adenomas,and heavy diet were all influential factors in the development of H.pylori infection after intestinal polypectomy.Binary multifactorial logistic regression analysis showed that age,BMI,and type of polyp pathology were independent predictors of the occurrence of H.pylori infection after intestinal polypectomy.The area under the receiver operating characteristic curve was 0.969[95%confidence interval(95%CI):0.928–1.000]and 0.898(95%CI:0.773–1.000)in the modeling and validation sets,respectively.The slope of the calibration curve of the graph was close to 1,and the goodness-of-fit test was P>0.05 in the two sets.The decision analysis curve showed a high rate of return in both sets.The results of the correlation analysis between different pathological types and the occurrence of H.pylori infection after colon polyp surgery showed that hyperplastic polyps,inflammatory polyps,and the occurrence of H.pylori infection were not significantly correlated.In contrast,adenomatous polyps showed a significant positive correlation with the occurrence of H.pylori infection.CONCLUSION Age,BMI,and polyps of the adenomatous type were independent predictors of H.pylori infection after intestinal polypectomy.Moreover,the further constructed column-line graph prediction model of H.pylori infection after intestinal polypectomy showed good predictive ability.
基金supported by the National Key Research and Development Program(2020YFA0712403,2020YFA0906900)National Natural Science Foundation of China(61922047,81890993,61721003,62133006)BNRIST Young Innovation Fund(BNR2020RC01009)。
文摘Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq(scRNA-seq)data.Compared with the commonly used variance-based methods,by mimicking the human maker selection in the 2D visualization of cells,a new feature selection method called HRG(Highly Regional Genes)is proposed to find the informative genes,which show regional expression patterns in the cell-cell similarity network.We mathematically find the optimal expression patterns that can maximize the proposed scoring function.In comparison with several unsupervised methods,HRG shows high accuracy and robustness,and can increase the performance of downstream cell clustering and gene correlation analysis.Also,it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.
基金National Science Centre,Poland Granted Through the Project 2020/39/B/ST8/02615。
文摘Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure.However,CV methods produce significantly more measurement errors.Thus,computer vision-based structural health monitoring(CVSHM)requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data.In this paper a complete CVSHM framework is proposed,and three damage assessment methods are tested.The first is the augmented inverse estimate(AIE),proposed by Peng et al.in 2021.This method is designed to work with highly contaminated measurement data,but it fails with a large noise provided by CV measurement.The second method,as proposed in this paper,is based on the AIE,but it introduces a weighting matrix that enhances the conditioning of the problem.The third method,also proposed in this paper,introduces additional constraints in the optimization process;these constraints ensure that the stiffness of structural elements can only decrease.Both proposed methods perform better than the original AIE.The latter of the two proposed methods gives the best results,and it is robust with respect to the selected coefficients,as required by the algorithm.
基金the Project"The Basic Research on Internet of Things Architecture"supported by National Key Basic Research Program of China(No.2011CB302704)supported by National Natural Science Foundation of China(No.60802034)+2 种基金Specialized Research Fund for the Doctoral Program of Higher Education(No.20070013026)Beijing Nova Program(No.2008B50)"New generation broadband wireless mobile communication network"Key Projects for Science and Technology Development(No.2011ZX03002-002-01)
文摘Sensors are ubiquitous in the Internet of Things for measuring and collecting data. Analyzing these data derived from sensors is an essential task and can reveal useful latent information besides the data. Since the Internet of Things contains many sorts of sensors, the measurement data collected by these sensors are multi-type data, sometimes contai- ning temporal series information. If we separately deal with different sorts of data, we will miss useful information. This paper proposes a method to dis- cover the correlation in multi-faceted data, which contains many types of data with temporal informa- tion, and our method can simultaneously deal with multi-faceted data. We transform high-dimensional multi-faeeted data into lower-dimensional data which is set as multivariate Gaussian Graphical Models, then mine the correlation in multi-faceted data by discover the structure of the multivariate Gausslan Graphical Models. With a real data set, we verifies our method, and the experiment demonstrates that the method we propose can correctly fred out the correlation among multi-faceted meas- urement data.
基金This work was supported in part by National Natural Science Foundation of China under Grant No.61502261,61572457,61379132Key Research and Development Plan Project of Shandong Province under Grant No.2016GGX101032+1 种基金Science,Technology Plan Project for Colleges and Universities of Shandong Province under Grant No.J14LN85the Natural Science Foundation of Shandong Province under Grant No.ZR2017PF013.
文摘The explosive growth of mobile data demand is becoming an increasing burden on current cellular network.To address this issue,we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic.The principle behind it is to select a few important users as seeds for data sharing.The three critical steps are detailed as follows.We first explore individual interests of users by the construction of user profiles,on which an interest graph is built by Gaussian graphical modeling.We then apply the extreme value theory to threshold the encounter duration of user pairs.So,a contact graph is generated to indicate the social relationships of users.Moreover,a contact-interest graph is developed on the basis of the social ties and individual interests of users.Corresponding on different graphs,three strategies are finally proposed for seed selection in an aim to maximize overloaded cellular data.We evaluate the performance of our algorithms by the trace data of real-word mobility.It demonstrates the effectiveness of the strategy of taking social relationships and individual interests into account.
基金This work was supported by the National Institutes of Health,National Institute of Neurological Disorders and Stroke grant R01NS093314 as well as Small Business Innovation Research grant 1R43NS103596-01.
文摘Magnetic resonance imaging(MRI)is a clinically relevant,real-time imaging modality that is frequently utilized to assess stroke type and severity.However,specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need.Consequently,the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke.Stroke was induced via permanent middle cerebral artery occlusion.At 24 hours post-stroke,MRI analysis revealed focal ischemic lesions,decreased diffusivity,hemispheric swelling,and white matter degradation.Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke.Gaussian graphical models identified specific MRI outputs and functional recovery variables,including white matter integrity and gait performance,that exhibited strong conditional dependencies.Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance.Consequently,these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities(e.g.,white matter composition)that have proven to be critical in ischemic stroke pathophysiology.The study was approved by the University of Georgia(UGA)Institutional Animal Care and Use Committee(IACUC;Protocol Number:A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5)on November 22,2017.
文摘In this article,we study different molecular structures such as Polythiophene network,PLY(n)for n=1,2,and 3,Orthosilicate(Nesosilicate)SiO4,Pyrosilicates(Sorosilicates)Si2O7,Chain silicates(Pyroxenes)(SiO3)n,and Cyclic silicates(Ring Silicates)Si3O9 for their cardinalities,chromatic numbers,graph variations,eigenvalues obtained from the adjacency matrices which are square matrices in order and their corresponding characteristics polynomials.We convert the general structures of these chemical networks in to mathematical graphical structures.We transform the molecular structures of these chemical networks which are mentioned above,into a simple and undirected planar graph and sketch them with various techniques of mathematics.The matrices obtained from these simple undirected graphs are symmetric.We also label the molecular structures by assigning different colors.Their graphs have also been studied for analysis.For a connected graph,the eigenvalue that shows its peak point(largest value)obtained from the adjacency matrix has multiplicity 1.Therefore,the gap between the largest and its smallest eigenvalues is interlinked with some form of“connectivity measurement of the structural graph”.We also note that the chemical structures,Orthosilicate(Nesosilicate)SiO4,Pyrosilicates(Sorosilicates)Si2O7,Chain silicates(Pyroxenes)(SiO3)n,and Cyclic silicates(Ring Silicates)Si3O9 generally have two same eigenvalues.Adjacency matrices have great importance in the field of computer science.
基金supported by the Floyd and Mary Schwall Fellowship in Medical Research and grants NIH R01-GM082802, R01-GM108711, R01-CA189532 and NSF DMS-1148643partly supported by grant U24 CA 210093, from the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC)
文摘We propose a novel conditional graphical model -- spaceMap -- to construct gene regulatory networks from multiple types of high dimensional omic profiles. A motivating application is to characterize the perturbation of DNA copy number alterations （CNAs） on downstream protein levels in tumors. Through a penalized multivariate regression framework, spaceMap jointly models high dimensional protein levels as responses and high dimensional CNAs as predictors. In this setup, spaceMap infers an undirected network among proteins together with a directed network encoding how CNAs perturb the protein network, spaceMap can be applied to learn other types of regulatory relationships from high dimensional molecular profiles, especially those exhibiting hub structures. Simulation studies show spaceMap has greater power in detecting regulatory relationships over competing methods. Additionally, spaceMap includes a network analysis toolkit for biological interpretation of inferred networks. We applies spaceMap to the CNAs, gene expression and proteomics data sets from CPTAC-TCGA breast （n = 77） and ovarian （n = 174） cancer studies. Each cancer exhibits disruption of＇ion transmembrane transport＇ and ＇regulation from RNA polymerase lI promoter＇ by CNA events unique to each cancer. Moreover, using protein levels as a response yields a more functionally-enriched network than using RNA expressions in both cancer types. The network results also help to pinpoint crucial cancer genes and provide insights on the functional consequences of important CNA in breast and ovarian cancers.
基金Under the sponsor of Unitéde Recherche Clinique Lariboisière St-Louis(URC)Assistance Publique-Hoitaux de Paris 200,rue du Fbg Saint-Denis 75010 Paris.
文摘In this article,we calculate various topological invariants such as symmetric division degree index,redefined Zagreb index,VL index,first and second exponential Zagreb index,first and second multiplicative exponential Zagreb indices,symmetric division degree entropy,redefined Zagreb entropy,VL entropy,first and second exponential Zagreb entropies,multiplicative exponential Zagreb entropy.We take the chemical compound named Proanthocyanidins,which is a very useful polyphenol in human’s diet.They are very beneficial for one’s health.These chemical compounds are extracted from grape seeds.They are tremendously anti-inflammatory.A subdivision formof this compound is presented in this article.The compound named subdivided grape seed Proanthocyanidins is abbreviated as SGSP_(3).This network SGSP_(3),is converted and modeled into its mathematical graphical formation with the support of the latest mathematical tools.We have also developed many closed formulas for the measurement of entropy for the general chemical structure of the subdivided grape seed Proanthocyanidins network.The achieved outcomes can be correlated with the chemical version of SGSP_(3) to get a better understanding of its biological as well as physical features.
基金This work was supported by the National Natural Science Foundation of China(No.60574075) and by Natural Science Foundation of ShaanxiProvince(No.2005A07).
文摘Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining. The experimental results show that the exact theoretical results and the approximations match very well.
基金supported by grants from the National Natural Science Foundation of China(31770268)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA24010303)+1 种基金the Fundamental Research Funds for the Central Universities(WK2070000091)University of Science and Technology of China(Start-up fund to S.M.).
文摘Transcription factors(TFs)regulate cellular activities by controlling gene expression,but a predictive model describing how TFs quantitatively modulate human transcriptomes is lacking.We construct a universal human gene expression predictor named EXPLICIT-Human and utilize it to decode transcriptional regulation.Using the expression of 1613 TFs,the predictor reconstitutes highly accurate transcriptomes for samples derived from a wide range of tissues and conditions.The broad applicability of the predictor indicates that it recapitulates the quantitative relationships between TFs and target genes ubiquitous across tissues.Significant interacting TF-target gene pairs are extracted from the predictor and enable downstream inference of TF regulators for diverse pathways involved in development,immunity,metabolism,and stress response.A detailed analysis of the hematopoiesis process reveals an atlas of key TFs regulating the development of different hematopoietic cell lineages,and a portion of these TFs are conserved between humans and mice.The results demonstrate that our method is capable of delineating the TFs responsible for fate determination.Compared to other existing tools,EXPLICIT-Human shows a better performance in recovering the correct TF regulators.
基金This work was supported in part by the National Science Foundation Project of P.R.China 484 under Grant No.52071349partially supported by Young and Middle-aged Talents Project of the State Ethnic Affairs 487 Commission.
文摘Text summarization creates subset that represents the most important or relevant information in the original content,which effectively reduce information redundancy.Recently neural network method has achieved good results in the task of text summarization both in Chinese and English,but the research of text summarization in low-resource languages is still in the exploratory stage,especially in Tibetan.What’s more,there is no large-scale annotated corpus for text summarization.The lack of dataset severely limits the development of low-resource text summarization.In this case,unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data.In this paper,we propose an unsupervised graph-based Tibetan multi-document summarization method,which divides a large number of Tibetan news documents into topics and extracts the summarization of each topic.Summarization obtained by using traditional graph-based methods have high redundancy and the division of documents topics are not detailed enough.In terms of topic division,we adopt two level clustering methods converting original document into document-level and sentence-level graph,next we take both linguistic and deep representation into account and integrate external corpus into graph to obtain the sentence semantic clustering.Improve the shortcomings of the traditional K-Means clustering method and perform more detailed clustering of documents.Then model sentence clusters into graphs,finally remeasure sentence nodes based on the topic semantic information and the impact of topic features on sentences,higher topic relevance summary is extracted.In order to promote the development of Tibetan text summarization,and to meet the needs of relevant researchers for high-quality Tibetan text summarization datasets,this paper manually constructs a Tibetan summarization dataset and carries out relevant experiments.The experiment results show that our method can effectively improve the quality of summarization and our method is competitive to previous unsupervised methods.
