Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network

Transfer learning could reduce the time and resources required by the training of new models and be therefore important for generalized applications of the trainedmachine learning algorithms.In this study,a transfer learningenhanced convolutional neural network(CNN)was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge.The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy.First of all,a CNN algorithm for bridge weigh-in-motion(B-WIM)technology was proposed to identify the axle weight and the gross weight of the typical two-axle,three-axle,and five-axle vehicles as they crossed the bridge with different loading routes and speeds.Then,the pre-trained CNN model was transferred by fine-tuning to weigh themoving vehicle on another bridge.Finally,the identification accuracy and the amount of training data required were compared between the two CNN models.Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%.Moreover,the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model,showing its promising potentials in the actual applications.

快速更换WLM头像

在这个追求个性的时代，大家都喜欢把WLM（Windows Live Messenger）的头像换成自定义的图片。通常的操作是点击头像，在弹出菜单中点击“更改显示图片”，然后在出现的图片窗口中选择需要的图片，非常麻烦。

代号0683WLM技巧直通车

前阵子，微软升级了WLMV8．0．0683，据说此版本的界面将不作改变，一直保持到最终版本。用户界面看起来柔和多了，重新设计了各个图标和提示信息，配色看起来也很舒服。

小波变换自适应滤波器及在主动噪声控制中的应用

讨论了传统的 L MS在信号处理中的不足 ,研究了小波变换自适应算法 (WL MS)的滤波器结构和实现 ,并将其应用于主动噪声控制。仿真结果表明 ,与传统的 L MS算法相比 ,WL

基于桥梁应变的高速动态车辆超载检测研究

针对动态车辆超重检测,基于桥梁应变建立了一种新型的高速动态超载检测方法。桥梁采用简支梁模型,车/桥相互作用模型化为两个具有固定间距点力匀速通过桥面。利用安装在桥梁下面的应变传感器测量车辆经过桥面引起的桥梁应变,通过积分运算消除车辆振动对计算结果的影响,进而获得动态车辆的载重值。试验结果证明该方法可以较准确的进行高速动态车辆超重的检测。

采用加权最小二乘法的恒星校正研究

针对光电探测设备恒星校正过程中的时效性问题,本文分析了产生时效性的原因以及在时效性存在的情况下,人们期待的两种方式。在此基础上,提出了基于加权最小二乘法拟合求解算法。算法能够实现两个方面的功能:提高了在局部区域内校正的精度;提高了在实时引导使用过程中的引导精度。本文依据两种情况,采用外场试验数据进行了仿真,验证了算法的可行性和有效性。

改进的自适应小波变换在信号去噪中的应用

讨论了传统的LMS在信号处理中的不足,基于离散小波变换提出一种自适应去噪方法。该方法利用多分辨率分析理论,把信号和噪声分解在不同的频率范围之内,使信号和噪声的频谱得到简化,从而减少了自适应滤波器的级数,使输入自相关矩阵阶数降低。仿真结果表明,与传统的LMS算法相比,WLMS算法的收敛速度和稳定性得到了显著的提高。

wIMS中间件平台集群方案的设计与实现

wIMS(Web 2.0 and CM-IMS Convergence;CM-IMS,China Mobile-IP Multimedia Subsystem)中间件平台是中国移动在CM-IMS基础上,打造的一个基础通信能力的开放平台。为了实现wIMS服务能力的可扩展性以及提升平台的稳定性,本文设计和实现了集群方案。通过基于tomcat的集群化部署wIMS,一方面提升了wIMS平台的性能,通过节点的增加就可以提高服务能力,实现了服务能力的高度可扩展性,另一方面,提高了平台的稳定性,从而为用户提供更好质量的服务。

A filtering-based bridge weigh-in-motion system on a continuous multi-girder bridge considering the influence lines of different lanes

A real-time vehicle monitoring is crucial for effective bridge maintenance and traffic management because overloaded vehicles can cause damage to bridges,and in some extreme cases,it will directly lead to a bridge failure.Bridge weigh-in-motion(BWIM)system as a high performance and cost-effective technology has been extensively used to monitor vehicle speed and weight on highways.However,the dynamic effect and data noise may have an adverse impact on the bridge responses during and immediately following the vehicles pass the bridge.The fast Fourier transform(FFT)method,which can significantly purify the collected structural responses(dynamic strains)received from sensors or transducers,was used in axle counting,detection,and axle weighing technology in this study.To further improve the accuracy of the BWIM system,the field-calibrated influence lines(ILs)of a continuous multi-girder bridge were regarded as a reference to identify the vehicle weight based on the modified Moses algorithm and the least squares method.In situ experimental results indicated that the signals treated with FFT filter were far better than the original ones,the efficiency and the accuracy of axle detection were significantly improved by introducing the FFT method to the BWIM system.Moreover,the lateral load distribution effect on bridges should be considered by using the calculated average ILs of the specific lane individually for vehicle weight calculation of this lane.

Weigh in Motion Based on Parameters Optimization

Dynamic tire forces are the main factor affecting the measurement accuracy of the axle weight of moving vehicle.This paper presents a novel method to reduce the influence of the dynamic tire forces on the weighing accuracy.On the basis of analyzing the characteristic of the dynamic tire forces,the objective optimization equation is constructed.The optimization algorithm is presented to get the optimal estimations of the objective parameters.According to the estimations of the parameters,the dynamic tire forces are separated from the axle weigh signal.The results of simulation and field experiments prove the effectiveness of the proposed method.

Weighing axle weight of moving vehicle based on empirical mode decomposition

Dynanfic forces are the main factor that influences the axle weight measurement accuracy of moving vehicle. Empirical mode decomposition （EMD） is presented to separate the dynamic forces contained in the axle weight signal. The concept and algorithm of EMD are introduced. The characteristic of the axle weight signal is analyzed. The method of judging pseudo intrinsic mode function （pseudo-IMF） is presented to improve the weighing accuracy. Numerical simulation and field experiments are conducted to evaluate the performance of EMD. The result shows effectiveness of the proposed method. Maximum weighing errors of the front axle, the rear axle and the gross weight at the speed of 15 km/h or lower are 2.22%, 6.26% and 4.11% respectively.

An Investigation on Safety Performance Assessment of Close-Following Behavior of Heavy Vehicle Using Empirical-Simulation Technique

One of the main causes of rear-end crashes is attributed to close-following and hazardous driving behavior. A study was conducted to investigate the close-following behavior of heavy vehicle under various heavy vehicle categories, travel speeds and gross vehicle weights (GVW). Investigation is based on data obtained from simulation and empirical observations. A safety performance assessment of close-following behavior of heavy vehicles by using empirical-simulation technique is proposed. The simulation, which incorporates vehicle dynamics, is to generate the minimum safe time gap (MSTG) for truck-following-car situations. MSTG is defined as the minimum time required by the following vehicle to decelerate and stop without hitting the leading vehicle when both leading and following vehicles apply the emergency brakes. Based on comparison between the actual time gap data and the MSTG, a safety performance assessment technique that considers vehicle type, vehicle braking characteristics, truck GVW and speed is proposed for truck-following-car situation.

Effect of Snow, Temperature and Their Interaction on Highway Truck Traffic

Based on statistical amount of traffic and weather data sets from three weigh-in-motion sites for the study period of from 2005 to 2009, permanent traffic counters and weather stations in Alberta, Canada, an investigation is carried out to study impacts of winter weather on volume of passenger car and truck traffic. Multiple regression models are developed to relate truck and passenger car traffic variations to winter weather conditions. Statistical validity of study results are confirmed by using statistical tests of significance. Considerable reductions in passenger car and truck volumes can be expected with decrease in cold temperatures. Such reductions are higher for passenger cars as compared to trucks. Due to cold and snow interactions, the reduction in car and truck traffic volume due to cold temperature could intensify with a rise in the amount of snowfall. For passenger cars, weekends experience higher traffic reductions as compared to weekdays. However, the impact of weather on truck traffic is generally similar for weekdays and weekends. Interestingly, an increase in truck traffic during severe weather conditions is noticed at one of the study sites. Such phenomenon is found statistically significant. None of the past studies in the literature have presented the possibility of traffic volume increases on highways during adverse weather conditions;which could happen due to shift of traffic from parallel roads with inadequate winter maintenance programs. It is believed that the findings of this study can benefit highway agencies in developing such programs and policies as efficient monitoring of passenger car and truck traffic, and plan for efficient winter roadway maintenance programs.

Estimating the frequency of traffic overloading on road bridges

Load limits,which appear to be routinely exceeded by trucks,occasionally result in road bridge failures.Therefore,predicting failures is crucial for safeguarding road safety.Past studies have largely focused on forecasting bridge failure event probability using the reliability analysis method,whilst occasionally accounting for vehicular overloading effects.Only recently,a study has investigated design traffic overloading event frequency using generalised linear regression models(GLRMs),including a power component and negative binomial regressions(NBRs).However,as far as the authors know,artificial neural network models(ANNMs)have never been applied to this field.This paper is an attempt to fill in these gaps.First a frequencybased metric of traffic overloading was adopted as a driver of failure probability.Second,two alternative‘frequency'models were specified,calibrated,and validated.The former was based on a GLRM,the latter on ANNMs.Then,these models were compared using regression plots(RPs),measures of errors(Mo Es)and the ratio between the number of observed vs predicted design load overcoming events to evaluate their performance.The models analysed more than 2 million weigh-in-motion(WIM)data records from a pilot station on a bridge on a heavily used ring road in Brescia(Italy).Results showed that ANNMs outperformed GLRMs.ANNMs have a higher correlation coefficient(between predicted and target frequencies),lower Mo Es,and a closer-to-unity ratio(between predicted and target frequencies).These findings may increase prediction accuracy of design traffic overloading events and give road authorities more effective traffic management to protect bridges from load hazards.

Hierarchical methodology to evaluate the quality of disparate axle load data sources for pavement design

Axle load data are an essential input for pavement design,yet for most North American agencies,there is uncertainty about the quality of axle load data obtained from weigh-inmotion(WIM)systems,the applicability of these data for pavement design,and potential opportunities to integrate axle load data from disparate sources.This article presents a novel and practical methodology to evaluate the quality of axle load data from WIM systems and roadside weigh scales through a series of hierarchical analyses designed to test data validity.When applied using data from Manitoba,Canada,the methodology quantified the uncertainty of axle loads measured at the weigh scales and piezo-quartz WIM,concluding that both could be used for pavement design applications.Data collected at piezo-polymer WIM sites exhibited poorer data validity;however,application of site-specific temperature correction factors significantly improved data validity at these sites.The article describes how other data quality dimensions,including spatial coverage,temporal coverage,and long-term data availability,could be considered when determining the suitability of disparate axle load data sources for pavement design.Application of the methodology enables a pragmatic evaluation of the quality and limitations of commonlyavailable axle load data,revealing uncertainties and data needs relevant for pavement design practice.

广东省居民肺癌与吸入氡关系的初步探讨

本文引用我所1985年对本省居民住宅内、外氡(气土)及其短寿子体浓度调查数据及1982年全省居民人口普查统计资料,采用挪威学者Stranden氏提出的氡与肺癌关系的估算模式,对广东省居民肺癌与吸入氡及其子体的关系进行了分析讨论,得出全部肺癌发病因素中由暴露氡所致份额为11.3%。

A practical multi-lane factor model of bridges based on multi-truck presence considering lane load disparities

Many bridge design specifications consider multi-lane factors(MLFs)a critical component of the traffic load model.Measured multi-lane traffic data generally exhibit significant lane disparities in traffic loads over multiple lanes.However,these disparities are not considered in current specifications.To address this drawback,a multi-coefficient MLF model was developed based on an improved probabilistic statistical approach that considers the presence of multiple trucks.The proposed MLF model and approach were calibrated and demonstrated through an example site.The model sensitivity analysis demonstrated the significant influence of lane disparity of truck traffic volume and truck weight distribution on the MLF.Using the proposed approach,the experimental site study yielded MLFs comparable with those directly calculated using traffic load effects.The exclusion of overloaded trucks caused the proposed approach,existing design specifications,and conventional approach of ignoring lane load disparity to generate comparable MLFs,while the MLFs based on the proposed approach were the most comprehensive.The inclusion of overloaded trucks caused the conventional approach and design specifications to overestimate the MLFs significantly.Finally,the benefits of the research results to bridge practitioners were discussed.