• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.37-42
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• 2022

In this paper, AI methods such as deep learning are applied to provide customized exercise therapy for patients with kidney disease. In order to apply deep learning, a dataset that can determine kidney disease is trained to determine whether it is a kidney disease, and 1RM, which is the user's physical information and muscle strength according to whether it is a disease, can also be calculated through deep learning. The calculated muscle strength of 1RM was converted into resistant exercise for each part through a calculation equation for each part of the body, and was configured to be provided with an aerobic exercise amount tailored to the user's body information. If continuous research is conducted in the manner proposed in this paper, customized exercise therapy can be provided for various diseases.

• Journal of Radiation Protection and Research
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• v.47 no.3
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• pp.111-133
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• 2022

The exciting advancement related to the "modeling of digital human" in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.243-244
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• 2020

본 논문에서는 기존 AI 기능을 탑재한 교육용 프로그래밍 언어 기반의 서비스들의 문제점을 개선할 수 있는 머신러닝 학습을 위한 교육용 프로그래밍 언어 기반 실습 플랫폼을 제안한다. 이번 연구에서는 기존 교육용 프로그래밍 언어 기반 서비스의 대표주자인 Scratch 3.0과 Tensorflow를 접목하여 AI에 대한 높은 이해도를 가질 수 있도록 하는 학습 방향을 제시하고 Gray-Box 형태의 학습 모델 서비스를 구현한다.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.1-11
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• 2020

Recently, autonomous things, which are pieces of equipment or devices that grasp the context of circumstances on their own and perform actions appropriate for the situation in the surrounding environment, are attracting much research interest. This is because autonomous things are expected to be able to interact with humans more naturally, supersede humans in many tasks, and further solve problems by themselves by collaborating with each other without human intervention. This prospect leans heavily on AI as deep learning has delivered astonishing breakthroughs recently and broadened its range of applications. This paper surveys application trends in deep learning-based AI techniques for autonomous things, especially autonomous driving vehicles, because they present a wide range of problems involving perception, decision, and actions that are very common in other autonomous things.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.632-633
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• 2023

Diabetic retinopathy (DR) is a prevalent complication of diabetes that can lead to vision impairment if not diagnosed and treated promptly. This study presents a novel approach for the automated grading of diabetic retinopathy in ultra-widefield fundus images (UFI) using deep learning techniques. We propose a method that involves preprocessing UFIs by cropping the central region to focus on the most relevant information. Subsequently, we employ state-of-the-art deep learning models, including ResNet50, EfficientNetB3, and Xception, to perform DR grade classification. Our extensive experiments reveal that Xception outperforms the other models in terms of classification accuracy, sensitivity, and specificity. his research contributes to the development of automated tools that can assist healthcare professionals in early DR detection and management, thereby reducing the risk of vision loss among diabetic patients.

• Electronics and Telecommunications Trends
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• v.36 no.2
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• pp.32-42
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• 2021

The rapid growth of deep-learning applications has invoked the R&D of artificial intelligence (AI) processors. A dedicated software framework such as a compiler and runtime APIs is required to achieve maximum processor performance. There are various compilers and frameworks for AI training and inference. In this study, we present the features and characteristics of AI compilers, training frameworks, and inference engines. In addition, we focus on the internals of compiler frameworks, which are based on either basic linear algebra subprograms or intermediate representation. For an in-depth insight, we present the compiler infrastructure, internal components, and operation flow of ETRI's "AI-Ware." The software framework's significant role is evidenced from the optimized neural processing unit code produced by the compiler after various optimization passes, such as scheduling, architecture-considering optimization, schedule selection, and power optimization. We conclude the study with thoughts about the future of state-of-the-art AI compilers.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.35-36
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• 2023

본 논문에서는 OpenAI Gym 환경에서 제공하는 Acrobot-v1에 대해 DQN(Deep Q-Networks) 강화학습으로 학습시키고, 이 때 적용되는 활성화함수의 성능을 비교분석하였다. DQN 강화학습에 적용한 활성화함수는 ReLU, ReakyReLU, ELU, SELU 그리고 softplus 함수이다. 실험 결과 평균적으로 Leaky_ReLU 활성화함수를 적용했을 때의 보상 값이 높았고, 최대 보상 값은 SELU 활성화 함수를 적용할 때로 나타났다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.389-396
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• 2024

Generative AI has recently been utilized across all fields, achieving expert-level advancements in deep data analysis. However, identifying regional names in scientific literature remains a challenge due to insufficient training data and limited AI application. This study developed a standardized dataset for effectively classifying regional names using address data from Korean institution-affiliated authors listed in the Web of Science. It tested and evaluated the applicability of machine learning and deep learning models in real-world problems. The BERT model showed superior performance, with a precision of 98.41%, recall of 98.2%, and F1 score of 98.31% for metropolitan areas, and a precision of 91.79%, recall of 88.32%, and F1 score of 89.54% for city classifications. These findings offer a valuable data foundation for future research on regional R&D status, researcher mobility, collaboration status, and so on.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.75-81
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• 2024

In this paper, we proposes a method for real-time processing of inter-floor noise problems by embedding TinyML, which includes a deep learning model, into ultra-low-power systems. The reason this method is feasible is because of lightweight deep learning model technology, which allows even systems with small computing resources to perform inference autonomously. The conventional method proposed to solve inter-floor noise problems was to send data collected from sensors to a server for analysis and processing. However, this centralized processing method has issues with high costs, complexity, and difficulty in real-time processing. In this paper, we address these limitations by employing On-Sensor AI using TinyML. The method presented in this paper is simple to install, cost-effective, and capable of processing problems in real-time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.