科创中国

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.

Friction compensation is particularly important for motion trajectory tracking control of pneumatic cylinders at low speed movement. However, most of the existing model-based friction compensation schemes use simple classical models, which are not enough to address applications with high-accuracy position requirements. Furthermore, the friction force in the cylinder is time-varying, and there exist rather severe unmodelled dynamics and unknown disturbances in the pneumatic system. To deal with these problems effectively, an adaptive robust controller with LuGre model-based dynamic friction compensation is constructed. The proposed controller employs on-line recursive least squares estimation(RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. In addition, in order to realize LuGre model-based friction compensation, the modified dual-observer structure for estimating immeasurable friction internal state is developed. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology is applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Finally, the proposed controller is tested for tracking sinusoidal trajectories and smooth square trajectory under different loads and sudden disturbance. The testing results demonstrate that the achievable performance of the proposed controller is excellent and is much better than most other studies in literature. Especially when a 0.5 Hz sinusoidal trajectory is tracked, the maximum tracking error is 0.96 mm and the average tracking error is 0.45 mm. This paper constructs an adaptive robust controller which can compensate the friction force in the cylinder.

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.

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.

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.

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.

针对工业零件含噪图像边缘检测,根据Canny算法原理,提出了一些改进策略,形成了一种矩形透镜最大梯度模边缘检测算法.采用中值滤波完成图像平滑,有效抑制了图像噪声;采用5X5邻域一阶偏导有限差分计算图像的梯度幅值,提高了边缘定位的精度;采用最大类间方差法(OTSU)求解了最优区域分割阈值,实现了边缘的自动检测.以磁环和极片工业零件图像边缘检测为例进行了实验,结果表明,该算法具有较好的去噪和边缘检测效果.图4参13

水泥混凝土路面具有良好的抗冲击、抗冻、抗裂性能,有利于延长路面使用寿命、能有效减小路面截面厚度等优点.结合2010年度新沂河整治水泥混凝土路面防汛道路工程,介绍混凝土路面工程结构及施工特点,并对施工质量控制具体措施、混凝土路面质量检测及评定方法进行探讨,旨在为水泥混凝土路面在水利堤防配套防汛道路工程中的推广及应用提供借鉴.

根据化学事故应急救援与处置现场环境污染快速、实时检测需要,提出了一种化学污染物微流控芯片实时检测系统的设计方案,进行了系统的总体设计和细化设计,论述了系统设计涉及的复杂基质样品微流控芯片预处理技术,特别是固相萃取样品预处理技术、快速灵敏的微流控芯片检测技术、微流控芯片的模拟仿真、设计、集成与控制技术,以及便携式微流控芯片分析仪器的硬件设计和软件开发技术等关键技术及其研究方法,探讨了微流控芯片分离与检测单元的设计与实验、复杂基质样品的微流控芯片预处理单元设计与实验、原理性样机的研制与实验、微流控分析检测方法库和数据库的建立等系统设计的主要研究工作内容及其技术途径.通过微流控芯片分析检测系统包括样品预处理、混合、分离等单元,检测器单元,控制系统和信息处理单元的科学设计,可构建高度集成化、便携化、微型化的微流控分析检测系统,使系统具有适合小分子化合物等环境污染物的分析检测架构.

目的 应用三维斑点追踪显像技术研究慢性心力衰竭患者(CHF)左室扭转的运动特征,探讨其临床价值.方法 CHF组患者30例,年龄匹配的健康志愿者(对照组)33例,采集标准的四腔心及两腔心切面,进行全容积图像存储,应用3D-trace软件进行脱机分析,软件自动分析计算左心室舒张末期容积(LVEDV),左室收缩末期容积(LVESV),左室射血分数(LVEF),左室基底段收缩期扭转角度峰值(BTW),中间段收缩期扭转角度峰值(MTW),心尖段收缩期扭转角度峰值(ATW),左室整体收缩期扭转角度峰值(LVTW).结果 CHF组LVEF,LVTW,MTW,ATW均较正常组减低,BTW、MTW、ATW、LVTW与LVEF之间有明显的相关性,相关系数分别为0.557,0.926,0.932,0.945.结论 CHF患者左心收缩功能明显降低.三维斑点追踪技术为评估左心室的扭转运动提供了全新的工具,该方法具有广阔的应用前景.