A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

Ran, Xiaojuan; Zhou, Xiangbing; Lei, Mu; Tepsan, Worawit; Deng, Wu

doi:10.3390/app112311202

Cited by 170 publications

(91 citation statements)

References 47 publications

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“…t-SNE, a method of manifold learning, is a technique for nonlinear dimensionality reduction between data sets (Li, Cerise, et al, 2017;Ran et al, 2021). The core principle of t-SNE is to perform modeling of the data points by using a normalized Gaussian kernel in high dimensional space and t distribution in low dimensional space (Devassy & George, 2020).…”

Section: T-distributed Stochastic Neighbor Embeddingmentioning

confidence: 99%

Identification of coumarin‐based food additives using terahertz spectroscopy combined with manifold learning and improved support vector machine

Chen

Tang

et al. 2022

Journal of Food Science

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The purpose of this paper is to use terahertz (THz) spectroscopy combined with manifold learning and improved support vector machine (SVM) model to identify the coumarin-based food additives. The 216 THz absorbance spectra (144 for calibration set and 72 for prediction set) of six coumarin-based food additives are measured by using THz time-domain spectroscopy (THz-TDS) in the range of 0.5-2.0 THz. The method (P-t-SNE) combined principal component analysis (PCA) with manifold learning t-distributed stochastic neighbor embedding (t-SNE) is used for feature extraction of the THz spectra. Then, an improved SVM using differential evolution (DE) to improve gray wolf optimization (GWO) to optimize parameters is proposed. Finally, the result shows that the prediction set accuracy of PCA-DEGWO-SVM, P-t-SNE-DEGWO-SVM, and P-t-SNE-GWO-SVM models are 97.22%, 98.61%, and 95.83%, respectively, indicating that the accuracy by P-t-SNE is increased by about 1.39% compared with that processed by PCA, and the accuracy by DEGWO is also increased by about 2.78% compared with that processed by GWO. In conclusion, the improved model (Pt-SNE-DEGWO-SVM) has the best identification effect, and it is proved to be an effective method to identify coumarin-based food additives.

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Section: T-distributed Stochastic Neighbor Embeddingmentioning

confidence: 99%

Identification of coumarin‐based food additives using terahertz spectroscopy combined with manifold learning and improved support vector machine

Chen

Tang

et al. 2022

Journal of Food Science

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“…As a result, these findings will be beneficial for future research in order to improve the MDFVI's conciseness. Furthermore, to extend the proposed MTL-CNN detection algorithm in a unsupervised one, k-means clustering techniques [65] can be useful for identifying the health cluster automatically as well. In addition, the dynamometer simulation considers a variety of motor loads.…”

Section: Experimental Setup and Dataset Descriptionmentioning

confidence: 99%

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Hasan

Islam

2021

Sensors

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Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (MDFVI), is highly effective in generating a unique pattern even with variable speeds and loads. Following that, these MDFVI images are fed to the proposed MTL-based CNN architecture to identify faults in variable speed and health conditions concurrently. The proposed method is tested on two benchmark datasets from the bearing experiment. The experimental results suggested that the proposed method outperformed state-of-the-arts in both datasets.

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“…Therefore, the CACO algorithm takes on a global optimization ability. In addition, some new optimization algorithms have been proposed in recent years [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Chaotic Ant Colony Optimization Algorithmmentioning

confidence: 99%

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

Cui

Guan

2021

Applied Sciences

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Aiming at the problems of poor decomposition quality and the extraction effect of a weak signal with strong noise by empirical mode decomposition (EMD), a novel fault diagnosis method based on cascaded adaptive second-order tristable stochastic resonance (CASTSR) and EMD is proposed in this paper. In the proposed method, low-frequency interference components are filtered by using high-pass filtering, and the restriction conditions of stochastic resonance theory are solved by using an ordinary variable-scale method. Then, a chaotic ant colony optimization algorithm with a global optimization ability is employed to adaptively adjust the parameters of the second-order tristable stochastic resonance system to obtain the optimal stochastic resonance, and noise reduction pretreatment technology based on CASTSR is developed to enhance the weak signal characteristics of low frequency. Next, the EMD is employed to decompose the denoising signal and extract the characteristic frequency from the intrinsic mode function (IMF), so as to realize the fault diagnosis of rolling bearings. Finally, the numerical simulation signal and actual bearing fault data are selected to prove the validity of the proposed method. The experiment results indicate that the proposed fault diagnosis method can enhance the decomposition quality of the EMD, effectively extract features of weak signals, and improve the accuracy of fault diagnosis. Therefore, the proposed fault diagnosis method is an effective fault diagnosis method for rotating machinery.

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A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

Cited by 170 publications

References 47 publications

Identification of coumarin‐based food additives using terahertz spectroscopy combined with manifold learning and improved support vector machine

Identification of coumarin‐based food additives using terahertz spectroscopy combined with manifold learning and improved support vector machine

Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning

Fault Diagnosis Using Cascaded Adaptive Second-Order Tristable Stochastic Resonance and Empirical Mode Decomposition

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