Makinarocks Co., Ltd.

Autoliv Development Ab

Sangwoo Shim

Ki Hyun Kim

Yongsub Lim

Byungchan Kim

Andre S Yoon

Sungho Yoon

JeongWoo Choi

Jongsun Shinn

Jong Seob Jeon

Hooncheol Shin

Jongseob Jeon

Hwiyeon Cho

Byoungwan Kim

Dae Sung Kim

Changwoo Lee

Hojin Lee

Seungmok Park

Young Jae Choung

Kyounghyun Mo

Disclosed is a method performed by a computing device for implementing a Graphical User Interface (GUI) providing a development environment, the method including: setting, by a computing device, a plurality of code blocks; designating two or more execution target blocks among the plurality of code blocks; constructing one or more pipelines defining a relationship between the two or more execution target blocks and connecting the two or more execution target blocks; and executing at least some of the two or more execution target blocks based on the connection relationship of the one or more pipelines.

Method for a development environment

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.

Systems and methods for detecting flaws on panels using images of the panels

The disclosed technology generally relates to novelty detection and more particularly to novelty detection methods using a deep learning neural network and apparatuses and non-transitory computer-readable media configured for performing the methods. In one aspect, a method for detecting novelty using a deep learning neural network model comprises providing a deep learning neural network model. The deep learning neural network model comprises an encoder comprising a plurality of encoder layers and a decoder comprising a plurality of decoder layers. The method additionally comprises feeding a first input into the encoder and successively processing the first input through the plurality of encoder layers to generate a first encoded input, wherein successively processing the first input comprises generating a first intermediate encoded input from one of the encoder layers prior to generating the first encoded input. The method additionally comprises feeding the first encoded input from the encoder into the decoder and successively processing the first encoded input through the plurality of decoder layers to generate a first reconstructed output. The method additionally comprises feeding the first reconstructed output from the decoder as a second or subsequent input into the encoder and successively processing the first reconstructed output through the plurality of encoder layers, wherein successively processing the first reconstructed output comprises generating a second intermediate encoded input from the one of the encoder layers. The method further comprises detecting a novelty of the original input based on a comparison of the first intermediate encoded input and the second intermediate encoded input.

Novelty detection using deep learning neural network

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.

Method and apparatus for detecting anomaly data

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.

Environment factor control device and training method thereof

According to an embodiment of the present disclosure for solving the aforementioned problem, disclosed is a computer program stored in a computer-readable storage medium executable by one or more processors, in which when the computer program is executed by one or more processors of a computer device, the computer program allows the one or more processors to perform the following operations for data processing, and the operations may include: an operation of generating a plurality of transformed data based on each of a plurality of data included in a data set; an operation of generating a test data set based on the plurality of data and the plurality of transformed data; and an operation of testing the performance of the model by calculating the test data set by using the model.

Computer program for performance testing of models

In an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium executable by one or more processors, in which when the computer program is executed by one or more processors of a computing device, the computer program performs operations below for processing data, the operations may include: selecting a plurality of different data from a data set including data formed of one or more feature groups, transforming a part of each data among the plurality of selected data, assigning a label to each of the plurality of transformed data, and computing the transformed data by using the model.

Method for generating abnormal data

Disclosed is a non-transitory computer readable medium storing a computer program. When the computer program is executed by one or more processors of a computing device, the computer program performs the following operations for processing data, and the operations may include: determining an uncertainty level with respect to labeling criteria for each of one or more data included in a dataset; determining a similarity level for one or more data included in a data subset; and selecting at least some of data included in the dataset based on the uncertainty level and the similarity level, and additionally labeling the selected data.

Method of training neural network classification model using selected data

According to an exemplary embodiment of the present disclosure, a computer program stored in a computer readable storage medium is disclosed. The computer program performs operations for processing input data when the computer program is executed by one or more processors of a computer device, the operations including: obtaining input data based on sensor data obtained during manufacturing of an article by using one or more manufacturing recipes in one or more manufacturing equipment; inputting the input data to a neural network model loaded to the computer device; generating an output by processing the input data by using the neural network model; and detecting an anomaly for the input data based on the output of the neural network model.

Anomaly detection

A method for detecting novelty using an encoder and a decoder comprises: feeding a first input into the encoder and processing the first input through a plurality of encoder layers to generate a first encoded input, wherein processing the first input comprises generating a first intermediate encoded input prior to generating the first encoded input, feeding the first encoded input from the encoder into the decoder and processing the first encoded input through a plurality of decoder layers to generate a first reconstructed output, feeding the first reconstructed output from the decoder as a second or subsequent input into the encoder and processing the first reconstructed output through the plurality of encoder layers, wherein processing the first reconstructed output comprises generating a second intermediate encoded input from the one of the encoder layers, and detecting a novelty based on the first intermediate encoded input and the second intermediate encoded input.

According to an exemplary embodiment of the present disclosure, disclosed is a computer program stored in a computer readable storage medium. When the computer program is executed in one or more processors, the computer program performs the following method for anomaly detection of data using a network function, and the method includes: generating an anomaly detection model including a plurality of anomaly detection sub models including a trained network function using a plurality of training data sub sets included in the training data set; calculating input data using at least one of the plurality of generated anomaly detection sub models; and determining whether there is an anomaly in the input data based on output data for input data of at least one of the plurality of generated anomaly detection sub models and the input data.

Anomaly detection independent of a normal pattern of input data

Provided is a method of manufacturing an emblem of an automobile part in which the emblem and the part are formed integrally. The method includes printing an emblem on a film wound up in a roll at predetermined intervals (S), three-dimensionally forming the emblem-printed film (S), cutting the three-dimensionally formed film using a cutter to obtain an emblem film (S), inserting the cut emblem film into upper and lower halves of a mold (S), and injecting a melted resin into the mold to integrally form the resin with the emblem film (S).

Growing community of inventors

Seoul, South Korea

Sangwoo Shim