Analyzing the Solar Energy Data Using a New Anderson-Darling Test under Indeterminacy

Aslam, Muhammad; Algarni, Ali

doi:10.1155/2020/6662389

Cited by 10 publications

(2 citation statements)

References 45 publications

(42 reference statements)

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“…As a distribution goodness-of-fit test algorithm, the ksample Anderson-Darling test [20][21][22][23][24][25] is a nonparametric statistical procedure that tests the hypothesis that the populations from which two or more groups of data were drawn are identical. Compared with the chi-square test (χ 2 test), it requires less sample length and the power-of-test is superior to other tests such as the Kolmogorov-Smirnov test (K-S test), 26 and has been widely used to determine the strength properties of composite materials.…”

Section: K-sample Anderson-darling Testmentioning

confidence: 99%

Comparative experimental investigation of design allowable of composite materials based on k-sample Anderson–Darling test

Sui,

Sun

2023

Journal of Reinforced Plastics and Composites

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The design allowable values of composite materials were experimentally studied by specimen from two different suppliers, and the test data of grouped samples for bending strength, tensile strength, and interlaminar shear strength were obtained. The k-sample Anderson–Darling test method was adopted to analyze the test results, and the correlation conclusions of bending strength, tensile strength, and interlaminar shear strength were achieved. The test and analysis results show that the fluctuation range of bending strength, tensile strength, and interlaminar shear strength of composite specimen manufactured by the two suppliers is small, and the error range is less than 10.5%. The A-D test critical value ADC corresponding to bending strength, tensile strength, and interlayer shear strength is greater than the corresponding test statistical value ADK. The test data of the samples processed by the two suppliers have strong correlation, which can be considered to be from the same population. The above conclusions provide guidance of ideas and methods for the strength performance consideration of composite structure selection and substitution.

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Section: K-sample Anderson-darling Testmentioning

confidence: 99%

Comparative experimental investigation of design allowable of composite materials based on k-sample Anderson–Darling test

Sui,

Sun

2023

Journal of Reinforced Plastics and Composites

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“…The Anderson-Darling test is [41] a nonparametric test that shows a superior performance while detecting departures from normality [41]. A K-sample is a type of Anderson-Darling test used to detect if multiple observations are generated from the same statistical distribution.…”

Section: Anderson-darlingmentioning

confidence: 99%

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Hamdi

Hamam

2022

Sensors

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Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model. Moreover, the proposed framework can dynamically react to all the changes occurring in the data series. Backbone traffic was used to validate the proposed method. As a result, the forecasting accuracy improved significantly with the proposed framework and with minimal data loss from the smoothing process. The results showed that the hybrid moving average LSTM (MLSTM) achieved the most remarkable improvement in the training and testing forecasts, with 28% and 24% for long-term evolution (LTE) time series and with 35% and 32% for the multiprotocol label switching (MPLS) time series, respectively, while robust locally weighted scatter plot smoothing and LSTM (RLWLSTM) achieved the most significant improvement for upstream traffic with 45%; moreover, the dynamic learning framework achieved improvement percentages that can reach up to 100%.

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Generalized Regressed Exponential Estimator for Estimation of Mean Under Neutrosophic Ranked Set Sampling

Khanna

2024

Natl. Acad. Sci. Lett.

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Analyzing the Solar Energy Data Using a New Anderson-Darling Test under Indeterminacy

Cited by 10 publications

References 45 publications

Comparative experimental investigation of design allowable of composite materials based on k-sample Anderson–Darling test

Comparative experimental investigation of design allowable of composite materials based on k-sample Anderson–Darling test

Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts

Generalized Regressed Exponential Estimator for Estimation of Mean Under Neutrosophic Ranked Set Sampling

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