2,713 research outputs found
Automatic Multi-Label Image Classification Model for Construction Site Images
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ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :๊ณต๊ณผ๋ํ ๊ฑด์ถํ๊ณผ,2019. 8. ๋ฐ๋ฌธ์.์ต๊ทผ ์ด๋ฏธ์ง ๋ถ์ ๊ธฐ์ ์ด ๋ฐ์ ํจ์ ๋ฐ๋ผ ๊ฑด์ค ํ์ฅ์์ ๋ค์ํ ๋ฐฉ๋ฉด์์ ํ์ฅ์์ ์์ง๋ ์ฌ์ง์ ํ์ฉํ์ฌ ๊ฑด์ค ํ๋ก์ ํธ๋ฅผ ๊ด๋ฆฌํ๊ณ ์ ํ๋ ์๋๊ฐ ์ด๋ฃจ์ด์ง๊ณ ์๋ค. ํนํ ์ดฌ์ ์ฅ๋น์ ๋ฐ์ ๋์ ๊ฑด์ค ํ์ฅ์์ ์์ฐ๋๋ ์ฌ์ง์ ์๊ฐ ๊ธ์ฆํ์ฌ ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ์ ์ฌ์ ์ธ ํ์ฉ๋๋ ๋์ฑ ๋ ๋์์ง๊ณ ์๋ค. ํ์ง๋ง ์ด๋ ๊ฒ ์์ฐ๋๋ ๋ง์ ์์ ์ฌ์ง์ ๋๋ถ๋ถ ์ ๋๋ก ๋ถ๋ฅ๋์ง ์์ ์ํ๋ก ๋ณด๊ด๋๊ณ ์๊ธฐ ๋๋ฌธ์ ํ์ฅ ์ฌ์ง์ผ๋ก๋ถํฐ ํ์ํ ํ๋ก์ ํธ ์ ๋ณด๋ฅผ ์ถ์ถํ๋ ๊ฒ์ ๋งค์ฐ ์ด๋ ค์ด ์ค์ ์ด๋ค. ํ์ฌ ํ์ฅ์์ ์ฌ์ง์ ๋ถ๋ฅํ๋ ๋ฐฉ์์ ์ฌ์ฉ์๊ฐ ์ง์ ๊ฐ๋ณ ์ฌ์ง์ ๊ฒํ ํ ๋ค ๋ถ๋ฅํ๊ธฐ ๋๋ฌธ์ ๋ง์ ์๊ฐ๊ณผ ๋
ธ๋ ฅ์ด ์๊ตฌ๋๊ณ , ์ด๋ฏธ์ง ๋ถ๋ฅ๋ฅผ ์ํ ํน์ง์ ์ง์ ์ ์ผ๋ก ์ถ์ถํ๋ ๊ธฐ์กด์ ์ด๋ฏธ์ง ๋ถ์ ๊ธฐ์ ์ญ์ ๋ณต์กํ ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ํน์ง์ ๋ฒ์ฉ์ ์ผ๋ก ํ์ตํ๋ ๋ฐ๋ ํ๊ณ๊ฐ ์๋ค.
์ด์ ๋ณธ ์ฐ๊ตฌ์์๋ ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ๋ชจ์ต์ด ๋งค์ฐ ๋ค์ํ๊ณ , ๋์ ์ผ๋ก ๋ณํ๋ ๊ฒ์ ๋์ํ๊ธฐ ์ํด ์ด๋ฏธ์ง ๋ถ๋ฅ์์ ๋์ ์ฑ๋ฅ์ ๋ณด์ด๊ณ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง(Deep Convolutional Neural Network) ์๊ณ ๋ฆฌ์ฆ์ ์ ์ฉํ์ฌ ๊ฐ๋ณ ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ์ ํฉํ ํค์๋๋ฅผ ์๋์ผ๋ก ํ ๋นํ ์ ์๋ ๋ชจ๋ธ์ ๊ฐ๋ฐํ๊ณ ์ ํ๋ค. ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง ๋ชจ๋ธ์ ๋ชจ๋ธ ๊ตฌ์กฐ๊ฐ ๊น์ด์ง์ ๋ฐ๋ผ ๋์ ์ฐจ์์ ํญ์์ฑ(invariant) ํน์ง๋ ํจ๊ณผ์ ์ผ๋ก ํ์ตํ ์ ์๋ ํน์ง์ด ์๊ธฐ ๋๋ฌธ์ ๋ณต์กํ ๊ฑด์ค ํ์ฅ ์ฌ์ง ๋ถ๋ฅ ๋ฌธ์ ์ ์ ํฉํ๋ค.
๋ฐ๋ผ์ ๋ณธ ์ฐ๊ตฌ์์๋ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง ๋ชจ๋ธ์ ํ ๋๋ก ํ์ฅ์์ ํ์ํ ์ฌ์ง์ ๋น ๋ฅด๊ณ ์ ํํ๊ฒ ์ฐพ์ ์ ์๋๋ก ๊ฐ ์ฌ์ง์ ์ ํฉํ ํค์๋๋ฅผ ์๋์ผ๋ก ํ ๋นํ๋ ๋ชจ๋ธ์ ๊ฐ๋ฐํ์๋ค. ํนํ, ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ๋๋ถ๋ถ์ด ํ๋ ์ด์์ ๋ ์ด๋ธ๊ณผ ์ฐ๊ด์ด ์๋ค๋ ์ ์ ๊ธฐ๋ฐํ์ฌ ๋ค์ค ๋ ์ด๋ธ ๋ถ๋ฅ ๋ชจ๋ธ์ ์ ์ฉํ์๋ค. ์ด๋ฅผ ํตํด ์ผ์ฐจ์ ์ผ๋ก๋ ๊ฑด์ค ์ฌ์ง์์ ํ๋ก์ ํธ์ ๊ด๋ จ๋ ๋ค์ํ ์ ๋ณด๋ฅผ ์ถ์ถํ์ฌ ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ํ์ฉ๋๋ฅผ ๊ฐ์ ํ๊ณ , ๋์๊ฐ ์ฌ์ง ๋ฐ์ดํฐ๋ฅผ ํ์ฉํ์ฌ ํจ์จ์ ์ธ ๊ฑด์ค ๊ด๋ฆฌ๋ฅผ ๋๋ชจํ๊ณ ์ ํ๋ค.
๋ณธ ์ฐ๊ตฌ์ ์งํ ์์๋ ๋ค์๊ณผ ๊ฐ๋ค. ์ฐ์ ๋ชจ๋ธ์ ํ์ต์ํค๊ธฐ ์ํด์ ์ค์ ๊ฑด์ค ํ์ฅ ๋ฐ ์คํ์์ค ๊ฒ์์์ง์ ํตํ์ฌ ์ด 6๊ฐ ๊ณต์ข
์ ์ฌ์ง์ ์์งํ๊ณ , ํ์ ๋ถ๋ฅ ๋ฒ์๋ฅผ ํฌํจํ ์ด 10๊ฐ ๋ ์ด๋ธ์ ๋ฐ์ดํฐ์
์ ๊ตฌ์ฑํ์ฌ ํ์ต์ ์งํํ๋ค. ๋ํ ๊ตฌ์ฒด์ ์ธ ๋ชจ๋ธ ์ ํ์ ์ํด ๋ํ์ ์ธ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง ๋ชจ๋ธ์ ๋น๊ต ๊ฒํ ํ์ฌ ๊ฐ์ฅ ์ฐ์ํ ์ฑ๋ฅ์ ๋ณด์ธ ResNet 18์ ์ต์ข
๋ชจ๋ธ๋ก ์ ํํ๋ค. ์คํ ๊ฒฐ๊ณผ ํ๊ท 91%์ ์ ํ๋๋ฅผ ๋ณด์ด๋ฉฐ ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ์๋์ผ๋ก ๋ถ๋ฅํ ์ ์๋ ๊ฐ๋ฅ์ฑ์ ํ์ธํ์๋ค.
๋ํ ๋ณธ ์ฐ๊ตฌ๋ ์ต๊ทผ ํ ๋ถ์ผ ์ด๋ฏธ์ง ๋ถ์์์ ์ข์ ์ฑ๊ณผ๋ฅผ ๋ณด์ธ ํฉ์ฑ๊ณฑ ์ ๊ฒฝ๋ง์ ํ์ฉํ์ฌ ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ์๋์ผ๋ก ๋ถ๋ฅํ ์ ์๋ค๋ ๊ฐ๋ฅ์ฑ์ ํ์ธํ๋ค๋ ์ ๊ณผ, ๊ฑด์ค ํ์ฅ ์ฌ์ง ๋ถ๋ฅ ๋ฌธ์ ์ ๋ค์ค ๋ ์ด๋ธ ๋ถ๋ฅ๋ฅผ ์ ์ฉํ ์ฒซ ์ฐ๊ตฌ๋ผ๋ ์ ์์ ์์๊ฐ ์๋ค. ์ค์ ํ์ฅ์์๋ ์ฌ์ง์ ์๋์ผ๋ก ๋ถ๋ฅํ ์ ์๊ฒ ๋จ์ ๋ฐ๋ผ ๊ธฐ์กด์ ๋ฒ๊ฑฐ๋ก์ด ์๋ ์ฌ์ง ๋ถ๋ฅ ์์
์ ์ค์ด๊ณ , ๊ฑด์ค ํ์ฅ ์ฌ์ง์ ํ์ฉ๋๋ฅผ ๋์ผ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.
ํ์ง๋ง ๋ณธ ์ฐ๊ตฌ๋ ๊ฐ ๋ ์ด๋ธ ๊ฐ์ ์ฐ๊ด์ฑ์ด๋ ์์กด์ฑ์ ๊ณ ๋ คํ์ง ์๊ธฐ ๋๋ฌธ์ ์ถํ ์ฐ๊ตฌ์์๋ ๊ฐ ์ฌ์ง ๊ฐ์ ๊ณ์ธต์ ๊ด๊ณ๋ฅผ ๋ชจ๋ธ์ ์ถ๊ฐ์ ์ผ๋ก ํ์ต์์ผ ์ ํ๋๋ฅผ ๋์ด๊ณ , ํ์ต ๋ ์ด๋ธ๋ ๋ ๋ฎ์ ๋จ๊ณ์ ํค์๋๊น์ง ํฌํจํ์ฌ ํ์ฅ ์ฌ์ง์ผ๋ก๋ถํฐ ๋ณด๋ค ๋ค์ํ ์ ๋ณด๋ฅผ ์ป์ ์ ์๋๋ก ๋ชจ๋ธ์ ๊ฐ์ ํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ๊ณ ์๋ค.Activity recognition in construction performs as the prerequisite step in the process for various tasks and thus is critical for successful project management. In the last several years, the computer vision community has blossomed, taking advantage of the exploding amount of construction images and deploying the visual analytics technology for cumbersome construction tasks. However, the current annotation practice itself, which is a critical preliminary step for prompt image retrieval and image understanding, is remained as both time-consuming and labor-intensive. Because previous attempts to make the process more efficient were inappropriate to handle dynamic nature of construction images and showed limited performance in classifying construction activities, this research aims to develop a model which is not only robust to a wide range of appearances but also multi-composition of construction activity images. The proposed model adopts a deep convolutional neural network model to learn high dimensional feature with less human-engineering and annotate multi-labels of semantic information in the images. The result showed that our model was capable of distinguishing different trades of activities at different stages of the activity. The average accuracy of 83% and a maximum accuracy of 91% holds promise in an actual implementation of automated activity recognition for construction operations. Ultimately, it demonstrated a potential method to provide automated and reliable procedure to monitor construction activity.Chapter 1. Introduction 1
1.1. Research Background 1
1.2. Research Objectives and Scope 5
1.3. Research Outline 7
Chapter 2. Preliminary Study 9
2.1. Challenges with Construction Activity Image Classification Task 10
2.2. Applications of Traditional Vision-based Algorithms in Construction Domain 13
2.3. Convolutional Neural Network-based Image Classification in Construction Domain 18
2.4. Summary 21
Chapter 3. Development of Construction Image Classification Model 22
3.1. Customized Construction Image Dataset Preparation 23
3.1.1. Construction Activity Classification System 23
3.1.2. Dataset Collection 24
3.1.3. Data Pre-Processing 25
3.2. Construction Image Classification Model Framework 27
3.2.1. Multi-label Image Classification 27
3.2.2. Base CNN Model Selection 28
3.2.3. Proposed ResNet Model Architecture 29
3.3. Model Training and Validation 33
3.3.1. Transfer Learning 33
3.3.2. Loss Computation and Model Optimization 33
3.3.3. Model Performance Indicator 35
3.4. Summary 37
Chapter 4. Experiment Results and Discussion 38
4.1. Experiment Results 38
4.2. Analysis of Experiment Results 42
4.3. Summary 44
Chapter 5. Conclusion 45
5.1. Research Summary 45
5.2. Research Contributions 46
5.3. Limitations and Further Study 47
References 49
Appendix 57
Abstract in Korean 63Maste
Improving Construction Project Schedules before Execution
The construction industry has been forever
blighted by delay and disruption. To address this
problem, this study proposes the Fitzsimmons
Method (FM method) to improve the scheduling
performance of activities on the Critical Path before
the project execution. The proposed FM method
integrates Bayesian Networks to estimate the
conditional probability of activity delay given its
predecessor and Support Vector Machines to
estimate the time delay. The FM method was trained
on 302 completed infrastructure construction
projects and validated on a ยฃ40 million completed
road construction project. Compared with
traditional Monte Carlo Simulation results, the
proposed FM method is 52% more accurate in
predicting the projectsโ time delay. The proposed
FM method contributes to leveraging the vast
quantities of data available to improve the
estimation of time risk on infrastructure and
construction projects
JURI SAYS:An Automatic Judgement Prediction System for the European Court of Human Rights
In this paper we present the web platform JURI SAYS that automatically predicts decisions of the European Court of Human Rights based on communicated cases, which are published by the court early in the proceedings and are often available many years before the final decision is made. Our system therefore predicts future judgements of the court. The platform is available at jurisays.com and shows the predictions compared to the actual decisions of the court. It is automatically updated every month by including the prediction for the new cases. Additionally, the system highlights the sentences and paragraphs that are most important for the prediction (i.e. violation vs. no violation of human rights)
Using attention methods to predict judicial outcomes
Legal Judgment Prediction is one of the most acclaimed fields for the
combined area of NLP, AI, and Law. By legal prediction we mean an intelligent
systems capable to predict specific judicial characteristics, such as judicial
outcome, a judicial class, predict an specific case. In this research, we have
used AI classifiers to predict judicial outcomes in the Brazilian legal system.
For this purpose, we developed a text crawler to extract data from the official
Brazilian electronic legal systems. These texts formed a dataset of
second-degree murder and active corruption cases. We applied different
classifiers, such as Support Vector Machines and Neural Networks, to predict
judicial outcomes by analyzing textual features from the dataset. Our research
showed that Regression Trees, Gated Recurring Units and Hierarchical Attention
Networks presented higher metrics for different subsets. As a final goal, we
explored the weights of one of the algorithms, the Hierarchical Attention
Networks, to find a sample of the most important words used to absolve or
convict defendants
Semantic discovery and reuse of business process patterns
Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse
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