Unfolding analysis of LaBr3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration

With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.

CALIBRATION OF A 6-DOF SPACE ROBOT USING GENETIC ALGORITHM

The kinematic error model of a 6-DOF space robot is deduced, and the cost function of kinematic parameter identification is built. With the aid of the genetic algorithm （GA） that has the powerful global adaptive probabilistic search ability, 24 parameters of the robot are identified through simulation, which makes the pose （position and orientation） accuracy of the robot a great improvement. In the process of the calibration, stochastic measurement noises are considered. Lastly, generalization of the identified kinematic parameters in the whole workspace of the robot is discussed. The simulation results show that calibrating the robot with GA is very stable and not sensitive to measurement noise. Moreover, even if the robot＇s kinematic parameters are relative, GA still has strong search ability to find the optimum solution.

Robot stereo vision calibration method with genetic algorithm and particle swarm optimization

Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.

Scanner external calibration algorithm based on fixed point in robot remanufacturing system

This paper deals with the scanner exterior calibration algorithm when the scanner is arranged by the robot and the object scanned is fixed on a rotate device in the Robot Remanufacturing System. The method of calibrating the relationship between the scanner coordinate and the robot Tool0, such as the rotation, Rx, Ry, Rz, and the transformation ,Y, Z is studied. The data of Tool0 can be directly obtained from the relationship with the robot base-coordinate. So, the coordinate relationship between the scanner coordinate and the robot base coordinate can be easily gotten. This paper explains the basic algorithm theory, computing method, data collecting process and the resulted data in detail. The calibration algorithm is deduced under the orthogonal coordinate.

An Adaptive Image Calibration Algorithm for Steganalysis

In this paper,a new adaptive calibration algorithm for image steganalysis is proposed.Steganography disturbs the dependence between neighboring pixels and decreases the neighborhood node degree.Firstly,we analyzed the effect of steganography on the neighborhood node degree of cover images.Then,the calibratable pixels are marked by the analysis of neighborhood node degree.Finally,the strong correlation calibration image is constructed by revising the calibratable pixels.Experimental results reveal that compared with secondary steganography the image calibration method significantly increased the detection accuracy for LSB matching steganography on low embedding ratio.The proposed method also has a better performance against spatial steganography.

An improved self-calibration approach based on adaptive genetic algorithm for position-based visual servo

An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper. Our approach introduces an extension of Mendonca-Cipolla and G. Chesi＇s self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix. Specifically, a suitable dynamic online cost function is generated according to the property of the three singular values. The visual servo process is carried out simultaneous to the dynamic self-calibration, and then the cost function is minimized using the adaptive genetic algorithm instead of the gradient descent method in G. Chesi＇s approach. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value, which is not constant in many cases. It is not necessary to know exactly the camera intrinsic parameters when using our approach, instead, coarse coding bounds of the five parameters are enough for the algorithm, which can be done once and for all off-line. Besides, this algorithm does not require knowledge of the 3D model of the object. Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbations of camera parameters, and it is an effective and efficient visual servo algorithm.

Calibration of Pipe Roughness Coefficient Based on Manning Formula and Genetic Algorithm

This paper presents a method to calibrate pipe roughness coefficient (i.e., Manning n-factor) with genetic algorithm (GA) under multiple loading conditions. Due to the old pipe age as well as deleting valves and blends in the skeleton of distribution network, most of the pipes in hydraulic model of practical water distribution system (WDS) are rough. The commonly used Hazen-Williams C-factor is therefore replaced by Manning n-factor in calibrating WDS hydraulic model. Adjustment to GA is designed, and the program efficiency is improved. A case study shows that the adjustment can save 60% of the total runtime. About 90% of the relative differences between simulated and observed pressures at monitoring locations are lower than 3%, which suggests that the proposed adjustment to the calibration is efficient and effective.

A new calibration algorithms of spinning projectile aerodynamic parameters

This paper demonstrates that the application of calibration algorithms of aerodynamic parameters for the trajectory of spinning projectile is successful. First, from the point of view of the trajectory simulation, a general summary of well-known trajectory models is given. A five degrees of freedom (5 DOF) model is developed that can match the projectile motion essentially in the vertex region, and the results obtained by 5 DOF model are in close agreement with those of a more sophisticated 6 DOF model for elevation angles above 45 degrees. Secondly, the calibration algorithms have been developed and are summarized. The methods of calibrating the flight trajectory models are compared, and these methods are shown to be effective in the representative cases. In addition, the method of Mach number calibration (MNC) is presented; some possible areas in MNC for further investigation are indicated together with benefits to be gained. The utilization of MNC schemes not only allow a worthwhile reduction of calibration rounds firing in range and accuracy (R&A) trial and production of firing tables (PFT) test, but also make PFT and fire control data (FCD) more cost effective.

Online calibration of laser-induced breakdown spectroscopy for detection of heavy metals in water

In order to reduce the fluctuation of LIBS detection spectrum of liquid sample,the full-spectrum sum method and the internal standardization method is adopted,using an equal-RSD normalization algorithm to calibrate the detection spectrum.Experiment result shows that the full-spectrum sum method reduced the RSD of parallel samples of Cd and Cr to 9.4% and 11.06% from 28.32% and 31.93% respectively,yielded better overall calibration than the singleelement internal standardization approach,thereby suggesting that the former method is convenient and effective for online calibration of LIBS for detection of aqueous heavy metals.

Second-order calibration applied to quantification of two active components of Schisandra chinensis in complex matrix

The effectiveness of traditional Chinese medicine (TCM) against various diseases urges more low cost, speed and sensitive analytical methods for investigating the phamacology of TCM and providing a theoretical basis for clinical use. The potential of second-order calibration method was validated for the quantification of two effective ingredients of Schisandra chinensis in human plasma using spectrofluorimetry. The results obtained in the present study demonstrate the advantages of this strategy for multi-target determination in complex matrices. Although the spectra of the analytes are similar and a large number of interferences also exist, second-order calibration method could predict the accurate concentrations together with reasonable resolution of spectral profiles for analytes of interest owing to its ‘second-order advantage’. Moreover, the method presented in this work allows one to simply experimental procedure as well as reduces the use of harmful chemical solvents.

New self-calibration approach to space robots based on hand-eye vision

To overcome the influence of on-orbit extreme temperature environment on the tool pose(position and orientation) accuracy of a space robot,a new self-calibration method based on a measurement camera(hand-eye vision) attached to its end-effector was presented.Using the relative pose errors between the two adjacent calibration positions of the space robot,the cost function of the calibration was built,which was different from the conventional calibration method.The particle swarm optimization algorithm(PSO) was used to optimize the function to realize the geometrical parameter identification of the space robot.The above calibration method was carried out through self-calibration simulation of a six-DOF space robot whose end-effector was equipped with hand-eye vision.The results showed that after calibration there was a significant improvement of tool pose accuracy in a set of independent reference positions,which verified the feasibility of the method.At the same time,because it was unnecessary for this method to know the transformation matrix from the robot base to the calibration plate,it reduced the complexity of calibration model and shortened the error propagation chain,which benefited to improve the calibration accuracy.

Automatic calibration for wobble errors in shallow water multibeam bathymetries

The wobble errors caused by the imperfect integration of motion sensors and transducers in multibeam echo-sounder systems(MBES)manifest as high-frequency wobbles in swaths and hinder the accurate expression of high-resolution seabed micro-topography under a dynamic marine environment.There are many types of wobble errors with certain coupling among them.However,those current calibration methods ignore the coupling and are mainly manual adjustments.Therefore,we proposed an automatic calibration method with the coupling.First,given the independence of the transmitter and the receiver,the traditional georeferenced model is modified to improve the accuracy of footprint reduction.Secondly,based on the improved georeferenced model,the calibration model associated with motion scale,time delay,yaw misalignment,lever arm errors,and soundings is constructed.Finally,the genetic algorithm(GA)is used to search dynamically for the optimal estimation of the corresponding error parameters to realize the automatic calibration of wobble errors.The simulated data show that the accuracy of the calibrated data can be controlled within 0.2%of the water depth.The measured data show that after calibration,the maximum standard deviation of the depth is reduced by about 5.9%,and the mean standard deviation of the depth is reduced by about 11.2%.The proposed method has significance in the precise calibration of dynamic errors in shallow water multibeam bathymetrie s.

An Automatic Optimization Technique for the Calibration of a Physically Based Hydrological Rainfall-Runoff Model

Models are tools widely used in the prediction of hydrological phenomena. The present study aims to contribute to the implementation of an automatic optimization strategy of parameters for the calibration of a hydrological model based on the least action principle (HyMoLAP). The Downhill Simplex method is also known as the Nelder-Mead algorithm, which is a heuristic research method, is used to optimize the cost function on a given domain. The performance of the model is evaluated by the Nash Stucliffe Efficiency Index (NSE), the Root Mean Square Error (RMSE), the coefficient of determination (R2), the Mean Absolute Error (MAE). A comparative estimation is conducted using the Nash-Sutcliffe Modeling Efficiency Index and the mean relative error to evaluate the performance of the optimization method. It appears that the variation in water balance parameter values is acceptable. The simulated optimization method appears to be the best in terms of lower variability of parameter values during successive tests. The quality of the parameter sets obtained is good enough to impact the performance of the objective functions in a minimum number of iterations. We have analyzed the algorithm from a technical point of view, and we have carried out an experimental comparison between specific factors such as the model structure and the parameter’s values. The results obtained confirm the quality of the model (NSE = 0.90 and 0.75 respectively in calibration and validation) and allow us to evaluate the efficiency of the Nelder-Mead algorithm in the automatic calibration of the HyMoLAP model. The developed hybrid automatic calibration approach is therefore one of the promising ways to reduce computational time in rainfall-runoff modeling.

A Comparison among Manual and Automatic Calibration Methods in VISSIM in an Expressway (Chihuahua, Mexico)

Traffic microsimulation is an essential tool in urban transportation and road planning. Its calibration is essential to attain representative results validated with real-world conditions. VISSIM (Verkehr in Städten—SIMulationsmodell) operates with the Wiedemann’s psycho-physical car-following model for freeway travel that considers safety distances (standstill and movement) during simulation. Calibration in this paper was achieved by using two different approaches: a) manual and b) genetic algorithm (with the GEH statistic formula) calibration techniques. Calibration and validation of this model were performed at the Periferico de la Juventud expressway in Chihuahua City, in northern Mexico. The Periferico de la Juventud (PDJ) has a N-S orientation and a length of ca. 20 km, with its northern section being its most congested portion. Its highest vehicle volume occurs at noon, with 3700 vehicles per hour, with 95% being passenger cars and the other 5% heavy goods vehicles. PDJ’s speed limit is 70 km·h-1, but the driver’s behavior has a tendency towards the aggressive performance. A total of 82 standstill and 82 look-ahead distances were obtained from unmanned aerial vehicles (UAV) images, with values ranging from 0.8 to 4.7 m and from 0.2 to 28 m, respectively. VISSIM calibrated parameter values were calculated for this expressway, being slightly above than the VISSIM default ones;and was validated with travel times and look-ahead distances. Results contribute information for the city’s future installment of public transportation systems, and should help decision makers deal with future urban planning.

Application of camera calibrating model to space manipulator with multi-objective genetic algorithm

The multi-objective genetic algorithm(MOGA) is proposed to calibrate the non-linear camera model of a space manipulator to improve its locational accuracy. This algorithm can optimize the camera model by dynamic balancing its model weight and multi-parametric distributions to the required accuracy. A novel measuring instrument of space manipulator is designed to orbital simulative motion and locational accuracy test. The camera system of space manipulator, calibrated by MOGA algorithm, is used to locational accuracy test in this measuring instrument. The experimental result shows that the absolute errors are [0.07, 1.75] mm for MOGA calibrating model, [2.88, 5.95] mm for MN method, and [1.19, 4.83] mm for LM method. Besides, the composite errors both of LM method and MN method are approximately seven times higher that of MOGA calibrating model. It is suggested that the MOGA calibrating model is superior both to LM method and MN method.

附加距离约束的导航相机快速标定方法

针对在轨条件下因观测条件匮乏导致巡视器导航相机标定精度受限的问题,提出了一种基于直线特征的导航相机快速在轨自标定方法。首先,利用SAM(segment anything model)对导航影像太阳能板区域进行图像分割,并对太阳能板OCR(optical control room)直线特征进行提取;然后,构建附加距离约束的导航相机自检校模型;最后,利用Levenberg-Marquardt方法对标定模型进行求解。开展了模拟实验及在轨实验。分析结果表明,相比光束法自标定、CAHVOR及Vanishing Points等标定方法,所提出方法标定参数重建的坐标精度平均值分别提升了20.60%、30.82%和35.92%,对太阳能板OCR距离恢复距离偏差范围在-1.4~1.5 mm之间。该方法可为巡视器导航相机在轨标定技术提供一定的价值参考。

基于电动方向盘的拖拉机自动导航转向控制方法

为提高轮式拖拉机自动导航过程中转向控制的精度与稳定性,该研究以雷沃欧豹M704-2H拖拉机作为试验平台,采用电动方向盘作为转向执行机构,分析转向机械间隙对控制精度的影响,针对转向间隙特性设计转向控制算法。首先,为了获得准确的转向角,利用GNSS(global navigation satellite system)与二轮车模型快速标定虚拟轮转角,标定结果表明:虚拟轮转角的最大误差为1.3°,平均误差为0.11°。然后,对转向系统的机械间隙进行分析,设计一种带有间隙补偿的模糊PD(proportional derivative)转向控制算法,并在Simulink中验证算法的可行性。实车试验结果表明,该算法跟踪方波转角信号的响应时间为1.1 s,最大稳态误差为0.65°,平均稳态绝对误差为0.132°。跟踪正弦波转角信号的平均延时为0.5 s,最大误差为1.91°,平均绝对误差为1.09°。与无间隙补偿算法相比,有间隙补偿算法跟踪方波信号最大稳态误差减小了0.022°,平均稳态绝对误差减少了0.112°,角度误差在±0.2°内的时间提升了71%;跟踪正弦波信号最大误差减小了0.68°,平均绝对误差减小0.23°。田间直线导航转向控制试验结果表明,转角跟踪的绝对平均误差为0.61°,最大跟踪误差为2.82°,转向控制跟踪精度较高,稳定性好,满足导航作业需求。

基于计算机视觉与Canny算法的服装纸样轮廓提取

为提高服装二维纸样轮廓信息采集转换的准确性及方便性,设计了一种基于计算机视觉的服装纸样轮廓提取方法。将手机相机作为图像采集设备,采用相机标定的方法进行图像畸变矫正,对图像灰度化处理后进行伽马变换。通过改进的Canny算法对纸样图像进行边缘信息的提取,使用自适应双边滤波保边去噪;在原Sobel算子上增加了45°和135°方向的梯度模板计算梯度;采用自适应双阈值确定边缘;融合形态学算法处理轮廓;最后按需进行轮廓骨架提取。结果表明:本文方法适用于二维纸样的轮廓提取,其提取误差在0.15~1.50 cm之间,可实现单独对服装纸样的外轮廓、内轮廓及内外轮廓图的提取,完成轮廓的无差别提取,减少后期人工对轮廓图的编辑,提高二维纸样数字化录入效率。

基于PE-ANGO的MIMU现场标定方法

针对当前微惯性测量单元(MIMU)的现场标定方法存在标定步骤复杂、不利于非专业人员操作等问题,提出了一种基于先验知识增强的自适应北苍鹰优化(PE-ANGO)算法的MIMU现场标定方法。首先分析了MIMU涉及的误差并建立了加速度计和陀螺仪的目标函数,然后使用PE-ANGO算法求解目标函数并得到最优参数。为了使得标定工作易于现场操作,引入了一种用于传感器数据采集的手持MIMU来验证所提算法。仿真结果表明:PE-ANGO算法的标定精度相较于北苍鹰优化算法提高了一个数量级。实测实验表明:标定前后对俯仰角和横滚角累积误差的抑制效果分别提高了约89%和87%;与传统标定方法相比,对俯仰角和横滚角累积误差的抑制效果分别提高了71%和68%,验证了所提方法的有效性。