• Title/Summary/Keyword: 인식적 비대칭성

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DEVELOPMENT OF THREE DIMENSIONAL MEASURING PROGRAM WITH FRONTAL AND LATERAL CEPHALOMETRIC RADIOGRAPHS -PART 1. COMPUTATION OF THE THREE-DIMENSIONAL COORDINATES BY COMPENSATION OF THE ERROR OF THE HEAD POSITION IN ORDINARY NON-BIPLANAR CEPHALOSTAT- (정모 및 측모 두부 방사선 규격사진을 이용한 3차원 계측 프로그램의 개발 -1. 단일 방사선원으로 촬영된 두부 방사선사진의 두부 위치 보정을 이용한 3차원 좌표의 산출-)

  • Lee, Geun-Ho;Lee, Sang-Han;Jang, Hyon-Joong;Kwon, Tae-Geon
    • Journal of the Korean Association of Oral and Maxillofacial Surgeons
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    • v.27 no.3
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    • pp.214-220
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    • 2001
  • The clinical application of the three-dimensional radiographic technique had been limited to standard Broadbent-Bolton cephalometer with biplanar stereoradiography. We developed a new method for compensating the error of head position in ordinary non-biplanar cephalostat. It became to possible to use the three dimensional cephalogram commonly in clinical bases. 1. The method of methemetical compensation of head positioning error in non-biplanar condition was evaluated with dry skull. The error of the method of first and the second trial was $0.46{\pm}1.21$, $0.33{\pm}0.90mm$, which means the error of the head positioning correction in conventional cephalogram was within clinical acceptance. 2. The reproducibility of this system for clinical application was 0.54 mm ($-2.99{\sim}2.26mm$) which defines the absolute mean difference of the first and second trial. Compare to the The landmark identification error $1.2{\pm}1.6mm$, the error of the measurement was within the range of landmark identification error. The result indicates the adequate clinical accuracy of the computation of three-dimensional coordinates by compensation of the error of the head position in ordinary non-biplanar cephalostat.

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우리나라의 출산력과 가정경제행태에 관한 연구

  • 노공균;조남훈
    • Korea journal of population studies
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    • v.10 no.2
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    • pp.17-45
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    • 1987
  • This study contributes to understanding women's labor market behavior by focusing on a particular set of labor force transitions - labor force withdrawal and entry during the period surrounding the first birth of a child. In particular, this study provides a dynamic analyses, using longitudinal data and event history analysis, to conceptualize labor force behaviors in a straightforward way. The main research question addresses which factors increase or decrease the hazard rates of leaving and entering the labor market. This study used piecewise Gompertz model, following the guide of the non-parametric analysis on the hazard rates, which allowed relatively detailed description on the distribution of timing of leave and entry to the labor market as parameters of interest. The results show that preferences and structural variables, as well as economic considerations, are very important factors to explain the labor market behavior of women in the period surrounding childbirth.

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The Characteristics and Performances of Manufacturing SMEs that Utilize Public Information Support Infrastructure (공공 정보지원 인프라 활용한 제조 중소기업의 특징과 성과에 관한 연구)

  • Kim, Keun-Hwan;Kwon, Taehoon;Jun, Seung-pyo
    • Journal of Intelligence and Information Systems
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    • v.25 no.4
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    • pp.1-33
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    • 2019
  • The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study (딥러닝 알고리즘을 이용한 저선량 디지털 유방 촬영 영상의 복원: 예비 연구)

  • Su Min Ha;Hak Hee Kim;Eunhee Kang;Bo Kyoung Seo;Nami Choi;Tae Hee Kim;You Jin Ku;Jong Chul Ye
    • Journal of the Korean Society of Radiology
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    • v.83 no.2
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    • pp.344-359
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    • 2022
  • Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.