• Journal of Family Resource Management and Policy Review
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• v.25 no.4
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• pp.55-70
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• 2021

The aim of this study was to analyze the work and life balance of unmarried working cases in single-person households. Eleven cases were interviewed with a semi-structured questionnaire. The results were as follows: When they were starting as a single-person household on their own initiative and with enough preparation and planning, they expressed higher satisfaction as a single person household. For their adaptation to being a single-person household, practice education and information about household work and self-care were useful. Their thought about 'life' are focusing on self improvement and leisure activities either alone or with their friends. Their family interactions were performed with financial, emotional, and housework support from each other. Resources such as money and time were the major factors that impacted work and life balance. For work and life balance of single person househods, the cases needed political support for financial aid and flexible work arrangements.

• Journal of Teachers' Pension
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• v.4
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• pp.175-205
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• 2019

본 연구의 목적은 폐교로 인해 퇴직한 사립학교 교직원을 대상으로 사학연금의 재해보상기금을 활용하여 실업급여를 제공하는 방안을 모색하는데 있다. 이를 위해 먼저 폐교 퇴직 교직원 현황 및 사학연금에서 폐교로 인한 퇴직 시 연금 지급규정, 재해보상 관련 쟁점, 고용보험 임의가입방안 관련 쟁점 등에 대해 고찰하였다. 다음으로 폐교로 인한 퇴직 교직원에 대한 정책방안들로 현행안, 재해보상기금을 활용한 구직급여 제공방안, 고용보험 임의가입안에 대한 재정추계를 실시하였다. 주요 분석결과는 다음과 같다. 첫째, 불과 5년 뒤인 2024학년도부터는 대입가능자원이 대학입학정원을 크게 밑돌 것으로 전망되고 이로 인해 대학의 통·폐합과 구조조정 등이 발생하여 현재보다 훨씬 더 많은 대학이 폐교될 것으로 예상된다. 둘째, 현행 사학연금법 상연금지급개시연령은 65세이나, 공무원연금법을 준용하고 있는 사학연금에서 폐교로 인해 퇴직하는 경우 퇴직 때부터 5년이 경과한 때 연금을 지급하는 규정으로 인해 여러 문제점들이 발생하고 있다. 셋째, 폐교로 인한 비자발적 퇴직 교직원의 소득활동 중단에 따른 소득 대체 및 생활안정을 위한 방안마련이 시급하다. 넷째, 산재보험과 공무원연금·군인연금 등 공적 재해보상제도들은 모두 업무상 또는 공무상 사고나 질병으로 부상·질병·장해·사망이 발생하면 재해로 정의하고 있어 사학연금에서 폐교를 재해로 포함시키는 것은 적합하지 않으며, 실업에 대응하는 사회보장제도인 고용보험에 포함시키는 것이 보다 타당할 것으로 보인다. 다섯째, 사립학교 교직원에 대한 고용보험적용 여부를 논의하기 위해서는 대학교수의 법적 지위와 근로기준법의 적용가능성이 중요한데, 향후 대법원이 대학교수를 근로기준법상 근로자로 판단한다면 사학 교직원의 고용보험 가입도 가능하게 될 것이다. 여섯째, 사학연금가입자들을 위한 고용보험 도입이 바람직하나, 이것이 단기간 내에 실행되기 어렵다면 고려 가능한 대안으로 공무원연금과는 다른 사학 교직원 직무에서 발생하는 재해의 특성을 고려하여 사학재해보상제도에서 폐교로 인한 퇴직을 재해보상에 포함시키고 구직급여를 제공하는 방안을 검토해 볼 수 있다. 또한 재정추계결과는 다음과 같다. 첫째, 현재 폐교로 인한 퇴직 시 제공되는 연금 대신에 재해보상기금으로 구직급여를 제공할 경우 사학연금기금에 재정적으로 상당히 긍정적 효과가 있을 것으로 예상된다. 둘째, 폐교로 인한 2018년 현재 연금수급자(285명)의 예상 연금급여액 추계에 의하면 2050년까지 누적해서 볼 경우, 재해보상기금을 활용하여 구직급여를 제공하는 방안이 폐교로 인한 퇴직 시 연금을 지급하는 현행안보다 2018년 현재가치로 123억 원 정도 유리한 것으로 나타났다. 셋째, 현재 수급자와 미래 수급자의 예상 연금급여액 추계에 의하면 2028년까지 누적해서 볼 경우, 재해보상기금을 활용하여 구직급여를 제공하는 방안이 현행안보다 2018년 현재가치로 747억 원 정도 재정적으로 유리한 것으로 나타났다.

• Journal of Conservation Science
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• v.37 no.5
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• pp.451-463
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• 2021

In this study we conducted component analyses of portable XRF detectors using four Au-Cu alloy standard samples to improve their accuracy by drawing up a calibration curve based on ICP-OES standard values. The portable XRF analysis found absolute errors of 0.3 to 3.7 wt% for Au and 0.2 to 8.2 wt% for Cu, confirming that the error range and standard deviation differed from one detector to another. Furthermore, the calibration curve improved their accuracy, such that the relative error rates of Au and Cu decreased from 9.8% and 14% to 3.5% and 3.7%, respectively. Accordingly, an experiment to confirm the calibration curve was conducted using unknown samples, finding that the measured values of the unknown samples fell on the calibration curve. Therefore, to accurately analyze the components of metal cultural heritage items, it is necessary to prepare a calibration curve for each element after checking whether the detector is suitable for the artifact.

• Convergence Security Journal
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• v.21 no.2
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• pp.57-66
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• 2021

Deep neural networks (DNNs) provide excellent performance for image, speech, and pattern recognition. However, DNNs sometimes misrecognize certain adversarial examples. An adversarial example is a sample that adds optimized noise to the original data, which makes the DNN erroneously misclassified, although there is nothing wrong with the human eye. Therefore studies on defense against adversarial example attacks are required. In this paper, we have experimentally analyzed the success rate of detection for adversarial examples by adjusting various parameters. The performance of the ensemble defense method was analyzed using fast gradient sign method, DeepFool method, Carlini & Wanger method, which are adversarial example attack methods. Moreover, we used MNIST as experimental data and Tensorflow as a machine learning library. As an experimental method, we carried out performance analysis based on three adversarial example attack methods, threshold, number of models, and random noise. As a result, when there were 7 models and a threshold of 1, the detection rate for adversarial example is 98.3%, and the accuracy of 99.2% of the original sample is maintained.

• The Journal of Internal Korean Medicine
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• v.42 no.5
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• pp.1001-1008
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• 2021

Objective: The aim of this case study was to describe a case of iris diagnosis of primary hyperhidrosis and the use of Korean medicine. Methods: A patient with symptoms of hyperhidrosis was diagnosed as having Taeeumin after assessment using the four basic Korean diagnostic methods. Iris diagnosis was used for further examination. The images obtained showed a remarkably defined collarette and increased nerve rings, which suggested an overactive sympathetic nerve system. Under the diagnosis of Taeeum, a Korean herbal medicine was prescribed with additional herbs to help alleviate the hyperactivity of the sympathetic nervous system. Results: The patient had been receiving treatment for hyperhidrosis for >30 years, with various medical attempts to relieve her symptoms, which were ineffective. She showed signs of improvement from day 4 into the treatment, and 80% of her symptoms were improved after completing a 6-week treatment course. Conclusion: The herbal medicine prescribed to the patient proved effective for reducing her chronic symptoms that had been unresponsive to previous medical treatments.

• Convergence Security Journal
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• v.21 no.1
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• pp.137-147
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• 2021

The purpose of this study is to summarize the variables for making the power expansion of terrorist organizations possible by analyzing the power transitions among the Somalia governments and terrorisms. For this, I created a framework after extracting the independent variables affecting the power transition using the theories of Hans J. Morgenthau, A.F.K Organski, K.J. Holsti. On the basis of these, I analyzed the power transitions among the organizations from August 2012(the foundation of Federal government) to August 2019. According to the results, the power area of the federal government is extremely narrowed, whereas the power areas of al-Shabaab, ISIS, and neutral conflict are largely expanded. The factors in the framework were embodied as the delay of powerful federal government foundation, the shortage of national military forces, the limitation of effective use of foreign forces, the continuation of cooperation and antagonism among armed terrorist organization, sanctuary environment of terrorists using the Sharia law, advantages of force recruitment resulted from the bad economic conditions, public support suitable for the Somalia environment. All of these factors work the causes of lasting anarchy condition of the federal government of Somalia by making the settlement of conflict against terrorist organizations.

• Journal of Science Education
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• v.45 no.2
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• pp.156-171
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• 2021

This study aims at investigating the characteristics of trends of future education over time though the literature review and examining the accuracy of the framework for forecasting future education proposed by the previous studies by comparing the outcomes between the literature review and media articles. Thus, this study collects the articles dealing with future education searched from the Web of Science and categorized them into four periods during the new millennium. The new articles from media were selected to find out the present of education so that we can figure out the appropriateness of the proposed framework to predict the future of education. Research findings reveal that gradual tendencies of topics could not be found except teacher education and they are diverse from characteristics of agents (students and teachers) to the curriculum and pedagogical strategies. On the other hand, the results of analysis on the media articles focuses more on the projects launched by the government and the immediate responses to the COVID-19, as well as educational technologies related to big data and artificial intelligence. It is surprising that only a few key words are occupied in the latest articles from the literature review and many of them have not been discussed before. This indicates that the predictive framework is not effective to establish the long-term plan for education due to the uncertainty of educational environment, and thus this study will give some implications for developing the model to forecast the future of education.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.523-529
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• 2021

In this paper, we designed and developed an Emotional Speech Synthesis Markup Language (SSML) processor. Multi-speaker emotional speech synthesis technology that can express multiple voice colors and emotional expressions have been developed, and we designed Emotional SSML by extending SSML for multiple voice colors and emotional expressions. The Emotional SSML processor has a graphic user interface and consists of following four components. First, a multi-speaker emotional text editor that can easily mark specific voice colors and emotions on desired positions. Second, an Emotional SSML document generator that creates an Emotional SSML document automatically from the result of the multi-speaker emotional text editor. Third, an Emotional SSML parser that parses the Emotional SSML document. Last, a sequencer to control a multi-speaker and emotional Text-to-Speech (TTS) engine based on the result of the Emotional SSML parser. Based on SSML which is a programming language and platform independent open standard, the Emotional SSML processor can easily integrate with various speech synthesis engines and facilitates the development of multi-speaker emotional text-to-speech applications.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.431-445
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• 2021

The purpose of this study was to provide implications for social work practice by examining the influence of career barrier on smart-phone addiction through self-esteem and depression among out-of-school adolescents. Using data from the Panel Survey of School Dropouts conducted by the National Youth Policy Institute, this study examined the research questions by analyzing the direct and indirect influences of career barrier on smart-phone addiction with the PROCESS macro method. First, the results of this study showed that out-of-school adolescents' career barrier statistically significantly increased smart-phone addiction. Also, out-of-school adolescents' depression mediated the influence of career barrier on smart-phone addiction, but out-of-school adolescents' self-esteem did not mediate the influence. However, out-of-school adolescents' self-esteem and depression dual-mediated the influence of career barrier on smart-phone addiction. The level of career barrier decreased the level of self-esteem, and in turn increased the level of depression, and finally increased the level of smart-phone addiction. Based on the findings, practical strategies to address the smart-phone addiction of out-of-school adolescents were discussed.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.368-376
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• 2021

Artificial intelligence clouds help to efficiently develop the autonomous things integrating artificial intelligence technologies and control technologies by sharing the learned models and providing the execution environments. The existing autonomous things development technologies only take into account for the accuracy of artificial intelligence models at the cost of the increment of the complexity of the models including the raise up of the number of the hidden layers and the kernels, and they consequently require a large amount of computation. Since resource-constrained computing environments, could not provide sufficient computing resources for the complex models, they make the autonomous things violate time criticality. In this paper, we propose a digital twin software development framework that selects artificial intelligence models optimized for the computing environments. The proposed framework uses a load estimation DNN model to select the optimal model for the specific computing environments by predicting the load of the artificial intelligence models with digital twin data so that the proposed framework develops the control software. The proposed load estimation DNN model shows up to 20% of error rate compared to the formula-based load estimation scheme by means of the representative CNN models based experiments.