scholarly journals MODELING A STRATEGIC HUMAN ENGINEERING DESIGN PROCESS: HUMAN-INSPIRED HEURISTIC GUIDANCE THROUGH LEARNED VISUAL DESIGN AGENTS

2020 ◽  
Vol 1 ◽  
pp. 355-364
Author(s):  
L. Puentes ◽  
A. Raina ◽  
J. Cagan ◽  
C. McComb
Keyword(s):  
Deep Learning ◽  
Initial Data ◽  
Domain Knowledge ◽  
Design Space ◽  
Visual Design ◽  
Human Engineering ◽  
Design Task ◽  
Design Heuristics ◽  
Engineering Design Process ◽  
Heuristic Guidance

AbstractHuman designers often work in a visual design space, projecting step-by-step design progression through evolving mental images. The strategic evolution of that design leverages heuristics based on experience and domain knowledge. The methodology presented in this paper brings together the visual nature of design problem solving and design heuristics in a deep learning computational agent framework that emulates and enables human-mirrored design. When applied to a truss design task, results demonstrate superior results to those of human designers who provided the initial data.

scholarly journals Domain knowledge integration into deep learning for typhoon intensity classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maiki Higa ◽  
Shinya Tanahara ◽  
Yoshitaka Adachi ◽  
Natsumi Ishiki ◽  
Shin Nakama ◽  
...  
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Knowledge Integration ◽  
Cluster Formation ◽  
Satellite Image ◽  
Image Features ◽  
Feature Maps ◽  
Typhoon Intensity ◽  
Intensity Class ◽  
Intensity Classification

AbstractIn this report, we propose a deep learning technique for high-accuracy estimation of the intensity class of a typhoon from a single satellite image, by incorporating meteorological domain knowledge. By using the Visual Geometric Group’s model, VGG-16, with images preprocessed with fisheye distortion, which enhances a typhoon’s eye, eyewall, and cloud distribution, we achieved much higher classification accuracy than that of a previous study, even with sequential-split validation. Through comparison of t-distributed stochastic neighbor embedding (t-SNE) plots for the feature maps of VGG with the original satellite images, we also verified that the fisheye preprocessing facilitated cluster formation, suggesting that our model could successfully extract image features related to the typhoon intensity class. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to highlight the eye and the cloud distributions surrounding the eye, which are important regions for intensity classification; the results suggest that our model qualitatively gained a viewpoint similar to that of domain experts. A series of analyses revealed that the data-driven approach using only deep learning has limitations, and the integration of domain knowledge could bring new breakthroughs.

Exploring Biases Between Human and Machine Generated Designs

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Christian E. Lopez ◽  
Scarlett R. Miller ◽  
Conrad S. Tucker
Keyword(s):  
Computer Simulation ◽  
Deep Learning ◽  
Domain Knowledge ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Design Tools ◽  
Generative Design ◽  
Generate Functional ◽  
Intended Function ◽  
2D Sketches

The objective of this work is to explore the possible biases that individuals may have toward the perceived functionality of machine generated designs, compared to human created designs. Toward this end, 1187 participants were recruited via Amazon mechanical Turk (AMT) to analyze the perceived functional characteristics of both human created two-dimensional (2D) sketches and sketches generated by a deep learning generative model. In addition, a computer simulation was used to test the capability of the sketched ideas to perform their intended function and explore the validity of participants' responses. The results reveal that both participants and computer simulation evaluations were in agreement, indicating that sketches generated via the deep generative design model were more likely to perform their intended function, compared to human created sketches used to train the model. The results also reveal that participants were subject to biases while evaluating the sketches, and their age and domain knowledge were positively correlated with their perceived functionality of sketches. The results provide evidence that supports the capabilities of deep learning generative design tools to generate functional ideas and their potential to assist designers in creative tasks such as ideation.

An Explorative Study of Virtual Product Placement

2011 ◽  
pp. 122-147
Author(s):  
Chia-Hu Chang ◽  
Ja-Ling Wu
Keyword(s):  
Domain Knowledge ◽  
Design Space ◽  
State Of The Art ◽  
Product Placement ◽  
The State ◽  
Explorative Study ◽  
Advertising Message ◽  
Computational Aesthetics ◽  
The Right ◽  
Virtual Product

With the aid of content-based multimedia analysis, virtual product placement opens up new opportunities for advertisers to effectively monetize the existing videos in an efficient way. In addition, a number of significant and challenging issues are raising accordingly, such as how to less-intrusively insert the contextually relevant advertising message (what) at the right place (where) and the right time (when) with the attractive representation (how) in the videos. In this chapter, domain knowledge in support of delivering and receiving the advertising message is introduced, such as the advertising theory, psychology and computational aesthetics. We briefly review the state of the art techniques for assisting virtual product placement in videos. In addition, we present a framework to serve the virtual spotlighted advertising (ViSA) for virtual product placement and give an explorative study of it. Moreover, observations about the new trend and possible extension in the design space of virtual product placement will also be stated and discussed. We believe that it would inspire the researchers to develop more interesting and applicable multimedia advertising systems for virtual product placement.

scholarly journals Zebrafish Embryo Vessel Segmentation Using a Novel Dual ResUNet Model

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Kun Zhang ◽  
Hongbin Zhang ◽  
Huiyu Zhou ◽  
Danny Crookes ◽  
Ling Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
Zebrafish Embryo ◽  
Fluorescent Protein ◽  
Vessel Segmentation ◽  
Dual Model ◽  
Shape Constraint ◽  
Learning Framework ◽  
Robust Segmentation ◽  
Characteristic Features

Zebrafish embryo fluorescent vessel analysis, which aims to automatically investigate the pathogenesis of diseases, has attracted much attention in medical imaging. Zebrafish vessel segmentation is a fairly challenging task, which requires distinguishing foreground and background vessels from the 3D projection images. Recently, there has been a trend to introduce domain knowledge to deep learning algorithms for handling complex environment segmentation problems with accurate achievements. In this paper, a novel dual deep learning framework called Dual ResUNet is developed to conduct zebrafish embryo fluorescent vessel segmentation. To avoid the loss of spatial and identity information, the U-Net model is extended to a dual model with a new residual unit. To achieve stable and robust segmentation performance, our proposed approach merges domain knowledge with a novel contour term and shape constraint. We compare our method qualitatively and quantitatively with several standard segmentation models. Our experimental results show that the proposed method achieves better results than the state-of-art segmentation methods. By investigating the quality of the vessel segmentation, we come to the conclusion that our Dual ResUNet model can learn the characteristic features in those cases where fluorescent protein is deficient or blood vessels are overlapped and achieves robust performance in complicated environments.

Computer-Automated Evolution of an X-Band Antenna for NASA's Space Technology 5 Mission

2011 ◽  
Vol 19 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Gregory. S. Hornby ◽  
Jason D. Lohn ◽  
Derek S. Linden
Keyword(s):  
Evolutionary Algorithms ◽  
Domain Knowledge ◽  
Current Practice ◽  
Design Space ◽  
Fitness Function ◽  
Antenna Design ◽  
Space Technology ◽  
X Band ◽  
Spacecraft Orbit

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASA's Space Technology 5 (ST5) spacecraft. Two evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three ST5 spacecraft, which were successfully launched into space on March 22, 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any application and is the first computer-evolved hardware in space.

scholarly journals CRISPRLearner: A Deep Learning-Based System to Predict CRISPR/Cas9 sgRNA On-Target Cleavage Efficiency

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1478 ◽  
Author(s):  
Giovanni Dimauro ◽  
Pierpasquale Colagrande ◽  
Roberto Carlucci ◽  
Mario Ventura ◽  
Vitoantonio Bevilacqua ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Its Sequence ◽  
Target Genome ◽  
Spearman Correlation ◽  
Design Task ◽  
Cleavage Efficiency ◽  
Sgrna Design

CRISPRLearner, the system presented in this paper, makes it possible to predict the on-target cleavage efficiency (also called on-target knockout efficiency) of a given sgRNA sequence, specifying the target genome that this sequence is designed for. After efficiency prediction, the researcher can evaluate its sequence and design a new one if the predicted efficiency is low. CRISPRLearner uses a deep convolutional neural network to automatically learn sequence determinants and predict the efficiency, using pre-trained models or using a model trained on a custom dataset. The convolutional neural network uses linear regression to predict efficiency based on efficiencies used to train the model. Ten different models were trained using ten different gene datasets. The efficiency prediction task attained an average Spearman correlation higher than 0.40. This result was obtained using a data augmentation technique that generates mutations of a sgRNA sequence, maintaining the efficiency value. CRISPRLearner supports researchers in sgRNA design task, predicting a sgRNA on-target knockout efficiency.

scholarly journals SCFont: Structure-Guided Chinese Font Generation via Deep Stacked Networks

2019 ◽  
Vol 33 ◽  
pp. 4015-4022 ◽  
Author(s):  
Yue Jiang ◽  
Zhouhui Lian ◽  
Yingmin Tang ◽  
Jianguo Xiao
Keyword(s):  
Deep Learning ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Automatic Generation ◽  
Chinese Characters ◽  
Generation System ◽  
Model Learning ◽  
High Quality ◽  
Large Numbers ◽  
Quantitative Assessments

Automatic generation of Chinese fonts that consist of large numbers of glyphs with complicated structures is now still a challenging and ongoing problem in areas of AI and Computer Graphics (CG). Traditional CG-based methods typically rely heavily on manual interventions, while recentlypopularized deep learning-based end-to-end approaches often obtain synthesis results with incorrect structures and/or serious artifacts. To address those problems, this paper proposes a structure-guided Chinese font generation system, SCFont, by using deep stacked networks. The key idea is to integrate the domain knowledge of Chinese characters with deep generative networks to ensure that high-quality glyphs with correct structures can be synthesized. More specifically, we first apply a CNN model to learn how to transfer the writing trajectories with separated strokes in the reference font style into those in the target style. Then, we train another CNN model learning how to recover shape details on the contour for synthesized writing trajectories. Experimental results validate the superiority of the proposed SCFont compared to the state of the art in both visual and quantitative assessments.

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