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Found 20 papers, 2 papers with code
Iterative distributed moving horizon estimation of linear systems with penalties on both system disturbances and noise
In this paper, partition-based distributed state estimation of general linear systems is considered.
Data-driven parallel Koopman subsystem modeling and distributed moving horizon state estimation for large-scale nonlinear processes
The local estimators are integrated via information exchange to form a distributed estimation scheme, which provides estimates of the unmeasured/unmeasurable state variables of the original nonlinear process in a linear manner.
What drives the European carbon market? Macroeconomic factors and forecasts
We rely on emissions and price forecasts to build market monitoring tools that track demand and price pressure in the EU ETS market.
A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions
During online monitoring, the temporal correlation dynamics of a query device is monitored for breach of the control limit derived in offline training.
LightGCNet: A Lightweight Geometric Constructive Neural Network for Data-Driven Soft sensors
Herein, guided by spatial geometric, a lightweight geometric constructive neural network, namely LightGCNet, is proposed, which utilizes compact angle constraint to assign the hidden parameters from dynamic intervals.
A Federated Learning-based Industrial Health Prognostics for Heterogeneous Edge Devices using Matched Feature Extraction
The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters.
Lithium-ion Battery Online Knee Onset Detection by Matrix Profile
Lithium-ion batteries (LiBs) degrade slightly until the knee onset, after which the deterioration accelerates to end of life (EOL).
A Slow-Shifting Concerned Machine Learning Method for Short-term Traffic Flow Forecasting
However, a crucial challenge in traffic flow forecasting is the slow shifting in temporal peaks between daily and weekly cycles, resulting in the nonstationarity of the traffic flow signal and leading to difficulty in accurate forecasting.
Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction
An accurate vehicle trajectory prediction benefits communication traffic management and network resource allocation for the real-time application of the V2X network.
Digital Twin for Real-time Li-ion Battery State of Health Estimation with Partially Discharged Cycling Data
To meet the fairly high safety and reliability requirements in practice, the state of health (SOH) estimation of Lithium-ion batteries (LIBs), which has a close relationship with the degradation performance, has been extensively studied with the widespread applications of various electronics.
A Hybrid Deep Learning Model-based Remaining Useful Life Estimation for Reed Relay with Degradation Pattern Clustering
To provide accurate remaining useful life (RUL) estimation for reed relay, a hybrid deep learning network with degradation pattern clustering is proposed based on the following three considerations.
A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation
A transferable multi-stage SOH estimation model is proposed to perform TL across batteries in the same stage, consisting of four steps.
Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization
Second, the canonical variates retrieved from the spatial path of the cycle-synchronized time series are used for distribution similarity analysis between the source and target batteries.
Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation
Similarly, the estimation accuracy of the milling machine has been improved by 23. 57% compared to LSTM and 19. 54% compared to CapsNet.
Lithium-ion Battery State of Health Estimation based on Cycle Synchronization using Dynamic Time Warping
The state of health (SOH) estimation plays an essential role in battery-powered applications to avoid unexpected breakdowns due to battery capacity fading.
IIoT-Enabled Health Monitoring for Integrated Heat Pump System Using Mixture Slow Feature Analysis
This revolution delivers a promising solution for the health monitoring problem of heat pump (HP) system, a vital device widely deployed in modern buildings for heating use, to timely evaluate its operation status to avoid unexpected downtime.
A Transfer Learning-based State of Charge Estimation for Lithium-Ion Battery at Varying Ambient Temperatures
After that, once the inability of the reference SoC estimation model is detected, consistent temporal dynamics between temperatures are selected for transfer learning.
Time-Series Regeneration with Convolutional Recurrent Generative Adversarial Network for Remaining Useful Life Estimation
Out of this consideration, an enhanced RUL framework focusing on data self-generation is put forward for both non-cyclic and cyclic degradation patterns for the first time.
Invariant learning based multi-stage identification for Lithium-ion battery performance degradation
By informing accurate performance (e. g., capacity), health state management plays a significant role in safeguarding battery and its powered system.
Extending iLQR method with control delay
Iterative linear quadradic regulator(iLQR) has become a benchmark method to deal with nonlinear stochastic optimal control problem.
