高压承载下的桥梁挠度形变检测方法仿真研究

研究桥梁挠度形变准确检测问题.桥梁受到外界压力的影响,会发生局部的断面挠度形变.由于挠度形变不均匀,且形变程度极小,因此过小的形变导致在远程坐标转换过程中,常常发生坐标对应错误,导致形变部位基准坐标点不准.传统的采用GPS技术,在检测中依靠形变部位的坐标值进行形变检测,转换后的坐标误差会导致形变特征对应不准,造成检测的准确性不高.提出了一种采用计算机视觉图像最大熵算法的桥梁形变检测方法.利用二值化方法,计算桥梁图像中边界像素的有效分布,对像素计算边界的最大熵,通过设定合理的阀值,计算桥梁形变,避免转换误差的发生.实验结果表明,提出的算法能够提高桥梁在高压力下的形变检测准确性.     

基于Bézier曲线的地震数据三维可视化研究

针对三维地震数据资料处理和解释环节中的科学计算三维可视化问题,提出一种基于 Bézier 曲线的处理方法。通过对地震采样数据进行插值拟合或抽样取点的方式,使数据样本点的密度与成像效率保持在一定的范围内。根据地震数据信号变化规律,利用三次 Bézier 曲线插值的方式,将地震数据映射到相应的颜色向量空间,实现地震数据的三维成像处理,并在构建图像化过程中给出两种参数量化的方法。实验结果表明,该曲线插值方法的图像层次感明显,反映出的地质特征性更强。     

电磁防护仿生技术研究的进展与展望

为了从生物中汲取灵感,进而设计抗干扰能力强、可靠性高的电子系统,对近些年新兴的电磁防护仿生技术的发展脉络进行了详细总结.阐述了电磁防护仿生技术主要研究内容,并展示了现阶段电磁防护仿生研究已取得的成果.生物的神经系统是高度复杂的信息处理和控制系统,具有自组织和自适应的优良特性,同时也具有很高的容错能力.因此,结合神经信息处理方式、编码特点和集成电路实现,进一步探讨和分析了进行电磁防护仿生研究的新思路.     

基于三次定位过程的车牌精确定位算法设计

为了实现对车牌区域的精确定位,提出了一种基于三次定位过程的车牌定位算法,综合改进了数学形态学和纹理分析等方法.对自然环境下摄像头采集的车辆图像进行预处理后,使用像素统计法对车牌进行粗略定位,使用纹理分析法去除车牌上下边框和干扰,使用阈值法去除车牌左右边框和干扰,得到精确的车牌区域.另外还对图像的预处理进行简化,保证实时性要求.实验结果表明,改进后的算法能够对车牌进行实时、精确定位,满足了系统的要求.     

具有温度补偿的膜片型光纤光栅温度压力传感器

阐述了具有温度补偿结构的膜片型光纤光栅温度压力传感器。传感器以弹性膜片为感压元件,在压力作用下产生轴向位移来压缩压力敏感光栅以实现压力传感;通过结构温度补偿消除压力敏感光栅的温度漂移,同时串入感温光栅进行实时修正并实现温度测量。对传感器的压力和温度特性进行了测量。试验结果表明:压力灵敏度为528 pm/MPa,温度灵敏度为8 pm/℃。     

求解多目标问题的Memetic免疫优化算法

将基于Pareto支配关系的局部下山算子和差分算子引入免疫多目标优化算法之中,提出了一种求解多目标问题的Memetic免疫优化算法(Memetic immune algorithm for multiobjective optimization,简称MIAMO).该算法利用种群中抗体在决策空间上的位置关系设计了两种有效的启发式局部搜索策略,提高了免疫多目标优化算法的求解效率.仿真实验结果表明,MIAMO与其他4种有效的多目标优化算法相比,不仅在求得Pareto最优解集的逼近性、均匀性和宽广性上有明显优势,而且算法的收敛速度与免疫多目标优化算法相比明显加快.     

Solution of integral equations via generalized orthogonal polynomials

A new approximation method using a generalized orthogonal polynomial (GOP) is employed for solving integral equations. The integration operational matrix of the GOP, which can represent all kinds of individual orthogonal polynomial, is developed. The dependent variables in the integral equation are assumed to be expressed by a GOP series. A set of algebraic equations is obtained from the integral equation. The calculation of coefficients is straightforward and easy. Examples are given, and the results obtained from individual orthogonal polynomial approximations are compared with each other. It is found that nearly all individual orthogonal polynomials, except Hermite polynomials, offer excellent results.     

New approach for parameter identification via generalized orthogonal polynomials

An effective method is presented for using generalized orthogonal polynomials (GOP) for identifying the parameters of a process whose behaviour can be modelled by a linear differential equation with time-invariant coefficients. The method is based on the differentiation operational matrix of the GOP, which can represent all kinds of individual orthogonal polynomials. The main advantage of using the differentiation operational matrix is that parameter estimation can be made starting at any time of interest, without the restriction of starting at zero time. Using the concept of GOP expansion for a state function and a control function, the differential input-output equation is converted into a set of over-determined linear algebraic equations. The unknown parameters are evaluated by a weighted least-squares estimation method. Two examples are given to demonstrate the validity of the method and good results are obtained.     

Application of a generalized block pulse function to a scaled system

A generalized block pulse function (GBPF) is employed to solve a functional differential equation. The operational matrix for integration and the functional operational matrix of the GBPF are introduced to solve the state equation in order to simplify the calculation procedure. The greatest advantage of using a GBPF is that the time interval of calculation can be adjusted arbitrarily. A small time interval is chosen for a steep change of state variable with time and a large time interval is chosen for a flat change of state variable with time. Therefore, the number of expansion coefficients is greatly reduced and the computer time is also minimized when using GBPF compared with the conventional BPF. An illustrative example is given. It is shown that computational results are more accurate for a steep change of state variable using a GBPF rather than a BPF.     

Adaptive tracking system for a manoeuvring target using images with correlated noises

An effective scheme is proposed for estimating the state parameters of a manoeuvring target from a noisy image sequence, providing the sequence contains correlated noises and the trajectory of the target is disturbed by an unknown acceleration. For the correlated noises, a first-order difference operator is applied to the original image sequence to generate an artificial measurement sequence with only uncorrelated white noises. For the unknown acceleration, a Kalman filter augmented by a semi-Markov process and the bayesian theory is applied to form an adaptive filter. In the proposed tracking system, the filter first generates an artificial measurement at each sampling time from the observation. It then utilizes an artificial measurement sequence up to the current time instance to predict the a posteriori probability of each possible acceleration state. The weighted average of acceleration, where the weight is the a posteriori probability, is applied to derive the optimal estimates of the state parameters. Several computer simulations with external force applied at unknown times are performed to demonstrate the applicability and superiority of the proposed system.