• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2003.05a
• /
• pp.1-1
• /
• 2003

No Abstract, See Full Text

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.11
• /
• pp.345-350
• /
• 2020

Based on the analysis of physical and learning characteristics and requirements of the silver generation, a video English dictionary was developed and evaluated as English learning contents. The video English dictionary was developed using OCR as an input method and video as an output method, and 17 silver generations were evaluated for academic achievement, learning satisfaction, and ease of use. As a result of the analysis, both the text English dictionary and the video English dictionary showed high learning satisfaction, but the video English dictionary showed higher results than the text English dictionary in an academic achievement and ease of use.

• International Journal of Advanced Culture Technology
• /
• v.11 no.4
• /
• pp.346-351
• /
• 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.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1994.06c
• /
• pp.338-341
• /
• 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.

• MALSORI
• /
• no.35_36
• /
• pp.131-143
• /
• 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.

• MALSORI
• /
• no.61
• /
• pp.87-100
• /
• 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.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.6
• /
• pp.8-12
• /
• 2017

This paper presents the mobile phone case for self-power generation and recharge for emergency calls or text messages at the discharge of a battery. If the user shakes his smart phone case, the interaction of electromagnetic coil and permanent magnet in an electric generator produces electric energy, which charges the lithium-ion battery. This enables the user to give a few calls or text messages. In addition, the vibration energy from humans walking at a frequency of 2 ~ 3Hz charges the battery. The electric generator was simulated using MAXWELL, a commercial electromagnetic analysis program, to analyze the electric power generation. Finally a prototype of the mobile phone case for self-power generation was built based on the analysis and its performance was verified.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3168-3186
• /
• 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.

• Journal of Korea Society of Industrial Information Systems
• /
• v.28 no.5
• /
• pp.15-30
• /
• 2023

Recently, generative models based on the Transformer architecture, such as ChatGPT, have been gaining significant attention. The Transformer architecture has been applied to various neural network models, including Google's BERT(Bidirectional Encoder Representations from Transformers) sentence generation model. In this paper, a method is proposed to create a text binary classification model for determining whether a comment on Korean movie review is positive or negative. To accomplish this, a pre-trained multilingual BERT sentence generation model is fine-tuned and transfer learned using a new Korean training dataset. To achieve this, a pre-trained BERT-Base model for multilingual sentence generation with 104 languages, 12 layers, 768 hidden, 12 attention heads, and 110M parameters is used. To change the pre-trained BERT-Base model into a text classification model, the input and output layers were fine-tuned, resulting in the creation of a new model with 178 million parameters. Using the fine-tuned model, with a maximum word count of 128, a batch size of 16, and 5 epochs, transfer learning is conducted with 10,000 training data and 5,000 testing data. A text sentiment binary classification model for Korean movie review with an accuracy of 0.9582, a loss of 0.1177, and an F1 score of 0.81 has been created. As a result of performing transfer learning with a dataset five times larger, a model with an accuracy of 0.9562, a loss of 0.1202, and an F1 score of 0.86 has been generated.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.8
• /
• pp.2278-2291
• /
• 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.