• 수산해양교육연구
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• 제21권2호
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• pp.306-324
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• 2009

The purpose of this study is to analyze green growth issues such as employment, education and training, social capital and nature's standing right from the complementary perspective between natural environment conservation and economic growth. Green growth can be defined as a growth which lowers an increasing rate of entropy and at the same time improves our living standard. Green growth paradigm requires a quite amount of understanding the laws of thermodynamics and the uncertainty principle as the highest orders which regulate our overall socio-economic behaviors. They suggest that socio-economic growth is a mere transformation process of natural energy from one form to another and they increases natural manmade entropy over time. The most important issue of green growth policy may be a problem concerning employment and/or unemployment since green growth may induce inevitable movement of resources from the existing industries to the green sector. In particular, green industries will demand more highly specialized manpower than the existing ones. Without a well-designed new training education system and social capital accumulation toward environmental concerns, green growth may accompany a substantial amount of structural involuntary frictional unemployment. This may increase not only wealth-distribution disparity but also political instability. In order to achieve harmonious green growth, we should recognize that there are important complementary relationships between green and growth. Our society should also be able to innovate the existing educational system to accumulate social capital, to create a new sharing system, and to admit nature's standing right. Although the 2003 lawsuit case of Korean Salamander in Cheonseong Mountain went against plaintiff, it would provide apparently our society with a way of green development ahead.

• 정보보호학회논문지
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• 제32권6호
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• pp.1165-1170
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• 2022

무선 센서 네트워크(Wireless Sensor Network: WSN)에서 센서들을 클러스터(Cluster) 단위로 그룹화하고 각 클러스터에서 통신 중계 역할을 하는 클러스터 헤드(Cluster Head: CH)를 선출하는 클러스터링 알고리즘이 에너지 보존과 중계 효율을 위해 제안되어 왔다. 한편, 오염된 노드(Compromised Node), 즉 내부공격자를 통해 CH 선출과정에 개입하여 네트워크 운영에 치명적인 영향을 미치는 공격기법들이 등장하였으나, 암호키 기반 대응방식과 같은 기존 대응방법은 내부공격자 방어에 한계가 있었다. 따라서, 본 연구에서는 클러스터링 알고리즘의 CH 선출 통계를 바탕으로 비정상적인 CH 선출 공격을 탐지하는 통계적 탐지기법을 제안한다. 대표적인 클러스터링 알고리즘인 LEACH와 HEED가 운영되는 환경에서 오염노드에 의한 비정상적인 CH 선출 공격을 설계하고, 제안기법의 공격탐지여부에 대한 실험을 통해 제안기법의 효과성을 확인하였다.

• 간호행정학회지
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• 제6권3호
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• pp.357-374
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• 2000

The purpose of this study was to validate knowledge outcomes included Nursing Outcomes Classification(NOC) developed by Johnson and Maas at the University of Iowa. A sample of 71 nurse experts working in university affiliated hospitals participated in this study. They were asked to rate indicators that examplified the outcomes on a scale of 1(indicator is not all characteristic) to 5(indicator is very characteristic). A questionnaire with an adaptation of Fehring's methodology was used to establish the content validity of outcomes. The results were as follow: 1. All indicators were considered to be 'supporting' and no indicators were considered to be 'nonsupporting'. 2. 'Knowledge: Treatment Regimen' attained and OCV score of 0.816 and was the highest OCV score among outcomes. 3. 'Knowledge: Energy Conservation' attained an OCV score of 0.748 and was the lowest OCV score among abuse outcomes. 4. 'Knowledge: Breastfeeding' attained an OCV score of 0.790 and was the highest indicator was 'description of benefits of breastfeeding'. 5. 'Knowledge: Child Safety' attained an OCV score of 0.778 and was the highest indicator was 'demonstration of first aids techniques'. 6. 'Knowledge: Diet' attained an OCV score of 0.779 and was the highest indicator was 'performance of self-monitoring activities'. 7. 'Knowledge: Disease Process' attained an OCV score of 0.815 and was the highest indicator was 'description of signs and symptoms'. 8. 'Knowledge: Health Behaviors' attained an OCV score of 0.800 and was the highest indicator was 'description of safe use of prescription drugs'. 9. 'Knowledge: Health Resources' attained an OCV score of 0.794 and was the highest indicator was 'description of need for follow-up care'. 10. 'Knowledge: Infection Control' attained an OCV score of 0.793 and was the highest indicator was 'description of signs and symptoms'. 11. 'Knowledge: Medication' attained an OCV score of 0.789 and was the highest indicator was 'description of correct administration of medication'. 12. 'Knowledge: Personal Safety' attained an OCV score of 0.804 and was the highest indicator was 'description of measures to reduce risk of accidental injury'. 13. 'Knowledge: Prescribed Activity' attained an OCV score of 0.810 and was the highest indicator was 'proper performance of exercise'. 14. 'Knowledge: Substance Use Control' attained an OCV score of 0.809 and was the highest indicator was 'description of signs of dependence during substance withdrawl'. 15. 'Knowledge: Treatment Procedure(s)' attained an OCV score of 0.795 and was the highest indicator was 'description of appropriate action for complications'. 16. 'Knowledge: Treatment Regimen' attained an OCV score of 0.816 and was the highest indicator was 'description of self-care responsibilities for emergency situations'. More outcomes need to be validated and outcomes sensitive to Korean culture need to be developed.