In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of convolutional autoencoder (CAE) is used to reduce the dimensions and extract features from the imaging data; (2) the bagging-based multiclustering model is proposed to obtain the classifications with high confidence at the cost of rejecting the disputed sources that are inconsistently voted. We apply this method on the sample of galaxies with H < 24.5 in CANDELS. Galaxies are clustered into 100 groups, each contains galaxies with analogous characteristics. To explore the robustness of the morphological classifications, we merge 100 groups into five categories by visual verification, including spheroid, early-type disk, late-type disk, irregular, and unclassifiable. After eliminating the unclassifiable category and the sources with inconsistent voting, the purity of the remaining four subclasses are significantly improved. Massive galaxies (M _* > 10¹⁰ M _⊙) are selected to investigate the connection with other physical properties. The classification scheme separates galaxies well in the U − V and V − J color space and Gini–M ₂₀ space. The gradual tendency of Sérsic indexes and effective radii is shown from the spheroid subclass to the irregular subclass. It suggests that the combination of CAE and multiclustering strategy is an effective method to cluster galaxies with similar features and can yield high-quality morphological classifications. Our study demonstrates the feasibility of UML in morphological analysis that would develop and serve the future observations made with China Space Station telescope.

Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive polar-coordinate transformation, we develop a rotationally-invariant supervised machine-learning (SML) method that ensures consistent classifications when rotating galaxy images, which is always required to be satisfied physically, but difficult to achieve algorithmically. The adaptive polar-coordinate transformation, compared with the conventional method of data augmentation by including additional rotated images in the training set, is proved to be an effective and efficient method in improving the robustness of the SML methods. In the previous work, we generated a catalog of galaxies with well-classified morphologies via our developed unsupervised machine-learning (UML) method. By using this UML data set as the training set, we apply the new method to classify galaxies into five categories (unclassifiable, irregulars, late-type disks, early-type disks, and spheroids). In general, the result of our morphological classifications following the sequence from irregulars to spheroids agrees well with the expected trends of other galaxy properties, including Sérsic indices, effective radii, nonparametric statistics, and colors. Thus, we demonstrate that the rotationally-invariant SML method, together with the previously developed UML method, completes the entire task of automatic classification of galaxy morphology.

Sewer networks (SNs) are susceptible to various factors that can lead to failures, resulting in economic losses and environmental pollution. Data-driven approaches based on sewage flow monitoring enhance the awareness and maintenance capabilities of SNs. However, the current research lacks early warning systems for flow anomalies. This presents a challenge for the application of supervised methods, primarily due to the scarcity of anomalous flow datasets. Even with the availability of such datasets, the effectiveness of these methods may vary due to environmental differences, since SNs are situated in diverse environments. Therefore, effectively achieving early warnings for anomalies in unlabeled flow data is a challenge that must be addressed in the field of flow monitoring. To address this challenge, we propose a detection method for effectively warning of anomalies in flow data. Since anomalies typically result in significant deviations from normal data, early warnings can be achieved by comparing the differences between current and historical data. The key to this early warning lies in establishing an adaptive threshold for detecting abnormal data changes. Our detection method employs an unsupervised bagging-based multi-anomaly detection algorithm to detect such abnormal data changes. Experiments conducted on Erhai Lake SNs flow data demonstrate that our method can predict anomalies 5-15 minutes in advance with a precision of 80.00%, a recall of 66.67%, and an F1 score of 0.73. Our approach not only achieves cost-effective and timely anomalies detection but also overcomes the challenges associated with limited dataset availability, making it applicable to various other industries.

Morphological classification conveys abundant information on the formation, evolution, and environment of galaxies. In this work, we refine a two-step galaxy morphological classification framework (USmorph), which employs a combination of unsupervised machine-learning and supervised machine-learning techniques, along with a self-consistent and robust data-preprocessing step. The updated method is applied to galaxies with I _mag < 25 at 0.2 < z < 1.2 in the COSMOS field. Based on their Hubble Space Telescope/Advanced Camera for Survey I-band images, we classify them into five distinct morphological types: spherical (SPH, 15,200), early-type disk (17,369), late-type disk (21,143), irregular disk (IRR, 28,965), and unclassified (UNC, 17,129). In addition, we have conducted both parametric and nonparametric morphological measurements. For galaxies with stellar masses exceeding 10⁹ M _☉, a gradual increase in effective radius from SPHs to IRRs is observed, accompanied by a decrease in the Sérsic index. Nonparametric morphologies reveal distinct distributions of galaxies across the Gini−M ₂₀ and C−A parameter spaces for different categories. Moreover, different categories exhibit significant dissimilarity in their G ₂ and Ψ distributions. We find morphology to be strongly correlated with redshift and stellar mass. The consistency of these classification results with expected correlations among multiple parameters underscores the validity and reliability of our classification method, rendering it a valuable tool for future studies.