• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.347-364
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• 2022

Fraudulent companies or sellers strategically manipulate reviews to influence customers' purchase decisions; therefore, the reliability of reviews has become crucial for customer decision-making. Since customers increasingly rely on online reviews to search for more detailed information about products or services before purchasing, many researchers focus on detecting manipulated reviews. However, the main problem in detecting manipulated reviews is the difficulties with obtaining data with manipulated reviews to utilize machine learning techniques with sufficient data. Also, the number of manipulated reviews is insufficient compared with the number of non-manipulated reviews, so the class imbalance problem occurs. The class with fewer examples is under-represented and can hamper a model's accuracy, so machine learning methods suffer from the class imbalance problem and solving the class imbalance problem is important to build an accurate model for detecting manipulated reviews. Thus, we propose an OpenAI-based reviews generation model to solve the manipulated reviews imbalance problem, thereby enhancing the accuracy of manipulated reviews detection. In this research, we applied the novel autoregressive language model - GPT-3 to generate reviews based on manipulated reviews. Moreover, we found that applying GPT-3 model for oversampling manipulated reviews can recover a satisfactory portion of performance losses and shows better performance in classification (logit, decision tree, neural networks) than traditional oversampling models such as random oversampling and SMOTE.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.329-333
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• 2003

최근 전문가의 지식과 경험정보가 데이터베이스로 구축된 전문가 시스템의 지식 정보를 이용하여 안전하고 효율적인 선박운항이 가능한 지능형 선박 시스템에 관한 연구가 활발하게 진행되고 있다. 인공지능기법을 이용하여 보다 인간 친화적인 시스템을 구현하고, 음성인식기술을 이용하여 원격으로 선박 조타기를 제어하여 조업자의 부담경감 및 인원절감의 효과를 가져올 수 있는 선박 조종시스템의 개발이 절실하다. 따라서, 본 논문에서는 PC를 기반으로 하여 일격으로 모형 선박의 조타기를 제어하는 시스템을 구축한다. 구체적인 연구방법으로는, 음성인식기술과 지능형 학습 기법을 바탕으로 음성지시기반학습 시스템을 구축하고, 퍼지 조타수 조작모델을 구현하여 PC 기반 원격 제어시스템을 구축한다. 또한, 구축된 원격 조타제어시스템을 축소된 선박모형(Miniature Ship) 시스템에 적용하여 그 효용성을 확인하였다.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.302-313
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• 2023

This paper aims to develop a framework that can fully automate the quality management of training data used in large-scale Artificial Intelligence (AI) models built by the Ministry of Science and ICT (MSIT) in the 'AI Hub Data Dam' project, which has invested more than 1 trillion won since 2017. Autonomous driving technology using AI has achieved excellent performance through many studies, but it requires a large amount of high-quality data to train the model. Moreover, it is still difficult for humans to directly inspect the processed data and prove it is valid, and a model trained with erroneous data can cause fatal problems in real life. This paper presents a dataset reconstruction framework that removes abnormal data from the constructed dataset and introduces strategies to improve the performance of AI models by reconstructing them into a reliable dataset to increase the efficiency of model training. The framework's validity was verified through an experiment on the autonomous driving dataset published through the AI Hub of the National Information Society Agency (NIA). As a result, it was confirmed that it could be rebuilt as a reliable dataset from which abnormal data has been removed.

• The Journal of Bigdata
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• v.8 no.2
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• pp.83-95
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• 2023

Big data analytics and AI play a critical role in the development of smart plants. This study presents a big data platform for plant data and an 'AutoML platform' for AI-based plant O&M(Operation and Maintenance). The big data platform collects, processes and stores large volumes of data generated in plants using Hadoop, Spark, and Kafka. The AutoML platform is a machine learning automation system aimed at constructing predictive models for equipment prognostics and process optimization in plants. The developed platforms configures a data pipeline considering compatibility with existing plant OISs(Operation Information Systems) and employs a web-based GUI to enhance both accessibility and convenience for users. Also, it has functions to load user-customizable modules into data processing and learning algorithms, which increases process flexibility. This paper demonstrates the operation of the platforms for a specific process of an oil company in Korea and presents an example of an effective data utilization platform for smart plants.

• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.137-144
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• 2024

In this study, we aim to devise an effective generative AI education direction for those learning game development. In the past, artificial intelligence technology was used to create game content, but with the emergence and rapid development of generative AI, its role has expanded to a tool for game development. This is changing the entire game development process. However, these developments brought not only opportunities but also anxiety to those in demand for education. This class is designed to relieve these anxieties, allow educational consumers to create part of the game development process using generative AI rather than traditional game development methods, and change their perception of generative AI through this experience. A post-training survey was conducted to explore perceptions of generative AI and capture the skills needed to use generative AI smoothly and demand for additional training areas. Through this, we propose a method for effectively teaching generative AI technology and suggest implications for the future direction of generative AI education.

• Industry Promotion Research
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• v.8 no.2
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• pp.21-27
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• 2023

This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.

• Journal of Platform Technology
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• v.11 no.6
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• pp.79-88
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• 2023

Recently, industrial accidents have continued to increase due to the industrialization, and worker safety management is recognized as essential to reduce losses due to hazardous factors at work places. To manage the safety of workers, it is required to apply customized safety management artificial intelligence technology that takes into account the characteristics of industrial sites, and a service for real-time risk detection and response to workers depending on the situation based on safety accident types and risk analysis for each task and process. The proposed safety management service consists of worker devices to acquire sensor data, edge devices to collect from IoT-based sensors, and a voice chatbot to support workers' disaster response. The voice chatbot plays a major role in interacting with workers at disaster sites to respond to risks. This paper focuses on real-time risk response using an IoT-based system and voice chatbot on a server for work safety according to the worker's situation. A Scenario-based voice chatbot is used to process responses at the edge level to provide safety management services.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.37-44
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• 2020

This study aimed to explore the relationship between Intelligent Robot Reacher(IRT)-student by examining the factors of their relationship perceived by middle school students. In doing so, we developed questionnaires based on the existing teacher-student relationship scale and conducted an online survey of 283 first graders in middle school. The collected date were analyzed using exploratory factor analyses with SPSS 23 and confirmatory factor analysis with Amos 21. The study findings identified four factors of IRT-student relationship namely "trust", "competence", "emotional exchange", and "tolerance". It is expected that the study can be used to discuss ways to enhance educationally significant interaction between students-IRT and teaching methods using intelligent robots(IRs). Also, the study will contribute to the understanding and development of various services using IRs. Based on the study finidngs, future studies should investigate the perception of various education stockholders (teachers, parets, etc) on IRT to elevate the Human-Robot Interaction in the education field.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.323-324
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• 2022

본 논문에서는 사람의 개입 없이 실내 환경에서 마스크 미 착용자를 스스로 발견한 후 방역수칙위반 사실에 대한 경고와 함께 마스크 착용을 권고하는 인공지능 기반의 자율주행 서비스 로봇을 개발한다. 제안한 시스템에서 로봇은 동시적 위치 추적 지도 작성 기법인 SLAM(Simultaneous Localization and Mapping)기술을 이용하여 지도를 작성한 후 사용자가 제공한 웨이포인트(Waypoint)를 기반으로 자율주행한다. 또한, YOLO(You Only Look Once) 알고리즘을 이용한 실시간 객체 인식 기술을 활용하여 보행자의 마스크 착용 여부를 판단한다. 실험을 통해 사전에 작성된 지도에 지정된 웨이포인트를 따라 로봇이 자율주행하는 것을 확인하였다. 또한, 충전소로 이동할 경우, 영상 처리 기법을 활용하여 충전소에 부착된 표식에 근접하도록 이동하여 충전이 진행됨을 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.43-44
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• 2022

기존 자연어 처리 모델은 문맥 단위 단어 임베딩을 처리하지 못하는 한계점을 가지고 있는 한편 최근 BERT 기반 사전학습 모델들은 문장 단위 임베딩이 가능하고 사전학습을 통해 학습 효율이 비약적으로 개선되었다는 특징이 있다. 본 논문에서는 사전학습 언어 모델들을 이용하여 음식점, 배달전문점 등 음식 업종에서 발생한 고객 발화 의도를 분류하고 모델별 성능을 비교하여 최적의 모델을 제안하고자 한다. 연구결과, 사전학습 모델의 한국어 코퍼스와 Vocab 사이즈가 클수록 고객의 발화 의도를 잘 예측하였다. 한편, 본 연구에서 발화자의 의도를 크게 문의와 요청으로 구분하여 진행하였는데, 문의와 요청의 큰 차이점인 '물음표'를 제거한 후 성능을 비교해본 결과, 물음표가 존재할 때 발화자 의도 예측에 좋은 성능을 보였다. 이를 통해 음식 업종에서 발화자의 의도를 예측하는 시스템을 개발하고 챗봇 시스템 등에 활용한다면, 발화자의 의도에 적합한 서비스를 정확하게 적시에 제공할 수 있을 것으로 기대한다.