The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents and detecting illegal activities. In this work, we describe research gaps and challenges in machine learning for vessel behavior change and …
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48 results for “maritime monitoring”
This paper discusses challenges and opportunities in vessel behavior detection using machine and deep learning.
problem Real-time analysis of vessel behaviors is crucial for maritime safety and protection.
method Comparison of classical machine learning and deep learning approaches for vessel event and anomaly detection.
result Novel methods and tools are needed to address challenges in vessel behavior detection.
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach -- referred to as GeoTrackNet -- for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn…
In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages t…
A Deep Zero-Inflated Model for Detecting North Atlantic Right Whale Presence
problem Balancing marine conservation and blue economy management
method Deep Zero-Inflated Bernoulli model
result Improved model adequacy and predictive performance
On the non-slippery slope: Some observations on a recent paper on rolling bodies published in Naturemath.DG
New method for rolling bodies on inclined planes, with applications to rescue operations.
problem Constructing solid bodies rolling along curves on inclined planes.
method Rigorous existence theorems and connections to maritime rescue operations.
result Comprehensive existence theorems for rolling bodies on inclined planes.
Adaptive rerouting reshapes impacts of maritime chokepoint disruptions
problem How disruptions to shipping traffic at chokepoints affect global economy
method Empirically calibrated full-scale agent-based model of global commercial shipping fleet
result Rerouting changes arrival losses under chokepoint closures
A novel spatio-temporal graph neural network with a learnable Tweedie head improves vessel traffic flow prediction in sparse maritime data.
problem Accurate vessel traffic flow prediction in sparse maritime data.
method A model-agnostic learnable Tweedie head attached to ST-GNN backbones.
result The proposed head consistently improves RMSE across multiple ST-GNN backbones, especially on non-zero events.
Study uses GPLFM to create Digital Twin for ferry quay health monitoring.
problem Deterioration of ferry quays due to harsh maritime environments and impacts.
method Gaussian Process Latent Force Model (GPLFM) integrating physics-based model and machine learning.
result GPLFM provides accurate acceleration response estimates, even under simplifying assumptions.
In this paper, we present our approach for solving the DEBS Grand Challenge 2018. The challenge asks to provide a prediction for (i) a destination and the (ii) arrival time of ships in a streaming-fashion using Geo-spatial data in the maritime context. Novel aspects of our approach include the use of ensemble learning …
This study aims to predict vessel stay and delay times at ports to optimize logistics.
problem Uncertainties in maritime logistics, including weather, cargo diversity, and port dynamics, lead to increased costs and inefficiencies.
method Developed predictive analytics to address shortcomings in previous works, using feature analysis and SHAP explanations.
result Predictive analytics can assist in efficient planning and scheduling of port operations, reducing costs and improving logistics.
Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel appro…
CVAE detects weak complex signals in maritime radar, improving detection over classical methods.
problem Detecting weak complex-valued signals in non-Gaussian, range-varying interference.
method Complex-valued Variational AutoEncoder (CVAE) trained on clutter-plus-noise, whitening, ANMF fusion.
result CVAE yields higher detection probability Pd at matched false-alarm rate Pfa, especially with whitening.
Evaluates six ETSC algorithms on various datasets.
problem Comparing ETSC algorithms for different data characteristics.
method Empirical comparison of six ETSC algorithms on public and new datasets.
result Provides insights into algorithm performance on real-life applications.
This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point…
Predicting illegal fishing on Patagonian Shelf using machine learning.
problem Improving detection of illegal fishing to conserve fish stocks.
method Combining vessel tracking data with oceanographic seascapes and machine learning models.
result Machine learning models predict illegal fishing with 69-96% confidence.
AI helps simplify complex ship finance processes.
problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.
Enhanced visibility forecasts using CAMS data improve accuracy.
problem Improving the accuracy of visibility predictions in weather forecasts.
method Statistical post-processing with historical observations and CAMS forecasts.
result Post-processed forecasts with CAMS data are substantially superior to raw and climatological predictions.
The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main …
Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Predictionstat.ML
Bayesian Neural ODEs improve vessel trajectory prediction with better uncertainty estimates.
problem Challenges in predicting vessel trajectories from irregular AIS data.
method Adopted a Gaussian process (GP) kernel-based prior on the vector field evaluated at measurement points, combined with probabilistic multiple shooting for long trajectories.
result Improved accuracy and uncertainty quantification in vessel trajectory predictions.
AI systems that explain their decisions can be monitored for harmful intentions.
problem Monitoring AI systems' decision-making processes for harmful intentions is imperfect and can miss some misbehavior.
method Monitoring the chain of thought (CoT) of AI systems that communicate in human language.
result CoT monitoring is a promising but fragile approach to AI safety.
In this work a novel ships dataset is proposed consisting of more than 56k images of marine vessels collected by means of web-scraping and including 12 ship categories. A YOLOv3 single-stage detector based on Keras API is built on top of this dataset. Current results on four categories (cargo ship, naval ship, oil ship…
The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as ear…
Simple online monitor detects unsafe LLM outputs.
problem LLMs generate unsafe outputs despite training.
method Thresholding external verifier signal to decide alarms.
result Simple design competitive with advanced methods.
This research tackles monitoring machine learning algorithms post-deployment, addressing performativity issues.
problem Monitoring machine learning algorithms after deployment, especially when they affect their own data-generating process.
method Uses causal inference techniques to navigate performativity and compares different monitoring criteria and data sources.
result Different monitoring systems have varying operating characteristics and implications for ML monitoring design.
Paper improves ETF tail-risk monitoring reliability.
problem Unreliable ETF risk monitoring under degraded data.
method Combines quality checks, prediction, scoring, and adjustment.
result Improves tail-risk monitoring, especially during stressed periods.
PITMonitor monitors model calibration over time with formal error guarantees.
problem Fixed-sample tests applied to models over time can lead to false alarms.
method PITMonitor uses mixture e-processes to detect distributional shifts in probability integral transforms.
result PITMonitor achieves competitive detection rates on river's FriedmanDrift benchmark.
The paper adds explanation to predictive process monitoring.
problem Equipping predictive business process monitoring with explanation capabilities.
method Used game theory of Shapley Values to obtain robust explanations.
result First time explanations given in predictive business process monitoring.
Linear Partial Monitoring for Sequential Decision-Making: Algorithms, Regret Bounds and Applicationscs.LG
IDS algorithm optimizes sequential decisions in various monitoring settings.
problem Optimizing sequential decisions in complex monitoring scenarios.
method Information-directed sampling (IDS) algorithm for linear partial monitoring.
result IDS achieves nearly worst-case rate optimality in finite-action games.
Registration-Free Monitoring of Unstructured Point Cloud Data via Intrinsic Geometrical Propertiescs.CV
A new method monitors unstructured 3D shapes without registration.
problem Error-prone registration and mesh reconstruction steps in PCD monitoring.
method Intrinsic geometric properties of shapes, using Laplacian and geodesic distances.
result Effective monitoring of defects without registration and mesh reconstruction.
Focuses on monitoring and explaining models in real-world applications.
problem Ensuring high quality machine learning services in production environments.
method Statistical techniques for model performance and data monitoring, explanations of predictions.
result Challenges and solutions for implementing monitoring and explanation in production models.
Neural system optimizes glucose levels in diabetics.
problem Limited research on continuous glucose maintenance devices.
method Differential predictive control with neural policy and differentiable modeling.
result Improves glucose level optimization in real-time.
Fluctuation identities with continuous monitoring and their application to price barrier optionsq-fin.CP
We present a numerical scheme to calculate fluctuation identities for exponential Lévy processes in the continuous monitoring case. This includes the Spitzer identities for touching a single upper or lower barrier, and the more difficult case of the two-barriers exit problem. These identities are given in the Fourier-L…
Optimal probing framework for scalable network monitoring.
problem Efficiently monitor growing cloud networks with limited budgets.
method A- and E-optimal experimental designs, Frank-Wolfe algorithm approximations.
result Significant reduction in probing budget with low estimation errors.
New monitoring method detects ML risk models' performance changes in medical interventions.
problem Monitoring ML risk models in healthcare is complicated by confounding medical interventions.
method Developed a new score-based CUSUM monitoring procedure with dynamic control limits.
result Valid inference is possible if conditional exchangeability or time-constant selection bias hold.
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Modelscs.LG
Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustain…
Risk monitoring detects when TTA models degrade at test time.
problem Detecting when TTA models degrade at test time.
method Extended risk monitoring tools based on sequential testing with confidence sequences.
result Demonstrated effectiveness of TTA monitoring framework across various datasets and methods.
Paper optimizes a big data and ML risk monitoring system for financial markets.
problem Traditional risk monitoring methods are inadequate for modern financial markets due to data complexity and volume.
method Four-layer architecture integrating big data and advanced ML algorithms (LSTM, RF, GB).
result Significantly enhances efficiency and accuracy in risk management, especially in market crash risk detection.
For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runtime neuron activation pattern monitoring - after the standard training process, one creates a monitor by feeding the training data to the ne…
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysiseess.SP
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…
Structural health monitoring is a condition-based field of study utilised to monitor infrastructure, via sensing systems. It is therefore used in the field of aerospace engineering to assist in monitoring the health of aerospace structures. A difficulty however is that in structural health monitoring the data input is …
A monitoring procedure improves machine learning forecasts for digital platforms.
problem Maintaining accurate and stable forecasts for data streams at digital platforms.
method Developed a monitoring procedure to determine when to retrain machine learning algorithms.
result Monitor-based retraining produces accurate forecasts compared to benchmarks.
Novel framework monitors cardiac image segmentation models in real-time.
problem Ensuring continuous high model performance and segmentation results in clinics.
method Formulated as anomaly detection, the framework derives surrogate quality measures for segmentation.
result Demonstrated accurate, fast, and scalable quality control monitoring.
New Thompson sampling algorithm for stochastic partial monitoring achieves logarithmic regret.
problem Limited feedback in sequential learning problems.
method Developed a novel Thompson-sampling-based algorithm to sample from the posterior distribution exactly.
result Achieved logarithmic regret bound of O(log T) for a linearized variant of the problem.
Paper monitors DNN accuracy to enhance trustworthiness.
problem Varying DNN accuracy in practice and lack of ground truth labels.
method Post-hoc accuracy monitor model using Monte-Carlo dropout ensemble.
result Accuracy monitor provides close-to-true accuracy estimation.
Proposes real-time risk monitoring for machine learning systems under unknown shifts.
problem Dynamic distribution shifts challenge real-world machine learning systems' risk assurances.
method Sequential hypothesis testing with 'testing by betting' to detect risk violations.
result Effective real-time risk monitoring under various unknown shifts.
Simultaneous Monitoring of Shape and Surface Color via 4D Point Clouds: A Registration-free Approachcs.CV
A registration-free framework monitors shape and color in 4D point clouds.
problem Monitoring shape and color changes in complex parts without registration.
method Laplace-Beltrami operator spectral properties for geometric and color feature capture; combined monitoring scheme for shape and color anomalies.
result Effective detection of shape deformations and color anomalies without registration or mesh reconstruction.
IDS optimizes regret in stochastic partial monitoring with linear rewards.
problem Optimizing decision-making in uncertain environments with linear rewards.
method Information Directed Sampling (IDS) for stochastic partial monitoring.
result Achieves optimal regret rates in all observable game regimes.