• Korean Journal of Cognitive Science
• /
• v.27 no.1
• /
• pp.27-41
• /
• 2016

Named entity recognition is to classify elements in text into predefined categories and used for various departments which receives natural language inputs. In this paper, we propose a method which can generate named entity training corpus automatically using knowledge bases. We apply two different methods to generate corpus depending on the knowledge bases. One of the methods attaches named entity labels to text data using Wikipedia. The other method crawls data from web and labels named entities to web text data using Freebase. We conduct two experiments to evaluate corpus quality and our proposed method for generating Named entity recognition corpus automatically. We extract sentences randomly from two corpus which called Wikipedia corpus and Web corpus then label them to validate both automatic labeled corpus. We also show the performance of named entity recognizer trained by corpus generated in our proposed method. The result shows that our proposed method adapts well with new corpus which reflects diverse sentence structures and the newest entities.

• Journal of the Korean Society for information Management
• /
• v.35 no.4
• /
• pp.141-164
• /
• 2018

Recently, as the amount of academic literature has increased rapidly and complex researches have been actively conducted, researchers have difficulty in analyzing trends in previous research. In order to solve this problem, it is necessary to classify information in units of academic papers. However, in Korea, there is no academic database in which such information is provided. In this paper, we propose an automatic classification system that can classify domestic academic literature into multiple classes. To this end, first, academic documents in the technical science field described in Korean were collected and mapped according to class 600 of the DDC by using K-Means clustering technique to construct a learning set capable of multiple classification. As a result of the construction of the training set, 63,915 documents in the Korean technical science field were established except for the values in which metadata does not exist. Using this training set, we implemented and learned the automatic classification engine of academic documents based on deep learning. Experimental results obtained by hand-built experimental set-up showed 78.32% accuracy and 72.45% F1 performance for multiple classification.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.27 no.5
• /
• pp.113-119
• /
• 2023

Most infrastructure structures were completed during periods of economic growth. The number of infrastructure structures reaching their lifespan is increasing, and the proportion of old structures is gradually increasing. The functions and performance of these structures at the time of design may deteriorate and may even lead to safety accidents. To prevent this repercussion, accurate inspection and appropriate repair are requisite. To this end, demand is increasing for computer vision and deep learning technology to accurately detect even minute cracks. However, deep learning algorithms require a large number of training data. In particular, label images indicating the location of cracks in the image are required. To secure a large number of those label images, a lot of labor and time are consumed. To reduce these costs as well as increase detection accuracy, this study proposed a learning structure based on mean teacher method. This learning structure was trained on a dataset of 900 labeled image dataset and 3000 unlabeled image dataset. The crack detection network model was evaluated on over 300 labeled image dataset, and the detection accuracy recorded a mean intersection over union of 89.23% and an F1 score of 89.12%. Through this experiment, it was confirmed that detection performance was improved compared to supervised learning. It is expected that this proposed method will be used in the future to reduce the cost required to secure label images.

• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.1
• /
• pp.30-45
• /
• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.5
• /
• pp.171-177
• /
• 2018

Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.86-98
• /
• 2022

Recently, many studies have been conducted to improve quality by applying machine learning models to semiconductor manufacturing process data. However, in the semiconductor manufacturing process, the ratio of good products is much higher than that of defective products, so the problem of data imbalance is serious in terms of machine learning. In addition, since the number of features of data used in machine learning is very large, it is very important to perform machine learning by extracting only important features from among them to increase accuracy and utilization. This study proposes an anomaly detection methodology that can learn excellently despite data imbalance and high-dimensional characteristics of semiconductor process data. The anomaly detection methodology applies the LIME algorithm after applying the SMOTE method and the RFECV method. The proposed methodology analyzes the classification result of the anomaly classification model, detects the cause of the anomaly, and derives a semiconductor process requiring action. The proposed methodology confirmed applicability and feasibility through application of cases.

• The Journal of Korea Robotics Society
• /
• v.18 no.3
• /
• pp.346-351
• /
• 2023

With the release of numerous open driving datasets, the demand for domain adaptation in perception tasks has increased, particularly when transferring knowledge from rich datasets to novel domains. However, it is difficult to solve the change 1) in the sensor domain caused by heterogeneous LiDAR sensors and 2) in the environmental domain caused by different environmental factors. We overcome domain differences in the semi-supervised setting with 3-stage model parameter training. First, we pre-train the model with the source dataset with object scaling based on statistics of the object size. Then we fine-tine the partially frozen model weights with copy-and-paste augmentation. The 3D points in the box labels are copied from one scene and pasted to the other scenes. Finally, we use the knowledge distillation method to update the student network with a moving average from the teacher network along with a self-training method with pseudo labels. Test-Time Augmentation with varying z values is employed to predict the final results. Our method achieved 3rd place in ECCV 2022 workshop on the 3D Perception for Autonomous Driving challenge.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.5
• /
• pp.479-487
• /
• 2021

In this paper, we propose an Sound Event Detection (SED) model using self-training based on a noisy student model. The proposed SED model consists of two stages. In the first stage, a mean-teacher model based on an Residual Convolutional Recurrent Neural Network (RCRNN) is constructed to provide target labels regarding weakly labeled or unlabeled data. In the second stage, a self-training-based noisy student model is constructed by applying different noise types. That is, feature noises, such as time-frequency shift, mixup, SpecAugment, and dropout-based model noise are used here. In addition, a semi-supervised loss function is applied to train the noisy student model, which acts as label noise injection. The performance of the proposed SED model is evaluated on the validation set of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge Task 4. The experiments show that the single model and ensemble model of the proposed SED based on the noisy student model improve F1-score by 4.6 % and 3.4 % compared to the top-ranked model in DCASE 2020 challenge Task 4, respectively.

• 한국어정보학회:학술대회논문집
• /
• 2016.10a
• /
• pp.181-185
• /
• 2016

최근 교육과정에서 학생들의 능력 평가는 단순 암기보다 학생들의 종합적인 사고력을 판단할 수 있는 서답형 문항을 늘리는 방향으로 변하고 있다. 그러나 서답형 문항의 경우 채점하는 데 시간과 비용이 많이 들고, 채점자의 주관에 따라 채점 결과의 일관성과 신뢰성을 보장하기 어렵다는 문제가 있다. 이런 점을 해결하기 위해 해외의 사례를 참고하여 국내에서도 서답형 문항에 자동채점 시스템을 적용하는 연구를 진행하고 있다. 본 논문에서는 2014년도에 개발된 '한국어 문장 수준 서답형 문항 자동채점 시스템'의 성능분석을 바탕으로 언어 처리 기능과 자동채점 성능을 개선한 2015년도 자동채점 시스템을 간략하게 소개하고, 각 자동채점 시스템의 성능을 비교 분석한다. 성능 분석 대상으로는 2014년도 국가수준 학업성취도평가의 서답형 문항을 사용했다. 실험 결과, 개선한 시스템의 평균 완전 일치도와 평균 정확률이 기존의 시스템보다 각각 9.4%p, 8.9%p 증가했다. 자동채점 시스템의 목적은 가능한 채점 시간을 단축하면서 채점 기준의 일관성과 신뢰성을 확보하는 데 있으므로, 보완한 2015년 자동채점 시스템의 성능이 향상되었다고 판단할 수 있다.

• Annual Conference on Human and Language Technology
• /
• 2016.10a
• /
• pp.181-185
• /
• 2016

최근 교육과정에서 학생들의 능력 평가는 단순 암기보다 학생들의 종합적인 사고력을 판단할 수 있는 서답형 문항을 늘리는 방향으로 변하고 있다. 그러나 서답형 문항의 경우 채점하는 데 시간과 비용이 많이 들고, 채점자의 주관에 따라 채점 결과의 일관성과 신뢰성을 보장하기 어렵다는 문제가 있다. 이런 점을 해결하기 위해 해외의 사례를 참고하여 국내에서도 서답형 문항에 자동채점 시스템을 적용하는 연구를 진행하고 있다. 본 논문에서는 2014년도에 개발된 '한국어 문장 수준 서답형 문항 자동채점 시스템'의 성능분석을 바탕으로 언어 처리 기능과 자동채점 성능을 개선한 2015년도 자동채점 시스템을 간략하게 소개하고, 각 자동채점 시스템의 성능을 비교 분석한다. 성능 분석 대상으로는 2014년도 국가수준 학업성취도평가의 서답형 문항을 사용했다. 실험 결과, 개선한 시스템의 평균 완전 일치도와 평균 정확률이 기존의 시스템보다 각각 9.4%p, 8.9%p 증가했다. 자동채점 시스템의 목적은 가능한 채점 시간을 단축하면서 채점 기준의 일관성과 신뢰성을 확보하는 데 있으므로, 보완한 2015년 자동채점 시스템의 성능이 향상되었다고 판단할 수 있다.