• 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.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1049-1058
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• 2022

HAR(Human Action Recognition) such as anomaly and object detection has become a trend in research field(s) that focus on utilizing Artificial Intelligence (AI) methods to analyze patterns of human action in crime-ridden area(s), media services, and industrial facilities. Especially, in real-time system(s) using video streaming data, HAR has become a more important AI-based research field in application development and many different research fields using HAR have currently been developed and improved. In this paper, we propose and analyze a deep-learning-based HAR that provides more efficient scheme(s) using an intelligent AI models, such system can be applied to media services using RGB video streaming data usage without feature extraction pre-processing. For the method, we adopt Slowfast based on the Deep Neural Network(DNN) model under an open dataset(HMDB-51 or UCF101) for improvement in prediction accuracy.

• 대한원격탐사학회지
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• 제38권6_2호
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• pp.1581-1587
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• 2022

이제는 딥러닝 없는 원격탐사 데이터 처리는 상상하기도 어려운 시대가 되었다. 원격탐사의 활용기술 개발을 위해서는 먼저 인공지능(artificial intelligence, AI)을 위한 데이터를 설계 및 구축하고, AI모델을 학습시키는 과정을 거친다. AI모델은 빠르게 발전하여 모델 정확도가 나날이 높아지고 있지만, 모델을 훈련시키는 사람에 따라 정확도의 편차가 발생하고 있다. 결국 AI모델을 훈련시킬 수 있는 숙련도 높은 전문가가 더욱 더 필요한 시대가 되어가고 있다. 특히, 딥러닝기술은 원격탐사활용에 있어 자동화라는 키워드를 제공하고 있다. 예전에는 60% 이하의 정확도만 있었던 기술도 이제는 90%를 넘어 100%의 시대로 가고 있다. 이 특별호에서는 딥러닝기술이 원격탐사에 어떻게 활용되고 있는지에 관한 13편의 논문을 소개한다.

• 한국지진공학회논문집
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• 제25권2호
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• 한국방사선학회논문지
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• 제17권6호
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• pp.873-879
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• 2023

본 연구는 인공지능(AI)을 사용하여 흉부 엑스레이 이미지에서 이물질을 탐지하는 방법을 탐구하였다. 의료영상학, 특히 흉부 엑스레이는 폐렴이나 폐암과 같은 질병을 진단하는 데 매우 중요한 역할을 한다. 영상의학 검사가 증가함에 따라 AI는 효율적이고 빠른 진단을 위한 중요한 도구가 되었다. 하지만 이미지에는 단추나 브래지어 와이어와 같은 일상적인 장신구를 포함한 이물질이 포함될 수 있어 정확한 판독을 방해할 수 있다. 본 연구에서는 이러한 이물질을 정확하게 식별하는 AI 알고리즘을 개발하였고, 미국 국립보건원 흉부 엑스레이 데이터셋을 가공하여 YOLOv8 모델을 기반으로 처리하였다. 그 결과 정확도, 정밀도, 리콜, F1-score가 모두 0.91에 가까울 정도로 높은 탐지 성능을 보였다. 이번 연구는 AI의 뛰어난 성능에도 불구하고 이미지 내 이물질로 인해 판독 결과가 왜곡될 수 있는 문제점을 해결함으로써 영상의학 분야에서 AI의 혁신적인 역할과 함께, 임상 구현에 필수적인 정확성에 기반하여 신뢰성을 강조하였다.

• 한국컴퓨터정보학회논문지
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• 제27권8호
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• pp.1-7
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• 2022

인공지능 기술이 발달하면서 뉴로사이언스 마이닝(NSM: NeuroScience Mining)과 AI를 접목하려는 시도가 증가하고 있다. 나아가 NSM은 뉴로사이언스와 비즈니스 애널리틱스의 결합으로 인해 연구범위가 확장되고 있다. 본 연구에서는 fNIRS 실험을 통해 확보한 뉴로 데이터를 분석하여 비즈니스 문제 해결 창의성(BPSC: business problem-solving creativity)을 예측하고 이를 통해 NSM의 잠재력을 조사한다. BPSC는 비즈니스에서 차별성을 가지게 하는 중요한 요소이지만, 인지적 자원의 하나인 BPSC의 측정 및 예측에는 한계가 존재한다. 본 논문에서는 BPSC 예측 성능을 높이는 방안으로 CNN, BiLSTM 그리고 어텐션 네트워크를 결합한 새로운 NSM 기법을 제안한다. 제안된 NSM 기법을 15만 개 이상의 fNIRS 데이터를 활용하여 유효성을 입증하였다. 연구 결과, 본 논문에서 제안하는 NSM 방법이 벤치마킹한 알고리즘(CNN, BiLSTM)에 비하여 우수한 성능을 가지는 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1545-1559
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• 2023

In the cloud environment, microservices are implemented through Kubernetes, and these services can be expanded or reduced through the autoscaling function under Kubernetes, depending on the service request or resource usage. However, the increase in the number of nodes or distributed microservices in Kubernetes and the unpredictable autoscaling function make it very difficult for system administrators to conduct operations. Artificial Intelligence for IT Operations (AIOps) supports resource management for cloud services through AI and has attracted attention as a solution to these problems. For example, after the AI model learns the metric or log data collected in the microservice units, failures can be inferred by predicting the resources in future data. However, it is difficult to construct data sets for generating learning models because many microservices used for autoscaling generate different metrics or logs in the same timestamp. In this study, we propose a cloud data refining module and structure that collects metric or log data in a microservice environment implemented by Kubernetes; and arranges it into computing resources corresponding to each service so that AI models can learn and analogize service-specific failures. We obtained Kubernetes-based AIOps learning data through this module, and after learning the built dataset through the AI model, we verified the prediction result through the differences between the obtained and actual data.

• 한국군사과학기술학회지
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• 제25권3호
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• pp.219-230
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• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.

• 식품보건융합연구
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• 제9권4호
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• pp.19-23
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• 2023

The integration of ChatGPT, an AI-powered language model, is causing a profound transformation within the food industry, impacting various domains. It offers novel capabilities in recipe creation, personalized dining, menu development, food safety, customer service, and culinary education. ChatGPT's vast culinary dataset analysis aids chefs in pushing flavor boundaries through innovative ingredient combinations. Its personalization potential caters to dietary preferences and cultural nuances, democratizing culinary knowledge. It functions as a virtual mentor, empowering enthusiasts to experiment creatively. For personalized dining, ChatGPT's language understanding enables customer interaction, dish recommendations based on preferences. In menu development, data-driven insights identify culinary trends, guiding chefs in crafting menus aligned with evolving tastes. It suggests inventive ingredient pairings, fostering innovation and inclusivity. AI-driven data analysis contributes to quality control, ensuring consistent taste and texture. Food writing and marketing benefit from ChatGPT's content generation, adapting to diverse strategies and consumer preferences. AI-powered chatbots revolutionize customer service, improving ordering experiences, and post-purchase engagement. In culinary education, ChatGPT acts as a virtual mentor, guiding learners through techniques and history. In food safety, data analysis prevents contamination and ensures compliance. Overall, ChatGPT reshapes the industry by uniting AI's analytics with culinary expertise, enhancing innovation, inclusivity, and efficiency in gastronomy.

• 스마트미디어저널
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• 제10권2호
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• pp.22-29
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• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.