• Structural Engineering and Mechanics
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• 제89권6호
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• pp.589-599
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• 2024

This study presents the usability of the high-rate single-frequency Precise Point Positioning (SF-PPP) technique based on 20 Hz Global Positioning Systems (GPS)-only observations in detecting dynamic motions. SF-PPP solutions were obtained from post-mission and real-time GNSS corrections. These include the International GNSS Service (IGS)-Final, IGS real-time (RT), real-time MADOCA (Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis), and real-time products from the Australian/New Zealand satellite-based augmentation systems (SBAS, known as SouthPAN). SF-PPP results were compared with LVDT (Linear Variable Differential Transformer) sensor and single-frequency relative positioning (SF-RP) solutions. The findings show that the SF-PPP technique successfully detects the harmonic motions, and the real-time products-based PPP solutions were as accurate as the final post-mission products. In the frequency domain, all GNSS-based methods evaluated in this contribution correctly detect the dominant frequency of short-term harmonic oscillations, while the differences in the amplitude values corresponding to the peak frequency do not exceed 1.1 mm. However, evaluations in the time domain show that SF-PPP needs high-pass filtering to detect accurate displacement since SF-PPP solutions include trends and low-frequency fluctuations, mainly due to atmospheric effects. Findings obtained in the time domain indicate that final, real-time, and MADOCA-based PPP results capture short-term dynamic behaviors with an accuracy ranging from 3.4 mm to 8.5 mm, and SBAS-based PPP solutions have several times higher RMSE values compared to other methods. However, after high-pass filtering, the accuracies obtained from PPP methods decreased to a few mm. The outcomes demonstrate the potential of the high-rate SF-PPP method to reliably monitor structural and earthquake-induced ground motions and vibration frequencies of structures.

• Journal of Chest Surgery
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• 제57권3호
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• pp.302-311
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• 2024

Background: Unexpected conversion to thoracotomy during planned video-assisted thoracoscopic surgery (VATS) can lead to poor outcomes and comparatively high morbidity. This study was conducted to assess preoperative risk factors associated with unexpected thoracotomy conversion and to develop a risk scoring model for preoperative use, aimed at identifying patients with an elevated risk of conversion. Methods: A retrospective analysis was conducted of 1,506 patients who underwent surgical resection for non-small cell lung cancer. To evaluate the risk factors, univariate analysis and logistic regression were performed. A risk scoring model was established to predict unexpected thoracotomy conversion during VATS of the lung, based on preoperative factors. To validate the model, an additional cohort of 878 patients was analyzed. Results: Among the potentially significant clinical variables, male sex, previous ipsilateral lung surgery, preoperative detection of calcified lymph nodes, and clinical T stage were identified as independent risk factors for unplanned conversion to thoracotomy. A 6-point risk scoring model was developed to predict conversion based on the assessed risk, with patients categorized into 4 groups. The results indicated an area under the receiver operating characteristic curve of 0.747, with a sensitivity of 80.5%, specificity of 56.4%, positive predictive value of 1.8%, and negative predictive value of 91.0%. When applied to the validation cohort, the model exhibited good predictive accuracy. Conclusion: We successfully developed and validated a risk scoring model for preoperative use that can predict the likelihood of unplanned conversion to thoracotomy during VATS of the lung.

• Journal of Veterinary Science
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• 제25권2호
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• pp.28.1-28.11
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• 2024

Background: Porcine circovirus type 2 (PCV2) infection is ubiquitous around the world. Diagnosis of the porcine circovirus-associated disease requires clinic-pathological elements together with the quantification of viral loads. Furthermore, given pig farms in regions lacking access to sufficient laboratory equipment, developing diagnostic devices with high accuracy, accessibility, and affordability is a necessity. Objectives: This study aims to investigate two newly developed diagnostic tools that may satisfy these criteria. Methods: We collected 250 specimens, including 170 PCV2-positive and 80 PCV2-negative samples. The standard diagnosis and cycle threshold (Ct) values were determined by quantitative polymerase chain reaction (qPCR). Then, two point-of-care (POC) diagnostic platforms, convective polymerase chain reaction (cPCR, qualitative assay: positive or negative results are shown) and EZtargex (quantitative assay: Ct values are shown), were examined and analyzed. Results: The sensitivity and specificity of cPCR were 88.23% and 100%, respectively; the sensitivity and specificity of EZtargex were 87.65% and 100%, respectively. These assays also showed excellent concordance compared with the qPCR assay (κ = 0.828 for cPCR and κ = 0.820 for EZtargex). The statistical analysis showed a great diagnostic power of the EZtargex assay to discriminate between samples with different levels of positivity. Conclusions: The two point-of-care diagnostic platforms are accurate, rapid, convenient and require little training for PCV2 diagnosis. These POC platforms can discriminate viral loads to predict the clinical status of the animals. The current study provided evidence that these diagnostics were applicable with high sensitivity and specificity in the diagnosis of PCV2 infection in the field.

• International Journal of Heart Failure
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• 제6권1호
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• 대한수의학회지
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• 제64권3호
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• pp.26.1-26.9
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• 2024

Japanese encephalitis virus (JEV) is a mosquito-borne virus that can infect pigs, horses, and other mammals, including humans. Sero-epidemiological investigations of JEV have been performed using hemagglutination inhibition (HI), virus neutralization (VN) tests and enzyme-linked immunosorbent assay (ELISA). A need exists for a new ELISA that can detect JEV antibodies in the sera of several animal species. We aimed to develop a blocking ELISA (B-ELISA) for detecting JEV antibodies in pig and horse serum samples. JEV antibodies in 218 pig and 315 horse serum samples were measured using HI and VN tests. The purified KV1899-306 strain was used as an antigen for B-ELISA. The purified antibody (7A13) was conjugated with horseradish peroxidase and used as a detector antibody. The sera of pigs and horses to measure antibody against JEV were subjected to B-ELISA and analyzed. The B-ELISA had a diagnostic sensitivity of 94.6% to 100%, a specificity of 91.2 to 100%, and an accuracy of 94.9 to 98.6% compared with those of the HI and VN tests in pig and horse sera. The B-ELISA had a higher correlation with pig sera (r = 0.89 and 0.90 for VN and HI) than with horse sera (r = 0.75 and to 0.79). The new B-ELISA could be useful in the sero-surveillance of JEV in pig and horse sera and replace indirect ELISA.

• 한국컴퓨터정보학회논문지
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• 제29권8호
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• pp.43-51
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• 2024

본 논문은 수중 활동을 주로 하는 다이버를 대상으로 특수 목적용 다이버 마스크를 이용해서 안구 데이터를 획득 및 분석하고, 이를 이용해서 사용자의 시선을 추적하는 방법에 대해 제안한다. 안구 데이터 분석을 위해 자체 제작한 안구 데이터 셋을 구축하였고, YOLOv8-nano 모델을 활용해서 학습 모델을 생성하였다. 학습 모델의 프레임 당 소요 시간은 평균 45.52ms를 달성하였고, 눈을 뜬 상태와 감는 상태를 구별하는 인식 성공률은 99%를 달성하였다. 안구 데이터 분석 결과를 바탕으로 현실 세계 좌표를 매칭할 수 있는 시선 추적 알고리즘을 개발하였다. 이 알고리즘의 검증 결과 x축은 약 1%, y축은 약 6%의 평균 오차율을 나타내는 것을 알 수 있었다.

• 한국컴퓨터그래픽스학회논문지
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• 제30권3호
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• pp.1-8
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• 2024

본 연구는 자율 주행을 위한 과수원 내 2차원 지도를 생성하는 새로운 알고리즘을 제안한다. 제안하는 알고리즘은 과수원의 과수가 일반적으로 열로 정렬되어 있다는 특성에 기반하여, 나무 열을 감지하고 이 정보를 지도에 투영하는 것을 목표로 한다. 이를 위해 본 연구는 우선 점 구름 데이터에서 점들의 분포를 분석하여 나무를 인식하는 방법을 제안한다. 또한, 인식된 과수의 위치를 기반으로 과수 열을 추출하는 방법을 소개하고, 이를 2차원 과수원 지도에 통합한다. 본 연구는 LiDAR를 통해 획득한 실제 과수원 점 구름 데이터를 사용하여 제안하는 알고리즘을 검증하였다. 그 결과, 90%의 높은 과수 감지 정확도와 정밀한 과수 열 맵핑 결과를 보여주었다. 또한, 생성된 지도가 과수원의 구조에 맞춘 자연스러운 자율 주행 경로를 생성하는 데 도움을 주는 것을 확인하였다.

• 한국수자원학회논문집
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• 제57권9호
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• pp.627-631
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• 2024

본 연구에서는 1980년대 도입되어 활용되고 있는 기상관측용 강우감지기의 관측 장애 및 오류 현황을 확인하고, 관측 효율 개선을 위해 강우감지기 1분 자료 수집, 산출 알고리즘 개선하고자 하였다. 오류 현황 분석 결과 강우감지기는 기상관측기 중 수동 품질관리를 가장 많이 시행되는 관측 장비로 이는 강수 산출 알고리즘 개선을 통해 강수 인식율 향상이 가능한 것으로 판단되었다. 국내외 강우감지기 알고리즘을 확인,선별하여 임의의 자료로 강수 인식율을 비교한 결과 10초 간격으로 강수를 측정 1회 이상 강수 측정 시 '강수'로 판별하는 알고리즘이 가장 높은 강수 인식율을 보였다. 해당 알고리즘이 강수를 과대모의하는 경향이 있으나 이는 원시자료 품질관리를 통해 개선 가능할 것으로 판단된다. 본 연구 결과를 토대로 강우감지기 오류율 감소와 정확도 향상에 기여할 수 있을 것으로 사료된다.

• 정보처리학회 논문지
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• 제13권9호
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• pp.468-473
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• 2024

도심 지역에서의 굴착 작업은 지반 변형을 유발할 수 있으며, 이는 인근 인프라에 피해를 줄 수 있다. 지반 변형으로 인해 작업 현장 근처의 보도블록에 변위가 발생할 수 있다. 변위를 정확하게 측정하여 지반 변형의 잠재적인 위험을 평가하는 지표로 사용할 수 있다. 본 논문에서는 UGV에 장착된 3D 레이저 라인 센서를 이용한 강건하고 효율적인 보도블록 이격 측정 방법을 제안한다. 제안 방법은 2D 투영 기반 객체 탐지와 CPLF 알고리즘을 통한 측정의 두 단계로 구성된다. 실험 결과, CPLF 알고리즘이 PLF에 비해 효율적임을 확인했으며, CPLF 알고리즘이 1.36 mm의 오차와 10.76ms의 처리 시간을 보여 제안 방법이 다양한 유형의 보도블록과 환경적 요인이 존재하는 실제 환경에서도 UGV에서 3D 레이저 라인센서를 이용하여 강건한 온라인 측정을 보장하면서도 높은 정확성을 유지할 수 있음을 확인했다.

• Journal of Digestive Cancer Research
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• 제12권2호
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• pp.90-101
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• 2024

The incidence of colon cancer in South Korea has recently been the highest among gastrointestinal cancers. Early diagnosis is critical, and image-enhanced endoscopy (IEE) is a key diagnostic method. Colon tumors primarily include serrated polyps, adenomatous polyps, and colon cancer. Early endoscopic techniques relied on simple visual inspection for diagnosis, with tumor size and shape being the primary considerations. Low-resolution images made these methods ineffective for detecting small or early-stage lesions. IEE now enables detailed examination using high-resolution images and various color and structure analyses. Techniques like narrow band imaging (NBI) allow precise observation of vascular patterns and surface structures. Hyperplastic polyps often appear similar in color to the surrounding mucosa, with no visible vascular pattern. Sessile serrated lesions have a cloudy surface with distinct boundaries and irregular patterns, often with black spots in the crypts. Adenomatous polyps are darker brown, with a visible white epithelial network and various pit patterns. Magnified images help differentiate between low- and high-grade dysplasia, with low-grade showing regular patterns and high-grade showing increased irregularities. The NBI International Colorectal Endoscopic classification identifies malignant colon tumors as brown or dark brown with disorganized vascular patterns. The Japan NBI Expert Team classification includes loose vascular areas and disrupted thick vessels. The Workgroup serrAted polypS and Polyposis classification aids in differentiating between hyperplastic polyps and sessile serrated lesions/adenomas when deciding whether to resect polyps larger than 5 mm. Suspected high-grade dysplasia warrants endoscopic submucosal dissection and follow-up. Future advancements in IEE are expected to further enhance early detection and diagnostic accuracy.