Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an eff...Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.展开更多
DNAN-based insensitive melt-cast explosives have been widely utilized in insensitive munition in recent years. When constrained DNAN-based melt-cast explosives are ignited under thermal stimulation, the base explosive...DNAN-based insensitive melt-cast explosives have been widely utilized in insensitive munition in recent years. When constrained DNAN-based melt-cast explosives are ignited under thermal stimulation, the base explosive exists in a molten liquid state, where high-temperature gases expand and react in the form of bubble clouds within the liquid explosive;this process is distinctly different from the dynamic crack propagation process observed in the case of solid explosives. In this study, a control model for the reaction evolution of burning-bubble clouds was established to describe the reaction process and quantify the reaction violence of DNAN-based melt-cast explosives, considering the size distribution and activation mechanism of the burning-bubble clouds. The feasibility of the model was verified through experimental results. The results revealed that under geometrically similar conditions, with identical confinement strength and aspect ratio, larger charge structures led to extended initial gas flow and surface burning processes, resulting in greater reaction equivalence and violence at the casing fracture.Under constant charge volume and size, a stronger casing confinement accelerated self-enhanced burning, increasing the internal pressure, reaction degree, and reaction violence. Under a constant casing thickness and radius, higher aspect ratios led to a greater reaction violence at the casing fracture.Moreover, under a constant charge volume and casing thickness, higher aspect ratios resulted in a higher internal pressure, increased reaction degree, and greater reaction violence at the casing fracture. Further,larger ullage volumes extended the reaction evolution time and increased the reaction violence under constant casing dimensions. Through a matching design of the opening threshold of the pressure relief holes and the relief structure area, a stable burning reaction could be maintained until completion,thereby achieving a control of the reaction violence. The proposed model could effectively reflect the effects of the intrinsic burning rate, casing confinement strength, charge size, ullage volume, and pressure relief structure on the reaction evolution process and reaction violence, providing a theoretical method for the thermal safety design and reaction violence evaluation of melt-cast explosives.展开更多
Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be so...Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process(AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.展开更多
The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling ...The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling efficiency of underground engineering,a modularized and parametric modeling cloud server is developed by using Python codes.The basic framework of the cloud server is as follows:input the modeling parameters into the web platform,implement Rhino software and FLAC3D software to model and run simulations in the cloud server,and return the simulation results to the web platform.The modeling program can automatically generate instructions that can run the modeling process in Rhino based on the input modeling parameters.The main modules of the modeling program include modeling the 3D geological structures,the underground engineering structures,and the supporting structures as well as meshing the geometric models.In particular,various cross-sections of underground caverns are crafted as parametricmodules in themodeling program.Themodularized and parametric modeling program is used for a finite element simulation of the underground powerhouse of the Shuangjiangkou Hydropower Station.This complicatedmodel is rapidly generated for the simulation,and the simulation results are reasonable.Thus,this modularized and parametric modeling program is applicable for three-dimensional finite element simulations and analyses.展开更多
To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage p...To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.展开更多
Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable c...Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.展开更多
The Water Cloud Model(WCM)plays a crucial role in active microwave soil moisture inversion applications.Empirical parameters are important factors affecting the accuracy of WCM simulation,but the current evaluation of...The Water Cloud Model(WCM)plays a crucial role in active microwave soil moisture inversion applications.Empirical parameters are important factors affecting the accuracy of WCM simulation,but the current evaluation of empirical parameters only considers the forward simulation process,and insufficient consideration is given to the model inversion problem.This study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective function.The effectiveness of the method is then verified using measured data.Simultaneously,this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM,resulting in the following conclusions.First,blindly utilizing vegetation parameters recommended by previous model studies is not advisable.To ensure the accuracy of the simulation,it is necessary to adjust the vegetation parameters appropriately.Second,to ensure the ability of the WCM solving both forward and inverse problems,it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost function.Third,soil backscatter simulations have an impact on the solution of vegetation parameters,and more accurate soil scattering models provide a better representation of the modeled vegetation.This study presents a dependable method for resolving the vegetation parameters of the WCM,thereby offering a valuable reference for the application of the model in surface parameter inversion research.展开更多
Cloud Computing is an uprising technology in the rapid growing IT world. The adaptation of cloud computing is increasing in very large scale business organizations to small institutions rapidly due to many advanced fe...Cloud Computing is an uprising technology in the rapid growing IT world. The adaptation of cloud computing is increasing in very large scale business organizations to small institutions rapidly due to many advanced features of cloud computing, such as SaaS, PaaS and IaaS service models. So, nowadays, many organizations are trying to implement Cloud Computing based ERP system to enjoy the benefits of cloud computing. To implement any ERP system, an organization usually faces many challenges. As a result, this research has introduced how easily this cloud system can be implemented in an organization. By using this ERP system, an organization can be benefited in many ways;especially Small and Medium Enterprises (SMEs) can enjoy the highest possible benefits from this system.展开更多
The method of cloud model with entropy weight was adopted for the prediction of rock burst classification. Some main factors of rock burst including the uniaxial compressive strength （σc）, the tensile strength （σ...The method of cloud model with entropy weight was adopted for the prediction of rock burst classification. Some main factors of rock burst including the uniaxial compressive strength （σc）, the tensile strength （σt）, the tangential stress （σθ）, the rock brittleness coefficient （σc/σt）, the stress coefficient （σθ /σc） and the elastic energy index （Wet） are chosen to establish evaluation index system. The entropy？cloud model and criterion are obtained through 209 sets of rock burst samples from underground rock projects. The sensitivity of indicators is analyzed and 209 sets of rock burst samples are discriminated by this model. The discriminant results of the entropy-cloud model are compared with those of Bayes, KNN and RF methods. The results show that the sensitivity order of those factors from high to low is σ_θ /σ_c, σ_θ, W_（ct）, σ_c/σ_t, σ_t, σ_c, and the entropy-cloud model has higher accuracy than Bayes, K-Nearest Neighbor algorithm （KNN） and Random Forest （RF） methods.展开更多
Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traf...Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.展开更多
In order to reduce amount of data storage and improve processing capacity of the system, this paper proposes a new classification method of data source by combining phase synchronization model in network clusteri...In order to reduce amount of data storage and improve processing capacity of the system, this paper proposes a new classification method of data source by combining phase synchronization model in network clustering with cloud model. Firstly, taking data source as a complex network, after the topography of network is obtained, the cloud model of each node data is determined by fuzzy analytic hierarchy process （AHP）. Secondly, by calculating expectation, entropy and hyper entropy of the cloud model, comprehensive coupling strength is got and then it is regarded as the edge weight of topography. Finally, distribution curve is obtained by iterating the phase of each node by means of phase synchronization model. Thus classification of data source is completed. This method can not only provide convenience for storage, cleaning and compression of data, but also improve the efficiency of data analysis.展开更多
Corneal topography serves as an essential reference for diagnostic treatment in ophthalmology.Accurate corneal topography is crucial for clinical practice.In this study,the refractive power calculation was performed b...Corneal topography serves as an essential reference for diagnostic treatment in ophthalmology.Accurate corneal topography is crucial for clinical practice.In this study,the refractive power calculation was performed based on the initial corneal information collected using the Placido disc.A corneal point cloud model was established in polar coordinates,and an interpolation algorithm was proposed to fill missing points of the local bicubic B-spline by searching control points in the selfdefined interpolation matrix.The grid interpolation of the point cloud information and the smooth imaging of the final topographic map were achieved by Delaunay triangulation and Gaussian kernel function smoothing.Experiment results show that the proposed interpolation algorithm has higher accuracy than previous algorithms.The mean absolute error between the measured diopter of the original detection and the reconstructed is less than 0.300 D,indicating that this algorithm is feasible.展开更多
The stability of the tunnel portal slope is crucial for ensuring safe tunnel construction.Thus,a sound stability evaluation is of significance.Given the unique geological characteristics of tunnel portal slopes,it is ...The stability of the tunnel portal slope is crucial for ensuring safe tunnel construction.Thus,a sound stability evaluation is of significance.Given the unique geological characteristics of tunnel portal slopes,it is necessary to establish a specific evaluation indicator system that differs from those used for ordinary slopes.Based on the unascertained measure method,uncertainties in the indicator are addressed by introducing the left and right half cloud asymmetric cloud model to optimize the linear membership function.The subjectivity of confidence criterion level identification is also improved by using the Euclidean distance method.Thus,a stability evaluation model for the tunnel portal slope is established based on the improved unascertained measure method.Finally,using the collected tunnel portal slope data,the results of four evaluation methods are compared with the safety factor levels.The evaluation methods include the traditional unascertained measure method,the method improved by using the left and right half cloud asymmetric cloud model,the method improved by using the Euclidean distance method,and the method improved by using both the left and right half cloud asymmetric cloud model and the Euclidean distance method.The results show that the accuracy rates of these four methods are 50%,55%,85%,and 90%,respectively.Among them,the joint improvement method has the slightest deviation,with only one level,while the other three methods had deviations of two levels.This result verifies the stability and effectiveness of the joint improvement method,providing a reference for tunnel portal slope stability evaluation.展开更多
As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid ...As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.展开更多
The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain facto...The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.展开更多
In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neura...In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.展开更多
With a focus on the difficulty of quantitatively describing the degree of nonuniformity of temporal and spatial distributions of water resources, quantitative research was carried out on the temporal and spatial distr...With a focus on the difficulty of quantitatively describing the degree of nonuniformity of temporal and spatial distributions of water resources, quantitative research was carried out on the temporal and spatial distribution characteristics of water resources in Guangdong Province from 1956 to 2000 based on a cloud model. The spatial variation of the temporal distribution characteristics and the temporal variation of the spatial distribution characteristics were both analyzed. In addition, the relationships between the numerical characteristics of the cloud model of temporal and spatial distributions of water resources and precipitation were also studied. The results show that, using a cloud model, it is possible to intuitively describe the temporal and spatial distribution characteristics of water resources in cloud images. Water resources in Guangdong Province and their temporal and spatial distribution characteristics are differentiated by their geographic locations. Downstream and coastal areas have a larger amount of water resources with greater uniformity and stronger stability in terms of temporal distribution. Regions with more precipitation possess larger amounts of water resources, and years with more precipitation show greater nonuniformity in the spatial distribution of water resources. The correlation between the nonuniformity of the temporal distribution and local precipitation is small, and no correlation is found between the stability of the nonuniformity of the temporal and spatial distributions of water resources and precipitation. The amount of water resources in Guangdong Province shows an increasing trend from 1956 to 2000, the nonuniformity of the spatial distribution of water resources declines, and the stability of the nonuniformity of the spatial distribution of water resources is enhanced.展开更多
Uncertainties existing in the process of dam deformation negatively influence deformation prediction.However,existing deformation pre-diction models seldom consider uncertainties.In this study,a cloud-Verhulst hybrid ...Uncertainties existing in the process of dam deformation negatively influence deformation prediction.However,existing deformation pre-diction models seldom consider uncertainties.In this study,a cloud-Verhulst hybrid prediction model was established by combing a cloud model with the Verhulst model.The expectation,one of the cloud characteristic parameters,was obtained using the Verhulst model,and the other two cloud characteristic parameters,entropy and hyper-entropy,were calculated by introducing inertia weight.The hybrid prediction model was used to predict the dam deformation in a hydroelectric project.Comparison of the prediction results of the hybrid prediction model with those of a traditional statistical model and the monitoring values shows that the proposed model has higher prediction accuracy than the traditional sta-tistical model.It provides a new approach to predicting dam deformation under uncertain conditions.展开更多
Recommender system is an important content in the research of E-commerce technology. Collaborative filtering recom-mendation algorithm has already been used successfully at recom-mender system. However,with the develo...Recommender system is an important content in the research of E-commerce technology. Collaborative filtering recom-mendation algorithm has already been used successfully at recom-mender system. However,with the development of E-commerce,the difficulties of the extreme sparsity of user rating data have become more and more severe. Based on the traditional similarity measuring methods,we introduce the cloud model and combine it with the item-based collaborative filtering recommendation algorithms. The new collaborative filtering recommendation algorithm based on item and cloud model （IC-Based CF） computes the similarity de-gree between items by comparing the statistical characteristic of items. The experimental results show that this method can improve the performance of the present item-based collaborative filtering algorithm with extreme sparsity of data.展开更多
Ventilation system is significant in underground metal mine of alpine region.Reasonable evaluation of ventilation effectiveness will lead to a practical improvement for the maintenance and management of ventilation sy...Ventilation system is significant in underground metal mine of alpine region.Reasonable evaluation of ventilation effectiveness will lead to a practical improvement for the maintenance and management of ventilation system.However,it is difficult to make an effective evaluation of ventilation system due to the lack of classification criteria with respect to underground metal mine in alpine region.This paper proposes a novel evaluation method called the cloud model-clustering analysis(CMCA).Cloud model(CM)is utilized to process collected data of ventilation system,and they are converted into cloud descriptors by CM.Cloud similarity(CS)based Euclidean distance(ED)is proposed to make clustering analysis of assessed samples.Then the classification of assessed samples will be identified by clustering analysis results.A case study is developed based on CMCA.Evaluation results show that ventilation effectiveness can be well classified.Moreover,CM is used alone to make comparison of evaluation results obtained by CMCA.Then the availability and validity of CMCA is verified.Meanwhile,difference of CS based ED and classical ED is analyzed.Two new clustering analysis methods are introduced to make comparison with CMCA.Then the ability of proposed CMCA to meet evaluation requirements of ventilation system is verified.展开更多
基金the National Key R&D Program of China(2017YFB1002702).
文摘Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.
基金supported by the National Natural Science Foundation of China (Grant No. 12002044)。
文摘DNAN-based insensitive melt-cast explosives have been widely utilized in insensitive munition in recent years. When constrained DNAN-based melt-cast explosives are ignited under thermal stimulation, the base explosive exists in a molten liquid state, where high-temperature gases expand and react in the form of bubble clouds within the liquid explosive;this process is distinctly different from the dynamic crack propagation process observed in the case of solid explosives. In this study, a control model for the reaction evolution of burning-bubble clouds was established to describe the reaction process and quantify the reaction violence of DNAN-based melt-cast explosives, considering the size distribution and activation mechanism of the burning-bubble clouds. The feasibility of the model was verified through experimental results. The results revealed that under geometrically similar conditions, with identical confinement strength and aspect ratio, larger charge structures led to extended initial gas flow and surface burning processes, resulting in greater reaction equivalence and violence at the casing fracture.Under constant charge volume and size, a stronger casing confinement accelerated self-enhanced burning, increasing the internal pressure, reaction degree, and reaction violence. Under a constant casing thickness and radius, higher aspect ratios led to a greater reaction violence at the casing fracture.Moreover, under a constant charge volume and casing thickness, higher aspect ratios resulted in a higher internal pressure, increased reaction degree, and greater reaction violence at the casing fracture. Further,larger ullage volumes extended the reaction evolution time and increased the reaction violence under constant casing dimensions. Through a matching design of the opening threshold of the pressure relief holes and the relief structure area, a stable burning reaction could be maintained until completion,thereby achieving a control of the reaction violence. The proposed model could effectively reflect the effects of the intrinsic burning rate, casing confinement strength, charge size, ullage volume, and pressure relief structure on the reaction evolution process and reaction violence, providing a theoretical method for the thermal safety design and reaction violence evaluation of melt-cast explosives.
基金Heilongjiang Provincial Natural Science Foundation of China (LH2021F009)。
文摘Anti-jamming performance evaluation has recently received significant attention. For Link-16, the anti-jamming performance evaluation and selection of the optimal anti-jamming technologies are urgent problems to be solved. A comprehensive evaluation method is proposed, which combines grey relational analysis (GRA) and cloud model, to evaluate the anti-jamming performances of Link-16. Firstly, on the basis of establishing the anti-jamming performance evaluation indicator system of Link-16, the linear combination of analytic hierarchy process(AHP) and entropy weight method (EWM) are used to calculate the combined weight. Secondly, the qualitative and quantitative concept transformation model, i.e., the cloud model, is introduced to evaluate the anti-jamming abilities of Link-16 under each jamming scheme. In addition, GRA calculates the correlation degree between evaluation indicators and the anti-jamming performance of Link-16, and assesses the best anti-jamming technology. Finally, simulation results prove that the proposed evaluation model can achieve the objective of feasible and practical evaluation, which opens up a novel way for the research of anti-jamming performance evaluations of Link-16.
基金The Construction S&T Project of the Department of Transportation of Sichuan Province(Grant No.2023A02)the National Natural Science Foundation of China(No.52109135).
文摘The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling efficiency of underground engineering,a modularized and parametric modeling cloud server is developed by using Python codes.The basic framework of the cloud server is as follows:input the modeling parameters into the web platform,implement Rhino software and FLAC3D software to model and run simulations in the cloud server,and return the simulation results to the web platform.The modeling program can automatically generate instructions that can run the modeling process in Rhino based on the input modeling parameters.The main modules of the modeling program include modeling the 3D geological structures,the underground engineering structures,and the supporting structures as well as meshing the geometric models.In particular,various cross-sections of underground caverns are crafted as parametricmodules in themodeling program.Themodularized and parametric modeling program is used for a finite element simulation of the underground powerhouse of the Shuangjiangkou Hydropower Station.This complicatedmodel is rapidly generated for the simulation,and the simulation results are reasonable.Thus,this modularized and parametric modeling program is applicable for three-dimensional finite element simulations and analyses.
基金supported by National Natural Science Foundation of China(Grant No.62073256)the Shaanxi Provincial Science and Technology Department(Grant No.2023-YBGY-342).
文摘To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.
基金National Natural Science Foundation of China under Grant 62203468Technological Research and Development Program of China State Railway Group Co.,Ltd.under Grant J2023G007+2 种基金Young Elite Scientist Sponsorship Program by China Association for Science and Technology(CAST)under Grant 2022QNRC001Youth Talent Program Supported by China Railway SocietyResearch Program of Beijing Hua-Tie Information Technology Corporation Limited under Grant 2023HT02.
文摘Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.
基金National Natural Science Foundation of China,Grant/Award Number:51779269。
文摘The Water Cloud Model(WCM)plays a crucial role in active microwave soil moisture inversion applications.Empirical parameters are important factors affecting the accuracy of WCM simulation,but the current evaluation of empirical parameters only considers the forward simulation process,and insufficient consideration is given to the model inversion problem.This study proposes a new estimation method for vegetation parameters in the WCM by combining the soil backscattering model and the objective function.The effectiveness of the method is then verified using measured data.Simultaneously,this study also analyzes the factors influencing the evaluation of vegetation parameters in the WCM,resulting in the following conclusions.First,blindly utilizing vegetation parameters recommended by previous model studies is not advisable.To ensure the accuracy of the simulation,it is necessary to adjust the vegetation parameters appropriately.Second,to ensure the ability of the WCM solving both forward and inverse problems,it is advisable to consider both soil backscatter and surface backscatter simulations in the construction of the cost function.Third,soil backscatter simulations have an impact on the solution of vegetation parameters,and more accurate soil scattering models provide a better representation of the modeled vegetation.This study presents a dependable method for resolving the vegetation parameters of the WCM,thereby offering a valuable reference for the application of the model in surface parameter inversion research.
文摘Cloud Computing is an uprising technology in the rapid growing IT world. The adaptation of cloud computing is increasing in very large scale business organizations to small institutions rapidly due to many advanced features of cloud computing, such as SaaS, PaaS and IaaS service models. So, nowadays, many organizations are trying to implement Cloud Computing based ERP system to enjoy the benefits of cloud computing. To implement any ERP system, an organization usually faces many challenges. As a result, this research has introduced how easily this cloud system can be implemented in an organization. By using this ERP system, an organization can be benefited in many ways;especially Small and Medium Enterprises (SMEs) can enjoy the highest possible benefits from this system.
基金Projects(51474252,51274253)supported by the National Natural Science Foundation of ChinaProject(2015CX005)supported by the Innovation Driven Plan of Central South University,ChinaProject(2016zzts095)supported by the Fundamental Research Funds for the Central Universities,China
文摘The method of cloud model with entropy weight was adopted for the prediction of rock burst classification. Some main factors of rock burst including the uniaxial compressive strength （σc）, the tensile strength （σt）, the tangential stress （σθ）, the rock brittleness coefficient （σc/σt）, the stress coefficient （σθ /σc） and the elastic energy index （Wet） are chosen to establish evaluation index system. The entropy？cloud model and criterion are obtained through 209 sets of rock burst samples from underground rock projects. The sensitivity of indicators is analyzed and 209 sets of rock burst samples are discriminated by this model. The discriminant results of the entropy-cloud model are compared with those of Bayes, KNN and RF methods. The results show that the sensitivity order of those factors from high to low is σ_θ /σ_c, σ_θ, W_（ct）, σ_c/σ_t, σ_t, σ_c, and the entropy-cloud model has higher accuracy than Bayes, K-Nearest Neighbor algorithm （KNN） and Random Forest （RF） methods.
基金The National Natural Science Foundation of China(No.71101014,50679008)Specialized Research Fund for the Doctoral Program of Higher Education(No.200801411105)the Science and Technology Project of the Department of Communications of Henan Province(No.2010D107-4)
文摘Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.
基金National Natural Science Foundation of China(No.61171057,No.61503345)Science Foundation for North University of China(No.110246)+1 种基金Specialized Research Fund for Doctoral Program of Higher Education of China(No.20121420110004)International Office of Shanxi Province Education Department of China,and Basic Research Project in Shanxi Province(Young Foundation)
文摘In order to reduce amount of data storage and improve processing capacity of the system, this paper proposes a new classification method of data source by combining phase synchronization model in network clustering with cloud model. Firstly, taking data source as a complex network, after the topography of network is obtained, the cloud model of each node data is determined by fuzzy analytic hierarchy process （AHP）. Secondly, by calculating expectation, entropy and hyper entropy of the cloud model, comprehensive coupling strength is got and then it is regarded as the edge weight of topography. Finally, distribution curve is obtained by iterating the phase of each node by means of phase synchronization model. Thus classification of data source is completed. This method can not only provide convenience for storage, cleaning and compression of data, but also improve the efficiency of data analysis.
基金Shanghai Science and Technology Program,China (No.20DZ2251400)。
文摘Corneal topography serves as an essential reference for diagnostic treatment in ophthalmology.Accurate corneal topography is crucial for clinical practice.In this study,the refractive power calculation was performed based on the initial corneal information collected using the Placido disc.A corneal point cloud model was established in polar coordinates,and an interpolation algorithm was proposed to fill missing points of the local bicubic B-spline by searching control points in the selfdefined interpolation matrix.The grid interpolation of the point cloud information and the smooth imaging of the final topographic map were achieved by Delaunay triangulation and Gaussian kernel function smoothing.Experiment results show that the proposed interpolation algorithm has higher accuracy than previous algorithms.The mean absolute error between the measured diopter of the original detection and the reconstructed is less than 0.300 D,indicating that this algorithm is feasible.
基金supported by the National Natural Science Foundation of China(Grant No.42377191,42072300)“The 14th Five Year Plan”Hubei Provincial advantaged characteristic disciplines(groups)project of Wuhan University of Science and Technology(Grant No.2023A0303).
文摘The stability of the tunnel portal slope is crucial for ensuring safe tunnel construction.Thus,a sound stability evaluation is of significance.Given the unique geological characteristics of tunnel portal slopes,it is necessary to establish a specific evaluation indicator system that differs from those used for ordinary slopes.Based on the unascertained measure method,uncertainties in the indicator are addressed by introducing the left and right half cloud asymmetric cloud model to optimize the linear membership function.The subjectivity of confidence criterion level identification is also improved by using the Euclidean distance method.Thus,a stability evaluation model for the tunnel portal slope is established based on the improved unascertained measure method.Finally,using the collected tunnel portal slope data,the results of four evaluation methods are compared with the safety factor levels.The evaluation methods include the traditional unascertained measure method,the method improved by using the left and right half cloud asymmetric cloud model,the method improved by using the Euclidean distance method,and the method improved by using both the left and right half cloud asymmetric cloud model and the Euclidean distance method.The results show that the accuracy rates of these four methods are 50%,55%,85%,and 90%,respectively.Among them,the joint improvement method has the slightest deviation,with only one level,while the other three methods had deviations of two levels.This result verifies the stability and effectiveness of the joint improvement method,providing a reference for tunnel portal slope stability evaluation.
基金supported by the Specialized Research Fund for the Doctoral Program of Higher Education(20114307120032)the National Natural Science Foundation of China(71201167)
文摘As for the drop of particle diversity and the slow convergent speed of particle in the late evolution period when particle swarm optimization（PSO） is applied to solve high-dimensional multi-modal functions,a hybrid optimization algorithm based on the cat mapping,the cloud model and PSO is proposed.While the PSO algorithm evolves a certain of generations,this algorithm applies the cat mapping to implement global disturbance of the poorer individuals,and employs the cloud model to execute local search of the better individuals;accordingly,the obtained best individuals form a new swarm.For this new swarm,the evolution operation is maintained with the PSO algorithm,using the parameter of pop distr to balance the global and local search capacity of the algorithm,as well as,adopting the parameter of mix gen to control mixing times of the algorithm.The comparative analysis is carried out on the basis of 4 functions and other algorithms.It indicates that this algorithm shows faster convergent speed and better solving precision for solving functions particularly those high-dimensional multi-modal functions.Finally,the suggested values are proposed for parameters pop distr and mix gen applied to different dimension functions via the comparative analysis of parameters.
文摘The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.
基金Sponsored by National High-tech Research and Development Project of China(2009AA04Z143)Natural Science Foundation of Hebei Province of China(E2006001038)Science and Technology Project of Hebei Province of China(10212101D)
文摘In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.
基金supported by the National Science and Technology Major Project of Water Pollution Control and Treatment(Grants No.2014ZX07405002,2012ZX07506007,2012ZX07506006,and 2012ZX07506002)the Natural Science Foundation of the Anhui Higher Education Institutions of China(Grant No.KJ2016A868)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘With a focus on the difficulty of quantitatively describing the degree of nonuniformity of temporal and spatial distributions of water resources, quantitative research was carried out on the temporal and spatial distribution characteristics of water resources in Guangdong Province from 1956 to 2000 based on a cloud model. The spatial variation of the temporal distribution characteristics and the temporal variation of the spatial distribution characteristics were both analyzed. In addition, the relationships between the numerical characteristics of the cloud model of temporal and spatial distributions of water resources and precipitation were also studied. The results show that, using a cloud model, it is possible to intuitively describe the temporal and spatial distribution characteristics of water resources in cloud images. Water resources in Guangdong Province and their temporal and spatial distribution characteristics are differentiated by their geographic locations. Downstream and coastal areas have a larger amount of water resources with greater uniformity and stronger stability in terms of temporal distribution. Regions with more precipitation possess larger amounts of water resources, and years with more precipitation show greater nonuniformity in the spatial distribution of water resources. The correlation between the nonuniformity of the temporal distribution and local precipitation is small, and no correlation is found between the stability of the nonuniformity of the temporal and spatial distributions of water resources and precipitation. The amount of water resources in Guangdong Province shows an increasing trend from 1956 to 2000, the nonuniformity of the spatial distribution of water resources declines, and the stability of the nonuniformity of the spatial distribution of water resources is enhanced.
基金supported by the National Natural Science Foundation of China(Grant No.51379162)the Water Conservancy Science and Technology Innovation Project of Guangdong Province(Grant No.2016-06)
文摘Uncertainties existing in the process of dam deformation negatively influence deformation prediction.However,existing deformation pre-diction models seldom consider uncertainties.In this study,a cloud-Verhulst hybrid prediction model was established by combing a cloud model with the Verhulst model.The expectation,one of the cloud characteristic parameters,was obtained using the Verhulst model,and the other two cloud characteristic parameters,entropy and hyper-entropy,were calculated by introducing inertia weight.The hybrid prediction model was used to predict the dam deformation in a hydroelectric project.Comparison of the prediction results of the hybrid prediction model with those of a traditional statistical model and the monitoring values shows that the proposed model has higher prediction accuracy than the traditional sta-tistical model.It provides a new approach to predicting dam deformation under uncertain conditions.
基金Supported by the National Basic Research Program of China (973 Program) (2006CB701305, 2007CB310804)the National Natural Science Foundation of China (60743001)+1 种基金Best National Thesis Fund (2005047)the Natural Science Foundation of Hubei Province (CDB132, 2010j0049)
文摘Recommender system is an important content in the research of E-commerce technology. Collaborative filtering recom-mendation algorithm has already been used successfully at recom-mender system. However,with the development of E-commerce,the difficulties of the extreme sparsity of user rating data have become more and more severe. Based on the traditional similarity measuring methods,we introduce the cloud model and combine it with the item-based collaborative filtering recommendation algorithms. The new collaborative filtering recommendation algorithm based on item and cloud model （IC-Based CF） computes the similarity de-gree between items by comparing the statistical characteristic of items. The experimental results show that this method can improve the performance of the present item-based collaborative filtering algorithm with extreme sparsity of data.
基金Project(2018YFC0808404)supported by National Key Research and Development Program of China。
文摘Ventilation system is significant in underground metal mine of alpine region.Reasonable evaluation of ventilation effectiveness will lead to a practical improvement for the maintenance and management of ventilation system.However,it is difficult to make an effective evaluation of ventilation system due to the lack of classification criteria with respect to underground metal mine in alpine region.This paper proposes a novel evaluation method called the cloud model-clustering analysis(CMCA).Cloud model(CM)is utilized to process collected data of ventilation system,and they are converted into cloud descriptors by CM.Cloud similarity(CS)based Euclidean distance(ED)is proposed to make clustering analysis of assessed samples.Then the classification of assessed samples will be identified by clustering analysis results.A case study is developed based on CMCA.Evaluation results show that ventilation effectiveness can be well classified.Moreover,CM is used alone to make comparison of evaluation results obtained by CMCA.Then the availability and validity of CMCA is verified.Meanwhile,difference of CS based ED and classical ED is analyzed.Two new clustering analysis methods are introduced to make comparison with CMCA.Then the ability of proposed CMCA to meet evaluation requirements of ventilation system is verified.
