The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disast...The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).展开更多
The progressive and mixing algorithm(PAMA) is a method for surface modeling and editing,which is developed for effective and flexible applications in many environments,such as computer-aided design(CAD) and computer-a...The progressive and mixing algorithm(PAMA) is a method for surface modeling and editing,which is developed for effective and flexible applications in many environments,such as computer-aided design(CAD) and computer-aided geometric design(CAGD).In this paper,the construction scheme and continuities of PAMA are discussed,which provide a mathematics analysis of PAMA.The analysis and results show that the PAMA provides a new method of surface modeling and editing with four more degrees of freedom for designers to manipulate a 3D object.展开更多
In order to resolve grid distortions in finite element method(FEM), the meshless numerical method which is called general particle dynamics(GPD) was presented to simulate the large deformation and failure of geomateri...In order to resolve grid distortions in finite element method(FEM), the meshless numerical method which is called general particle dynamics(GPD) was presented to simulate the large deformation and failure of geomaterials. The Mohr-Coulomb strength criterion was implemented into the code to describe the elasto-brittle behaviours of geomaterials while the solid-structure(reinforcing pile) interaction was simulated as an elasto-brittle material. The Weibull statistical approach was applied to describing the heterogeneity of geomaterials. As an application of general particle dynamics to slopes, the interaction between the slopes and the reinforcing pile was modelled. The contact between the geomaterials and the reinforcing pile was modelled by using the coupling condition associated with a Lennard-Jones repulsive force. The safety factor, corresponding to the minimum shear strength reduction factor "R", was obtained, and the slip surface of the slope was determined. The numerical results are in good agreement with those obtained from limit equilibrium method and finite element method. It indicates that the proposed geomaterial-structure interaction algorithm works well in the GPD framework.展开更多
In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale par...In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,Bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that Bayesian estimates have a good performance.展开更多
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero....Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.展开更多
PI (proportional-integral) control algorithm is applied to control WlP (work-in-progress) in a discrete manufacturing system, where the cascade control of PI controllers is presented. It is in the frequency domain...PI (proportional-integral) control algorithm is applied to control WlP (work-in-progress) in a discrete manufacturing system, where the cascade control of PI controllers is presented. It is in the frequency domain that the PI controller is designed with constraints on sensitivity options to ensure the stability and robustness of its parameters. A case is evaluated on a motorcycle engine crankcase production system, whose simulation results confirm that demand fluctuations can be compensated by PI controllers under a normal demand. PI controllers also possess low sensitivity to the distribution of production times.展开更多
基金funded by Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,under grant No.(PNURSP2022R161).
文摘The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).
基金the 2009 Excellent Going Abroad Experts Training Program in Hebei Province,China(No.[2010]76)
文摘The progressive and mixing algorithm(PAMA) is a method for surface modeling and editing,which is developed for effective and flexible applications in many environments,such as computer-aided design(CAD) and computer-aided geometric design(CAGD).In this paper,the construction scheme and continuities of PAMA are discussed,which provide a mathematics analysis of PAMA.The analysis and results show that the PAMA provides a new method of surface modeling and editing with four more degrees of freedom for designers to manipulate a 3D object.
基金Projects(51325903,51279218)supported by the National Natural Science Foundation of ChinaProject(cstc2013kjrcljrccj0001)supported by the Natural Science Foundation Project of CQ CSTC,ChinaProject(20130191110037)supported by Research fund by the Doctoral Program of Higher Education of China
文摘In order to resolve grid distortions in finite element method(FEM), the meshless numerical method which is called general particle dynamics(GPD) was presented to simulate the large deformation and failure of geomaterials. The Mohr-Coulomb strength criterion was implemented into the code to describe the elasto-brittle behaviours of geomaterials while the solid-structure(reinforcing pile) interaction was simulated as an elasto-brittle material. The Weibull statistical approach was applied to describing the heterogeneity of geomaterials. As an application of general particle dynamics to slopes, the interaction between the slopes and the reinforcing pile was modelled. The contact between the geomaterials and the reinforcing pile was modelled by using the coupling condition associated with a Lennard-Jones repulsive force. The safety factor, corresponding to the minimum shear strength reduction factor "R", was obtained, and the slip surface of the slope was determined. The numerical results are in good agreement with those obtained from limit equilibrium method and finite element method. It indicates that the proposed geomaterial-structure interaction algorithm works well in the GPD framework.
基金Supported by the National Natural Science Foundation of China(71571144,71401134,71171164,11701406) Supported by the International Cooperation and Exchanges in Science and Technology Program of Shaanxi Province(2016KW-033)
文摘In this paper, we construct a Bayesian framework combining Type-Ⅰ progressively hybrid censoring scheme and competing risks which are independently distributed as exponentiated Weibull distribution with one scale parameter and two shape parameters. Since there exist unknown hyper-parameters in prior density functions of shape parameters, we consider the hierarchical priors to obtain the individual marginal posterior density functions,Bayesian estimates and highest posterior density credible intervals. As explicit expressions of estimates cannot be obtained, the componentwise updating algorithm of Metropolis-Hastings method is employed to compute the numerical results. Finally, it is concluded that Bayesian estimates have a good performance.
基金supported by the Scientific Research Project of Xiang Jiang Lab(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28)+5 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72071205)the Hunan Graduate Research Innovation Project(ZC23112101-10)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)the Science and Technology Project for Young and Middle-aged Talents of Hunan(2023TJ-Z03)the Science and Technology Innovation Program of Humnan Province(2023RC1002)。
文摘Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
基金Science Fund of Key Laboratory of Intel-ligent Control Theory and Application of High Academies in Liaoning Province (No.200521303)
文摘PI (proportional-integral) control algorithm is applied to control WlP (work-in-progress) in a discrete manufacturing system, where the cascade control of PI controllers is presented. It is in the frequency domain that the PI controller is designed with constraints on sensitivity options to ensure the stability and robustness of its parameters. A case is evaluated on a motorcycle engine crankcase production system, whose simulation results confirm that demand fluctuations can be compensated by PI controllers under a normal demand. PI controllers also possess low sensitivity to the distribution of production times.