Accurate Evaluation of Free-form Surface Profile Error Based on Quasi Particle Swarm Optimization Algorithm and Surface Subdivision

Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline（NURBS） for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system（DCS） and measurement coordinate system（MCS） for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization（QPSO） is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.

Robustness and precision evaluation of the form error of micro-structured surfaces using real coded genetic algorithm

To obtain the form error of micro-structured surfaces robustly and accurately, a form er- ror evaluation method was developed based on the real coded genetic algorithm （RCGA）. The meth- od employed the average squared distance as the matching criterion. The point to surface distance was achieved by use of iterative method and the modeling of RCGA for the surface matching was also presented in detail. Parameter selection for RCGA including the crossover rate and population size was discussed. Evaluation results of series simulated surfaces without form error show that this method can achieve the accuracy of root mean square deviation （ Sq ） less than 1 nm and surface pro- file error （ St ） less than 4 nm. Evaluation of the surfaces with different simulated errors illustrates that the proposed method can also robustly obtain the form error with nano-meter precision. The e- valuation of actual measured surfaces further indicates that the proposed method is capable of pre- cisely evaluating micro-structured surfaces.

Radiological evaluation of patellofemoral instability and possible causes of assessment errors

Patellofemoral instability(PI)is the disruption of the patella’s relationship with the trochlear groove as a result of abnormal movement of the patella.To identify the presence of PI,conventional radiographs(anteroposterior,lateral,and axial or skyline views),magnetic resonance imaging,and computed tomography are used.In this study,we examined four main instability factors:Trochlear dysplasia,patella alta,tibial tuberosity–trochlear groove distance,and patellar tilt.We also briefly review some of the other assessment methods used in the quantitative and qualitative assessment of the patellofemoral joint,such as patellar size and shape,lateral trochlear inclination,trochlear depth,trochlear angle,and sulcus angle,in cases of PI.In addition,we reviewed the evaluation of coronal alignment,femoral anteversion,and tibial torsion.Possible causes of error that can be made when evaluating these factors are examined.PI is a multi-factorial problem.Many problems affecting bone structure and muscles morphologically and functionally can cause this condition.It is necessary to understand normal anatomy and biomechanics to make more accurate radiological measurements and to identify causes.Knowing the possible causes of measurement errors that may occur during radiological measurements and avoiding these pitfalls can provide a more reliable road map for treatment.This determines whether the disease will be treated medically and with rehabilitation or surgery without causing further complications.

Geometric Approximation Technique for Minimum Zone Sphericity Error

The mathematical modeling for evaluation of the sphericity error is proposed with minimum radial separation center. To obtain the minimum sphericity error from the form data, a geometric approximation technique was devised. The technique regarded the least square sphere center as the initial center of the concentric spheres containing all measurement points, and then the center was moved gradually to reduce the radial separation till the minimum radial separation center was got where the constructed concentric spheres conformed to the minimum zone condition. The method was modeled firstly, then the geometric approximation process was analyzed, and finally,the software for data processing was programmed. As evaluation example, five steel balls were measured and the measurement data were processed with the developed program. The average iteration times of the approximation technique is 4.2, and on average the obtained sphericity error is 0. 529μm smaller than the least square solution,with accuracy increased by 7. 696%.

Time Synchronized Velocity Error for Trajectory Compression

Nowadays,distance is usually used to evaluate the error of trajectory compression.These methods can effectively indicate the level of geometric similarity between the compressed and the raw trajectory,but it ignores the velocity error in the compression.To fill the gap of these methods,assuming the velocity changes linearly,a mathematical model called SVE(Time Synchronized Velocity Error)for evaluating compression error is designed,which can evaluate the velocity error effectively,conveniently and accurately.Based on this model,an innovative algorithm called SW-MSVE(Minimum Time Synchronized Velocity Error Based on Sliding Window)is proposed,which can minimize the velocity error in trajectory compression under the premise of local optimization.Two elaborate experiments are designed to demonstrate the advancements of the SVE and the SW-MSVE respectively.In the first experiment,we use the PED,the SED and the SVE to evaluate the error under four compression algorithms,one of which is the SW-MSVE algorithm.The results show that the SVE is less influenced by noise with stronger performance and more applicability.In the second experiment,by marking the raw trajectory,we compare the SW-MSVE algorithm with three others algorithms at information retention.The results show that the SW-MSVE algorithm can take into account both velocity and geometric structure constraints and retains more information of the raw trajectory at the same compression ratio.

Prediction of constrained modulus for granular soil using 3D discrete element method and convolutional neural networks

To efficiently predict the mechanical parameters of granular soil based on its random micro-structure,this study proposed a novel approach combining numerical simulation and machine learning algorithms.Initially,3500 simulations of one-dimensional compression tests on coarse-grained sand using the three-dimensional(3D)discrete element method(DEM)were conducted to construct a database.In this process,the positions of the particles were randomly altered,and the particle assemblages changed.Interestingly,besides confirming the influence of particle size distribution parameters,the stress-strain curves differed despite an identical gradation size statistic when the particle position varied.Subsequently,the obtained data were partitioned into training,validation,and testing datasets at a 7:2:1 ratio.To convert the DEM model into a multi-dimensional matrix that computers can recognize,the 3D DEM models were first sliced to extract multi-layer two-dimensional(2D)cross-sectional data.Redundant information was then eliminated via gray processing,and the data were stacked to form a new 3D matrix representing the granular soil’s fabric.Subsequently,utilizing the Python language and Pytorch framework,a 3D convolutional neural networks(CNNs)model was developed to establish the relationship between the constrained modulus obtained from DEM simulations and the soil’s fabric.The mean squared error(MSE)function was utilized to assess the loss value during the training process.When the learning rate(LR)fell within the range of 10-5e10-1,and the batch sizes(BSs)were 4,8,16,32,and 64,the loss value stabilized after 100 training epochs in the training and validation dataset.For BS?32 and LR?10-3,the loss reached a minimum.In the testing set,a comparative evaluation of the predicted constrained modulus from the 3D CNNs versus the simulated modulus obtained via DEM reveals a minimum mean absolute percentage error(MAPE)of 4.43%under the optimized condition,demonstrating the accuracy of this approach.Thus,by combining DEM and CNNs,the variation of soil’s mechanical characteristics related to its random fabric would be efficiently evaluated by directly tracking the particle assemblages.

接地(绝缘)电阻测试仪示值误差测量结果的不确定度评定

接地电阻测试仪和绝缘电阻测试仪都是通信工程建设上经常用到的重要仪表。文中介绍了这两种仪表的测量原理及其示值误差测量结果不确定度的评定过程、方法及计算方法。

Boundary evaluation and error correction on pseudorandom spread spectrum photon counting system

The Cramer–Rao lower bound on range error is modeled for pseudo-random ranging systems using Geiger-mode avalanche photodiodes. The theoretical results are shown to agree with the Monte Carlo simulation, satisfying boundary evaluations. Experimental tests prove that range errors caused by the fluctuation of the number of photon counts in the laser echo pulse leads to the range drift of the time point spread function. The function relationship between the range error and the photon counting ratio is determined by using numerical fitting.Range errors due to a different echo energy is calibrated so that the corrected range root mean square error is improved to 1 cm.

Efficient Compression of Vector Data Map Based on a Clustering Model

This paper proposes a new method for the compression of vector data map. Three key steps are encompassed in the proposed method, namely, the simplification of vector data map via the elimination of vertices, the compression of re- moved vertices based on a clustering model, and the decoding of the compressed vector data map. The proposed compres- sion method was implemented and applied to compress vector data map to investigate its performance in terms of the com- pression ratio and distortions of geometric shapes. The results show that the proposed method provides a feasible and effi- cient solution for the compression of vector data map and is able to achieve a promising ratio of compression and maintain the main shape characteristics of the spatial objects within the compressed vector data map.

Adaptive Ultra-short-term Wind Power Prediction Based on Risk Assessment

A risk assessment based adaptive ultra-short-term wind power prediction(USTWPP)method is proposed in this paper.In this method,features are first extracted from the historical data,and then each wind power time series(WPTS)is split into several subsets defined by their stationary patterns.A WPTS that does not match any of the stationary patterns is then included in a subset of non-stationary patterns.Each WPTS subset is then related to a USTWPP model that is specially selected and optimized offline based on the proposed risk assessment index.For online applications,the pattern of the last short WPTS is first recognized,and the relevant prediction model is then applied for USTWPP.Experimental results confirm the efficacy of the proposed method.