• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.40.1-40.1
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• 2016

Recent integral-field spectrograph surveys have found that similar-looking early type galaxies have wide range of rotational properties (Emsellem et al. 2007). This finding initiated a new point of view to the galaxies; rotation of galaxy as the first parameter of galaxy classification (Emsellem et al. 2011, Cappellari et al. 2011, for example). Some theoretical studies tried to address the origin of galaxy rotation. Idealized galaxy merger simulations have shown that galaxy-galaxy interactions have significant effects on the rotation of galaxies. Cosmological simulations by Naab et al. 2014 also added some more insights to the rotation of galaxies. However, previous studies either lack cosmological background or have not enough number of samples. Running a set of cosmological hydrodynamic zoom-in simulations using the AMR code RAMSES(Teyssier 2002). we have constructed a sample of thousands of galaxies in 20 clusters. Here we present a kinematic analysis of a large sample of galaxies in the cosmological context. The overall distribution of rotation parameter of simulated galaxies suggests a single peak corresponding to fast rotating galaxies. But when divided by mass, we find a strong mass dependency of galaxy rotation, and massive galaxies are distinctively slow rotating. The cumulated effective of mergers seems to neutralize galaxy rotation as suggested by previous studies (Khochfar et al. 2011, Naab et al. 2014, and Moody et al. 2014). This is consistent with the fact that massive galaxies tend to rotate more slowly after numerous mergers. However, if seen individually, merger can either increase or decrease galaxy rotation depending on mass ratio, orbital parameter, and relative rotation axis of the two galaxies. This explains the existence of some non-slow rotating massive early type galaxies.

• Journal of the Korean Institute of Navigation
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• v.8 no.2
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• pp.51-70
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• 1984

In the North Pacific Ocean a lot of large waves set up in winter, affected by continued winds and swells owing to severe extratropical cyclones. Under this sea condition, if the ship is about 100,000L/T (in deadweight capacity tonnage), we can't find the danger involved in the ship at sea apparently. But when we compare the seaworthiness of ship's building strength with the stress given to the hull by waves, we can't insist that the former be more stronger than the latter. As a result, VLCC is in danger of destroying and cutting for lack of longitudinal strength in heavy weather. Up to this time, Naval Architects have actively studied the relation between ship's longitudinal strength and waves as a ship's projector; however, actually, they have never made more profound study on the problem of longitudinal strength in relation to navigation. The main puprpose of this thesis is to clarify these vivid actual states of ship's trouble unknown to ship's masters. In this thesis we picked up VLCC Pan Yard, a vessel of Pan Ocean Bulk Carrier company's, as a model ship. And in the North Pacific Ocean, we have chosen for this research the basins where the wind speed and the wave height are greater than average. The data used this thesis are quotes from the "winds and waves of the North Pacific Ocean('64-'73)", and wind speed more than 30 knots was made use of as an ocject of this study. By usinh the ITTC wave spectrum, we found out the significant waves for every 5 knots within the range of 20 knots to 45 knots of wind speed. According to this H1/1000 was calculated. The stress of ship's hull is determined by ship's speed and wave height. We compared the ship's longitudinal strength with a planned wave height by rules of several famous classification societies in the world. In the last analysis, we found out that ship's present planned strength in heavy weather is not enough. Finally we made a graph for avoiding heavy weather, with which we studied safe ship's handling in the North pacafic Ocean in winter.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2550-2572
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• 2023

Epidemiological survey is an important means for the prevention and control of infectious diseases. Due to the particularity of the epidemic survey, 1) epidemiological survey in epidemic prevention and control has a wide range of people involved, a large number of data collected, strong requirements for information disclosure and high timeliness of data processing; 2) the epidemiological survey data need to be disclosed at different institutions and the use of data has different permission requirements. As a result, it easily causes personal privacy disclosure. Therefore, traditional access control technologies are unsuitable for the privacy protection of epidemiological survey data. In view of these situations, we propose a black box-assisted fine-grained hierarchical access control scheme for epidemiological survey data. Firstly, a black box-assisted multi-attribute authority management mechanism without a trusted center is established to avoid authority deception. Meanwhile, the establishment of a master key-free system not only reduces the storage load but also prevents the risk of master key disclosure. Secondly, a sensitivity classification method is proposed according to the confidentiality degree of the institution to which the data belong and the importance of the data properties to set fine-grained access permission. Thirdly, a hierarchical authorization algorithm combined with data sensitivity and hierarchical attribute-based encryption (ABE) technology is proposed to achieve hierarchical access control of epidemiological survey data. Efficiency analysis and experiments show that the scheme meets the security requirements of privacy protection and key management in epidemiological survey.

• Journal of IKEEE
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• v.28 no.2
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• pp.168-173
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• 2024

Speaker recognition refers to a technology that analyzes voice frequencies that are different for each individual and compares them with pre-stored voices to determine the identity of the person. Deep learning-based speaker recognition is being applied to many fields, and pet robots are one of them. However, the hardware performance of pet robots is very limited in terms of the large memory space and calculations of deep learning technology. This is an important problem that pet robots must solve in real-time interaction with users. Lightening deep learning models has become an important way to solve the above problems, and a lot of research is being done recently. In this paper, we describe the results of research on lightweight speaker recognition for pet robots by constructing a voice data set for pet robots, which is a specific command type, and comparing the results of models using residuals. In the conclusion, we present the results of the proposed method and Future research plans are described.

• Women's Health Nursing
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• v.5 no.2
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• pp.204-221
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• 1999

Due to the ever increasing life-span of human beings, the average woman is living well into her 70's. Henceforth, they spend at least one-third of their life after menopause. Climacteric encompasses the time preceding, during, and proceeding menopause : a transitional period of shifting from the period of being fertile to the period of senescence. In other words, this is the time at which they lose the ability to reproduce. Menopause can influence a woman's sexual life immensely due to the physiological, social, and psychological changes that occur during that period. In korea, where some women still live according to Confucian Culture in terms of sexology, nurses can play a vital role in the dissemination of sexual facts to women. This study was designed to clarity the characteristic and satisfaction of sexual life. This was done according to the classification of three types of climacteric women : pre-meno-pausal, menopausal, and post-menopausal. I studied 159 climacteric women between the ages of 45 & 59, living in or around the vicinity of Taegu City. This study utilized the investigative tools for sexual life patterns which were constructed with field experts' consultation and reference review by the author. The sexual satisfaction was translated from DSFI. The climacteric symptoms were invented by Kupperman, This research is descriptive. The data was collected between July 15, 1998 and October 14, 1998. Statistical analysis was performed using ANOVA and Pearson Correlations, and was computed with the SPSS program. The results obtained are summarized as follows; The frequency of sexual activity was highest in pre-menopause and declined after menopause. The frequency of genital caressing declined after menopause. The difference between the frequency of masturbation and coital pain was not significant according to the classification set forth in this experiment. Congruently, the frequency of orgasms declined after menopause. The mean satisfaction of sexual life is 27.1 years. The range of expected score is 9-45 years of age. Satisfaction was highest in the pre-menopausal group and proved to be statistically significant. Some of the general characteristics that affected the satisfaction a woman's sexual life in the climacteric stage during this project were ; age, income, satisfaction in younger pre-menopausal and the large income group than the other experimental groups. The climacteric symptoms were conversely correlated with the satisfaction in one's sexual life.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.148-153
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• 2020

In order to obtain high accuracy with an convolutional neural network(CNN), it is necessary to set the optimal hyperparameters. However, the exact value of the hyperparameter that can make high performance is not known, and the optimal hyperparameter value is different based on the type of the dataset, therefore, it is necessary to find it through various experiments. In addition, since the range of hyperparameter values is wide and the number of combinations is large, it is necessary to find the optimal values of the hyperparameters after the experimental design in order to save time and computational costs. In this paper, we suggest an algorithm that use the design of experiments and grid search algorithm to determine the optimal hyperparameters for a classification problem. This algorithm determines the optima values of the hyperparameters that yields high performance using the factorial design of experiments. It is shown that the amount of computational time can be efficiently reduced and the accuracy can be improved by performing a grid search after reducing the search range of each hyperparameter through the experimental design. Moreover, Based on the experimental results, it was shown that the learning rate is the only hyperparameter that has the greatest effect on the performance of the model.

• Annals of Occupational and Environmental Medicine
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• v.34
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• pp.40.1-40.9
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• 2022

Background: According to the occupational accident status analysis in 2020, of 1,180 occupational deaths, 463 were caused by cardiovascular disease (CVD). Workers should be assessed for CVD risk at regular intervals to prevent work-related CVD in accordance with the rules on occupational safety and health standards. However, no previous study has addressed risk and mortality. Therefore, this longitudinal study was conducted to evaluate the relationship between 10-year cardiovascular risk of the general health checkup and mortality. Methods: The study included 545,859 participants who visited Kangbuk Samsung Total Healthcare Centers from January 1, 2002, to December 31, 2017. We performed 10-year cardiovascular risk assessment for the participants and the risk was divided into 4 groups (low, moderate, high, and very high). The study used death data from the Korea National Statistical Office for survival status as an outcome variable by December 31, 2019, and the cause of death based on the International Classification of Diseases, 10^th Revision (ICD-10) was identified. Statistical analysis was performed using Cox proportional hazards regression analysis, and the sum of the periods from the first visit to the date of death or December 31, 2019, was used as a time scale. We also performed a stratified analysis for age at baseline and sex. Results: During 5,253,627.9 person-years, 4,738 overall deaths and 654 cardiovascular deaths occurred. When the low-risk group was set as a reference, in the multivariable-adjusted model, the hazard ratios (HRs) (95% confidence interval [CI]) for overall mortality were 3.36 (2.87-3.95) in the moderate-risk group, 11.08 (9.27-13.25) in the high-risk group, and 21.20 (17.42-25.79) in the very-high-risk group, all of which were statistically significant. In cardiovascular deaths, the difference according to the risk classification was more pronounced. The HRs (95% CI) were 8.57 (4.95-14.83), 38.95 (21.77-69.69), and 78.81 (42.62-145.71) in each group. As a result of a subgroup analysis by age and sex, the HRs of all-cause mortality and cardiovascular mortality tended to be higher in the high-risk group. Conclusions: This large-scale longitudinal study confirmed that the risk of death increases with the 10-year cardiovascular risk of general health checkup.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.4
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• pp.591-598
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• 2002

In the ship structural design, the material cost of hull weight and the overall cost of construction processes should be minimized considering safety and reliability. In the past, minimum weight design has been mainly focused on reducing material cost and increasing dead weight reflect the interests of a ship's owner. But, in the past experience, the minimum weight design has been inevitably lead to increasing the construction cost. Therefore, it is necessary that the designer of ship structure should consider both structural weight and construction cost. In this point of view, multi-objective optimization technique is proposed to design the ship structure in this study. According to the proposed algorithm, the results of optimization were compared to the structural design of actual VLCC(Very Large Crude Oil Carrier). Objective functions were weight cost and construction cost of VLCC, and ES(Evolution Strategies), one of the stochastic search methods, was used as an optimization solver. For the scantlings of members and the estimations of objectives, classification rule was adopted for the longitudinal members, and the direct calculation method, GSDM(Generalized Slope Deflection Method), lot the transverse members. To choose the most economical design point among the results of Pareto optimal set, RFR(Required Freight Rate) was evaluated for each Pareto point, and compared to actual ship.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.101-108
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• 2020

Since the performance of deep learning techniques has recently been proven in the field of image processing, there are many attempts to perform classification, analysis, and detection of images using such techniques in various fields. Among them, the expectation of medical image analysis software, which can serve as a medical diagnostic assistant, is increasing. In this study, we are attention to the capsule endoscope image, which has a large data set and takes a long time to judge. The purpose of this paper is to distinguish the gastrointestinal landmarks and to estimate the gastrointestinal transition location that are common to all patients in the judging of capsule endoscopy and take a lot of time. To do this, we designed CNN-based Classifier that can identify gastrointestinal landmarks, and used it to estimate the gastrointestinal transition location by filtering the results. Then, we estimate gastrointestinal transition location about seven of eight patients entered the suspected gastrointestinal transition area. In the case of change from the stomach to the small intestine(pylorus), and change from the small intestine to the large intestine(ileocecal valve), we can check all eight patients were found to be in the suspected gastrointestinal transition area. we can found suspected gastrointestinal transition area in the range of 100 frames, and if the reader plays images at 10 frames per second, the gastrointestinal transition could be found in 10 seconds.