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ANOMALOUS ITEM DETERMINATION METHOD 新技術説明会

外国特許コード F190009839
整理番号 (GI-H29-18)
掲載日 2019年7月25日
出願国 世界知的所有権機関(WIPO)
国際出願番号 2018JP037352
国際公開番号 WO 2019073923
国際出願日 平成30年10月5日(2018.10.5)
国際公開日 平成31年4月18日(2019.4.18)
優先権データ
  • 特願2017-196758 (2017.10.10) JP
発明の名称 (英語) ANOMALOUS ITEM DETERMINATION METHOD 新技術説明会
発明の概要(英語) Provided is an anomalous item determination method with which it is possible to determine an anomalous item accurately by performing machine learning using a large amount of normal data and a small quantity of anomalous data. Data relating to a plurality of items to be determined are input into an encoder-decoder structure network, features of the items to be determined are extracted, and a discriminator determines whether the distribution of the features of the items to be determined is in accordance with a normal distribution. Updating of the encoder-decoder structure network, updating of the discriminator, and updating of an encoder are each repeated to minimize a feature extraction error. The encoder, using a feature obtained by the updating, calculates an anomaly degree of the items to be determined, subjects the anomaly degree to threshold value processing, and determines whether the items to be determined are normal items or anomalous items. The step of determining whether the distribution of the features of the items to be determined is in accordance with a normal distribution comprises a step of inputting data in accordance with a normal distribution into the discriminator and calculating an error between the data and the features of the items to be determined extracted by the encoder-decoder structure network. Using the result of determination by the discriminator allows the features of the items to be determined that are used by the encoder for anomaly degree calculation to converge so as to be distributed in accordance with a normal distribution.
従来技術、競合技術の概要(英語) BACKGROUND ART
By repeated learning data into the computer, as a mathematical equation or numerical feature data contained in the statistical computer is extracted, further, using the extracted features, the machine learning as a method of identification.
Machine learning as one method, referred to as the auto-encoder of the encoder and the (self-encoder), using the network decoder structure has been known a method of extracting the feature amount. And the auto-encoder, the input and output are the same as in the neural network to learn. Encoder input once the one-dimensional feature by a small chute, the decoder input so as to reproduce among the repeated output, the feature amount is extracted which represent the input.
Using the auto-encoder of the normal product if the feature extraction, using this feature, a normal product and the defective products from an aggregate of the determination target object are mixed, the defective products is determined with high accuracy and can be extracted.
Non-Patent Document 1 is, a kind of neural network 'Convolutional Neural Network (hereinafter, also referred to as a convolutional neural network) ' discloses a technique related to. CNN is, mainly used in the field of image recognition in a neural network, a local feature extraction of the convolution layer is responsible for, for each local feature in the feature pooling layers are summarized in a feature of the repeated structure. In general, including CNN for learning of the neural network, supervised learning using a large amount of training samples is required. However, the number of samples of the defective products is learned when it is difficult to secure a sufficient number is, the learning problem that cannot be performed successfully.
Non-Patent Document 2 is, a kind of neural network 'Autoencoder (hereinafter, the auto-encoder, also referred to as self-encoder) ' discloses a technique related to. Non-Patent Document 2 discloses a neural network, a multi-layered neural network parameters are initialized after unsupervised learning, supervised learning and relearning. Non-Patent Document 2 is of the auto-encoder, compressed two-dimensional input, wherein the abstract of the input vector quantity is converted into a feature vector, the feature vector from the reproduced input. However, the auto-encoder as to how to obtain and the shape distribution, it cannot be manipulated.
Non-Patent Document 3 is, a kind of neural network 'Adversarial Autoencoder (hereinafter, also referred to as a hostile self-encoder) ' discloses a technique related to. The encoder is self-deception, a hostile to auto-encoder incorporating learning, while the features are extracted which represent the input, in order to follow the feature distribution of an arbitrary technique.
Non-Patent Document 4 is, the Hotelling's T2 is a literature which disclosed method. T2 Method, only a large amount of normal data, abnormal data or a large amount of normal data and a small amount of the feature vector used to create a model from the normal, the abnormality level of each of the unknown data is computed, is a statistical method for detecting abnormal data. However, the distribution of the feature amount of the normal distribution is in accordance with the assumption that, if the data does not follow the normal distribution, sufficient detection cannot be performed. Hotelling T2 is applied to the image recognition method in the art, according to the normal distribution needs to be selected features.
  • 出願人(英語)
  • ※2012年7月以前掲載分については米国以外のすべての指定国
  • GIFU UNIVERSITY
  • 発明者(英語)
  • KATO Kunihito
  • NAKATSUKA Shunsuke
  • AIZAWA hiroaki
国際特許分類(IPC)
指定国 National States: AE AG AL AM AO AT AU AZ BA BB BG BH BN BR BW BY BZ CA CH CL CN CO CR CU CZ DE DJ DK DM DO DZ EC EE EG ES FI GB GD GE GH GM GT HN HR HU ID IL IN IR IS JO JP KE KG KH KN KP KR KW KZ LA LC LK LR LS LU LY MA MD ME MG MK MN MW MX MY MZ NA NG NI NO NZ OM PA PE PG PH PL PT QA RO RS RU RW SA SC SD SE SG SK SL SM ST SV SY TH TJ TM TN TR TT TZ UA UG US UZ VC VN ZA ZM ZW
ARIPO: BW GH GM KE LR LS MW MZ NA RW SD SL SZ TZ UG ZM ZW
EAPO: AM AZ BY KG KZ RU TJ TM
EPO: AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR
OAPI: BF BJ CF CG CI CM GA GN GQ GW KM ML MR NE SN ST TD TG
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