Effect of the Unit Floor Plan Characteristics on the Prices of Small Officetels

2020 ◽  
Vol 30 (2) ◽  
pp. 57-67
Author(s):  
Ho Kwan Jang ◽  
Jae Won Lee ◽  
Sang Youb Lee
Keyword(s):  
Floor Plan

scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

scholarly journals DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2000
Author(s):  
Marius Laska ◽  
Jörg Blankenbach
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Success Probability ◽  
Local Area ◽  
Position Estimation ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Area Network ◽  
Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

Floor plan for the congress

1996 ◽  
Vol 19 (S1) ◽  
pp. S16-S16
Keyword(s):  
Floor Plan

scholarly journals Exhibition Floor Plan

2017 ◽  
Vol 23 (1_suppl) ◽  
pp. 350-350
Keyword(s):  
Floor Plan

Some Remarks on the Study of Prehistoric Structures

1971 ◽  
Vol 36 (4) ◽  
pp. 451-455
Author(s):  
Slavomil Vencl
Keyword(s):  
Present Article ◽  
University Of Michigan ◽  
Floor Plan ◽  
English Version ◽  
House Floor ◽  
Positive Results

AbstractThe present article responds to questions raised by Marshall (1969). Based on European material, the author concludes that engineering principles cannot be universally applied, despite some obviously positive results. Before reconstructing an object, the completeness of its preserved remains should be taken into account and the preserved floor plan should be supplemented with those elements that have vanished. To use only what actually remains of a house floor in making a reconstruction presupposes that the layout is fully preserved, and this is of course not always the case. The English version of this paper was translated from the original Czech manuscript by H. Martin Wobst, University of Michigan.

scholarly journals Floor Plan

2014 ◽  
Vol 10 ◽  
pp. 218-218
Keyword(s):  
Floor Plan
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