Eunu Kim | Selected Works 2021 by Eunu Kim

EUNU K I M

Architectural Designer + Computer Scientist Selected Works 2021

Table of Contents Computational

Drone Urbanism Simulator

HORIZONS (pp. 12 - 21)

(pp. 4 - 11)

50 New

(pp. 22

w Towns

2 - 31)

Architectural

Cadavre Exquis

Repurposing Kagawa Prefectural Gymnasium

(pp. 32 - 47)

(pp. 48 - 59)

Drone Urbanism Simulator (2020-2021) With the rapid development of unmanned control technology and revisions of the related rules, drones flying around in cities are going to be the reality sooner than we think. A number of traditional aircraft vendors such as Airbus and Boeing as well as new-born drone specilized manufacturers have already successfully demonstrated their capabilites to produce safe unmanned vehicles for passengers and logistics. Many cities have established tangible milestones to guarantee safe operations of drones. Lastly, goverment agencies such as FAA (Federal Aviation Administration) and NASA (National Aeronautics and Space Administration) have been aligning policies, rules, and standards towards the same goal. However, other than safety, there is another extremely important factor that must be considered in the process of shaping policies and designing infrastructures for drone urbanism: the impact of drones on quality-of-life of the residents. As drones cause noise, privacy violation, energy consumption, air pollution, and other adverse impacts on a city, introduction of urban drones without considering these factors is prone to degrade various urban experiences. Unfortunately, there has not been any tool that accurately projects the impacts of the drones. To aid this urgent matter, this projects aims to develop a highly extensible and adaptable tool that visualizes different aspects of quality-of-life of the urban residents that will be affected by the introduction of drones. The simulator acquires urban information (buildings, air spaces, etc.) in real-time from GIS database, provides flexibility to configure the infrastructures (parking facilities, aerodromes, drone routes, and so on), and analysizes and visualizes the impact on different parts of the city. * Performed as a part of Future of Air Travel project at Laboratory for Design Technology, Harvard Graduate School of Design Role in the project : Simulation engine development Collaborated with: Gavin Ruedisueli (UI development), Andrew Witt (Supervisor)

User Interface

Analyzer

Traffic Control (2 Types)

Route Generator

Drone Agents (3 Types)

Simulation Engine Building/Terrain Database

Infrastructure Database

Airspace Database

Online GIS Database

Drone Database

Pre-modeled 3D objects and specifications

Structure of Drone Urbanism Simulator

Region View

Add New City

Modify Existing City

Get Airspace Information

City View (Simulation View)

Add Aerodromes

Add Parking Structures

Toggle Overlay Layers

Modify Infrastructure

Add Restriction Zones

Drone Information

Landing Corridors Separation

Corridor Dro

Buildings Affected by Noise

Low Altitude Drones

ones

Drone Corridor

Simulator demo available at:

(2) Landing Granted

r1 do rri Co

1st Landing Stand-by Point

Land

uide ing G

e Guid ing Land

Nth Landing Stand-by Point Landing Guide

Landing Guide

State = Pending → Land

1st Landing Stand-by Point

ide

Co rri do r1

idor

N dor

rri do

Corr

i Corr Nth Landing Stand-by Point

State = Pending

(3) Landing

u ing G Land

(1) Landing Pended

Landing Granted

State = Move

Take-off Completed

State = Land → Wait

Aerodrome (Busy → Idle )

Aerodrome (Idle)

Queue Drone in 1st Landing Queue Drone in Nth Landing Queue

ff -o ke ed Ta ant Gr

Aerodr

Queue

Idle

Drone in 1st Landing Queue Drone in Nth Landing Queue

W ai Ex t Tim pir e ed r

Take-off

ff -o ke ed Ta ant Gr

Wait

Stand-by Point Reached

Pending

W ai Ex t Tim pir e ed r

Landing Point Reached

Land

De s Re tin ac at he ion d

Pending

Drone State Machine

ff -o ke ed Ta ant Gr

Wait

Move

g in d nd te La ran G

Drone State Machine

Landing Granted or Take-off Granted

Idle

Drone in

Stand-by Point Reached

Land

De s Re tin ac at he ion d

Idle

Drone in

Take-off

Landing Point Reached

Move

Queue

Take-off Stand-by Point Reached

Move De s Re tin ac at he ion d

Pen

Drone Sta

Landing Granted or Take-off Granted

Busy

Idle

Take-off Completed or Parking Completed

Landing or Take-o

Busy Take-off Completed or Parking Completed

Aerodrome State Machine

Idle

Take-off or Parkin

Aerodrome State Machine

Aerodrome S

Traffic Control Cycle of Aerod

(4) Wait Timer Expired and Take-off Granted

(5) Take-off Completed

do r1

Co rri do r1

N dor

N idor Corr

i Corr

Co rri

g Completed

Nth Landing Stand-by Point

uide

e Guid

Land

ing Land

ide Gu rture D e pa

Landing Guide

ing G

Nth Landing Stand-by Point

State = Take-off → Move

State = Wait→ Idle → Take-off Wait timer Expired

Take-off Completed

Take-off Granted Wait timer initialized

Aerodrome (Idle)

Aerodrome (Busy)

rome (Busy)

Queue

Idle

nding

Drone in 1st Landing Queue Drone in Nth Landing Queue

1st Landing Queue Nth Landing Queue

W ai Ex t Tim pir e ed r

ff -o ke ed Ta ant Gr

Wait Landing Point Reached

Land g in d nd te La ran G

ate Machine

Idle

W ai Ex t Tim pir e ed r

Take-off

ff -o ke ed Ta ant Gr

Wait

Stand-by Point Reached

Landing Point Reached

Move

Land

De s Re tin ac at he ion d

Pending

Completed ng Completed

State Machine

Idle

Wait

Stand-by Point Reached

Landing Point Reached

Move

Land

Pending

g in d nd te La ran G

Drone State Machine

Landing Granted or Take-off Granted

Busy

W ai Ex t Tim pir e ed r

Take-off

De s Re tin ac at he ion d

g in d nd te La ran G

Drone State Machine

Granted off Granted

Idle

Landing Granted or Take-off Granted

Busy Take-off Completed or Parking Completed

Idle

Busy Take-off Completed or Parking Completed

Aerodrome State Machine

Aerodrome State Machine Internal Signal Signal from Drone Signal from Aerodrome

drome and Parking Structure

HORIZONS (2018) HORIZONS is a journey into a city dreamed by neural networks. Inspired by the Ed Ruscha book “Every Building on the Sunset Strip,” the artists trained special AI software to generate an endless street view of a fantasy city. The piece has five related, infinite horizontal views, from top to bottom: (1) The images that the neural net learns from – from Hong Kong, the Netherlands, Cappadocia, and more (2) An endless list of possible patterns (3) The endless elevation of the dream city, composed of a selection of fantasy buildings (4) The unique “fingerprints” of each new building (5) An archive of every new building created by the neural net. HORIZONS invites you to step into a city that bridges human and machine imagination.

* This project has been exhibited at: 1) Le Laboratoire, Cambridge, MA 2) The Factory Contemporary Art Center, Ho Chi Minh City, Vietnam 3) Storrs Gallery, UNC Charlotte, Charlotte, NC Role in the project : AI development and training, training set curation, video production Collaborated with: Gavin Ruedisueli (video production), Andrew Witt (Supervisor), Tobias Nolte (Supervisor)

CMP Datatset · Background · Wall · Door · Window · Window Sill · Window Header · Shutter · Balcony · Trim · Cornice · Column · Entrance

Hong Kong (R=0, G=6, B=217) (R=13, G=61, B=251) (R=165, G=0, B=0) (R=0, G=117, B=255) (R=104, G=248, B=152) (R=29, G=255, B=221) (R=238, G=237, B=40) (R=184, G=255, B=56) (R=255, G=146, B=4) (R=255, G=68, B=1) (R=246, G=0, B=1) (R=0, G=201, B=255)

Frame House · Background · Wall · Door · Window · Window Sill · Window Header · Shutter · Balcony · Trim · Cornice · Structure · Entrance

· Background · Wall · Door · Window · Window Sill · Window Header · Shutter · Balcony · Trim · Cornice · Column · Entrance

(R= (R= (R= (R= (R= (R= (R= (R= (R= (R= (R= (R=

Paris (R=0, G=6, B=217) (R=13, G=61, B=251) (R=165, G=0, B=0) (R=0, G=117, B=255) (R=104, G=248, B=152) (R=29, G=255, B=221) (R=238, G=237, B=40) (R=184, G=255, B=56) (R=255, G=146, B=4) (R=255, G=68, B=1) (R=246, G=0, B=1) (R=0, G=201, B=255)

· Background · Wall · Door · Window · Balcony · Cornice · Storefront

(R= (R= (R= (R= (R= (R= (R=

Training Set o

=0, G=6, B=217) =13, G=61, B=251) =165, G=0, B=0) =0, G=117, B=255) =104, G=248, B=152) =29, G=255, B=221) =238, G=237, B=40) =184, G=255, B=56) =255, G=146, B=4) =255, G=68, B=1) =246, G=0, B=1) =0, G=201, B=255)

=0, G=6, B=217) =13, G=61, B=251) =165, G=0, B=0) =0, G=117, B=255) =184, G=255, B=56) =255, G=68, B=1) =0, G=201, B=255)

Amsterdam · Background · Wall · Door · Window · Window Sill · Window Header · Shutter · Balcony · Trim · Cornice · Column · Entrance

(R=0, G=6, B=217) (R=13, G=61, B=251) (R=165, G=0, B=0) (R=0, G=117, B=255) (R=104, G=248, B=152) (R=29, G=255, B=221) (R=238, G=237, B=40) (R=184, G=255, B=56) (R=255, G=146, B=4) (R=255, G=68, B=1) (R=246, G=0, B=1) (R=0, G=201, B=255)

Capadoccia · Background · Wall · Holes · Forground

of HORIZONS

(R=0, G=6, B=217) (R=13, G=61, B=251) (R=0, G=117, B=255) (R=255, G=68, B=1)

Outline Profile Generation

Facade Component Allocation

Elevation Generation

CMP Rectilinear Outline

Hong Kong Grid Mapping

Convex Outline

Amsterdam Subdivision

Concave Hull Outline

Frame House Graphical Pattern

Exisiting Building Profile

Capadoccia Paris

Facade Generation Pipeline

Concave Hull Convex

Rectilinear

Existing Profile

Outline Profile Generation

Grid Mapping

Subdivision

Graphical Pattern

Maps components onto a grid generated in an outline profile

Maps components onto subdivisions generated in an outline profile

Facade Component Allocation

Maps components to black areas of a black and white graphical pattern mapped into an outline profile

Generated Schematic

CMP

Hong Kong

Amsterdam

Frame House

Paris

Capadoccia

Generated Schematic

CMP

Hong Kong

Amsterdam

Frame House

Paris

Capadoccia

Rectilinear Outline + Graphical Pattern

Convex Polygon + Grid Mapping

Elevation Generation

· Select a cell (C) and place windows at: · Create a frame · Fill background · Fill outline · Create a grid map

· Place doors and entrances among the bottom cells

· Allocate cornices (Spans the entire witdh)

· Allocate columns (Spans the entire height)

· Repeat above until target number of windows are allocated · Paint the region difference between the original and offset outlint with the color of the wall · Find the curves that has no obstacles above to -y direction and extrude them to mark roof line

Grid Map Facade Component Allocation

· Create a frame · Fill background · Fill outline

· Create a plan tree map in the offset outline

· Allocate windows · Allocate head, sill and shutter adjcent to windows

· Allocate cornices · Allocate columns

· Allocate doors and entrances

· Find the curves that has no obstacles above to -y direction and extrude them to mark roof line

Subdivision Facade Component Allocation

· Fit image and center of the frame · Crop the parts outside outline

· Randomly choose black pixels and start flood fill with "window" color

· Fill background · Fill outline

· Choose a black pixel from the bottom row and start floodfill with the "door" color

· Color other components

· Color the region difference between the original and offset outline as "wall" color

Graphical Pattern Facade Component Allocation

Input

Original Output

Corrected Output

Input

Original Pair

Original Training Set

Original Output

Corrected Output

New Training Set

Enhancing Output Quality via Manual Correction and Additional Training

Building Figerprint (Color Map) Extraction

Video available at: https://player.vimeo.com/video/280089207

50 New Towns: Scanning, Classification and (re)Generation of Vernacular Architecture Typologies (2018) Morphological and planimetric studies of vernacular architecture significantly contribute to the invention of new typologies that well serves the traditional local needs reshaped by recent changes. Unfortunately, there is not an extensive documentation on most vernacular architectures, as they are usually in suburban settings which have drawn much less attention than urbans. Furthermore, the wide range of their taxonomy makes it difficult to be manually documented. This project presents a novel AI-based methodology to remedy the absence of documentation for vernacular architectures. Scanning aerial photos of suburbs, exploiting a neural network originally developed for biomedical image segmentation, the proposed methodology identifies different vernacular typologies. Taking the scan results and augmenting with existing samples of planimetric information of the typologies, it trains a generative adversarial neural network (GAN) that learns and generates geneology of the deep structures in relation to the formal aspects of the typologies. Through the analysis of the results, lastly, it creates the taxonomy of the vernacular architectures that captures the extensive variety of each typology.

* Performed as a part of “Evergrande Times New City” project, Office for Urbanization, Harvard Graduate School of Design Role in the project : AI system design and engineering, deep-structure generation, dataset preparation, annotation, documentation Collaborated with: Sharon Deng (annotation), Saif Haobsh (annotation, deep-structure generation, data visualization), Eunsu Kim (annotation, data visualization), Andrew Witt (Supervisor), Charles Waldheim (PI)

Initial Training

Stitching

Training

Scanning

Annotation

Additional Training

U-NET1

Tiling

Retraining

Scanning

Initial Scan

Additional Training

Retraining

Scanning

Scan after Additional Training

Boundary Extraction

Image-to-Vector Conversion

Boundary Extraction

Deep Structure Generation

Pix2Pix2

Footprint Analysis Training

Validation

Building Footprint Analysis

Deep Structure Generation

Data Generation Training

Validation

Deep Structure Generation 1) Ronneberger et. al., “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Computer Vision and Pattern Recognition 2015, Jul. 2015, Boston, MA, USA. 2) Isola et. al., “Image-to-Image Translation with Conditional Adversarial Networks,” Computer Vision and Pattern Recognition 2017, Jul. 2017, Honolulu, HI, USA.

Scan Examples

Scan Result 01 - Guanzhong Skinny Courtyard

Scan Result 04 -Fujian Tulou

Scan Result 02 - Han Large Courtyard

Scan Result 05 - Hakka Encircling Dragons

Scan Result 03 - Qinghai Nest Courtyard

27 Three-sided Skywell Scan Result 06 - Chaoshan

Scanned Image

Boundary Detection

Vector Conversion

Jin Multi-storied Courtyard

Qinghai Nest Courtyard

“Three-space” Dwelling

Hainan Courtyard Strings

Boundary Detection Procedure and Examples

Validation & Clean-up

Xinjiang Courtyard

Chaoshan Three-sided Skywell

Training (Existing Boudary & Deep Structure)

Raster Deep Structure Generation (Scanned Boudary to Deep Structure)

Raster Clean-up (Image Thresholding)

Jin Multi-storied Courtyard

Qinghai Nest Courtyard

“Three-space” Dwelling

Hainan Courtyard Strings

Deep Structure Generation Procedure and Examples

Vector Conversion (Image Thinning)

Xinjiang Courtyard

Chaoshan Three-sided Skywell

ARRAY 01 TYPE A-2

50 NEW TOWNS AGRARIAN TYPES SET A ARRAY 02 TYPE A-2 Caption:

Orientation x Area

ARRAY 01 TYPE A-5

officer: certain measures

item type: drawing item description: plans keywords: officer: certain measures

50 NEW TOWNS AGRARIAN TYPES SET A ARRAY 02 Caption: TYPE A-5 Orientation x Area

Lorem ipsum dolor sit amet, Jin Multi-storied Courtyard (Orientation - Area) consectetur adipiscing elit, Annotation:

Qinghai Nest Courtyar

sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Caption:

Annotation:

Aspect Ratio x Solid-Void ipsum dolor sit amet, Jin Multi-storied Courtyard (Aspect Ratio - Solid to Lorem Void Ratio) Ratio consectetur adipiscing elit,

sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Qinghai Nest Courtyard (Aspe

Caption: Aspect Ratio x Solid-Void Ratio

officer: certain measures

TYPE C-3

item type: drawing item description: plans keywords: officer: certain measures Annotation: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

50 NEW TOWNS AGRARIAN TYPES SET A ARRAY 02 Caption: TYPE xC-3 Orientation Area

rd (Orientation - Area)

item type: drawing item description: plans keywords: officer: certain measures Annotation:

Lorem ipsum dolor sit amet, Yunnan Three-sided Skywell (Orientation - Area) consectetur adipiscing elit,

sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Annotation: ect Ratio - Solid to Void Ratio) Lorem ipsum dolor sit amet,

consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Caption: Aspect Ratio x Solid-Void Ratio

Annotation: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

Yunnan Three-sided Skywell (Aspect Ratio - Solid to Void Ratio)

Cadavre Exquis: Suturing Buildings Using Digital Techniques (2020, GSD Thesis Project) In this era of new machine age, computational techniques are rapidliy expanding its territory in design process of architecture, questioning the boundary of agencies of human designers. Under this context, this thesis presents a design process consists of a series of computational techniques that generates volumetirc cadavre exquis. with the examination of inherent visual and spatial implications of each technique, this thesis seeks a reciprocal relationship between human and machine that resist the homogenizing force inherent to the computational techniques. Thesis Advisor: Andrew Witt

Cadavre Exquis Process

A. Breton and Artists, “Cadavre Exquis” (1928)

A. Breton and Artists, “Cadavre Exquis” (1938)

John Soane, “Bank of England Building” (1790 - 1827)

OMA, “Dutch Parliament Proposal” (1978)

Zaha Hadid Architects, “Antwerp’s Port Authority Extension (2016)

OMA, “Whitney Museum Extension” (2001)

Cadavre Exquis in Architecture

Collage Material

Massing

Circulation

Facade actual building plan/section

actual building texture

Building Geometry

Volumetric Nesting

Topological Analysis

Plan/Section

3d Pattern Blending

Building Cadavre Exquis Generation Methodology

3D Model of Boston

Architectural Documents Stock Feed: City of Boston

* 3D model is obtained from Boston Planning and Development Agency * Architectural (construction) documents are obtained from Inspectional Services Department of Boston Public Record Database

Vector Tweening

Measures : Footprint Aspect Ratio X Area-to-Height Ratio Median Building

Largest cluster

3D Scanning & Shape Comparison

Data Clustering and Selection

Collage Material Acquisition

10 Denton Street (Roslindale)

11 Melrose Street (Bay Village)

8 Kneeland Street (Chinatown)

15 Sheafe Street (North End)

197 Friend Street (West End)

23 Bartlett Street (Charlestown)

35 Charles Street (Hyde Park)

252 Marlborough Street (Back Bay)

52 Chauncy Street (Downtown)

1175 Boylston Street (Fenway)

29 Easton Street (Allston)

35 Draper Street (Dorchester)

103 School Street (Roxbury)

63 Revere Street (Beacon Hill)

15 Sheafe Street (North End)

83 Paul Gore Street (Jamaica Plain)

86 Ormond Strret (Mattapan)

111 Woodard Street (West Roxbury)

105 West Concord Street (South End)

15 Sheafe Street (North End)

259 Fanueil Street (Brighton)

195 Heath Street (Mission Hill)

239 Princeton Street (East Boston)

Acquired Collage Materials

Initial Massing: 3D Nesting Algorithm

Nesting Iterations

Initial Massing Arrangement

Initial Massing before Circulation Configuration

Circulation Configuration: Connectivity Analysis

Extended Cores Existing Cores Existing Internal Vertical Circulation Extended Internal Vertical Circulation Added Exterior Circulation

Configured Circulations

Facade 3D Blending Method

Blended Facade (North Elevation)

South El

levation

Original Buildings

Matching Blended Facade

Pattern Recognition

Pattern Tweening

Planmaking Through Pa

Plan Pers

Nesting Result

Content-Aware fill

Manual Blending

Vector Tweening

Figure/Ground

Poche

Figure/Ground

Poche

Figure/Ground

Poche

Figure/Ground

Poche

Figure/Ground

Poche

Figure/Ground

Poche

attern Blending Methods

spectives

Plan Pers

spectives

Pattern Transit

Section O

tion in Section

Oblique

Repurposing Kagawa Prefectural Gymnasium for Setouchi Art Triennale Archive (2019) Setouchi art triennale is one of the most significant events in Kagawa prefecture. The art triennale is distinguished from other art festivals around the world, as it takes place on the islands in the area and exhibits numerous experiential art projects. However, these special features of the triennale pose difficulty in archiving the works because of their sizes and tight relationship with the original site. Partly due to this reason, the trajectories of the past triennales have not been visibly and even further, tangibly accessible. To preserve valuable memories of triennale, this project proposes to repurpose Kagawa prefectural gymnasium into a dedicated archive for Setouchi art triennale. Taking advantage of the iconic presence of the gym, it aims to create a symbol that signifies the new identity of the prefecture. Without intervening with the existing structure, the project removes the existing rectilinear walls and inserts new infill whose forms resemble the profile of the islands participating in the triennale. On the site, the profiles of islands that may participate in triennale in future form a pavilion. The curvy paths, not straight corridors, formed between the volumes create the experience of freely flowing around as if people meander around the islands during the actual triennale. * Architecture Design Studio Project at Harvard Graduate School of Design Instrcuctor: Toshiko Mori

Current - Courtesy of Take, licensed under CC BY-NC-ND 2.0, Link

Proposed - Outdoor Pavilion Rendering (Ground Floor)

Program

Sub Category

Requirement

Size

Archive Exhibition

Photo Exhibition

Flat Wall

500 m2

Video Exhibition

Theater/Seatings

Document Exhibition

Tables

Seats 500 X 2 250 m2

Lobby

Triennale Archive

Research

Service

450 m2

Film Archive

Shelves

Controlled Light

100 m2

Document/Photo Archive

Shelves

Controlled Light

125 m2

Document Production

Shelves, Desks

100 m2

Film Editing

Facility, Controlled Light

120 m2

Archive Organization

Desks

85 m2

Reading Room

Desks, Shelves

110 m2

Ticketing

50 m2

Cafe

100 m2

Docent Office

120 m2

Resting Area

80 m2

Loading Zone/Dock

100 m2

Receving

50 m2

Bathrooms

25 m2 X 6

Program Specification

Entrance

Lobby

Ticketing

Receiving

Film Editing

Archive Organization

1F Docent’s Office

Bathroom

Stair Ramp

Photo Archive

EV Bathroom

Cafe

Resting Area

Document Exhibition

Photo Exhibition

Theater

Bathroom

Program Topology

Film Archive

Document Archive

Document Production

Reading Room

Concave Hull

Filleting Corner

AA’

Actual Island Profile

2nd Floor Plan Diagram

3rd Floor Plan Diagram

BB’

Ground Floor Plan Diagram

Material

Program

Area

Opaque

Photo Exhibition

500 m2

Opaque

Document Exhibition

250 m2

Opaque

Document/Photo Archive

125 m2

Opaque

Film Editing

120 m2

Translucent

Docent Office

120 m2

Naoshima

Translucent

Reading Room

110 m2

Honjima

Film Archive

100 m2

Document Production

100 m

Cafe

100 m2

Opaque Translucent Open Translucent

Island

CC’

Awashima

85 m2

Transparent

Resting Area

80 m

Transparent

Ticketing

50 m2

Bathrooms

25 m2

Mapping Island Profiles to Space Profiles and Materials

Megijima Takamijima Ogijima Oshima Inujima Ibukijima Shamijima

DD’

Teshima

Archive Organization

Opaque

Shodoshima

* /

F11

9 6 5

3 2 Enter

L K

J H

T F

D S

9 8

7 6

5 R

W A

F4 4

3 2 Q Lock

, M

Alt

B V

C X

Ground Floor Plan EE’

1Cap

8 7

F12

F10 F9

F3 F1 Esc

EE’

Second Floor Plan

EE’

Third Floor Plan

Section AA’

Secti

ion CC’

Section BB’

Exhibition Hall Entrance (3rd Floor)