• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.81-88
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• 2021

Post COVID-19, the medical legacy system will be transformed for utilizing medical resources efficiently, minimizing medical service imbalance, activating remote medical care, and strengthening private-public medical cooperation. This can be realized by achieving an entire medical paradigm shift and not simply via the application of advanced technologies such as AI. We propose a medical system configuration named "Medical AI Hub" that can realize the shift of the existing paradigm. The development stage of this configuration is categorized into "AI Cooperation Hospital," "AI Base Hospital," and "AI Hub Hospital." In the "AI Hub Hospital" stage, the medical intelligence in charge of individual patients cooperates and communicates autonomously with various medical intelligences, thereby achieving synchronous evolution. Thus, this medical intelligence supports doctors in optimally treating patients. The core technologies required during configuration development and their current R&D trends are described in this paper. The realization of the central configuration of medical AI through the development of these core technologies will induce a paradigm shift in the new medical system by innovating all medical fields with influences at the individual, society, industry, and public levels and by making the existing medical system more efficient and intelligent.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.524-525
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• 2023

설명 가능한 심층 강화 학습 지식 증류 방법(ERL-KD)이 제안하였다. 이 방법은 모든 하위 에이전트로부터 점수를 수집하며, 메인 에이전트는 주 교사 네트워크 역할을 하고 하위 에이전트는 보조 교사 네트워크 역할을 한다. 글로벌 최적 솔루션은 샤플리 값과 같은 해석 가능한 방법을 통해 얻어진다. 또한 유사도 제약이라는 개념을 도입하여 교사 네트워크와 학생 네트워크 간의 유사도를 조정함으로써 학생 네트워크가 자유롭게 탐색할 수 있도록 유도한다. 실험 결과, 학생 네트워크는 아타리 2600 환경에서 대규모 교사 네트워크와 비슷한 성능을 달성하는 것으로 나타났다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.719-720
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• 2024

센서 기반의 자동화재탐지설비의 역할을 지원할 목적으로, 합성곱 신경망 기반의 AI 화재 감시장비등이 연구되어왔다. ai 기반 화재 감지에 사용되는 알고리즘은 전이학습을 주로 이용하고 있고, 이는 화재 감지에 기여도가 낮은 프로세스가 내장되어 있을 가능성이 존재하여, 딥러닝 모델의 복잡성을 가중시키는 원인이 될 수 있다. 본 연구에서는 이러한 모델의 복잡성을 개선하고자 다양한 딥러닝 및 해석 기술들을 분석하였고, 분석 결과를 토대로 화재 감지에 최적화된 아키텍처인 "IF2bNet" 을 제안한다. 구현한 아키텍처의 성능을 비교한 결과 동일한 성능을 내면서, 파라미터를 약 0.1 배로 경량화 하여, 복잡성을 완화하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.586-589
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• 2024

본 실험 연구에서는 주의 메커니즘과 컨볼루션 신경망을 결합하여 모델을 개선하는 방법을 탐색하는 딥 러닝 기술을 소개한다. 이 기술은 지도 학습 방식을 위해 공개 데이터 세트의 쓰레기 분류 데이터를 사용하고, Grad-CAM 기술과 채널 주의 메커니즘 SE 를 적용하여 모델의 분류 의사 결정 과정을 더 잘 이해하기 위해 히트 맵을 생성한다. Grad-CAM 기술을 사용하여 히트 맵을 생성하면 분류 중에 모델이 집중하는 영역을 시각화할 수 있다. 이는 모델의 분류 결정을 설명하는 방법을 제공하여 다양한 이미지 카테고리에 대한 모델 결정의 기초를 더 잘 이해할 수 있다. 실험 결과는 전통적인 합성곱 신경망과 비교하여 제안한 방법이 쓰레기 분류 작업에서 더나은 성능을 달성한다는 것을 보여준다. 주의 메커니즘과 히트맵 해석을 결합함으로써 우리 모델은분류 정확도를 향상시킬 수 있다. 이는 실제 응용 분야의 이미지 분류 작업에 큰 의미가 있으며 해석 가능성에 대한 딥 러닝 연구 진행을 촉진하는 데 도움이 된다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.19-28
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• 2021

Esophagogastroduodenoscopy is a method commonly used for early diagnosis of upper gastrointestinal lesions. However, 10-20 percent of the gastric lesions are reported to be missed, due to human error. And countries including the US, the UK, and Japan, the World Endoscopy Organization (WEO) suggested guidelines about essential gastrointestinal parts to take pictures of so that all gastric lesions are observed. In this paper, we propose deep learning techniques for classification of anatomical sites, aiming for the system that informs practitioners whether they successfully did the gastroscopy without blind spots. The proposed model uses pre-processing modules and data augmentation techniques suitable for gastroscopy images. Not only does the experiment result with a maximum F1 score of 99.6%, but it also shows a error rate of less than 4% based on the actual data. Given the performance results, we found the model to be explainable with the potential to be utilized in the clinical area.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.121-138
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• 2024

Artificial intelligence is crucial to manufacturing productivity. Understanding the difficulties in producing disruptions, especially in linear feed robot systems, is essential for efficient operations. These mechanical tools, essential for linear movements within systems, are prone to damage and degradation, especially in the LM guide, due to repetitive motions. We examine how explainable artificial intelligence (XAI) may diagnose wafer linear robot linear rail clearance and ball screw clearance anomalies. XAI helps diagnose problems and explain anomalies, enriching management and operational strategies. By interpreting the reasons for anomaly detection through visualizations such as Class Activation Maps (CAMs) using technologies like Grad-CAM, FG-CAM, and FFT-CAM, and comparing 1D-CNN with 2D-CNN, we illustrates the potential of XAI in enhancing diagnostic accuracy. The use of datasets from accelerometer and torque sensors in our experiments validates the high accuracy of the proposed method in binary and ternary classifications. This study exemplifies how XAI can elucidate deep learning models trained on industrial signals, offering a practical approach to understanding and applying AI in maintaining the integrity of critical components such as LM guides in linear feed robots.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.109-126
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• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1224-1232
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• 2022

Whole body bone scan is the most frequently performed nuclear medicine imaging to evaluate bone metastasis in cancer patients. We evaluated the performance of a VGG16-based transfer learning classifier for bone scan images in which metastatic bone lesion was present. A total of 1,000 bone scans in 1,000 cancer patients (500 patients with bone metastasis, 500 patients without bone metastasis) were evaluated. Bone scans were labeled with abnormal/normal for bone metastasis using medical reports and image review. Subsequently, gradient-weighted class activation maps (Grad-CAMs) were generated for explainable AI. The proposed model showed AUROC 0.96 and F1-Score 0.90, indicating that it outperforms to VGG16, ResNet50, Xception, DenseNet121 and InceptionV3. Grad-CAM visualized that the proposed model focuses on hot uptakes, which are indicating active bone lesions, for classification of whole body bone scan images with bone metastases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.49-57
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• 2022

Recently, various uses of artificial intelligence have been made possible through the deep artificial neural network structure of machine learning, demonstrating human-like capabilities. Unfortunately, the deep structure of the artificial neural network has not yet been accurately interpreted. This part is acting as anxiety and rejection of artificial intelligence. Among these problems, we solve the capability part of artificial neural networks. Calculate the size of the artificial neural network structure and calculate the size of data that the artificial neural network can process. The calculation method uses the group method used in mathematics to calculate the size of data and artificial neural networks using an order that can know the structure and size of the group. Through this, it is possible to know the capabilities of artificial neural networks, and to relieve anxiety about artificial intelligence. The size of the data and the deep artificial neural network are calculated and verified through numerical experiments.