• 성인간호학회지
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• 제22권1호
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• pp.60-69
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• 2010

Purpose: The aim of this study was to investigate the level of resilience and related factors in patients with Parkinson's disease (PD) in Korea. Methods: Data were obtained from 148 patients using the Resilience Scale (RS), Beck's Depression Inventory (BDI), and Spielberger's Anxiety Inventory (AI). Results: The mean scores of the RS, BDI, and AI were $127.7{\pm}21.6$, $12.9{\pm}9.3$, and $41.9{\pm}11.1$, respectively. The RS score was strongly correlated with the BDI score (r=-.531, p<.001) and the AI (r=-.572, p<.001). The resilience was significantly revealed by household income (F=4.002, p=.009) and presence of a hobby (t=-3.300, p=.001). In addition, resilience was significantly correlated with age of disease onset (r=.164, p=.046), years of living with PD (r=-.262, p=.001), and the length of treatment with levodopa (r=-.283, p<.001). From the stepwise multiple regression analysis, the most important factors related to the RS score were the AI score, household income, and length of treatment with levodopa. Conclusion: Understanding these factors is essential for developing effective interventions to improve resilience in patients with PD.

• 한국군사과학기술학회지
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• 제25권4호
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• pp.372-380
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• 2022

This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.

• Journal of Gastric Cancer
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• 제23권3호
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• pp.375-387
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• 2023

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

• 대한원격탐사학회지
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• 제40권1호
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• 한국산업융합학회 논문집
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• 제24권5호
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• pp.555-562
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• 2021

In this paper, we propose a sorting method for extracting accurate license plate information, which is currently used in Korea, after detecting objects using YOLO. We propose sorting methods for the five types of vehicle license plates managed by the Ministry of Land, Infrastructure and Transport by classifying the plates with the number of lines, Korean characters, and numbers. The results of experiments with 5 license plates show that the proposed algorithm identifies all license plate types and information by focusing on the object with high reliability score in the result label file presented by YOLO and deleting unnecessary object information. The proposed method will be applicable to all systems that recognize license plates.

• 정보보호학회논문지
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• 제34권4호
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• pp.587-596
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• 2024

사이버 공격, 위협이 복잡해지고 빠르게 진화하면서, 4차 산업 혁명의 핵심 기술인 인공지능(AI)을 이용하여 사이버 위협 탐지 시스템 구축이 계속해서 주목받고 있다. 특히, 기업 및 정부 조직의 보안 운영 센터(Security Operations Center)에서는 보안 오케스트레이션, 자동화, 대응을 뜻하는 SOAR(Security Orchestration, Automation and Response) 솔루션 구현을 위해 AI를 활용하는 사례가 증가하고 있으며, 이는 향후 예견되는 근거를 바탕으로 한 지식인 사이버 위협 인텔리전스(Cyber Threat Intelligence, CTI) 구축 및 공유를 목적으로 한다. 본 논문에서는 네트워크 트래픽, 웹 방화벽(WAF) 로그 데이터를 대상으로 한 사이버 위협 탐지 기술 동향을 소개하고, TF-IDF(Term Frequency-Inverse Document Frequency) 기술과 자동화된 머신러닝(AutoML)을 이용하여 웹 트래픽 로그 공격 유형을 분류하는 방법을 제시한다.

• Tuberculosis and Respiratory Diseases
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• 제84권2호
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• pp.115-124
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• 2021

Background: This study aimed to determine the parameters for worsening oxygenation in non-severe coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective cohort study included cases of confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as that with SpO₂ ≤94% or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening oxygenation group that did not experience any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest computed tomography (CT) was calculated using artificial intelligence (AI) and measured visually by a radiologist. Results: We included 136 patients, with 32 (23.5%) patients in the worsening oxygenation group; of whom, two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups; however, univariate logistic regression analysis revealed that a variety of parameters on admission were associated with an increased risk of a desaturation event. A subset of patients was studied to eliminate potential bias, that ferritin ≥280 ㎍/L (p=0.029), lactate dehydrogenase ≥240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52). Conclusion: Our study suggests that initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation on admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

• 한국산학기술학회논문지
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• 제20권2호
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• pp.750-762
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• 2019

기업의 대고객 접점으로써 콜센터의 중요성은 커지고 있다. 하지만, 콜센터는 상담사의 지식 부족과 업무 부적응에 따른 잦은 이직으로 인해 상담사 운영이 어렵고, 이로 인한 고객 서비스 품질 저하의 문제를 안고 있다. 이에 본 연구에서는 상담사에게 업무 지식에 대한 부하를 줄이고 서비스 품질을 향상 시키기 위해 음성 인식 기술과 자연어 처리 및 질의응답을 지원하는 AI 기술과 PBX, CTI 등의 콜센터 정보시스템을 결합하여 실시간으로 상담사에게 고객의 질의 내용에 대한 답변을 제공해주는 "실시간 상담 도우미" 시스템 개발 방안에 대해 N은행 콜센터 사례를 통해 연구하였다. 사례연구 결과, 실시간 통화 분석을 위한 음성인식 시스템의 구성방안과, 질의응답 시스템의 자연어처리 성능 향상을 위한 말뭉치 구축 방안을 확인 할 수 있었으며, 특히 개체명 인식기의 경우 도메인에 맞는 말뭉치 학습 후 정확도가 31% 향상됨을 확인하였다. 또한, 상담 도우미 시스템을 적용한 후 상담 도우미의 답변에 대한 상담사들의 긍정적 피드백 비율이 93.1%로써 충분히 상담사 업무에 도움을 주고 있음을 확인하였다.

• Journal of Animal Science and Technology
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• 제63권1호
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• pp.36-45
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• 2021

Presently, there is an increased demand for livestock products all over the world which has led to more devotion on improving livestock population. Although goats have been bred for a long time in Korea, but there is not much research conducted on traditional Korean black goat (Capra hircus coreanae) compared to other livestock populations. Mutton consumption has been dramatically changing from medicinal use to edible meat and this trend directs the black goat populations declining and also mutton import quantities are increasing consistently. The present study introduced a new estrus synchronizing technique with subsequent artificial insemination (AI) for Korean black goats to enable crossbreeding with non-native breeds for the small or subsistent farmers. Our data highlighted that, the percentage of motile sperm from the electro-ejaculated samples declined significantly after freezing and melting. In addition, the sperm motility significantly declined with regard to sperm incubation period (0, 5, 60, and 120 min at 37℃) and was negatively correlated (64.2 ± 7.9%, 63.3 ± 5.8%, 49.9 ± 6.3%, and 35.9 ± 7.6%, respectively) in frozen-thawed sperm samples. Moreover, the E2 levels were unchanged even 24 h after controlled internal drug releas (CIDR) withdrawal. But, 48 h and 72 h after CIDR removal, E2 levels increased significantly. These data helps us to consider the two time points for AI; CIDR removal after 24 h, at which E2 decreases, and after 48 h, as the time at which progesterone increases. Additionally, the AI after 48 h of CIDR removal group exhibited significantly higher pregnancy and parturition rates (42.9%) compared to AI after 24 h after CIDR removal 28.6% group. In conclusion, these studies will propose an optimal estrus synchronisation process with subsequent timing of AI and also will promote the Korean black goat breeding industry.

• 대한영상의학회지
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• 제83권3호
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• pp.473-485
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• 2022

현대사회가 점차 고령화 사회가 됨에 따라 퇴행성 뇌질환의 발병률이 증가하고 있으며, 이러한 퇴행성 뇌질환에 관한 많은 연구들이 이루어지고 있다. 퇴행성 뇌질환의 진단에서 영상분석은 영상표지자로서 중요한 역할을 하고 있다. 영상분석에서 객관적이고 일관성 있는 평가는 퇴행성 뇌질환의 조기 진단 및 정확한 진단에 중요하다. 이에 다양한 퇴행성 뇌질환과 관련한 영상연구에 자기공명영상(이하 MRI)을 이용한 인공지능이 조기 진단과 최적의 치료 방향 계획 및 결정에 도움이 될 가능성을 보여주었다. 특히 MRI 기반의 뇌용적 측정과 분획화 및 특성을 포착하는 인공지능 소프트웨어들이 개발되고 연구되기 시작했다. 본 고찰에서는 우리나라에서 퇴행성 뇌질환과 관련하여 사용되고 있는 인공지능 소프트웨어의 현재 상황과 향후 인공지능 소프트웨어의 퇴행성 뇌질환 연구에의 활용, 그리고 인공지능 소프트웨어의 한계에 대해서 다루고자 한다.