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허수정(Soojung Hur),박희란(Heeran Park) 한국국정관리학회 2019 현대사회와 행정 Vol.29 No.2
우리나라의 노인 인구가 증가함에 따라 노인 정책 역시 확대되고 있다. 대표적인 노인 정책인 기초연금제도는 노인들의 안정적인 삶을 지원하고자 매년 연금액을 증액하고 수급자 선정기준을 완화하고 있다. 다른 노인 정책인 고령자 근로 정책은 근로를 통해 노인들이 안정적인 삶을 영위하도록 하고 있다. 두 정책 모두 노인의 안정적인 삶을 위해 시행되는 정책이지만 노인들이 기초연금을 받게 되면서 근로를 더 이상 지속하지 않게 되어 정책 간의 상충이 발생할 수 있는바, 본 연구에서는 기초연금으로 인하여 노인들의 근로소득이 변하는지를 살펴보았다. 연구 결과 기초연금은 노인들의 전체소득을 유의미하게 증가시켰으며 기초연금을 수급한다고 해서 근로소득이 감소하는 결과는 도출되지 않았다. 이를 통해 기초연금정책과 고령자 근로 정책 간에 서로 부정적인 관계는 없는 것을 확인할 수 있었다. As the elderly population increases, the number of policy for elderly increases. The basic pension system, for instance, has been increasing the amount of pension to provide a stable basis of income for the elderly. Another elderly policy in Korea called the elderly labor policy is also designed to provide a stable basis of income for the elderly. These two policies have the same purpose, however, the pension might cause the reduction of the number of working elderly. Therefore, in this study, we conduct the study to find the relationship between the basic pension and earned income of those who receive the basic pension. We find that the basic pension increases net income of elderly. However, the elderly who receives the basic pension does not reduce their labor income.
이무현,허수정,박용완,Lee, Moohyun,Hur, Soojung,Park, Yongwan 대한임베디드공학회 2015 대한임베디드공학회논문지 Vol.10 No.3
We propose obstacle classification method based on 2D LIDAR(Light Detecting and Ranging) database. The existing obstacle classification method based on 2D LIDAR, has an advantage in terms of accuracy and shorter calculation time. However, it was difficult to classifier the type of obstacle and therefore accurate path planning was not possible. In order to overcome this problem, a method of classifying obstacle type based on width data of obstacle was proposed. However, width data was not sufficient to improve accuracy. In this paper, database was established by width, intensity, variance of range, variance of intensity data. The first classification was processed by the width data, and the second classification was processed by the intensity data, and the third classification was processed by the variance of range, intensity data. The classification was processed by comparing to database, and the result of obstacle classification was determined by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that calculation time declined in comparison to 3D LIDAR and it was possible to classify obstacle using single 2D LIDAR.
Predictining ADHD in children using Vibraimage technology
Imran Ashraf(임란 아시라프),Soojung Hur(허수정),Gunzung Kim(김건정),Seung-Pil Jung(정승필),Yongwan Park(박용완) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.2
Attention deficit/hypersensitivity deficiency (ADHD) is one of the most common neurodevelopmental problems in children and adolescents. Differential diagnosis of ADHD is very difficult because its symptoms overlap with other diseases. Currently, ADHD is identified using subjective diagnosis which involves observing for various impulsive symptoms during different activities. An objective approach needs to be devised to diagnose ADHD for in time treatment. This study proposes Vibraimage technology to diagnose ADHD in children using a non-invasive approach.
단일 2차원 라이다 기반의 다중 특징 비교를 이용한 장애물 분류 기법
이무현(Moohyun Lee),허수정(Soojung Hur),박용완(Yongwan Park) 제어로봇시스템학회 2016 제어·로봇·시스템학회 논문지 Vol.22 No.4
We propose an obstacle classification method using multi-decision factors and decision sections based on Single 2D LiDAR. The existing obstacle classification method based on single 2D LiDAR has two specific advantages: accuracy and decreased calculation time. However, it was difficult to classify obstacle type, and therefore accurate path planning was not possible. To overcome this problem, a method of classifying obstacle type based on width data was proposed. However, width data was not sufficient to enable accurate obstacle classification. The proposed algorithm of this paper involves the comparison between decision factor and decision section to classify obstacle type. Decision factor and decision section was determined using width, standard deviation of distance, average normalized intensity, and standard deviation of normalized intensity data. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 2D LiDAR-based method, thus demonstrating the possibility of obstacle type classification using single 2D LiDAR.