In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlatio...In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlation optimization is proposed.On the basis of airframe damage feature analysis,the multi-dimensional feature entropy is defined to realize the full fusion of multiple feature information of the image,and the division method is extended to multi-threshold to refine the damage division and reduce the impact of the damage adjacent region’s morphological changes on the division.Through the correlation parameter optimization algorithm,the problem of low efficiency of multi-dimensional multi-threshold division method is solved.Finally,the proposed method is compared and verified by instances of airframe damage image.The results show that compared with the traditional threshold division method,the damage region divided by the proposed method is complete and accurate,and the boundary is clear and coherent,which can effectively reduce the interference of many factors such as uneven luminance,chromaticity deviation,dirt attachment,image compression,and so on.The correlation optimization algorithm has high efficiency and stable convergence,and can meet the requirements of aircraft intelligent maintenance.展开更多
Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class unifo...Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.展开更多
A way of resolving spreading code mismatches in blind multiuser detection with a particle swarm optimization （PSO） approach is proposed. It has been shown that the PSO algorithm incorporating the linear system of th...A way of resolving spreading code mismatches in blind multiuser detection with a particle swarm optimization （PSO） approach is proposed. It has been shown that the PSO algorithm incorporating the linear system of the decorrelating detector, which is termed as decorrelating PSO （DPSO）, can significantly improve the bit error rate （BER） and the system capacity. As the code mismatch occurs, the output BER performance is vulnerable to degradation for DPSO. With a blind decorrelating scheme, the proposed blind DPSO （BDPSO） offers more robust capabilities over existing DPSO under code mismatch scenarios.展开更多
车辆临近交叉口的变道行为会制约交叉口通行效率的提升。基于此,本文提出一种网联车辆环境下城市道路交通流分段协同控制方法(Segmented Cooperative cOntrol Method for Urban Road Traffic Flow,SCOM-URTF),该方法采用双层优化模型,...车辆临近交叉口的变道行为会制约交叉口通行效率的提升。基于此,本文提出一种网联车辆环境下城市道路交通流分段协同控制方法(Segmented Cooperative cOntrol Method for Urban Road Traffic Flow,SCOM-URTF),该方法采用双层优化模型,实现路段功能区动态划分和路段—交叉口交通流的协同优化。上层模型设计了一种分车道速度诱导错位变道策略(Misaligned Lane-changing with Separated Lane Speed Guidance,ML-SLSG),通过纵向空间错位排列促成左转和右转车辆的快速变道,最小化车辆变道区长度,并均衡车道组交通流量;下层模型以最小化车均延误为目标,基于动态规划法协同优化网联车辆的轨迹与交叉口信号配时参数。仿真结果表明,ML-SLSG策略能有效缩短变道长度,在低、中和高这3种交通负荷下,本文提出的车辆纵向轨迹优化模型能使交叉口车均延误减少5.9%~8.0%,且与信号配时协同优化后,车均延误可再降低3.7%~22.8%。与同类方法对比研究表明,SCOM-URTF更适合多种驾驶行为相互协调的交通环境。敏感性分析显示,更高的CAV渗透率和道路限速有助于降低车均延误;增大交叉口间距可在初期减少车均延误,但达到临界点后会出现延误反弹,而轨迹与信号的协同优化能有效遏制延误的反弹。展开更多
基金supported by the Aeronautical Science Foundation of China(No.20151067003)。
文摘In order to obtain the image of airframe damage region and provide the input data for aircraft intelligent maintenance,a multi-dimensional and multi-threshold airframe damage region division method based on correlation optimization is proposed.On the basis of airframe damage feature analysis,the multi-dimensional feature entropy is defined to realize the full fusion of multiple feature information of the image,and the division method is extended to multi-threshold to refine the damage division and reduce the impact of the damage adjacent region’s morphological changes on the division.Through the correlation parameter optimization algorithm,the problem of low efficiency of multi-dimensional multi-threshold division method is solved.Finally,the proposed method is compared and verified by instances of airframe damage image.The results show that compared with the traditional threshold division method,the damage region divided by the proposed method is complete and accurate,and the boundary is clear and coherent,which can effectively reduce the interference of many factors such as uneven luminance,chromaticity deviation,dirt attachment,image compression,and so on.The correlation optimization algorithm has high efficiency and stable convergence,and can meet the requirements of aircraft intelligent maintenance.
基金Supported by the CRSRI Open Research Program(CKWV2013225/KY)the Priority Academic Program Development of Jiangsu Higher Education Institution+2 种基金the Open Project Foundation of Key Laboratory of the Yellow River Sediment of Ministry of Water Resource(2014006)the State Key Lab of Urban Water Resource and Environment(HIT)(ES201409)the Open Project Program of State Key Laboratory of Food Science and Technology,Jiangnan University(SKLF-KF-201310)
文摘Since the logarithmic form of Shannon entropy has the drawback of undefined value at zero points,and most existing threshold selection methods only depend on the probability information,ignoring the within-class uniformity of gray level,a method of reciprocal gray entropy threshold selection is proposed based on two-dimensional(2-D)histogram region oblique division and artificial bee colony(ABC)optimization.Firstly,the definition of reciprocal gray entropy is introduced.Then on the basis of one-dimensional(1-D)method,2-D threshold selection criterion function based on reciprocal gray entropy with histogram oblique division is derived.To accelerate the progress of searching the optimal threshold,the recently proposed ABC optimization algorithm is adopted.The proposed method not only avoids the undefined value points in Shannon entropy,but also achieves high accuracy and anti-noise performance due to reasonable 2-D histogram region division and the consideration of within-class uniformity of gray level.A large number of experimental results show that,compared with the maximum Shannon entropy method with 2-D histogram oblique division and the reciprocal entropy method with 2-D histogram oblique division based on niche chaotic mutation particle swarm optimization(NCPSO),the proposed method can achieve better segmentation results and can satisfy the requirement of real-time processing.
基金supported by the NSC under Grant No.NSC 101-2221-E-275-007
文摘A way of resolving spreading code mismatches in blind multiuser detection with a particle swarm optimization （PSO） approach is proposed. It has been shown that the PSO algorithm incorporating the linear system of the decorrelating detector, which is termed as decorrelating PSO （DPSO）, can significantly improve the bit error rate （BER） and the system capacity. As the code mismatch occurs, the output BER performance is vulnerable to degradation for DPSO. With a blind decorrelating scheme, the proposed blind DPSO （BDPSO） offers more robust capabilities over existing DPSO under code mismatch scenarios.
文摘车辆临近交叉口的变道行为会制约交叉口通行效率的提升。基于此,本文提出一种网联车辆环境下城市道路交通流分段协同控制方法(Segmented Cooperative cOntrol Method for Urban Road Traffic Flow,SCOM-URTF),该方法采用双层优化模型,实现路段功能区动态划分和路段—交叉口交通流的协同优化。上层模型设计了一种分车道速度诱导错位变道策略(Misaligned Lane-changing with Separated Lane Speed Guidance,ML-SLSG),通过纵向空间错位排列促成左转和右转车辆的快速变道,最小化车辆变道区长度,并均衡车道组交通流量;下层模型以最小化车均延误为目标,基于动态规划法协同优化网联车辆的轨迹与交叉口信号配时参数。仿真结果表明,ML-SLSG策略能有效缩短变道长度,在低、中和高这3种交通负荷下,本文提出的车辆纵向轨迹优化模型能使交叉口车均延误减少5.9%~8.0%,且与信号配时协同优化后,车均延误可再降低3.7%~22.8%。与同类方法对比研究表明,SCOM-URTF更适合多种驾驶行为相互协调的交通环境。敏感性分析显示,更高的CAV渗透率和道路限速有助于降低车均延误;增大交叉口间距可在初期减少车均延误,但达到临界点后会出现延误反弹,而轨迹与信号的协同优化能有效遏制延误的反弹。
