科创中国

以大型会展活动中产生的互联网新闻报道为研究对象,重点阐述如何及时、准确地识别出特定应用场景下的系列事件和把握事件爆发前后的地域空间分布状态,然后结合领域知识和内容分析、信号分析等方法对这些信息进行合理解读,从而完成相关情报分析活动.

中断响应时间是衡量操作系统性能的重要参数和指标,它直接反映了系统对外部触发事件的响应能力,也是衡量操作系统实时性的一个重要指标.本文设计了一种测试操作系统中断响应时间的新方法,该方法采用CPCI板卡硬件,利用FPGA计时器并配合上层软件对中断响应时间进行测试.通过该方法实现了对Windows XP操作系统在不同负载情况下ISR、DPC和EVENT 3种中断响应方式响应时间的测量,并得出了中断响应时间的统计规律.依据这些统计规律,可以根据实时性的需求,选用合理的中断响应方式.

根据物流跨行业、跨部门、综合性较强的特点,阐述了大型体育赛事物流信息平台的目标、定位、设计原则,进而分析了大型体育赛事物流信息平台系统结构:中心管理平台结构与模块、赛区物流管理系统、物流服务提供商业务管理系统、供应商业务系统等构成及功能,最后探讨了大型体育赛事物流信息平台可能面临的风险与防范措施.

探讨了体育赛事物流服务的基本内容和主要特征,构建体育赛事物流服务能力的评价指标体系,并采用信息熵和未确知测度模型方法,结合案例具体地评估了体育赛事物流的综合服务能力,为科学、客观地评价体育物流服务能力提供一种新的方法.

Multi-way principal component analysis(MPCA)has received considerable attention and been widely used in process monitoring.A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces.However,low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model.This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information.The MPCA model and the knowledge base are built based on the new subspace.Then,fault detection and isolation with the squared prediction error(SPE)statistic and the Hotelling(T2)statistic are also realized in process monitoring.When a fault occurs,fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables.For fault isolation of subspace based on the T2 statistic,the relationship between the statistic indicator and state variables is constructed,and the constraint conditions are presented to check the validity of fault isolation.Then,to improve the robustness of fault isolation to unexpected disturbances,the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation.Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system(ASCS)to prove the correctness and effectiveness of the algorithm.The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model,and sets the relationship between the state variables and fault detection indicators for fault isolation.

对目前微博信息挖掘技术中的微博内容挖掘及用户关系挖掘的研究情况及相关方法进行介绍及归纳,认为其中微博内容挖掘主要包括微博短文本挖掘、话题趋势检测、情感倾向性分析等方面,用户关系挖掘主要包括用户群体特性、用户社区发现、意见领袖挖掘及微博传播模式等方面;指出这些方法的局限性,并对微博信息挖掘的发展进行展望,以为进一步研究提供参考.

Eddy current pulsed thermography(ECPT) is an emerging Non-destructive testing and evaluation(NDT & E) technique, which uses hybrid eddy current and thermography NDT & E techniques that enhances the detectability from their compensation. Currently, this technique is limited by the manual selection of proper contrast frames and the issue of improving the efficiency of defect detection of complex structure samples remains a challenge. In order to select a specific frame from transient thermal image sequences to maximize the contrast of thermal variation and defect pattern from complex structure samples, an energy driven approach to compute the coefficient energy of wavelet transform is proposed which has the potential of automatically selecting both optimal transient frame and spatial scale for defect detection using ECPT. According to analysis of the variation of different frequency component and the comparison study of the detection performance of different scale and wavelets, the frame at the end of heating phase is automatically selected as an optimal transient frame for defect detection. In addition, the detection capabilities of the complex structure samples can be enhanced through proper spatial scale and wavelet selection. The proposed method has successfully been applied to low speed impact damage detection of carbon fibre reinforced polymer(CFRP) composite as well as providing the guidance to improve the detectability of ECPT technique.

The existing research of steering efficiency mainly focuses on the mechanism efficiency of steering system,aiming at designing and optimizing the mechanism of steering system.In the development of assist steering system especially the evaluation of its comfort,the steering efficiency of driver physiological output usually are not considered,because this physiological output is difficult to measure or to estimate,and the objective evaluation of steering comfort therefore cannot be conducted with movement efficiency perspective.In order to take a further step to the objective evaluation of steering comfort,an estimating method for the steering efficiency of the driver was developed based on the research of the relationship between the steering force and muscle activity.First,the steering forces in the steering wheel plane and the electromyography(EMG)signals of the primary muscles were measured.These primary muscles are the muscles in shoulder and upper arm which mainly produced the steering torque,and their functions in steering maneuver were identified previously.Next,based on the multiple regressions of the steering force and EMG signals,both the effective steering force and the total force capacity of driver in steering maneuver were calculated.Finally,the steering efficiency of driver was estimated by means of the estimated effective force and the total force capacity,which represented the information of driver physiological output of the primary muscles.This research develops a novel estimating method for driver steering efficiency of driver physiological output,including the estimation of both steering force and the force capacity of primary muscles with EMG signals,and will benefit to evaluate the steering comfort with an objective perspective.

Forming limit curves(FLCs) are commonly used for evaluating the formability of sheet metals. However, it is difficult to obtain the FLCs with desirable accuracy by experiments due to that the friction effects are non-negligible under warm/hot stamping conditions. To investigate the experimental errors, experiments for obtaining the FLCs of the AA5754 are conducted at 250℃. Then, FE models are created and validated on the basis of experimental results. A number of FE simulations are carried out for FLC test-pieces and punches with different geometry configurations and varying friction coefficients between the test-piece and the punch. The errors for all the test conditions are predicted and analyzed. Particular attention of error analysis is paid to two special cases, namely, the biaxial FLC test and the uniaxial FLC test. The failure location and the variation of the error with respect to the friction coefficient are studied as well. The results obtained from the FLC tests and the above analyses show that, for the biaxial tension state, the friction coefficient should be controlled within 0.15 to avoid significant shifting of the necking location away from the center of the punch; for the uniaxial tension state, the friction coefficient should be controlled within 0.1 to guarantee the validity of the data collected from FLC tests. The conclusions summarized are beneficial for obtaining accurate FLCs under warm/hot stamping conditions.

Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the Ada Boost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.