New method combines neural networks and data assimilation for indoor air quality prediction.
problem Accurate and fast prediction of indoor air quality using real-time data.
method Combines Data Assimilation and Machine Learning using a Convolutional neural network and Long-Short-Term-Memory.
result Improved accuracy of dynamic system representation by integrating real data.
AirRL uses RL to infer urban air quality from selected stations.
problem Inferring fine-grained urban air quality from limited monitoring stations.
method Reinforcement learning model with a dynamic station selector and air quality regressor.
result AirRL achieves highest performance in air quality inference experiments.
DGPs improve air quality inference from sparse data.
problem Accurate air quality monitoring in unmonitored areas.
method Deep Gaussian Processes with Doubly Stochastic Variational Inference.
result DGPs outperform state-of-the-art models in AQ inference.
Surveying low-cost sensors for air quality monitoring and calibration.
problem Limited spatial resolution due to expensive environmental monitoring stations.
method Low-cost sensors with machine learning for calibration.
result Machine learning improves sensor accuracy over time.
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality rel…
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., m…
Paper uses Gaussian Processes to monitor air quality in Kampala.
problem Monitoring air pollution in cities with limited sensor coverage.
method Gaussian Processes for nowcasting and forecasting air pollution.
result Demonstrates the effectiveness of Gaussian Processes in air quality monitoring.
Engine forecasts NO2, O3, PM2.5, PM10 with high accuracy.
problem Accurate long-term air quality forecasting.
method Convolutional LSTM network trained on grid data.
result 4-day forecasts significantly outperform simple benchmarks.
New model clusters mixed-type data with missing values, improving air quality analysis.
problem Clustering mixed-type data with missing values and regime persistence.
method Statistical jump model incorporating regime persistence and handling missing data.
result Superior performance in inferring persistent air quality regimes compared to traditional methods.
Low-cost sensors improve air quality prediction accuracy significantly.
problem Improving air quality monitoring networks with affordable sensors.
method Developed a high-resolution air quality prediction engine using low-cost sensors and official data.
result The use of low-cost sensors improves prediction accuracy by 25% and 15% for PM2.5 and PM10 respectively in densely monitored areas.
A new model combines data-driven and model-based methods for accurate air quality prediction.
problem Accurate air quality forecasts are crucial for public health.
method Combines model-based strategy and data-driven method using PTC model.
result The PTC model achieves excellent performance compared to baseline models.
Autoencoder neural networks reconstruct missing indoor environment data.
problem Missing data in building operation data sets.
method Three different autoencoder neural networks trained to reconstruct missing data.
result Reconstructing variables with average RMSEs of 0.42 °C, 1.30 % and 78.41 ppm.
New model predicts citywide air pollution using deep learning.
problem Predicting air quality for entire cities is challenging due to spatiotemporal factors.
method Convolutional Long Short-Term Memory (ConvLSTM) model for spatiotemporal data.
result ConvLSTM model outperforms other methods in predicting citywide air pollution.
Engine predicts real-time air quality with high resolution.
problem Real-time prediction of air pollutants for health monitoring.
method Combines official data, models, land cover, traffic data for high-resolution predictions.
result Engine produces predictions with resolution of a few dozen meters.
The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and t…
DCK improves air quality index prediction with probabilistic spatial models.
problem Non-Gaussian, complex spatial structure of air quality index.
method Deep classifier kriging (DCK) for non-Gaussian, nonlinear spatial prediction.
result DCK outperforms conventional methods in predictive accuracy and uncertainty quantification.
GraphSVR forecasts urban air pollution robustly across stations and seasons.
problem Nonlinear, nonstationary, spatiotemporally dependent urban air pollution forecasting challenges.
method Combines graph convolutional learning and support vector regression.
result GraphSVR improves predictive accuracy and maintains stable performance across seasons and outlier-prone episodes.
Work addresses long-term accuracy issues in IoT air quality sensors.
problem Limited accuracy of IoT air quality sensors in long-term field deployments.
method Adaptive machine learning strategies for network calibration.
result Prolongs the validity of multisensor calibration models for continuous learning.
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
problem Calibrating LCAQ sensors against regulatory-grade monitors is expensive and time-consuming.
method PROvably outlier-resistant semi-parametric regression technique.
result RESPIRE offers improved prediction in cross-site, cross-season, and cross-sensor settings.
The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration measurem…
Improves magnetic field mapping using an array of magnetometers with noisy input.
problem Improving magnetic field maps in indoor environments with noisy magnetometer data.
method Uses Gaussian process regression with an array of magnetometers, incorporating known array positions and relative magnetometer locations.
result The method produces higher quality magnetic field maps compared to using a single magnetometer.
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demons…
New deep learning model optimizes energy use in buildings.
problem Optimizing energy use and comfort in large buildings.
method Transformer-based metamodel trained with simulation and sensor data, calibrated with CMA-ES, optimized with multi-objective algorithms.
result Optimal settings reduce energy loads while maintaining thermal comfort and air quality.
Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.
problem Accurate forecasting of urban air pollution for public health and policy guidance.
method Investigated lightweight additive models (FBP, NP) vs. deep learning and hybrid pipelines on Beijing PM2.5 and PM10 data.
result Facebook Prophet consistently outperformed NeuralProphet and traditional models, achieving high R2 values. Thermal preferences vary from person to person and may change over time. The main objective of this paper is to sequentially pose intelligent queries to occupants in order to optimally learn the indoor air temperature values which maximize their satisfaction. Our central hypothesis is that an occupant's preference rela…
Study shows reducing anthropogenic emissions significantly lowers PM2.5 levels but has little effect on O3 in Delhi.
problem Understanding and mitigating the effects of anthropogenic emissions on air pollution in Delhi.
method Predictive modeling, causal inference, Gaussian Process modeling, Granger causality analysis.
result Reductions in anthropogenic emissions lead to significant decreases in PM2.5 levels but have little effect on O3. Machine learning helps predict smoke types for safer forest burns.
problem Determining which fuels to burn safely to reduce wildfire risk and minimize smoke.
method Machine learning techniques like spectral clustering and manifold learning.
result Interpretable representations and tools for differentiating smoke types.
MMformer improves forecasting of environmental time series data.
problem Accurately forecasting environmental change trends for policy-making.
method Meta-learning MTS model combining self-attention and adaptive transferable multi-head attention.
result MMformer outperforms other models in air quality and climate datasets, reducing prediction errors by 50% in MSE and 20% in MAE.
Artificial Neural Network predicts PM2.5 pollution with low-cost sensors.
problem Costly and bulky PM2.5 monitoring instruments limit real-time, high-resolution data.
method Analytical equations derived using Artificial Neural Network (ANN).
result RMSE of 1.7973 ug/m3 and R2 of 0.9986 for eight predictors; 7.5372 ug/m3 and 0.9708 for three predictors.
Predicts local AQI using mobile sensor data, improving accuracy by 71.654 MSE.
problem Inaccurate AQI data from sparse sensors in developing countries.
method Spatio-temporal GNNs for fine-grained AQI forecasting.
result Significant improvement in AQI prediction accuracy (71.654 MSE reduction).
Study compares geostatistical and machine learning models for PM2.5 prediction.
problem Improving accuracy of hourly PM2.5 maps across California.
method Traditional geostatistical methods (kriging, land use regression) and machine learning models (neural networks, random forests, support vector machines) were evaluated.
result Ensemble model enhanced predictive accuracy of PM2.5 concentration by correcting PurpleAir data bias.
The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the traini…
Paper proposes a feature-wise change detection method for improving indoor positioning accuracy.
problem Improving the quality of reference fingerprint maps in indoor positioning systems.
method Inspired by RANSAC, the paper uses resampling of features to estimate intermediate locations and identifies candidate locations using MJI.
result The approach improves positioning accuracy by 20% and achieves 90% change detection accuracy.
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental sci…
New model estimates indoor radon distribution with higher spatial resolution.
problem Accurate estimation of indoor radon concentration for health assessment.
method Quantile regression forest and probabilistic Monte Carlo sampling.
result Approximate lognormal distribution of indoor radon in Germany with specific exceedance probabilities.
This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e.g., vision impairments, physical disabilities, etc.), path planni…
Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.
problem Forecasting high NO2 concentration episodes for effective air quality management.
method Compared 10 probabilistic forecasting models for NO2 concentration prediction.
result Quantile gradient boosted trees model outperformed others in predicting NO2 concentration distributions.
Spatial information is not always necessary for spatio-temporal models.
problem The necessity of including spatial information in spatio-temporal models.
method Comparison of spatial agnostic neural networks with state-of-the-art models on ten datasets.
result Spatial information is not always needed in most spatio-temporal models.
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
Proposes efficient calibration for indoor localization models.
problem Calibration data scarcity in wireless indoor localization.
method Uses synthetic labels and prediction sets to fine-tune a predictor and estimate bias.
result Yields rigorous coverage guarantees for prediction sets.
SLAM-net learns to navigate visually in challenging indoor environments.
problem Challenges in SLAM for visual robot navigation, especially in noisy conditions.
method Differentiable SLAM Network (SLAM-net) that encodes a particle filter SLAM algorithm in a differentiable graph and learns components through backpropagation.
result Significantly outperforms ORB-SLAM in noisy conditions and improves the Habitat Challenge 2020 PointNav task.
This paper improves indoor positioning accuracy by deploying reference nodes to ensure Line-of-Sight.
problem Systematic bias errors in indoor positioning due to non-LoS propagation.
method Model indoor service area as a graph, partition into cliques for reference nodes, set minimum distance and angle parameters.
result Guaranteed LoS to reference nodes improves indoor positioning accuracy and precision.
The paper uses Bayesian Surprise to identify unexpected structures in indoor environments.
problem Identifying unexpected structures in indoor environments.
method Bayesian Surprise applied to Isovist Analysis of 2D floor plans.
result Surprise regions in indoor environments can be used to focus on important areas in LBS.
The paper develops methods to reduce deployment risk under dynamic covariate shifts.
problem Reduction of deployment risk under dynamic covariate shifts.
method Time-domain Poincare inequality and Jacobian-velocity theorem to identify and control directional tangent energy.
result Drift-aligned tangent regularization (DTR) reduces risk volatility and directional gain in low-rank drift regimes.
RETR improves indoor radar perception with a novel transformer model.
problem Indoor radar perception lacks models tailored for multi-view radar settings.
method RETR extends DETR architecture with depth-prioritized feature similarity, tri-plane loss, and radar-to-camera transformation.
result RETR outperforms state-of-the-art methods by 15.38+ AP for object detection and 11.91+ IoU for instance segmentation.
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that di…
Deep learning adapts HVAC models to new buildings.
problem Adapting thermal dynamics models to new buildings with limited data.
method Deep supervised domain adaptation (DSDA) using LSTM-based Sequence to Sequence model.
result Deep supervised domain adaptation improves predictive performance over learning from scratch.
Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards …