• Journal of Korean Institute of Industrial Engineers
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• v.41 no.4
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• pp.338-343
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• 2015

We suggest a method to configure feature functions of continuous conditional random field (C-CRF). Regression tree and similarity analysis are introduced to construct the first and second feature functions of C-CRF, respectively. Rules from the regression tree are transformed to logic functions. If a logic in the set of rules is true for a data then it returns the corresponding value of leaf node and zero, otherwise. We build an Euclidean similarity matrix to define neighborhood, which constitute the second feature function. Using two feature functions, we make a C-CRF model and an illustrate example is provided.

• The Korean Journal of Health Service Management
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• v.4 no.2
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• pp.23-31
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• 2010

This study analyzed whether the patients' visits to oriental medicine hospitals were influenced by the Euclidean distance from patients' residence to oriental medicine hospitals. Patient who visited two oriental medicine hospitals in a metropolitan area were selected for study sample. The number of patient from each Dong (which is the smallest administrative district) to two hospitals was calculated based on claims data in 2008. ArcGIS was used to calculate the distance. Distance variable was not statistically significant in regression analysis after controlling the difference of socio-economic status of people in each Dong. It seems that distance factor did not play an important role in deciding whether to use the services of oriental medicine hospitals in a metropolitan area.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.42
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• pp.37.1-37.13
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• 2020

Background: The human mandible is variable in shape, size and position and any deviation from normal can affect the facial appearance and dental occlusion. Objectives: The objectives of this study were to determine whether the Sassouni cephalometric analysis could help predict two-dimensional mandibular shape in humans using cephalometric planes and landmarks. Materials and methods: A retrospective computerised analysis of 100 lateral cephalometric radiographs taken at Kingston Hospital Orthodontic Department was carried out. Results: Results showed that the Euclidean straight-line mean difference between the estimated position of gonion and traced position of gonion was 7.89 mm and the Euclidean straight-line mean difference between the estimated position of pogonion and the traced position of pogonion was 11.15 mm. The length of the anterior cranial base as measured by sella-nasion was positively correlated with the length of the mandibular body gonion-menton, r = 0.381 and regression analysis showed the length of the anterior cranial base sella-nasion could be predictive of the length of the mandibular body gonion-menton by the equation 22.65 + 0.5426x, where x = length of the anterior cranial base (SN). There was a significant association with convex shaped palates and oblique shaped mandibles, p = 0.0004. Conclusions: The method described in this study can be used to help estimate the position of cephalometric points gonion and pogonion and thereby sagittal mandibular length. This method is more accurate in skeletal class I cases and therefore has potential applications in craniofacial anthropology and the 'missing mandible' problem in forensic and archaeological reconstruction.

• The Korean Journal of Health Service Management
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• v.13 no.3
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• pp.13-26
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• 2019

Objectives: To investigate whether medical institutions can prevent accidents by analyzing the root cause of a medical accident and identifying the tendencies. Methods: A total of 345 medical cases were used for the RCA(Root Cause Analysis). The root causes were classified using the SHELL model. The suitability of the model was confirmed by SPSS's MDPREF and Euclidean distance. An SPSS20.0 hierarchical regression analysis was used as an influencing factor on the degree of injury resulting from medical accidents. Results: The SHELL model was suitable for classification. The rates of accident causes were LS49%, L34%, LL10.2%, LE3.7%, LH2.3%. The order in which the degree of a patient's injury was affected were: Risk Threshold (${\beta}=.180$), Time (${\beta}=.175$), Surgical stage (${\beta}=-.166$), Do not use procedure (${\beta}=.147$). Conclusions: Health care institutions should remove priorities through system improvement and training. For patients' safety, the five factors of the SHELL model should be managed in harmony.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.134-138
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• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.1-12
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• 2024

In many real-world data, multiple response variables are often dependent on the same set of explanatory variables. In particular, if several response variables are correlated with each other, simultaneous estimation considering the correlation between response variables might be more effective way than individual analysis by each response variable. In this multivariate regression analysis, least distance estimator (LDE) can estimate the regression coefficients simultaneously to minimize the distance between each training data and the estimates in a multidimensional Euclidean space. It provides a robustness for the outliers as well. In this paper, we examine the least distance estimation method in multivariate linear regression analysis, and furthermore, we present the penalized least distance estimator (PLDE) for efficient variable selection. The LDE technique applied with the adaptive group LASSO penalty term (AGLDE) is proposed in this study which can reflect the correlation between response variables in the model and can efficiently select variables according to the importance of explanatory variables. The validity of the proposed method was confirmed through simulations and real data analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.225-230
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• 2020

Machine learning is an algorithm which learns a computer based on the data so that the computer can identify the trend of the data and predict the output of new input data. Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a way of learning a machine with given label of data. In other words, a method of inferring a function of the system through a pair of data and a label is used to predict a result using a function inferred about new input data. If the predicted value is continuous, regression analysis is used. If the predicted value is discrete, it is used as a classification. A result of analysis, no. 8 (5, 3.4, setosa), 27 (5, 3.4, setosa), 41 (5, 3.5, setosa), 44 (5, 3.5, setosa) and 40 (5.1, 3.4, setosa) in Table 3 were classified as the most similar Iris flower. Therefore, theoretical practical are suggested.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• Journal of the Korean Geographical Society
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• v.29 no.1
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• pp.39-45
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• 1994

One of the major problems comparing the old map, Daedongyojido, with modern maps in order to measure the spatial error of the map it a projection of the modern map selected. This study focuses on the map projection of Daedongyojido. Three different approaches are used: comparing the spatial pattern of Daedongyojido with maps of different projection, examining materials related to old maps and books which were written by Jeongho Kim directly or indirectly, and the data sets which were available at the mid nineteenth century and could be used for map production. A couple of map projections are possible to that of Daedongyojido, but TM(Transverse Mercator) projection is one of the closest projections.

• Korean Journal of Environment and Ecology
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• v.25 no.5
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• pp.691-709
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• 2011

This study was performed to classify habitat types depending on environmental factors and to find out distribution characteristics of functional feeding groups of aquatic insects which were collected at that habitat types. Field survey was conducted twice in a year for every spring and fall from 2007 to 2008 for 38 sites in the Geum River. During the field survey 15 environmental factors were measured at each 38 sites and analyzed by similarity analysis method to classify habitat types. The result of similarity analysis showed that the 38 sites could be grouped into 7 classes like as C1 and C3 class belong to Head water(HD), C2 and C4 and C5 class belong to Middle stream(MS), C6 and C7 class belong to Large River(LR) based on euclidean distances 4. And also, we could extract the main environmental factors affecting the classification of habitat types such as Stream Width and Elevation of physical environmental factors, Water Temperature, Conductivity and DO of chemical environmental factors, percentages of Sand, Silt and Gravel of substrate factors. Total 142 species of aquatic insects in 46 families, 9 orders were collected during the field surveys and the occurrence number of species and individuals showed high correlation with the Velocity factor and the percentage of Sand factor of each habitat types. In addition, correlation analysis between functional feeding groups and environmental factors represented that (1) Filtering-collectors(FC) affected by Velocity, Stream Width and Silt, (2) Gathering-collector(GC) affected by Velocity, (3) Predator(P) affected by Elevation, Velocity, Boulder, Conductivity and Sand, (4) Plant-piecer(PP) affected by Water Width and Silt, (5) Scraper(SC) affected by Elevation and Conductivity, (6) Shredder(SH) affected by Elevation, Boulder, DO, pH, Conductivity and Water Temperature respectively. As a result of this study, Elevation, Stream Width, Velocity, Conductivity, Water Temperature and percentage of Sand factors which were deduced by stepwise multiple regression analysis had correlations($r{\geqq}0.600$, p<0.01) with biota community inhabitation. Therefore these six environmental factors were regarded as major environmental factors that might affect highly the distribution of functional feeding groups in stream ecosystem of the Geum River.