Box models for axisymmetric geometry

2020 ◽  
pp. 593-601
Keyword(s):  
Box Models

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals Branch and Bound Algorithm Based on Prediction Error of Metamodel for Computational Electromagnetics

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6749
Author(s):  
Reda El Bechari ◽  
Stéphane Brisset ◽  
Stéphane Clénet ◽  
Frédéric Guyomarch ◽  
Jean Claude Mipo
Keyword(s):  
Branch And Bound ◽  
Prediction Error ◽  
Global Solutions ◽  
Black Box ◽  
High Fidelity ◽  
Electromagnetic Devices ◽  
Box Models ◽  
Element Simulation ◽  
The Cost ◽  
Black Box Models

Metamodels proved to be a very efficient strategy for optimizing expensive black-box models, e.g., Finite Element simulation for electromagnetic devices. It enables the reduction of the computational burden for optimization purposes. However, the conventional approach of using metamodels presents limitations such as the cost of metamodel fitting and infill criteria problem-solving. This paper proposes a new algorithm that combines metamodels with a branch and bound (B&B) strategy. However, the efficiency of the B&B algorithm relies on the estimation of the bounds; therefore, we investigated the prediction error given by metamodels to predict the bounds. This combination leads to high fidelity global solutions. We propose a comparison protocol to assess the approach’s performances with respect to those of other algorithms of different categories. Then, two electromagnetic optimization benchmarks are treated. This paper gives practical insights into algorithms that can be used when optimizing electromagnetic devices.

scholarly journals Controlling Safety of Artificial Intelligence-Based Systems in Healthcare

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 102
Author(s):  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Krzysztof Fiok ◽  
Thomas Wan ◽  
Hamid R. Parsaei
Keyword(s):  
Artificial Intelligence ◽  
Rating Scale ◽  
Black Box ◽  
Healthcare Industry ◽  
Emergency Plans ◽  
Controlling System ◽  
Box Models ◽  
Key Dimensions ◽  
Value Model ◽  
And Control

In response to the need to address the safety challenges in the use of artificial intelligence (AI), this research aimed to develop a framework for a safety controlling system (SCS) to address the AI black-box mystery in the healthcare industry. The main objective was to propose safety guidelines for implementing AI black-box models to reduce the risk of potential healthcare-related incidents and accidents. The system was developed by adopting the multi-attribute value model approach (MAVT), which comprises four symmetrical parts: extracting attributes, generating weights for the attributes, developing a rating scale, and finalizing the system. On the basis of the MAVT approach, three layers of attributes were created. The first level contained six key dimensions, the second level included 14 attributes, and the third level comprised 78 attributes. The key first level dimensions of the SCS included safety policies, incentives for clinicians, clinician and patient training, communication and interaction, planning of actions, and control of such actions. The proposed system may provide a basis for detecting AI utilization risks, preventing incidents from occurring, and developing emergency plans for AI-related risks. This approach could also guide and control the implementation of AI systems in the healthcare industry.

Mathematical Modeling of CSTR Bioreactor Control for Production of Recombinant Protein

2014 ◽  
Vol 894 ◽  
pp. 311-315
Author(s):  
Xiao Yi Jia ◽  
Yu Tian Lin ◽  
Hui Bin Lin ◽  
Ling Gao ◽  
Jian Qun Lin ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Recombinant Protein ◽  
Fermentation Process ◽  
Protein Production ◽  
Continuous Stirred Tank Reactor ◽  
High Productivity ◽  
Box Models ◽  
Microbial Cultivation ◽  
Continuous Stirred Tank ◽  
Black Box Models

Fermentation process using recombinant strain for production of recombinant protein is widely used in commercialization of the biotechnologies. The continuous stirred tank reactor (CSTR) is a typical microbial cultivation method, has the major advantage of high productivity. Mathematical modeling and simulation is useful for analysis and optimization of the CSTR fermentation process. Most of the mathematical models developed for CSTR are black box models without information of the intracellular dynamics and regulations. In this research, a mathematical model is built based on gene regulation for recombinant protein production using CSTR, and simulation is made using this model.

scholarly journals Identifiability and physical interpretability of hybrid, gray-box models - a case study

2021 ◽  
Vol 54 (3) ◽  
pp. 389-394
Author(s):  
M. Hotvedt ◽  
B. Grimstad ◽  
L. Imsland
Keyword(s):  
Box Models

An overview of deep learning techniques

2018 ◽  
Vol 66 (9) ◽  
pp. 690-703 ◽  
Author(s):  
Michael Vogt
Keyword(s):  
Deep Learning ◽  
Single Layer ◽  
Visual Image ◽  
Image Understanding ◽  
Number Of Layers ◽  
Box Models ◽  
Learning Techniques ◽  
Engineering Work ◽  
Computational Resources ◽  
Black Box Models

Abstract Deep learning is the paradigm that profoundly changed the artificial intelligence landscape within only a few years. Although accompanied by a variety of algorithmic achievements, this technology is disruptive mainly from the application perspective: It considerably pushes the border of tasks that can be automated, changes the way products are developed, and is available to virtually everyone. Subject of deep learning are artificial neural networks with a large number of layers. Compared to earlier approaches with ideally a single layer, this allows using massive computational resources to train black-box models directly on raw data with a minimum of engineering work. Most successful applications are found in visual image understanding, but also in audio and text modeling.

Intercomparison of Two Box Models of the Chemical Evolution in Biomass-Burning Smoke Plumes

2006 ◽  
Vol 55 (3) ◽  
pp. 273-297 ◽  
Author(s):  
Sherri A. Mason ◽  
Jörg Trentmann ◽  
Tanja Winterrath ◽  
Robert J. Yokelson ◽  
Theodore J. Christian ◽  
...  
Keyword(s):  
Chemical Evolution ◽  
Biomass Burning ◽  
Box Models ◽  
Smoke Plumes

Joint fixity effect on structural design of a box wing aircraft

2012 ◽  
Vol 116 (1178) ◽  
pp. 363-372
Author(s):  
P. O. Jemitola ◽  
J. Fielding ◽  
P. Stocking
Keyword(s):  
Finite Element ◽  
Structural Design ◽  
Vortex Lattice ◽  
Computational Study ◽  
Requirements Analysis ◽  
Stress Distributions ◽  
Box Models ◽  
Wing Structure

Abstract A computational study was performed to compare the stress distributions in finite element torsion box models of a box wing structure that result from employing four different wing/end fin joint fixities. All considered wings were trimmed in pitch. The joint fixities refer to the type of attachment that connects the tip of the fore and aft wings to the end fin. Using loads from a vortex lattice tool, the analysis determined the best wing-joint fixity of a statically loaded idealised box wing configuration by comparing the stress distributions resulting from the different wing joints in addition to other essential aerodynamic requirements. Analysis of the wing joint fixity indicates that the rigid joint is the most suitable.

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