K-means clustering analysis and artificial neural network classification of fatigue strain signals

Yunoh, Mohd Faridz Mod; Abdullah, Shahrum; Saad, Mohamad Hanif Md; Nopiah, Zulkifli Mohd; Nuawi, Mohd. Zaki

doi:10.1007/s40430-016-0559-x

Cited by 13 publications

(5 citation statements)

References 14 publications

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“…The classification process is performed by using an artificial neural network (ANN) for optimal pattern recognition. Experiments show that their algorithm is about 92% accurate [ 5 ]. Wang et al predict natural disasters by modeling meteorological disasters.…”

Section: Introductionmentioning

confidence: 99%

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

2022

Computational Intelligence and Neuroscience

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The era people live in is the era of big data, and massive data carry a large amount of information. This study aims to analyze RFID data based on big data and clustering algorithms. In this study, a RFID data extraction technology based on joint Kalman filter fusion is proposed. In the system, the proposed data extraction technology can effectively read RFID tags. The data are recorded, and the KM-KL clustering algorithm is proposed for RFID data, which combines the advantages of the K-means algorithm. The improved KM-KL clustering algorithm can effectively analyze and evaluate RFID data. The experimental results of this study prove that the recognition error rate of the RFID data extraction technology based on the joint Kalman filter fusion is only 2.7%. The improved KM-KL clustering algorithm also has better performance than the traditional algorithm.

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Section: Introductionmentioning

confidence: 99%

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

2022

Computational Intelligence and Neuroscience

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“…After K-means clustering algorithm was proposed, it has been widely studied and applied in different disciplines. K-means clustering has been extensively applied in the field of dam safety evaluation, including clustering of displacement, seepage, and stress [18][19][20][21].…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Outlier Detection Based on Multivariable Panel Data and K‐Means Clustering for Dam Deformation Monitoring Data

Song

Zhang

Tong

et al. 2021

Advances in Civil Engineering

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A dam is a super-structure widely used in water conservancy engineering fields, and its long-term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long-time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K-means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K-means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping-I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.

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“…et al applied ANN to the classification of fatigue strain signal to obtain the best pattern recognition. The classification accuracy of ANN is 92%, and five levels of fatigue damage are obtained [ 20 ]. The ANN has a high demand for computing power and a long training time, so it is not suitable for the scene with real-time requirements.…”

Section: Introductionmentioning

confidence: 99%

Plantar Pressure Detection System Based on Flexible Hydrogel Sensor Array and WT-RF

Liu

Xiao

Wang

et al. 2021

Sensors

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This paper presents a hydrogel-based flexible sensor array to detect plantar pressure distribution and recognize the gait patterns to assist those who suffer from gait disorders to rehabilitate better. The traditional pressure detection array is composed of rigid metal sensors, which have poor biocompatibility and expensive manufacturing costs. To solve the above problems, we have designed and fabricated a novel flexible sensor array based on AAM/NaCl (Acrylamide/Sodium chloride) hydrogel and PI (Polyimide) membrane. The proposed array exhibits excellent structural flexibility (209 KPa) and high sensitivity (12.3 mV·N−1), which allows it to be in full contact with the sole of the foot to collect pressure signals accurately. The Wavelet Transform-Random Forest (WT-RF) algorithm is introduced to recognize the gaits based on the plantar pressure signals. Wavelet transform realizes the signal filtering and normalization, and random forest is responsible for the classification of the processed signals. The classification accuracy of the WT-RF algorithm reaches 91.9%, which ensures the precise recognition of gaits.

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K-means clustering analysis and artificial neural network classification of fatigue strain signals

Cited by 13 publications

References 14 publications

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

RFID Data Analysis and Evaluation Based on Big Data and Data Clustering

Outlier Detection Based on Multivariable Panel Data and K‐Means Clustering for Dam Deformation Monitoring Data

Plantar Pressure Detection System Based on Flexible Hydrogel Sensor Array and WT-RF

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