Prediction and Inverse Design of Structural Colors of Nanoparticle Systems via Deep Neural Network

Noniridescent and nonfading structural colors generated from metallic and dielectric nanoparticles with extraordinary optical properties hold great promise in applications such as image display, color printing, and information security. Yet, due to the strong wavelength dependence of optical constants and the radiation pattern, it is difficult and time-consuming to design nanoparticles with the desired hue, saturation, and brightness. Herein, we combined the Monte Carlo and Mie scattering simulations and a bidirectional neural network (BNN) to improve the design of gold nanoparticles’ structural colors. The optical simulations provided a dataset including color properties and geometric parameters of gold nanoparticle systems, while the BNN was proposed to accurately predict the structural colors of gold nanoparticle systems and inversely design the geometric parameters for the desired colors. Taking the human chromatic discrimination ability as a criterion, our proposed approach achieved a high accuracy of 99.83% on the predicted colors and 98.5% on the designed geometric parameters. This work provides a general method to accurately and efficiently design the structural colors of nanoparticle systems, which can be exploited in a variety of applications and contribute to the development of advanced optical materials.

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Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation

Journal of High Energy Physics ◽

10.1007/jhep05(2021)208 ◽

2021 ◽

Vol 2021 (5) ◽

Author(s):

Garvita Agarwal ◽

Lauren Hay ◽

Ia Iashvili ◽

Benjamin Mannix ◽

Christine McLean ◽

...

Keyword(s):

Network Performance ◽

Deep Neural Network ◽

Relevant Information ◽

Jet Substructure ◽

Discrimination Ability ◽

Low Level ◽

Performance Improvements ◽

Intermediate Layers ◽

Explainable Ai ◽

General Method

Abstract A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs (“eXpert AUGmented” variables, or XAUG variables), then apply layerwise relevance propagation (LRP) to networks both with and without XAUG variables. The XAUG variables are concatenated with the intermediate layers after network-specific operations (such as convolution or recurrence), and used in the final layers of the network. The results of comparing networks with and without the addition of XAUG variables show that XAUG variables can be used to interpret classifier behavior, increase discrimination ability when combined with low-level features, and in some cases capture the behavior of the classifier completely. The LRP technique can be used to find relevant information the network is using, and when combined with the XAUG variables, can be used to rank features, allowing one to find a reduced set of features that capture part of the network performance. In the studies presented, adding XAUG variables to low-level DNNs increased the efficiency of classifiers by as much as 30-40%. In addition to performance improvements, an approach to quantify numerical uncertainties in the training of these DNNs is presented.

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Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

Complexity ◽

10.1155/2020/5896813 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Yuxiang Wang ◽

Zhangwei Chen ◽

Hongfei Zu ◽

Xiang Zhang ◽

Chentao Mao ◽

...

Keyword(s):

Neural Network ◽

Manufacturing Systems ◽

Robot Manipulator ◽

Joint Stiffness ◽

Calibration Method ◽

Geometric Parameters ◽

Heavy Load ◽

Positioning Accuracy ◽

Robot Positioning ◽

Residual Errors

The positioning accuracy of a robot is of great significance in advanced robotic manufacturing systems. This paper proposes a novel calibration method for improving robot positioning accuracy. First of all, geometric parameters are identified on the basis of the product of exponentials (POE) formula. The errors of the reduction ratio and the coupling ratio are identified at the same time. Then, joint stiffness identification is carried out by adding a load to the end-effector. Finally, residual errors caused by nongeometric parameters are compensated by a multilayer perceptron neural network (MLPNN) based on beetle swarm optimization algorithm. The calibration is implemented on a SIASUN SR210D robot manipulator. Results show that the proposed method possesses better performance in terms of faster convergence and higher precision.

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Predicting geometric parameters of assemblies with neural network models

Journal of Physics Conference Series ◽

10.1088/1742-6596/1096/1/012198 ◽

2018 ◽

Vol 1096 ◽

pp. 012198 ◽

Cited By ~ 3

Author(s):

M A Bolotov ◽

V A Pechenin ◽

N V Ruzanov ◽

I A Grachev

Keyword(s):

Neural Network ◽

Network Models ◽

Geometric Parameters ◽

Neural Network Models

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Luminescent photonic crystals with multi-functionality and tunability

Chemical Science ◽

10.1039/c6sc01703g ◽

2016 ◽

Vol 7 (9) ◽

pp. 5692-5698 ◽

Cited By ~ 18

Author(s):

Hong Wang ◽

Xinggui Gu ◽

Rongrong Hu ◽

Jacky W. Y. Lam ◽

Deqing Zhang ◽

...

Keyword(s):

Photonic Crystals ◽

Aggregation Induced Emission ◽

Structural Colors ◽

Response To Water ◽

General Method

We develop a general method to incorporate aggregation-induced emission luminogens into photonic crystals (PCs) and the resulting luminescent PCs display diverse structural colors in response to water stimulation.

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Redox-Active Ionic-Liquid-Assisted One-Step General Method for Preparing Gold Nanoparticle Thin Films: Applications in Refractive Index Sensing and Catalysis

Langmuir ◽

10.1021/la103084t ◽

2010 ◽

Vol 26 (22) ◽

pp. 17568-17580 ◽

Cited By ~ 17

Author(s):

Enakshi Dinda ◽

Md. Harunar Rashid ◽

Mrinmoy Biswas ◽

Tarun K. Mandal

Keyword(s):

Thin Films ◽

Refractive Index ◽

Ionic Liquid ◽

Gold Nanoparticle ◽

Refractive Index Sensing ◽

Redox Active ◽

One Step ◽

General Method

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Neural Network Identification and Control of a Parametrically Excited Structural Dynamic Model of an F-15 Tail Section

Shock and Vibration ◽

10.1155/2000/530231 ◽

2000 ◽

Vol 7 (6) ◽

pp. 355-361 ◽

Cited By ~ 3

Author(s):

Ayman A. El-Badawy ◽

Ali H. Nayfeh ◽

Hugh Van Landingham

Keyword(s):

Neural Network ◽

Computer Simulation ◽

Dynamic Model ◽

Parametric Excitation ◽

Vibration Suppression ◽

Great Promise ◽

Structural Dynamic ◽

Neural Network Controller ◽

Structural Dynamic Model ◽

Computer Simulation Studies

We investigated the design of a neural-network-based adaptive control system for a smart structural dynamic model of the twin tails of an F-15 tail section. A neural network controller was developed and tested in computer simulation for active vibration suppression of the model subjected to parametric excitation. First, an emulator neural network was trained to represent the structure to be controlled and thus used in predicting the future responses of the model. Second, a neurocontroller to determine the necessary control action on the structure was developed. The control was implemented through the application of a smart material actuator. A strain gauge sensor was assumed to be on each tail. Results from computer-simulation studies have shown great promise for control of the vibration of the twin tails under parametric excitation using artificial neural networks.

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Defect Characteristic Prediction of Pipeline by Means of Wavelet Neural Network Based on the Hierarchical Clustering Algorithm

2004 International Pipeline Conference, Volumes 1, 2, and 3 ◽

10.1115/ipc2004-0722 ◽

2004 ◽

Cited By ~ 5

Author(s):

Maoan Wei ◽

Shijiu Jin ◽

Likun Wang ◽

Yan Zhou

Keyword(s):

Neural Network ◽

Hierarchical Clustering ◽

Clustering Algorithm ◽

Great Influence ◽

Wavelet Neural Network ◽

Geometric Parameters ◽

Hierarchical Clustering Algorithm ◽

Defect Simulation ◽

The Relationship

It is very difficult to generalize the relationship between MFL signal and the defect geometric parameters of the pipeline because the relationship is nonlinear. Many applications of wavelet neural network on this field show that the defect geometric parameters can be obtained with this method to some extent. However, the initial centers have great influence on performance of the network. Hierarchical clustering algorithm is proposed in this paper and applied to classification of defect samples, centers selection and calculation of basis function width. With this algorithm, clusters similarity is computed to create tree structure and the perfect clustering is obtained. The sample set created from finite element defect simulation are used to train and validate the efficiency and reliability of the network based on hierarchical clustering algorithm. The experiment shows that the training speed and the prediction precision of the network can be improved simulataneously.

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A general method to predict the performance of closed pulsating heat pipe by artificial neural network

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2019.113761 ◽

2019 ◽

Vol 157 ◽

pp. 113761 ◽

Cited By ~ 7

Author(s):

Xuehui Wang ◽

Yuying Yan ◽

Xin Meng ◽

Guangming Chen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Pipe ◽

Pulsating Heat Pipe ◽

Artificial Neural ◽

General Method

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JointNet: A Common Neural Network for Road and Building Extraction

Remote Sensing ◽

10.3390/rs11060696 ◽

2019 ◽

Vol 11 (6) ◽

pp. 696 ◽

Cited By ~ 13

Author(s):

Zhengxin Zhang ◽

Yunhong Wang

Keyword(s):

Neural Network ◽

Receptive Field ◽

Loss Function ◽

Large Scale ◽

Connectivity Pattern ◽

Automatic Extraction ◽

Network Module ◽

Building Extraction ◽

Road Extraction ◽

General Method

Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method. We propose such a method, JointNet, which is a novel neural network to meet extraction requirements for both roads and buildings. The proposed method makes three contributions to road and building extraction: (1) in addition to the accurate extraction of small objects, it can extract large objects with a wide receptive field. By switching the loss function, the network can effectively extract multi-type ground objects, from road centerlines to large-scale buildings. (2) This network module combines the dense connectivity with the atrous convolution layers, maintaining the efficiency of the dense connection connectivity pattern and reaching a large receptive field. (3) The proposed method utilizes the focal loss function to improve road extraction. The proposed method is designed to be effective on both road and building extraction tasks. Experimental results on three datasets verified the effectiveness of JointNet in information extraction of road and building objects.

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