This paper proposes an online algorithm for detecting transient patterns with an unpredictable duration from an astronomical data stream. The key idea appears in the binning technique which is often useful for noise filtering. It is used to split the original data stream into bins with a fixed length. A suitable length for binning depends on the transient patterns to be detected, although their prior information is not available in advance. The proposed method addresses this problem by introducing a dynamic mechanism which enables to adjust the bins with an adequate length. Consequently, it prevents from missing transient patterns with arbitrary duration Our extensive experiments using real astronomy dataset reveals that the proposed method outperforms the existing methods with high accuracy.

Ensuring the availability and reliability of electronic components or materials during operation necessitates conducting a sampling inspection of their lifespan. This is particularly crucial when comparing equipment or materials deployed in various areas with diverse environments, conditions, and situations. Many studies rely on one-method parameter estimation methods, which fall short in identifying the two explicit parameters of the Weibull distribution. Moreover, inaccurate parameter estimation methods impede the attainment of reliable analysis results. Consequently, this paper introduces three analytical estimation methods to determine the parameters of the Weibull distribution. The accuracy of these methods is evaluated using the mean square error (MSE). Furthermore, we utlized the Kolmogorov–Smirnov and Anderson–Darling test on real datasets, confirming that the data follow a Weibull distribution. Simulation results indicate that the maximum likelihood estimate outperforms the other estimators by minimizing the MSE and yielding optimal parameters. These optimal parameters were then applied to real CCTV datasets, demonstrating a good fit and enabling assessment of CCTV lifespan through the mean time to failure, which is estimated to be 6.6 years. This holds true even when the equipment operates in environments with different conditions and situations.

Time-domain astrophysics analysis (TDAA) involves observational surveys of celestial phenomena that may contain irrelevant information because of several factors, one of which is the sensitivity of the optical telescopes. Data binning is a typical technique for removing inconsistencies and clarifying the main characteristic of the original data in astrophysics analysis. It splits the data sequence into smaller bins with a fixed size and subsequently sketches them into a new representation form. In this study, we introduce a novel approach, called elastic data binning (EBinning), to automatically adjust each bin size using two statistical metrics based on the Student's t-test for linear regression and Hoeffding inequality. We demonstrate the successful representation of various characteristics in the light curve data gathered from the Kiso Schmidt telescope using our approach and the applicability of our approach for transient pattern analysis using real world data.