• 대한임상검사과학회지
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• 제51권3호
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• pp.329-335
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• 2019

매년 호흡기 바이러스 감염으로 인해 수 백만명의 소아들이 사망한다. 호흡기 바이러스 감염의 원인 병원체 중 Human rhinovirus (HRV)는 코감기의 주요 원인 균으로 면역력이 약한 영, 유아, 노인 그리고 천식 환자에게 심각한 호흡기 감염의 원인으로 작용하는 병원체이다. 2012년 1월부터 2018년 12월까지 천안 단국대학교 병원 진단검사의학과에 호흡기 바이러스 검사가 의뢰된 호흡기 검체 9,010개의 검체를 real time reverse transcription PCR (real time RT-PCR) 방법으로 검사했다. 총 12종의 호흡기 바이러스를 real-time RT-PCR로 검출했다. 연구기간 중 평균 검출률은 21.3%이었고, HRV 양성 환자의 평균 연령은 6.5세였다. 7월의 검출률이 32.4%로 가장 높게 나타났고 2월이 8.3%로 가장 낮았다. 연령대별로 검출률을 분석해봤을 때 10세 미만의 검출률이 가장 높았다. HRV의 중복 감염률은 35.3%이고, 가장 흔한 조합은 Adenovirus와의 조합이었다. 호흡기 바이러스 감염증은 비슷한 임상 증상을 가지고 있어 빠른 진단이 이루어 져야 적절한 시기에 적절한 치료를 할 수 있다. 호흡기 바이러스 감염은 보통 면역력이 약한 어린아이와 노인에서 주로 발생하는 것으로 알려져 있다. 하지만 본 연구에서는 10세 미만에 이어 10대 환자들의 검출률이 높았다. 그리고 1,2월을 제외하고 15% 이상의 detection rate를 보였다. HRV의 감염 양상에 대한 꾸준한 연구가 필요할 것으로 사료된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• Asian Pacific Journal of Cancer Prevention
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• 제17권7호
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• pp.3637-3641
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• 2016

To determine the prevalence of human papillomavirus (HPV) among women with atypical squamous cells of undetermined significance (ASC-US) referred to colposcopy and the implications for clinical management in low- and middle-income countries (LMIC), the present study was conducted. We included 200 women living in $Maring{\acute{a}}$/Brazil referred to colposcopy service between August 2012 and March 2013 due to an abnormal cytology from ASC-US until high-grade intraepithelial lesion (HSIL). HPV was detected and genotyped by polymerase chain reaction (PCR). The mean age was $36.8{\pm}10.5$ years, and women with and without ASC-US had similar mean ages ($37.4{\pm}11.5$ and $36.4{\pm}9.96$ years, respectively). The highest prevalence of ASC-US occurred at 20-24 years (40%). HPV-DNA was positive in 164 (82.0%) women.Of the 57 women with ASC-US, 30 (52.6%) were HPV-DNA-positive and 21 (70%) were high-risk HPV-positive (HR-HPV); the latter was similar to women without ASC-US (76.9%) but with other abnormal cytological findings present. Our data demonstrated that performing tests for HR-HPV can be used for management of women with ASC-US to support the decision of which women should be referred for an immediate or later colposcopy. The same conclusions can be applied to other LMICs for which HPV testing for primary screening has not been adopted.

• Parasites, Hosts and Diseases
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• 제58권2호
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• pp.181-184
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• 2020

Blastocystis, an enteric protist, has been reported to be an important cause of protozoal gastrointestinal manifestations in humans and animals worldwide. Animals harboring certain Blastocystis subtypes (STs) may serve as a potential source of human infection. However, information about the prevalence and genetic diversity of Blastocystis in alpacas is limited. In the present study, a total of 366 fecal samples from alpacas in Shanxi Province, northern China, were examined for Blastocystis by PCR amplification of the small subunit rRNA gene, followed by sequencing and phylogenetic analysis. The prevalence of Blastocystis in alpacas was 23.8%, and gender difference in the prevalence of Blastocystis was observed. The most predominant Blastocystis ST was ST10, followed by ST14 and ST5. The detection of ST5, a potentially zoonotic genotype, indicates that alpacas harboring ST5 could be a potential source of human infection with Blastocystis. These data provide new insight into the prevalence and genetic diversity of Blastocystis in alpacas.

• 로봇학회논문지
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• 제16권4호
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• pp.336-344
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• 2021

This paper presents a robust deep learning-based human tracking framework in crowded environments. For practical human tracking applications, a target must be robustly tracked even in undetected or overcrowded situations. The proposed framework consists of two parts: robust deep learning-based human detection and tracking while recognizing the aforementioned situations. In the former part, target candidates are detected using Detectron2, which is one of the powerful deep learning tools, and their weights are computed and assigned. Subsequently, a candidate with the highest weight is extracted and is utilized to track the target human using a Kalman filter. If the bounding boxes of the extracted candidate and another candidate are overlapped, it is regarded as a crowded situation. In this situation, the center information of the extracted candidate is compensated using the state estimated prior to the crowded situation. When candidates are not detected from Detectron2, it means that the target is completely occluded and the next state of the target is estimated using the Kalman prediction step only. In two experiments, people wearing the same color clothes and having a similar height roam around the given place by overlapping one another. The average error of the proposed framework was measured and compared with one of the conventional approaches. In the error result, the proposed framework showed its robustness in the crowded environments.

• 데이타베이스연구회지:데이타베이스연구
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• 제34권3호
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• pp.45-57
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• 2018

주행 중에 발생하는 졸음은 큰 사고로 직결될 수 있는 매우 위험한 운전자 상태이다. 졸음을 방지하기 위하여 운전자의 상태를 파악하는 전통적인 졸음 감지 방법들이 존재하지만 운전자들이 가지는 개개인의 특성을 모두 반영한 일반화 된 운전자 상태 인식에는 한계가 있다. 최근에는 운전자의 상태를 인식하기 위한 딥 러닝기반의 상태인식 연구들이 제안되었다. 딥 러닝은 인간이 아닌 기계가 특징을 추출하여 보다 일반화된 인식모델을 도출할 수 있는 장점이 있다. 본 연구에서는 운전자의 상태를 파악하기 위해 이미지와 PPG를 동시에 학습하여 기존 딥 러닝 방식보다 정확한 상태 인식 모델을 제안한다. 본 논문은 운전자의 이미지와 PPG 데이터가 졸음 감지에 어떤 영향을 미치는지, 함께 사용되었을 때 학습 모델의 성능을 향상시키는지 실험을 통해 확인하였다. 이미지만을 사용했을 때 보다 이미지와 PPG를 함께 사용하였을 때 3%내외의 정확도 향상을 확인했다. 또한, 운전자의 상태를 세 가지로 분류하는 멀티모달 딥 러닝 기반의 모델을 96%의 분류 정확도를 보였다.

• 분석과학
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• 제33권1호
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• pp.42-48
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• 2020

Bromate is a disinfection by-product generated mainly from the oxidation of bromide during the ozonation and disinfection process in order to remove pathogenic microorganism of drinking water, and classified as a possible human carcinogen by International Agency for Research of Cancer (IARC) and World Health Organization (WHO). For the purpose of determining the trace level concentration of bromate, several sensitive techniques are applied mostly based on suppressed conductivity detection and UV/Visible detection after postcolumn reaction (PCR). In this study, the suppressed conductivity detection method and the PCR-UV/Visible detection method through the triiodide reaction were compared to analyze the trace bromate in water samples and estimated for the availability of these analytical methods. In addtion, the state-of-the-art techniques was applied for the determination of trace level bromate in various water matrices, i.e., soft drinking water, hard drinking water, mineral water, swimming pool water, and raw water. In comparison of two analytical methods, it was found that the conductivity detection had the suitable advantage to simultaneously analyze bromate and inorganic anions, however, the bromate might not be precisely quantified due to the matrix effect especially by chloride ion. On the other hand, the trace bromate was analyzed effectively by the method of PCR-UV/Visible detection through triiodide reaction to satisfactorily minimize the matrix interference of chloride ion in various water samples, showing the good linearity and reproducibility. Furthermore, the method detection limit (MDL) and recovery were 0.161 ㎍/L and 101.0-108.1 %, respectively, with a better availability compared to conductivity detection.

• 디지털산업정보학회논문지
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• 제12권4호
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• pp.1-12
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• 2016

Digital imaging technology has developed into a state-of-the-art IT convergence, composite industry beyond the limits of the multimedia industry, especially in the field of smart object recognition, face - Application developed various techniques have been actively studied in conjunction with the phone. Recently, face recognition technology through the object recognition technology and evolved into intelligent video detection recognition technology, image recognition technology object detection recognition process applies to skills through is applied to the IP camera, the image object recognition technology with face recognition and active research have. In this paper, we first propose the necessary technical elements of the human factor technology trends and look at the human object recognition based spFACS (Smile Progress Facial Action Coding System) for detecting smiles study plan of the image recognition technology recognizes objects. Study scheme 1). ASM algorithm. By suggesting ways to effectively evaluate psychological research skills through the image object 2). By applying the result via the face recognition object to the tooth area it is detected in accordance with the recognized facial expression recognition of a person demonstrated the effect of extracting the feature points.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2023년도 제35회 한글 및 한국어 정보처리 학술대회
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• pp.525-530
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• 2023

최근 소셜 네트워크 서비스(SNS) 및 모바일 서비스가 증가함에 따라 사용자들은 다양한 종류의 위험에 직면하고 있다. 특히 온라인 그루밍과 온라인 루머 같은 위험은 한 개인의 삶을 완전히 망가뜨릴 수 있을 정도로 심각한 문제로 자리 잡았다. 그러나 많은 경우 이러한 위험들을 판단하는 시점은 사건이 일어난 이후이고, 주로 법적인 증거채택을 위한 위험성 판별이 대다수이다. 따라서 본 논문은 이러한 문제를 사전에 예방하는 것에 초점을 맞추었고, 계속적으로 발생하는 대화와 같은 event를 실시간으로 감지하고, 위험을 사전에 탐지할 수 있는 Real-Time Early Risk Detection(RERD) 문제를 정의하고자 한다. 온라인 그루밍과 루머를 실시간 조기 위험 감지(RERD) 문제로 정의하고 해당 데이터셋과 평가지표를 소개한다. 또한 RERD 문제를 정확하고 신속하게 해결할 수 있는 강화학습 기반 새로운 방법론인 RT-ERD 모델을 소개한다. 해당 방법론은 RERD 문제를 이루고 있는 온라인 그루밍, 루머 도메인에 대한 실험에서 각각 기존의 모델들을 뛰어넘는 state-of-the-art의 성능을 달성하였다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1998년도 Proceedings ICPE 98 1998 International Conference on Power Electronics
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• pp.719-723
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• 1998

Prevention of the islanding phenomena is one of the most important issues because it can damage electrical equipment connected to the utility system and endanger human life. It is very difficult to detect an islanding condition of a power distribution line with conventional voltage of frequency relays, while the output power and the load power of utility interactive PV inverter units are in nearly balanced state in both active power and reactive power. This paper describes the protective equipment that prevents the PV system connected to the utility grid from starting islanding. Both predictive ocntrol method and harmonic injection method are used for a current control and islanding detection for operating safety.