scholarly journals A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
P.-Y. Chen ◽  
C.-H. Chen ◽  
H. Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Problems ◽  
Computation Time ◽  
General Purpose ◽  
Optimization Process ◽  
Electromagnetic Properties ◽  
Design Parameters ◽  
Pass Filter ◽  
Low Pass Filter

This study proposes a neural network-family competition genetic algorithm (NN-FCGA) for solving the electromagnetic (EM) optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN) and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA) to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

Optimum Design of a High Power Density Magnetic-Gel Brake Base Using Neural Network Model and Genetic Algorithm

2010 ◽  
Vol 102-104 ◽  
pp. 184-188
Author(s):  
Hong Zhan Lv ◽  
Xi Chang Liang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Power Density ◽  
Network Model ◽  
Neural Network Model ◽  
High Power ◽  
Computation Time ◽  
Integrated Approach ◽  
Design Parameters ◽  
High Power Density

An integrated approach based on genetic algorithm (GA) and an artificial neural network (ANN) is presented for structural optimization of a high power density magneto-gel brake. For this method, the GA method is employed for obtaining the optimal configuration of the brake by minimizing the dimensions of the brake. Subsequently, a two-layer BP neural network model is trained to obtain the correlation between main design parameters and performance of the brake, and then it is used to predict the performance of the magnetic gel brake with high power density. The coupled method incorporating the ANN with GA can reduce substantially the computation time during optimizing brake performance. Meanwhile, the proposed method’s validity is demonstrated by comparisons between the experiment and existing data.

Driver Steering Control and a New Perspective on Car Handling Qualities

2005 ◽  
Vol 219 (10) ◽  
pp. 1041-1051 ◽  
Author(s):  
R S Sharp
Keyword(s):  
White Noise ◽  
Gaussian White Noise ◽  
Design Parameters ◽  
Steering Wheel ◽  
Steering Control ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Handling Qualities ◽  
Gaussian White Noise Process ◽  
Low Pass

The article is about steering control of cars by drivers, concentrating on following the lateral profile of the roadway, which is presumed visible ahead of the car. It builds on previously published work, in which it was shown how the driver's preview of the roadway can be combined with the linear dynamics of a simple car to yield a problem of discrete-time optimal-linear-control-theory form. In that work, it was shown how an optimal ‘driver’ of a linear car can convert the path preview sample values, modelled as deriving from a Gaussian white-noise process, into steering wheel displacement commands to cause the car to follow the previewed path with an attractive compromise between precision and ease. Recognizing that real roadway excitation is not so rich in high frequencies as white-noise, a low-pass filter is added to the system. The white-noise sample values are filtered before being seen by the driver. Numerical results are used to show that the optimal preview control is unaltered by the inclusion of the low-pass filter, whereas the feedback control is affected diminishingly as the preview increases. Then, using the established theoretical basis, new results are generated to show time-invariant optimal preview controls for cars and drivers with different layouts and priorities. Tight and loose controls, representing different balances between tracking accuracy and control effort, are calculated and illustrated through simulation. A new performance criterion with handling qualities implications is set up, involving the minimization of the preview distance required. The sensitivities of this distance to variations in the car design parameters are calculated. The influence of additional rear wheel steering is studied from the viewpoint of the preview distance required and the form of the optimal preview gain sequence. Path-following simulations are used to illustrate relatively high-authority and relatively low-authority control strategies, showing manoeuvring well in advance of a turn under appropriate circumstances. The results yield new insights into driver steering control behaviour and vehicle design optimization. The article concludes with a discussion of research in progress aimed at a further improved understanding of how drivers control their vehicles.

Neural Network-Based Metaheuristic Parameterization with Application to the Vehicle Matching Problem in Ride-Hailing Services

2019 ◽  
Vol 2673 (10) ◽  
pp. 311-320
Author(s):  
Arslan Ali Syed ◽  
Irina Gaponova ◽  
Klaus Bogenberger
Keyword(s):  
Neural Network ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Computation Time ◽  
Problem Instance ◽  
Neighborhood Search ◽  
Matching Problem ◽  
Solution Quality ◽  
Actual Problem ◽  
Parameter Values

The majority of transportation problems include optimizing some sort of cost function. These optimization problems are often NP-hard and have an exponential increase in computation time with the increase in the model size. The problem of matching vehicles to passenger requests in ride hailing (RH) contexts typically falls into this category.Metaheuristics are often utilized for such problems with the aim of finding a global optimal solution. However, such algorithms usually include lots of parameters that need to be tuned to obtain a good performance. Typically multiple simulations are run on diverse small size problems and the parameters values that perform the best on average are chosen for subsequent larger simulations.In contrast to the above approach, we propose training a neural network to predict the parameter values that work the best for an instance of the given problem. We show that various features, based on the problem instance and shareability graph statistics, can be used to predict the solution quality of a matching problem in RH services. Consequently, the values corresponding to the best predicted solution can be selected for the actual problem. We study the effectiveness of above described approach for the static assignment of vehicles to passengers in RH services. We utilized the DriveNow data from Bavarian Motor Works (BMW) for generating passenger requests inside Munich, and for the metaheuristic, we used a large neighborhood search (LNS) algorithm combined with a shareability graph.

A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK

2013 ◽  
Vol 37 (3) ◽  
pp. 459-465
Author(s):  
Chih-Ta Yen ◽  
Ing-Jr Ding ◽  
Zong-Wei Lai
Keyword(s):  
Neural Network ◽  
Digital Watermarking ◽  
Median Filter ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Noise Interference ◽  
Low Pass ◽  
Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh–Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First method, we used a low-pass filter and median filter to remove noise interferences. The second one, we used a back-propagation neural network algorithm to suppress noises. We removed nearly all noise and recovered the originally embedded watermarks of Walsh–Hadmard codes.

scholarly journals Detection of Computer Graphics Using Attention-Based Dual-Branch Convolutional Neural Network from Fused Color Components

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4743
Author(s):  
Peisong He ◽  
Haoliang Li ◽  
Hongxia Wang ◽  
Ruimei Zhang
Keyword(s):  
Neural Network ◽  
Computer Graphics ◽  
Convolutional Neural Network ◽  
Video Game ◽  
Detection Methods ◽  
Post Processing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Generalized Patterns ◽  
Color Components

With the development of 3D rendering techniques, people can create photorealistic computer graphics (CG) easily with the advanced software, which is of great benefit to the video game and film industries. On the other hand, the abuse of CGs has threatened the integrity and authenticity of digital images. In the last decade, several detection methods of CGs have been proposed successfully. However, existing methods cannot provide reliable detection results for CGs with the small patch size and post-processing operations. To overcome the above-mentioned limitation, we proposed an attention-based dual-branch convolutional neural network (AD-CNN) to extract robust representations from fused color components. In pre-processing, raw RGB components and their blurred version with Gaussian low-pass filter are stacked together in channel-wise as the input for the AD-CNN, which aims to help the network learn more generalized patterns. The proposed AD-CNN starts with a dual-branch structure where two branches work in parallel and have the identical shallow CNN architecture, except that the first convolutional layer in each branch has various kernel sizes to exploit low-level forensics traces in multi-scale. The output features from each branch are jointly optimized by the attention-based fusion module which can assign the asymmetric weights to different branches automatically. Finally, the fused feature is fed into the following fully-connected layers to obtain final detection results. Comparative and self-analysis experiments have demonstrated the better detection capability and robustness of the proposed detection compared with other state-of-the-art methods under various experimental settings, especially for image patch with the small size and post-processing operations.

Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

2008 ◽  
Vol 12 (3) ◽  
pp. 284-289 ◽  
Author(s):  
Jenn-Long Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Social Learning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Design Parameters ◽  
Search Process ◽  
Swarm Optimization ◽  
Learning Rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

scholarly journals Spatio-temporal prediction of suspended sediment concentration in the coastal zone using an artificial neural network and a numerical model

2012 ◽  
Vol 14 (3) ◽  
pp. 574-584 ◽  
Author(s):  
B. Bhattacharya ◽  
T. van Kessel ◽  
D. P. Solomatine
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Numerical Model ◽  
Series Data ◽  
Ann Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Temporal Prediction ◽  
Artificial Neural

A problem of predicting suspended particulate matter (SPM) concentration on the basis of wind and wave measurements and estimates of bed shear stress done by a numerical model is considered. Data at a location at 10 km offshore from Noordwijk in the Dutch coastal area is used. The time series data have been filtered with a low pass filter to remove short-term fluctuations due to noise and tides and the resulting time series have been used to build an artificial neural network (ANN) model. The accuracy of the ANN model during both storm and calm periods was found to be high. The possibilities to apply the trained ANN model at other locations, where the model is assisted by the correctors based on the ratio of long-term average SPM values for the considered location to that for Noordwijk (for which the model was trained), have been investigated. These experiments demonstrated that the ANN model's accuracy at the other locations was acceptable, which shows the potential of the considered approach.

scholarly journals Inverse design of organic light-emitting diode structure based on deep neural networks

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sanmun Kim ◽  
Jeong Min Shin ◽  
Jaeho Lee ◽  
Chanhyung Park ◽  
Songju Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Inverse Problem ◽  
Optical Properties ◽  
Extraction Efficiency ◽  
Optimization Problems ◽  
Light Extraction ◽  
Light Extraction Efficiency ◽  
Design Parameters ◽  
Light Emitting

Abstract The optical properties of thin-film light emitting diodes (LEDs) are strongly dependent on their structures due to light interference inside the devices. However, the complexity of the design space grows exponentially with the number of design parameters, making it challenging to optimize the optical properties of multilayer LEDs with rigorous electromagnetic simulations. In this work, we demonstrate an artificial neural network that can predict the light extraction efficiency of an organic LED structure in 30 ms, which is ∼103 times faster than the rigorous simulation in a single-treaded execution with root-mean-squared error of 1.86 × 10−3. The effective inference time per structure is brought down to ∼0.6 μs with unaltered error rate with parallelization. We also show that our neural networks can efficiently solve the inverse problem – finding a device design that exhibits the desired light extraction spectrum – within the similar time scale. We investigate the one-to-many mapping issue of the inverse problem and find that the degeneracy can be lifted by incorporating additional emission spectra at different observing angles. Furthermore, the forward neural network is combined with a conventional genetic algorithm to address additional large-scale optimization problems including maximization of light extraction efficiency and minimization of angle dependent color shift. Our approach establishes a platform for tackling computation-heavy optimization tasks with one-time computational cost.

scholarly journals Hybrid Integrated System for Air Bending Optimal Design

2015 ◽  
Vol 1 (1) ◽  
pp. 30
Author(s):  
Jaber Eid Abu Qudeiri ◽  
Fayiz Abu Khadra ◽  
Usama Umer
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Metal Forming ◽  
Optimization Problems ◽  
Fe Analysis ◽  
Integrated System ◽  
Computational Time ◽  
Optimal Solutions ◽  
Optimal Process ◽  
Search Spaces

Genetic algorithm (GA) is widely accepted method for handling optimization problems. GA can find optimal solutions for large and irregular search spaces. However, finding optimal solutions using GA is associated with high computational time when coupled with finite element (FE) code, since FE analysis should be applied to each individual of GA populations. A neural network metamodel (NNM) is introduced to reduce the computational time.GA utilizes the NNMas an approximation tool instead of FE. Application examples results show that the metamodelcan be used efficiently to obtainthe optimal process parameters of metal forming operations with large saving in time.

Effectiveness of Neural Network and Genetic Algorithm in Hull Form Design

2001 ◽  
Author(s):  
M. Reaz H. Khondoker ◽  
Chowdhury Mofizur Rahman ◽  
Mohammad Mahfuzul Islam
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Weight Matrix ◽  
Design Parameters ◽  
Close Relative ◽  
Convergence Criterion ◽  
Hull Form ◽  
Form Design ◽  
Hull Form Design ◽  
Improved Design

Abstract This paper aims at developing a fully automated hull form design technique employing an Neural Network and Genetic Algorithm methods resulting in accelerated convergence. For generating an input data that will be, by and large, a close relative of the desired hull, a linear relation has been assumed between the half breadth of different sections and principal dimensions (length, breadth, draft or (displacement)1/3) of a particular type of vessel. Compared to starting with a random value of the input, this technique resulted in faster convergence. The weight matrix for each of these parameters is produced from data obtained from the population. The half-breadth table for a new vessel can be obtained by multiplying the weight matrix with corresponding parameter. However, the half-breadth table obtained in such way may not provide the required displacement and speed of the vessel. Therefore, some readjustments of some of the principal dimensions are required. Neural Networks (Wasserman, 1989) has been used to find the required values of such improved design parameters (principal dimensions). The final design process consists of searching for the exact solution by examining several generations generated by the GA (Goldberg, 1989). The convergence criterion is the summed offset error, which is to be within the envelope defined by the tolerances. Since GA doesn’t guarantee fairness of the surface of the hull form, B-spline curve fitting method is used to obtain a fair hull. Thus, the hull form generated through this process is fully automated, accurate and having fair surface. The technique is also found to be an efficient one.

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