Structural health monitoring(SHM)in service has attracted increasing attention for years.Load localization on a structure is studied hereby.Two algorithms,i.e.,support vector machine(SVM)method and back propagation ne...Structural health monitoring(SHM)in service has attracted increasing attention for years.Load localization on a structure is studied hereby.Two algorithms,i.e.,support vector machine(SVM)method and back propagation neural network(BPNN)algorithm,are proposed to identify the loading positions individually.The feasibility of the suggested methods is evaluated through an experimental program on a carbon fiber reinforced plastic laminate.The experimental tests involve in application of four optical fiber-based sensors for strain measurement at discrete points.The sensors are specially designed fiber Bragg grating(FBG)in small diameter.The small-diameter FBG sensors are arrayed in 2-D on the laminate surface.The testing results indicate that the loading position could be detected by the proposed method.Using SVM method,the 2-D FBG sensors can approximate the loading location with maximum error less than 14 mm.However,the maximum localization error could be limited to about 1 mm by applying the BPNN algorithm.It is mainly because the convergence conditions(mean square error)can be set in advance,while SVM cannot.展开更多
Taking a reservoir in South China as an example, we use rainfall-runoff unit hydrograph method to analyze the time changing process of surface runoff inflow, which generated by typical design rainfall. On the basis of...Taking a reservoir in South China as an example, we use rainfall-runoff unit hydrograph method to analyze the time changing process of surface runoff inflow, which generated by typical design rainfall. On the basis of time series data of flow and water quality in control section of the main rivers in Xili Reservoir, we establish mathematical response relation between non-point source pollutants flux, such as flux of COD, flux of NH3-H, in catchment area of control section and runoff. Then we simulate the time dynamic change progress of non-point source pollution load which generate with the initial stage runoff that generated by design rainfall and flow into reservoir. It can provide technical parameters for the design of non-point source which generate from early runoff treatment project.展开更多
<span style="font-family:Verdana;">This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes...<span style="font-family:Verdana;">This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes into account the non-linearity of SHPs—something which is not possible using traditional controllers. Most intelligent methods use two-</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">input fuzzy controllers, but because such controllers are expensive, there is </span><span style="font-family:Verdana;">economic interest in the relatively cheaper single-input controllers. A n</span><span style="font-family:Verdana;">on-</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">linear control model based on one-input fuzzy logic PI (FLPI) controller was developed and applied to control the non-linear SHP. Using MATLAB/Si</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">mulink SimScape, the SHP was simulated with linear and non-linear plant models. The performance of the FLPI controller was investigated and compared with that of the conventional PI/PID controller. Results show that the settling time for the FLPI controller is about 8 times shorter;while the overshoot is about 15 times smaller compared to the conventional PI/PID controller. Therefore, the FLPI controller performs better than the conventional PI/PID controller not only in meeting the LFC control objective but also in ensuring increased dynamic stability of SHPs.</span>展开更多
脑小血管病(cerebral small vessel disease,CSVD)是引起人群认知功能障碍最重要的原因之一,随着我国人口老龄化趋势的加重及医学影像技术的发展,CSVD的发病率呈逐年上升趋势,其所引起的认知功能障碍也越来越受到关注。因脑小血管病起...脑小血管病(cerebral small vessel disease,CSVD)是引起人群认知功能障碍最重要的原因之一,随着我国人口老龄化趋势的加重及医学影像技术的发展,CSVD的发病率呈逐年上升趋势,其所引起的认知功能障碍也越来越受到关注。因脑小血管病起病隐匿、进展缓慢、早期无明显临床表现,出现症状时已进入认知功能障碍的中晚期或者已经形成痴呆,往往带给患者不能逆转的损伤及沉重的医疗负担。本文就不同影像学类型脑小血管病及其MRI总负荷对认知功能的影响进行综述,进一步了解CSVD与认知功能的关系,为CSVD所致认知功能障碍的识别和预防提供帮助。展开更多
基金supported by the National Natural Science Foundation of China(Nos.11402112,51405223)
文摘Structural health monitoring(SHM)in service has attracted increasing attention for years.Load localization on a structure is studied hereby.Two algorithms,i.e.,support vector machine(SVM)method and back propagation neural network(BPNN)algorithm,are proposed to identify the loading positions individually.The feasibility of the suggested methods is evaluated through an experimental program on a carbon fiber reinforced plastic laminate.The experimental tests involve in application of four optical fiber-based sensors for strain measurement at discrete points.The sensors are specially designed fiber Bragg grating(FBG)in small diameter.The small-diameter FBG sensors are arrayed in 2-D on the laminate surface.The testing results indicate that the loading position could be detected by the proposed method.Using SVM method,the 2-D FBG sensors can approximate the loading location with maximum error less than 14 mm.However,the maximum localization error could be limited to about 1 mm by applying the BPNN algorithm.It is mainly because the convergence conditions(mean square error)can be set in advance,while SVM cannot.
文摘Taking a reservoir in South China as an example, we use rainfall-runoff unit hydrograph method to analyze the time changing process of surface runoff inflow, which generated by typical design rainfall. On the basis of time series data of flow and water quality in control section of the main rivers in Xili Reservoir, we establish mathematical response relation between non-point source pollutants flux, such as flux of COD, flux of NH3-H, in catchment area of control section and runoff. Then we simulate the time dynamic change progress of non-point source pollution load which generate with the initial stage runoff that generated by design rainfall and flow into reservoir. It can provide technical parameters for the design of non-point source which generate from early runoff treatment project.
文摘<span style="font-family:Verdana;">This study presents an intelligent approach for load frequency control (LFC) of small hydropower plants (SHPs). The approach which is based on fuzzy logic (FL), takes into account the non-linearity of SHPs—something which is not possible using traditional controllers. Most intelligent methods use two-</span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">input fuzzy controllers, but because such controllers are expensive, there is </span><span style="font-family:Verdana;">economic interest in the relatively cheaper single-input controllers. A n</span><span style="font-family:Verdana;">on-</span></span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">linear control model based on one-input fuzzy logic PI (FLPI) controller was developed and applied to control the non-linear SHP. Using MATLAB/Si</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">mulink SimScape, the SHP was simulated with linear and non-linear plant models. The performance of the FLPI controller was investigated and compared with that of the conventional PI/PID controller. Results show that the settling time for the FLPI controller is about 8 times shorter;while the overshoot is about 15 times smaller compared to the conventional PI/PID controller. Therefore, the FLPI controller performs better than the conventional PI/PID controller not only in meeting the LFC control objective but also in ensuring increased dynamic stability of SHPs.</span>
文摘脑小血管病(cerebral small vessel disease,CSVD)是引起人群认知功能障碍最重要的原因之一,随着我国人口老龄化趋势的加重及医学影像技术的发展,CSVD的发病率呈逐年上升趋势,其所引起的认知功能障碍也越来越受到关注。因脑小血管病起病隐匿、进展缓慢、早期无明显临床表现,出现症状时已进入认知功能障碍的中晚期或者已经形成痴呆,往往带给患者不能逆转的损伤及沉重的医疗负担。本文就不同影像学类型脑小血管病及其MRI总负荷对认知功能的影响进行综述,进一步了解CSVD与认知功能的关系,为CSVD所致认知功能障碍的识别和预防提供帮助。
