Muhammad Chowdhury

Abstract Accurate, fast, and interpretable fault identification on electrical transmission lines is essential for maintaining power system stability and reducing outage durations. In this study, we propose a hybrid 1D convolutional neural network–Decision Tree (1D-CNN–DT) for transmission line fault detection and classification, in which the 1D-CNN acts solely as a feature extractor. During this process, the Decision Tree performs the final, interpretable classification.

Introduction In recent years, several rehabilitation strategies have been used to address the motor sequelae of brain and spinal cord injuries. One example is the Functional Electrical Stimulation (FES) technique, used in neurological rehabilitation programs to improve motor function and manual dexterity (Popovic et al., 2001).

On the 20th March 2003, fifteen years ago today, at around 5:30 in the morning Baghdad time, a US led coalition of armed forces began a military invasion of Iraq. There was no declaration of war. They began by launching a “shock and awe” bombing campaign and within less than a month, Baghdad had fallen. On the 1st of May, President Bush gave a speech on board the USS Abraham Lincoln while a banner stating “Mission Accomplished” was used as a backdrop. [1] But it was far from mission accomplished.