Jang et al. [36] introduced a Convolutional Variational Autoencoder (CVAE) that models the variability in ECG patterns through learned latent distributions, facilitating clustering and anomaly ...

Acceldata, a leading provider of data observability and agentic data management solutions, is announcing a new capability designed to amplify the power of agentic reasoning within the company's xLake ...

Transformer outages significantly impact the reliability and cost efficiency of power systems. Studies indicate that approximately 30% of transformer failures stem from issues with on-load tap ...

TSI’s Nano LPM™ System tackles these limitations with a patented approach that ensures accurate detection of particles at 10 nm, giving semiconductor facilities a robust and consistent ...

To solve these problems, based on the knowledge distillation framework, this paper proposes an unsupervised anomaly detection algorithm—Bidirectional knowledge distillation AD (BKD). This algorithm ...

Emerging Tech CBP still working toward anomaly detection algorithms at the border A series of privacy threshold analyses were recently released by the agency.

Leveraging this phenomenon, this paper proposes a slope failure mode identification method based on a deep convolutional autoencoder. The method utilizes monitoring data from the normal operation ...

I am excited to announce that our publication,"Convolutional Autoencoder Anomaly Detection and Classification based on Distribution PMU Measurements", has been awarded the Top Downloaded Article ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of data clustering and anomaly detection using the DBSCAN (Density Based Spatial Clustering of Applications ...

Currently, it is used for image synthesis, text generation, and anomaly detection in art, natural language processing, and cybersecurity applications. For example, game developers can use it to ...