Effect of Data Normalization on Accuracy and Error of Fault Classification for an Electrical Distribution System

Khond, Sarang V.

doi:10.1080/23080477.2020.1799135

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…The results showed that the Z-score technique provided a good degree of accuracy and min-max could not be recommended. This result is also supported by Khond [6] when conducting data normalization experiments using minmax, softmax, decimal scaling, and Z-score techniques. He concluded that the Z-score normalization method is an effective and robust technique of data normalization when input data differ largely on the scale.…”

Section: Introductionsupporting

confidence: 65%

“…Research on data normalization techniques as a stage of data preprocessing in NNBP has been widely carried out [4][5][6][7][8][9][10][11][12]. So, it can be known that normalization techniques include several data normalization techniques, namely, Z-score, min-max, mean-MAD, median-MAD, sigmoid, decimal scaling, tanh estimator, vector, and softmax.…”

Section: Introductionmentioning

confidence: 99%

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Investigations on Impact of Feature Normalization Techniques for Prediction of Hydro-Climatology Data Using Neural Network Backpropagation with Three Layer Hidden

Syaharuddin¹,

Fatmawati²,

Suprajitno³

2022

IJSDP

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Data normalization techniques are a very important initial stage to be carried out in order to obtain a good predictive data approach. Many researchers get different prediction and error results in each use of these data normalization techniques. Thereby, in this article discusses the accuracy rate of the seven normalization techniques at the preprocessing stage in the Neural Network Backpropagation (NNBP) architecture including decimal scaling, Z-score, min-max (there are 6 types), sigmoid, tanh estimators, mean-MAD, and median-MAD. We used two data patterns: seasonal data (rainfall) and stationary data (air humidity) that taken over the past 10 years (at 10-day intervals). We use accuracy rate parameters including number of epochs, MAE, and MSE when conducting training, testing, and predictions. The results showed that the Z-score technique was very good for the normalization of rainfall data with epochs of 10, MAE of 0.051, and MSE of 0.004. In the case of air humidity data, mean-MAD and Z-score techniques can be recommended with the number of mean-MAD technique epochs of 8, MAE of 0.013, MSE of 0.0004, while the number of epochs of Z-score techniques of 7, MAE of 0.018, and MSE of 0.0006. Thus, we conclude that when other researchers predict seasonal data or stationary data can use the Z-score technique for data normalization.

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Section: Introductionsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Investigations on Impact of Feature Normalization Techniques for Prediction of Hydro-Climatology Data Using Neural Network Backpropagation with Three Layer Hidden

Syaharuddin¹,

Fatmawati²,

Suprajitno³

2022

IJSDP

View full text Add to dashboard Cite

show abstract

“…By further investigation, we found out that a normalization approach can be beneficial to create a smooth image along with increasing the contrast of illumination near the border of the organs. So, to overcome the mentioned problems and enhance the result of the segmentation, a Z score normalization technique is employed so that all the nonzero values inside the image have a unit variance and zero mean ([ 23 – 25 ]; Jafarzadeh [ 26 ]). Equation ( 1 ) outlines how to apply Z score normalization.…”

Section: Methodsmentioning

confidence: 99%

Lung Infection Segmentation for COVID‐19 Pneumonia Based on a Cascade Convolutional Network from CT Images

Ranjbarzadeh

Ghoushchi

Bendechache

et al. 2021

BioMed Research International

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The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c -means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2 % .

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“…A method of power distribution network multi-source heterogeneous data standardized processing based on the Box-Cox transformation Z-score is proposed in this study. Because Z-score data standardization assumes that the multisource heterogeneous data factor obeys the law of normal distribution, the time-series data of skewness and kurtosis makes data processing in the process of the influence of certain factor scores small or large (Khond, 2020;Kou et al, 2021). The Box-Cox transformation, as a generalized power transformation method, can effectively handle the case where the continuous response variables do not satisfy the normal distribution.…”

Section: Power Distribution Internet Of Things Multi-source Data Stan...mentioning

confidence: 99%

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

Yuan

Kou

et al. 2022

Front. Energy Res.

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With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this study, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, the multi-source sensor data in the distribution network is unified in dimension and magnitude. By introducing the Box–Cox transform to improve the data offset problem in the Z-score normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box–Cox transform Z-score is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.

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Effect of Data Normalization on Accuracy and Error of Fault Classification for an Electrical Distribution System

Cited by 16 publications

References 20 publications

Investigations on Impact of Feature Normalization Techniques for Prediction of Hydro-Climatology Data Using Neural Network Backpropagation with Three Layer Hidden

Investigations on Impact of Feature Normalization Techniques for Prediction of Hydro-Climatology Data Using Neural Network Backpropagation with Three Layer Hidden

Lung Infection Segmentation for COVID‐19 Pneumonia Based on a Cascade Convolutional Network from CT Images

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

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