Bentley Systems, Inc.

Rony Kalfarisi

Zheng Yi Wu

Atiqur Rahman

Jianping Cai

Alvin Chew Wei Ze

Jocelyn Pok

Meng Xue

In various example embodiments, techniques are provided for training and/or using a semantic deep learning model, such as a segmentation-enabled CNN model, to detect corrosion and enable its quantitative evaluation. An application may include a training dataset generation tool capable of semi-automatic generation of a training dataset that includes images with labeled corrosion segments. The application may use the labeled training dataset to train a semantic deep learning model to detect and segment corrosion in images of an input dataset at the pixel-level. The application may apply an input dataset to the trained semantic deep learning model to produce a semantically segmented output dataset that includes labeled corrosion segments. The application may include an evaluation tool that quantitatively evaluates corrosion in the semantically segmented output dataset, to allow severity of the corrosion to be classified.

Semantic deep learning and rule optimization for surface corrosion detection and evaluation

Techniques are provided for near real-time anomaly event detection and classification with trend change detection for smart water grid operation management. In the first phase, a trend change is detected in each of one or more sensors by comparing new sensor data of a sensor with a historical trend pattern of the same sensor. In the second phase, and after the trend changes are detected, a valid event evaluation time window can be determined based on combining and analyzing the detected trend changes for flow and pressure sensors, e.g., at least one flow sensor and at least one pressure sensor from the same supply zone of the smart water grid. The valid event evaluation time window can be used with anomaly events that are detected in near-real time to classify the anomaly events in near-real time as valid, e.g., true anomaly events, or invalid, e.g., false alarms.

Systems, methods, and media for near real-time anomaly event detection and classification with trend change detection for smart water grid operation management

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Singapore, Singapore

Rony Kalfarisi