Validation of the CMS Magnetic Field Map

The Compact Muon Solenoid (CMS) is a general purpose detector, designed to run at the highest luminosity at the CERN Large Hadron Collider (LHC). Its distinctive features include a 4 T superconducting solenoid with 6-m-diameter by 12.5-m-length free bore, enclosed inside a 10,000-ton return yoke made of construction steel. The return yoke consists of five dodecagonal three-layered barrel wheels and four end-cap disks at each end comprised of steel blocks up to 620 mm thick, which serve as the absorber plates of the muon detection system. To measure the field in and around the steel, a system of 22 flux loops and 82 3-D Hall sensors is installed on the return yoke blocks. A TOSCA 3-D model of the CMS magnet is developed to describe the magnetic field everywhere outside the tracking volume measured with the field-mapping machine. The magnetic field description is compared with the measurements and discussed.Comment: 7 pages, 5 figures, presented at 4th International Conference on Superconductivity and Magnetism 2014, April 27 - May 2, 2014, Antalya, Turkey. arXiv admin note: substantial text overlap with arXiv:1605.08778; text overlap with arXiv:1212.165

An Exploratory Study of Field Failures

Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable. Improving verification and validation activities before deployment can identify and timely remove many but not all faults, and users may still experience a number of annoying problems while using their software systems. This paper investigates the nature of field failures, to understand to what extent further improving in-house verification and validation activities can reduce the number of failures in the field, and frames the need of new approaches that operate in the field. We report the results of the analysis of the bug reports of five applications belonging to three different ecosystems, propose a taxonomy of field failures, and discuss the reasons why failures belonging to the identified classes cannot be detected at design time but shall be addressed at runtime. We observe that many faults (70%) are intrinsically hard to detect at design-time

Validation issues in educational data mining:the case of HTML-Tutor and iHelp

Validation is one of the key aspects in data mining and even more so in educational data mining (EDM) owing to the nature of the data. In this chapter, a brief overview of validation in the context of EDM is given and a case study is presented. The field of the case study is related to motivational issues, in general, and disengagement detection, in particular. There are several approaches to eliciting motivational knowledge from a learner’s activity trace; in this chapter the validation of such an approach is presented and discussed

DEVELOPMENT OF LEARNING MODULE MENGOLAH DATA DENGAN MICROSOFT ACCESS 2003 ON COMPUTER SKILLS AND MANAGEMENTINFORMATION LESSONS IN SMK NEGERI 2 SUKOHARJO

This study aims to: 1) Make learning modules process the data with Microsoft Access 2003, 2) Examine the feasibility of learning module to process data with Microsoft Access 2003 as a medium of teaching in SMK Negeri 2 Sukoharjo, and 3) Determine the effectiveness of using learning modules process data with Microsoft Access 2003 on competency achievement data processing applications. This research is a research and development use development model Brog & Gall. Research and development is carried out by five steps: 1) Conducting analysis of products, 2) To develop the initial product, 3) Validation of expert and revision, 4) small-scale field trials and revisions, and 5) large-scale field tests and the final product. The subject of research on small-scale field trials were 12 students and research subjects on a large scale field tests of 78 students with a sampling technique that is purposive sampling. Determination of eligibility is done in an expert validation and small-scale field trials using a questionnaire while the effectiveness is done in large scale field tests using the results of the assessment practices. The data analysis technique for the feasibility of using descriptive statistics while the effectiveness of using two-sample t-test independent. According to expert assessment of materials and media expert in the expert validation, the learning module fit for use while in the small-scale field trials of learning modules fit for use by percentage of 83.33%. The results obtained by t-test t = 24.028 with df = 74 and p = 0.000, so there is a difference between the practicum students who use learning modules that do not use the learning modules. The mean of the lab for a class that uses a module that is 90.618 while the class does not use a module that is 69.405. As a whole class using the modules stated thoroughly in the competence and who do not use the module there are 5 students who need to make improvements

Revision of empirical electric field modeling in the inner magnetosphere using Cluster data

Using Cluster data from the Electron Drift (EDI) and the Electric Field and Wave (EFW) instruments, we revise our empirically-based, inner-magnetospheric electric field (UNH-IMEF) model at 22.662 mV/m; K-p\u3c1, 1K(p)\u3c2, 2K(p)\u3c3, 3K(p)\u3c4, 4K(p)\u3c5, and K(p)4(+). Patterns consist of one set of data and processing for smaller activities, and another for higher activities. As activity increases, the skewed potential contour related to the partial ring current appears on the nightside. With the revised analysis, we find that the skewed potential contours get clearer and potential contours get denser on the nightside and morningside. Since the fluctuating components are not negligible, standard deviations from the modeled values are included in the model. In this study, we perform validation of the derived model more extensively. We find experimentally that the skewed contours are located close to the last closed equipotential, consistent with previous theories. This gives physical context to our model and serves as one validation effort. As another validation effort, the derived results are compared with other models/measurements. From these comparisons, we conclude that our model has some clear advantages over the others

Creating a Database of Helicopter Main Rotor Acoustics for Validation of CFD Methods

The work presents recent experiments at the Kazan National Technical University (KNRTU-KAI), related to helicopter acoustics. The objective is to provide a database of near-field experimental data suitable for CFD validation. The obtained set of data corresponds to a Mach-scaled rotor of known planform. An advantage of the current dataset is that direct near-field acoustic data is made available and this allows easy and direct comparisons with CFD predictions, without the need to use far-field aeroacoustic methods

Field validation of a dusting cloth for mycological surveillance of surfaces

Efficient monitoring of surfaces for spores of filamentous fungi is essential for detecting minor contamination even when air samples test negative for fungi. This study evaluates and compares a pad prepared using a dusting cloth with Rodac contact plates and humidified swabs for detecting mycological contamination, and concludes that the new method is superior and cheaper

Theoretical and practical validation tests for a near-field to far-field transformation algorithm using spherical wave expansion

The use of spherical wave expansion of the solution of the wave equation to predict Far-Field values from data measured in the Near-Field region is a well known technique, typically used to perform antenna measurements in compact anechoic chambers. However, when designing the computing algorithm it is fundamental to validate the results and to quantify the numerical error of the method. In this regard, a computer application that samples the electric Near-Field and calculates the values of the electric Far-Field region using spherical wave expansion was developed to measure antenna radiation patterns in the Fresnel zone inside a fully anechoic chamber. In order to validate the code, this paper describes three validation methods: firstly, using the theoretical electric Near-Field values of an infinitesimal dipole as the input to the algorithm to compare the output with the response analytically expected; secondly, using a Far-Field electric field data of a calibrated half wavelength dipole measured in an anechoic chamber and finally, using an electric Near-Field data of a calibrated half wavelength dipole measured in the same chamber. These methods provide simple procedures to calculate the error introduced by the code in different scenarios that should be considered to estimate the measurement uncertainty.Postprint (published version

Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line vis-NIR spectroscopy measurements of soil total nitrogen and total carbon

Accurate and detailed spatial soil information about within-field variability is essential for variable-rate applications of farm resources. Soil total nitrogen (TN) and total carbon (TC) are important fertility parameters that can be measured with on-line (mobile) visible and near infrared (vis-NIR) spectroscopy. This study compares the performance of local farm scale calibrations with those based on the spiking of selected local samples from both fields into an European dataset for TN and TC estimation using three modelling techniques, namely gradient boosted machines (GBM), artificial neural networks (ANNs) and random forests (RF). The on-line measurements were carried out using a mobile, fiber type, vis-NIR spectrophotometer (305-2200 nm) (AgroSpec from tec5, Germany), during which soil spectra were recorded in diffuse reflectance mode from two fields in the UK. After spectra pre-processing, the entire datasets were then divided into calibration (75%) and prediction (25%) sets, and calibration models for TN and TC were developed using GBM, ANN and RF with leave-one-out cross-validation. Results of cross-validation showed that the effect of spiking of local samples collected from a field into an European dataset when combined with RF has resulted in the highest coefficients of determination (R-2) values of 0.97 and 0.98, the lowest root mean square error (RMSE) of 0.01% and 0.10%, and the highest residual prediction deviations (RPD) of 5.58 and 7.54, for TN and TC, respectively. Results for laboratory and on-line predictions generally followed the same trend as for cross-validation in one field, where the spiked European dataset-based RF calibration models outperformed the corresponding GBM and ANN models. In the second field ANN has replaced RF in being the best performing. However, the local field calibrations provided lower R-2 and RPD in most cases. Therefore, from a cost-effective point of view, it is recommended to adopt the spiked European dataset-based RF/ANN calibration models for successful prediction of TN and TC under on-line measurement conditions