• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.2
• /
• pp.38-46
• /
• 2017

In this study, a query language was developed to supplement the information retrieval and model regeneration in the case of Industry Foundation Classes (IFC)-based civil infrastructure information models. First, the IFC objects to represent the structural components, entities to manage the related properties, and relationships to connect with the mentioned elements were analyzed in a point of information flow. The results confirmed that the end-users could have problems with access and comprehend the properties and its relationships in the IFC file. Second, the IfcPropertySet-focused query method and applicable stand-alone module were proposed referring to the previous Building Information Model Query Language (BimQL). The availabilities of the proposed method were examined using the rail and sleeper information models through information retrieval and model regeneration. The most important advantage of the proposed approach is the IFC-based information retrievals that can guarantee the interoperability between software packages.

• English Language & Literature Teaching
• /
• v.11 no.4
• /
• pp.365-389
• /
• 2005

The purpose of this study is to present the task model that is the most effective in English language methodology based on the investigation of task-based performance in Korean EFL classroom practice. The subjects were 538 high school students and 126 high school teachers, each of whom had common experiences using the materials of task-based activities for more than one year. To analyze the data, the program SPSS WIN 11.0 including frequency distribution and chi-square analysis was used. The results of the questionnaire analysis showed that both teachers and students had a comparatively high level of satisfaction in task rationale, but that they had some mixed responses in the fields of input data, settings, and activity types. To conclude, a few suggestions are made to provide some meaningful considerations for the EFL teachers and material developers: a) task goals and rationale that encourage the learner's positive motivation; b) authenticity of input data based on the real-world context; c) collaborative learning environment that enhances communicative interaction; d) proportional representation of the creative problem-solving activities related to discussions and decision-making processes; e) systematic introduction of integrated language skills. It also suggests that the multi-lateral task model, which has some positive assets compared to previous task models, be newly introduced and applied to the second language learning classrooms.

• Smart Media Journal
• /
• v.13 no.6
• /
• pp.35-43
• /
• 2024

During the post-pandemic period, the interest in foreign language learning surged, leading to increased usage of language-learning apps. With the rising demand for these apps, analyzing app reviews becomes essential, as they provide valuable insights into user experiences and suggestions for improvement. This research focuses on extracting insights into users' opinions, sentiments, and overall satisfaction from reviews of HelloTalk, one of the most renowned language-learning apps. We employed topic modeling and emotion analysis approaches to analyze reviews collected from the Google Play Store. Several experiments were conducted to evaluate the performance of sentiment classification models with different settings. In addition, we identified dominant emotions and topics within the app reviews using feature importance analysis. The experimental results show that the Random Forest model with topics and emotions outperforms other approaches in accuracy, recall, and F1 score. The findings reveal that topics emphasizing language learning and community interactions, as well as the use of language learning tools and the learning experience, are prominent. Moreover, the emotions of 'admiration' and 'annoyance' emerge as significant factors across all models. This research highlights that incorporating emotion scores into the model and utilizing a broader range of emotion labels enhances model performance.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2024.05a
• /
• pp.830-833
• /
• 2024

As Large Language Models (LLM) like OpenAI's ChatGPT[1] continue to grow in popularity, new applications and services are expected to emerge. This paper introduces an experimental study on a smart web-builder application assistance system that combines Computer Vision with GUI object recognition and the ChatGPT (LLM). First of all, the research strategy employed computer vision technology in conjunction with Microsoft's "ChatGPT for Robotics: Design Principles and Model Abilities"[2] design strategy. Additionally, this research explores the capabilities of Large Language Model like ChatGPT in various application design tasks, specifically in assisting with web-builder tasks. The study examines the ability of ChatGPT to synthesize code through both directed prompts and free-form conversation strategies. The researchers also explored ChatGPT's ability to perform various tasks within the builder domain, including functions and closure loop inferences, basic logical and mathematical reasoning. Overall, this research proposes an efficient way to perform various application system tasks by combining natural language commands with computer vision technology and LLM (ChatGPT). This approach allows for user interaction through natural language commands while building applications.

• Journal of Internet Computing and Services
• /
• v.22 no.6
• /
• pp.141-150
• /
• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• Proceedings of the Korean Society for Language and Information Conference
• /
• 1996.02a
• /
• pp.347-356
• /
• 1996

• Proceedings of the Korean Society for Language and Information Conference
• /
• 1985.02a
• /
• pp.79-93
• /
• 1985

• Korean Journal of Radiology
• /
• v.25 no.8
• /
• pp.687-690
• /
• 2024

• 한국정보디스플레이학회:학술대회논문집
• /
• 2008.10a
• /
• pp.431-434
• /
• 2008

The physical OLED analog behavioral model for SPICE simulation has been described using Verilog-A language. The model is based on the carrier-balance between the hole and electron injected through Schottky barrier at anode and cathode. The accuracy of this model was examined by comparing with the results from device simulation.

• Language and Information
• /
• v.8 no.1
• /
• pp.101-116
• /
• 2004

This paper proposes a statistical model for the translation of prepositions in English-Korean machine translation. In the proposed model, statistical information acquired from unlabeled Korean corpora is used to choose the best translation from several possible translations. Such information includes functional word-verb co-occurrence information, functional word-verb distance information, and noun-postposition co-occurrence information. The model was evaluated with 443 sentences, each of which has a prepositional phrase, and we attained 71.3% accuracy.