scholarly journals Evaluation and Design Method for Product Form Aesthetics Based on Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Aimin Zhou ◽  
Hongbin Liu ◽  
Shutao Zhang ◽  
Jinyan Ouyang
Keyword(s):  
Deep Learning ◽  
Design Method ◽  
Product Form

scholarly journals Acoustic Structure Inverse Design and Optimization Using Deep Learning

2021 ◽  
Author(s):  
Xuecong Sun ◽  
Han Jia ◽  
Yuzhen Yang ◽  
Han Zhao ◽  
Yafeng Bi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Acoustic Waves ◽  
Design Method ◽  
Helmholtz Resonator ◽  
Structure Design ◽  
Acoustic Structure ◽  
Design And Optimization ◽  
Wide Range ◽  
Potential Applications ◽  
Coupling Parameters

Abstract From ancient to modern times, acoustic structures have been used to control the propagation of acoustic waves. However, the design of acoustic structures has remained a time-consuming and computational resource-consuming iterative process. In recent years, deep learning has attracted unprecedented attention for its ability to tackle hard problems with large datasets, achieving state-of-the-art results in various tasks. In this work, an acoustic structure design method is proposed based on deep learning. Taking the design of multiorder Helmholtz resonator as an example, we experimentally demonstrate the effectiveness of the proposed method. Our method is not only able to give a very accurate prediction of the geometry of acoustic structures with multiple strong-coupling parameters, but also capable of improving the performance of evolutionary approaches in optimization for a desired property. Compared with the conventional numerical methods, our method is more efficient, universal and automatic, and it has a wide range of potential applications, such as speech enhancement, sound absorption and insulation.

scholarly journals Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning

2020 ◽  
Vol 10 (15) ◽  
pp. 5284 ◽  
Author(s):  
Houdi Xiao ◽  
Zhipeng Qu ◽  
Mingyun Lv ◽  
Yi Jiang ◽  
Chuanzhi Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Design Method ◽  
Pattern Generation ◽  
Generation Process ◽  
The Future ◽  
Short Time ◽  
New Generation ◽  
The Military ◽  
Self Adaptive

Traditional digital camouflage is mainly designed for a single background and state. Its camouflage performance is appealing in the specified time and place, but with the change of place, season, and time, its camouflage performance is greatly weakened. Therefore, camouflage technology, which can change with the environment in real-time, is the inevitable development direction of the military camouflage field in the future. In this paper, a fast-self-adaptive digital camouflage design method based on deep learning is proposed for the new generation of adaptive optical camouflage. Firstly, we trained a YOLOv3 model that could identify four typical military targets with mean average precision (mAP) of 91.55%. Secondly, a pre-trained deepfillv1 model was used to design the preliminary camouflage texture. Finally, the preliminary camouflage texture was standardized by the k-means algorithm. The experimental results show that the camouflage pattern designed by our proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance in optical camouflage. Meanwhile, the whole pattern generation process takes a short time, less than 0.4 s, which meets the camouflage design requirements of the near-real-time camouflage in the future.

Target-Specific Drug Design Method Combining Deep Learning and Water Pharmacophore

2020 ◽  
Author(s):  
Minsup Kim ◽  
Kichul Park ◽  
Wonsang Kim ◽  
Sangwon Jung ◽  
Art E. Cho
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Design Method ◽  
Specific Drug

scholarly journals New Design Method for Fabricating Multilayer Membranes Using CO2-Assisted Polymer Compression Process

Molecules ◽  
2020 ◽  
Vol 25 (24) ◽  
pp. 5786
Author(s):  
Takafumi Aizawa
Keyword(s):  
Experimental Data ◽  
Deep Learning ◽  
Design Method ◽  
Sufficient Accuracy ◽  
Compression Process ◽  
Compression Behavior ◽  
Multilayer Membranes ◽  
Learning Data

It was verified that deep learning can be used in creating multilayer membranes with multiple porosities using the CO2-assisted polymer compression (CAPC) method. To perform training while reducing the number of experimental data as much as possible, the experimental data of the compression behavior of two layers were expanded to three layers for training, but sufficient accuracy could not be obtained. However, the accuracy was dramatically improved by adding the experimental data of the three layers. The possibility of only simulating process results without the necessity for a model is a merit unique to deep learning. Overall, in this study, the results show that by devising learning data, deep learning is extremely effective in designing multilayer membranes using the CAPC method.

scholarly journals Deep Learning Enabled Nanophotonics

2020 ◽  
Author(s):  
Lujun Huang ◽  
Lei Xu ◽  
Andrey E. Miroshnichenko
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
High Performance ◽  
Recent Progress ◽  
Design Method ◽  
Computation Time ◽  
Chiral Metamaterials ◽  
Nanophotonic Devices ◽  
Optical Media ◽  
Optical Neural Networks

Deep learning has become a vital approach to solving a big-data-driven problem. It has found tremendous applications in computer vision and natural language processing. More recently, deep learning has been widely used in optimising the performance of nanophotonic devices, where the conventional computational approach may require much computation time and significant computation source. In this chapter, we briefly review the recent progress of deep learning in nanophotonics. We overview the applications of the deep learning approach to optimising the various nanophotonic devices. It includes multilayer structures, plasmonic/dielectric metasurfaces and plasmonic chiral metamaterials. Also, nanophotonic can directly serve as an ideal platform to mimic optical neural networks based on nonlinear optical media, which in turn help to achieve high-performance photonic chips that may not be realised based on conventional design method.

Research on Design Method of Electromechanical Product Form Based on MA-GA

2011 ◽  
Vol 55-57 ◽  
pp. 1350-1356
Author(s):  
Xiao An Yang ◽  
Guang Long Sun ◽  
Hao Chen ◽  
Xiao Hong Liu ◽  
Wei She Zhang
Keyword(s):  
Design Method ◽  
Fitness Function ◽  
Product Form ◽  
Product Modeling ◽  
Application Process ◽  
Bp Network ◽  
Form Design ◽  
Product Form Design ◽  
Form Features ◽  
Form Scheme

In electromechanical products form design, there still exist problems such as CAD supporting incompletely and low-usage of existent excellent case form information, this paper proposed a comprehensive method of product form design, which integrated morphological matrix, BP network, Analytical Hierarchy Process (AHP), Gray Relating Analyze (GRA) and Genetic Algorithms. According to the heritability of product form features, this method deconstructed case product modeling components and form elements to obtain the form features, then the features and feature values of product form were represented by morphologic matrix, finally product form design scheme were combined with reasonable solutions. Grading method and AHP were used to quantify the modeling components feature, form elements and perceptual image vocabularies of case product, and a BP network model was trained for form scheme image prediction. This article constructed the genetic algorithm fitness function with the weighed grey relation grade to optimize the product form scheme based on customers’ requirements. VC++ was applied to develop the design software, the design of electric bicycle form scheme was taken to illustrate the application process of this method.

scholarly journals Evolutionary design of molecules based on deep learning and a genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngchun Kwon ◽  
Seokho Kang ◽  
Youn-Suk Choi ◽  
Inkoo Kim
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Deep Learning ◽  
Design Method ◽  
Network Models ◽  
Molecular Structures ◽  
Evolutionary Design ◽  
Neural Network Models ◽  
Practical Challenge ◽  
Significant Attention

AbstractEvolutionary design has gained significant attention as a useful tool to accelerate the design process by automatically modifying molecular structures to obtain molecules with the target properties. However, its methodology presents a practical challenge—devising a way in which to rapidly evolve molecules while maintaining their chemical validity. In this study, we address this limitation by developing an evolutionary design method. The method employs deep learning models to extract the inherent knowledge from a database of materials and is used to effectively guide the evolutionary design. In the proposed method, the Morgan fingerprint vectors of seed molecules are evolved using the techniques of mutation and crossover within the genetic algorithm. Then, a recurrent neural network is used to reconstruct the final fingerprints into actual molecular structures while maintaining their chemical validity. The use of deep neural network models to predict the properties of these molecules enabled more versatile and efficient molecular evaluations to be conducted by using the proposed method repeatedly. Four design tasks were performed to modify the light-absorbing wavelengths of organic molecules from the PubChem library.

scholarly journals Research on Urban Renewal Public Space Design Based on Convolutional Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jixin Wan ◽  
Huosai Shi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Public Space ◽  
Urban Space ◽  
Design Method ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Space Design ◽  
Generation Mode ◽  
The City

By establishing a database of urban space cases, machine learning algorithms and deep learning algorithms can be used to train computers to learn how to design urban spaces. Based on the basic concepts of machine learning and deep learning and their procedural logic, this paper explores the generation mode of traffic road network, neighborhood space form, and building function layout of urban space and uses the northern extension of the central green axis of the city as an application case to confirm its feasibility in order to seek a set of artificial intelligence-based urban space generation design method and provide a new idea for the innovative development of urban design methods.

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