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Found 32 papers, 3 papers with code
Performance triggered adaptive model reduction for soil moisture estimation in precision irrigation
Soil moisture estimation based on limited soil moisture sensors is crucial for obtaining comprehensive soil moisture information when dealing with large-scale agricultural fields.
Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra
The approach shown here has the potential to be applied to facilitate accurate detection and analysis of persistent organic pollutants by surface-enhanced Raman spectroscopy.
Multi-Agent Robust Control Synthesis from Global Temporal Logic Tasks
This paper focuses on the heterogeneous multi-agent control problem under global temporal logic tasks.
Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation
Additionally, we introduce the detail-enhanced DepthNet with an extra full-scale branch in the encoder and a grid decoder to enhance the restoration of fine details in depth maps.
Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning
Most of the processes for industrial mAb production rely on batch operations, which result in significant downtime.
Integrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling
The results demonstrate the superiority of the proposed scheduler compared to a traditional irrigation scheduling method in terms of water use efficiency and crop yield improvement for both growing seasons.
Maximizing soil moisture estimation accuracy through simultaneous hydraulic parameter estimation using microwave remote sensing: Methodology and application
Improving the accuracy of soil moisture estimation is desirable from the perspectives of irrigation management and water conservation.
Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness
This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the advantages of utilizing the explicit form of CIS to improve stability guarantees and sampling efficiency.
Robust MPC with Zone Tracking
We propose a robust nonlinear model predictive control design with generalized zone tracking (ZMPC) in this work.
Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy
By utilizing this representation, a generic subsystem decomposition method is proposed to partition the entire IES vertically based on the dynamic time scale and horizontally based on the closeness of interconnections between the operating units.
Control invariant set enhanced reinforcement learning for process control: improved sampling efficiency and guaranteed stability
This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the benefits of CIS to improve stability guarantees and sampling efficiency.
State estimation of a carbon capture process through POD model reduction and neural network approximation
Multi-layer perceptron (MLP) neural networks capture the dominant dynamics of the process and train the network parameters with low-dimensional data obtained from open-loop simulations.
SoftCLIP: Softer Cross-modal Alignment Makes CLIP Stronger
During the preceding biennium, vision-language pre-training has achieved noteworthy success on several downstream tasks.
Sensor network design for post-combustion CO2 capture plants: economy, complexity and robustness
The sensor selection problem is converted to an optimization problem, and is efficiently solved by a one-by-one removal approach through sensitivity analysis.
Progressive Motion Context Refine Network for Efficient Video Frame Interpolation
Recently, flow-based frame interpolation methods have achieved great success by first modeling optical flow between target and input frames, and then building synthesis network for target frame generation.
ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation
Video frame interpolation is a classic and challenging low-level computer vision task.
A sensitivity-based approach to optimal sensor selection for process networks
The minimum number of sensors is determined in a way such that the local sensitivity matrix is full column rank.
SMPL: Simulated Industrial Manufacturing and Process Control Learning Environments
Traditional biological and pharmaceutical manufacturing plants are controlled by human workers or pre-defined thresholds.
Economic model predictive control of integrated energy systems: A multi-time-scale framework
Subsequently, the CEMPC, which includes slow economic model predictive control (EMPC), medium EMPC and fast EMPC, is developed.
Computing control invariant sets of nonlinear systems: decomposition and distributed computing
In this work, we present a distributed framework based on the graph algorithm for computing control invariant set for nonlinear cascade systems.
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Large-scale vision-language pre-training has achieved promising results on downstream tasks.
Model predictive control of agro-hydrological systems based on a two-layer neural network modeling framework
To handle the tracking offset caused by the plant-model-mismatch of the proposed NN framework, a shrinking target zone is proposed for the ZMPC.
An efficient implementation of graph-based invariant set algorithm for constrained nonlinear dynamical systems
The graph-based invariant set (GIS) algorithm is a promising set-based technique for computing the largest (with respect to inclusion) control invariant set of general discrete-time nonlinear dynamical systems.
Robust economic MPC of the absorption column in post-combustion carbon capture through zone tracking
To ensure that the zone tracking objective is achieved in the presence of model uncertainties and time-varying flue gas flow rate, we propose a method to modify the original target zone with a control invariant set.
Sensitivity-based dynamic performance assessment for model predictive control with Gaussian noise
To this end, a sensitivity-based performance assessment approach is proposed to pre-evaluate the dynamic economic and tracking performance of them in this work.
Adaptive model reduction and state estimation of agro-hydrological systems
Furthermore, the adaptive MHE algorithm is developed based on an adaptive reduced model.
LSTM-based model predictive control with discrete inputs for irrigation scheduling
The development of well-devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation.
Knowledge-based optimal irrigation scheduling of agro-hydrological systems
The typical agricultural irrigation scheduler provides information on how much to irrigate and when to irrigate.
Subsystem decomposition and state estimation of nonlinear processes with implicit time-scale multiplicity
In this work, we propose a subsystem decomposition approach and a distributed estimation scheme for a class of implicit two-time-scale nonlinear systems.
Robust economic model predictive control with zone tracking
To optimize the economic performance within the zone in the presence of disturbances, we introduce the notion of risk factor in the controller design.
A comparative study of model approximation methods applied to economic MPC
Economic model predictive control (EMPC) has attracted significant attention in recent years and is recognized as a promising advanced process control method for the next generation smart manufacturing.
Soil moisture map construction using microwave remote sensors and sequential data assimilation
Secondly, measurements obtained from the microwave sensors are assimilated into the field model using the extended Kalman filter to form an information fusion system, which will provide frequent soil moisture estimates and predictions in the form of moisture content maps.
