Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Marko...Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.展开更多
为了研究不同经验驾驶人在高速公路特长隧道环境下的注视转移特性,在高速公路特长隧道中开展实车实验,利用i View X HED型眼动仪采集了32名不同经验驾驶人的眼动数据。运用动态聚类方法,对驾驶人注视区域进行划分,分析了职业与非职业驾...为了研究不同经验驾驶人在高速公路特长隧道环境下的注视转移特性,在高速公路特长隧道中开展实车实验,利用i View X HED型眼动仪采集了32名不同经验驾驶人的眼动数据。运用动态聚类方法,对驾驶人注视区域进行划分,分析了职业与非职业驾驶员在高速公路隧道不同段与普通路段的注视转移规律与注意力分配特性。结果表明:相较于非职业驾驶人,职业驾驶员具有较强的注视前瞻性,且在隧道的不同段主要注视的区域因行车环境不同变化较小;驾驶人对同一目标需要重复注视才能提取足够的信息,且当行车环境复杂度增加或驾驶员驾驶经验不足时,重复注视概率增加;驾驶人在不同路段行车时,主要通过注视中间区域获取信息;行车环境与驾驶经验对驾驶人在中间近处、左侧区域及内后视镜区域的注视平稳分布存在显著的交互作用。展开更多
基金supported by the National Natural Science Foundation of China (50879085)the Program for New Century Excellent Talents in University(NCET-07-0778)the Key Technology Research Project of Dynamic Environmental Flume for Ocean Monitoring Facilities (201005027-4)
文摘Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years' data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.
文摘为了研究不同经验驾驶人在高速公路特长隧道环境下的注视转移特性,在高速公路特长隧道中开展实车实验,利用i View X HED型眼动仪采集了32名不同经验驾驶人的眼动数据。运用动态聚类方法,对驾驶人注视区域进行划分,分析了职业与非职业驾驶员在高速公路隧道不同段与普通路段的注视转移规律与注意力分配特性。结果表明:相较于非职业驾驶人,职业驾驶员具有较强的注视前瞻性,且在隧道的不同段主要注视的区域因行车环境不同变化较小;驾驶人对同一目标需要重复注视才能提取足够的信息,且当行车环境复杂度增加或驾驶员驾驶经验不足时,重复注视概率增加;驾驶人在不同路段行车时,主要通过注视中间区域获取信息;行车环境与驾驶经验对驾驶人在中间近处、左侧区域及内后视镜区域的注视平稳分布存在显著的交互作用。
