A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

Vitola, Jaime; Pozo, Francesc; Burgos, Diego Alexander Tibaduiza; Anaya, Maribel

doi:10.3390/s17020417

Cited by 120 publications

(76 citation statements)

References 24 publications

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“…It is very common in the literature—when using the sensor data fusion as in Vitola et al —to merge the data that come from different actuation phases in a single data matrix. In this study, we also use the approach with a single data matrix; however, we additionally examine the case where each actuation phase has a classifier, in Section .…”

Section: Case Studies: Aluminum Plate With Four Pztsmentioning

confidence: 99%

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

Agis

Burgos

Pozo

2020

Struct Control Health Monit

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Summary This paper describes a structural health monitoring strategy to detect and classify structural changes in structures that can be equipped with sensors. The proposed approach is based on the t‐distributed stochastic neighbor embedding ( t‐SNE), a nonlinear technique that can represent the local structure of high‐dimensional data collected from multiple sensors in a plane or spatial representation. We propose the following basic steps for the detection and classification. First, the raw data are preprocessed: We scale the data using the mean‐centered group scaling and apply principal component analysis to reduce the dimensionality of the scaled data. Second, t‐SNE is applied to represent the scaled and reduced data as points in a plane, defining a cluster for each structural state. Finally, the current structure to be diagnosed is associated with a cluster (or structural state) using three different strategies: (a) the smallest point‐centroid distance; (b) the majority voting; and (c) the sum of the inverse distances. The combination of t‐SNE with our preprocessing and the three proposed classification strategies significantly improves the quality of the clusters that represent different structural states. We evaluate the performance of our method using experimental data from an aluminum plate instrumented with piezoelectric transducers. Results are presented in the time domain, and they reveal the high classification accuracy and strong performance of this method, with a percentage of correct decisions close to 100% in several scenarios.

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Section: Case Studies: Aluminum Plate With Four Pztsmentioning

confidence: 99%

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

Agis

Burgos

Pozo

2020

Struct Control Health Monit

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“…The weight KNN uses a distance weight as in Eq(4). The last three types have the same number of neighbours as Medium KNN [15]. When the numbers of neighbours are decreased, the accuracy of the classifier increases.…”

Section: K-nearest Neighbours (Knn)mentioning

confidence: 99%

Diabetes Diagnosis Based on KNN

Ali

Alrubei

Hassan

et al. 2020

IIUMEJ

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Diabetes is a life-threatening syndrome occurring around the world; it can have huge complications and is documented by large amounts of medical data. Therefore, attempts at early detection of this disease took a large area of research and many methods were used to deal with diabetes. In this paper, different types of KNN algorithm have been used to classify diabetes disease using Matlab. The dataset was generated by the criteria of the American diabetes association. For the training stage, 4900 samples have been used by the classifier learner tool to observe the results. Then, 100 of the data samples were used for the test. The results show that the KNN types (Fine, Weighted, Medium and Cubic) give high accuracy over the Coarse and the Cosine methods. Fine KNN is considered the most suitable according to its accuracy of classified samples. ABSTRAK: Penyakit kencing manis adalah sindrom penyakit ancaman nyawa yang berlaku di seluruh dunia dan ia mempunyai data perubatan yang besar serta komplikasi tinggi. Oleh itu, cubaan dalam mengesan awal penyakit ini mempunyai potensi luas dalam kajian dan banyak kaedah telah digunakan bagi mengkaji penyakit kencing manis. Dalam kajian ini, pelbagai jenis algoritma KNN telah digunakan bagi mengelas penyakit kencing manis menggunakan Matlab. Setdata dihasilkan berdasarkan kriteria Kesatuan Kencing Manis Amerika. Pada peringkat latihan, sebanyak 4900 sampel telah digunakan oleh pelatih alat pengelasan bagi memantau dapatan kajian. Kemudian, 100 daripada sampel data telah digunakan bagi ujian. Keputusan menunjukkan jenis KNN (Halus, Berat, Sederhana dan Kubik) lebih tepat berbanding kaedah Kasar dan Kosinus. KNN Halus di dapati lebih sesuai berdasarkan ketepatan sampel pengelasan.

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“…From previous works that use PCA, 30,35 the influence of the number of scores is well known on the analysis of data. The reason is that the variability of the data cannot be concentrated in the first components.…”

Section: Data Set For Validationmentioning

confidence: 99%

A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue

Leon-Medina

Flechas

Burgos

2019

International Journal of Distributed Sensor Networks

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Electronic tongue-type sensor arrays are devices used to determine the quality of substances and seek to imitate the main components of the human sense of taste. For this purpose, an electronic tongue-based system makes use of sensors, data acquisition systems, and a pattern recognition system. Particularly, in the latter, machine learning techniques are useful in data analysis and have been used to solve classification and regression problems. However, one of the problems in the use of this kind of device is associated with the development of reliable pattern recognition algorithms and robust data analysis. In this sense, this work introduces a taste recognition methodology, which is composed of several steps including unfolding data, data normalization, principal component analysis for compressing the data, and classification through different machine learning models. The proposed methodology is tested using data from an electronic tongue with 13 different liquid substances; this electronic tongue uses multifrequency large amplitude pulse signal voltammetry. Results show that the methodology is able to perform the classification accurately and the best results are obtained when it includes the use of K-nearest neighbor machine in terms of accuracy compared with other kinds of machine learning approaches. Besides, the comparison to evaluate the methodology is made with different classification performance measures that show the behavior of the process in a single number.

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A Sensor Data Fusion System Based on k-Nearest Neighbor Pattern Classification for Structural Health Monitoring Applications

Cited by 120 publications

References 24 publications

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

Vibration‐based detection and classification of structural changes using principal component analysis and ‐distributed stochastic neighbor embedding

Diabetes Diagnosis Based on KNN

A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue

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