Bayesian detector improves defect detection on rough surfaces.
problem Defect detection on motion blurred images with rough texture.
method Developed Bayesian detector using reflected non-local prior distributions.
result Significantly enhanced accuracy in identifying defects.
A new ride-hailing subsidy system uses deep causal networks to estimate consumer elasticity.
problem Estimating consumer elasticity with subsidies in ride-hailing industry.
method Introduces a consumer subsidizing system using deep causal networks to address confounding effects.
result Effective in estimating the uplift effect of subsidies without confounding.
Optimizes e-hailing drivers' passenger seeking to reduce congestion and pollution.
problem Reduces congestion and pollution by optimizing e-hailing drivers' passenger seeking.
method Uses Markov Decision Process (MDP) and imitation learning to model and optimize drivers' decisions.
result Achieves a 17.5% improvement in passenger return rate over a heuristic strategy.
Recently, the online car-hailing service, Didi, has emerged as a leader in the sharing economy. Used by passengers and drivers extensive, it becomes increasingly important for the car-hailing service providers to minimize the waiting time of passengers and optimize the vehicle utilization, thus to improve the overall u…
In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the metho…
Model predicts passenger origin-destination for online taxi-hailing systems.
problem Predicting passenger origin-destination for efficient transportation planning.
method K-means clustering, non-negative matrix factorization, stacked recurrent neural network.
result Proposed model reduces MAPE by 5-7% for 1-hour windows and 14% for 30-minute windows.
New method detects structural damage quickly without knowing post-damage feature distribution.
problem Quickly identify structural damage with unknown post-damage feature distribution.
method Sequential change-point detection using maximum likelihood estimation.
result Accurately identifies structural damage in various tests.
Robots adapt to damage with a single policy and diagnosis.
problem Robotic failure due to damage during mission-critical tasks.
method Damage-aware control architecture using supervised learning for diagnosis and policy adaptation.
result Single-shot diagnosis and adaptation achieved with a single policy.
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
problem Repositioning idle supply before future demand is observed in ride-hailing.
method A predict-then-optimize approach using calibrated demand regimes, a similarity gate, and spatial queue-regret decomposition.
result The spatial gate reduces mean wait time to 82.3s compared to 85.3s for a hand-tuned similarity gate and 85.8s for a distributional-only baseline.
This paper reviews traditional and modern methods for detecting structural damage using vibrations.
problem Early warning of structural damage to maintain civil structures.
method Vibration-based methods and ML/DL algorithms.
result ML and DL algorithms show superior performance in detecting structural damage.
Proposes a multi-objective variational autoencoder for smart infrastructure damage detection.
problem Detecting and diagnosing damage in smart infrastructure using multi-way data.
method Multi-objective variational autoencoder (MVA) method for smart infrastructure damage detection and diagnosis.
result The method accurately detects structural damage, estimates severity, and captures damage locations.
Gaussian Process Regression improves damage assessment in structural health monitoring.
problem Inaccurate damage quantification in varying conditions and environments.
method Gaussian Process Regression models with Damage Indices for probabilistic damage assessment.
result The method provides probability-based decision-making for structural health monitoring.
Paper improves disaster damage detection using modern transfer learning.
problem Harder to obtain damaged examples than undamaged ones.
method Revisits classical bagging with modern transfer learning.
result Significantly outperforms existing methodologies.
Optimizes package types for e-commerce to reduce damage and costs.
problem Sub-optimal package types lead to damaged shipments and high costs.
method Multi-stage approach that balances shipment and damage costs using a scalable algorithm.
result Significant cost savings of tens of millions of dollars achieved.
Machine learning detects building damage in satellite images.
problem Extracting damage information from satellite imagery is slow and labor-intensive.
method Used four convolutional neural network models to detect damaged buildings.
result Models performed well in detecting damaged buildings in the 2010 Haiti earthquake.
Generative Adversarial Networks create synthetic data for structural damage detection.
problem Data scarcity in structural damage detection.
method 1-D Wasserstein Deep Convolutional Generative Adversarial Networks (1-D WDCGAN-GP) for synthetic data generation.
result Generated synthetic data improves damage detection accuracy in 1-D Deep Convolutional Neural Networks.
Tornadoes are the most violent of all atmospheric storms. In a typical year, the United States experiences hundreds of tornadoes with associated damages on the order of one billion dollars. Community preparation and resilience would benefit from accurate predictions of these economic losses, particularly as populations…
A model predicts building damage locations in near real-time using intensity-based features.
problem Accurate and timely damage diagnosis of building structures after extreme events.
method Support vector machines and Bayesian optimization for probabilistic hazard intensity determination.
result The model achieves 83.1% accuracy in identifying damage locations in a reinforced concrete moment frame.
Generative model predicts remaining life of damaged structures.
problem Prognosis of damage and remaining useful life of structures.
method Generative adversarial networks (GANs) in a population-based SHM framework.
result Algorithm provides confident predictions about structures' remaining useful life.
Research shows higher damages may encourage more disclosure in corporate disputes.
problem How to resolve disputes over undisclosed material events in a way that encourages voluntary disclosure.
method Dynamic continuous-time model of management's equilibrium disclosure decision.
result Increased damages may lead to an endogenous increase in voluntary disclosure.
A new framework combines CNN and GRU for better structural damage detection.
problem Improving damage detection in structural engineering using machine learning.
method Hierarchical CNN and Gated Recurrent Unit (GRU) framework to model spatial and temporal relations.
result The proposed HCG framework significantly outperforms existing methods for structural damage detection.
This paper classifies typhoon damage features using aerial photography.
problem Typhoon damages and slow recovery times.
method Aerial photography for classification of eight classes including land covers and disaster areas.
result Visualize and explain typhoon disaster features using convolutional activation maps.
Structural damage due to excessive loading or environmental degradation typically occurs in localized areas in the absence of collapse. This prior information about the spatial sparseness of structural damage is exploited here by a hierarchical sparse Bayesian learning framework with the goal of reducing the source of …
Deep learning improves damage localization in ultrasonic waves under uncertainty.
problem Uncertainty in wave propagation due to environmental factors and noise.
method Deep learning model trained on simulated wave data with uncertainty.
result Deep learning model learns robust representations of wave data with uncertainty.
Method uses pseudo-samples to improve RCT data in ride-hailing pricing studies.
problem Small and biased RCT data leads to significant bias when generalizing to broader user base.
method Pseudo-sample matching to expand and match RCT data with observational data.
result 0.41% improvement in profit through pseudo-sample matching.
Machine learning improves measuring climate adaptation impacts.
problem Measuring adaptation to climate change using weather damage elasticities.
method Debiased machine learning approach in panel data settings.
result Long-run impacts of damaging heat exposure significantly offset short-run impacts.
The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular le…
We introduce economic models based on Boolean Delay Equations: this formalism makes easier to take into account the complexity of the interactions between firms and is particularly appropriate for studying the propagation of an initial damage due to a catastrophe. Here we concentrate on simple cases, which allow to und…
Paper compares RNNs for short-term traffic flow prediction.
problem Accurate short-term traffic flow prediction for smart cities.
method Used three types of RNNs (simple RNN, GRU, LSTM) and compared with other models.
result Simple RNNs and GRU outperform LSTM in accuracy and training time.
The main aim of this paper is to inspect the properties of survey based on households inflation expectations, conducted by Reserve Bank of India. It is theorized that the respondents answers are exaggerated by extreme response bias. Latent class analysis has been hailed as a promising technique for studying measurement…
Formulates LGFO to measure fair ML systems using legal signals.
problem Formally incompatible measures of unfairness in ML systems.
method Uses legal signals to measure social cost of unfairness.
result LGFO aligns with societal view of unfairness.
Reducing barriers to entry in large-scale ML markets, study shows multi-objective learning can lower data requirements.
problem Barriers to entry in emerging markets for large-scale machine learning models.
method Defined a multi-objective high-dimensional regression framework to study reputational damage and data requirements.
result The number of data points needed for a new company to enter the market can be significantly smaller than the incumbent company's dataset size.
Researchers predict butt rot volume using harvester data and remote sensing.
problem Predicting butt rot volume in Norway spruce stands for optimal forest management.
method Used random forest models with harvester information, remote sensing, and environmental data.
result Remotely sensed predictor variables were more important than environmental variables.
Researchers developed a generic model to account for structural variability in SHM.
problem Variability in natural frequency due to operational and environmental conditions limits SHM technologies.
method An overlapping mixture of Gaussian processes (OMGP) was used to generate a generic representation of normal condition.
result The OMGP model provided a generic representation (form) to characterise the normal condition of structures.
This study predicts parking availability using multi-source data and a self-supervised learning enhanced transformer.
problem Accurate parking availability prediction to support urban planning and management.
method Proposes SST-iTransformer, a self-supervised learning enhanced spatio-temporal inverted transformer, integrating multi-source data.
result SST-iTransformer achieves state-of-the-art performance in parking availability prediction.
Self-supervised learning improves RUL prediction with limited data in fatigue damage prognosis.
problem Limited labelled data for RUL prediction in fatigue damage prognosis.
method Pre-training deep learning models on unlabelled sensor data using self-supervised learning.
result Self-supervised pre-trained models significantly outperform non-pre-trained models in RUL prediction with scarce labelled data.
Model shows how discount rates affect intergenerational equity in climate mitigation.
problem Intergenerational equity in climate mitigation decisions.
method Extended DICE model with stochastic discount rates and financing extensions.
result Discount-rate uncertainty amplifies intergenerational inequality in climate mitigation.
This paper optimizes driver repositioning using MARL and reward design for better service and traffic management.
problem Unserved passenger requests due to drivers' cruising behavior during passenger seeking.
method Mean field multi-agent reinforcement learning (MARL) with a reward design scheme and Bayesian optimization (BO) to solve bilevel optimization problems.
result Optimal toll charges and service charges can improve platform and city planner objectives by significant margins, leading to better traffic conditions.
It is illustrated a methodology to compute the pure premium for the automobile insurance (claim frequency and severity) using generalized linear models. It is obtained the pure premium for the partial damage loss cover (PPD) using a set of automobile insurance policies with an exposition of a year. It is found that the…
Paper proposes model to assess financial risk of grid-ignited wildfires.
problem Financial risk and solvency threats from grid-ignited wildfires.
method Integrated model to evaluate damage costs and risk levels.
result Identifies high-risk areas for preemptive actions.
DASC combines social media and car sensors to improve disaster response.
problem Inconsistent reliability and inconsistent availability of human sensors.
method Hybrid social-car sensing system using game theory, feedback control, and MDP.
result DASC improves detection accuracy and efficiency in disaster response.
Paper proposes a novel approach to improve spatiotemporal precipitation forecasts.
problem Improving accuracy of spatiotemporal precipitation forecasts for flood damage mitigation.
method Introduces a rain-code fusion approach using ConvLSTM and multi-frame fusion for spatiotemporal precipitation code-to-code forecasting.
result Demonstrates enhanced accuracy in precipitation forecasts beyond 3 timesteps using the rain-code fusion.
Forecaster uses graph Transformers to forecast spatial and time-dependent data.
problem Complex spatial and temporal dependencies in data.
method Graph Transformer architecture with sparsification for spatial and temporal dependencies.
result Forecaster significantly outperforms state-of-the-art baselines in taxi demand forecasting.
Tensor analysis improves structural health monitoring of complex aerospace systems.
problem Highly redundant and correlated sensor data in structural health monitoring.
method Tensor-based learning for multi-way structural data analysis.
result Tensor-based approach successfully detects damage in aeroservoelastic models.
Machine learning predicts failure in brittle materials with high accuracy.
problem Predicting failure in brittle materials under repetitive loads.
method Phase-field model combined with supervised machine learning.
result Framework predicts failure with acceptable accuracy even in noisy data.
Paper introduces ML tools for guided wave behaviour in composite materials.
problem Difficult assessment of guided wave behaviour in complex materials.
method Data-driven model using Gaussian processes with physical constraints.
result Structured machine learning models offer advantages like extrapolation and physical interpretation.
Physics-informed model predicts beam stiffness and monitors structural health.
problem Predicting and monitoring the stiffness of Euler-Bernoulli beams.
method Physics-informed Gaussian process model using the Euler-Bernoulli beam equation.
result Model accurately predicts bending stiffness and detects structural damage.
One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fi…