• Asian-Australasian Journal of Animal Sciences
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• v.25 no.8
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• pp.1073-1082
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• 2012

We first sought to clarify the effects of discounted rate, survival rate, and lactation persistency as a component trait of the selection index on net merit, defined as the first five lactation milks and herd life (HL) weighted by 1 and 0.389 (currently used in Japan), respectively, in units of genetic standard deviation. Survival rate increased the relative economic importance of later lactation traits and the first five lactation milk yields during the first 120 months from the start of the breeding scheme. In contrast, reliabilities of the estimated breeding value (EBV) in later lactation traits are lower than those of earlier lactation traits. We then sought to clarify the effects of applying single nucleotide polymorphism (SNP) on net merit to improve the reliability of EBV of later lactation traits to maximize their increased economic importance due to increase in survival rate. Net merit, selection accuracy, and HL increased by adding lactation persistency to the selection index whose component traits were only milk yields. Lactation persistency of the second and (especially) third parities contributed to increasing HL while maintaining the first five lactation milk yields compared with the selection index whose only component traits were milk yields. A selection index comprising the first three lactation milk yields and persistency accounted for 99.4% of net merit derived from a selection index whose components were identical to those for net merit. We consider that the selection index comprising the first three lactation milk yields and persistency is a practical method for increasing lifetime milk yield in the absence of data regarding HL. Applying SNP to the second- and third-lactation traits and HL increased net merit and HL by maximizing the increased economic importance of later lactation traits, reducing the effect of first-lactation milk yield on HL (genetic correlation ($r_G$) = -0.006), and by augmenting the effects of the second- and third-lactation milk yields on HL ($r_G$ = 0.118 and 0.257, respectively).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.462-483
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• 2016

The research on optimization of cellular network's energy efficiency (EE) towards environmental and economic sustainability has attracted increasing attention recently. In this survey, we discuss the opportunities, trends and challenges of this challenging topic. Two major contributions are presented namely 1) survey of proposed energy efficiency metrics; 2) survey of proposed energy efficient solutions. We provide a broad overview of the state of-the-art energy efficient methods covering base station (BS) hardware design, network planning and deployment, and network management and operation stages. In order to further understand how EE is assessed and improved through the heterogeneous network (HetNet), BS's energy-awareness and several typical HetNet deployment scenarios such as macrocell-microcell and macrocell-picocell are presented. The analysis of different HetNet deployment scenarios gives insights towards a successful deployment of energy efficient cellular networks.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.48.3-48.3
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• 2021

SomangNet is a project that started in 2020 with a network of ten 0.4 to 1.0 m telescopes owned by Korean institutes. By coordinating observations with multiple facilities around the world, we hope to maximize the usefulness of small telescopes which are still competitive for carrying out time-domain astronomy projects. In this talk, we will give an overview of the project, outlining SomangNet facilities, its organization, and current science projects. We hope to open SomangNet for common use in 2021B, and we will present our plan regarding the use of SomangNet.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.967-970
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• 2006

정보 보호의 목표는 정보자산의 기밀성, 무결성, 가용성을 보장함으로써 정보시스템의 신뢰성과 안전성을 확보하고 이를 통하여 기관이나 조직에서 추구하는 사업에 대한 영속성 보장의 기반을 제공하는 것이다. 접근 제어는 정보 보호의 목표인 기밀성, 무결성을 하기 위한 수단으로 많이 사용된다. 즉, 인가받지 않은 주체에게는 접근을 허용하지 않고, 인가된 주체에 대해서는 신뢰성 있는 정보를 제공하기 위해 정보에 대한 접근 제어를 한다. 그러나 가용성 측면을 무시하고 기밀성과 무결성만을 지나치게 강조할 경우 사용자에게 제공되는 정보는 이미 과거의 정보가 되어 아무런 가치가 없을 수도 있다. 이에 본 논문에서는 정보 보호의 3대 목표를 모두 만족하는 접근제어 시스템을 구축하는데 있어서 바람직한 방향을 제시하고자 한다.

• 한국약용작물학회:학술대회논문집
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• 2011.09a
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• pp.352-353
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• 2011

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.1214-1217
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• 2007

대부분의 기관들은 악의적인 행위들을 포착하거나 시스템과 데이터를 보호하고 사고에 대응하기 위한 시도들을 지원하기 위해 몇 가지 형태의 네트워크 기반 보안솔루션을 사용하고 있다. 하지만 기존 네트워크 레벨 보안의 한계로 인하여 시스템 상에서 일어나는 행위를 제어하기 위한 차세대 보안솔루션으로 보안운영체제를 도입하고 있다. 최근에는 전자금융거래법 등의 세칙에 의해 정보처리 시스템 내의 정보의 유출, 변조 및 파괴 등을 보호하는 것은 물론 세부 작업 내역의 로깅에 대한 요구가 지속적으로 증가하고 있다. 이에 본 논문에서는 보안레이블에 의한 시스템 보안 강화 기술을 소개하고 강제적 접근 제어 결과에 의해 생성되는 보안 로그에 대한 구체적인 관리 전략을 제시한다.

• Journal of the Korean Wood Science and Technology
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• v.45 no.6
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• pp.797-808
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• 2017

Automatic wood species identification systems have enabled fast and accurate identification of wood species outside of specialized laboratories with well-trained experts on wood species identification. Conventional automatic wood species identification systems consist of two major parts: a feature extractor and a classifier. Feature extractors require hand-engineering to obtain optimal features to quantify the content of an image. A Convolutional Neural Network (CNN), which is one of the Deep Learning methods, trained for wood species can extract intrinsic feature representations and classify them correctly. It usually outperforms classifiers built on top of extracted features with a hand-tuning process. We developed an automatic wood species identification system utilizing CNN models such as LeNet, MiniVGGNet, and their variants. A smartphone camera was used for obtaining macroscopic images of rough sawn surfaces from cross sections of woods. Five Korean softwood species (cedar, cypress, Korean pine, Korean red pine, and larch) were under classification by the CNN models. The highest and most stable CNN model was LeNet3 that is two additional layers added to the original LeNet architecture. The accuracy of species identification by LeNet3 architecture for the five Korean softwood species was 99.3%. The result showed the automatic wood species identification system is sufficiently fast and accurate as well as small to be deployed to a mobile device such as a smartphone.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.4
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• pp.409-418
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• 2010

Purpose: The purpose of this study was to compare the costs and benefits of home nursing care services between public health centers (PHC) and private hospitals. Method: Participants were 105 patients who had received home nursing care services from a private hospital or public health center. From a societal perspective, the researcher identified the costs and benefits of the services using performance data and calculated the net benefit and benefit/cost ratio. Result: The net benefit of the home nursing care service based in the PHC was 165.9 million won and benefit/cost ratio was 2.0, while the net benefit of the home nursing care services by the private hospital was 141.1 million won and benefit/cost ratio was 1.7. Both types of programs were economically validated. Conclusion: Home nursing care services were basically efficient as the results showed a positive net benefit. A cost-benefit analysis indicated that the PHC-based home nursing care services were more efficient than that of the private hospital. With limited human resources and management standards in public health centers, results suggest the need for a more systematic management of the home nursing care service to improve the health of this vulnerable community population.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.425-431
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• 2005

The systems-level analysis of microbes with myriad of heterologous data generated by omics technologies has been applied to improve our understanding of cellular function and physiology and consequently to enhance production of various bioproducts. At the heart of this revolution resides in silico genome-scale metabolic model, In order to fully exploit the power of genome-scale model, a systematic approach employing user-friendly software is required. Metabolic flux analysis of genome-scale metabolic network is becoming widely employed to quantify the flux distribution and validate model-driven hypotheses. Here we describe the development of an upgraded MetaFluxNet which allows (1) construction of metabolic models connected to metabolic databases, (2) calculation of fluxes by metabolic flux analysis, (3) comparative flux analysis with flux-profile visualization, (4) the use of metabolic flux analysis markup language to enable models to be exchanged efficiently, and (5) the exporting of data from constraints-based flux analysis into various formats. MetaFluxNet also allows cellular physiology to be predicted and strategies for strain improvement to be developed from genome-based information on flux distributions. This integrated software environment promises to enhance our understanding on metabolic network at a whole organism level and to establish novel strategies for improving the properties of organisms for various biotechnological applications.