With the increasing maturity of automated guided vehicles(AGV)technology and the widespread application of flexible manufacturing systems,enhancing the efficiency of AGVs in complex environments has become crucial.Thi...With the increasing maturity of automated guided vehicles(AGV)technology and the widespread application of flexible manufacturing systems,enhancing the efficiency of AGVs in complex environments has become crucial.This paper analyzes the challenges of path planning and scheduling in multi-AGV systems,introduces a map-based path search algorithm,and proposes the BFS algorithm for shortest path planning.Through optimization using the breadth-first search(BFS)algorithm,efficient scheduling of multiple AGVs in complex environments is achieved.In addition,this paper validated the effectiveness of the proposed method in a production workshop experiment.The experimental results show that the BFS algorithm can quickly search for the shortest path,reduce the running time of AGVs,and significantly improve the performance of multi-AGV scheduling systems.展开更多
In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocatio...In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.展开更多
随着矿井开采深度不断增加,工程活动对地下水系统的干扰日益显著,导致突水风险显著升高,不仅增加事故发生次数,突水规模与危害性也明显上升。本研究针对这一问题,构建了巷道网络水流漫延模型,模拟单点突水后水流的动态传播过程。通过将...随着矿井开采深度不断增加,工程活动对地下水系统的干扰日益显著,导致突水风险显著升高,不仅增加事故发生次数,突水规模与危害性也明显上升。本研究针对这一问题,构建了巷道网络水流漫延模型,模拟单点突水后水流的动态传播过程。通过将矿井巷道网络视为一个有向图G=(V, E, w),节点表示端点,边表示巷道区段并赋予三维欧氏距离权重,基于广度优先搜索(Breadth-First Search,BFS)的动态模拟算法,逐时段追踪水峰演进过程,从而得到各端点的水流到达的时刻和各巷道的充满水时间。在此基础上,建立了时间依赖的巷道通行网络,采用改进的Dijkstra算法,求解出矿工在突水情境下到达安全出口的最短逃生路径以及所需时间。结果表明,该方法能够有效模拟水害漫延过程并规划出可靠的疏散路径,可为矿井水灾应急决策与人员救援提供重要技术支持。展开更多
首先分析潮流转移的原因及伴随的现象。其次讨论潮流转移区域以及区域界定,对传统广度优先遍历(breadth first search,BFS)算法进行改进,提出潮流转移影响区域的界定方法。对安全评估工作的理论基础——3个基本概念(模型量化、平均功率...首先分析潮流转移的原因及伴随的现象。其次讨论潮流转移区域以及区域界定,对传统广度优先遍历(breadth first search,BFS)算法进行改进,提出潮流转移影响区域的界定方法。对安全评估工作的理论基础——3个基本概念(模型量化、平均功率角和潮流转移灵敏度)分别进行定义。提出潮流转移模型及其灵敏度的表达式。提出安全评估的评估方法,建立安全评估的数学模型,最终得到安全评估的综合指标,并阐述了指标的使用。开发潮流转移灵敏度及安全评估程序,利用该程序对真实电网算例进行仿真验证。展开更多
文摘With the increasing maturity of automated guided vehicles(AGV)technology and the widespread application of flexible manufacturing systems,enhancing the efficiency of AGVs in complex environments has become crucial.This paper analyzes the challenges of path planning and scheduling in multi-AGV systems,introduces a map-based path search algorithm,and proposes the BFS algorithm for shortest path planning.Through optimization using the breadth-first search(BFS)algorithm,efficient scheduling of multiple AGVs in complex environments is achieved.In addition,this paper validated the effectiveness of the proposed method in a production workshop experiment.The experimental results show that the BFS algorithm can quickly search for the shortest path,reduce the running time of AGVs,and significantly improve the performance of multi-AGV scheduling systems.
文摘In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.
文摘针对非物质文化遗产蓝印花布数字化生成技术发展较慢的问题,提出了一种基于图像拼接技术的蓝印花布边缘纹样快速生成算法,实现了对边缘纹样的拼接延展.对于蓝印花布样本存在颜色和噪点问题,提出了一种预处理算法,可统一待拼接图像样本的颜色并消除噪点.在拼接算法设计中,通过对特征提取、匹配、提纯及融合等关键环节的算法进行对比实验,系统性优化各环节的算法组合,形成高效的拼接算法架构.实验结果表明,该算法可以实现蓝印花布边缘纹样的快速拼接;采用基于FAST算法的纹样特征点的检测时间比SIFT(Scale-Invariant Feature Transform)和SURF(Speeded Up Robust Features)算法时间分别减少了74.6%和89.8%;采用基于BF算法的纹样特征点的平均匹配时间比FLANN(Fast Library for Approximate Nearest Neighbors)算法时间减少了88.6%;采用基于PROSAC算法的纹样匹配特征点的提纯时间平均比RANSAC(Random Sample Consensus)算法时间减少了20%;总体拼接时间平均比传统算法时间减少了1.0718 s.
文摘随着矿井开采深度不断增加,工程活动对地下水系统的干扰日益显著,导致突水风险显著升高,不仅增加事故发生次数,突水规模与危害性也明显上升。本研究针对这一问题,构建了巷道网络水流漫延模型,模拟单点突水后水流的动态传播过程。通过将矿井巷道网络视为一个有向图G=(V, E, w),节点表示端点,边表示巷道区段并赋予三维欧氏距离权重,基于广度优先搜索(Breadth-First Search,BFS)的动态模拟算法,逐时段追踪水峰演进过程,从而得到各端点的水流到达的时刻和各巷道的充满水时间。在此基础上,建立了时间依赖的巷道通行网络,采用改进的Dijkstra算法,求解出矿工在突水情境下到达安全出口的最短逃生路径以及所需时间。结果表明,该方法能够有效模拟水害漫延过程并规划出可靠的疏散路径,可为矿井水灾应急决策与人员救援提供重要技术支持。
文摘首先分析潮流转移的原因及伴随的现象。其次讨论潮流转移区域以及区域界定,对传统广度优先遍历(breadth first search,BFS)算法进行改进,提出潮流转移影响区域的界定方法。对安全评估工作的理论基础——3个基本概念(模型量化、平均功率角和潮流转移灵敏度)分别进行定义。提出潮流转移模型及其灵敏度的表达式。提出安全评估的评估方法,建立安全评估的数学模型,最终得到安全评估的综合指标,并阐述了指标的使用。开发潮流转移灵敏度及安全评估程序,利用该程序对真实电网算例进行仿真验证。
