Rescaled Data Research Articles

Restoration of the current transformer secondary current under core saturation conditions based on ANN

Relay protection and emergency automation is an integral part of electric power systems. The algorithms of the relay protection and emergency automation are based on the use of analysis of the parameters of the electrical mode - current and voltage - obtained from current and voltage instrument transformers. As a rule, these transformers operate on the basis of the laws of electromagnetism. Operational experience shows that the magnetic core of current transformers under fault conditions can be saturated. As a result, the shape of the measured current is distorted which can lead to false operation of the relay protection and emergency automation system. In this paper a comparative analysis of the existing methods is presented. The advantages and disadvantages of them are described. The problem of current transformer core saturation is reduced to a regression problem. To solve the problem an artificial neural networks-based method is proposed. Computational experiments for both proposed and existing methods are performed. The results of experiments show that the efficiency of the proposed method is 1.25 times higher relative to the most effective existing method. The method of re-scaling data at real-time work applied to the restoration of the distorted shape of current is proposed.

Ensuring cross-cultural data comparability by means of anchoring vignettes in heterogeneous refugee samples

BackgroundConfigural, metric, and scalar measurement invariance have been indicators of bias-free statistical cross-group comparisons, although they are difficult to verify in the data. Low comparability of translated questionnaires or the different understanding of response formats by respondents might lead to rejection of measurement invariance and point to comparability bias in multi-language surveys. Anchoring vignettes have been proposed as a method to control for the different understanding of response categories by respondents (the latter is referred to as differential item functioning related to response categories or rating scales: RC-DIF). We evaluate the question whether the cross-cultural comparability of data can be assured by means of anchoring vignettes or by considering socio-demographic heterogeneity as an alternative approach.MethodsWe used the Health System Responsiveness (HSR) questionnaire and collected survey data in English (n = 183) and Arabic (n = 121) in a random sample of refugees in the third largest German federal state. We conducted multiple-group Confirmatory Factor Analyses (MGCFA) to analyse measurement invariance and compared the results when 1) using rescaled data on the basis of anchoring vignettes (non-parametric approach), 2) including information on RC-DIF from the analyses with anchoring vignettes as covariates (parametric approach) and 3) including socio-demographic covariates.ResultsFor the HSR, every level of measurement invariance between the Arabic and English languages was rejected. Implementing rescaling or modelling on the basis of anchoring vignettes provided superior results over the initial MGCFA analysis, since configural, metric and – for ordered categorical analyses—scalar invariance could not be rejected. A consideration of socio-demographic variables did not show such an improvement.ConclusionsSurveys may consider anchoring vignettes as a method to assess cross-cultural comparability of data, whereas socio-demographic variables cannot be used to improve data comparability as a standalone method. More research on the efficient implementation of anchoring vignettes and further development of methods to incorporate them when modelling measurement invariance is needed.

COFFEE: Cross-Layer Optimization for Fast and Efficient Executions of Sinkhorn-Knopp Algorithm on HPC Systems

In this paper, we present COFFEE, cross-layer optimization for fast and efficient executions of the Sinkhorn-Knopp (SK) algorithm on HPC systems with clusters of compute nodes by exploring some architectural features of the system. By analyzing the performance of a typical implementation of the SK algorithm on such a system, a huge performance gap is observed between the row rescaling and column rescaling of the algorithm, where the latter requires much more time than the former. We also found that the costly MPI communication of the column rescaling seriously hinders the exploitation of parallelism. By observing and leveraging unique architectural characteristics across different system optimizations, such as column rescaling redesign, data blocking, micro-kernel design, enhanced intra-node and inter-node communication in MPI, etc., COFFEE is able to explore cross-layer optimization opportunities that enable fast and efficient execution of the SK algorithm. Our experimental results show that COFFEE provides up to 7.5X with an average of 2.0X performance improvement over the typical implementation on a single node, and up to 2.9X with an average of 1.6X performance improvement over the state-of-the-art MPI Allreduce algorithms on Tianhe-1 supercomputer.

A unified formalism for estimating photon absorbed fractions in spherical biovolumes: analytical equations without fitting parameters

A new analytical formalism, previously developed for estimating electron-absorbed fractions, was extended for estimating photon absorbed fractions in soft-tissue spheres, containing uniformly distributed photon-emitter. Analytical equations were formulated for calculating values of photon-absorbed fractions. The method involves a rescaling procedure with transformation of real biological sizes to unitless effective ones, combining information of photon energy, object’s size, and material. Rescaling was applied to large published datasets of photon absorbed fractions in soft-tissue spheres, computed with Monte Carlo codes. A new effect was demonstrated in which the rescaled data formed a single smooth ‘unified curve’ with saturation. The unified curve for photon absorbed fractions was described analytically, using simple equations without fitting parameters. The new method was tested for a wide range of spheres—from 1 mg up to 1000 kg, and wide range of photon energies—from 0.02 up to 5 MeV. For larger spheres, a close agreement between analytical values and Monte Carlo datasets was demonstrated. For small biovolumes, analytical equations predict higher values than available Monte Carlo data. The unified formalism is now available for direct calculating radiation absorbed fractions in soft-tissue spherical organs and organisms without Monte Carlo codes.

Разработка и тестирование модели поглощения энергии в биообъектах от инкорпорированных излучателей фотонов

A new analytical method, previously developed for estimating beta absorbed fractions, was ex-tended for analytical estimating photon absorbed fractions in soft-tissue bodies from internal uni-formly distributed gamma emitters. The method involves a rescaling procedure with transfor-mation of real biological sizes to unitless effective ones combining information of both photon energy and object’s size. The rescaling was applied to large published datasets of photon ab-sorbed fractions in soft-tissue spheres. A new effect was demonstrated: the rescaled data were united into a single smooth “universal curve” with saturation. The universal curve for photon ab-sorbed fractions was described analytically as a function of a single argument – a rescaled effec-tive radius. Practical applicability of the approach for non-spherical convex bodies (ellipsoids) was demonstrated. Examples of analytical estimating the internal dose coefficients for repre-sentative terrestrial animals were calculated. The new method now available for electron and photon internal dosimetry, provides an effective analytical tool for calculating radiation absorbed fractions in organs and bodies of soft-tissue organisms.

Cluster‐scaled principal component analysis

AbstractCluster‐scaled analysis means exploiting the cluster‐based scaling to conventional data analysis to obtain more accurate results or results that we cannot obtain by using ordinary analysis. Our target data is complex and large amounts of data. For this type of data, it is well known that ordinary statistical methods do not always work well, or theoretically, we know that we cannot obtain a correct result. As a tool of this implementation, we utilize fuzzy clustering, which is well known as a robust clustering to a complex and large amount of data. That is, we use the fuzzy clustering result as a scale of data and apply the rescaled data by the cluster‐scale to another target analysis. Our target analysis in this article is principal component analysis, which is a well‐known dimensional reduction method. A numerical example shows a better performance of the cluster‐scaled principal component analysis.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification

Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality

Increasing popularity of high-dynamic-range (HDR) image and video content brings the need for metrics that could predict the severity of image impairments as seen on displays of different brightness levels and dynamic range. Such metrics should be trained and validated on a sufficiently large subjective image quality dataset to ensure robust performance. As the existing HDR quality datasets are limited in size, we created a Unified Photometric Image Quality dataset (UPIQ) with over 4000 images by realigning and merging existing HDR and standard-dynamic-range (SDR) datasets. The realigned quality scores share the same unified quality scale across all datasets. Such realignment was achieved by collecting additional cross-dataset quality comparisons and re-scaling data with a psychometric scaling method. Images in the proposed dataset are represented in absolute photometric and colorimetric units, corresponding to light emitted from a display. We use the new dataset to retrain existing HDR metrics and show that the dataset is sufficiently large for training deep architectures. We show the utility of the dataset on brightness aware image compression.

A theoretically-based novel protocol for the analytic treatment of the glass failure stresses associated with coaxial double ring test method

With the aim of validating a new standardized Coaxial Double Ring testing procedure, without overpressure and with fixed geometry, an ad hoc theoretical approach has been proposed here to rearrange the laboratory outcomes accounting for the effects of the geometric non-linearities associated with such a testing configuration. By borrowing this idea, once the experimental values of the failure load have been determined, it has been possible to obtain an expression in closed form (fully defined by only two coefficients) of the maximum tensile stress (σmax) in the core of the specimen. Following this, in order to make the laboratory outcomes comparable and homogeneous, the σmax-values have been then re-scaled to a common reference condition (equibiaxial stress on a reference area, σeqbx), by means of the use of a correction coefficient (K) able to determine, under a condition of equal probability of failure, the effective area (Aeff) of the tested specimens. After being corrected to account for the effects of the stress corrosion cracking (static fatigue effect), all re-scaled data have been finally interpreted using a Weibull-type statistical distribution to determine the main fractile values of the glass strength. Doing so, despite some unavoidable approximations, this procedure furnished a highly effective means of determining the bending strength of float glass. Unlike the pure numerical approach proposed in codes and literature, which requires to correct the experimental data via FEM simulation, the rationale behind the proposed approach is in fact to elaborate the experimental data through an analytic treatment of the problem, which would greatly facilitate the interpretation of the data as well as the standardization of the testing procedure.

A new analytical method for estimating electron-absorbed fractions in soft-tissue biological volumes.

A new analytical methodology was developed for estimating electron-absorbed fractions in soft-tissue biological volumes from mono-energy emitters, uniformly distributed within these volumes. The approach was originally developed for soft-tissue spheres and was extended to ellipsoids. The method involves a procedure of size rescaling to the electron CSDA ranges. The rescaling was applied to large published datasets of electron-absorbed fractions in soft-tissue spheres. A new effect was demonstrated, i.e., that it is possible to describe the rescaled data on absorbed fractions by a single smooth 'universal curve'. A simple analytical formula is suggested, which describes the curve as a function of a single argument (the so-called rescaled radius) with saturation. Practical application of the method for estimating internal doses to non-human biota was demonstrated. It is concluded that the method provides an effective analytical tool for calculating the electron-absorbed fractions in soft-tissue bio-volumes relevant to various organisms and organs.

Assessing Goodness-of-Fit in Marked Point Process Models of Neural Population Coding via Time and Rate Rescaling.

Marked point process models have recently been used to capture the coding properties of neural populations from multiunit electrophysiological recordings without spike sorting. These clusterless models have been shown in some instances to better describe the firing properties of neural populations than collections of receptive field models for sorted neurons and to lead to better decoding results. To assess their quality, we previously proposed a goodness-of-fit technique for marked point process models based on time rescaling, which for a correct model produces a set of uniform samples over a random region of space. However, assessing uniformity over such a region can be challenging, especially in high dimensions. Here, we propose a set of new transformations in both time and the space of spike waveform features, which generate events that are uniformly distributed in the new mark and time spaces. These transformations are scalable to multidimensional mark spaces and provide uniformly distributed samples in hypercubes, which are well suited for uniformity tests. We discuss the properties of these transformations and demonstrate aspects of model fit captured by each transformation. We also compare multiple uniformity tests to determine their power to identify lack-of-fit in the rescaled data. We demonstrate an application of these transformations and uniformity tests in a simulation study. Proofs for each transformation are provided in the appendix.

Measuring Corruption and Comparison of Corruption Indices - Focused on CPI, CCI and IPI -

부패지수 혹은 부패통제 지수는 부패인식 제고나 반부패 정책 수립 등 다양한 목적을 위하여 사용된다. 이런 목적을 달성하기 위하여 부패지수의 속성이나 구성에 대해서 정확한 이해가 필요하다. 이 논문에서 CCI, CPI, IPI 부패지수의 작성원리와 속성을 비교 검토하여 각 지표의 장단점을 알아보았다. 모든 부패지수가 다수의 개별 자료나 지수를 활용하여 작성된 복합지수이고 개별 자료들은 인식지수를 포함하고 있다. 각 지수는 개별자료의 선택, 자료의 표준화, 합산 그리고 새로운 지수의 생성 과정을 거쳐서 만들어지는데 통계적으로 오차를 줄이기 위해서 노력하고 있지만, 지수 작성과정에서 다양한 편의(bias)가 존재한다. 좋은 부패지수를 만들기 위해서는 타당성 있고 신뢰할만한 개별자료를 확보하는 것이 중요하다. 부패 현상이나 거버넌스 자체가 측정이 어렵기 때문에 경험자료든 인식자료든 모두 고유한 편의를 가진다. 100개 이상의 국가를 대상으로 발표되는 부패지수는 모든 나라가 동일한 개별자료를 사용하는 것이 아니기 때문에도 편의가 발생한다. 기초자료의 유사성을 고려하면 경제발전의 정도가 비슷한 국가 사이의 부패지수 비교는 상당히 타당도가 높고 한 국가의 부패지수 변화 역시 일관성을 갖추고 있는 것으로 판단된다. 그러나 경제발전의 정도 다른 국가의 부패지수를 비교하는 것은 주의가 필요하다.The purpose of this study is to investigate the property of various corruption measures such as the corruption perception index(CPI) of Transparency International, the control of corruption index(CCI) of the World Bank and the index of public integrity(IPI) of The European Research Centre for Anti-Corruption and State-Building to better understand the meaning of corruption measure indexes. All three corruption indices are built following four steps:selection of source data, rescaling source data, aggregating the rescaled data and then reporting a measure for uncertainty. Although each organization is cautious to minimize errors and biases related to the steps. there exist several biases. To construct a good corruption index, a good quality resource data should be secured first. Due to the difficulties of measuring corruption or governance, all corruption measures exhibit some biases regardless of experience-based or perception-based measures. Because all corruption indexes do not use the same data source, we should be cautious interpreting the corruption index of a country. It is safe to compare indices of countries when the countries are in similar economic development level.

Low Information Omnibus (LIO) Priors for Dirichlet Process Mixture Models

Dirichlet process mixture (DPM) models provide flexible modeling for distributions of data as an infinite mixture of distributions from a chosen collection. Specifying priors for these models in individual data contexts can be challenging. In this paper, we introduce a scheme which requires the investigator to specify only simple scaling information. This is used to transform the data to a fixed scale on which a low information prior is constructed. Samples from the posterior with the rescaled data are transformed back for inference on the original scale. The low information prior is selected to provide a wide variety of components for the DPM to generate flexible distributions for the data on the fixed scale. The method can be applied to all DPM models with kernel functions closed under a suitable scaling transformation. Construction of the low information prior, however, is kernel dependent. Using DPM-of-Gaussians and DPM-of-Weibulls models as examples, we show that the method provides accurate estimates of a diverse collection of distributions that includes skewed, multimodal, and highly dispersed members. With the recommended priors, repeated data simulations show performance comparable to that of standard empirical estimates. Finally, we show weak convergence of posteriors with the proposed priors for both kernels considered.

A novel approach based on multiple correspondence analysis for monitoring social networks with categorical attributed data

ABSTRACTIn in most cases, the distribution of communications is unknown and one may summarize social network communications with categorical attributes in a contingency table. Due to the categorical nature of the data and a large number of features, there are many parameters to be considered and estimated in the model. Hence, the accuracy of estimators decreases. To overcome the problem of high dimensionality and unknown communications distribution, multiple correspondence analysis is used to reduce the number of parameters. Then the rescaled data are studied in a Dirichlet model in which the parameters should be estimated. Moreover, two control charts, Hotelling’s T2 and multivariate exponentially weighted moving average (MEWMA), are developed to monitor the parameters of the Dirichlet distribution. The performance of the proposed method is evaluated through simulation studies in terms of average run length criterion. Finally, the proposed method is applied to a real case.

A Large-Eddy Simulation-Based Assessment of the Risk of Wind Turbine Failures Due to Terrain-Induced Turbulence over a Wind Farm in Complex Terrain

The first part of the present study investigated the relationship among the number of yaw gear and motor failures and turbulence intensity (TI) at all the wind turbines under investigation with the use of in situ data. The investigation revealed that wind turbine #7 (T7), which experienced a large number of failures, was affected by terrain-induced turbulence with TI that exceeded the TI presumed for the wind turbine design class to which T7 belongs. Subsequently, a computational fluid dynamics (CFD) simulation was performed to examine if the abovementioned observed wind flow characteristics could be successfully simulated. The CFD software package that was used in the present study was RIAM-COMPACT, which was developed by the first author of the present paper. RIAM-COMPACT is a nonlinear, unsteady wind prediction model that uses large-eddy simulation (LES) for the turbulence model. RIAM-COMPACT is capable of simulating flow collision, separation, and reattachment and also various unsteady turbulence–eddy phenomena that are caused by flow collision, separation, and reattachment. A close examination of computer animations of the streamwise (x) wind velocity revealed the following findings: As we predicted, wind flow that was separated from a micro-topographical feature (micro-scale terrain undulations) upstream of T7 generated large vortices. These vortices were shed downstream in a nearly periodic manner, which in turn generated terrain-induced turbulence, affecting T7 directly. Finally, the temporal change of the streamwise (x) wind velocity (a non-dimensional quantity) at the hub-height of T7 in the period from 600 to 800 in non-dimensional time was re-scaled in such a way that the average value of the streamwise (x) wind velocity for this period was 8.0 m/s, and the results of the analysis of the re-scaled data were discussed. With the re-scaled full-scale streamwise wind velocity (m/s) data (total number of data points: approximately 50,000; time interval: 0.3 s), the time-averaged streamwise (x) wind velocity and TI were evaluated using a common statistical processing procedure adopted for in situ data. Specifically, 10-min moving averaging (number of sample data points: 1932) was performed on the re-scaled data. Comparisons of the evaluated TI values to the TI values from the normal turbulence model in IEC61400-1 Ed.3 (2005) revealed the following: Although the evaluated TI values were not as large as those observed in situ, some of the evaluated TI values exceeded the values for turbulence class A, suggesting that the influence of terrain-induced turbulence on the wind turbine was well simulated.

Rescaling of 2D ESEEM Data as a Tool for Inverse Problem Solving

A rescaling procedure is proposed for electron spin echo envelope modulation spectra observed at several electron paramagnetic resonance transitions. Analytical expressions describing the relations between the rescaled frequencies and hyperfine and quadrupole parameters of the remote nucleus are obtained. The dependences of the rescaled data on the external magnetic field and spin projections of the ion nucleus and the remote nuclei are used to derive the parameters of the nuclear state in the crystals Y2SiO5 and YVO4 doped by ion Nd3+.

Monitoring Period Variations of Variable Stars using Precise Photometric Surveys

AbstractThe period variations of rotating, pulsating and eclipsing variable stars bear valuable astrophysical information about the presence of companions, evolutionary effects, and the inner structure of the stars. This talk described a universal method for de-trending and re-scaling precise photometric data (Kepler, MOST, CoRoT, OGLE, …) appropriate for period-change diagnostics of periodic variables. We demonstrated the potential of the method by analysing the period variability of one of the newly-identified Kepler magnetic chemically-peculiar (mCP) stars. We showed that, surprisingly, our target star displays near-sinusoidal changes in its observed light-variations, with a period of 2.85(6) years, which is apparently the result of the presence of a nearby stellar companion. The expected long-term changes of the rotational period, as have been observed in several mCP stars, have not been identified among the sample of Kepler mCP stars.

Magnetic phase transition and magnetocaloric properties of Mn1-xSnxCoGe alloys

Magnetic phase transitions and the magnetocaloric effect of the polycrystalline Mn1-xSnxCoGe samples with x=0.02, 0.04, and 0.06 have been investigated. The transformation temperature, room temperature magnetocaloric effect and magnetic orders of these alloys could be effectively tuned by Sn substitution. A detailed study on the critical behavior was performed by various techniques, such as modified Arrott plot, Kouvel–Fisher method, and critical isotherm analysis, to determine the critical exponents (β, γ, and δ). Those exponents calculated from the critical magnetization isotherm were found to obey the Widom scaling relation remarkably well and the validity of the obtained critical exponents was also verified by the prediction of the scaling theory in the critical region. All the results are similar to the theoretical values of the mean-field model, indicating the presence of long-range ferromagnetic coupling in this system. Moreover, we made an attempt to contrast the universal curve for magnetocaloric effect in the present system, finding that rescaled entropy data for all alloys collapse into a universal curve.

A new front surface heat flux calibration method for a 1-D nonlinear thermal system with a time-varying back boundary condition

A novel calibration methodology is presented for resolving the front surface heat flux in a one-dimensional nonlinear sample with an unknown time-varying back boundary condition. This method combines the attributes of the recently reported linear two-probe calibration equation and the nonlinear one-probe calibration equation. The proposed calibration formulation is expressed in terms of a Volterra integral equation of the first type. This functional equation relates the rescaled unknown front surface heat flux to two rescaled calibration front surface heat fluxes and corresponding rescaled temperature data at the two in-depth probes when the sample is subjected to the unknown boundary conditions. A localized Tikhonov regularization scheme is introduced for generating the family of predictions based on the Tikhonov parameter spectrum. The L-curve strategy is used to extract the optimal prediction. This paper studies the effectiveness of this new calibration equation for two engineering materials, stainless steel 304 and a carbon composite. Results show that properly regularized predictions are stable and accurate in the presence of significant noise. Moreover, it is demonstrated that a preferred back boundary condition strategy for calibration tests reduces the ill-conditioning effects of the kernel. This new calibration method does not require the specification of the probe locations, although knowledge of the thermophysical properties is necessary.

Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the understanding of vegetation distribution and heterogeneity at multiple scales. Information detailing the fine-scale spatial distribution of tundra communities provided by high resolution vegetation mapping, is needed to understand the relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g., ~1 m chamber flux) and those encompassing multiple vegetation communities (e.g., ~300 m eddy covariance measurements). The objectives of this study were: (1) to determine whether dominant Arctic tundra vegetation communities found in different locations are spectrally distinct and distinguishable using field spectroscopy methods; and (2) to test which combination of raw reflectance and vegetation indices retrieved from field and satellite data resulted in accurate vegetation maps and whether these were transferable across locations to develop a systematic method to map dominant vegetation communities within larger eddy covariance tower footprints distributed along a 300 km transect in northern Alaska. We showed vegetation community separability primarily in the 450–510 nm, 630–690 nm and 705–745 nm regions of the spectrum with the field spectroscopy data. This is line with the different traits of these arctic tundra communities, with the drier, often non-vascular plant dominated communities having much higher reflectance in the 450–510 nm and 630–690 nm regions due to the lack of photosynthetic material, whereas the low reflectance values of the vascular plant dominated communities highlight the strong light absorption found here. High classification accuracies of 92% to 96% were achieved using linear discriminant analysis with raw and rescaled spectroscopy reflectance data and derived vegetation indices. However, lower classification accuracies (~70%) resulted when using the coarser 2.0 m WorldView-2 data inputs. The results from this study suggest that tundra vegetation communities are separable using plot-level spectroscopy with hand-held sensors. These results also show that tundra vegetation mapping can be scaled from the plot level (<1 m) to patch level (<500 m) using spectroscopy data rescaled to match the wavebands of the multispectral satellite remote sensing. We find that developing a consistent method for classification of vegetation communities across the flux tower sites is a challenging process, given the spatial variability in vegetation communities and the need for detailed vegetation survey data for training and validating classification algorithms. This study highlights the benefits of using fine-scale field spectroscopy measurements to obtain tundra vegetation classifications for landscape analyses and use in carbon flux scaling studies. Improved understanding of tundra vegetation distributions will also provide necessary insight into the ecological processes driving plant community assemblages in Arctic environments.

SU-F-R-08: Can Normalization of Brain MRI Texture Features Reduce Scanner-Dependent Effects in Unsupervised Machine Learning?

Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysis was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.