84,465 research outputs found
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곡νκ³Ό, 2021. 2. μ‘°μ±μ€.Predictive modeling is a type of supervised learning to find the functional relationship between the input variables and the output variable. Predictive modeling is used in various aspects in manufacturing systems, such as automation of visual inspection, prediction of faulty products, and result estimation of expensive inspection. To build a high-performance predictive model, it is essential to secure high quality data. However, in manufacturing systems, it is practically impossible to acquire enough data of all kinds that are needed for the predictive modeling. There are three main difficulties in the data acquisition in manufacturing systems. First, labeled data always comes with a cost. In many problems, labeling must be done by experienced engineers, which is costly. Second, due to the inspection cost, not all inspections can be performed on all products. Because of time and monetary constraints in the manufacturing system, it is impossible to obtain all the desired inspection results. Third, changes in the manufacturing environment make data acquisition difficult. A change in the manufacturing environment causes a change in the distribution of generated data, making it impossible to obtain enough consistent data. Then, the model have to be trained with a small amount of data. In this dissertation, we overcome this difficulties in data acquisition through active learning, active feature-value acquisition, and domain adaptation. First, we propose an active learning framework to solve the high labeling cost of the wafer map pattern classification. This makes it possible to achieve higher performance with a lower labeling cost. Moreover, the cost efficiency is further improved by incorporating the cluster-level annotation into active learning. For the inspection cost for fault prediction problem, we propose a active inspection framework. By selecting products to undergo high-cost inspection with the novel uncertainty estimation method, high performance can be obtained with low inspection cost. To solve the recipe transition problem that frequently occurs in faulty wafer prediction in semiconductor manufacturing, a domain adaptation methods are used. Through sequential application of unsupervised domain adaptation and semi-supervised domain adaptation, performance degradation due to recipe transition is minimized. Through experiments on real-world data, it was demonstrated that the proposed methodologies can overcome the data acquisition problems in the manufacturing systems and improve the performance of the predictive models.μμΈ‘ λͺ¨λΈλ§μ μ§λ νμ΅μ μΌμ’
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Όλ¬Έμμλ μ΄λ° λ°μ΄ν° νλμ μ΄λ €μμ 극볡νκΈ° μν΄ λ₯λ νμ΅, λ₯λ νΌμ³κ° νλ, λλ©μΈ μ μ λ°©λ²μ νμ©νλ€. λ¨Όμ , μ¨μ΄νΌ 맡 ν¨ν΄ λΆλ₯ λ¬Έμ μ λμ λΌλ²¨λ§ λΉμ©μ ν΄κ²°νκΈ° μν΄ λ₯λνμ΅ νλ μμν¬λ₯Ό μ μνλ€. μ΄λ₯Ό ν΅ν΄ μ μ λΌλ²¨λ§ λΉμ©μΌλ‘ λμ μ±λ₯μ λΆλ₯ λͺ¨λΈμ ꡬμΆν μ μλ€. λμκ°, κ΅°μ§ λ¨μμ λΌλ²¨λ§ λ°©λ²μ λ₯λνμ΅μ μ λͺ©νμ¬ λΉμ© ν¨μ¨μ±μ νμ°¨λ‘ λ κ°μ νλ€. μ ν λΆλ μμΈ‘μ νμ©λλ κ²μ¬ λΉμ© λ¬Έμ λ₯Ό ν΄κ²°νκΈ° μν΄μλ λ₯λ κ²μ¬ λ°©λ²μ μ μνλ€. μ μνλ μλ‘μ΄ λΆνμ€μ± μΆμ λ°©λ²μ ν΅ν΄ κ³ λΉμ© κ²μ¬ λμ μ νμ μ νν¨μΌλ‘μ¨ μ μ κ²μ¬ λΉμ©μΌλ‘ λμ μ±λ₯μ μ»μ μ μλ€. λ°λ체 μ μ‘°μ μ¨μ΄νΌ λΆλ μμΈ‘μμ λΉλ²νκ² λ°μνλ λ μνΌ λ³κ²½ λ¬Έμ λ₯Ό ν΄κ²°νκΈ° μν΄μλ λλ©μΈ μ μ λ°©λ²μ νμ©νλ€. λΉκ΅μ¬ λλ©μΈ μ μκ³Ό λ°κ΅μ¬ λλ©μΈ μ μμ μμ°¨μ μΈ μ μ©μ ν΅ν΄ λ μνΌ λ³κ²½μ μν μ±λ₯ μ νλ₯Ό μ΅μννλ€. λ³Έ λ
Όλ¬Έμμλ μ€μ λ°μ΄ν°μ λν μ€νμ ν΅ν΄ μ μλ λ°©λ²λ‘ λ€μ΄ μ μ‘°μμ€ν
μ λ°μ΄ν° νλ λ¬Έμ λ₯Ό 극볡νκ³ μμΈ‘ λͺ¨λΈμ μ±λ₯μ λμΌ μ μμμ νμΈνμλ€.1. Introduction 1
2. Literature Review 9
2.1 Review of Related Methodologies 9
2.1.1 Active Learning 9
2.1.2 Active Feature-value Acquisition 11
2.1.3 Domain Adaptation 14
2.2 Review of Predictive Modelings in Manufacturing 15
2.2.1 Wafer Map Pattern Classification 15
2.2.2 Fault Detection and Classification 16
3. Active Learning for Wafer Map Pattern Classification 19
3.1 Problem Description 19
3.2 Proposed Method 21
3.2.1 System overview 21
3.2.2 Prediction model 25
3.2.3 Uncertainty estimation 25
3.2.4 Query wafer selection 29
3.2.5 Query wafer labeling 30
3.2.6 Model update 30
3.3 Experiments 31
3.3.1 Data description 31
3.3.2 Experimental design 31
3.3.3 Results and discussion 34
4. Active Cluster Annotation for Wafer Map Pattern Classification 42
4.1 Problem Description 42
4.2 Proposed Method 44
4.2.1 Clustering of unlabeled data 46
4.2.2 CNN training with labeled data 48
4.2.3 Cluster-level uncertainty estimation 49
4.2.4 Query cluster selection 50
4.2.5 Cluster-level annotation 50
4.3 Experiments 51
4.3.1 Data description 51
4.3.2 Experimental setting 51
4.3.3 Clustering results 53
4.3.4 Classification performance 54
4.3.5 Analysis for label noise 57
5. Active Inspection for Fault Prediction 60
5.1 Problem Description 60
5.2 Proposed Method 65
5.2.1 Active inspection framework 65
5.2.2 Acquisition based on Expected Prediction Change 68
5.3 Experiments 71
5.3.1 Data description 71
5.3.2 Fault prediction models 72
5.3.3 Experimental design 73
5.3.4 Results and discussion 74
6. Adaptive Fault Detection for Recipe Transition 76
6.1 Problem Description 76
6.2 Proposed Method 78
6.2.1 Overview 78
6.2.2 Unsupervised adaptation phase 81
6.2.3 Semi-supervised adaptation phase 83
6.3 Experiments 85
6.3.1 Data description 85
6.3.2 Experimental setting 85
6.3.3 Performance degradation caused by recipe transition 86
6.3.4 Effect of unsupervised adaptation 87
6.3.5 Effect of semi-supervised adaptation 88
7. Conclusion 91
7.1 Contributions 91
7.2 Future work 94Docto
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
The Semantic Web is an extension of the current web in which information is
given well-defined meaning. The perspective of Semantic Web is to promote the
quality and intelligence of the current web by changing its contents into
machine understandable form. Therefore, semantic level information is one of
the cornerstones of the Semantic Web. The process of adding semantic metadata
to web resources is called Semantic Annotation. There are many obstacles
against the Semantic Annotation, such as multilinguality, scalability, and
issues which are related to diversity and inconsistency in content of different
web pages. Due to the wide range of domains and the dynamic environments that
the Semantic Annotation systems must be performed on, the problem of automating
annotation process is one of the significant challenges in this domain. To
overcome this problem, different machine learning approaches such as supervised
learning, unsupervised learning and more recent ones like, semi-supervised
learning and active learning have been utilized. In this paper we present an
inclusive layered classification of Semantic Annotation challenges and discuss
the most important issues in this field. Also, we review and analyze machine
learning applications for solving semantic annotation problems. For this goal,
the article tries to closely study and categorize related researches for better
understanding and to reach a framework that can map machine learning techniques
into the Semantic Annotation challenges and requirements
Challenges and solutions for Latin named entity recognition
Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English. Data sparsity in Latin presents a number of challenges for traditional Named Entity
Recognition techniques. Solving such challenges and enabling reliable Named Entity Recognition in Latin texts can facilitate many down-stream applications, from machine translation to digital historiography, enabling Classicists, historians, and archaeologists for instance, to track
the relationships of historical persons, places, and groups on a large scale. This paper presents the first annotated corpus for evaluating Named Entity Recognition in Latin, as well as a fully supervised model that achieves over 90% F-score on a held-out test set, significantly outperforming a competitive baseline. We also present a novel active learning strategy that predicts how many and which sentences need to be annotated for named entities in order to attain a specified degree
of accuracy when recognizing named entities automatically in a given text. This maximizes the productivity of annotators while simultaneously controlling quality
Global stellar variability study in the field-of-view of the Kepler satellite
We present the results of an automated variability analysis of the Kepler
public data measured in the first quarter (Q1) of the mission. In total, about
150 000 light curves have been analysed to detect stellar variability, and to
identify new members of known variability classes. We also focus on the
detection of variables present in eclipsing binary systems, given the important
constraints on stellar fundamental parameters they can provide. The methodology
we use here is based on the automated variability classification pipeline which
was previously developed for and applied successfully to the CoRoT exofield
database and to the limited subset of a few thousand Kepler asteroseismology
light curves. We use a Fourier decomposition of the light curves to describe
their variability behaviour and use the resulting parameters to perform a
supervised classification. Several improvements have been made, including a
separate extractor method to detect the presence of eclipses when other
variability is present in the light curves. We also included two new
variability classes compared to previous work: variables showing signs of
rotational modulation and of activity. Statistics are given on the number of
variables and the number of good candidates per class. A comparison is made
with results obtained for the CoRoT exoplanet data. We present some special
discoveries, including variable stars in eclipsing binary systems. Many new
candidate non-radial pulsators are found, mainly Delta Sct and Gamma Dor stars.
We have studied those samples in more detail by using 2MASS colours. The full
classification results are made available as an online catalogue.Comment: 15 pages, 5 figures, Accepted for publication in Astronomy and
Astrophysics on 09/02/201
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