Precision irrigation plays an important socio-economic and environmental role in our
society, reducing water and electricity consumption and increasing food production. An …

The rise of the Internet of Things allowed higher spatial–temporal resolution soil moisture
data captured through in situ sensing. Such abundance of data enables machine learning …

Anomaly detection of time series has always been a hot topic in academia and industry.
However, many existing multivariant time series methods suffer from common challenges …

Anomaly detection for time-series data is crucial in the management of systems for
streaming applications, computational services, and cloud platforms. The majority of current …

Rapid and accurate detection of battery pack anomalies and timely fault-tolerant control is of
great importance to the safe operation of electric vehicles (EVs). The occurrence of battery …

This article investigates the use of hybrid machine learning (HML) for the detection of
anomalous multivariate time-series (MVTS). Focusing on a specific industrial use-case from …

The proposed framework consists of three modules as an outlier detection method for indoor
air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based …

The quality of the acquisition data in the energy system of steel industry is the basis of
prediction analysis and scheduling operation. Facing with its multi-dimensional and high …

Geological carbon sequestration (GCS) is a method to reduce the emissions of CO 2 into the
atmosphere. During GCS operations CO 2 is captured from the atmosphere or industrial …

Detecting anomalies in massive volumes of multivariate time series data, particularly in the
IoT domain, is critical for maintaining stable systems. Existing anomaly detection models …