Chung-Ang University

About: Chung-Ang University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Thin film. The organization has 13381 authors who have published 26978 publications receiving 416735 citations. The organization is also known as: CAU & Chung.

Burden of osteoporosis in adults in Korea: a national health insurance database study

Abstract: We evaluated the number of osteoporosis patients under treatment and secular trends in 2005–2008 in South Korea. We investigated nationwide data regarding the number of osteoporosis patients under treatment in South Korea using data from the Health Insurance Review Agency (HIRA), which includes nationwide information. Reimbursement records from the HIRA database between 1 January 2004 and 31 December 2008 were investigated. Patients aged ≥30 years old with osteoporosis were identified based on a study-defined algorithm using prescription data and diagnostic codes. During the study periods, the number of patients receiving medical treatment related to osteoporosis increased from 1,034,399 to 1,392,189 for women and from 120,496 to 171,902 for men. The calculated proportion of osteoporosis patients under treatment in the general population over 50 years of age was 6.1% for men and 33.3% for women, and in the general population over 30 years of age was 2.7% for men and 16.6% for woman. More than 40% of patients (59.1% for women; 41.2% for men) were treated with medication indicated only for osteoporosis. About 4–7% of osteoporosis patients had a past medical history suggesting a secondary cause of osteoporosis. More than 80% of all osteoporosis patients were women older than 50 years, reflecting the pronounced burden of osteoporosis among postmenopausal women. This study demonstrated a substantial increasing trend in medical claims related to osteoporosis in 2005–2008 among adults in Korea and a pronounced burden of osteoporosis among postmenopausal women.

Switching Strategy Based on Model Predictive Control of VSI to Obtain High Efficiency and Balanced Loss Distribution

Abstract: This paper proposes the switching strategy based on finite control set model predictive control (FCS-MPC) method, to reduce switching losses and obtain balanced loss distribution of the voltage-source inverters (VSIs). Unlike the conventional FCS-MPC method with no explicit information of the reference voltage, the developed voltage-based FCS-MPC scheme produces the future reference voltage vector with the Lyapunov function every sampling period. With information of both the future reference voltage and the future load current vectors, the proposed switching strategy instantaneously determines one optimum clamped phase among the three legs in the VSI every sampling period. By optimally determining the clamping phase and its duration on the basis of every sampling period, the proposed switching strategy can successfully reduce the VSI switching losses. In addition, the proposed switching method can yield a balanced loss distribution among the switches in the VSI, contrary to the conventional FCS-MPC. The balanced loss generation as well as the switching loss reduction by the proposed method, which is optimal at the sampling period scale, is directly incorporated with the platform of the FCS-MPC algorithm, since the FCS-MPC operates on the basis of the sampling period. Thus, the proposed switching operation based on the voltage-based FCS-MPC algorithm enables the future VSI output currents to track the future reference current vector, as well as results in the reduced switching losses and the balanced loss performance.

Segmentation of Regions of Interest in Mammograms in a Topographic Approach

Abstract: This paper presents a novel method for the segmentation of regions of interest in mammograms. The algorithm concurrently delineates the boundaries of the breast boundary, the pectoral muscle, as well as dense regions that include candidate masses. The resulting representation constitutes an analysis of the global structure of the object in the mammogram. We propose a topographic representation called the isocontour map, in which a salient region forms a dense quasi-concentric pattern of contours. The topological and geometrical structure of the image is analyzed using an inclusion tree that is a hierarchical representation of the enclosure relationships between contours. The ?saliency? of a region is measured topologically as the minimum nesting depth. Features at various scales are analyzed in multiscale isocontour maps, and we demonstrate that the multiscale scheme provides an efficient way of achieving better delineations. Experimental results demonstrate that the proposed method has potential as the basis for a prompting system in mammogram mass detection.