Model-based Optimization Approach Research Articles

Improving the insulin therapy for diabetic patients using optimal impulsive disturbance rejection: Continuous time approach

The paper proposes a new model-based optimization approach to improve the clinical efficiency of compensatory insulin bolus treatment in diabetic patients, aiming to mitigate the consequences of diabetes. The most important contribution of this paper is a novel methodology for determining the optimal parameters of insulin treatment, namely the size and timing of insulin boluses, to effectively compensate for carbohydrate intake. This concept can be seen as the so-called optimal model-based bolus calculator. The presented theoretical framework deals with the problem of optimal disturbance rejection in impulsive systems by minimizing an integral quadratic cost function. The methodology considers a personalized empirical transfer function model with static gains and time constants as the only parameters assumed to be known, making the bolus calculator more straightforward to implement in clinical practice. Contrary to other techniques, the proposed methodology considers impulsive insulin administration in the form of boluses, which is more feasible than continuous infusion. In contrast to the conventional bolus calculator, the proposed algorithm allows for maximizing therapy performance by optimizing the relative time of insulin bolus administration with respect to carbohydrate intake. Another feature to highlight is that the solution of the optimization problem can be obtained analytically, hence no numerical iterative solvers are required. Additionally, the continuous-time domain approach allows for a much finer adjustments of the insulin administration timing compared to discrete-time models. The proposed approach was validated in an in-silico study, which demonstrated the importance of systematically determined insulin–carbohydrate ratio and the relative delay between disturbance and its compensation. The results showed that the proposed optimal bolus calculator outperforms the traditional suboptimal formula.

Resilience-Oriented Coordination of Networked Microgrids: A Shapley Q-Value Learning Approach

High-impact and low-probability extreme events have occurred more frequently than before because of rapid climate change, which can seriously damage distribution systems. However, conventional distribution management can be dysfunctional after an event, destroying its centralized supervision towards resilience enhancement. In this context, networked microgrids (NMGs) with distributed energy resources provide a viable solution for the resilience enhancement of distribution systems. Existing literature tends to employ model-based optimization approaches for resilient operations of NMGs, which require complete system models and can be time-consuming. To address these challenges, this paper suggests a decentralized framework for resilience-oriented coordination of NMGs and proposes a novel multi-agent reinforcement learning (MARL) method to solve it. Specifically, the proposed MARL method develops an efficient credit assignment scheme for NMGs to learn their contributions to the distribution system resilience via the Shapley Q-value technique with more efficient resilience enhancement. Case studies based on two modified IEEE 15- and 69-bus distribution networks are conducted to validate the effectiveness of the proposed MARL method in enabling effective coordination among NMGs and providing a high resilience level.

A Survey on Model-Based, Heuristic, and Machine Learning Optimization Approaches in RIS-Aided Wireless Networks

A Survey on Model-Based, Heuristic, and Machine Learning Optimization Approaches in RIS-Aided Wireless Networks

Optimization Assisted by Neural Network-Based Machine Learning in Electromagnetic Applications

We introduce an optimization assisted by a neural network (ONN) predictor to the electromagnetic community. ONN belongs to the class of the surrogate model-based optimization approaches, and approximates the objective function using a non-linear approximator – neural network. We provide a comprehensive description of the ONN algorithm and the details of its mathematical formulations. We apply ONN to optimize three popular benchmark functions and compare its performance with some commonly used optimization algorithms, namely particle swarm optimization, genetic algorithm, and Bayesian optimization. For the first time, we demonstrate ONN’s applicability and effectiveness in antenna design problems by optimizing a six-element Yagi-Uda antenna and by solving a challenging ten-dimensional dual-band slotted patch antenna constrained optimization problem. To achieve this, ONN is linked with a full-wave electromagnetic simulation solver through an application user interface. The optimized slotted patch is fabricated and measured to demonstrate how ONN can be part of the full antenna design process. Our empirical results indicate that ONN requires less objective function evaluations to reach the same qualitative point and reaches better optimal points for the same number of iterations for the studied benchmark functions and antenna optimization problems compared to the aforementioned baseline optimization algorithms.

Graph Learning-Based Voltage Regulation in Distribution Networks With Multi-Microgrids

Microgrids (MGs), as localized small power systems, can effectively provide voltage regulation services for distribution networks by integrating and managing various distributed energy resources. Existing literature employs model-based optimization approaches to formulate the voltage regulation problem of multi-MGs, which require complete system models. However, this assumption is normally impractical due to time-varying environment and privacy issues. To fill this research gap, this paper suggests a data-driven decentralized framework for the cost-effective voltage regulation of a distribution network with multi-MGs. A novel multi-agent reinforcement learning method featuring an augmented graph convolutional network and a proximal policy optimization algorithm is proposed to solve this problem. Furthermore, the techniques of critical bus and electrical distance enhance the capability of feature extractions from the distribution network, allowing for the decentralized training with privacy preserving. Simulation results based on modified IEEE 33-bus, 69-bus, and 123-bus networks are developed to validate the effectiveness of the proposed method in enabling multi-MGs to provide distribution network voltage regulation.

Towards Attack-Resistant Service Function Chain Migration: A Model-Based Adaptive Proximal Policy Optimization Approach

Network function virtualization (NFV) supports the rapid development of service function chain (SFC), which efficiently connects a sequence of network virtual function instances (VNFIs) placed into physical infrastructures. Current SFC migration mechanisms usually keep static SFC deployment after finishing certain objectives, and deployment methods mostly provide static resource allocation for VNFIs. Therefore, the adversary has enough time to plan for devastating attacks for in-service SFCs. Fortunately, moving target defense (MTD) was proposed as a game-changing solution to dynamically adjust network configurations. However, existing MTD methods mostly depend on attack-defense models, and lack adaptive mutation period. In this paper, we propose an Intelligence-Driven Service Function Chain Migration (ID-SFCM) scheme. Firstly, we model a Markov decision process (MDP) to formulate the dynamic arrival or departure of SFCs. To remove infeasible actions from the action space of MDP, we formalize the SFC deployment as a constrained satisfaction problem. Then, we design a deep reinforcement learning (DRL) algorithm named model-based adaptive proximal policy optimization (MA-PPO) to enable attack-resistant migration decisions and adaptive migration period. Finally, we evaluate the defense performance by multiple attack strategies and two realistic datasets called CICIDS-2017 and LYCOS-IDS2017 respectively. Simulation results highlight the effectiveness of ID-SFCM compared with representative solutions.

An ensemble learning with sequential model-based optimization approach for pavement roughness estimation using smartphone sensor data

Pavement roughness monitoring has always been a concern in the field of road asset management. However, the utilization of laser profilometers to measure pavement roughness is costly and inconvenient. To address these challenges, this study developed a low-cost, lightweight, and rapid approach for pavement roughness estimation using smartphone sensor data. Firstly, the feasibility of the chosen smartphone in vibration acceleration acquisition was examined through micro-scale shaking table tests. Then, the Categorical Boosting (CatBoost) model with sequential model-based optimization (SMBO) approach was developed for smartphone-based pavement roughness estimation. Finally, the feature importance and feature interaction effects in the pavement roughness estimation task were interpreted using Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP), respectively. The results show that the chosen smartphone has great potential in vibration data acquisition, and the recorded data are highly consistent with the results obtained by the professional accelerometer. The CatBoost model outperforms the other reference models in terms of pavement roughness estimation accuracy, with coefficient of determination (R2) of 0.807, root mean square error (RMSE) of 0.553, and mean absolute error (MAE) of 0.433. Furthermore, the feature interpretation results indicate that the smartphone-based pavement roughness estimation approach relies on the coupling effects of vibration and velocity features. This study provides a novel insight into the lightweight pavement roughness estimation approach, which has potential implications for assisting pavement maintenance decision-making in road asset management.

Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging.

Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively.

Model-Based Optimization Approach for PID Control of Pitch–Roll UAV Orientation

This study focuses on the modeling and control optimization of the pitch–roll angles orientation in a quad-rotor UAV (unmanned aerial vehicle) drone system. To address the control challenge, PID (proportional–integral–derivative) controllers are used, which have been tuned through a model-based optimization approach. A non-linear model of the system is obtained and further validated experimentally. The model is implemented in Simulink to conduct model-based optimization. The controller design uses an IMC (internal model control) model-based approach to establish the initial parameters for a PID controller. In the subsequent design phase, a multiobjective approach is taken, incorporating weighted cost functions that account for IAE (integral absolute error) and ITAE (integral of time-weighted absolute error) forms, along with battery usage. The outcomes of this design are showcased through simulations and real-world platform testing.

Model-based optimization of drug release rate from a size distributed population of biodegradable polymer carriers

In the present study, a comprehensive polymer degradation-drug diffusion model is developed to describe the polymer degradation kinetics and quantify the release rate of an active pharmaceutical ingredient (API) from a size-distributed population of drug-loaded poly(lactic-co-glycolic) acid (PLGA) carriers in terms of material and morphological properties of the drug carriers. To take into account the spatial–temporal variation of the drug and water diffusion coefficients, three new correlations are developed in terms of spatial–temporal variation of the molecular weight of the degrading polymer chains. The first one relates the diffusion coefficients with the time-spatial variation of the molecular weight of PLGA and initial drug loading and, the second one with the initial particle size, and the third one with evolution of the particle porosity due to polymer degradation. The derived model, comprising a system of partial differential and algebraic equations, is numerically solved using the method of lines and validated against published experimental data on the drug release rate from a size distributed population of piroxicam-PLGA microspheres. Finally, a multi-parametric optimization problem is formulated to calculate the optimal particle size and drug loading distributions of drug-loaded PLGA carriers to realize a desired zero-order drug release rate of a therapeutic drug over a specified administration period of several weeks. It is envisaged that the proposed model-based optimization approach will aid the optimal design of new controlled drug delivery systems and, consequently, the therapeutic outcome of an administered drug.

Development of a population pharmacokinetic model and optimal dosing regimen of leflunomide in Korean population

PurposeLeflunomide is an immunosuppressive drug indicated for the treatment of rheumatoid arthritis (RA). While the pharmacokinetics (PK) of its active metabolite A771726 reportedly show large interindividual variability, no efficient dose individualization strategy is currently available. The goal of this work was to develop a population PK model for A771726 and propose an optimal individualized dosing strategy. MethodsA771726 plasma concentration data were collected from 50 healthy male volunteers participating in two leflunomide PK studies given a single oral dose of 40 mg. Concentrations were elevated in low body weight (WT) subjects and showed multiple peaks. Thus, A771726 PK modeling was conducted incorporating allometry scaling and enterohepatic circulation (EHC). For dose optimization, simulating a set of 1000 virtual subjects from the developed model and dividing the subjects into 5 groups with WT of 50, 60, 70, 80, 90 kg, respectively, the optimal dose was explored that achieves the drug concentration most similar to the target, which was defined as the concentration for the 70 kg subject treated with the current standard dosage regimen (the loading dose of 100 mg QD for 3 days, followed by the maintenance dose of 20 mg QD). ResultsThe data were best described by a two compartment model with first order absorption incorporating EHC with the bile released into the intestine. None of the covariates tested was found to be significant other than WT used in allometry. Simulation showed that the optimal loading dose increased by 15 mg for every 10 kg increment in WT while the optimal maintenance dose was 15 and 25 mg for 50 and 90 kg groups, respectively, and the same (= 20 mg) for the others. Large concentration differences from the target observed in low and high WT groups disappeared when optimal doses were given. ConclusionsThis work demonstrates the importance of a population PK model-based dose optimization approach in maintaining drug therapeutic concentrations in leflunomide treatment.

Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach

The large-scale integration of distributed energy resources into the energy industry enables the fast transition to a decarbonized future but raises some potential challenges of insecure and unreliable operations. Multi-energy Microgrids (MEMGs), as localized small multi-energy systems, can effectively integrate a variety of energy components with multiple energy sectors, which have been recently recognized as a valid solution to improve the operational security and reliability. As a result, a massive amount of research has been conducted to investigate MEMG energy management problems, including both model-based optimization and model-free learning approaches. Compared to optimization approaches, reinforcement learning is being widely deployed in MEMG energy management problems owing to its ability to handle highly dynamic and stochastic processes without knowing any system knowledge. However, it is still difficult for conventional model-free reinforcement learning methods to capture the physical constraints of the MEMG model, which may therefore destroy its secure operation. To address this research challenge, this paper proposes a novel safe reinforcement learning method by learning a dynamic security assessment rule to abstract a physical-informed safety layer on top of the conventional model-free reinforcement learning energy management policy, which can respect all the physical constraints through mathematically solving an action correction formulation. In this setting, the secure energy management of the MEMG can be guaranteed for both training and test procedures. Extensive case studies based on two integrated systems (i.e., a small 6-bus power and 7-node gas network, and a large 33-bus power and 20-node gas network) are carried out to verify the superior performance of the proposed physical-informed reinforcement learning method in achieving a cost-effective MEMG energy management performance while respecting all the physical constraints, compared to conventional reinforcement learning and optimization approaches.

Population pharmacokinetics of buprenorphine and naloxone sublingual combination in Chinese healthy volunteers and patients with opioid use disorder: Model-based dose optimization.

The sublingual combination of buprenorphine (BUP) and naloxone (NLX) is a new treatment option for opioid use disorder (OUD) and is effective in preventing drug abuse. This study aimed to explore rational dosing regimen for OUD patients in China via a model-based dose optimization approach. BUP, norbuprenorphine (norBUP), and NLX plasma concentrations of 34 healthy volunteers and 12 OUD subjects after single or repeated dosing were included. A parent-metabolite population pharmacokinetics (popPK) model with transit compartments for absorption was implemented to describe the pharmacokinetic profile of BUP-norBUP. In addition, NLX concentrations were well captured by a one-compartment popPK model. Covariate analysis showed that every additional swallow after the administration within the observed range (0-12) resulted in a 3.5% reduction in BUP bioavailability. This provides a possible reason for the less-than-dose proportionality of BUP. There were no differences in the pharmacokinetic characteristics between BUP or NLX in healthy volunteers and OUD subjects. Ethnic sensitivity analysis demonstrated that the dose-normalized peak concentration and area-under-the-curve of BUP in Chinese were about half of Puerto Ricans, which was consistent with a higher clearance observed in Chinese (166 vs. 270 ). Furthermore, Monte Carlo simulations showed that an 8mg three-times daily dose was the optimized regimen for Chinese OUD subjects. This regimen ensured that opioid receptor occupancy remained at a maximum (70%) in more than 95% of subjects, at the same time, with NLX plasma concentrations below the withdrawal reaction threshold (4.6 ).

Learning Reactive Power Control Polices in Distribution Networks Using Conditional Value-at-Risk and Artificial Neural Networks

Scalable coordination of photovoltaic (PV) inverters, considering the uncertainty in PV and load in distribution networks (DNs), is challenging due to the lack of real-time communications. Decentralized PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks (ANNs). To this end, we first use an offline, centralized data-driven conservative convex approximation of chance-constrained optimal power flow (CVaR-OPF) in which conditional value-at-risk (CVaR) is used to compute reactive power setpoints of PV inverter, taking into account PV and load uncertainties in DNs. Following that, an artificial neural network (ANN) controller is trained for each PV inverter to emulate the optimal behavior of the centralized control set- points of PV inverter in a decentralized fashion. Additionally, the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs (local controllers) developed using model-based learning (regression-based controller), optimization (affine feedback controller), and case-based learning (mapping) approaches. Numerical tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.

Sequential Model-based Optimization Approach Deep Learning Model for Classification of Multi-class Traffic Sign Images

Sequential Model-based Optimization Approach Deep Learning Model for Classification of Multi-class Traffic Sign Images

Towards a Model-Based Approach for the Optimization of the Mechanical and Economical Properties of Laser-Based Plastic-Metal Joints

The application of laser-structured metal surfaces to combine plastics and metals is a promising option to enable low-cost lightweight and resource-efficient multi-material joints. The mechanical properties (especially strength), as well as production time and costs, depend on the microstructure of the metal surface (e.g. the number, distance and shape of cavities). Thus, in order to design optimal joints, the properties from the mechanical, as well as production and cost domain, must be considered simultaneously during product development. Therefore, in this paper, a model-based optimization approach is presented with the goal of identifying an optimum between the strength of the joint and laser manufacturing costs. Parameterized models for the strength estimation, as well as calculation of laser manufacturing time and costs, are developed. The models are linked in an optimization workflow and the optimum positioning and shape of the cavities on the joining zone is determined using a genetic optimization algorithm. The results show that, compared to a benchmark, the manufacturing costs can be reduced by 82 % for the same strength using the proposed model-based optimization approach.

Optimal dispatch of an energy hub with compressed air energy storage: A safe reinforcement learning approach

With the advancements in renewable energy and energy storage technologies, the energy hubs (EH) have been emerging in recent years. The scheduling of EH is a challenging task due to the need to incorporate uncertainties at energy supply and load side. The existing model-based optimization approaches proposed for the above purpose have limitations in terms of solution accuracy and computational efficiency, which makes hinders their applications. This paper proposes a model-free, safe deep reinforcement learning (DRL) approach, using primal-dual optimization and imitation learning, for optimal scheduling of an EH that includes a tri-generative advanced adiabatic compressed air energy storage (AA-CAES). First, the operation of an AA-CAES under off-design conditions is modeled and linearized using a mixed-integer linear programming (MILP). Then, a safe DRL approach is proposed with training and testing steps considering a case study. The performance of the proposed approach in reducing operational cost and satisfying constraints is compared to the state-of-the-art DRL algorithms as well as a deterministic MILP approach. In addition, the generalization of the proposed approach is examined on a test-set. Finally, the effect of off-design conditions of a tri-generative AA-CAES on the optimal dispatch strategy is investigated. The results indicate that the proposed approach can effectively reduce the operational cost and satisfy the operational constraints.

Modeling Moisture Absorption of Flax/Sisal Reinforced Hybrid Biocomposites Using Fick’s and ANN Methods

ABSTRACT Natural fiber composites are being increasingly used in several fields, owing to their considerable cost, weight, and environmental benefits. The objective of this research is to study the effect of water absorption on the behavior of laminated biocomposites using flax (F) and sisal (S) fiber biocomposites with different stacking sequences (3S and 3F) and fiber orientation at 90° (F090s and S090s), as well as hybridizations (F/4S90/F and S/4F90/S) to reinforce an epoxy matrix. The kinetics of water diffusion within the proposed biocomposites was monitored and analyzed through a model-based optimization approach using artificial neural network (ANN) method. The results of this study showed that the amount of water absorbed by different types of biocomposites increased with time according to Fick’s law. Non-hybrid flax and sisal fiber composites (UD and (0/90°)s oriented) had lower water absorption rates than the hybrid orientation. It was also observed that sisal fiber was most sensitive to water absorption than flax. A high correlation of experimental data with the model was revealed which denoted that the ANN learning process was perfectly performed. Therefore, it was inferred that the ANN approach can accurately predict biocomposites water absorption.

Recent advances on the modeling and optimization of CO2 capture processes

This work presents a critical review on the recent developments in the design and optimization of efficient post-combustion CO2 capture processes from point sources, focusing on absorption, adsorption, and membrane separations. Emphasis is placed on the efficient modeling and simulation frameworks, synthesis and design studies, and model-based optimization approaches. Furthermore, the interactions between material (solvent, adsorbent, membrane) development and process design and optimization, are explored. Comparison among the different carbon capture processes, shows that absorption is still the most mature technology for post-combustion CO2 capture. Amines are the most appropriate solvents for CO2 absorption, although significant amount of energy is required for solvent regeneration. On the other hand, the development of materials used in adsorption and membrane-based separations has not reached a mature level that can lead to efficient CO2 capture, employing simple process configurations. Consequently, more complex configurations and design specifications are necessary in these processes, which usually result in an increased separation cost.

R2ogs5: Calibration of Numerical Groundwater Flow Models with Bayesian Optimization in R.

Large-scale and high-resolution groundwater models are currently becoming increasingly important in order to clarify the extent to which climate trends and extreme weather affect the groundwater balance regionally. As a result, the parameterization of groundwater models is becoming more detailed and more complex, making conventional calibration methods too time-consuming. Moderating the computational demand to find optimal solutions for the resulting potentially multi-modal objective function requires intelligent and efficient global optimization methods. Moreover, the increasing use of modern scripting languages R and Python to craft environmental analysis workflows calls to integrate groundwater flow simulators in such. Here we introduce and exemplify r2ogs5, a tool that integrates version 5 of the open-source simulation software OpenGeoSys into the programming language R. r2ogs5 allows for calibration of numerical groundwater flow models with a sequential model-based optimization approach that combines Bayesian optimization (BO) with surrogate modeling. Here, we describe the structure and function of r2ogs5 as well as the implemented calibration method. We then demonstrate the calibration method by calibrating 4 and 12 parameters of two simple groundwater flow models. The results indicate that this method needs fewer runs of the groundwater flow model than conventional gradient search and Latin hypercube sampling in case of the 12 parameter model. We believe that our method offers the potential to calibrate computationally expensive groundwater flow models. r2ogs5 supports groundwater flow modelers to access the statistical analysis and visualization capabilities of the R language and is a valuable tool for geoscientists already using R.