For any β 〉 1, let （[0, 1],Tβ） be the beta dynamical system. For a positive function ψ ： N → R＋ and a real number x0 E [0, 1], we define D（Tβ, ψ, xo） the set of ψ-well approximable points by xo as {x C [...For any β 〉 1, let （[0, 1],Tβ） be the beta dynamical system. For a positive function ψ ： N → R＋ and a real number x0 E [0, 1], we define D（Tβ, ψ, xo） the set of ψ-well approximable points by xo as {x C [0, 1] ： ]Tβ^nx - x0｜ （ψ（n） for infinitely many n ∈ N}.In this note, by proving a structure lemma that any ball B（x, r） contains a regular cylinder of comparable length with r, we determine the Hausdorff dimension of the set D（Tβ, ψb, x0） completely for any β 〉 1 and any positive function ψ.展开更多
Dear editor,The advent of modern molecular mechanism’s approach to disease treatment is highly advancing to mitigate/normalize the symptoms of disease i.e.hyperglycemia by targeting at least eight different pathophys...Dear editor,The advent of modern molecular mechanism’s approach to disease treatment is highly advancing to mitigate/normalize the symptoms of disease i.e.hyperglycemia by targeting at least eight different pathophysiological approaches popularly known as omnious octet[1].Importantly,type 2 diabetes is a展开更多
With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on ...With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.展开更多
Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzz...Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.展开更多
A novel data association algorithm is developed based on fuzzy geneticalgorithms (FGAs). The static part of data association uses one FGA to determine both the lists ofcomposite measurements and the solutions of m-bes...A novel data association algorithm is developed based on fuzzy geneticalgorithms (FGAs). The static part of data association uses one FGA to determine both the lists ofcomposite measurements and the solutions of m-best S-D assignment. In the dynamic part of dataassociation, the results of the m-best S-D assignment are then used in turn, with a Kalman filterstate estimator, in a multi-population FGA-based dynamic 2D assignment algorithm to estimate thestates of the moving targets over time. Such an assignment-based data association algorithm isdemonstrated on a simulated passive sensor track formation and maintenance problem. The simulationresults show its feasibility in multi-sensor multi-target tracking. Moreover, algorithm developmentand real-time problems are briefly discussed.展开更多
This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-gu...This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.展开更多
目的:探究问题导引-目标链接式护理干预对糖尿病患者低血糖发生率的影响。方法:选择2022年2月—2023年1月泰州市第三人民医院收治的86例糖尿病患者为研究对象,采用计算机法以01~086对患者进行编号,设置01~043患者为对照组(n=43),实施常...目的:探究问题导引-目标链接式护理干预对糖尿病患者低血糖发生率的影响。方法:选择2022年2月—2023年1月泰州市第三人民医院收治的86例糖尿病患者为研究对象,采用计算机法以01~086对患者进行编号,设置01~043患者为对照组(n=43),实施常规护理,设置044~086患者为观察组(n=43),实施问题导引-目标链接式护理干预。比较两组血糖水平、糖尿病自我管理行为及低血糖发生率。结果:护理前,两组血糖水平比较,差异无统计学意义(P>0.05);护理后,两组空腹血糖(FBG)、餐后2 h血糖(2 h PBG)及糖化血红蛋白(HbA1c)水平均下降,观察组低于对照组,差异有统计学意义(P<0.05)。护理前,两组糖尿病患者自我管理行为量表(SDSCA)评分比较,差异无统计学意义(P>0.05);护理14 d后,两组SDSCA评分升高,观察组高于对照组,差异有统计学意义(P<0.05);观察组低血糖发生率低于对照组,差异有统计学意义(P<0.05)。结论:在糖尿病患者的护理中采用问题导引-目标链接式护理干预,能有效控制血糖水平,提升患者自护能力,降低低血糖发生率。展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.10901066 and 51149008)Hunan Natural Science Foundation(Grant No.09JJ3001)
文摘For any β 〉 1, let （[0, 1],Tβ） be the beta dynamical system. For a positive function ψ ： N → R＋ and a real number x0 E [0, 1], we define D（Tβ, ψ, xo） the set of ψ-well approximable points by xo as {x C [0, 1] ： ]Tβ^nx - x0｜ （ψ（n） for infinitely many n ∈ N}.In this note, by proving a structure lemma that any ball B（x, r） contains a regular cylinder of comparable length with r, we determine the Hausdorff dimension of the set D（Tβ, ψb, x0） completely for any β 〉 1 and any positive function ψ.
文摘Dear editor,The advent of modern molecular mechanism’s approach to disease treatment is highly advancing to mitigate/normalize the symptoms of disease i.e.hyperglycemia by targeting at least eight different pathophysiological approaches popularly known as omnious octet[1].Importantly,type 2 diabetes is a
基金supported by the National Natural Science Foundation of China(61471191)the Aeronautical Science Foundation of China(20152052026)the Foundation of Graduate Innovation Center in NUAA(kfjj20170313)
文摘With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.
基金supported by The National Natural Science Foundation of China under Grant Nos.61402517, 61573375The Foundation of State Key Laboratory of Astronautic Dynamics of China under Grant No. 2016ADL-DW0302+2 种基金The Postdoctoral Science Foundation of China under Grant Nos. 2013M542331, 2015M572778The Natural Science Foundation of Shaanxi Province of China under Grant No. 2013JQ8035The Aviation Science Foundation of China under Grant No. 20151996015
文摘Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.
文摘A novel data association algorithm is developed based on fuzzy geneticalgorithms (FGAs). The static part of data association uses one FGA to determine both the lists ofcomposite measurements and the solutions of m-best S-D assignment. In the dynamic part of dataassociation, the results of the m-best S-D assignment are then used in turn, with a Kalman filterstate estimator, in a multi-population FGA-based dynamic 2D assignment algorithm to estimate thestates of the moving targets over time. Such an assignment-based data association algorithm isdemonstrated on a simulated passive sensor track formation and maintenance problem. The simulationresults show its feasibility in multi-sensor multi-target tracking. Moreover, algorithm developmentand real-time problems are briefly discussed.
文摘This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.
文摘目的:探究问题导引-目标链接式护理干预对糖尿病患者低血糖发生率的影响。方法:选择2022年2月—2023年1月泰州市第三人民医院收治的86例糖尿病患者为研究对象,采用计算机法以01~086对患者进行编号,设置01~043患者为对照组(n=43),实施常规护理,设置044~086患者为观察组(n=43),实施问题导引-目标链接式护理干预。比较两组血糖水平、糖尿病自我管理行为及低血糖发生率。结果:护理前,两组血糖水平比较,差异无统计学意义(P>0.05);护理后,两组空腹血糖(FBG)、餐后2 h血糖(2 h PBG)及糖化血红蛋白(HbA1c)水平均下降,观察组低于对照组,差异有统计学意义(P<0.05)。护理前,两组糖尿病患者自我管理行为量表(SDSCA)评分比较,差异无统计学意义(P>0.05);护理14 d后,两组SDSCA评分升高,观察组高于对照组,差异有统计学意义(P<0.05);观察组低血糖发生率低于对照组,差异有统计学意义(P<0.05)。结论:在糖尿病患者的护理中采用问题导引-目标链接式护理干预,能有效控制血糖水平,提升患者自护能力,降低低血糖发生率。
