• 한국주조공학회지
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• 제42권6호
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• pp.369-376
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• 2022

다이캐스팅 공정은 다양한 산업군의 인프라 역할을 수행하는 중요한 공정이지만, 높은 불량률로 인하여 관련 기업들의 수익성 및 생산성의 한계가 있는 상황이다. 이를 타개하기 위하여, 본 연구에서는 다이캐스팅 공정의 불량 검출을 위한 산업인공지능 기반 모듈을 구성하였다. 개발된 불량 검출 모듈은 제공되는 데이터의 특징에 따라서 3단계로 동작되는 모델로 구성된다. 1단계 모델은 비지도학습 기반 이상 검출을 진행하며, 레이블이 없는 데이터셋을 대상으로 작동한다. 2단계 모델은 반지도학습 기반으로 이상 검출을 진행하며, 양품 데이터의 레이블만 존재하는 데이터셋을 대상으로 작동하며, 3단계 모델은 소수의 불량 데이터가 제공된 상황의 지도학습 모델을 기반으로 작동한다. 개발된 모델은 실제 다이캐스팅 양품 데이터를 바탕으로 96% 이상의 우수한 양품 검출 성능을 보였다.

• 정보보호학회논문지
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• 제33권4호
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• pp.621-631
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• 2023

적대적 학습은 적대적 샘플에 대한 딥러닝 모델의 강건성을 향상시킨다. 하지만 기존의 적대적 학습 기법은 입력단계의 작은 섭동마저도 은닉층의 특징에 큰 변화를 일으킨다는 점을 간과하여 adversarial loss function에만집중한다. 그 결과로 일반 샘플 또는 다른 공격 기법과 같이 학습되지 않은 다양한 상황에 대한 정확도가 감소한다. 이 문제를 해결하기 위해서는 특징 표현 능력을 향상시키는 모델 아키텍처에 대한 분석이 필요하다. 본 논문에서는 입력 이미지의 attention map을 생성하는 attention module을 일반 모델에 적용하고 PGD 적대적학습을수행한다. CIFAR-10 dataset에서의 제안된 기법은 네트워크 구조에 상관없이 적대적 학습을 수행한 일반 모델보다 적대적 샘플에 대해 더 높은 정확도를 보였다. 특히 우리의 접근법은 PGD, FGSM, BIM과 같은 다양한 공격과 더 강력한 adversary에 대해서도 더 강건했다. 나아가 우리는 attention map을 시각화함으로써 attention module이 적대적 샘플에 대해서도 정확한 클래스의 특징을 추출한다는 것을 확인했다.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• 스마트미디어저널
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• 제13권2호
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• pp.44-51
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• 2024

스트레스 측정용 데이터셋의 구축은 건강, 의료분야, 심리향동, 교육분야 등 현대의 다양한 응용 분야에서 핵심적인 역할을 수행하교 있다. 특히, 스트레스 측정용 인공지능 모델의 효율적인 훈련을 위해서는 다양한 편향성을 제거하고 품질 관리된 데이터셋을 구축하는 것이 중요하다. 본 논문에서는 다양한 편향성 제거를 통한 품질의 관리된 스트레스 측정용 데이터셋 구축에 관하여 제안하였다. 이를 위해 스트레스 정의 및 측정도구 소개, 스트레스 인공지능 데이터 셋 구축과정, 품질향상을 위한 편향성 극복 전략 그리고 스트레스 데이터 수집시 고려사항을 제시하였다. 특히, 데이터셋 품질을 관리하기 위해 데이터셋 구축시 고려사항과, 발생할 수 있는 선택편향, 측정편향, 인과관계편향, 확증편향, 인공지능편향과 같은 다양한 편향서에 대해 검토하였다. 본 논문을 통해 스트레스 데이터 수집시 고려사항과 스트레스 데이터셋의 구축에서 발생할 수 있는 다양한 편향성을 체계적으로 이해하고, 이를 극복하여 품질이 보장된 데이터셋을 구축하는데 기여할 것으로 기대된다.

• 대한토목학회논문집
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• 제43권1호
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• pp.99-107
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• 2023

최근 건설 현장의 안전사고 비율은 전체 산업에서 가장 높은 비중을 차지한다. 인공지능 기술을 건설 현장에 접목하기 위해서는 기초 학습 자료로 활용될 수 있는 데이터셋 확보가 필수적이다. 본 논문에서는 실제 현장 확보를 통해 원천 데이터를 수집하였으며, 토목 현장에서 주로 운용되고 있는 주요 건설장비 객체를 선정하고 약 9만장의 정지영상 데이터셋 가공을 통해 최적의 학습 데이터셋 구축을 완료하였다. 또한, 객체 인식분야의 대표적인 모델인 YOLO를 활용하여 구축된 데이터의 검증 작업을 수행하였고 90 % 근접한 검출 성능을 확인해 데이터 신뢰성을 확보하였다. 본 연구에서 사용되는 학습 데이터셋은 공공데이터포털에서 활용 가능하도록 공개를 완료하였다. 본 데이터셋은 향후 건설안전 분야의 객체 인식 기술의 건설현장 적용을 위한 기반 데이터로 활용 가능하리라 판단된다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• Nuclear Engineering and Technology
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• 제54권10호
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• pp.3943-3948
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• 2022

Single-photon emission computed tomography is one of the reliable pin-by-pin verification techniques for spent-fuel assemblies. One of the challenges with this technique is to increase the total fuel assembly verification speed while maintaining high verification accuracy. The aim of the present study, therefore, was to develop an artificial intelligence (AI) algorithm-based tomographic image analysis technique for partial-defect verification of fuel assemblies. With the Monte Carlo (MC) simulation technique, a tomographic image dataset consisting of 511 fuel-rod patterns of a 3 × 3 fuel assembly was generated, and with these images, the VGG16, GoogLeNet, and ResNet models were trained. According to an evaluation of these models for different training dataset sizes, the ResNet model showed 100% pattern estimation accuracy. And, based on the different tomographic image qualities, all of the models showed almost 100% pattern estimation accuracy, even for low-quality images with unrecognizable fuel patterns. This study verified that an AI model can be effectively employed for accurate and fast partial-defect verification of fuel assemblies.

• 말소리와 음성과학
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• 제15권3호
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• pp.83-88
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• 2023

This study investigates the fine-tuning of large-scale Automatic Speech Recognition (ASR) models, specifically OpenAI's Whisper model, for domain-specific applications using the KsponSpeech dataset. The primary research questions address the effectiveness of targeted lexical item emphasis during fine-tuning, its impact on domain-specific performance, and whether the fine-tuned model can maintain generalization capabilities across different languages and environments. Experiments were conducted using two fine-tuning datasets: Set A, a small subset emphasizing specific lexical items, and Set B, consisting of the entire KsponSpeech dataset. Results showed that fine-tuning with targeted lexical items increased recognition accuracy and improved domain-specific performance, with generalization capabilities maintained when fine-tuned with a smaller dataset. For noisier environments, a trade-off between specificity and generalization capabilities was observed. This study highlights the potential of fine-tuning using minimal domain-specific data to achieve satisfactory results, emphasizing the importance of balancing specialization and generalization for ASR models. Future research could explore different fine-tuning strategies and novel technologies such as prompting to further enhance large-scale ASR models' domain-specific performance.

• 한국군사과학기술학회지
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• 제27권2호
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• pp.167-176
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• 2024

Lip-reading is the task of inferring the speaker's utterance from silent video based on learning of lip movements. It is very challenging due to the inherent ambiguities present in the lip movement such as different characters that produce the same lip appearances. Recent advances in deep learning models such as Transformer and Temporal Convolutional Network have led to improve the performance of lip-reading. However, most previous works deal with English lip-reading which has limitations in directly applying to Korean lip-reading, and moreover, there is no a large scale Korean lip-reading dataset. In this paper, we introduce the first large-scale Korean lip-reading dataset with more than 120 k utterances collected from TV broadcasts containing news, documentary and drama. We also present a preprocessing method which uniformly extracts a facial region of interest and propose a transformer-based model based on grapheme unit for sentence-level Korean lip-reading. We demonstrate that our dataset and model are appropriate for Korean lip-reading through statistics of the dataset and experimental results.

• 인터넷정보학회논문지
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• 제25권2호
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.