• Korean Journal of Agricultural and Forest Meteorology
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• v.16 no.2
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• pp.114-124
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• 2014

For classifying fire damaged areas and analyzing burn severity of two large-fire areas damaged over 100 ha in 2011, three methods were employed utilized supervised classification, unsupervised classification and Normalized Difference Vegetation Index (NDVI). In this paper, the post-fire imageries of SPOT were used to compute the Maximum Likelihood (MLC), Minimum Distance (MIN), ISODATA, K-means, NDVI and to evaluate large-scale patterns of burn severity from 1 m to 5 m spatial resolutions. The result of the accuracy verification on burn severity from satellite images showed that average overall accuracy was 88.38 % and the Kappa coefficient was 0.8147. To compare the accuracy between burn severity and field survey at Uljin and Youngduk, two large fire sites were selected as study areas, and forty-four sampling plots were assigned in each study area for field survey. The burn severities of the study areas were estimated by analyzing burn severity (BS) classes from SPOT images taken one month after the occurrence of the fire. The applicability of composite burn index (CBI) was validated with a correlation analysis between field survey data and burn severity classified by SPOT5, and by their confusion matrix. The result showed that correlation between field survey data and BS by SPOT5 were closely correlated in both Uljin (r = -0.544 and p<0.01) and Youngduk (r = -0.616 and p<0.01). Thus, this result supported that the proposed burn severity analysis is an adequate method to measure burn severity of large fire areas in Korea.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.354-362
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• 2000

Increasing amount of road tragic in 90's has drawn much attention in Korea due to its influence on safety problems. Various types of data analyses are done in order to analyze the relationship between the severity of road traffic accident and driving conditions based on traffic accident records. Accurate results of such accident data analysis can provide crucial information for road accident prevention policy. In this paper, we apply several data fusion, ensemble and clustering algorithms in an effort to increase the accuracy of individual classifiers for the accident severity. An empirical study results indicated that clustering works best for road traffic accident classification in Korea.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.151-157
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• 2018

Most previous studies of software fault prediction have focused on supervised learning models for binary classification that determines whether an input module has faults or not. However, binary classification model determines only the presence or absence of faults in the module without considering the complex characteristics of the fault, and supervised model has the limitation that it requires a training data set that most development groups do not have. To solve these two problems, this paper proposes severity-based ternary classification model using unsupervised learning algorithms, and experimental results show that the proposed model has comparable performance to the supervised models.

• Journal of Korean Clinical Nursing Research
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• v.14 no.1
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• pp.161-172
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• 2008

Purpose: This study was to classify patient severity score for hemodialysis patients. Method: The subject of this study was 1,575 patients. To study the severity of the patients, we used t-test and ANOVA. The congruity was measured by Kappa coefficient and the severity in each medical facility was analyzed by ANOVA. Result: The results showed that there was a significant difference according to the levels of medical center (F=171.187, p<.0001). Categorizing the severity of the patients in each medical facility, group II and III of the secondary medical institution had higher ratio than the primary medical institution. There was not a single patient coming under group IV in both of the primary or secondary medical institutions. However, the tertiary medical institutions had more subjects in group II and III than the primary and secondary medical institutions. The group IV with the highest severity had 11 patients(1.5%), demonstrating that the tertiary medical institution had higher severity patients than the primary or secondary medical institutions. Conclusion: The results of this study appropriately reflects the repayment system of medical expenses by the government. Also, it provides the fundamental information to develop nursing fee system taken into account of the systemic differences among the primary, secondary and tertiary medical institutions.

• Journal of the Korean Society of Safety
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• v.34 no.2
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• pp.1-6
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• 2019

This study focuses on the evaluation of severity measures used for accidents associated with industrial machines and devices. In particular, duration of medical treatment, duration of work loss, number of deaths in an individual accident associated with industrial machines and devices are evaluated in various ways to assess the severity of the accident. The number of accidents with work loss of longer than 1 year as the severity measure and the number of accidents as the frequency measure appeared to be the most discriminating information and allow risk assessment based on these frequency and severity measures for grouping of industrial machines and devices. Results of such risk assessment further confirmed the re-classification of industrial machines and devices that are currently subject to safety certification (SC) and self-declaration of conformity (SDC) or selection of those machines and devices that are newly subject to SC and SDC.

• Archives of Craniofacial Surgery
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• v.23 no.3
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• pp.111-118
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• 2022

Background: Although the zygomatic arch is an important structure determining facial prominence and width, no consensus exists regarding the classification of isolated zygomatic arch fractures, and the literature on this topic is scarce. To date, five papers have subdivided zygomatic arch fractures; however, only one of those proposed classifications includes the injury vector, although the injury vector is one of the most important factors to consider in fracture cases. Furthermore, the only classification that does include the injury vector is too complicated to be suitable for daily practice. In addition, the existing classifications are clinically limited because they do not consider greenstick fractures, nondisplaced fractures, or coronoid impingement. In the present study, we present a rearrangement of the previously published classifications and propose a modified classification of isolated zygomatic arch fractures that maximizes the advantages and overcomes the disadvantages of previous classification systems. Methods: The classification criteria for isolated zygomatic arch fractures described in five previous studies were analyzed, rearranged, and supplemented to generate a modified classification. The medical records, radiographs, and facial bone computed tomography findings of 134 patients with isolated zygomatic arch fractures who visited our hospital between January 2010 and December 2019 were also retrospectively analyzed. Results: We analyzed major classification criteria (displacement, the force vector of the injury, V-shaped fracture, and coronoid impingement) for isolated zygomatic arch fracture from the five previous studies and developed a modified classification by subdividing zygomatic arch fractures. We applied the modified classification to cases of isolated zygomatic arch fracture at our hospital. The surgery rate and injury severity differed significantly from fracture types I to VI. Conclusion: Using our modified classification, we could determine that both the injury force and the injury vector meaningfully influenced the surgery rate and the severity of the injuries.

• Journal of Korean Dental Science
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• v.16 no.2
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• pp.156-163
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• 2023

Purpose: The aim of this study was to evaluate the feasibility of deep learning techniques to classify the morphology and severity of peri-implantitis bone defects based on periapical radiographs. Materials and Methods: Based on a pre-trained and fine-tuned ResNet-50 deep learning algorithm, the morphology and severity of peri-implantitis bone defects on periapical radiographs were classified into six groups (class I/II and slight/moderate/severe). Accuracy, precision, recall, and F1 scores were calculated to measure accuracy. Result: A total of 971 dental images were included in this study. Deep-learning-based classification achieved an accuracy of 86.0% with precision, recall, and F1 score values of 84.45%, 81.22%, and 82.80%, respectively. Class II and moderate groups had the highest F1 scores (92.23%), whereas class I and severe groups had the lowest F1 scores (69.33%). Conclusion: The artificial intelligence-based deep learning technique is promising for classifying the morphology and severity of peri-implantitis. However, further studies are required to validate their feasibility in clinical practice.

• Health Policy and Management
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• v.11 no.1
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• pp.1-18
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• 2001

The purpose of the present study was to assess the agreement of survival probability estimated by International Classification of Diseases l0th Edition(ICD-10) based International Classification of Diseases based Injury Severity Score(ICISS) with professional panel's judgment on preventable death. ICISS has a promise as an alternative to Trauma and Injury Severity Score(TRISS) which have served as a standard measure of trauma severity, but requires more validation studies. Furthermore as original version of ICISS was based ICD-9CM, it is necessary to test its performance employing ICD-10 which has been used in Korea and is expected to replace ICD-9 in many countries sooner or later. Methods : For 1997 and 1998 131 trauma deaths and 1,785 blunt trauma inpatients from 6 emergency medical centers were randomly sampled and reviewed. Trauma deaths were reviewed by professional panels with hospital records and survival probability of trauma inpatients was assessed using ICD-10 based ICISS. For trauma mortality degree of agreement between ICISS survival probability with judgment of professional panel on preventable death was assessed and correlation between W-score and preventable death rate by each emergency medical center was assessed. Results : Overall agreement rate of ICISS survival probability with preventable death judged by professional panel was 66.4%(kappa statistic 0.36). Spearman's correlation coefficient between W-score and preventable death rate by each emergency medical center was -0.77(p=0.07) and Pearson's correlation coefficient between them was -0.90(p=0.01). Conclusions : The agreement rate of ICD-10 based ICISS survival probability with of professional panel's judgment on preventable death was similar to TRISS. The W-scores of emergency medical centers derived from ICD-10 based ICISS were highly correlated with preventable death rates of them with marginal statistical significance.

• Journal of Yeungnam Medical Science
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• v.38 no.1
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• pp.10-18
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• 2021

Endometriosis is a chronic disease associated with pelvic pain and infertility. Several classification systems for the severity of endometriosis have been proposed. Of these, the revised American Society for Reproductive Medicine classification is the most well-known. The ENZIAN classification was developed to classify deep infiltrating endometriosis and focused on the retroperitoneal structures. The endometriosis fertility index was developed to predict the fertility outcomes in patients who underwent surgery for endometriosis. Finally, the American Association of Gynecological Laparoscopists classification is currently being developed, for which 30 endometriosis experts are analyzing and researching data by assigning scores to categories considered important; however, it has not yet been fully validated and published. Currently, none of the classification systems are considered the gold standard. In this article, we review the classification systems, identify their pros and cons, and discuss what improvements need to be made to each system in the future.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1814-1820
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• 2023

Safety classification of systems, structures, and components (SSC) is an essential activity for nuclear reactor design and operation. The current regulatory trend is to require risk-informed safety classification that considers first, the severity, but also the frequency of SSC failures. While safety classification for nuclear power plants is covered in many regulatory and scientific publications, research reactors received less attention. Research reactors are typically of lower power but, at the same time, are less standardized i.e., have more variability in the design, operational modes, and operating conditions. This makes them more challenging when considering safety classification. This work presents the Integrated Risk-Informed Safety Classification (IRISC) procedure which is a novel extension of the IAEA recommended process with dedicated probabilistic treatment of research reactor designs. The article provides the details of probabilistic analysis performed within safety classification process to a degree that is often missing in most literature on the topic. The article presents insight from the implementation of the procedure in the safety classification for the MARIA Research Reactor operated by the National Center for Nuclear Research in Poland.