Earticle

개체명 인식(Named Entity Recognition) 시스템은 문서에서 인명(PS), 지명(LC), 단체명(OG)과 같은 개체명을 가지는 단어나 어구를 해당 개체명으로 인식하는 시스템이다. 개체명 인식을 하기위한 전통적인 연구방법 으로는 hand-craft된 자질(feature)을 기반으로 모델을 학습하는 통계 기반의 모델이 있다. 최근에는 딥러닝 기반의 RNN(Recurrent Neural Networks), LSTM(Long-short Term Memory)과 같은 모델을 이용하여 문장을 표현하 는 자질을 구성하고 이를 개체명 인식과 같이 순서 라벨링(sequence labeling) 문제 해결에 이용한 연구가 제안되었 다. 본 연구에서는 한국어 개체명 인식 시스템의 성능 향상을 위해, end-to-end learning 방식이 가능한 딥러닝 기반의 모델에 미리 구축되어 있는 hand-craft된 자질이나 품사 태깅 정보 및 기구축 사전(lexicon) 정보를 추가로 활용하여 자질을 보강(augmentation)하는 방법을 제안한다. 실험 결과 본 논문에서 제안하는 방법에 따라 자질을 보강한 한국어 개체명 인식 시스템의 성능 향상을 확인하였다. 또한 본 연구의 결과를 한국어 자연어처리(NLP) 및 개체명 인식 시스템을 연구하는 연구자들과의 향후 협업 연구를 위해 github를 통해 공개하였다.

The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.

This study introduced a novel methodology for enhancing Automatic Identification System (AIS) trajectory forecasting in regions characterized by significant tidal variations through feature augmentation, specifically indirect incorporation of sea level data via the nearest tidal gauge. Traditional AIS prediction models predominantly utilize features such as latitude, longitude, speed over ground (SOG), and course over ground (COG) for time series forecasting. However, these models often overlook the influence of tidal fluctuations, which can significantly impact prediction accuracy in areas with pronounced tidal changes. To address this limitation, we proposed a feature augmentation approach by incorporating the Haversine distance to the nearest tidal gauge and the real-time sea level at that gauge as additional features. Direct access to sea level data at a vessel's precise location presents practical challenges, making this indirect method an efficient and effective solution. Through comprehensive analyses across multiple deep learning models and test scenarios, our results demonstrate that this augmented feature set can substantially improve AIS forecasting performance in regions with significant tidal variation surrounding the Korean Peninsula.

영아는 비언어적 의사 소통 방식인 울음이라는 수단을 사용한다[1]. 하지만 영아의 울음소리를 파악하는 것에는 어려움이 따른다. 영아의 울음소리를 해석하기 위해 많은 연구가 진행되었다[2,3]. 이에 본 논문에서는 다양한 음성 데이터 증강을 통한 3D 특징 벡터를 이용한 영아의 울음소리 분류를 제안한다. 연구에서는 총 5개의 클래스 복통, 하품, 불편함, 배고픔, 피곤함(belly pain, burping, discomfort, hungry, tired)로 분류된 데이터 세트를 사용한다. 데이터들은 5가지 기법(Pitch, Tempo, Shift, Mixup-noise, CutMix)을 사용하여 증강한다. 증강 기법 중에서 Tempo, Shift, CutMix 기법을 적용하였을 때 성능의 향상을 보여주었다. 최종적으로 우수한 데이터 증강 기법들을 동시 적용한 결과 단일 특징 벡터와 오리지널 데이터를 사용한 모델보다 17.75%의 성능 향상을 도출하였다.

Infants utilize crying as a non-verbal means of communication [1]. However, deciphering infant cries presents challenges. Extensive research has been conducted to interpret infant cry audios [2,3]. This paper proposes the classification of infant cries using 3D feature vectors augmented with various audio data techniques. A total of 5 classes (belly pain, burping, discomfort, hungry, tired) are employed in the study dataset. The data is augmented using 5 techniques (Pitch, Tempo, Shift, Mixup-noise, CutMix). Tempo, Shift, and CutMix augmentation techniques demonstrated improved performance. Ultimately, applying effective data augmentation techniques simultaneously resulted in a 17.75% performance enhancement compared to models using single feature vectors and original data.

As digital twin technology for implementing smart shipyards advances, research on object detection using 3D point cloud has become increasingly necessary. Shipyards, in particular, are complex environments with coexisting indoor and outdoor settings, large structures, and diverse equipment, making the analysis of features from 3D point cloud objects essential for effective object detection. However, these environments, characterized by intricate geometries, varied equipment scales, and high-resolution data, generate massive datasets. Such large volumes of data lead to challenges in storage, transmission, processing, and learning resources and costs, notably intensifying computational and memory loads during the training of deep learning-based object detection models. Therefore, a lightweighting solution that ensures efficient processing while maintaining data quality is required. Accordingly, this study extracted geometric local features using Fast Point Feature Histograms (FPFH) from 3D point cloud acquired in a shipyard. An augmented dataset was then constructed based on these extracted feature points. Subsequently, an object detection model was trained using a 3D point cloud-based deep learning architecture to analyze the impact of these features on object detection accuracy. The results indicated that the FPFH-applied dataset maintained comparable accuracy to the original dataset. This research is anticipated to be valuably utilized for the development of automation technologies and digital twins aimed at constructing smart shipyards.

As digital twin technology for implementing smart shipyards advances, research on object detection using 3D point cloud has become increasingly necessary. Shipyards, in particular, are complex environments with coexisting indoor and outdoor settings, large structures, and diverse equipment, making the analysis of features from 3D point cloud objects essential for effective object detection. However, these environments, characterized by intricate geometries, varied equipment scales, and high-resolution data, generate massive datasets. Such large volumes of data lead to challenges in storage, transmission, processing, and learning resources and costs, notably intensifying computational and memory loads during the training of deep learning-based object detection models. Therefore, a lightweighting solution that ensures efficient processing while maintaining data quality is required. Accordingly, this study extracted geometric local features using Fast Point Feature Histograms (FPFH) from 3D point cloud acquired in a shipyard. An augmented dataset was then constructed based on these extracted feature points. Subsequently, an object detection model was trained using a 3D point cloud-based deep learning architecture to analyze the impact of these features on object detection accuracy. The results indicated that the FPFH-applied dataset maintained comparable accuracy to the original dataset. This research is anticipated to be valuably utilized for the development of automation technologies and digital twins aimed at constructing smart shipyards.