• Journal of Korean Philosophical Society
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• v.116
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• pp.257-280
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• 2010

I try to formalize the system of modal logic and interpret it in view of constructivism through this study. As to the meaning of a sentence, as we saw, Frege endorsed extensions in view of the fact that they are enough to provide for a compositional account for truth, in particular that (1) the assignment of extensions to expressions is compositional ; (2) the assignment of extensions to sentences coincides with the assignment of truth values. But nobody would be willing to admit that a truth value is what a sentence means and that consequently all true sentences are synonymous. So, if what we are after is meaning in the intuitive sense, then extensions would not do. This consideration has later become the point of departure of modal and intensional semantics. So, it is clear that the language of modal logic do not allow for an extensional interpretation. ${\square}$ is syntactically on a par with ${\vdash}$, hence within the extensional framework it would have to denote a unary truth function. This means that if modal logic is to be interpreted, we need a semantics which is not extensional. The first attempt to build a feasible intensional semantics was presented by Saul Kripke. He came to the conclusion that we must let sentences denote not truth values, but rather subsets of a given set. He called elements of the underlying set possible world. Hence each sentence is taken to denote the set of those possible world in which it is true. This lets us explicate necessity as 'truth in every possible world' and possibility as 'truth in at least one possible world'. But it is clear that the system of modal logic is not only an enlargement of propositional logic, as long as the former contains the new symbols, but that it is of an other nature. In fact, the modal logic is intensional, in that the operators do not determine the functions of truth any more. But this new element is not given a priori, but a posteriori from construction by logicist.

• Asian Spine Journal
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• v.12 no.6
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• pp.973-980
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• 2018

Study Design: Retrospective review. Purpose: To identify the trends in stimulator use, pair those trends with various grafting materials, and determine the influence of stimulators on the risk of revision surgery. Overview of Literature: A large number of studies has reported beneficial effects of electromagnetic energy in healing long bone fractures. However, there are few clinical studies regarding the use of electrical stimulators in spinal fusion. Methods: We used insurance billing codes to identify patients with lumbar disc degeneration who underwent anterior lumbar interbody fusion (ALIF). Comparisons between patients who did and did not receive electrical stimulators following surgery were performed using logistic regression analysis, chi-square test, and odds ratio (OR) analysis. Results: Approximately 19% of the patients (495/2,613) received external stimulators following ALIF surgery. There was a slight increase in stimulator use from 2008 to 2014 (multi-level $R^2=0.08$, single-level $R^2=0.05$). Patients who underwent multi-level procedures were more likely to receive stimulators than patients who underwent single-level procedures (p<0.05; OR, 3.72; 95% confidence interval, 3.02-4.57). Grafting options associated with most frequent stimulator use were bone marrow aspirates (BMA) plus autograft or allograft for single-level and allograft alone for multi-level procedures. In both cohorts, patients treated with bone morphogenetic proteins were least likely to receive electrical stimulators (p<0.05). Patients who received stimulation generally had higher reimbursements. Concurrent posterior lumbar fusion (PLF) (ALIF+PLF) increased the likelihood of receiving stimulators (p<0.05). Patients who received electrical stimulators had similar revision rates as those who did not receive stimulation (p>0.05), except those in the multilevel ALIF+PLF cohort, wherein the patients who underwent stimulation had higher rates of revision surgery. Conclusions: Concurrent PLF or multi-level procedures increased patients' likelihood of receiving stimulators, however, the presence of comorbidities did not. Patients who received BMA plus autograft or allograft were more likely to receive stimulation. Patients with and without bone stimulators had similar rates of revision surgery.

• Food Engineering Progress
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• v.15 no.4
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• pp.324-331
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• 2011

Hyperspectral reflectance imaging technology was used to predict internal quality of cherry tomatoes with the spectral range of 400-1000 nm. Partial least square (PLS) regression method was used to predict firmness, sugar content, and acid content. The PLS models were developed with several preprocessing methods, such as normalization, standard normal variate (SNV), multiplicative scatter correction (MSC), and derivative of Savitzky Golay. The performance of the prediction models were investigated to find the best combination of the preprocessing and PLS models. The coefficients of determination ($R^{2}_{p}$) and standard errors of prediction (SEP) for the prediction of firmness, sugar content, and acid content of cherry tomatoes from green to red ripening stages were 0.876 and 1.875kgf with mean of normalization, 0.823 and $0.388^{\circ}Bx$ with maximum of normalization, and 0.620 and 0.208% with maximum of normalization, respectively.

• Food Engineering Progress
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• v.15 no.2
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• pp.136-142
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• 2011

The objective of this research was to classify intact soybeans and foreign objects using near-infrared (NIR) spectroscopy. Intact soybeans and foreign objects were scanned using a NIR spectrometer equipped with scanning monochromator. NIR spectra of intact soybeans and foreign objects in the wavelength range from 900 to 1800 nm were collected. The classification of intact soybeans and foreign objects were conducted by using partial least-square discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) multivariate methods. Various types of data pretreatments were tested to develop the classification models. Intact soybeans and foreign objects were successfully classified by the PLS-DA prediction model with mean normalization pretreatment. These results showed the potential of NIR spectroscopy combined with multivariate analysis as a method for classifying intact soybeans and foreign objects.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.3
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• pp.129-141
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• 2019

In recent years, fast growing cities in Korea are showing signs of being vulnerable to more disasters as their population and facilities increase and intensify. In particular, fire is one of the most common disasters in Korea's cities, along with traffic accidents. Therefore, in this study, we analyze what type of factors affect the fire that threatens urban people. Fire data were acquired for 10 years, from 2007 to 2017, in Jinju, Korea. Spatial distribution pattern of fire occurrence in Jinju was assessed through the spatial autocorrelation analysis. First, spatial autocorrelation analysis was carried out to grasp the spatial distribution pattern of fire occurrence in Jinju city. In addition, correlation and multiple regression analysis were used to confirm spatial dependency and abnormality among factors. Based on this, OLS (Ordinary Least Square) regression analysis was performed using space weighting considering fire location and spatial location of each facility. As a result, First, LISA (Local Indicator of Spatial Association) analysis of the occurrence of fire in Jinju shows that the most central commercial area are fire department, industrial area, and residential area. Second, the OLS regression model was analyzed by applying spatial weighting, focusing on the most derived factors of multiple regression analysis, by integrating population and social variables and physical variables. As a result, the second kind of neighborhood living facility showed the highest correlation with the fire occurrence, followed by the following in the order of single house, sales facility, first type of neighborhood living facility, and number of households. The results of this study are expected to be useful for analyzing the fire occurrence factors of each facility in urban areas and establishing fire safety measures.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.295-302
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• 2018

Face recognition (FR) has been widely used in various applications, such as smart surveillance systems, immigration control in airports, user authentication in smart devices, and so on. FR in well-controlled conditions has been extensively studied and is relatively mature. However, in unconstrained conditions, FR performance could degrade due to undesired characteristics of the input face image (such as irregular facial pose variations). To overcome this problem, this paper proposes a new method for determining if an input image is suitable for FR. In the proposed method, for an input face image, reconstruction error is computed by using a predefined set of reference face images. Then, suitability can be determined by comparing the reconstruction error with a threshold value. In order to reduce the effect of illumination changes on the determination of suitability, a preprocessing algorithm is applied to the input and reference face images before the reconstruction. Experimental results show that the proposed method is able to accurately discriminate non-frontal and/or incorrectly aligned face images from correctly aligned frontal face images. In addition, only 3 ms is required to process a face image of $64{\times}64$ pixels, which further demonstrates the efficiency of the proposed method.

• Journal of Technologic Dentistry
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• v.40 no.4
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• pp.217-224
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• 2018

Purpose: The aim of this study was to evaluate accuracy of three type complete mandibular denture of before and after polymerization. Methods: Mandibular edentulous model was selected as the master model. 15 study models were made by Type IV stone. Wax complete mandibular dentures were produced by the denture base and artificial teeth. Before and after curing, STL files were obtained using a blue scanner. By superimposing the digitized complete mandibular denture data(after curing) with the CAD-reference(before curing) three-dimensionally, visual fit-discrepancies were drawn by calculating the root mean square (RMS) and visualized on a color-difference map. Each calculated RMS-value was statistically analyzed by 1-way analysis of variance(ANOVA) (${\alpha}=.05$). Results: Mean(SD) RMS-values was OM group $88.98(6.10){\mu}m$, BM group $82.35(13.46){\mu}m$, BDM group $77.83(9.46){\mu}m$. The results of the 1-way ANOVA showed no statistically significant differences in the RMS values of the Three groups for the material (P > .241). Conclusion : Deformation of artificial teeth position was observed in all groups after resin polymerization. But the values, all group were within the clinically acceptable range. The values of BDM group showed the least deformation than the other two groups.

• Management & Information Systems Review
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• v.38 no.2
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• pp.119-137
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• 2019

This paper analyzes the factors affecting the relationship characteristics of firms and public research institutes, in-addition to analyzing the effect of relationship characteristics with the industry-public research institute cooperation. We selected factors influencing relationship characteristics of firms and public research institutes based on the study of existing industry-university-institute cooperation. These factors include the willingness of the industry-public research institute cooperation, firm's R&D capabilities, and the firm's perceived public research institutes competence. The relationship between these factors, their relationship characteristics, and the performance of the industry-public research institute cooperation was studied using six hypotheses. In order to test this research model, we conducted a survey on industry-public research institute cooperation firms and received responses from 116 companies. Partial Least Square-Multi Group Analysis (PLS-MGA) was used to test the research model. The results of the empirical analysis show the following results. First, the willingness of the industry-public research institute cooperation, firm's R&D capabilities and firm's perceived public research institutes competence have a positive impact on the relationship characteristics of firms and public research institutes. Second, the relationship characteristics of firms and public research institutes have a positive impact on firm performance in the industry-public research institute cooperation. Third, firm's R&D capabilities and firm's perceived public research institutes competence directly affect firm performance in the industry-public research institute cooperation. These results provide policy and practical implications for enhancing firm performance of the industry-public research institute cooperation.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.1
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• pp.49-56
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• 2019

Urban green spaces have been contributing to the improvement of environmental, mental, and physical health for humans. In addition, recent studies showed the potential role of vegetation in reducing the amount of crime in inner-city neighborhoods at the micro-scale level. However, little is known about the positive role of urban green areas in improving urban safety at the regional level. The purpose of this study is to examine the relationship between urban green areas and actual outdoor crime rates, while also considering socio-demographic factors. The study area is the city of Austin, Texas, USA, which consists of 506 block groups. This study utilized socio-demographic factors based on U.S. Census data and vegetation-related factors utilizing GIS and ENVI software. For analyses, the analysis of variance (ANOVA) and an ordinary least square (OLS) regression were utilized. The results from ANOVA showed that yearly crime rates per acre for areas having 0%~25% trees in their neighborhoods were 0.46% and 1.05% higher than those of having 25%~50% and >50% trees in the neighborhoods, respectively. The results from the OLS regression represented that income, NDVI and park rates in neighborhoods were negatively associated with the crime rate per acre, whereas the percentage of minorities and the percentage of teenage school dropouts were positively associated with the crime rate per acre. This study implies that urban green areas may help to improve the safety of urban areas.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.1-9
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• 2021

In this paper, we propose a training algorithm of support vector machine (SVM) with a sensitive variable. Although machine learning models enable automatic decision making in the real world applications, regulations prohibit sensitive information from being used to protect privacy. In particular, the privacy protection of the legally protected attributes such as race, gender, and disability is compulsory. We present an efficient least square SVM (LSSVM) training algorithm using a fully homomorphic encryption (FHE) to protect a partial sensitive attribute. Our framework posits that data owner has both non-sensitive attributes and a sensitive attribute while machine learning service provider (MLSP) can get non-sensitive attributes and an encrypted sensitive attribute. As a result, data owner can obtain the encrypted model parameters without exposing their sensitive information to MLSP. In the inference phase, both non-sensitive attributes and a sensitive attribute are encrypted, and all computations should be conducted on encrypted domain. Through the experiments on real data, we identify that our proposed method enables to implement privacy-preserving sensitive LSSVM with FHE that has comparable performance with the original LSSVM algorithm. In addition, we demonstrate that the efficient sensitive LSSVM with FHE significantly improves the computational cost with a small degradation of performance.