Histogram density for the Feature Selective Validation (FSV) method

This paper identifies several important aspects of current Feature Selective Validation (FSV) methodologies that are embodied in IEEE Standard 1597.1 for the Validation of CEM Computer Modeling and Simulations. The FSV method facilitates comparisons of sets of electromagnetic (EM) observable data for a given problem to determine “levels of agreement” across amplitude and feature variables. Areas of future revision to this standard are presented that will further enhance the standard's utility for performing Computational Electromagnetic (CEM) technique validation for a wide range of problems. In particular, we consider the utility of the N-dimensional FSV and revisit applications of the Amplitude Difference Measure (ADM), Feature Difference Measure (FDM) and the Global Difference Measure (GDM). This is discussed within the context of large complex system problems that present interesting challenges to the FSV method due to the potentially wide dynamic range of the data. Certain use cases for scattering cross section, system-level coupling, and large system-level EMC problems require a somewhat modified approach in computing the GDM based on how the FDM and ADM are weighted. For the current 1-D FSV, unweighted or incorrectly weighted amplitude and feature measures can potentially lead to inconclusive or even misleading results. These issues are addressed and future revisions to the IEEE Standard 1597.1 are highlighted.

Predicting the High Intensity Radiated Field (HIRF) coupling in complex system is highly required in the aeronautic industry, which can be performed by electromagnetic simulation. But the validation of electromagnetic simulation is a difficult issue which needs proper flowchart and validation methods. In this paper, the numerical simulation by the parallel Finite-Difference Time-Domain (FDTD) method is validated on a simplify model of a metallic case with apertures under the HIRF environment. Concretely, the uncertainty quantification analysis on the input parameter of incident angles is carried out for the electric field in the center of the model with the method of nonintrusive polynomial chaos (NIPC). Based on this, the feature selective validation (FSV) is performed to quantify the comparison of the experimental data and the best simulation estimate. And the results obtained from the FSV method provide a high reliability for the numerical simulation.

This paper is concerned with the usability of the feature selective validation (FSV) method for comparing validation data. It addresses how it can help in the validation process and how the resulting validation data can be interpreted. It does this by considering two different case studies. The first considers two approaches to modeling electric fields in an equipment rack, and the second compares a Spice model for coupled circuit boards with a reference full wave model. The paper concludes that while the single value summary metrics are helpful to give an overall level of agreement, the detailed point-by-point information is very helpful when considering how to improve the models or measurements involved.

It has been found that the feature-selective validation (FSV) method may demonstrate inconsistencies in the results when applied to the comparison of zero-crossing datasets. This type of data is typical of time-domain-based electromagnetic compatibility validation. This paper investigates the source of this inconsistency and proposes a solution. The reason was investigated using a set of typical transient data, which was related to the derivation of the formulas of FSV. It is demonstrated that the problem can be alleviated by enhancing the definition of the offset difference measure. The resulting enhanced performance of FSV is assessed by comparing the results with visual assessment. It is demonstrated that the improvement increases the agreement between FSV prediction and visual assessment. Meanwhile, this modification has a very limited effect on the comparison of other data structures which do not cross the axis.

Feature Select Validation (FSV) is a widely used validation method for data comparison. FSV provides a quantitative standard to describe the similarity between two sets of data. In this paper, the application of the FSV technique is extended to data optimization. The raw data obtained from simulations or measurements are often non-ideal for further processing. Several techniques, such as data perturbation, can be used to improve the data quality in certain aspects. However, after modifications the new data could be very different to the original one. Using FSV as an objective function for the optimization process is discussed in this paper, in an example of causality enforcement, to ensure the enforced casual data has the minimum deviations from the original data. The results demonstrate that the proposed approach in this paper is effective for data modification and optimization.

The use of Ethernet cables is a vital, if under- discussed element of the infrastructure for the internet of things (IOT). While there are many cable types on the market, one worrying trend is the wide availability of copper clad aluminum (CCA) cables, which are widely considered unsuitable for infrastructure deployment. The availability of these copper clad aluminum (CCA) cables frequently disguised as compliant Ethernet communication cables calls for a method of assessing their performance, as this is crucial to ensuring quality of service delivery. This paper presents a method of analyzing the measured return loss and impedance profile due to handling stress. In this research, four Ethernet cables of which one of them was copper CCA cable were subjected to three rounds of coiling and uncoiling tests to represent stress from handling during installation. The Feature Selective Validation (FSV) method and Kolmogorov-Smirnov (KS) tests were used to quantify the variations between the tests. The results indicate that the CCA cable has the lowest resilience to physical stress with high potential for degradation.

Objective validation and ranking of measurements and simulations may be done by methods, such as FSV (Feature Selective Validation). In this paper, FSV is used to compare two EMC-measurement results. Due to the noisy nature of this type of data, the FSV results are corrupted. The reasons are discussed and solutions are proposed to make FSV feasible in a broader area of applications. The final solution is a combination of denoising the data and changing the weight of the data to be in accordance with our visual interpretation.

The Feature Selective Validation (FSV) method has demonstrated a number of advantages over alternative assessment methods for validation of computational electromagnetics. For example, the availability of diagnostic information and the ability to mirror human perception. However, the FSV is a heuristically based technique and, in order to have confidence in the method itself, an understanding of how the output varies as the internal coefficients vary is important. The first, important, step in FSV is to filter the original signals into ‘low’ and ‘high’ transform portions, and this paper looks at the effect of varying the coefficients associated with this operation. It supports that the current partitioning is optimum but notes the areas where substantial care is needed in selection of the parameters.

To accurately predict High Intensity Radiated Field (HIRF) effects in various systems is highly required for most commercial as well as defense applications, which is mainly based on electromagnetic simulation. Further, the validation of the simulated HIRF effects at different levels is also required. However, it is often difficult and needs some appropriate techniques and facilities. Here, in-house developed parallel Finite-Difference Time-Domain (FDTD) solver is at first employed for simulating the HIRF effects of two metallic cases with multiple slots and apertures, which is carried out on a high performance computer with tens of thousands of processors. The uncertainty analysis of the incident plane wave angle related to the electric field distribution in the metallic cases is finished with the help of non-intrusive polynomial chaos (NIPC) method, and the feature selective validation (FSV) is performed to quantify the difference between the measured and simulated field results. It is shown that the results obtained from the FSV method provide high reliability and confidence for our numerical characterization of HIRF effects in the metallic cases and even other complex structures.

In this study, measurements inside the fuselage of an unmanned air vehicle (UAV) are conducted using a novel double-loaded loop probe. The characterization and evaluation of electromagnetic interference (EMI) within VAVs is a main research topic and requires state-of-the-art measurement methodologies. The developed probe is capable of obtaining accurate measurements as commercial reference E-Field broadband probes, introducing new advantages based on the reduction of the probe's electronics and the availability of the full time-domain data. Allowing to compute simultaneously the electric and the magnetic field and to measure high-amplitude electromagnetic pulses. The laser-probe works together with multi-channel full-time domain system computing the electromagnetic fields using an oscilloscope. Regarding the double-loaded loop probe characterization and validation, it has been carried out by comparing the results with different validation methods, like Feature Selective Validation Method and the Integrated Error against Logarithmic Frequency.

A numerical analysis technique is demonstrated for the measurement of the shielding effectiveness (SE) of a conducting box cavity with apertures based on the use of a vibrating intrinsic reverberation chamber (VIRC) with the nested chamber approach. VIRC generates a multipath electromagnetic environment and can measure the SE for various polarizations and angles of incidence compared with the conventional plane-wave excitation method. The finite-difference time-domain method is applied to model a configuration for shielding effectiveness measurements and simulation of the configuration behavior. We investigated its usability based on the application of computational electromagnetics validation methods, such as the feature selective validation method, with reference results obtained with the conventional plane-wave excitation approach. The proposed VIRC numerical analysis platform used for the prediction of the SE of conducting cavities with apertures yields good outcomes even when the cavity exhibits a dominant resonance.

In this study, SPICE models of a multi‐antenna system for transmitting and receiving are proposed. The radiation fields at a given direction can be predicted by the transmitting SPICE model when the system is excited by a set of excitation sources. The receiving SPICE model can be used to calculate the coupling voltages on each antenna's load when the antenna system is illuminated by plane waves. The proposed modelling method for transmitting and receiving is a three‐step approach. In the first step, the radiation properties of the system are characterized by a minimal number of linearly independent radiation fields. Then, the transmitting and receiving equivalent models are established based on the radiation properties of the system. Finally, the equivalent models are approximated by rational functions and converted to SPICE models. Once these models have been established, the characteristic properties of the excitation signals can be changed arbitrarily without re‐establishing the models. Besides, both models can be incorporated with any terminations. The proposed SPICE models have been validated by a commercial full‐wave simulator. The feature selective validation method is used to quantify the models' results. Experiments are also conducted to validate the models.

Analysing vaccine efficacy evaluated in phase 3 clinical trials carried out during outbreaks

Background Early detection of mandibular bone changes is critical for preventing complications during dental surgeries. Radiographic assessment of mandibular trabeculae using visual index assessment and fractal dimension (FD) analysis represents a significant advancement in dental diagnostics. These approaches enhance the identification of individuals at risk of low bone mass and facilitate the monitoring of bone changes, thereby optimizing treatment planning. Objective This study aimed to visually assess changes in the trabecular pattern of the mandible on digital panoramic images and compare these changes with fractal dimension analysis to support optimal treatment planning. Methods This retrospective cross-sectional study was conducted in the Department of Oral Diagnostic Sciences, Aminu Kano Teaching Hospital, Kano. Two regions of interest (ROIs)—between the interdental region of the second premolar and first molar—were analyzed on both sides of the mandible using dedicated software for visual and FD assessment. Data on age, sex, trabecular pattern type, and FD values were recorded. Statistical analyses, including Chi-square, independent t-test, and ANOVA, were performed to assess associations among variables. Results A total of 412 digital panoramic images were analyzed, comprising 219 (53.2%) males and 193 (46.8%) females, with a mean age (SD) of 35.3 (11.9) years. Sparse trabecular patterns were more prevalent among females (56.0% right; 50.3% left), while significantly higher FD values were observed in males (p = 0.02 right; p = 0.006 left). Both visual assessment and FD analysis identified sparse trabecular patterns on the right (51.7%, 51.9%) and left (44.2%, 48.3%) sides. Agreement between methods was moderate on the right and fair on the left side of the mandible (Kappa = 0.47 and 0.36, respectively). Conclusion Both visual index and FD assessment methods effectively identified sparse trabecular patterns, though neither alone is entirely sufficient. A combined approach yields more reliable data for improved treatment planning.

The aim of this clinical study was to investigate the reliability of the clinical assessment (visual and goniometric) of the range of motion of the knee joint. 30 patients were assessed concerning the range of motion of their knee joints by visual and goniometric measurements. Assessment was performed by three investigators. The obtained data were used to analyse the intra- and interobserver reliability. Statistical analysis was performed using the Spearman coefficient of correlation [r (s)]. Intraobserver agreement was consistent across observers regarding the visual and goniometric assessment of flexion (r (s) > 0.6), whereas reliability was uniformly low for both measurements regarding the assessment of extension (r (s) < 0.6). Interobserver agreement was consistent across all three goniometric and two out of three visual assessments regarding the measurement of flexion (r (s) > 0.6); the interobserver reproducibility of extension, however, was uniformly low both for the visual and goniometric measurements (r (s) < 0.6). Reliability of clinical assessment of range of motion should be taken critically into consideration whilst performing classical function-related scoring systems when measuring outcome after total joint arthroplasty, since these scoring systems are strongly based on a valid and reliable assessment of range of motion.