Dynamic accuracy analysis of a 5PSS/UPU parallel mechanism based on rigid-flexible coupled modeling

Abstract In order to improve the low output accuracy caused by the elastic deformations of the branch chains, a finite element-based dynamic accuracy analysis method for parallel mechanisms is proposed in this paper. First, taking a 5–prismatic–spherical–spherical (PSS)/universal–prismatic–universal (UPU) parallel mechanism as an example, the error model is established by a closed vector chain method while its influence on the dynamic accuracy of the parallel mechanism is analyzed through numerical calculation and simulation. According to the structural and error characteristics of the parallel mechanism, a vector calibration algorithm is proposed to reduce the position and pose errors along the whole motion trajectory. Then, considering the elastic deformation of the rod, the rigid-flexible coupling dynamic equations of the mechanism are established by combining the finite element method with the Lagrange method, and the equations are vectorially superimposed by means of internal force cancellation to synthesize the elastic dynamics equation of the connecting rod. Based on the constraint condition of each moving part, the elastodynamic model of the whole machine is obtained. Furthermore, the effect of component flexibility on the dimensionless root mean square error of the displacement, velocity and acceleration of the moving platform is investigated by using a Newmark method, and the dynamic accuracy influenced by these dimensionless root mean square errors is further studied. The research work establishes an important theoretical foundation for the development of the prototype.

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Development of a Compact 1-D Micromanipulator with Flexure Manufactured Using Rapid Prototyping

International Journal of Intelligent Mechatronics and Robotics ◽

10.4018/ijimr.2012040104 ◽

2012 ◽

Vol 2 (2) ◽

pp. 47-57 ◽

Cited By ~ 4

Author(s):

Su Zhao ◽

Yan Naing Aye ◽

Cheng Yap Shee ◽

Wei Tech Ang

Keyword(s):

Finite Element ◽

Rapid Prototyping ◽

Root Mean Square ◽

Piezoelectric Actuator ◽

Trajectory Tracking ◽

Nonlinear Stiffness ◽

Mean Square ◽

Manufacturing Costs ◽

Simulation Results ◽

Mean Square Errors

Presented is the design and initial experimental results of a compact 1-D micromanipulator. The presented manipulator is a piezoelectric actuator based complaint mechanism with a compact translational flexure manufactured using rapid prototyping. Rapid prototyping allows complicated designs with low manufacturing costs. Analytical and Finite Element (FE) models for designing the flexure are presented. The simulation results are compared with experiments conducted on a prototype. Nonlinear stiffness is measured and evaluated. The importance of pre-loading force is investigated. Trajectory tracking tests at different frequencies are performed on the manipulator. The maximum and root mean square errors are analyzed.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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Stereolithography vs. Direct Light Processing for Rapid Manufacturing of Complete Denture Bases: An In Vitro Accuracy Analysis

Journal of Clinical Medicine ◽

10.3390/jcm10051070 ◽

2021 ◽

Vol 10 (5) ◽

pp. 1070 ◽

Cited By ~ 1

Author(s):

Alexey Unkovskiy ◽

Franziska Schmidt ◽

Florian Beuer ◽

Ping Li ◽

Sebastian Spintzyk ◽

...

Keyword(s):

Root Mean Square ◽

Complete Denture ◽

Rapid Manufacturing ◽

Accuracy Analysis ◽

Mean Square ◽

Heat Map ◽

Denture Base ◽

Multiple Comparison Test ◽

Direct Light

The topical literature lacks any comparison between stereolithography (SLA) and direct light processing (DLP) printing methods with regard to the accuracy of complete denture base fabrication, thereby utilizing materials certified for this purpose. In order to investigate this aspect, 15 denture bases were printed with SLA and DLP methods using three build angles: 0°, 45° and 90°. The dentures were digitalized using a laboratory scanner (D2000, 3Shape) and analyzed in analyzing software (Geomagic Control X, 3D systems). Differences between 3D datasets were measured using the root mean square (RMS) value for trueness and precision and mean and maximum deviations were obtained for each denture base. The data were statistically analyzed using two-way ANOVA and Tukey’s multiple comparison test. A heat map was generated to display the locations of the deviations within the intaglio surface. The overall tendency indicated that SLA denture bases had significantly higher trueness for most build angles compared to DLP (p < 0.001). The 90° build angle may provide the best trueness for both SLA and DLP. With regard to precision, statistically significant differences were found in the build angles only. Higher precision was revealed in the DLP angle of 0° in comparison to the 45° and 90° angles.

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Evidence That Supertriangles Exist in Nature from the Vertical Projections of Koelreuteria paniculata Fruit

Symmetry ◽

10.3390/sym14010023 ◽

2021 ◽

Vol 14 (1) ◽

pp. 23

Author(s):

Yuping Li ◽

Brady K. Quinn ◽

Johan Gielis ◽

Yirong Li ◽

Peijian Shi

Keyword(s):

Root Mean Square ◽

Goodness Of Fit ◽

Mean Square ◽

Sea Stars ◽

Different Types ◽

Koelreuteria Paniculata ◽

Two Parameters ◽

Mean Square Errors

Many natural radial symmetrical shapes (e.g., sea stars) follow the Gielis equation (GE) or its twin equation (TGE). A supertriangle (three triangles arranged around a central polygon) represents such a shape, but no study has tested whether natural shapes can be represented as/are supertriangles or whether the GE or TGE can describe their shape. We collected 100 pieces of Koelreuteria paniculata fruit, which have a supertriangular shape, extracted the boundary coordinates for their vertical projections, and then fitted them with the GE and TGE. The adjusted root mean square errors (RMSEadj) of the two equations were always less than 0.08, and >70% were less than 0.05. For 57/100 fruit projections, the GE had a lower RMSEadj than the TGE, although overall differences in the goodness of fit were non-significant. However, the TGE produces more symmetrical shapes than the GE as the two parameters controlling the extent of symmetry in it are approximately equal. This work demonstrates that natural supertriangles exist, validates the use of the GE and TGE to model their shapes, and suggests that different complex radially symmetrical shapes can be generated by the same equation, implying that different types of biological symmetry may result from the same biophysical mechanisms.

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The Accuracy of Real-Time hmF2 Estimation from Ionosondes

Remote Sensing ◽

10.3390/rs12172671 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2671

Author(s):

Carlo Scotto ◽

Dario Sabbagh

Keyword(s):

Data Analysis ◽

Real Time ◽

Root Mean Square ◽

Magnetic Activity ◽

Mean Square ◽

Incoherent Scatter Radar ◽

Incoherent Scatter ◽

Scatter Radar ◽

Mean Square Errors

A total of 4991 ionograms recorded from April 1997 to December 2017 by the Millstone Hill Digisonde (42.6°N, 288.5°E) were considered, with simultaneous Ne(h)[ISR] profiles recorded by the co-located Incoherent Scatter Radar (ISR). The entire ionogram dataset was scaled with both the Autoscala and ARTIST programs. The reliability of the hmF2 values obtained by ARTIST and Autoscala was assessed using the corresponding ISR values as a reference. Average errors Δ and the root mean square errors RMSE were computed for the whole dataset. Data analysis shows that both the Autoscala and ARTIST systems tend to underestimate hmF2 values with |Δ| in all cases less than 10 km. For high magnetic activity ARTIST offers better accuracy than Autoscala, as evidenced by RMSE[ARTIST] < RMSE[Autoscala], under both daytime and nighttime conditions, and considering all hours of the day. Conversely, under low and medium magnetic activity Autoscala tends to estimate hmF2 more accurately than the ARTIST system for both daytime and nighttime conditions, when RMSE[Autoscala] < RMSE[ARTIST]. However, RMSE[Autoscala] slightly exceeds RMSE[ARTIST] for the day as a whole. RMSE values are generally substantial (RMSE > 16 km in all cases), which places a limit on the results obtainable with real-time models that ingest ionosonde data.

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Validation of ESA Sentinel-2 L2A Aerosol Optical Thickness and Columnar Water Vapour during 2017–2018

Remote Sensing ◽

10.3390/rs11141649 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1649 ◽

Cited By ~ 2

Author(s):

María Ángeles Obregón ◽

Gonçalo Rodrigues ◽

Maria Joao Costa ◽

Miguel Potes ◽

Ana Maria Silva

Keyword(s):

Water Vapour ◽

Root Mean Square ◽

Optical Thickness ◽

Spectral Response ◽

Aerosol Optical Thickness ◽

Mean Bias Error ◽

Mean Square ◽

Response Functions ◽

Mean Square Errors ◽

Sentinel 2

This study presents a validation of aerosol optical thickness (AOT) and integrated water vapour (IWV) products provided by the European Space Agency (ESA) from multi-spectral imager (MSI) measurements on board the Sentinel-2 satellite (ESA-L2A). For that purpose, data from 94 Aerosol Robotic Network (AERONET) stations over Europe and adjacent regions, covering a wide geographical region with a variety of climate and environmental conditions and during the period between March 2017 and December 2018 have been used. The comparison between ESA-L2A and AERONET shows a better agreement for IWV than the AOT, with normalized root mean square errors (NRMSE) of 5.33% and 9.04%, respectively. This conclusion is also reflected in the values of R2, which are 0.99 and 0.65 for IWV and AOT, respectively. The study period was divided into two sub-periods, before and after 15 January 2018, when the Sentinel-2A spectral response functions of bands 1 and 2 (centered at 443 and 492 nm) were updated by ESA, in order to investigate if the lack of agreement in the AOT values was connected to the use of incorrect spectral response functions. The comparison of ESA-L2A AOT with AERONET measurements showed a better agreement for the second sub-period, with root mean square error (RMSE) values of 0.08 in comparison with 0.14 in the first sub-period. This same conclusion was attained considering mean bias error (MBE) values that decreased from 0.09 to 0.01. The ESA-L2A AOT values estimated with the new spectral response functions were closer to the correspondent reference AERONET values than the ones obtained using the previous spectral response functions. IWV was not affected by this change since the retrieval algorithm does not use bands 1 and 2 of Sentinel-2. Additionally, an analysis of potential uncertainty sources to several factors affecting the AOT comparison is presented and recommendations regarding the use of ESA-L2A AOT dataset are given.

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A STUDY OF MODELS FOR HANDGRIP FORCE PREDICTION FROM SURFACE ELECTROMYOGRAPHY OF EXTENSOR MUSCLE

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237209001131 ◽

2009 ◽

Vol 21 (02) ◽

pp. 81-88 ◽

Cited By ~ 1

Author(s):

Wensheng Hou ◽

Xiaolin Zheng ◽

Yingtao Jiang ◽

Jun Zheng ◽

Chenglin Peng ◽

...

Keyword(s):

Root Mean Square ◽

Surface Electromyography ◽

Random Sequence ◽

Maximum Voluntary Contraction ◽

Force Production ◽

Voluntary Contraction ◽

Mean Square ◽

Force Level ◽

Force Output ◽

Mean Square Errors

Force production involves the coordination of multiple muscles, and the produced force levels can be attributed to the electrophysiology activities of those related muscles. This study is designed to explore the activity modes of extensor carpi radialis longus (ECRL) using surface electromyography (sEMG) at the presence of different handgrip force levels. We attempt to compare the performance of both the linear and nonlinear models for estimating handgrip forces. To achieve this goal, a pseudo-random sequence of handgrip tasks with well controlled force ranges is defined for calibration. Eight subjects (all university students, five males, and three females) have been recruited to conduct both calibration and voluntary trials. In each trial, sEMG signals have been acquired and preprocessed with Root–Mean–Square (RMS) method. The preprocessed signals are then normalized with amplitude value of Maximum Voluntary Contraction (MVC)-related sEMG. With the sEMG data from calibration trials, three models, Linear, Power, and Logarithmic, are developed to correlate the handgrip force output with the sEMG activities of ECRL. These three models are subsequently employed to estimate the handgrip force production of voluntary trials. For different models, the Root–Mean–Square–Errors (RMSEs) of the estimated force output for all the voluntary trials are statistically compared in different force ranges. The results show that the three models have different performance in different force ranges. Linear model is suitable for moderate force level (30%–50% MVC), whereas a nonlinear model is more accurate in the weak force level (Power model, 10%–30% MVC) or the strong force level (Logarithmic model, 50%–80% MVC).

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Prediction of Wheat Grain Protein by Coupling Multisource Remote Sensing Imagery and ECMWF Data

Remote Sensing ◽

10.3390/rs12081349 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1349 ◽

Cited By ~ 3

Author(s):

Xiaobin Xu ◽

Cong Teng ◽

Yu Zhao ◽

Ying Du ◽

Chunqi Zhao ◽

...

Keyword(s):

Remote Sensing ◽

Linear Model ◽

Root Mean Square ◽

Vegetation Index ◽

Meteorological Data ◽

Hierarchical Linear Model ◽

Grain Protein ◽

Landsat 8 ◽

Mean Square ◽

Mean Square Errors

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R2 = 1.00) and RE (R2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R2 = 0.524) demonstrated higher accuracy than the empirical linear model (R2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction.

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A rapidly converging initialisation method to simulate the present-day Greenland ice sheet using the GRISLI ice sheet model (version 1.3)

Geoscientific Model Development ◽

10.5194/gmd-12-2481-2019 ◽

2019 ◽

Vol 12 (6) ◽

pp. 2481-2499 ◽

Cited By ~ 2

Author(s):

Sébastien Le clec'h ◽

Aurélien Quiquet ◽

Sylvie Charbit ◽

Christophe Dumas ◽

Masa Kageyama ◽

...

Keyword(s):

Root Mean Square ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Ice Thickness ◽

Strong Impact ◽

Good Representation ◽

Mean Square ◽

Method Performance ◽

Ice Flow ◽

Mean Square Errors

Abstract. Providing reliable projections of the ice sheet contribution to future sea-level rise has become one of the main challenges of the ice sheet modelling community. To increase confidence in future projections, a good knowledge of the present-day state of ice flow dynamics, which is critically dependent on basal conditions, is strongly needed. The main difficulty is tied to the scarcity of observations at the ice–bed interface at the scale of the whole ice sheet, resulting in poorly constrained parameterisations in ice sheet models. To circumvent this drawback, inverse modelling approaches can be developed to infer initial conditions for ice sheet models that best reproduce available data. Most often such approaches allow for a good representation of the mean present-day state of the ice sheet but are accompanied with unphysical trends. Here, we present an initialisation method for the Greenland ice sheet using the thermo-mechanical hybrid GRISLI (GRenoble Ice Shelf and Land Ice) ice sheet model. Our approach is based on the adjustment of the basal drag coefficient that relates the sliding velocities at the ice–bed interface to basal shear stress in unfrozen bed areas. This method relies on an iterative process in which the basal drag is periodically adjusted in such a way that the simulated ice thickness matches the observed one. The quality of the method is assessed by computing the root mean square errors in ice thickness changes. Because the method is based on an adjustment of the sliding velocities only, the results are discussed in terms of varying ice flow enhancement factors that control the deformation rates. We show that this factor has a strong impact on the minimisation of ice thickness errors and has to be chosen as a function of the internal thermal state of the ice sheet (e.g. a low enhancement factor for a warm ice sheet). While the method performance slightly increases with the duration of the minimisation procedure, an ice thickness root mean square error (RMSE) of 50.3 m is obtained in only 1320 model years. This highlights a rapid convergence and demonstrates that the method can be used for computationally expensive ice sheet models.

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