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Found 13 papers, 9 papers with code
Scalable Methods for Brick Kiln Detection and Compliance Monitoring from Satellite Imagery: A Deployment Case Study in India
We show that 90% of brick kilns in Delhi-NCR violate a density-based policy.
Deep Gaussian Processes for Air Quality Inference
Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce pollution.
Challenges in Gaussian Processes for Non Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances.
Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning
Gaussian processes are Bayesian non-parametric models used in many areas.
Merged-GHCIDR: Geometrical Approach to Reduce Image Data
We propose two variations: Geometrical Homogeneous Clustering for Image Data Reduction (GHCIDR) and Merged-GHCIDR upon the baseline algorithm - Reduction through Homogeneous Clustering (RHC) to achieve better accuracy and training time.
Geometrical Homogeneous Clustering for Image Data Reduction
The intuition behind the first approach, RHCKON, is that the boundary points contribute significantly towards the representation of clusters.
SpiroMask: Measuring Lung Function Using Consumer-Grade Masks
According to the World Health Organisation (WHO), 235 million people suffer from respiratory illnesses and four million people die annually due to air pollution.
PlantDoc: A Dataset for Visual Plant Disease Detection
Early detection of plant diseases remains difficult due to the lack of lab infrastructure and expertise.
Active Collaborative Sensing for Energy Breakdown
However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits.
How good is good enough? Re-evaluating the bar for energy disaggregation
Since the early 1980s, the research community has developed ever more sophisticated algorithms for the problem of energy disaggregation, but despite decades of research, there is still a dearth of applications with demonstrated value.
Neighbourhood NILM: A Big-data Approach to Household Energy Disaggregation
Many existing techniques are not able to disaggregate these loads at all.
Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets
In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances.
Other Computer Science
NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.
Applications
