• 전기의세계
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• v.26 no.2
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• pp.66-71
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• 1977

A mathematical model, which can be used for the study of an influenza epidemic, was derived. The model of influenza takes into full consideration the incubation period and inapparent infection. That was analysed by means of digital computer under the conditions of changing the infection rate, .betha., from 4 to 5, for three types of communities (First type: the initial distribution of population, x$_{1}$(0)=89% susceptibles, x$_{2}$(0)=3% incubatives, x$_{3}$(0)=0.5% carriers, x$_{4}$(0)=7.5% immunes; Second type: x$_{1}$(0)=79%, x$_{2}$(0)=3%, x$_{3}$(0)=0.5%, x$_{4}$(0)=17.5%; Third type: x$_{1}$(0)=69%, x$_{2}$(0)=3%, x$_{3}$(0)=0.5%, x$_{4}$(0)=27.5%, considering the rate of population increase, in Seoul. In conclusion, the outcomes of this study are summarized as follow. 1) The new model is quite reasonable in representing many phenomena connected with influenza spread. 2) The more influenza does prevail, the smaller the valve of attack rate becomes, while the contagious period becomes slightly longer. 3) The average infection rate, .betha., of influenza is approximately 5 per week time and X$_{4}$(0) is about 27.5 percent of the total population in Seoul spring 1961. 4) The number of carriers of influenza in Seoul spring 1961 becomes maximum within approximately 2.4 weeks after the attack of diseases. 5) About 68 percent of all cases in the contagious period is infected with influenza from 5 to 15 days after the attack of diseases. The auther believes that the method to study the influenza models in this paper will be helpful to study the characteristics of other epidemics. It will also contribute to public healthe management and the preventive policy decision against epidemics.

• Structural Engineering and Mechanics
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• v.52 no.2
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• pp.405-426
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• 2014

In this paper, a new meta-heuristic algorithm named Ranked Particles Optimization (RPO), is presented. This algorithm is not inspired from natural or physical phenomena. However, it is based on numerous researches in the field of meta-heuristic optimization algorithms. In this algorithm, like other meta-heuristic algorithms, optimization process starts with by producing a population of random solutions, Particles, located in the feasible search space. In the next step, cost functions corresponding to all random particles are evaluated and some of those having minimum cost functions are stored. These particles are ranked and their weighted average is calculated and named Ranked Center. New solutions are produced by moving each particle along its previous motion, the ranked center, and the best particle found thus far. The robustness of this algorithm is verified by solving some mathematical and structural optimization problems. Simplicity of implementation and reaching to desired solution are two main characteristics of this algorithm.

• Journal of Preventive Medicine and Public Health
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• v.49 no.1
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• pp.18-22
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• 2016

Ethical considerations are essential in planning for and responding to outbreaks of infectious diseases. During the outbreak of Middle East respiratory syndrome coronavirus (MERS-CoV) in the Republic of Korea in 2015, serious challenges emerged regarding important ethical issues, such as transparency and the protection of privacy. The development of bioethics in Korea has been influenced by individualistic perspectives applied in clinical contexts, leading to a paucity of ethical perspectives relevant to population-level phenomena such as outbreaks. Alternative theories of public health ethics include the perspectives of relational autonomy and the patient as victim and vector. Public health actions need to incorporate clear and systematic procedures founded upon ethical principles. The MERS-CoV epidemic in Korea created significant public support for more aggressive early interventions in future outbreaks. This trend makes it all the more imperative for ethical principles and procedures to be implemented in future planning and responses to outbreaks in order to promote perceptions of legitimacy and civic participation.

• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.50.1-50.1
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• 2015

The origin of globular cluster (GC) color bimodality, which is one of the salient phenomena observed in most large galaxies, has not yet been fully resolved. The phenomenon has conventionally been interpreted as a bimodal metallicity distribution based on an assumption of linear GC color-metallicity relations (CMRs). Recent studies however suggest that nonlinear GC CMRs can cause a bimodal color distribution even from a single-peaked metallicity spread. Using photometric and spectroscopic data on GCs in NGC 5128 (Cen A) and NGC 4594 (Sombrero), we investigate the nonlinearity of GC CMRs and compare the observed GC CMRs with the predictions of stellar population simulation models. Our careful selection of old GCs effectively reduces the scatter and reveals the nonlinear nature of the GC CMRs for various colors. The overall shape of the observed CMRs agrees well with that of the modeled CMRs, while offsets are present for some colors. We discuss the implications of our results in terms of the GC color bimodality and GC formation in NGC 5128 and NGC 4594.

• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.799-804
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• 2009

In this paper, disasters are increasing rapidly due to increase of population, giantish of cities, advancement of traffics, and abnormal weather phenomena, and the scale of damages is also getting wider. So we study of the disaster safety management system and find recognition comprehensions and the problem points which are basic in our system. We dedicate this research on improvement analysis and main discussion issues of the system that based on diagnostics in developed countries' system. And Disaster management activities are increasing to efficiency through the disaster standardization trend.

• Journal of Computational Design and Engineering
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• v.3 no.1
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• pp.24-36
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• 2016

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.434-437
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• 2007

Maps contain and effectively visualize a number of spatial information. Advances in GIS enable researchers to analyze and represent spatial information through digital maps. Choropleth maps represent different quantities showing usually rates, percentages or densities. Generally, researchers make choropleth maps using raw rates. But, if the events are rare, raw rates cannot be sufficient in representing spatial phenomena. That is to say, if the population is large and events are rare, we cannot be sure that the raw rate is correct. The objective of this study is to make choropleth maps by several rate calculation methods and compare them. We use three methods in choropleth mapping; a raw rate, empirical Bayesian method, and spatial rate method which use prior probabilities. The experiments reveal that maps are somewhat different by used methods. We suggest that a raw rate method can not be an only way to make a rate map and researchers should choose an appropriate method for their objectives.

• Journal of Applied Biological Chemistry
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• v.43 no.4
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• pp.218-221
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• 2000

By the end of the $21^st$ century, the world population will surely far exceed the current 6.6 billion, threatening the essential requirements for life due to environmental deterioration and shortened food supply. To overcome this looming threat, we must develop new biotechnologies. There are so many known natural phenomena that we may have neglected, not perceiving them as blessings of nature. Many more remain unknown. We must examine each of them carefully since the many tricky and complicated mechanisms behind simple natural workings could provide us with attractive research targets. How then do we apply these complicated natural mechanisms to agricultural/ industrial production?.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.113-116
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• 2001

This paper analyzes the evolutionary process of a fuzzy logic controller using evolutionary activity. An evolutionary algorithm is commonly used to find solutions for given problems. However, little has been done on the analysis of the evolutionary pathways to the optimal solutions. This paper uses a genetic algorithm to construct a fuzzy logic controller for a mobile robot and applies evolutionary activity to measure the adaptability quantitatively. Evolutionary activity can be defined as the rate at which useful genetic innovations are absorbed in the population. By measuring the evolutionary activities, we will show quantitatively that the optimal fuzzy logic controller is not from other genetic phenomena like chance or necessity, but from the adaptability to a given encironment.