3D Atomic Structure of Supported Metallic Nanoparticles Estimated from 2D ADF STEM Images: A Combination of Atom‐Counting and a Local Minima Search Algorithm

Small Methods ◽  
2021 ◽  
pp. 2101150
Author(s):  
Ece Arslan Irmak ◽  
Pei Liu ◽  
Sara Bals ◽  
Sandra Van Aert
Keyword(s):  
Atomic Structure ◽  
Metallic Nanoparticles ◽  
Search Algorithm ◽  
Local Minima ◽  
Atom Counting

scholarly journals Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft

2021 ◽  
Vol 11 (7) ◽  
pp. 3103
Author(s):  
Kyuman Lee ◽  
Daegyun Choi ◽  
Donghoon Kim
Keyword(s):  
Collision Avoidance ◽  
Search Algorithm ◽  
Unmanned Aircraft ◽  
Artificial Potential Field ◽  
Tree Search ◽  
Local Minima ◽  
Collision Risk ◽  
Motion Primitives ◽  
Multiple Uavs ◽  
Tree Search Algorithm

Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF.

Deterministic and probabilistic occupancy detection with a novel heuristic optimization and Back-Propagation (BP) based algorithm

2021 ◽  
pp. 1-13
Author(s):  
Nuzhat Fatema ◽  
Saeid Gholami Farkoush ◽  
Mashhood Hasan ◽  
H Malik
Keyword(s):  
Convergence Rate ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Hybrid Approach ◽  
Experimental Studies ◽  
Back Propagation ◽  
Heuristic Optimization ◽  
Local Minima ◽  
Occupancy Detection ◽  
Trapping Problem

In this paper, a novel hybrid approach for deterministic and probabilistic occupancy detection is proposed with a novel heuristic optimization and Back-Propagation (BP) based algorithms. Generally, PB based neural network (BPNN) suffers with the optimal value of weight, bias, trapping problem in local minima and sluggish convergence rate. In this paper, the GSA (Gravitational Search Algorithm) is implemented as a new training technique for BPNN is order to enhance the performance of the BPNN algorithm by decreasing the problem of trapping in local minima, enhance the convergence rate and optimize the weight and bias value to reduce the overall error. The experimental results of BPNN with and without GSA are demonstrated and presented for fair comparison and adoptability. The demonstrated results show that BPNNGSA has outperformance for training and testing phase in form of enhancement of processing speed, convergence rate and avoiding the trapping problem of standard BPNN. The whole study is analyzed and demonstrated by using R language open access platform. The proposed approach is validated with different hidden-layer neurons for both experimental studies based on BPNN and BPNNGSA.

FINDING MULTIPLE LOCAL MINIMA USING CHAOTIC JUMP

1998 ◽  
Vol 07 (01) ◽  
pp. 105-115 ◽  
Author(s):  
CHANGKYU CHOI ◽  
JU-JANG LEE
Keyword(s):  
Search Algorithm ◽  
Gaussian Function ◽  
Search Space ◽  
Planning Problem ◽  
Local Minima ◽  
Chaotic Motions ◽  
Quality Function ◽  
Unstructured Search ◽  
Free Search ◽  
Minimum Points

In this paper, the local minima free search algorithm using chaos is proposed for an unstructured search space. The problem is that given the quality function, find the value of a configuration that minimizes the quality function. The proposed algorithm started basically from the gradient search technique but at the prescribed points, that is, local minimum points, which are to be automatically detected the chaotic jump is introduced by the dynamics of a chaotic neuron. Chaotic motions are mainly because of the Gaussian function having a hysteresis as a refractoriness. In order to enhance the probability of finding the global minimum, a parallel search strategy is also given. The validity of the proposed method wil be verified in simulation examples of the function minimization problem and the motion planning problem of a mobile robot.

scholarly journals Visualising the Three-dimensional Morphology and Surface Structure of Metallic Nanoparticles at Atomic Resolution by Automated HAADF STEM Atom Counting

2014 ◽  
Vol 20 (S3) ◽  
pp. 60-61
Author(s):  
Lewys Jones ◽  
Vidar T. Fauske ◽  
Katherine E. MacArthur ◽  
Antonius T. J. van Helvoort ◽  
Peter D. Nellist
Keyword(s):  
Surface Structure ◽  
Metallic Nanoparticles ◽  
Three Dimensional ◽  
Atomic Resolution ◽  
Atom Counting

scholarly journals Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Nazri Mohd Nawi ◽  
Abdullah Khan ◽  
M. Z. Rehman ◽  
Haruna Chiroma ◽  
Tutut Herawan
Keyword(s):  
Gradient Descent ◽  
Search Algorithm ◽  
Error Function ◽  
Back Propagation ◽  
Data Classification ◽  
Cuckoo Search ◽  
Recurrent Network ◽  
Training Algorithms ◽  
Local Minima ◽  
Classification Problems

Recurrent neural network (RNN) has been widely used as a tool in the data classification. This network can be educated with gradient descent back propagation. However, traditional training algorithms have some drawbacks such as slow speed of convergence being not definite to find the global minimum of the error function since gradient descent may get stuck in local minima. As a solution, nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. This paper proposes a new metaheuristic search algorithm called Cuckoo Search (CS) based on Cuckoo bird’s behavior to train Elman recurrent network (ERN) and back propagation Elman recurrent network (BPERN) in achieving fast convergence rate and to avoid local minima problem. The proposed CSERN and CSBPERN algorithms are compared with artificial bee colony using BP algorithm and other hybrid variants algorithms. Specifically, some selected benchmark classification problems are used. The simulation results show that the computational efficiency of ERN and BPERN training process is highly enhanced when coupled with the proposed hybrid method.

scholarly journals Novel Back Propagation Optimization by Cuckoo Search Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jiao-hong Yi ◽  
Wei-hong Xu ◽  
Yuan-tao Chen
Keyword(s):  
Search Algorithm ◽  
Back Propagation ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Prediction Performance ◽  
General Regression Neural Network ◽  
Parameter Study ◽  
Local Minima ◽  
Bp Network ◽  
General Regression

The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.

Nelder-Mead Evolutionary Hybrid Algorithms

2011 ◽  
pp. 1191-1196 ◽  
Author(s):  
Sanjoy Das
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Simplex Algorithm ◽  
Stochastic Methods ◽  
Great Success ◽  
Local Minima ◽  
Swarm Optimization

Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular as they are derivativefree techniques, are not as prone to getting trapped in local minima, and can be tailored specifically to suit any given problem. The performance of evolutionary algorithms can be improved further by adding a local search component to them. The Nelder-Mead simplex algorithm (Nelder & Mead, 1965) is a simple local search algorithm that has been routinely applied to improve the search process in evolutionary algorithms, and such a strategy has met with great success. In this article, we provide an overview of the various strategies that have been adopted to hybridize two wellknown evolutionary algorithms - genetic algorithms (GA) and particle swarm optimization (PSO).

Calculating all local minima on liquidus surfaces using the FactSage software and databases and the Mesh Adaptive Direct Search algorithm

2011 ◽  
Vol 43 (9) ◽  
pp. 1323-1330 ◽  
Author(s):  
Aimen E. Gheribi ◽  
Christian Robelin ◽  
Sébastien Le Digabel ◽  
Charles Audet ◽  
Arthur D. Pelton
Keyword(s):  
Search Algorithm ◽  
Direct Search ◽  
Local Minima ◽  
Mesh Adaptive Direct Search ◽  
Direct Search Algorithm ◽  
Factsage Software

scholarly journals Characterising the Atomic Structure of Mono-Metallic Nanoparticles from X-Ray Scattering Data Using Conditional Generative Models

2020 ◽  
Author(s):  
Andy Sode Anker ◽  
Emil T. S. Kjær ◽  
Erik B. Dam ◽  
Simon J. L. Billinge ◽  
Kirsten M. Ø. Jensen ◽  
...  
Keyword(s):  
Atomic Structure ◽  
Metallic Nanoparticles ◽  
Chemical Structure ◽  
Distance Geometry ◽  
Generative Models ◽  
Scattering Data ◽  
Face Centered Cubic ◽  
Distance Information ◽  
X Ray ◽  
Chemical Structures

The development of new nanomaterials for energy technologies is dependent on understanding the intricate relation between material properties and atomic structure. It is, therefore, crucial to be able to routinely characterise the atomic structure in nanomaterials, and a promising method for this task is Pair Distribution Function (PDF) analysis. The PDF can be obtained through Fourier transformation of x-ray total scattering data, and represents a histogram of all interatomic distances in the sample. Going from the distance information in the PDF to a chemical structure is an unassigned distance geometry problem (uDGP), and solving this is often the bottleneck in nanostructure analysis. In this work, we propose to use a Conditional Variational Autoencoder (CVAE) to automatically solve the uDGP to obtain valid chemical structures from PDFs. We use a simple model system of hypothetical mono-metallic nanoparticles containing up to 100 atoms in the face centered cubic (FCC) structure as a proof of concept. The model is trained to predict the assigned distance matrix (aDM) from a simulated PDF of the structure as the conditional input. We introduce a novel representation of structures by projecting them inside a unit sphere and adding additional anchor points or satellites to help in the reconstruction of the chemical structure. The performance of the CVAE model is compared to a Deterministic Autoencoder (DAE) showing that both models are able to solve the uDGP reasonably well. We further show that the CVAE learns a structured and meaningful latent embedding space which can be used to predict new chemical structures.

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