• 대한물리의학회지
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• 제18권1호
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• pp.77-86
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• 2023

PURPOSE: This study developed an experimental algorithm, which is similar or identical to semantic linking for KCF codes, even if it converted existing semantic code linking methods to morphological code extraction methods. The purpose of this study was to verify the applicability of the system. METHODS: An experimental algorithm was developed as a morphological extraction method using code-specific words in the KCF code descriptions. The algorithm was designed in five stages that extracted KCF code using natural language paragraphs. For verification, 80 clinical natural language experimental cases were defined. Data acquisition for the study was conducted with the deliberation and approval of the bioethics committee of the relevant institution. Each case was linked by experts and was extracted through the System. The linking accuracy index model was used to compare the KCF code linking by experts with those extracted from the system. RESULTS: The accuracy was checked using the linking accuracy index model for each case. The analysis was divided into five sections using the accuracy range. The section with less than 25% was compared; the first experimental accuracy was 61.24%. In the second, the accuracy was 42.50%. The accuracy was improved to 30.59% in the section by only a weight adjustment. The accuracy can be improved by adjusting several independent variables applied to the system. CONCLUSION: This paper suggested and verified a way to easily extract and utilize KCF codes even if they are not experts. KCF requires the system for utilization, and additional study will be needed.

• The Journal of Korean Physical Therapy
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• 제35권3호
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• pp.57-63
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• 2023

Purpose: The aim of this study was to conduct a systematic review of randomized controlled studies from 2012 to present that explore the diagnostic accuracy of clinical tests used for diagnosing anterior cruciate ligament (ACL) injury. Methods: Study design: Systematic review. Literature search of the PubMed and Scholar databases was conducted using keywords related to diagnostic accuracy of clinical tests for ACL injury. The PRISMA Guidelines were followed to conduct this study. The Cochrane Risk of Bias Tool was utilized to assess the quality of each included study. Results: As a result, 8 studies were included, and 6 clinical tests used in ACL tears were evaluated for diagnostic accuracy. The pivot shift test was reported as having the highest +LR (29.5) value with a sensitivity of 59% and a specificity of 98%. However, the test with the lowest -LR value was the lever test, and the values were as follows: -LR (0.08), +LR (4.7), specificity (80%), sensitivity (94%). Conclusion: In this study, it was concluded that a single clinical test is not sufficient to determine the presence of ACL injury. Test combinations have a higher diagnostic accuracy than a single test. In this study, the accuracy of the clinical tests was examined without considering the amount of ACL rupture and acute-chronic condition. Further research is required to examine the impact of these two factors on diagnostic accuracy of clinical test.

• 한국차세대컴퓨팅학회논문지
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• 제13권5호
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• pp.124-130
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• 2017

문자 인코딩은 문자나 기호를 컴퓨터로 표현하기 위해 사용되는 방법이며 문자 인코딩 판별 소프트웨어들이 존재한다. 기존의 널리 쓰이는 인코딩 판별 소프트웨어인"uchardet"의 경우 변조되지 않은 일반 문서의 인코딩 판별 정확도는 91.39% 이지만 언어 판별 정확도는 32.09%에 불과하다. 또한 문서가 치환 암호에 의해 암호화 된 경우 인코딩 판별 정확도는 3.55%, 언어 판별 정확도는 0.06%로 매우 낮은 정확도를 보였다. 따라서 본 논문에서는 Deep learning 알고리즘인 LSTM(Long Short-Term Memory)을 이용한 문서의 인코딩 및 언어 판별 방법을 제안하며, 기존의 인코딩 판별 소프트웨어"uchardet"보다 뛰어난 결과를 보였다. 제안하는 방법을 이용한 일반 문서의 인코딩 판별 정확도는 99.89%이며, 언어 판별 정확도는 99.92%이다. 또한 문서가 치환 암호에 의해 암호화된 경우에는 제안하는 방법의 인코딩 판별 정확도는 99.26%이며, 언어 판별 정확도는 99.77%로 매우 뛰어나다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.235-235
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• 2022

Genome selection is a promising tool for plant and animal breeding, which uses genome-wide molecular marker data to capture large and small effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, the prediction accuracy of 10 agricultural traits in the wheat core group with 567 points was compared. We used a cross-validation approach to train and validate prediction accuracy to evaluate the effects of training population size and training model.As for the prediction accuracy according to the model, the prediction accuracy of 0.4 or more was evaluated except for the SVN model among the 6 models (GBLUP, LASSO, BayseA, RKHS, SVN, RF) used in most all traits. For traits such as days to heading and days to maturity, the prediction accuracy was very high, over 0.8. As for the prediction accuracy according to the training group, the prediction accuracy increased as the number of training groups increased in all traits. It was confirmed that the prediction accuracy was different in the training population according to the genetic composition regardless of the number. All training models were verified through 5-fold cross-validation. To verify the prediction ability of the training population of the wheat core collection, we compared the actual phenotype and genomic estimated breeding value using 35 breeding population. In fact, out of 10 individuals with the fastest days to heading, 5 individuals were selected through genomic selection, and 6 individuals were selected through genomic selection out of the 10 individuals with the slowest days to heading. Therefore, we confirmed the possibility of selecting individuals according to traits with only the genotype for a shorter period of time through genomic selection.

• The Journal of Advanced Prosthodontics
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• 제15권6호
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• pp.315-332
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• 2023

PURPOSE. This study aimed to investigate whether the accuracy of intraoral scanners is influenced by different scanning strategies in an in vitro setting, through a systematic review and meta-analysis. MATERIALS AND METHODS. This review was conducted in accordance with the PRISMA 2020 standard. The following PICOS approach was used: population, tooth impressions; intervention, the use of intraoral scanners with scanning strategies different from the manufacturer's instructions; control, the use of intraoral scanners following the manufacturers' requirements; outcome, accuracy of intraoral scanners; type of studies, in vitro. A comprehensive literature search was conducted across various databases including Embase, SciELO, PubMed, Scopus, and Web of Science. The inclusion criteria were based on in vitro studies that reported the accuracy of digital impressions using intraoral scanners. Analysis was performed using Review Manager software (version 5.3.5; Cochrane Collaboration, Copenhagen, Denmark). Global comparisons were made using a standardized mean difference based on random-effect models, with a significance level of α = 0.05. RESULTS. The meta-analysis included 15 articles. Digital impression accuracy significantly improved under dry conditions (P < 0.001). Moreover, trueness and precision were enhanced when artificial landmarks were used (P ≤ 0.02) and when an S-shaped pattern was followed (P ≤ 0.01). However, the type of light used did not have a significant impact on the accuracy of the digital intraoral scanners (P ≥ 0.16). CONCLUSION. The accuracy of digital intraoral scanners can be enhanced by employing scanning processes using artificial landmarks and digital impressions under dry conditions.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.

• Animal Bioscience
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• 제37권4호
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• pp.555-566
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• 2024

Objective: This study aimed to assess the genetic parameters and accuracy of genomic predictions for twenty-four linear body conformation traits and overall conformation scores in Korean Holstein dairy cows. Methods: A dataset of 2,206 Korean Holsteins was collected, and genotyping was performed using the Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The traits investigated included body traits (stature, height at front end, chest width, body depth, angularity, body condition score, and locomotion), rump traits (rump angle, rump width, and loin strength), feet and leg traits (rear leg set, rear leg rear view, foot angle, heel depth, and bone quality), udder traits (udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement), and overall conformation score. Accuracy of genomic predictions was assessed using the single-trait animal model genomic best linear unbiased prediction method implemented in the ASReml-SA v4.2 software. Results: Heritability estimates ranged from 0.10 to 0.50 for body traits, 0.21 to 0.35 for rump traits, 0.13 to 0.29 for feet and leg traits, and 0.05 to 0.46 for udder traits. Rump traits exhibited the highest average heritability (0.29), while feet and leg traits had the lowest estimates (0.21). Accuracy of genomic predictions varied among the twenty-four linear body conformation traits, ranging from 0.26 to 0.49. The heritability and prediction accuracy of genomic estimated breeding value (GEBV) for the overall conformation score were 0.45 and 0.46, respectively. The GEBVs for body conformation traits in Korean Holstein cows had low accuracy, falling below the 50% threshold. Conclusion: The limited response to selection for body conformation traits in Korean Holsteins may be attributed to both the low heritability of these traits and the lower accuracy estimates for GEBVs. Further research is needed to enhance the accuracy of GEBVs and improve the selection response for these traits.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 춘계학술발표회논문집
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• pp.259-262
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• 2004

There is the case to make the image map by using color aerial photo, DEM gives the accuracy of the Ortho image. Therefore We evaluate the accuracy by digital photogrammetric system after generating the DEM of Color Aerial Image.

• 대한수학회지
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• 제46권2호
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• pp.281-294
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• 2009

Accuracy of the scaling function is very crucial in wavelet theory, or correspondingly, in the study of wavelet filter banks. We are mainly interested in vector-valued filter banks having matrix factorization and indicate how to choose block central symmetric matrices to construct multi-wavelets with suitable accuracy.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2007년도 춘계학술발표회 논문집
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• pp.231-235
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• 2007

As the centralization of city, underground facilities is increasing and being more important. And there were many underground facilities accidents in korea. It is very important to update and to have high quality accuracy of underground facilities maps. But the underground facilities maps are mapped and updated by each institutes. So it is difficult to verify the accuracy of the data. In this study, To analyze the accuracy of maps, Surveying of undergroud facilities for 18 GUs in Seoul is performed during 3 years using Electromegnetic Induction Method, Total Station and GPS. In conclusion, Horizontal accuracy of 6 underground facilities management institutes is 73cm(2004), 78cm(2005) and 75cm(2006).