Improved Temperature Compensation of Atomic Clocks and INS Instruments using Multivariate Model-based Design Optimized for Real-world Operating Conditions.

2018 ◽  
Author(s):  
Andrew V. Dowd
Keyword(s):  
Real World ◽  
Temperature Compensation ◽  
Multivariate Model ◽  
Operating Conditions ◽  
Atomic Clocks ◽  
Model Based

Study On Automatic Adaptation for Control-Oriented Model of Advanced Diesel Engine

2021 ◽  
Author(s):  
Shunki Nishii ◽  
Yudai Yamasaki
Keyword(s):  
Operating Conditions ◽  
Physical Models ◽  
Model Parameters ◽  
Training Methods ◽  
Model Based Control ◽  
Practical Applications ◽  
Model Based ◽  
Automatic Adaptation ◽  
Automobile Engines ◽  
Adaptation Method

Abstract To achieve high thermal efficiency and low emission in automobile engines, advanced combustion technologies using compression autoignition of premixtures have been studied, and model-based control has attracted attention for their practical applications. Although simplified physical models have been developed for model-based control, appropriate values for their model parameters vary depending on the operating conditions, the engine driving environment, and the engine aging. Herein, we studied an onboard adaptation method of model parameters in a heat release rate (HRR) model. This method adapts the model parameters using neural networks (NNs) considering the operating conditions and can respond to the driving environment and the engine aging by training the NNs onboard. Detailed studies were conducted regarding the training methods. Furthermore, the effectiveness of this adaptation method was confirmed by evaluating the prediction accuracy of the HRR model and model-based control experiments.

scholarly journals The Design of an Instructional Model Based on Connectivism and Constructivism to Create Innovation in Real World Experience

2018 ◽  
Vol 11 (3) ◽  
pp. 12 ◽  
Author(s):  
Kanokrat Jirasatjanukul ◽  
Namon Jeerungsuwan
Keyword(s):  
Real World ◽  
Model Evaluation ◽  
Sampling Technique ◽  
Instructional Model ◽  
Model Design ◽  
Research Results ◽  
Purposive Sampling ◽  
Evaluation Form ◽  
Model Based ◽  
High Level

The objectives of the research were to (1) design an instructional model based on Connectivism and Constructivism to create innovation in real world experience, (2) assess the model designed–the designed instructional model. The research involved 2 stages: (1) the instructional model design and (2) the instructional model rating. The sample consisted of 7 experts, and the Purposive Sampling Technique was used. The research instruments were the instructional model and the instructional model evaluation form. The statistics used in the research were means and standard division. The research results were (1) the Instructional Model based on Connectivism and Constructivism to Create innovation in Real World Experience consisted of 3 components. These were Connectivism, Constructivism and Innovation in Real World Experience and (2) the instructional model rating was at a high level (=4.37, S.D.=0.41). The research results revealed that the Instructional Model Based on Connectivism and Constructivism to Create Innovation in Real World Experience was a model that can be used in learning, in that it promoted the creation of real world experience innovation.

Real-world model based validation of blending bed simulation techniques

2016 ◽  
pp. 261-266
Author(s):  
Michael P. Cipold ◽  
Pradyumn Kumar Shukla ◽  
Christian Autenrieth ◽  
Marvin Baral ◽  
Lukas Lihotzki
Keyword(s):  
Real World ◽  
World Model ◽  
Model Based ◽  
Simulation Techniques

scholarly journals Improving medication safety: Development and impact of a multivariate model-based strategy to target high-risk patients

PLoS ONE ◽  
2017 ◽  
Vol 12 (2) ◽  
pp. e0171995 ◽  
Author(s):  
Tri-Long Nguyen ◽  
Géraldine Leguelinel-Blache ◽  
Jean-Marie Kinowski ◽  
Clarisse Roux-Marson ◽  
Marion Rougier ◽  
...  
Keyword(s):  
High Risk ◽  
Medication Safety ◽  
Multivariate Model ◽  
High Risk Patients ◽  
Model Based ◽  
Risk Patients

scholarly journals Improving Model-Based Genetic Programming for Symbolic Regression of Small Expressions

2020 ◽  
pp. 1-27 ◽  
Author(s):  
M. Virgolin ◽  
T. Alderliesten ◽  
C. Witteveen ◽  
P. A. N. Bosman
Keyword(s):  
Genetic Programming ◽  
Real World ◽  
Symbolic Regression ◽  
New Approach ◽  
Model Based ◽  
Real World Datasets ◽  
Linkage Learning ◽  
Optimal Mixing ◽  
Crucial Parameter

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a model-based EA framework that has been shown to perform well in several domains, including Genetic Programming (GP). Differently from traditional EAs where variation acts blindly, GOMEA learns a model of interdependencies within the genotype, that is, the linkage, to estimate what patterns to propagate. In this article, we study the role of Linkage Learning (LL) performed by GOMEA in Symbolic Regression (SR). We show that the non-uniformity in the distribution of the genotype in GP populations negatively biases LL, and propose a method to correct for this. We also propose approaches to improve LL when ephemeral random constants are used. Furthermore, we adapt a scheme of interleaving runs to alleviate the burden of tuning the population size, a crucial parameter for LL, to SR. We run experiments on 10 real-world datasets, enforcing a strict limitation on solution size, to enable interpretability. We find that the new LL method outperforms the standard one, and that GOMEA outperforms both traditional and semantic GP. We also find that the small solutions evolved by GOMEA are competitive with tuned decision trees, making GOMEA a promising new approach to SR.

scholarly journals Nonlinear Model-Based Inferential Control of Moisture Content of Spray Dried Coconut Milk

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1177
Author(s):  
Zalizawati Abdullah ◽  
Farah Saleena Taip ◽  
Siti Mazlina Mustapa Kamal ◽  
Ribhan Zafira Abdul Rahman
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Nonlinear Model ◽  
Milk Powder ◽  
Operating Conditions ◽  
Coconut Milk ◽  
Inferential Control ◽  
Model Based ◽  
The Neural Network ◽  
Control Response

The moisture content of a powder is a parameter crucial to be controlled in order to produce stable products with a long shelf life. Inferential control is the best solution to control the moisture content due to difficulty in measuring this variable online. In this study, fundamental and empirical approaches were used in designing the nonlinear model-based inferential control of moisture content of coconut milk powder that was produced from co-current spray dryer. A one-dimensional model with integration of reaction engineering approach (REA) model was used to represent the dynamic of the spray drying process. The empirical approach, i.e., nonlinear autoregressive with exogenous input (NARX) and neural network, was used to allow fast and accurate prediction of output response in inferential control. Minimal offset (<0.0003 kg/kg) of the responses at various set points indicate high accuracy of the neural network estimator. The nonlinear model-based inferential control was able to provide stable control response at wider process operating conditions and acceptable disturbance rejection. Nevertheless, the performance of the controller depends on the tuning rules used.

scholarly journals Optical atomic phase reference and timing

2017 ◽  
Vol 375 (2099) ◽  
pp. 20160241 ◽  
Author(s):  
L. Hollberg ◽  
E. H. Cornell ◽  
A. Abdelrahmann
Keyword(s):  
Special Relativity ◽  
Real World ◽  
High Performance ◽  
Cold Atoms ◽  
Cold Atom ◽  
New Physics ◽  
Atomic Clocks ◽  
Commercial World ◽  
Commercial Applications ◽  
Quantum Technology

Atomic clocks based on laser-cooled atoms have made tremendous advances in both accuracy and stability. However, advanced clocks have not found their way into widespread use because there has been little need for such high performance in real-world/commercial applications. The drive in the commercial world favours smaller, lower-power, more robust compact atomic clocks that function well in real-world non-laboratory environments. Although the high-performance atomic frequency references are useful to test Einstein's special relativity more precisely, there are not compelling scientific arguments to expect a breakdown in special relativity. On the other hand, the dynamics of gravity, evidenced by the recent spectacular results in experimental detection of gravity waves by the LIGO Scientific Collaboration, shows dramatically that there is new physics to be seen and understood in space–time science. Those systems require strain measurements at less than or equal to 10 −20 . As we discuss here, cold atom optical frequency references are still many orders of magnitude away from the frequency stability that should be achievable with narrow-linewidth quantum transitions and large numbers of very cold atoms, and they may be able to achieve levels of phase stability, Δ Φ / Φ total  ≤ 10 −20 , that could make an important impact in gravity wave science. This article is part of the themed issue ‘Quantum technology for the 21st century’.

Model-Based Calibration of a Biogas Engine Control System

2012 ◽  
Author(s):  
Michael Flory ◽  
Joel Hiltner ◽  
Clay Hardenburger
Keyword(s):  
Control System ◽  
Natural Gas ◽  
Electrical Power ◽  
Engine Performance ◽  
Operating Conditions ◽  
High Quality ◽  
Model Based ◽  
Crank Angle ◽  
Short Period ◽  
Exhaust Gas Emission

Pipeline natural gas composition is monitored and controlled in order to deliver high quality, relatively consistent gas quality in terms of heating value and detonation characteristics to end users. The consistency of this fuel means gas-fired engines designed for electrical power generation (EPG) applications can be highly optimized. As new sources of high quality natural gas are found, the demand for these engines is growing. At the same time there is also an increasing need for EPG engines that can handle fuels that have wide swings in composition over a relatively short period of time. The application presented in this paper is an engine paired with an anaerobic digester that accepts an unpredictable and varying feedstock. As is typical in biogas applications, there are exhaust stream contaminants that preclude the use of an oxygen or NOx sensor for emissions feedback control. The difficulty with such a scenario is the ability to hold a given exhaust gas emission level as the fuel composition varies. One challenge is the design of the combustion system hardware. This design effort includes the proper selection of compression ratio, valve events, ignition timing, turbomachinery, etc. Often times simulation tools, such as a crank-angle resolved engine model, are used in the development of such systems in order to predict performance and reduce development time and hardware testing. The second challenge is the control system and how to implement a robust control capable of optimizing engine performance while maintaining emissions compliance. Currently there are limited options for an OEM control system capable of dealing with fuels that have wide swings in composition. Often times the solution for the engine packager is to adopt an aftermarket control system and apply this in place of the control system delivered on the engine. The disadvantage to this approach is that the aftermarket controller is not calibrated and so the packager is faced with the task of developing an entire engine calibration at a customer site. The controller must function well enough that it will run reliably during plant start-up and then eventually prove capable of holding emissions under typical operating conditions. This paper will describe the novel use of a crank-angle resolved four-stroke engine cycle model to develop an initial set of calibration values for an aftermarket control system. The paper will describe the plant operation, implementation of the aftermarket controller, the model-based calibration methodology and the commissioning of the engine.

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