An application of K-Nearest Neighbor interpolation on calibrating corrosion measurements collected by two non-destructive techniques

Hamed, Yaman; Shafie, Afza; Mustaffa, Zahiraniza; Idris, Naila Rusma Binti

doi:10.1109/icsima.2015.7559030

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…The KNN model is a statistical tool for estimating the value of an unknown point based on its nearest neighbors [ 69 ]. The nearest neighbors are usually calculated as the points with the shortest distance to the unknown point [ 70 ]. Several techniques are used to measure the distance between the neighbors.…”

Section: Machine Learningmentioning

confidence: 99%

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

et al. 2021

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Several design parameters affect the reliability of wafer-level type advanced packaging, such as upper and lower pad sizes, solder volume, buffer layer thickness, and chip thickness, etc. Conventionally, the accelerated thermal cycling test (ATCT) is used to evaluate the reliability life of electronic packaging; however, optimizing the design parameters through ATCT is time-consuming and expensive, reducing the number of experiments becomes a critical issue. In recent years, many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. This study demonstrates AI-assisted DoS technology by combining artificial intelligence and simulation technologies to predict wafer level package (WLP) reliability. In order to ensure reliability prediction accuracy, the simulation procedure was validated by several experiments prior to creating a large AI training database. This research studies several machine learning models, including artificial neural network (ANN), recurrent neural network (RNN), support vector regression (SVR), kernel ridge regression (KRR), K-nearest neighbor (KNN), and random forest (RF). These models are evaluated in this study based on prediction accuracy and CPU time consumption.

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Section: Machine Learningmentioning

confidence: 99%

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

et al. 2021

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“…The authors in [26] introduce a new application of k-Nearest Neighbor interpolation to calibrate corrosion measurements obtained from a Magnetic Flux Leakage intelligent system using readings from an ultrasonic testing scan device. By applying this interpolation method, enhanced metrics are derived, which are then utilized in the integrity assessment report of the pipeline.…”

Section: Machine Learning and Artificial Intelligence In Eddy Current...mentioning

confidence: 99%

Rapid Identification of Material Defects Based on Pulsed Multifrequency Eddy Current Testing and the k-Nearest Neighbor Method

Grochowalski,

Chady

2023

Materials

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The article discusses the utilization of Pulsed Multifrequency Excitation and Spectrogram Eddy Current Testing (PMFES-ECT) in conjunction with the supervised learning method for the purpose of estimating defect parameters in conductive materials. To obtain estimates for these parameters, a three-dimensional finite element method model was developed for the sensor and specimen containing defects. The outcomes obtained from the simulation were employed as training data for the k-Nearest Neighbors (k-NN) algorithm. Subsequently, the k-NN algorithm was employed to determine the defect parameters by leveraging the available measurement outcomes. The evaluation of classification accuracy for different combinations of predictors derived from measured data is also presented in this study.

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“…Therefore, it results in large computation and long algorithm time and difficult to keep up with the real-time dynamic response of the system. 12,13 (3) Look-up table: It just obtains the battery characteristic data from the standard table by calculating the standard mathematical model in advance or the experimental data. The method can calculate quickly, but there is a contradiction between accuracy and data capacity when directly using the look-up table data.…”

Section: Introductionmentioning

confidence: 99%

Method of voltage setting for power battery simulator using successive nearest-neighbor interpolation

Tan

Zhang

et al. 2022

Measurement and Control

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The power battery simulator is important equipment in new energy vehicle test platform and other industrial fields, and the battery model is the key to emulate the battery characteristics accurately. A novel method of given voltage for the power battery simulator is proposed in the paper. According to the state of charge (SOC) of the power battery, three sub-tables with different resolutions are established corresponding to the initial, stationary, and final stage of SOC. To ensure the accuracy of a given voltage under small data capacity, a successive nearest-neighbor interpolation (SNNI) algorithm is proposed for processing battery model data and the iterative calculation is carried out for the data obtained by looking up the table until it approaches the required value. Simulations and experiments are done on various capacity data tables, which are obtained by employed different sampling ratios to reduce the data capacity, and the influence of iteration number selection on the accuracy of the algorithm is discussed. The results show that, after 4–5 iterations, the given voltage accuracy is controlled within 0.03 V by the SNNI algorithm with only 1.6% capacity of the standard model table, which is greatly improved compared with the traditional method, and the feasibility is verified by executing the proposed algorism in a DSP system.

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An application of K-Nearest Neighbor interpolation on calibrating corrosion measurements collected by two non-destructive techniques

Cited by 11 publications

References 7 publications

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

An Overview of AI-Assisted Design-on-Simulation Technology for Reliability Life Prediction of Advanced Packaging

Rapid Identification of Material Defects Based on Pulsed Multifrequency Eddy Current Testing and the k-Nearest Neighbor Method

Method of voltage setting for power battery simulator using successive nearest-neighbor interpolation

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