A CNN-Based Method for Counting Grains within a Panicle

The number of grains within a panicle is an important index for rice breeding. Counting manually is laborious and time-consuming and hardly meets the requirement of rapid breeding. It is necessary to develop an image-based method for automatic counting. However, general image processing methods cannot effectively extract the features of grains within a panicle, resulting in a large deviation. The convolutional neural network (CNN) is a powerful tool to analyze complex images and has been applied to many image-related problems in recent years. In order to count the number of grains in images both efficiently and accurately, this paper applied a CNN-based method to detecting grains. Then, the grains can be easily counted by locating the connected domains. The final error is within 5%, which confirms the feasibility of CNN-based method for counting grains within a panicle.

In-Country Method Validation of a Paper-based, Smartphone-assisted Iron Sensor for the Food Fortification Programs

Objectives Food fortification programs and food companies in low-income settings, such as in the case of Mexico, lack the ability to monitor the micronutrients added to staples entering local markets. The purpose of the present work is to validate a user-friendly sampling kit and quantify the final error parameters of a paper-based, smartphone-assisted sensor (Nu3px) for the determination of iron in corn flours within the context of Mexico's food fortification program. Methods Corn flour samples (n = 45) from local brands (n = 6) were collected from supermarkets, convenience stores, and directly from companies in the States of Querétaro, Cuautitlán, Saltillo, and Cuetzalan, Mexico. Iron content was analyzed using atomic emission spectroscopy (AES) and Nu3px. The final error parameters were quantified via method validation final experiments, i.e., replication and comparison of methods experiments. Qualitative categorization of samples (i.e., accept/reject batch) was applied to evaluate Nu3px's against Mexico's fortification policy (cutoff = 40 μg Fe/g flour). A user-centered design process was applied to develop and evaluate a sampling kit consisting of low-cost measuring utensils. Results Iron content in fortified Mexican corn flours varied widely (23–39%). Nu3px's random error was 12% (replication experiment, n = 5) and its bias was 1.79 ± 9.99 μg Fe/g flour (comparison of methods experiment, n = 45). The true mean difference between Nu3px and AES was zero (p > 0.05) and both methods had similar variance (F = 2.40; P > 0.05). AES and Nu3px classified the iron content above/below the cutoff in the same way (100% match, Χ2 = 16.41, P = 0.01). The affordable and user-friendly sampling kit added some random error, but the mean difference was equal to zero (P > 0.05). Both sampling procedures were correlated (r = 0.952, P = 0.01). Conclusions An affordable, user-friendly, and equipment-free sample preparation kit for corn flour samples showed similar precision to using analytical tools. The sample preparation kit along with the paper-based, smartphone-assisted assay measure iron within the performance parameters required for its application to monitor batch quality in the corn flour fortification program in Mexico. Funding Sources Fulbright Garcia-Robles Fellowship, 2019.

Modification of Holter ECG Monitoring Based on Arduino Uno with Data Storage

Heart monitoring is needed in order to know the patient’s heart condition. In monitoring, storage media of heart monitor result is needed in order to help doctors and paramedics examine the patients. Therefore, a research to plan and create logger data system of heart signal as a modification of electrocardiograph was conducted. This research used amplifier, active High Pass Filter, active Low Pass Filter, Noch Filter, summing adder, and Microcontroller, and Delphi 7 Software to make interface program on personal computer. Heart Rate score (BPM) on 2x16 LCD indicated final error of 2,8%. Meanwhile, the final error of Software module was 1,4%. The measurement of lead II R signal amplitude resulted the final error of -4,5% and the final error on the distance length from R to R was -1,1%. Heart Rate score (BPM) with two human samples indicated error on sample one on sitting position of -3,8% and on sample two -6,3%, Sample one on Standing Position 2,9% and sample two -4,1%, Sample one on Roll Call -1,7% and sample one -8,4% and sample one on running in place -4,6% and sample two 4,1%. The result reading on Delphy 7 Software the errors were -0,3% on sample one and -6,5% on sample two. On standing position, sample one was -0,6% and sample two -2,2%, roll call position for sample one 1,7% and sample two -8,4%, and running in place for sample one 4,1% and sample two 3,3%. The score of R signal amplitude and the distance length from R of R on human sample indicated error for amplitude in sitting position -9,6%, standing -1,3%, roll call -3,0%, running in place -5,9%. The error on distance from R to R on sitting position was -5,9%, standing -17,3%, roll call 6,7%, and running in place 13,8%.

StartReact increases probability of muscle activity and distance in severe/moderate stroke survivors during two-dimensional reaching task

ObjectiveStartReact elicits faster, larger, and more appropriate muscle activation in stroke survivors but has been only cursorily studied to date during multi-jointed reaching. Our objective was to evaluate StartReact on unrestricted, two-dimensional point-to-point reaching tasks post-stroke.MethodData from 23 individuals with stroke was collected during point-to-point reaching. Voluntary and StartReact trials were compared between mild, severe/moderate, and the unimpaired arm.ResultsStartReact showed an increase in probability of muscle activity, larger muscle activity amplitude and faster muscle activity onset. Despite changes in muscle activity, metrics of movement (distance, final error, linear deviation) were largely the same between StartReact and Voluntary trials except in severe/moderate stroke who had larger reaching distances during StartReact.ConclusionWhile StartReact impacted many metrics of muscle activity, the most profound effect was on probability of muscle activity increasing 34% compared to Voluntary which allowed severe/moderate subjects to increase reaching distance but did not translate to decrease in final error suggesting that the additional movement was not always directed towards the appropriate target.SignificanceThese results indicate that SR has the capacity to activate paralyzed muscle in severe/moderate patients, but future studies are needed to explore the possible use of SR in the rehabilitation.

The cosmological analysis of X-ray cluster surveys

Context. This paper is the fourth of a series evaluating the ASpiX cosmological method, based on X-ray diagrams, which are constructed from simple cluster observable quantities, namely: count rate (CR), hardness ratio (HR), core radius (rc), and redshift. Aims. Following extensive tests on analytical toy catalogues (Paper III), we present the results of a more realistic study over a 711 deg2 template-based maps derived from a cosmological simulation. Methods. Dark matter haloes from the Aardvark simulation have been ascribed luminosities, temperatures, and core radii, using local scaling relations and assuming self-similar evolution. The predicted X-ray sky-maps were converted into XMM event lists, using a detailed instrumental simulator. The XXL pipeline runs on the resulting sky images, produces an observed cluster catalogue over which the tests have been performed. This allowed us to investigate the relative power of various combinations of the CR, HR, rc, and redshift information. Two fitting methods were used: a traditional Markov chain Monte Carlo (MCMC) approach and a simple minimisation procedure (Amoeba) whose mean uncertainties are a posteriori evaluated by means of synthetic catalogues. The results were analysed and compared to the predictions from the Fisher analysis (FA). Results. For this particular catalogue realisation, assuming that the scaling relations are perfectly known, the CR-HR combination gives σ8 and Ωm at the 10% level, while CR-HR-rc-z improves this to ≤3%. Adding a second HR improves the results from the CR-HR1-rc combination, but to a lesser extent than when adding the redshift information. When all coefficients of the mass-temperature relation (M-T, including scatter) are also fitted, the cosmological parameters are constrained to within 5–10% and larger for the M-T coefficients (up to a factor of two for the scatter). The errors returned by the MCMC, those by Amoeba and the FA predictions are in most cases in excellent agreement and always within a factor of two. We also study the impact of the scatter of the mass-size relation (M-Rc) on the number of detected clusters: for the cluster typical sizes usually assumed, the larger the scatter, the lower the number of detected objects. Conclusions. The present study confirms and extends the trends outlined in our previous analyses, namely the power of X-ray observable diagrams to successfully and easily fit at the same time, the cosmological parameters, cluster physics, and the survey selection, by involving all detected clusters. The accuracy levels quoted should not be considered as definitive. A number of simplifying hypotheses were made for the testing purpose, but this should affect any method in the same way. The next publication will consider in greater detail the impact of cluster shapes (selection and measurements) and of cluster physics on the final error budget by means of hydrodynamical simulations.

Geometric Error Modeling and Sensitivity Analysis of Single-Axis Assembly in Three-Axis Vertical Machine Center Based on Jacobian-Torsor Model

The influence of component errors on the final error is a key point of error modeling of computer numerical control (CNC) machine tool. Nevertheless, the mechanism by which the errors in mechanical parts accumulate to result in the component errors and then impact the final error of CNC machine tool has not been identified; the identification of this mechanism is highly relevant to precision design of CNC machine. In this study, the error modeling based on the Jacobian-torsor theory is applied to determine how the fundamental errors in mechanical parts influence and accumulate to the comprehensive error of single-axis assembly. First, a brief introduction of the Jacobian-torsor theory is provided. Next, the Jacobian-torsor model is applied to the error modeling of a single-axis assembly in a three-axis machine center. Furthermore, the comprehensive errors of the single-axis assembly are evaluated by Monte Carlo simulation based on the synthesized error model. The accuracy and efficiency of the Jacobian-torsor model are verified through a comparison between the simulation results and the measured data from a batch of similar vertical machine centers. Based on the Jacobian-torsor model, the application of quantitative sensitivity analysis of single-axis assembly is investigated, along with the analysis of key error sources to the synthetical error ranges of the single-axis assembly. This model provides a comprehensive method to identify the key error source of the single-axis assembly and has the potential to enhance the tolerance/error allocation of the single axis and the whole machine tool.

Modified Jacobian-Torsor Based Error Modeling and Quantitative Sensitivity Analysis for Single Axis Assembly of Machine Tool

The influence of component errors on the final error is a key aspect of error modeling of CNC machine tool. Nevertheless, the mechanism by which the errors in mechanical parts accumulate to result in the component errors and then impact the final error of CNC machine, has not been identified; the identification of this mechanism is highly relevant to precision design of CNC machine. In this study, error modeling based on the Jacobian-torsor theory is founded to determine the mechanism by which fundamental errors in mechanical parts influence the comprehensive error of single-axis assembly. Firstly, the constraints of small displacement torsors (SDTs) for typical features and the statistical solution are proposed to perfect the modified Jacobian-torsor model theoretically. Next, the modified Jacobian-torsor model is applied to the error modeling of a single-axis assembly in a three-axis machine center. Furthermore, the comprehensive errors of the single-axis assembly are evaluated by Monte Carlo simulation based on the synthesized error model. The accuracy and efficiency of the modified Jacobian-torsor model are verified through a comparison between the simulation results and the measured data from a batch of similar vertical machine centers. Based on the modified Jacobian-torsor model, the application of quantitative sensitivity analysis of single-axis assembly is investigated, along with an analysis of the analysis of key error sources to the synthetical error ranges of the single-axis assembly. This model is providing a comprehensive method for the better understanding of the key error source of the machine tool and has the potential to enable error allocation and precision improvement of the assembly and the whole machine tool in future.

ANALISIS KAUSALITAS EKSPOR NON MIGAS DENGAN PERTUMBUHAN EKONOMI MENGGUNAKAN METODE FINAL PREDICTION ERROR

This research titled "Causality analysis non petrol export with economic growth using final error prediction methods". Goal which needs to find the answer in this research is to know that non petrol export variable affecting to economic growth variable and economic growth variable affecting non petrol export variable. And also to know final prediction error with existence of long term equilibrium between non petrol export with economic growth. Hypothesis proposed is non petrol export having positive effect to economic growth and economic growth have positive effect to non petrol export. Final error prediction with existence of relation between long term equilibrium and non petrol export and economic growth have positive effect, and final error prediction with existence of relation between long term equilibrium with economic growth and non petrol export have positive effect.Based on analysis there's only one direction causality relation between economic growth and non petrol export. From facts above can be concluded that economic growth will bring creation process and expanding strong domestic market because export is not a starting point or initial destination of economic growth but export only a economic growth process

A Taboo Search Optimization of the Control Law of Nonlinear Systems with Bounded Uncertainties

The aim of this paper is to propose a method to determine among the eligible controls of a nonlinear system, with bounded perturbations, the one which minimizes the final error. The approach is based on the implementation of aggregation techniques using vector norms in order to determine a comparison system used to calculate an attractor in view of its minimization by implementation of metaheuristics.

Efficient volumetric error compensation technique for additive manufacturing machines

Purpose – This paper aims to present a methodology for volumetric error compensation. This technique is applied to an Objet Eden350V 3D printer and involves a custom measurement strategy. Design/methodology/approach – The kinematic model of the printer is explained, and its error model is simplified to 18 independent error functions. Each error function is defined by a cubic Legendre polynomial. The coefficients of the polynomials are obtained through a Levenberg–Marquardt optimization process. This optimization process compares, in an iterative algorithm, nominal coordinates with actual values of the cloud of points. The points are built in the faces of a gauge artefact as conical sockets defining one unique point for each socket. These points are measured by a coordinate measuring machine self-centring measurement process. Findings – Most of the errors of the 3D printer are systematic. It is possible to obtain an improvement of 70 per cent in terms of global mean error reduction in single points within a volume of 120 × 120 × 40 mm. The forecast of the final error compensation fully matches the actual final error. Practical implications – This methodology can be used for accuracy improvement in additive manufacturing machines. Originality/value – Unlike the calculation of geometric errors, the proposed parametric determination through optimization of the error model allows global error reduction, which decreases all sort of systematic errors concurrently. The proposed measurement strategy allows high reliability, high speed and operator independence in the measurement process, which increases efficiency and reduces the cost. The proposed methodology is easily translated to other rapid prototyping machines and allows scalability when replicating artefacts covering any working volume.