• 한국컴퓨터정보학회논문지
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• 제23권6호
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• pp.1-7
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• 2018

A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 I
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• pp.386-389
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• 2003

This paper proposes the architecture of a multiple ring based optical network with single hop between edge nodes using either the concept of circuit switching or multi-wavelength label. The structure of the multi-wavelength label, be shown through the single wavelength-band and the multiple wavelength-band that can reduce number of ring. To avoid the collision of the optical packets at an outward port, we proposed the dynamic allocation scheme of the outward optical packets based on the fiber do]ay lines(FDLs).

• 한국컴퓨터정보학회논문지
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• 제22권10호
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• pp.121-128
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• 2017

In this paper, we propose a multi-label classification method in which multi-label classification estimation techniques are applied to resolving location prediction problem. Most of previous studies related to location prediction have focused on the use of single-label classification by using contextual information such as user's movement paths, demographic information, etc. However, in this paper, we focused on the case where users are free to visit multiple locations, forcing decision-makers to use multi-labeled dataset. By using 2373 contextual dataset which was compiled from college students, we have obtained the best results with classifiers such as bagging, random subspace, and decision tree with the multi-label classification estimation methods like binary relevance(BR), binary pairwise classification (PW).

• 한국음향학회지
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• 제42권6호
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• pp.536-543
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• 2023

다수의 화자가 존재하는 음성에서 "누가 언제 발화했는가?"에 대해 레이블링하는 화자 분할은 발화 중첩 구간에 대한 레이블링과 화자 분할 모델의 최적화를 위해 심층 신경망 기반의 종단 간 방법에 대해 연구되었다. 대부분 심층 신경망 기반의 종단 간 화자 분할 시스템은 음성의 각 프레임에서 발화한 모든 화자의 레이블들을 추정하는 다중 레이블 분류 문제로 분할을 수행한다. 다중 레이블 기반의 화자 분할 시스템은 임계값을 어떤 값으로 설정하는지에 따라 모델의 성능이 많이 달라진다. 본 논문에서는 임계값 없이 화자 분할을 수행할 수 있도록 단일 레이블 분류를 이용한 화자 분할 시스템에 대해 연구하였다. 제안하는 화자 분할 시스템은 기존의 화자 레이블을 단일 레이블 형태로 변환하여 모델의 출력으로부터 레이블을 바로 추정한다. 훈련에서는 화자 레이블 순열을 고려하기 위해 Permutation Invariant Training(PIT) 손실함수와 교차 엔트로피 손실함수를 조합하여 사용하였다. 또한 심층 구조를 갖는 모델의 효과적인 학습을 위해 화자 분할 모델에 잔차 연결 구조를 추가하였다. 실험은 Librispeech 데이터베이스를 이용해 화자 2명에 대한 시뮬레이션 잡음 데이터를 생성하여 사용하였다. Diarization Error Rate(DER) 성능 평가 지수를 이용해 제안한 방법과 베이스라인 모델을 비교 평가했을 때, 제안한 방법이 임계값 없이 분할이 가능하며, 약 20.7 %만큼 향상된 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권11호
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• pp.5546-5559
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• 2019

Facial action units (AUs) have recently drawn increased attention because they can be used to recognize facial expressions. A variety of methods have been designed for frontal-view AU detection, but few have been able to handle multi-view face images. In this paper we propose a method for multi-view facial AU detection using a fused multilayer, multi-task, and multi-label deep learning network. The network can complete two tasks: AU detection and facial view detection. AU detection is a multi-label problem and facial view detection is a single-label problem. A residual network and multilayer fusion are applied to obtain more representative features. Our method is effective and performs well. The F1 score on FERA 2017 is 13.1% higher than the baseline. The facial view recognition accuracy is 0.991. This shows that our multi-task, multi-label model could achieve good performance on the two tasks.

• 대한원격탐사학회지
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• 제28권6호
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• pp.671-682
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• 2012

Strong backscattering features from high-resolution Synthetic Aperture Rader (SAR) image provide useful information to analyze earth surface characteristics such as man-made objects in urban areas. The SAR image has, however, some limitations on description of detail information in urban areas compared to optical images. In this paper, we propose a new classification method using a fused SAR and Electro-Optical (EO) image, which provides more informative classification result than that of a single-sensor SAR image classification. The experimental results showed that the proposed method achieved successful results in combination of the SAR image classification and EO image characteristics.

• 한국임상약학회지
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• 제16권1호
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• pp.9-13
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• 2006

Patient counseling is emerging as one of the most important roles of community pharmacists because the information on the standard labeling for the prescription drug is not sufficient to ensure the correct use of the drug. However, excessive workload of the community pharmacists in Korea discourages the provision of the effective patient counseling. The use of auxiliary label may be an efficient tool to help patients correctly use the prescription drug in this situation. As a preliminary study to encourage the use of auxiliary label, we have performed a survey analysis of familiarity and willingness of community pharmacists to use the auxiliary label. About three quarters of the participating community pharmacists have heard of the auxiliary label, however, there was not a single pharmacist who uses the label. Furthermore, only one fifth of the participating pharmacists were willing to use the label if they have to purchase. Therefore, it is recommended that governmental and non-profit organizations such as Korean Pharmaceutical Association educate community pharmacists regarding usefulness of the auxiliary label with focus on enhancing patient compliance and constrainment of healthcare expense.

• 한국산업정보학회논문지
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• 제25권4호
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• pp.29-38
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• 2020

본 논문에서는 다중 레이블 콘볼루션 신경회로망(Multi-label Convolution Neural Networks)을 이용하는 능동펄스 식별 알고리즘을 제안한다. 제안하는 기법은 기존의 단일 레이블 기반 알고리즘이 여러 개의 신경회로망 구조로 되어 있던 것과 달리 하나의 신경회로망 구조로 능동펄스의 정보를 한번에 획득할 수 있으며, 학습과정 역시 간편해지는 장점이 있다. 제안한 능동펄스 식별 알고리즘을 검증하기 위해서 해상실험 데이터를 이용하여 신경회로망을 학습시키고 성능을 분석하였다. 분석결과 제안한 능동펄스 식별 알고리즘이 수렴하는 것을 확인하였으며, 오차행렬(Confusion Matrix) 분석을 통하여 우수한 능동펄스 식별성능을 가지는 것을 확인하였다.

• Nuclear Engineering and Technology
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• 제56권2호
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권12호
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• pp.555-560
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• 2021

최근 안면 피부 미용에 대한 사람들의 관심이 높아짐에 따라 딥 러닝을 활용한 안면 피부 미용을 위한 피부 질환 인식 연구가 진행되고 있다. 이러한 연구들은 여드름을 비롯한 다양한 피부 질환을 인식한다. 기존의 연구들은 단일 피부 질환만을 인식하지만, 안면에 발생하는 피부 질환은 더 다양하고 복합적으로 발생할 수 있다. 따라서 본 논문에서는 Inception-ResNet V2 모델을 활용하여 다중 레이블 분류 방법으로 여드름, 블랙헤드, 주근깨, 검버섯, 일반 피부, 화이트헤드에 관한 복합적인 피부 질환을 인식한다. 사용한 평가 지표 중 정확도는 98.8%, 해밍 손실은 0.003을 달성하였고, 단일 클래스별 정밀도, 재현율, F1-점수는 모두 96.6% 이상을 달성하였다.