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이용빈(Lee, Yongbin),이주영(Lee, Juyoung),인진환(In, Jinhwan),이지은(Lee, Jeeun),조용훈(Cho, Yonghoon) 한국주거학회 2020 한국주거학회 학술대회논문집 Vol.32 No.2
The purpose of the research is to discover the social problems of the elderly as they enter into an 1)aging society and utilize them to plan the overall plan of the city, compare the 2)lifestyle and patterns of the users and analyze the physical activity ability of the elderly. Specifically, the proposal to connect a single 4)garden path in the form of a 3)platform-type cohabitation is intended to try to resolve the isolation, depression, and social isolation of the elderly through a community between various users in the 3)platform and the 4)garden.
Design optimization using support vector regression
Yongbin Lee,Sangyup Oh,최동훈 대한기계학회 2008 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.22 No.2
Polynomial regression (PR) and kriging are standard meta-model techniques used for approximate optimization (AO). Support vector regression (SVR) is a new meta-model technique with higher accuracy and a lower standard deviation than existing techniques. In this paper, we propose a sequential approximate optimization (SAO) method using SVR. Inherited latin hypercube design (ILHD) is used as the design of experiment (DOE), and the trust region algorithm is used as the model management technique, both adopted to increase efficiency in problem solving. We demonstrate the superior accuracy and efficiency of the proposed method by solving three mathematical problems and two engineering design problems. We also compare the proposed method with other meta-models such as kriging, radial basis function (RBF), and polynomial regression.
Active data dissemination for mobile sink groups in wireless sensor networks
Lee, Jeongcheol,Oh, Seungmin,Park, Soochang,Yim, Yongbin,Kim, Sang-Ha,Lee, Euisin Elsevier 2018 AD HOC NETWORKS Vol.72 No.-
<P><B>Abstract</B></P> <P>In wireless sensor networks, a mobile sink group brings out many challenging issues with regard to data dissemination due to its twofold mobility: group mobility and individual one. All member sinks of a group should move together toward the same destination in relation to the group mobility, but each member sink can also move randomly within a certain group area in relation to the individual mobility. For supporting such groups, geocasting may decrease data delivery ratio due to continuous group area shifting by the group mobility, and multicasting may increase energy consumption due to frequent multicast tree reconstructions by the individual sink mobility. Recently, mobile geocasting protocols have been proposed, which enable a mobile sink group to periodically register its current group area information to a source and member sinks in the group to passively receive data from the source by flooding within the registered group area. However, due to the passive data dissemination, they suffer from excessive energy consumption of sensor nodes due to flooding data within the large group area and result in high data delivery failures of member sinks on edge of the group due to asynchrony between the registered group area and the actual group area. Therefore, we propose an active data dissemination protocol that exploits a local data area constructed by considering the moving direction and pattern of a mobile sink group. In the proposed protocol, a source sends data to nodes in the local data area in advance, and member sinks in the group actively receive the data from the local data area when they potentially pass it. To efficiently construct a local data area, we investigate the pattern of group mobility and classify into three major categories according to the prediction level: a regular movement, a directional movement, and a random movement. We then present three different data dissemination schemes with an efficient local data area to effectively operate for each mobility pattern. Experimental results conducted in various environments show that the proposed protocol has better performance than previous protocols in terms of the data delivery ratio and the energy consumption.</P>
Blocking of p53-Snail Binding, Promoted by Oncogenic K-Ras, Recovers p53 Expression and function
Lee, Sun-Hye,Lee, Su-Jin,Jung, Yeon Sang,Xu, Yongbin,Kang, Ho Sung,Ha, Nam-Chul,Park, Bum-Joon Elsevier 2009 Neoplasia Vol.11 No.1
<P>Differentially from other kinds of Ras, oncogenic K-Ras, which is mutated approximately 30% of human cancer, does not induce apoptosis and senescence. Here, we provide the evidence that oncogenic K-Ras abrogates p53 function and expression through induction of Ataxia telangiectasia-mutated and Rad3-related mediated Snail stabilization. Snail directly binds to DNA binding domain of p53 and diminishes the tumor-suppressive function of p53. Thus, elimination of Snail through si-RNA can induce p53 in K-Ras-mutated cells, whereas Snail and mutant K-Ras can suppress p53 in regardless of K-Ras status. Chemicals, isolated from inhibitor screening of p53-Snail binding, can block the Snail-mediated p53 suppression and enhance the expression of p53 as well as the transcriptional activity of p53 in an oncogenic K-Ras-dependent manner. Among the chemicals, two are very similar in structure. These results can answer why K-Ras can coexist with wild type p53 and propose the Snail-p53 binding as the new therapeutic target for K-Ras-mutated cancers including pancreatic, lung, and colon cancers.</P>
Hyo Jung Park,Yongbin Shin,Jisuk Park,Hyosang Kim,In Seob Lee,Dong-Woo Seo,Jimi Huh,Tae Young Lee,박태용,Jeongjin Lee,김경원 대한영상의학회 2020 Korean Journal of Radiology Vol.21 No.1
Objective: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images. Materials and Methods: A fully convolutional network-based segmentation system was developed using a training dataset of 883 CT scans from 467 subjects. Axial CT images obtained at the inferior endplate level of the 3rd lumbar vertebra were used for the analysis. Manually drawn segmentation maps of the skeletal muscle, visceral fat, and subcutaneous fat were created to serve as ground truth data. The performance of the fully convolutional network-based segmentation system was evaluated using the Dice similarity coefficient and cross-sectional area error, for both a separate internal validation dataset (426 CT scans from 308 subjects) and an external validation dataset (171 CT scans from 171 subjects from two outside hospitals). Results: The mean Dice similarity coefficients for muscle, subcutaneous fat, and visceral fat were high for both the internal (0.96, 0.97, and 0.97, respectively) and external (0.97, 0.97, and 0.97, respectively) validation datasets, while the mean cross-sectional area errors for muscle, subcutaneous fat, and visceral fat were low for both internal (2.1%, 3.8%, and 1.8%, respectively) and external (2.7%, 4.6%, and 2.3%, respectively) validation datasets. Conclusion: The fully convolutional network-based segmentation system exhibited high performance and accuracy in the automatic segmentation of abdominal muscle and fat on CT images.
개선된 Padding기법을 이용한 Intra Prediction 성능 향상
김태영(Taeyoung Kim),이용빈(Yongbin Lee),이선율(Seonyul Lee),이선주(Seongjoo Lee) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
This paper analyzes prediction performance of intra prediction by combination of existing Padding technique (zero, half, mirror, circular, replication) and propose better Padding technique that can be used. Analyzed padding technique half & mirror, circular, replication, zero & mirror, circular, replication which is combination of two padding technique, and ‘Quartile padding’ using average. A simulation was performed on a grayscale 8-bit image using ‘visual studio 2019’. Prediction accuracy was evaluated by MSE (Mean Square Error) of the residual block.