arXiv research

A novel ensemble classifier improves vibration-based quality monitoring accuracy.

This paper reviews traditional and modern methods for detecting structural damage using vibrations.

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence e…

Novel framework monitors cardiac image segmentation models in real-time.

Low-cost sensors improve air quality prediction accuracy significantly.

Generative Adversarial Networks create synthetic data for structural damage detection.

AirRL uses RL to infer urban air quality from selected stations.

Paper improves ETF tail-risk monitoring reliability.

Study improves data quality assessment for structural monitoring data.

Paper uses Gaussian Processes to monitor air quality in Kampala.

Work addresses long-term accuracy issues in IoT air quality sensors.

Study uses GPLFM to create Digital Twin for ferry quay health monitoring.

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…

Amazon SageMaker Model Monitor detects drift in deployed ML models.

Deep CNN monitors AM quality with high accuracy.

Focuses on monitoring and explaining models in real-world applications.

Infrastructure monitors AI/ML radiology models across multiple sites.

This work models variability in composite blades' vibrations.

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…

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approa…

RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.

The paper develops methods for monitoring TPL machine health.

The paper proposes a method to detect relevant model degradations without over-alerting.

Operations is a key challenge in the domain of machine learning pipeline deployments involving monitoring and management of real-time prediction quality. Typically, metrics like accuracy, RMSE etc., are used to track the performance of models in deployment. However, these metrics cannot be calculated in production due …

Machine learning models estimate nutrient concentrations from water quality surrogates.

Study predicts coastal water quality using machine learning, identifying salinity as key factor.

Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale High Energy Physics experiment. This paper focuses on the use of artificial neural networks for supervised and semi-supervised problems related to the identification of anomalies in the d…

A new method uses active learning to monitor industrial processes more accurately.

DGPs improve air quality inference from sparse data.

Deep autoencoder detects anomalies in wastewater sensor data.

Deployment of machine learning (ML) algorithms in production for extended periods of time has uncovered new challenges such as monitoring and management of real-time prediction quality of a model in the absence of labels. However, such tracking is imperative to prevent catastrophic business outcomes resulting from inco…

Roads are critically important infrastructure to societal and economic development, with huge investments made by governments every year. However, methods for monitoring those investments tend to be time-consuming, laborious, and expensive, placing them out of reach for many developing regions. In this work, we develop…

New model clusters mixed-type data with missing values, improving air quality analysis.

GraphSVR forecasts urban air pollution robustly across stations and seasons.

Artificial Neural Network predicts PM2.5 pollution with low-cost sensors.

Deep neural nets classify tool wear in blanking processes.

Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties. In order to protect organizations database DAM systems have two main roles, monitoring (documenting activity) and alerting to anomalous activity. Due to high-velocity …

Research develops a water quality prediction model using LSTM.

The paper proposes incentivizing human annotators with 'golden questions' to improve data quality.

Physics-informed model predicts beam stiffness and monitors structural health.

Study compares geostatistical and machine learning models for PM2.5 prediction.

The study forecasts water quality from satellite data using machine learning.

We describe the concept of logical scaffolds, which can be used to improve the quality of software that relies on AI components. We explain how some of the existing ideas on runtime monitors for perception systems can be seen as a specific instance of logical scaffolds. Furthermore, we describe how logical scaffolds ma…

The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of wid…

Continuous, ubiquitous monitoring through wearable sensors has the potential to collect useful information about users' context. Heart rate is an important physiologic measure used in a wide variety of applications, such as fitness tracking and health monitoring. However, wearable sensors that monitor heart rate, such …

The paper improves neural network predictions by integrating process knowledge.

We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep on six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and causal model search to find the most probable graphical model accounting for sel…

We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and cau…