scholarly journals Effect of Objective Function on Data-Driven Greedy Sparse Sensor Optimization

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 46731-46743
Author(s):  
Kumi Nakai ◽  
Keigo Yamada ◽  
Takayuki Nagata ◽  
Yuji Saito ◽  
Taku Nonomura
Keyword(s):  
Objective Function ◽  
Data Driven ◽  
Sensor Optimization

scholarly journals A Data-Driven Approach to the Development and Understanding of Chiroptical Sensors for Alcohols with Remote γ-Stereocenters

2021 ◽  
Author(s):  
Jordan Dotson ◽  
Eric Anslyn ◽  
Matthew Sigman
Keyword(s):  
Density Functional ◽  
Level Density ◽  
Enantiomeric Excess ◽  
Data Driven ◽  
Functional Theory ◽  
Dynamic Covalent Chemistry ◽  
Sensor Optimization ◽  
Data Driven Approach ◽  
High Level ◽  
Sensor Molecules

Dynamic covalent chemistry-based sensors have recently emerged as powerful tools to rapidly determine the enantiomeric excess of organic small molecules. While a bevy of sensors have been developed, those for flexible molecules with stereocenters remote to the functional group that binds the chiroptical sensor remain scarce. In this study, we develop an iterative, data-driven workflow to design and analyze a chiroptical sensor capable of assessing challenging acyclic γ-stereogenic alcohols. Fol-lowing sensor optimization, the mechanism of sensing was probed with a combination of computational parameterization of the sensor molecules, statistical modeling, and high-level density functional theory (DFT) calculations. These were used to elucidate the mechanism of stereochemical recognition and revealed that competing attractive non-covalent interactions (NCIs) determine the overall performance of the sensor. It is anticipated that the data-driven workflows developed herein will be generally applicable to the development and understanding of dynamic covalent and supramolecular sensors.

Data-Driven Decisions for Problems with an Unspecified Objective Function

2019 ◽  
Vol 31 (1) ◽  
pp. 2-20 ◽  
Author(s):  
Zhen Sun ◽  
Milind Dawande ◽  
Ganesh Janakiraman ◽  
Vijay Mookerjee
Keyword(s):  
Objective Function ◽  
Data Driven ◽  
Data Driven Decisions

Combined Plant and Controller Design Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems

2017 ◽  
Author(s):  
Ali Baheri ◽  
Joseph Deese ◽  
Christopher Vermillion
Keyword(s):  
System Dynamics ◽  
Objective Function ◽  
Closed Form ◽  
Controller Design ◽  
Center Of Mass ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Closed Form Expression ◽  
Optimization Strategy ◽  
Form Model

This paper presents a novel data-driven nested optimization framework that aims to solve the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamics is unobtainable and simulations or experiments are necessary. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve the nested optimization problem. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent control or plant parameters. The proposed framework differs from the majority of co-design literature where there exists a closed-form model of the system dynamics. We validate the proposed framework for Altaeros’ Buoyant Airborne Turbine (BAT). We choose the horizontal stabilizer area and longitudinal center of mass relative to center of buoyancy (plant parameters) and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that plant and control parameters converge to optimal values within only a few iterations.

Data Driven Approach to Production / Injection Optimization in Oil & Gas field in Abu Dhabi

2021 ◽  
Author(s):  
Dorzhi Badmaev ◽  
Luigi Saputelli ◽  
Carlos Mata
Keyword(s):  
Simulation Model ◽  
Objective Function ◽  
Optimization Procedure ◽  
Production Costs ◽  
Data Driven ◽  
Abu Dhabi ◽  
Phase Flow ◽  
Water Production ◽  
Fractional Flow ◽  
Data Driven Approach

Abstract Production and Injection rate target optimization plays an important role in waterflooded field management in order to ensure hydrocarbon recovery. In line with ADNOC Digital transformation and waterflood excellence initiatives CRM and Optimization technology has been progressed to maximize opportunities in oil recovery increase. The optimization means that producing well delivers a maximum amount of oil with minimal water production along with maintaining proper Voidage Replacement Ratio (VRR) to support reservoir pressure. To reach such goal, the optimization procedure needs to run multiple rate scenarios to calculate the objective function value. The conventional way is to perform multiple runs on simulation model, which can be very time-consuming. The data driven approach described in this paper suggests faster and convenient methodology to solve this problem. The process applied to this approach consists of data preparation/ data cleansing stage, CRM (Capacitance Resistance Model) and optimization procedure based on the objective function with a penalty to imbalanced VRR at the pattern level. The CRM algorithm can calculate fraction of injection distributed from each injecting well to connected producing wells at any timestep. These calculated injection allocation factors are considered in the rate optimization procedure in order to define optimal injection and production rates along with balancing of VRR at the pattern level. The method also considers 3-phase flow across wells and reservoir intervals. The objective function calculates overall profit from oil production, costs for water and gas handling, and the penalty for the production injection difference at the producing well level. At the end, the output of this optimization process is to recommend production and injection rates targets for each well and short term forecast of the production based on fractional flow model. The data driven approach shows quite good efficiency in terms of time and efforts, the injection allocation factors based on CRM model are comparatively same as it is calculated in streamline simulation model but with better granularity at the pattern level. The optimization procedure works quite fast, and the results have shown decrease of water production rate and increase of recovery factor due to maintaining VRR close to the target level. The data driven approach described in the paper implements a new way to apply CRM in fields with waterflooding and gas injection with the enhancement of managing 3-phase flow. The in-house developed optimization function and its implementation is a novel approach in terms of practical application to the fields in Abu Dhabi area.

Combined Plant and Controller Design Using Batch Bayesian Optimization: A Case Study in Airborne Wind Energy Systems

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Ali Baheri ◽  
Chris Vermillion
Keyword(s):  
Objective Function ◽  
Closed Form ◽  
Economies Of Scale ◽  
Controller Design ◽  
Center Of Mass ◽  
Bayesian Optimization ◽  
Data Driven ◽  
Closed Form Expression ◽  
Plant Design ◽  
Optimization Strategy

This paper presents a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored toward instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian process (GP); then, Bayesian optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent control or plant parameters. The proposed framework differs from the majority of codesign literature where there exists a closed-form model of the system dynamics. Furthermore, we utilize the idea of batch Bayesian optimization at the plant optimization level to generate a set of plant designs at each iteration of the overall optimization process, recognizing that there will exist economies of scale in running multiple experiments in each iteration of the plant design process. We validate the proposed framework for Altaeros' buoyant airborne turbine (BAT). We choose the horizontal stabilizer area, longitudinal center of mass relative to center of buoyancy (plant parameters), and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that these plant and control parameters converge to their respective optimal values within only a few iterations.

scholarly journals A Data-Driven Intermittent Online Coverage Path Planning Method for AUV-Based Bathymetric Mapping

2020 ◽  
Vol 10 (19) ◽  
pp. 6688
Author(s):  
Jianguang Shi ◽  
Mingxi Zhou
Keyword(s):  
Path Planning ◽  
Objective Function ◽  
Autonomous Underwater Vehicles ◽  
Data Gathering ◽  
Cost Effective ◽  
Coverage Rate ◽  
Data Driven ◽  
Seafloor Topography ◽  
Coverage Path Planning ◽  
Bathymetric Mapping

Bathymetric mapping with Autonomous Underwater Vehicles (AUVs) receives increased attentions in recent years. AUVs offer a lower operational cost and smaller carbon footprint with reduced ship usage, and they can provide higher resolution data when surveying the seabed at a closer distance if compared to ships. However, advancements are still needed to improve the data quality of AUV-based surveys. Unlike mobile robots with deterministic mapping performance, multibeam sonars used in AUV-based bathymetric mapping often yields inconsistent swath width due to the varied seabed elevation and surficial properties. As a result, mapping voids may exist between planned lawnmower transects. Although this could be solved by planning closer lawnmower paths, mission time increases proportionally. Therefore, an onboard path planner is demanded to assure the defined survey objective, i.e., coverage rate. Here in this paper, we present a new data-driven coverage path planning (CPP) method, in which the vehicle automatically updates the waypoints intermittently based on an objective function constructed using the information about the exploration preference, sonar performance, and coverage efficiency. The goal of the proposed method is to plan a cost-effective path on-the-fly to obtain high quality mapping result meeting the requirements in coverage rate and uncertainty. The proposed CPP method has been evaluated in a simulated environment with a 6DOF REMUS AUV model and a realistic seafloor topography. A series of trials has been conducted to investigate the performance affected by the parameters in the objective function. We also compared the proposed method with traditional lawnmower and spiral paths. The results show that the weight assignment in the objective function is critical as they affect the overall survey performance. With proper weight settings, the AUV yields better survey performance, coverage rate and coverage efficiency, compared to traditional approaches. Moreover, the proposed method can be easily adjusted or modified to achieve different coverage goals, such as rapid data gathering of the entire region, survey of irregular workspace, or maintaining real time path planning.

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