• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2003년도 제20회 춘계학술대회 논문집
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• pp.1-1
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• 2003

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• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제9권11호
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• pp.345-350
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• 2020

본 논문은 실버세대 영어학습자를 위한 모바일 학습 콘텐츠를 구현하고 이를 평가하여 이들을 위한 콘텐츠 설계시 고려해야 할 사항에 대해 분석하고자 하였다. 실버세대의 신체적, 학습적 특징과 요구사항 분석을 근거로 하여 영어학습 콘텐츠로 동영상 영어사전을 개발하였고 이를 평가하였다. 동영상 영어사전은 입력방식으로 OCR을, 출력방식으로 동영상을 활용하여 개발하였고 17명의 실버세대들을 대상으로 학업성취도, 학습만족도, 사용의 용이성을 평가하였다. 분석결과 문자 영어사전과 동영상 영어사전 모두 학습만족도가 높은 것으로 나타났으나 학업성취도와 사용의 용이성에서는 문자로 된 영어사전보다 동영상 영어사전이 더 높은 결과를 나타냈다.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.346-351
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• 2023

With the rapid development of Artificial Intelligence (AI) technology, there is an increasing variety of methods for creating 3D models. These include innovations such as text-only generation, 2D images to 3D models, and combining images with cue words. Each of these methods has unique advantages, opening up new possibilities in the field of 3D modeling. The purpose of this study is to explore and summarize these methods in-depth, providing researchers and practitioners with a comprehensive perspective to understand the potential value of these methods in practical applications. Through a comprehensive analysis of pure text generation, 2D images to 3D models, and images with cue words, we will reveal the advantages and disadvantages of the various methods, as well as their applicability in different scenarios. Ultimately, this study aims to provide a useful reference for the future direction of AI modeling and to promote the innovation and progress of 3D model generation technology.

• 한국음향학회:학술대회논문집
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• 한국음향학회 1994년도 제11회 음성통신 및 신호처리 워크샵 논문집 (SCAS 11권 1호)
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• pp.338-341
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• 1994

In this paper we propose a method to generate a multisyllable onsense wordest for the purpose of synthetic speech assessment and applies th ewordest to assess one commercial text-to-speech system. Some results about the experiment is suggested and it is verified that the generated nonsense wordset can be used to assess the intelligibility of the synthesizer in phoneme level or in phonemic environmental level. From the experimental results it is verified that such multi-syllable nonsense wordset can be useful for the assessment of synthesized speech.

• 대한음성학회지:말소리
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• 제35_36호
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• pp.131-143
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• 1998

This study describes an algorithm for the F0 contour generation system for Korean sentences and its evaluation results. 400 K-ToBI labeled utterances were used which were read by one male and one female announcers. F0 contour generation system uses two classification trees for prediction of K-ToBI labels for input text and 11 regression trees for prediction of F0 values for the labels. Evaluation results of the system showed 77.2% prediction accuracy for prediction of IP boundaries and 72.0％ prediction accuracy for AP boundaries. Information of voicing and duration of the segments was not changed for F0 contour generation and its evaluation. Evaluation results showed 23.5Hz RMS error and 0.55 correlation coefficient in F0 generation experiment using labelling information from the original speech data.

• 대한음성학회지:말소리
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• 제61호
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• pp.87-100
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• 2007

A punctuation generation system which combines prosodic information with acoustic and language model information is presented. Experiments have been conducted first for the reference text transcriptions. In these experiments, prosodic information was shown to be more useful than language model information. When these information sources are combined, an F-measure of up to 0.7830 was obtained for adding punctuation to a reference transcription. This method of punctuation generation can also be applied to the 1-best output of a speech recogniser. The 1-best output is first time aligned. Based on the time alignment information, prosodic features are generated. As in the approach applied in the punctuation generation for reference transcriptions, the best sequence of punctuation marks for this 1-best output is found using the prosodic feature model and an language model trained on texts which contain punctuation marks.

• 한국산학기술학회논문지
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• 제18권6호
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• pp.8-12
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• 2017

본 논문에서는 휴대폰의 배터리 방전 시에도 비상전화나 문자 메시지의 사용을 가능하게 해주는 자가발전기에 관한 연구를 진행하여 휴대폰 케이스를 통한 휴대폰의 재충전 방법을 제시한다. 사용자가 스마트 폰 케이스를 흔들게 되면 전자기 코일과 발전기의 영구 자석의 상호 작용을 통해 리튬 이온 배터리를 충전 하며 전기 에너지가 생성된다. 이 때 생성된 전기에너지는 사용자가 몇 분의 통화 또는 문자 메시지를 사용할 수 있는 전력을 제공 해준다. 또한 사람이 걸을 때 위상의 변화에 의해 2 ~ 3 Hz 주파수의 진동 에너지가 발생하는데, 이를 획득하는 에너지 하베스팅 과정을 통해 배터리를 충전한다. 그리고 발전량을 분석하는 것은 상용 전자기 해석 프로그램인 MAXWELL을 사용하여 모델링 후 시뮬레이션 하였다. 마지막으로 분석한 결과를 바탕으로 자가 발전을 위한 휴대폰 케이스의 프로토타입을 구축하고 그 측정값과 시뮬레이션 값을 비교함으로써 프로토타입의 성능을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3168-3186
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• 2020

Automatic topic essay generation (TEG) is a controllable text generation task that aims to generate informative, diverse, and topic-consistent essays based on multiple topics. To make the generated essays of high quality, a reasonable method should consider both diversity and topic-consistency. Another essential issue is the intrinsic link of the topics, which contributes to making the essays closely surround the semantics of provided topics. However, it remains challenging for TEG to fill the semantic gap between source topic words and target output, and a more powerful model is needed to capture the semantics of given topics. To this end, we propose a pretraining-based contextual self-attention (PC-SAN) model that is built upon the seq2seq framework. For the encoder of our model, we employ a dynamic weight sum of layers from BERT to fully utilize the semantics of topics, which is of great help to fill the gap and improve the quality of the generated essays. In the decoding phase, we also transform the target-side contextual history information into the query layers to alleviate the lack of context in typical self-attention networks (SANs). Experimental results on large-scale paragraph-level Chinese corpora verify that our model is capable of generating diverse, topic-consistent text and essentially makes improvements as compare to strong baselines. Furthermore, extensive analysis validates the effectiveness of contextual embeddings from BERT and contextual history information in SANs.

• 한국산업정보학회논문지
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• 제28권5호
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• pp.15-30
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• 2023

근래에 트랜스포머(Transformer) 구조를 기초로 하는 ChatGPT와 같은 생성모델이 크게 주목받고 있다. 트랜스포머는 다양한 신경망 모델에 응용되는데, 구글의 BERT(bidirectional encoder representations from Transformers) 문장생성 모델에도 사용된다. 본 논문에서는, 한글로 작성된 영화 리뷰에 대한 댓글이 긍정적인지 부정적인지를 판단하는 텍스트 이진 분류모델을 생성하기 위해서, 사전 학습되어 공개된 BERT 다국어 문장생성 모델을 미세조정(fine tuning)한 후, 새로운 한국어 학습 데이터셋을 사용하여 전이학습(transfer learning) 시키는 방법을 제안한다. 이를 위해서 104 개 언어, 12개 레이어, 768개 hidden과 12개의 집중(attention) 헤드 수, 110M 개의 파라미터를 사용하여 사전 학습된 BERT-Base 다국어 문장생성 모델을 사용했다. 영화 댓글을 긍정 또는 부정 분류하는 모델로 변경하기 위해, 사전 학습된 BERT-Base 모델의 입력 레이어와 출력 레이어를 미세 조정한 결과, 178M개의 파라미터를 가지는 새로운 모델이 생성되었다. 미세 조정된 모델에 입력되는 단어의 최대 개수 128, batch_size 16, 학습 횟수 5회로 설정하고, 10,000건의 학습 데이터셋과 5,000건의 테스트 데이터셋을 사용하여 전이 학습시킨 결과, 정확도 0.9582, 손실 0.1177, F1 점수 0.81인 문장 감정 이진 분류모델이 생성되었다. 데이터셋을 5배 늘려서 전이 학습시킨 결과, 정확도 0.9562, 손실 0.1202, F1 점수 0.86인 모델을 얻었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권8호
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• pp.2278-2291
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• 2023

Imported ornamental fish should be quarantined because they can have dangerous diseases depending on their habitat. The quarantine requires a lot of time because quarantine officers collect various information on the imported ornamental fish. Inefficient quarantine processes reduce its work efficiency and accuracy. Also, long-time quarantine causes the death of environmentally sensitive ornamental fish and huge financial losses. To improve existing quarantine systems, information on ornamental fish was collected and structured, and a server was established to develop quarantine performance support software equipped with a text search engine. However, the long names of ornamental fish in general can cause many typos and time bottlenecks when we type search words for the target fish information. Therefore, we need a technique that can correct typos. Typical typo character calibration compares input text with all characters in a calibrated candidate text dictionary. However, this approach requires computational power proportional to the number of typos, resulting in slow processing time and low calibration accuracy performance. Therefore, to improve the calibration accuracy of characters, we propose a fusion system of simple Artificial Neural Network (ANN) models and character preprocessing methods that accelerate the process by minimizing the computation of the models. We also propose a typo character generation method used for training the ANN models. Simulation results show that the proposed typo character correction system is about 6 times faster than the conventional method and has 10% higher accuracy.