scholarly journals Bayesian sparse reconstruction: a brute-force approach to astronomical imaging and machine learning

2018 ◽  
Author(s):  
Edward Higson ◽  
Will Handley ◽  
Michael Hobson ◽  
Anthony Lasenby
Keyword(s):  
Machine Learning ◽  
Sparse Reconstruction ◽  
Brute Force ◽  
Astronomical Imaging ◽  
Brute Force Approach

scholarly journals Build Orientation Optimization for Strength Enhancement of FDM Parts Using Machine Learning based Algorithm

2019 ◽  
Author(s):  
Manoj Malviya ◽  
Kaushal A Desai
Keyword(s):  
Machine Learning ◽  
Brute Force ◽  
Initial Sample ◽  
Bayesian Algorithm ◽  
Build Orientation ◽  
Optimization Framework ◽  
Sample Data ◽  
Artificial Neural Network Ann ◽  
Brute Force Approach ◽  
Orientation Optimization

The layered fabrication approach induces directional anisotropy and impacts mechanical strength of FDM components significantly. This paper proposes generalized machine learning based parameter optimization framework to determine optimal build orientation for FDM components. The algorithm determines ideal build orientation by maximizing the minimum Factor of Safety (FoS) for the component under prescribed loading conditions ensuring its even distribution. An Artificial Neural Network (ANN) coupled with Bayesian algorithm has been employed to accelerate the optimization process. The algorithm begins with an initial sample data collected using brute force approach; uses single layered ANN for approximation and optimization is achieved using Bayesian algorithm. A series of computational experiments considering five different test components has been devised to evaluate the performance and efficacy of the proposed algorithm. These experiments demonstrated that the proposed algorithm can determine the optimum building orientation effectively with certain limitations

scholarly journals Feature partitioning for robust tree ensembles and their certification in adversarial scenarios

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Stefano Calzavara ◽  
Claudio Lucchese ◽  
Federico Marcuzzi ◽  
Salvatore Orlando
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Brute Force ◽  
Accuracy Improvement ◽  
Safe Environment ◽  
Feature Partitioning ◽  
Feature Based ◽  
Brute Force Approach ◽  
The Given ◽  
Certification Method

AbstractMachine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work, we focus on evasion attacks, where a model is trained in a safe environment and exposed to attacks at inference time. The attacker aims at finding a perturbation of an instance that changes the model outcome.We propose a model-agnostic strategy that builds a robust ensemble by training its basic models on feature-based partitions of the given dataset. Our algorithm guarantees that the majority of the models in the ensemble cannot be affected by the attacker. We apply the proposed strategy to decision tree ensembles, and we also propose an approximate certification method for tree ensembles that efficiently provides a lower bound of the accuracy of a forest in the presence of attacks on a given dataset avoiding the costly computation of evasion attacks.Experimental evaluation on publicly available datasets shows that the proposed feature partitioning strategy provides a significant accuracy improvement with respect to competitor algorithms and that the proposed certification method allows ones to accurately estimate the effectiveness of a classifier where the brute-force approach would be unfeasible.

scholarly journals Build Orientation Optimization for Strength Enhancement of 3-D Printed Parts Using Machine Learning based Algorithm

2019 ◽  
Author(s):  
Manoj Malviya
Keyword(s):  
Machine Learning ◽  
Brute Force ◽  
Initial Sample ◽  
Bayesian Algorithm ◽  
Build Orientation ◽  
Optimization Framework ◽  
Sample Data ◽  
Artificial Neural Network Ann ◽  
Brute Force Approach ◽  
Orientation Optimization

The layered fabrication approach induces directional anisotropy and impacts the mechanical strength of FDM components significantly. This paper proposes a generalized machine learning based parameter optimization framework to determine optimal build orientation for FDM components. The algorithm determines ideal build orientation by maximizing the minimum Factor of Safety (FoS) for the component under prescribed loading conditions ensuring its even distribution. An Artificial Neural Network (ANN) coupled with Bayesian algorithm has been employed to accelerate the optimization process. The algorithm begins with an initial sample data collected using a brute force approach; uses single layered ANN for approximation and optimization is achieved using a Bayesian algorithm. A series of computational experiments considering five different test components have been devised to evaluate the performance and efficacy of the proposed algorithm. These experiments demonstrated that the proposed algorithm can determine the optimum building orientation effectively with certain limitations.

scholarly journals LHC physicists embrace brute-force approach to particle hunt

Nature ◽  
2018 ◽  
Vol 560 (7718) ◽  
pp. 293-294
Author(s):  
Davide Castelvecchi
Keyword(s):  
Brute Force ◽  
Brute Force Approach

scholarly journals Efficient Parameterized Word Matching Using Bit-Parallelism and Partitioning the Text

2016 ◽  
Vol 2 (2) ◽  
pp. 20
Author(s):  
Rajesh Prasad
Keyword(s):  
Experimental Results ◽  
Brute Force ◽  
White Space ◽  
Matching Problem ◽  
One To One ◽  
Brute Force Approach ◽  
Better Than

Word matching problem is to find all the exact occurrences of a pattern P[0...m-1] in the text T[0...n-1], where P neither contains any white space nor preceded and followed by space. In the parameterized word matching problem, a given word P[0...m-1] is said to match with a sub-word t of the text T[0...n-1], if there exists a one-to-one correspondence between the symbols of P and the symbols of t. Exact Word Matching (EWM) problem has been previously solved by partitioning the text into number of tables in the pre-processing phase and then applying either brute force approach or fast hashing during the searching process. This paper presents an extension of EWM problem for parameterized word matching. It first split the text into number of tables in the pre-processing phase and then applying prev-encoding and bit-parallelism technique, Parameterized Shift-Or (PSO) during the searching phase. Experimental results show that this technique performs better than PSO.

scholarly journals A Hybrid Computational Intelligence based Technique for Automatic Cryptanalysis of Playfair Ciphers

2020 ◽  
Vol 70 (6) ◽  
pp. 612-618
Author(s):  
Maiya Din ◽  
Saibal K. Pal ◽  
S. K. Muttoo ◽  
Sushila Madan
Keyword(s):  
Computational Intelligence ◽  
Text Message ◽  
Search Space ◽  
Population Based ◽  
English Text ◽  
Hybrid Technique ◽  
Brute Force ◽  
Symmetric Key ◽  
Brute Force Approach ◽  
Multiple Particle

The Playfair cipher is a symmetric key cryptosystem-based on encryption of digrams of letters. The cipher shows higher cryptanalytic complexity compared to mono-alphabetic cipher due to the use of 625 different letter-digrams in encryption instead of 26 letters from Roman alphabets. Population-based techniques like Genetic algorithm (GA) and Swarm intelligence (SI) are more suitable compared to the Brute force approach for cryptanalysis of cipher because of specific and unique structure of its Key Table. This work is an attempt to automate the process of cryptanalysis using hybrid computational intelligence. Multiple particle swarm optimization (MPSO) and GA-based hybrid technique (MPSO-GA) have been proposed and applied in solving Playfair ciphers. The authors have attempted to find the solution key applied in generating Playfair crypts by using the proposed hybrid technique to reduce the exhaustive search space. As per the computed results of the MPSO-GA technique, correct solution was obtained for the Playfair ciphers of 100 to 200 letters length. The proposed technique provided better results compared to either GA or PSO-based technique. Furthermore, the technique was also able to recover partial English text message for short Playfair ciphers of 80 to 120 characters length.

Method and Verification for Material Calibration of the Chaboche Plasticity Model for Multiple Material Directions

2021 ◽  
Author(s):  
Charles R. Krouse ◽  
Grant O. Musgrove ◽  
Taewoan Kim ◽  
Seungmin Lee ◽  
Muhyoung Lee ◽  
...  
Keyword(s):  
Experimental Data ◽  
Kinematic Hardening ◽  
Material Model ◽  
Brute Force ◽  
Hardening Material ◽  
Material Constants ◽  
Automated Method ◽  
Chaboche Model ◽  
Multiple Material ◽  
Brute Force Approach

Abstract The Chaboche model is a well-validated non-linear kinematic hardening material model. This material model, like many models, depends on a set of material constants that must be calibrated for it to match the experimental data. Due to the challenge of calibrating these constants, the Chaboche model is often disregarded. The challenge with calibrating the Chaboche constants is that the most reliable method for doing the calibration is a brute force approach, which tests thousands of combinations of constants. Different sampling techniques and optimization schemes can be used to select different combinations of these constants, but ultimately, they all rely on iteratively selecting values and running simulations for each selected set. In the experience of the authors, such brute force methods require roughly 2,500 combinations to be evaluated in order to have confidence that a reasonable solution is found. This process is not efficient. It is time-intensive and labor-intensive. It requires long simulation times, and it requires significant effort to develop the accompanying scripts and algorithms that are used to iterate through combinations of constants and to calculate agreement. A better, more automated method exists for calibrating the Chaboche material constants. In this paper, the authors describe a more efficient, automated method for calibrating Chaboche constants. The method is validated by using it to calibrate Chaboche constants for an IN792 single-crystal material and a CM247 directionally-solidified material. The calibration results using the automated approach were compared to calibration results obtained using a brute force approach. It was determined that the automated method achieves agreeable results that are equivalent to, or supersede, results obtained using the conventional brute force method. After validating the method for cases that only consider a single material orientation, the automated method was extended to multiple off-axis calibrations. The Chaboche model that is available in commercial software, such as ANSYS, will only accept a single set of Chaboche constants for a given temperature. There is no published method for calibrating Chaboche constants that considers multiple material orientations. Therefore, the approach outlined in this paper was extended to include multiple material orientations in a single calibration scheme. The authors concluded that the automated approach can be used to successfully, accurately, and efficiently calibrate multiple material directions. The approach is especially well-suited when off-axis calibration must be considered concomitantly with longitudinal calibration. Overall, the automated Chaboche calibration method yielded results that agreed well with experimental data. Thus, the method can be used with confidence to efficiently and accurately calibrate the Chaboche non-linear kinematic hardening material model.

A Method for Solving Multi-Objective Optimization Problems Containing an Infinite Number of Parameterized Objectives

2020 ◽  
Author(s):  
Eliot Rudnick-Cohen
Keyword(s):  
Decision Making ◽  
Infinite Number ◽  
Optimization Problems ◽  
Pareto Frontier ◽  
Brute Force ◽  
Multi Objective Optimization ◽  
New Approach ◽  
Multi Objective ◽  
Brute Force Approach ◽  
Better Than

Abstract Multi-objective decision making problems can sometimes involve an infinite number of objectives. In this paper, an approach is presented for solving multi-objective optimization problems containing an infinite number of parameterized objectives, termed “infinite objective optimization”. A formulation is given for infinite objective optimization problems and an approach for checking whether a Pareto frontier is a solution to this formulation is detailed. Using this approach, a new sampling based approach is developed for solving infinite objective optimization problems. The new approach is tested on several different example problems and is shown to be faster and perform better than a brute force approach.

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