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.

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.

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.

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 Math 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.

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.

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.

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.

A Flexible Calibration Method of Laser Light-Sectioning System for Online 3D Measurement

A flexible calibration method based on a front-coated flat mirror is proposed for a laser light-sectioning three-dimensional(3D)measurement system. Since the calibration target and its mirror image are spatially separated and can be recorded in an image by a camera,the proposed method requires only a single composite image that contains a non-planar checkerboard pattern,a laser strip projected on the target and their mirror images to complete the calibration of the camera and the laser plane in one step. Levenberg-Marquardt(LM)algorithm is used to optimize the system parameters,and the measurement accuracy and speed are improved to enable online 3D inspection. Static and dynamic online 3D measurements are carried out on a cup and a triple stepped shaft,respectively,to validate the proposed method. The shaft has two steps with the depth of(0.5±0.01)mm and(2±0.01)mm to be measured online when the shaft is rotated and translated at the same time. The measurement results can be output at a frequency of 7 to 11 readings per second with standard deviations of 0.040 mm and 0.051 mm. The experimental results verify the effectiveness and flexibility of the proposed method.

六自由度机械臂快速路径规划与手眼标定研究

针对双向快速随机扩展树(rapidly-exploring random trees-connect,RRT-Connect)算法的随机性强、搜索效率低、路径规划时间过长等问题,提出一种改进的RRT-Connect算法。该算法在起始点与目标点连线的中垂线上设置第三节点,采用高斯分布限制第三节点的采样区域,避免第三采样节点距离中点较远导致的路径冗余。算法通过第三节点分别向起始点和目标点生成2棵随机树,结合贪婪算法思想以及引入动态步长的方法,提高算法的规划效率。仿真结果表明,改进的RRT-Connect算法相较于传统RRT-Connect算法,平均运行时间缩短了48.7%,平均迭代次数减少了38.9%,平均路径长度减少了25.2%。另外,针对传统的九点标定法精度的问题,提出一种改进的九点标定方法,该方法通过获取机械臂在空间同一点的多组位姿计算机械臂第六轴长度,在已知机械臂各关节角和轴长情况下,计算得到机械臂末端执行器安装后第六轴的长度,从而提高手眼标定的精度。试验结果表明,改进的方法相较于传统九点标定法其精度平均提高了2.09%。最后,在机械臂平台验证改进的RRT-Connect算法和改进的九点标定法,试验结果表明,改进的RRT-Connect算法相较于DRRT-Connect(dynamicRRT-Connect)算法在路径规划总时间和总长度上分别减少了8.28%和4.79%,改进的九点标定法相较于传统的九点标定法抓取精度提高了3%。

WOFOST伴随率定三温模型的玉米农田遥感蒸散发估算方法

通过遥感蒸散发模型估算实际蒸散发量的方法已被广泛使用,但精度提升仍是研究热点。作物生长模型在模拟作物蒸腾方面具有良好的机理性和精度。本文结合WOFOST作物生长模型和三温遥感蒸散发模型,提出了一种新的玉米农田遥感蒸散发估算方法。核心思路是本地化WOFOST模型,在验证其模拟精度后,利用其模拟的作物蒸腾数据,构造伴随率定函数,率定三温模型的蒸腾组分,然后合并率定后的土壤蒸发组分,得到玉米农田实际蒸散发估算值。以涡度相关系统观测的实际蒸散发量为参照,评估了该方法的估算精度和适用性。结果表明,未经率定的三温模型蒸散发、作物蒸腾和土壤蒸发的相关系数分别为0.61、0.71、0.12,均方根误差为1.76 mm·d^(-1)、1.91 mm·d^(-1)、3.02 mm·d^(-1),纳什效率系数均为负。仅率定土壤蒸发后,相关系数提高至0.77,但误差仍然较大(1.91 mm·d^(-1)),纳什效率系数为-0.74。利用WOFOST模拟的作物蒸腾率定三温模型后,估算值与实际观测的相关系数显著提高至0.89,均方根误差降至0.65 mm·d^(-1),纳什效率系数达到0.79,表明该方法有效提高了三温遥感蒸散发模型的估算精度,并对其他遥感蒸散发模型的精度提升具有参考意义。

梯度重构与迭代极小值标定的分水岭分割算法

分割是面向对象遥感影像分类的首要步骤和重要环节,但是传统的分水岭分割算法往往产生过分割现象,导致分割的地物影像对象过于细碎。研究发现,可以从微分算子计算梯度和区域合并2个方面对分水岭分割算法进行改进。利用Sobel算子进行梯度运算时,引入各波段熵值,以每个波段的熵值在全波段的比率作为梯度权重,进行梯度重构;同时对区域合并极小值阈值标定进行改进,通过前后景标定后,代入前景均值与后景均值的迭代计算,获得极小值阈值,完成影像分割。经过结果对比分析,此方法在较好地解决过分割的同时,也尽可能地保留了地物的边缘信息,能够更好地支持后续的地物对象分析和检测。

High speed robust image registration and localization using optimized algorithm and its performances evaluation

Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.