Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, in which when the computer program is executed by one or more processors of a computing device, the computer program performs operations to provide methods for detecting flaws, and the operations may include: extracting a flaw patch from a flaw image including a flaw; preprocessing at least one of the flaw image or non-flaw image not including a flaw; extracting a non-flaw patch from at least one of the preprocessed flaw image or non-flaw image; and training a neural network model for classifying patches to flaw or non-flaw with a training data set including the flaw patch and the non-flaw patch.

Abstract: An anomaly data detecting method performed by a computing device having at least one processor includes acquiring first time-series data, dividing the first time-series data into a plurality of sub time-series data, adjusting scales of variable values included in at least one sub time-series data among the plurality of sub time-series data and determining whether the first time-series data is abnormal by inputting scaled first time-series data to a neural network based detection model.

Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, wherein the computer program includes instructions to perform following steps for data processing when the computer program is executed by one or more processors, the steps including: recognizing at least one continuous section from each raw data subset; determining at least one serialization point, based on a start point and an end point of each of the at least one continuous section for each of the raw data subset; and generating a training data set by generating serialized training data, based on the at least one serialization point.

Abstract: An anomaly data detecting method performed by a computing device having at least one processor includes acquiring first time-series data, dividing the first time-series data into a plurality of sub time-series data, adjusting scales of variable values included in at least one sub time-series data among the plurality of sub time-series data and determining whether the first time-series data is abnormal by inputting scaled first time-series data to a neural network based detection model.

Abstract: Disclosed is a method for performing an operation related to an auto-encoder model, which is performed by a computing device including at least one processor, which has optimizing an auto-encoder model as a problem to be solved. Specifically, disclosed is a method including: measuring a reconstruction error (RE) value for noise with respect to at least one of a trained auto-encoder model or an auto-encoder model being trained based on a data set; and performing at least one operation of an operation of changing a size of the trained auto-encoder model or an operation of stopping training of the auto-encoder model being trained, based on the reconstruction error value for the noise.

Abstract: A method for calculating an anomaly score performed by a computing device including at least one processor is performed using an auto-encoder model to calculate an anomaly score based on the selection. The method includes calculating a reconstruction error for a plurality of data, based on an auto-encoder model; determining a reconstruction error for one or more data among the plurality of data as an exclusion object; and calculating the anomaly score based on the remaining reconstruction errors excluding the exclusion object, among reconstruction errors for the plurality of data.

Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, wherein the computer program includes instructions to perform following steps for data processing when the computer program is executed by one or more processors, the steps including: recognizing at least one continuous section from each raw data subset; determining at least one serialization point, based on a start point and an end point of each of the at least one continuous section for each of the raw data subset; and generating a training data set by generating serialized training data, based on the at least one serialization point.

Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, wherein the computer program includes instructions to perform following steps for data processing when the computer program is executed by one or more processors, the steps including: recognizing at least one continuous section from each raw data subset; determining at least one serialization point, based on a start point and an end point of each of the at least one continuous section for each of the raw data subset; and generating a training data set by generating serialized training data, based on the at least one serialization point.

Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, wherein the computer program includes instructions to perform following steps for data processing when the computer program is executed by one or more processors, the steps including: recognizing at least one continuous section from each raw data subset; determining at least one serialization point, based on a start point and an end point of each of the at least one continuous section for each of the raw data subset; and generating a training data set by generating serialized training data, based on the at least one serialization point.

Abstract: Disclosed is a non-transitory computer readable medium storing a computer program, in which when the computer program is executed by one or more processors of a computing device, the computer program performs operations to provide methods for detecting flaws, and the operations may include: extracting a flaw patch from a flaw image including a flaw; preprocessing at least one of the flaw image or non-flaw image not including a flaw; extracting a non-flaw patch from at least one of the preprocessed flaw image or non-flaw image; and training a neural network model for classifying patches to flaw or non-flaw with a training data set including the flaw patch and the non-flaw patch.