Venice used 'helicopter money' to subsidize during famine and plague, but it caused instability.
problem Subsidizing inhabitants during containment policies while preventing long-term debt increase.
method Net-worth helicopter money strategy, equivalent to monetary expansion generating losses to the issuer.
result The strategy caused much monetary instability and had to be quickly reversed.
Novel method detects faults in helicopter transmissions using healthy data only.
problem Detecting faults in rare events in helicopter transmissions.
method Probabilistic anomaly detection using Bayesian measures.
result Method achieves competitive detection performance in real-world and benchmark applications.
This work uses a scalable approach to identify partially observed nonlinear systems.
problem Offline identification of partially observed nonlinear systems.
method Certainty-equivalent expectation-maximization (CEEM) as block coordinate-ascent.
result The CEEM approach can identify high-dimensional systems reliably and efficiently.
Improved anomaly detection in time series data using kervolutional neural networks.
problem Anomaly detection in time series data.
method Mixed kervolutional and convolutional layers in a temporal auto-encoder.
result The mixed model detects anomalies more sensitively in time series data.
This study benchmarks anomaly detection methods in a functional setup.
problem Efficient anomaly detection in high-frequency sensor data.
method Functional analysis approach for multivariate data.
result Comparison of anomaly detection methods on real datasets.
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.
Developed a cost and revenue model for HEMS to estimate breakeven transport volumes under different reimbursement and labor cost assumptions.
problem Estimating breakeven transport volumes for HEMS under varying reimbursement and labor cost assumptions.
method Developed a two-part model: cost framework and actuarial revenue model using healthcare encounter data and payer reimbursement rates.
result Estimated breakeven transport volumes under different reimbursement and labor cost assumptions.
A fundamental problem in control is to learn a model of a system from observations that is useful for controller synthesis. To provide good performance guarantees, existing methods must assume that the real system is in the class of models considered during learning. We present an iterative method with strong guarantee…
Current UAV-recorded datasets are mostly limited to action recognition and object tracking, whereas the gesture signals datasets were mostly recorded in indoor spaces. Currently, there is no outdoor recorded public video dataset for UAV commanding signals. Gesture signals can be effectively used with UAVs by leveraging…
This work models variability in composite blades' vibrations.
problem Variability in structural dynamics due to manufacturing differences.
method Defining a general model for frequency response functions using mixtures of Gaussian processes.
result A general model for frequency response functions of composite blades.
RL agent learns to avoid market spoofing.
problem Avoiding subtle non-normative behavior in trading agents.
method Learned recognizer incorporated into RL agent's reward function.
result RL agent avoids spoofing while remaining profitable.
The paper tackles exact linearization and control of flat discrete-time systems.
problem Exact linearization and control of flat nonlinear discrete-time systems.
method Investigates conditions for choosing new inputs and feedbacks that may depend on forward-shifts of the new input.
result Easily verifiable conditions for choosing a feasible input and a new input that minimizes forward-shifts of the flat output.
Deep learning transforms time series into images for anomaly detection in industrial assets.
problem Detecting anomalies in time series data from industrial assets.
method Transforming time series data into image-like representations and using them as inputs for deep learning models.
result Some encodings provide competitive results for anomaly detection in industrial asset monitoring.