• Journal of Veterinary Science
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• 제22권1호
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• pp.9.1-9.11
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• 2021

Background: Scalding burn injuries can occur in everyday life but occur more frequently in young children. Therefore, it is important to develop more effective burn treatments. Objectives: This study examined the effects of bee venom (BV) stimulation on scalding burn injury-induced nociception in mice as a new treatment for burn pain. Methods: To develop a burn injury model, the right hind paw was immersed temporarily in hot water (65℃, 3 seconds). Immediately after the burn, BV (0.01, 0.02, or 0.1 mg/kg) was injected subcutaneously into the ipsilateral knee area once daily for 14 days. A von Frey test was performed to assess the nociceptive response, and the altered walking parameters were evaluated using an automated gait analysis system. In addition, the peripheral and central expression changes in substance P (Sub P) were measured in the dorsal root ganglion and spinal cord by immunofluorescence. Results: Repeated BV treatment at the 2 higher doses used in this study (0.02 and 0.1 mg/kg) alleviated the pain responses remarkably and recovered the gait performances to the level of acetaminophen (200 mg/kg, intraperitoneal, once daily), which used as the positive control group. Moreover, BV stimulation had an inhibitory effect on the increased expression of Sub P in the peripheral and central nervous systems by a burn injury. Conclusions: These results suggest that a peripheral BV treatment may have positive potency in treating burn-induced pain.

• 한국기계가공학회지
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• 제18권7호
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• pp.56-62
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• 2019

The high-precision laser scriber carries out scribing alumina ceramic substrates for manufacturing ultra-small chip resistors. The ceramic substrates are loaded, aligned, scribed, transferred, and unloaded. The entire process is fully automated, thereby minimizing the scribing cycle time of the ceramic substrates and improving the throughput. The scriber consists of the laser optical system, pick-up module of ceramic substrates, pre-alignment module, TH axis drive work table, automation module for substrate loading / unloading, and high-speed scribing control S/W. The loader / unloader unit, which has the greatest influence on the scribing cycle time of the substrates, carries the substrates to the work table that carries out the cutting line work by driving the X and Y axes as well as by adsorbing the ceramic substrates. The loader / unloader unit consists of the magazine up / down part, X-axis drive part for conveying the substrates to the left and right direction, and the vision part for detecting the edge of the substrate for the primary pre-alignment of the substrates. In this paper, the laser scribing machining simulation is performed by applying the instrument mechanism of each component module. Through this study, the scribing machining process is first verified by analyzing the process operation and work area of each module in advance. In addition, the scribing machining process is optimized by comparing and analyzing the scribing cycle time of one ceramic substrate according to the alignment stage module speed.

• 한국건설관리학회논문집
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• 제22권5호
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• pp.48-54
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• 2021

고용노동부의 2020년 산업재해 사고 사망 통계 발표에 따르면 최근 5년간 발생한 업무상 사고 사망 재해의 절반 이상이 건설업에서 발생하고 있다. 그중 건설기계와 관련된 충돌 및 협착 사고가 사망 재해의 큰 부분을 차지하고 있다는 것을 알 수 있다. 정부는 건설 현장에서 발생하는 사고를 예방하기 위해 "건설안전특별법" 발의, 사고 예방을 위한 새로운 기술의 도입 장려 등 큰 노력을 기울이고 있지만, 여전히 건설 현장에서 수많은 안전사고가 발생하고 있다. 이에 본 연구는 초음파 스캐닝 기술을 통해 반경 내 대상의 종류와 위치를 인식하여 건설기계와 작업자 간의 충돌사고를 예방할 수 있는 기술을 개발했다. 본 연구는 파일럿 실험을 수행하였으며, 결과 분석을 통해 대상 인식과 위치 추정 모두에서 높은 정확도로 기술의 실현 가능성을 증명하였다. 개발한 기술은 건설 현장에서 발생하는 충돌사고를 예방하고, 자동화된 건설기계 충돌사고 예방 기술 개발에 이바지할 수 있을 것으로 기대된다.

• 정보화정책
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• 제27권4호
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• pp.47-66
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• 2020

머신러닝과 딥러닝 등 인공지능 기술의 급속한 발전은 행정-정책 분야에도 영향을 확대하고 있다. 이 논문은 데이터분석과 알고리즘의 발전으로 자동화된 구성과 운용을 설계하는 인공지능 시대의 정책의사결정에 관한 탐색적 연구이다. 이 연구의 의의는 정책의사결정에서의 주요 연구 중 하나인 정책 문제의 문제구조화를 기반으로 하여, 문제정의가 잘 구조화된 정도에 따른 유형으로 이론적 틀을 구성하여 성공과 실패 사례를 구분하고 분석해서 시사점을 도출하였다. 즉 문제구조화가 어려운 유형일수록 인공지능을 활용한 의사결정의 실패 혹은 부작용의 우려가 크다는 것이다. 또한 알고리즘의 중립성여부에 대한 우려도 제시하였다. 정책적 제언으로는 우리나라 인공지능 추진체계구축 시 기술적 측면과 사회적 측면의 전문가들이 전문적으로 역할을 하는 소위원회를 병렬적으로 두고 이 소위원회들이 종합적, 융합적으로도 작동할 수 있는 운영의 묘를 발휘하는 거버넌스 추진체계 구축이 필요함을 제시하고 있다.

• 한국미생물·생명공학회지
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• 제50권1호
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• pp.147-156
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• 2022

Urinary tract infections (UTIs) are one of the most common infections in different age groups, including children. Bacteria are the main etiological agents of UTIs. The aim of the present study was to isolate, identify, and determine the antibiotic susceptibility of bacteria isolated from children with UTIs from Baghdad, Iraq. Three hundred and two urine samples were collected from children aged 6 months to 12 years. The samples were cultured on blood agar and MacConkey agar. The selected colonies were subjected to biochemical tests and antibiotic susceptibility analysis using the Vitek^® 2 Compact automated microbial identification system. In this sample, 299 bacteria were identified, of which, 267 were gram-negative bacteria, and 32 were gram-positive bacteria. Escherichia coli (56%) was the most commonly isolated gram-negative bacteria, followed by Pseudomonas aeruginosa (14%), Enterobacter spp. (10.48%), Klebsiella pneumoniae (9.36%), Proteus spp. (7.8%), Acinetobacter baumannii (1.5%), and Morganella morganii (0.37%). Enterococcus faecalis (62.5%) was the most commonly detected gram-positive bacteria, followed by Staphylococcus aureus (37.5%). E. coli and P. aeruginosa were the most antibiotic-resistant bacteria. Among the tested antibiotics, meropenem showed 100% sensitivity, followed by imipenem (97.4%), amikacin (91.8%), and tobramycin (83.5%). In contrast, the high frequencies of resistance were observed with cefixime (93.2%), cefotaxime (78.7%), and ceftriaxone/cefotaxime (71.2%). In conclusion, carbapenems and aminoglycosides are highly recommended for the empirical treatment of UTIs, while, Quinolones, penicillins, and cephalosporins are not suggested. Frequent antibiotics susceptibility testing are warranted to determine the resistance pattern of UTI bacteria.

• 동의생리병리학회지
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• 제35권6호
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• pp.267-273
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• 2021

Mibyeong (sub-health) is a concept that represents the sub-health in traditional East Asian medicine. Assuming that the nose sizes and shapes are related to respiratory function, in this study, we hypothesized that the nose size and shape features are related to the self-rated health (SRH) level and self-rated Mibyeong severity, and aimed to assess this relationship using a fully automated image analysis system. The nose size features were evaluated from the frontal and profile face images of 810 participants. The nose size features consisted of five length features, one area feature, and one volume feature. The level of SRH and the Mibyeong severity were determined using a questionnaire. The normalized nasal height was negatively associated with the self-rated health score (SRHS) (partial ρ = -0.125, p = 3.53E-04) and the Mibyeong score (MBS) (partial ρ = -.172, p = 9.38E-07), even after adjustment for sex, age, and body mass index. The normalized nasal volume (ρ = -.105, p = 0.003), the normalized nasal tip protrusion length (ρ = -.087, p = 0.014), and the normalized nares width (ρ = -.086, p = .015) showed significant correlation with the SRHS. The normalized nasal area (ρ = -.118, p = 0.001), the normalized nasal volume (ρ = -.107, p = .002) showed significant correlation with the MBS. The wider, longer, and larger the nose, the lower the SRHS and MBS, indicating that health status can be estimated based on the size and shape features of the nose.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권11호
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• pp.473-478
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• 2022

온라인 저지 시스템 지원하기 위한 표절 검사, 소스코드 분석 및 자동화된 튜터링 기법이 연구되고 있다. 최근 딥러닝 기술 기반의 소스코드 유사도 분석을 통한 표절 감지 기술들이 제안되었으나, 자동화된 튜터링을 지원하기 위한 딥러닝 기반의 연구는 미흡한 실정이다. 따라서 본 논문에서는 자바 바이트코드와 문제정보를 결합하여 학습하고, 학습자가 온라인 저지 시스템에 코드를 제출하기 전에 pass/fail 여부를 예측할 수 있는 GRU 기반의 Input / Output side FiLM 모델을 제안한다. 또한 온라인 저지에 수집되는 데이터의 특성상 비대칭이 발생하기 때문에 밸런스 샘플링 기법을 적용하여 데이터를 균등하게 분포시켜 두 상황을 제안한 모델로 학습하였다. 실험 결과 Input side FiLM 모델이 가장 높은 73.63%의 성능을 보였다. 이를 기반으로 학습자들이 온라인 저지의 평가를 받기 전에 pass/faill 여부를 확인하여 소스코드 개선에 대한 피드백 기능에 적용 가능할 것으로 예상된다.

• 산업경영시스템학회지
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• 제46권1호
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• 정보보호학회논문지
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• 제34권1호
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• pp.21-29
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• 2024

ICT와 network의 발달로 규모가 커진 IT 인프라의 보안 관리가 매우 어려워지고 있다. 많은 회사나 공공기관에서 시스템과 네트워크 보안 관리에 어려움을 겪고 있다. 또한 하드웨어와 소프트웨어의 복잡함이 커짐에 따라 사람이 모든 보안을 관리한다는 것은 불가능에 가까워지고 있다. 따라서 네트워크 보안 관리에 AI가 필수적이다. 하지만 실제 네트워크 환경에 공격 모델을 구동하는 것은 매우 위험하기에 실제와 유사한 네트워크 환경을 구현하여 강화학습을 통해 사이버 보안 시뮬레이션 연구를 진행하였다. 이를 위해 본 연구는 강화학습을 네트워크 환경에 적용하였고, 에이전트는 학습이 진행될수록 해당 네트워크의 취약점을 정확하게 찾아냈다. AI를 통해 네트워크의 취약점을 발견하면, 자동화된 맞춤 대응이 가능해진다.

• 대한의용생체공학회:의공학회지
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• 제45권2호
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• pp.95-100
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• 2024

Vitiligo is a condition characterized by the destruction or dysfunction of melanin-producing cells in the skin, resulting in a loss of skin pigmentation. Facial vitiligo, specifically affecting the face, significantly impacts patients' appearance, thereby diminishing their quality of life. Evaluating the efficacy of facial vitiligo treatment typically relies on subjective assessments, such as the Facial Vitiligo Area Scoring Index (F-VASI), which can be time-consuming and subjective due to its reliance on clinical observations like lesion shape and distribution. Various machine learning and deep learning methods have been proposed for segmenting vitiligo areas in facial images, showing promising results. However, these methods often struggle to accurately segment vitiligo lesions irregularly distributed across the face. Therefore, our study introduces a framework aimed at improving the segmentation of vitiligo lesions on the face and providing an evaluation of vitiligo lesions. Our framework for facial vitiligo segmentation and lesion evaluation consists of three main steps. Firstly, we perform face detection to minimize background areas and identify the face area of interest using high-quality ultraviolet photographs. Secondly, we extract facial area masks and vitiligo lesion masks using a semantic segmentation network-based approach with the generated dataset. Thirdly, we automatically calculate the vitiligo area relative to the facial area. We evaluated the performance of facial and vitiligo lesion segmentation using an independent test dataset that was not included in the training and validation, showing excellent results. The framework proposed in this study can serve as a useful tool for evaluating the diagnosis and treatment efficacy of vitiligo.