Abstract: A method comprises obtaining original data representing one or more characteristics of a target object; selecting an artificial intelligence generation model from among a plurality of artificial intelligence generation models based on the original data; generating reconstructed data based on the original data using the selected artificial intelligence generation model; calculating an anomaly score by comparing the original data and the reconstructed data; and outputting detection result data indicating whether the target object is abnormal based on the anomaly score.

Abstract: An outlier detection device sets a weight for a kernel center of a grid cell based on a distribution of the data disposed on the grid cell region, calculates a cumulative change of a weight for each corresponding kernel center, sets a stationary region in the grid cell region based on the cumulative change, maintains a density of a kernel center of the stationary region as a previous density, calculates a density of a kernel center excluding the stationary region to update the calculated density, estimates a density of multidimensional data at the current time, and detects an arbitrary number of outliers based on a relative difference between the density of the multidimensional data and a density of a kernel center nearest to the multidimensional data.