Abstract With the proposal of smart grid, demand both source and load for fine monitoring control power is becoming increasingly prominent. Non-intrusive a technical means to better meet this demand. However, research at home abroad focuses on existing data sets labeled improve accuracy identification, while training method model under massive unlabeled in actual scene still relatively blank st...

At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within context of generalization performance, failing to meet accuracy requirements when applied novel scenarios. To address this issue, a based on instance-batch normalization network proposed. This uses an encoder-decoder structure with attention mechanism, in which skip connections ...

With the ever-growing number of elevators coupled with aging workforce, diminishing new installations and limited use maintenance technology, it is increasingly challenging for owners responsible parties to maintain safe reliable operation lift systems. To address this issue, a non-intrusive artificial intelligence (AI) based diagnosis system, aiming at providing fault detection potential predi...

Abstract To meet the human perceived quality of experience (QoE) while communicating over various Voice Internet protocol (VoIP) applications, for example Google Meet, Microsoft Skype, Apple FaceTime, etc. a precise speech assessment metric is needed. The should be able to detect and segregate different types noise degradations present in surroundings before measuring monitoring real-time. Our ...

From a single meter that measures the entire home’s electrical demand, energy disaggregation calculates appliance-by-appliance electricity consumption. Non-intrusive load monitoring (NILM), also known as disaggregation, tries to decompose aggregated consumption data and estimate each appliance’s contribution. Recently, methodologies based on Artificial Intelligence (AI) have been proposed commo...