• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.207-218
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• 1997

We consider two models for the growth of population with overlaping generations. First, the model we will describe is basically the model given by Leslie(1945). This is only a one-sex model of population age structure and growth. Next, we introduce a model in which couples must be formed before reproduction occurs. If the maximum number of couples is formed, and if the couples are only formed from fermales of age x-a and males of age x at time t, $\alpha$ > 0. Then, we will solve the renewal equations for the reproductive value.

• 한국지리정보학회지
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• 제10권2호
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• pp.11-22
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• 2007

본 논문은 인구 중심점 개념을 이용하여 GIS 공간 모형에서 보다 정확한 수요 규모를 추정할 수 있는 방법을 제시하고자 하는 연구이다. 이를 위하여 본 연구에서는 면적 속성의 수요 데이터와 점형 속성의 인구 중심점(population centroid)의 개념을 활용하여 보다 정확한 수요 규모를 추정할 수 있는 방법을 제시하고 다양한 지역 및 공간 단위에도 적용될 수 있는 가능성을 제시하였다. 이를 위하여 기존의 수요 데이터 이용 방법의 한계와 제한점을 보완하고 보다 정확한 수요 규모의 추정을 위해서 주택 유형별 가중치 기반의 인구 중심점 추정 방법을 제시하였다. 추출된 인구 중심점을 기반으로 각 수요점의 위치와 수요 규모를 추정하고 인구 중심점과 수요 지점간의 거리 측정 방법을 통하여 실제 GIS 공간모형의 적용 가능성을 살펴 보았다. 이를 위하여 입지-배분 공간 모형을 사례로 시설물 입지를 위한 기본적인 수요 규모와 서비스 배분을 위한 GIS 공간 모형의 적용 가능성을 확인하였다.

• The Korean Journal of Pain
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• 제33권2호
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• pp.153-165
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• 2020

Background: Well-validated risk prediction models help to identify individuals at high risk of diseases and suggest preventive measures. A recent systematic review reported lack of validated prediction models for low back pain (LBP). We aimed to develop prediction models to estimate the 8-year risk of developing LBP and its recurrence. Methods: A population based prospective cohort study using data from 435,968 participants in the National Health Insurance Service-National Sample Cohort enrolled from 2002 to 2010. We used Cox proportional hazards models. Results: During median follow-up period of 8.4 years, there were 143,396 (32.9%) first onset LBP cases. The prediction model of first onset consisted of age, sex, income grade, alcohol consumption, physical exercise, body mass index (BMI), total cholesterol, blood pressure, and medical history of diseases. The model of 5-year recurrence risk was comprised of age, sex, income grade, BMI, length of prescription, and medical history of diseases. The Harrell's C-statistic was 0.812 (95% confidence interval [CI], 0.804-0.820) and 0.916 (95% CI, 0.907-0.924) in validation cohorts of LBP onset and recurrence models, respectively. Age, disc degeneration, and sex conferred the highest risk points for onset, whereas age, spondylolisthesis, and disc degeneration conferred the highest risk for recurrence. Conclusions: LBP risk prediction models and simplified risk scores have been developed and validated using data from general medical practice. This study also offers an opportunity for external validation and updating of the models by incorporating other risk predictors in other settings, especially in this era of precision medicine.

• 4차산업연구
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• 제3권2호
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• pp.1-10
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• 2023

Purpose - The main purpose of the paper is to examine the variables affecting carbon emissions in different nations around the world. Research design, data, and methodology - To measure its impact on carbon emissions, secondary data has data of the top 50 Countries have been taken. The stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model have been used to quantify the factors that affect carbon emissions. A modified version using Industry 4.0 and region in fundamental STIRPAT model has been applied with the ordinary least square approach. The outcome has been measured using both the basic and extended STIRPAT models. Result - Technology found a positive determinant as well as statistically significant at the alpha level of 0.001models indicating that technological innovation helps reduce carbon emissions. In total, 4 models have been derived to test the best fit and find the highest explaining capacity of variance. Model 3 is found best fit in explanatory power with the highest adjusted R2 (97.95%). Conclusion - It can be concluded that the selected explanatory variables population and Industry 4.0 are found important indicators and causal factors for carbon emission and found constant with all four models for total CO2 and Co2 per capita.

• 대한임상약리학회:학술대회논문집
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• 대한임상약리학회 2006년도 제15차 추계학술대회
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• pp.37-38
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• 2006

• 한국전자통신학회논문지
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• 제17권1호
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• pp.91-98
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• 2022

본 연구에서는 오픈소스 하드웨어인 라즈베리파이와 딥러닝 기술 기반 객체 탐지 알고리즘을 이용해 부경대학교 교내 유동인구 조사 및 분석을 수행하였다. 라즈베리파이를 이용하여 이미지를 수집한 후 YOLO3의 IMAGEAI, YOLOv5 모델을 사용하여 수집한 이미지의 인물 검출을 진행하였으며 정확도 비교 분석을 위해 Haar-like features, HOG 모델을 사용하였다. 분석결과, 개교기념일로 인한 휴교에 가장 적은 유동인구가 관측되었다. 대체적으로 입구의 유동인구가 출구의 유동인구보다 많았으며, 입구와 출구 모두 학교의 기념일과 행사에 따라 유동인구가 많은 영향을 받는 것으로 나타났다.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• 제4권3호
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• pp.115-126
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• 2023

Understanding the carrying capacity of a habitat is crucial for effectively managing populations of wild boars (Sus scrofa), which are designated as harmful wild animal species in national parks. Carrying capacity refers to the maximum population size supported by a park's environmental conditions. This study aimed to estimate the appropriate wild boar population size by integrating population characteristics and habitat suitability for wild boars in the Bukhansan National Park using the HexSim program. Population characteristics included age, survival, reproduction, and movement. Habitat suitability, which reflects prospecting and resource acquisition, was determined using the Maximum Entropy model. This study found that the optimal population size for wild boar ranged from 217 to 254 individuals. The population size varied depending on the amount of resources available within the home range, indicating fewer individuals in a larger home range. The estimated wild boar population size was 217 individuals for the minimum amount of resources (50% minimum convex polygon [MCP] home range), 225 individuals for the average amount of resources (95% MCP home range), and 254 individuals for the maximum amount of resources (100% MCP home range). The results of one-way analysis of variance revealed a significant difference in wild boar population size based on the amount of resources within the home range. These findings provide a basis for the development and implementation of effective management strategies for wild boar populations.

• Genomics & Informatics
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• 제17권4호
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• pp.37.1-37.8
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• 2019

Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST, and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.

• 대한원격탐사학회지
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• 제36권1호
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• pp.83-92
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• 2020

The purpose of this work was to apply geographical information system (GIS) for geostatistical analyzing by selecting a semi-variogram model to quantify the spatial correlation of the population distribution with residential neighborhoods in the both sides of Mosul city. Two hundred and sixty-eight sample sites in 240 ㎢ are adopted. After determining the population distribution with respect to neighborhoods, data were inserted to ArcGIS10.3 software. Afterward, the datasets was subjected to the semi-variogram model using ordinary kriging interpolation. The results obtained from interpolation method showed that among the various models, Spherical model gives best fit of the data by cross-validation. The kriging prediction map obtained by this study, shows a particular spatial dependence of the population distribution with the neighborhoods. The results obtained from interpolation method also indicates an unbalanced population distribution, as there is no balance between the size of the population neighborhoods and their share of the size of the population, where the results showed that the right side is more densely populated because of the small area of residential homes which occupied by more than one family, as well as the right side is concentrated in economic and social activities.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.220-225
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• 2021

Human population growth rate is an important parameter for real-world planning. Common approaches rely upon fixed parameters like human population, mortality rate, fertility rate, which is collected historically to determine the region's population growth rate. Literature does not provide a solution for areas with no historical knowledge. In such areas, machine learning can solve the problem, but a multitude of machine learning algorithm makes it difficult to determine the best approach. Further, the missing feature is a common real-world problem. Thus, it is essential to compare and select the machine learning techniques which provide the best and most robust in the presence of missing features. This study compares 17 machine learning techniques (base learners and ensemble learners) performance in predicting the human population growth rate of the country. Among the 17 machine learning techniques, random forest outperformed all the other techniques both in predictive performance and robustness towards missing features. Thus, the study successfully demonstrates and compares machine learning techniques to predict the human population growth rate in settings where historical data and feature information is not available. Further, the study provides the best machine learning algorithm for performing population growth rate prediction.