Computational Design _ Wenjie Huang

Portfolio

Computational Design Selected Works 2017-2021

Wenjie Huang Computer Science

Wenjie Huang Tel : +86 13719192209 Email : JankinHuang0302@163.com

EDUCATION 17-22

Architecture, Bachelor of Architecture in Architecture Shenzhen University, College of Architecture and Urban Planning

INTERNSHIPS 21-22

XKoolTech，Back-End development with Python and C# , Research on Deep Learning in Architecture

19

11 Architect，Research on Educational Architecture

CONTENT

P4 - 9

EXPERIENCE 21

2021 DigitalFUITURES: Workshops Artificial Intelligence & Big data Features prediction system Tongji Univerisity, Philip F.Yuan, Hao Zheng

21

Kaggle – Shopee Price Matching Competition Data exploratory analysis and Multimodal prediction

20

2020 DigitalFUITURES: Workshops Convolution NeuralNetwork in Architectural Design Tongji Univerisity, Philip F.Yuan, Hao Zheng

Multi-Modal Price Matching [Efficient-Net, NF-Net,Bert]

P10 - 17

Deep Learning + Design Projects [CNN,GAN,Big data Features Prediction System]

HONORS 21/20

DigitalFUITURES: Workshops excellent team member

21

Kaggle – Shopee Price Matching Competition Top11%

20

Second Prize in National Scholarship

19/20

First Prize Excellent Student Cadre Scholarship

SKILLS Skilled in Python development and Deep learning applications Skilled in Computational design and Architectural robotics

LANGUAGE Native in Mandarin Chinese, Native in Cantonese, Fluent in English

P18 - 23

Market Memory Storage [YOLO_v3,Object Detection,Architecture Design]

01 Multi-Modal Price Match Guarantee Efficient-Net, NF-Net,Bert Kaggle : Shopee-Price Match Guarantee Teammate : Zoey Xu zoeyxu@aliyun.com Duration : 2021.5 Competition : Team Work (Leader)

Do you scan online retailers in search of the best deals?In this competition, we applied our machine learning skills to build a model that predicts which items are the same products.

Captained my teammates to conduct data exploratory analysis, feature processing, building baseline (TFIDF+ResNet18+Result Splicing) and models optimization iteration.

Responsible for parameters adjustment of Finetune and TFIDF for BERT model, and NFNet training for CV, the similarity algorithm selection, models’ fusion and prediction results splicing, ranked top 11 among over 3000 teams.

ABSTRACT

IMAGE

Kaggle : Shopee-Price Match Guarantee

Efficientnet B3 + Efficientnet B5 + Nfnet l0

Description : Retail companies use a variety of methods to assure customers that their products are the cheapest. Among them is product matching, which allows a company to offer products at rates that are competitive to the same product sold by another retailer.

In this competition, well applied our machine learning skills to build a model that predicts which items are the same products.

The applications go far beyond Shopee or other retailers. We contributions to product matching could support more accurate product categorization and uncover marketplace spam.

Evaluation : F1 score = 2 × (Precision × Recall) / (Precision + Recall)

DATASETS 34250 (Image + Titles + Phash) 34250 images in the training set roughly 70,000 images in the hidden test set

[train/test].csv the training set metadata. posting_id - the ID code for the posting. image - the image id/md5sum. image_phash - a perceptual hash of the image. title - the product description for the posting. label_group - ID code for all postings that map to the same product. Not provided for the test set.

[train/test]images the images associated with the postings.(34250)

... × 34250

OVERVIEW My Solution in the Competition Top 11%

TEXT BERT + TFIDF

PRECDITION

CONCLUSION

KNN & Cosine Similarity

Limitations & Future works

KNN -k-nearest neighbors algorithm

COMPARISON Comparison to Top 1% Kernel in the Competition

Future Works : After comparing with the winning kernel of the competition. The most serious deficiency of our kernel is post-processing and vote in prediction. According to the prediction results of the optimal LB score model in the early stage, we found that there are a large number of unmatched samples in the statistical results, that is, the matching results only match themselves, so we can reduce the matching standard (increase the matching distance threshold or reduce the similarity threshold) Comparison: Baseline ( TFIDF + ResNet18 + Result Splicing) : LB Score: 0.653 Top 80% My Solution (EfficientNet-B3 + EfficientNet-B5 + Nfnet-l0 + TFIDF + BERT + (Multi-Modal)) : LB Score: 0.734 Top 11% Top Solution (Nfnet-l0 + swin-small + EfficientNet-B0 + BERT + Indonesian-BERT + Multlingual-v1 + TFIDF) : LB Score:0.746 Top 1%

02 Deep Learning + Design Projects CNN,GAN,Big data Features Prediction System Projects in DigitalFUTURES and XkoolTech Tutor : Hao Zheng,Philip.F.Yuan,Tai Li Duration : 2020-2021 Design : 2.2/2.3 Individual Work 2.1 Team Work (Leader)

2.1 Urban Big data Features Prediction System Using the API of Amap to collect POI and geographic data of Shanghai, combined with the popularity and housing prices crawled of the Lianjia agent as basic dataset, adopted pix2pixHD model to establish forecast model for second-hand housing distribution map.

2.2 Reform the Ancient Façade Used Style Transfer technology to explore the possible applications of deep CNN in architecture design, studied the façade style of the Parthenon Temple and successfully applied the ancient Greek architectural style to modern vehicles 2.3 Landscape Generation with GAN We collected 120 landscape planes on pinterest and ArchiDaily.

By training pix2pixHD model, the automatic generation of landscape planes is realized.

ABSTRACT

OVERVIEW

2.1 Urban Big data Features Prediction System

Data Processing + Generative Adversarial Networks (GAN)

Description : Using the API of Amap to collect POI and geographic data of Shanghai, combined with the popularity and housing prices crawled of the Lianjia agent as basic dataset, adopted pix2pixHD model to establish forecast model for secondhand housing distribution map.

DATASETS POI Data + Graph Data

POI (Points of Interest) Data (From Amap.api)

Index : Index of second-hand houses in Shanghai.(Total 6000) location: Longitude and latitude of each sample.

Hospnums: Number of hospitals within 1000m.

Subnumbs: Number of hospitals within 800m.

Edubumbs: Number of hospitals within 500m.

Total_Price: The price of the whole house.

Unit_Price: Unit price per square meter.

Follow_Count: Number of followers on lianjia.com.

Househeat: Househeat(θ) = α / log2(β） α = Number of followers on lianjia.com. β = Duration on sale. *Numbs are Based on the service radius of each POI point.

Graphs Data (From Baidu Map &ArcGis)

ALGORITHM Generative Adversarial Networks (GAN)

Encoder

Architectural outline of Shanghai

Decoder

Urban road Network of Shanghai

District Centroid Distance: Sum of coefficients of sample distance from each urban center. ( Closest 5 Urban Center)

Generator

Discriminator

DATA PROCESSING

MODEL TRAINING Ⅰ

Data Processing of POI Data of Shanghai Central Area

Several different attempts of model training

image segmentation (Training set) Input Feature

Input Label

POI_Combination_Img

POI_Segmentation

Househeat_Img

Househeat_Segmentation

8000×8000

225×512×512

8000×8000

225×512×512

Model 01 input

Combie/Househeat

POI_Image

real

Househeat_Image

synthesized

input

real

synthesized

epoch195 - epoch200

POI_Image: Subway only, 225 Segmentation

Result: Training set over fitting, Test set under fitting

Househeat_Image: Visualization based on thermal radius

Reason: Subway POI only is not comprehensive enough, and the visualization method of househeat has errors

Model: Pix2Pix, 200 epochs, Tesla V100 Combie/Unitprice

Model 02 input

real

synthesized

input

real

synthesized

Hospital_numbs

Edu_numbs POI_Image

District Centroid Distance

EDA and Structure Data

Househeat_Image

epoch75 - epoch80

POI_Image: The urban map of green space and river is added

Result: Training set under fitting

Househeat_Image: Visualization based on thermal radius

Reason: The visualization method of househeat has errors,It's hard to connect with the urban map.

Model: Pix2Pix, 200 epochs, Tesla V100

PRECDITION

MODEL TRAINING Ⅱ

Using model 5 to predict Househeat in Beijing

Several different attempts of model training

Model 03 input

POI_Image

real

Househeat_Image

synthesized

input

real

synthesized

epoch190 - epoch200

POI_Image: Full features, 50 selected Segmentation

Result: Training set over fitting, Test set under fitting

Househeat_Image: Visualization based on heat

Reason: The selected 50 fragments are easy to cause over fitting and poor generalization

Model: Pix2Pix, 200 epochs, Tesla V100

Model 04 input

POI_Image

real

Househeat_Image

synthesized

input

real

epoch09- epoch14

POI_Image: Full features, 225 Segmentation

Result: Training set under fitting

Househeat_Image: Visualization based on small radius

Reason: Too many black areas in the labels Delivered inaccurate information.

Model: Pix2Pix, 200 epochs, Tesla V100

synthesized

Model 05 input

POI_Image

Househeat_Image

real

synthesized

input

real

synthesized

epoch190 - epoch200

POI_Image: RGB_Full features, 225 Segmentation, with Mask.

Result: Good performance in training set and test set

Househeat_Image: Visualization based on Weighted value.

Reason: RGB color distinguishes different features well, which can also be learned by machines. Mask reduces interference in non residential areas

Model: Pix2Pix, 200 epochs, Tesla V100

03 Market Memory Storage YOLO_v3,Object Detection,Architecture Design

AI + Architecture Exhibition in Designsociety Tutor : Fei Qu fqu@szu.edu.cn Duration : 2021.12 Location : Shenzhen China Competition : Team Work (Leader)

Design society, china’s first dedicated cultural design hub, has opened its doors to the public in shenzhen. the new institution is housed within the sea world culture and arts center (SWCAC), a building designed by japanese architect fumihiko maki, and houses a new V&A gallery.

Shekou fishing Market will be demolished by the end of November 2020. Invited by Design society and Huawei cloud , we will build an art installation in the sea world culture and arts center (SWCAC) to continue Shekou people's memory of Shekou fishing Market. This art installation combining AI and architecture will be displayed in the museum.

We will place the camera in the market and use Yolo_v3 algorithm carries out human shape detection in real time.

Through stepping motors and art devices in the museum, we conveys the same sense of crowding to exhibitors as in Shekou market. This sense of crowding will also be preserved in the form of data.

ABSTRACT

OVERVIEW

AI × Architecture Interactive art installation

Market + Algorithm + Museum

Description : Shekou fishing Market will be demolished by the end of November 2020. Invited by Design society and Huawei cloud , we will build an art installation in the sea world culture and arts center (SWCAC) to continue Shekou people's memory of Shekou fishing Market. This art installation combining AI and architecture will be displayed in the museum.

Interactive: We will place the camera in the market and use Yolo_v3 algorithm carries out human shape detection in real time. Through stepping motors and art devices in the museum, we conveys the same sense of crowding to exhibitors as in Shekou market. This sense of crowding will also be preserved in the form of data.

SHEKOU Design Society Museum © Maki and Associates

ALGORITHM YOLO_v3 Object Detection

DBL×5

Darknet-53 without FC Layer DBL

Res1

Res2

Res8

Res8

Res4

416×416×3

DBL

Shekou Market since 1956

Design Society Museum since 2017

CAMERA LOCATION Three Cameras in Shekou Market to record People's activities

DBL

DBL

conv

Up Sampling

concat

DBL

DBL

conv

DBL×5

Yolo_v3_Structure

DBL

Up Sampling

concat

DBL

=

conv

BN

Res_unit Leaky relu

res unit

Y2 26×26×255

DBL

DBL

conv

DBL×5

Darknetconv2D_BN_Leaky

Y1 13×13×255

Y3 52×52×255

Resblock_body

=

DBL

DBL

Add

resn

=

Zero Padding

DBL

res unit Res_unit×n

YOLO_v3 Algorithm Structure

Video of Shekou Market

Yolo_v3 Loss Function ©Keyird

STRUCTURE Elastic Fabric + Stepper Motor + Prefabricated Frame

MOTION Controled by Programmable Logic Controller (PLC)

PLC Control the movement of the Stepper Motor

EXHIBITION Exhibition from December 2020 to February 2021

Market - Museum Movement in each time period

2D Plan Drawing of PLC