Coordinates Of Cluster Centers Research Articles

Abnormal discharge detection using adaptive neuro-fuzzy inference method with probability density-based feature and modified subtractive clustering

Abnormal discharge (AD) is a discharge mode in electroencephalogram (EEG) with sharp outlines. Lack of related open-access datasets and insufficient annotation hamper the development of AD detection, while cognitive neuroscience, clinical research and pilots’ neural screening demand stable and reliable AD detection methods. An adaptive neuro-fuzzy inference method for AD detection is proposed in this work. First, a probability-density-based (PD) method is proposed to extract lognormal amplitude features from EEG envelopes. Second, the subtractive clustering method (SCM) is modified so that clustering radii can adapt to cluster shapes for each dimension instead of using identical radii for each cluster. The outputs of modified SCM (mSCM), coordinates of cluster centers and adjusting rates for radius components are used to automatically initialize Gaussian membership functions of adaptive-network-based fuzzy inference system (ANFIS). Finally, we conducted multiple experiments to validate mSCM-based ANFIS, comparing it with traditional machine learning classifiers (support vector machines, multi-layer perceptrons, decision trees, and random forests) and the state-of-the-art deep learning-based time-series classification methods InceptionTime and Minirocket, using synthetic data, small-size datasets and a private EEG dataset. Results show that combining PD features with features proposed in previous studies, such as smoothed nonlinear energy operator features and discrete wavelet transform features, achieved higher accuracy (95.13 ± 0.86%) and recall (89.17 ± 3.06%) than other feature combinations. mSCM created more suitable cluster boundaries than SCM on small-size datasets, and its clustering results demonstrated potential in helping interpretation of classification rules built in the ANFIS network. Results of comparison experiments showed that mSCM-based ANFIS produced competitive results in accuracy and recall for AD detection, with lower computational cost, compared to the top 2 results obtained by InceptionTime and Minrocket.

Usefulness of subclassification of adult diabetes mellitus among inpatients in Japan.

Aims/IntroductionWe aimed to replicate a new diabetes subclassification based on objective clinical information at admission in a diabetes educational inpatient program. We also assessed the educational outcomes for each cluster.MethodsWe included diabetes patients who participated in the educational inpatient program during 2009–2020 and had sufficient clinical information for the cluster analysis. We applied a data‐driven clustering method proposed in a previous study and further evaluated the clinical characteristics of each cluster. We investigated the association between the clusters and changes in hemoglobin A1c level from the start of the education program. We also assessed the risk of re‐admission for the educational program.ResultsWe divided a total of 651 patients into five clusters. Their clinical characteristics followed the same pattern as in previous studies. The intercluster ranking of the cluster center coordinates showed strong correlation coefficients with those of the previous studies (mean ρ = 0.88). Patients classified as severe insulin‐resistant diabetes (cluster 3) showed a more pronounced progression of renal dysfunction than patients classified as the other clusters. The patients classified as severe insulin‐deficient diabetes (cluster 2) had the highest rate of reduction in hemoglobin A1c level from the start of the program (P < 0.01) and a tendency toward a lower risk of re‐admission for the education program (hazard ratio 0.47, P = 0.09).ConclusionWe successfully replicated the diabetes subclassification using objective clinical information at admission for the education program. In addition, we showed that severe insulin‐deficient diabetes patients tended to have better educational outcomes than patients classified as the other clusters.

A New Vibration Analysis Approach for Monitoring the Working Condition of a High-Voltage Shunt Reactor

With the rapid development of power grids, the voltage level and capacity of high-voltage shunt reactors (HVSRs) are increasing year by year, and HVSR faults are increasing, especially HVSRs core winding faults. The HVRSs core winding faults seriously threaten the safe and stable operation of power grids. To solve this problem, we propose a feature extraction method for HVSR core winding faults identification to improve the accuracy of faults identification. This method relies on the combined application of qualitative analysis of phase space reconstruction (PSR) and quantitative calculation of an improved K-means clustering method. First, we employ PSR to qualitatively analyze the HVSR vibration signal and extract the phase trajectory feature quantity of the vibration signal. Then, this paper uses an improved K-means clustering optimized by an improved grasshopper optimization algorithm (GOA) to cluster the phase trajectory features, so as to obtain the cluster center coordinates. Furthermore, we employ the distance from the cluster center displacement vector to the origin and the angle change to perform quantitative calculations to realize fault identification. Finally, the fault simulation experimental data sets of 10kV and 20kV HVSRs verify the effectiveness of the proposed method. The experiment results show that the proposed method has better accuracy and can truly reflect the state characteristics of the HVSR core winding. The proposed high-accuracy method helps to improve the efficiency of on-site HVSRs condition assessment and maintenance.

ASteCA: Automated Stellar Cluster Analysis

We present ASteCA (Automated Stellar Cluster Analysis), a suit of tools designed to fully automatize the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of positional and photometric data to obtain precise and objective values for a given cluster's center coordinates, radius, luminosity function and integrated color magnitude, as well as characterizing through a statistical estimator its probability of being a true physical cluster rather than a random overdensity of field stars. ASteCA incorporates a Bayesian field star decontamination algorithm capable of assigning membership probabilities using photometric data alone. An isochrone fitting process based on the generation of synthetic clusters from theoretical isochrones and selection of the best fit through a genetic algorithm is also present, which allows ASteCA to provide accurate estimates for a cluster's metallicity, age, extinction and distance values along with its uncertainties. To validate the code we applied it on a large set of over 400 synthetic MASSCLEAN clusters with varying degrees of field star contamination as well as a smaller set of 20 observed Milky Way open clusters (Berkeley 7, Bochum 11, Czernik 26, Czernik 30, Haffner 11, Haffner 19, NGC 133, NGC 2236, NGC 2264, NGC 2324, NGC 2421, NGC 2627, NGC 6231, NGC 6383, NGC 6705, Ruprecht 1, Tombaugh 1, Trumpler 1, Trumpler 5 and Trumpler 14) studied in the literature. The results show that ASteCA is able to recover cluster parameters with an acceptable precision even for those clusters affected by substantial field star contamination. ASteCA is written in Python and is made available as an open source code which can be downloaded ready to be used from it's official site.

The main parameters of 25 un-studied open star clusters

This paper is a continuation of a series, aiming to determine the main parameters of rarely or un-studied open star clusters using Near-IR JHK photometry. Cluster center coordinates, angular diameter, distances, reddening, age, and metallicity are estimated for 25 un-studied open clusters, most of them in the first quadrant. A morphological analysis of 2MASS database (the digital “Two Micron All Sky Survey”) has been used.

Extension Neural Network-Type 2 and Its Applications

A supervised learning pattern classifier, called the extension neural network (ENN), has been described in a recent paper. In this sequel, the unsupervised learning pattern clustering sibling called the extension neural network type 2 (ENN-2) is proposed. This new neural network uses an extension distance (ED) to measure the similarity between data and the cluster center. It does not require an initial guess of the cluster center coordinates, nor of the initial number of clusters. The clustering process is controlled by a distanced parameter and by a novel extension distance. It shows the same capability as human memory systems to keep stability and plasticity characteristics at the same time, and it can produce meaningful weights after learning. Moreover, the structure of the proposed ENN-2 is simpler and the learning time is shorter than traditional neural networks. Experimental results from five different examples, including three benchmark data sets and two practical applications, verify the effectiveness and applicability of the proposed work.