![]() The proposed scheme could eliminate redundant information contained in the data, improve performance of clustering methods in identifying abnormal samples, and reduce the amount of calculation. This scheme makes full use of the advantages of the deep autoencoder (DAE) to generate low-dimensional representation and reconstruction errors for the input high-dimensional or multidimensional data and uses them to reconstruct the input samples. The deep autoencoder is trained to learn the compressed representation of the input data and then feed it to clustering approach. In order to improve the performance of unsupervised anomaly detection, we propose an anomaly detection scheme based on a deep autoencoder (DAE) and clustering methods. Recent studies have shown that the deep autoencoder (DAE) can solve this problem well. Although the methods of dimension reduction and density estimation have made great progress in recent years, most dimension reduction methods are difficult to retain the key information of original data or multidimensional data. The key to anomaly detection is density estimation. Click here to start this process.The unsupervised anomaly detection task based on high-dimensional or multidimensional data occupies a very important position in the field of machine learning and industrial applications especially in the aspect of network security, the anomaly detection of network data is particularly important. ![]() Hoske, content manager, Control Engineering, CFE Media, Artificial intelligence (AI), machine learning (ML), predictive maintenanceĪre you using AI most effectively for predictive maintenance and smart factory applications?ĭo you have experience and expertise with the topics mentioned in this content? You should consider contributing to our CFE Media editorial team and getting the recognition you and your company deserve. Matt Dentino is vice president, client engagement, Braintrust and Mitsuo Baba is senior director, IoT and infrastructure business unit, at Renesas Electronics Corp. ![]() The webcast includes many more details, examples and will answer questions at the end. To help with a diversity of applications, higher performance with new AI, and human interaction services, three key requirements for deploying AI applications are flexibility, power efficiency and real-time operation.ĪI tools applied at the edge of applications resolve numerous challenges present in smart factories, including predictive maintenance challenges.
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