• 대기
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• 제18권4호
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• pp.507-524
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• 2008

In this paper, future climate changes over East Asia($20^{\circ}{\sim}50^{\circ}N$, $100^{\circ}{\sim}150^{\circ}E$) are projected by anthropogenic forcing of greenhouse gases and aerosols using coupled atmosphere-ocean general circulation model (AOGCM) simulations based on Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) B1, A1B and A2 scenarios. Before projection future climate, model performance is assessed by the $20^{th}$ Century (20C3M) experiment with bias, root Mean Square Error (RMSE), ratio of standard deviation, Taylor diagram analysis. The result of examination of the seasonal uncertainty of T2m and PCP shows that cold bias, lowered than that of observation, of T2m and wet bias, larger than that of observation, of PCP are found over East Asia. The largest wet bias is found in winter and the largest cold bias is found in summer. The RMSE of temperature in the annual mean increases and this trend happens in winter, too. That is, higher resolution model shows generally better performances in simulation T2m and PCP. Based on IPCC SRES scenarios, East Asia will experience warmer and wetter climate in the coming $21^{st}$ century. It is predict the T2m increase in East Asia is larger than global mean temperature. As the latitude goes high, the warming over the continents of East Asia showed much more increase than that over the ocean. An enhanced land-sea contrast is proposed as a possible mechanism of the intensified Asian summer monsoon. But, the inter-model variability in PCP changes is large.

• 대한기계학회논문집B
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• 제29권11호
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• pp.1265-1276
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• 2005

The elliptic conceptual second moment models for turbulent heat fluxes, which are proposed on the basis of elliptic-blending and elliptic-relaxation equations, are applied to calculate the combined forced and natural turbulent convection in a vertical plane channel. The models satisfy the near-wall balance between viscous diffusion, viscous dissipation and temperature-pressure gradient correlation, and also have the characteristics of approaching its respective conventional high Reynolds number model far away from the wall. Also the models are closely linked to the elliptic blending model which is used for the prediction of Reynolds stress. In order to calibrate the heat flux models, firstly, the distributions of mean temperature and scala flux in fully developed channel flow with constant wall difference temperature are solved by the present models. The buoyancy effect on the turbulent characteristics including the mean velocity and temperature, the Reynolds stress tensor, and the turbulent heat flux vector are examined. In the opposing flow, the turbulent transport is greatly enhanced with both the Reynolds stresses and the turbulent heat fluxes being remarkably increased; whereas, in the aiding flow, the opposite change is observed. The results of prediction are directly compared to the DNS to assess the performance of the model predictions and show that the behaviors of the turbulent heat transfer in the whole flow region are well captured by the present models.

• Nuclear Engineering and Technology
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• 제22권4호
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• pp.326-336
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• 1990

최근 수행된 일련의 실험들을 통하여 핵연료 집합체 지지격자(Spacer Grid)의 존재가 냉각재상실사고시에 핵연료봉으로부터의 열제거에 긍정적인 효과를 미치고 있음이 밟혀졌다. 그 이유는 열원이 없는 지지격자가 연료봉보다 먼저 ？칭이 일어나며 물방울이 지지격자에 부딪쳐서 잘게 부수어져 증발이 쉽게 일어나게 되고 또한 난류효과를 증대시키는 요인이 되기 때문이다. 따라서 냉각재상실사고의 진행 과정에서 첨두피복관온도가 발생하는 재관수 구간의 수면 위쪽에서 유지되는 DFFB에서의 정확한 열전달을 계산하기 위해서는 이들의 고려가 필요하다. 본 논문에서는 DFFB에서 지지격자의 존재로 인해 물방울이 잘게 부수어져 증발이 쉽게 이루어지도록 하는 Droplet Breakup Model을 냉각재상실사고 최적해석 코드인 RELAP5/MOD2에 삽입하였다. 재관수 구간에서 지지격자의 영향을 체계적으로 조사한 FEBA실험에 대해서 검증계산을 수행하여 실험자료와 비교하였다.

• 영어어문교육
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• 제8권1호
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• pp.165-198
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• 2002

The purpose of this research is to improve the writing ability of students by an ideal test model of English writing based on strategies of procedural learning stages enhancing the level of students' writing ability. Assessment of writing in the field of English education has been limited so far to very restricted areas with no appropriate scientific scrutiny. Assessment is really meaningful only when it exactly estimates the ability of students. Since English writing competence has become indispensable in this era of global village, writing instruction should be most emphasized. The most forceful method of busting writing instruction is to utilize the so-called washback effect of testing. So, to develop a good test model of writing, the first thing that is required is to inspect writing strategy in steps and, then, testing itself. First of all, analyzed with a special reference to the 6th high school English curriculum were the goals and contents of the syllabus reflected in one kind of junior high textbook and eight different kinds of senior high textbooks. Then questionnaires on the whole area of writing and tendencies of English writing classes were given to 100 English teachers, 300 students. The results of questionnaires were statistically analyzed. Then, some suggestions and opinions about the questioning method were made: the procedural strategy in steps, English writing instruction and test model of assessment were applied to the syllabus referring to teaching plans. On the bases of the results of the questionnaires, three pretests and a final test of English writing were administered to verify the effect of enhanced English writing competence which had been gradually promoted and, through the promotion, produced the test criteria of English writing. In conclusion, guidance and evaluation of English writing through in steps are really indispensable to increase student's practical ability and, accordingly, we are in need of the development of a testing method of useful writing practiced in school class above anything else. So, it is necessary to further the study on methods to assess writing ability on the bases of participation and fluency of students with their keen interest in English. Also, to intensify the effect of the test model, more accommodating reorganization of syllabus is required in our education. For instance, we need a flexible operation in organizing time units from the current 50 minutes to 100-130 minutes.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2008년도 춘계 종합학술대회 논문집
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• pp.161-165
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• 2008

본 연구는 지식정보화 사회의 경쟁력인 창의성을 기르기 위하여 수정된 ASSURE 모델을 기반으로 한 영상제작 활동 교수 학습 과정을 설계하고 적용하는 데 그 목적이 있다. 연구의 목적을 달성하기 위하여 다음과 같은 절차와 방법을 수행하였다. 먼저 영상매체 교육과 창의성 교육에 대하여 이론적 고찰을 한 후, 이 둘의 관계를 규명 해보고 그 결과로 수정된 ASSURE 모델을 적용한 교수 학습과정을 설계하였다. 그리고 영상제작 활동 교수 학습과정 전개 시 창의성 계발학습 모형을 적용하여 수업의 효과성을 높였다. 본 연구 결과 및 의의는 다음과 같다. 첫째, 영상제작활동 교수학습과정 적용의 결과 학생들의 창의적 성향이 신장되었다. 적용 전에 이던 것이 적용 후에는 로 나타났으며 통계적으로는 t=(p<0.05)의 유의미한 차이가 있다. 둘째, 영상제작 활동 교수학습 과정 적용을 통하여 문화적 프로슈머의 발판을 마련하였다. 덧붙여서 본 연구를 함께 한 모든 학생이 디지털미디어 시대의 리더그룹으로 성장하기를 기대해 본다.

• Biomolecules & Therapeutics
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• 제29권6호
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• pp.615-629
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• 2021

An active compound, triterpene saponin, astersaponin I (AKNS-2) was isolated from Aster koraiensis Nakai (AKNS) and the autophagy activation and neuroprotective effect was investigated on in vitro and in vivo Parkinson's disease (PD) models. The autophagy-regulating effect of AKNS-2 was monitored by analyzing the expression of autophagy-related protein markers in SH-SY5Y cells using Western blot and fluorescent protein quenching assays. The neuroprotection of AKNS-2 was tested by using a 1-methyl-4-phenyl-2,3-dihydropyridium ion (MPP⁺)-induced in vitro PD model in SH-SY5Y cells and an MPTP-induced in vivo PD model in mice. The compound-treated SH-SY5Y cells not only showed enhanced microtubule-associated protein 1A/1B-light chain 3-II (LC3-II) and decreased sequestosome 1 (p62) expression but also showed increased phosphorylated extracellular signal-regulated kinases (p-Erk), phosphorylated AMP-activated protein kinase (p-AMPK) and phosphorylated unc-51-like kinase (p-ULK) and decreased phosphorylated mammalian target of rapamycin (p-mTOR) expression. AKNS-2-activated autophagy could be inhibited by the Erk inhibitor U0126 and by AMPK siRNA. In the MPP⁺-induced in vitro PD model, AKNS-2 reversed the reduced cell viability and tyrosine hydroxylase (TH) levels and reduced the induced α-synuclein level. In an MPTP-induced in vivo PD model, AKNS-2 improved mice behavioral performance, and it restored dopamine synthesis and TH and α-synuclein expression in mouse brain tissues. Consistently, AKNS-2 also modulated the expressions of autophagy related markers in mouse brain tissue. Thus, AKNS-2 upregulates autophagy by activating the Erk/mTOR and AMPK/mTOR pathways. AKNS-2 exerts its neuroprotective effect through autophagy activation and may serve as a potential candidate for PD therapy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• Journal of Biosystems Engineering
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• 제27권2호
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• pp.117-124
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• 2002

This research was conducted to develop a method to remove the effect of surface temperature of Shingo pears for sugar content measurement. Sugar content was measured by a near-infrared spectrum analysis technique. Reflected spectrum and sugar content of a pear were used for developing regression models. For the model development, reflected spectrums having wavelengths in the range of 654 to 1,052nm were used. To remove the effect of surface temperature, special sample preparation techniques and partial least square (PLS) regression models were proposed and tested. 71 Shingo pears stored in a cold storage, which had 2$^{\circ}C$ inside temperature, were taken out and left in a room temperature for a while. Temperature and reflected spectrum of each pear was measured. To increase the temperature distribution of samples, temperature and reflected spectrum of each pear was measured four times with one hour twenty minutes interval. During the experiment, temperature of pears increased up to 17 $^{\circ}C$. The total number of measured spectrum was 284. Three groups of spectrum data were formed according to temperature distribution. First group had surface temperature of 14$^{\circ}C$ and total number of 51. Second group consisted of the first and the fourth experiment data which contained the minimum and the maximum temperatures. Third group consisted of 155 data with normal temperature-distribution. The rest data set were used for model evaluation. Results shelved that PLS model I, which was developed by using the first data group, was inadequate for measuring sugar content of pears which had different surface temperatures from 14$^{\circ}C$. After temperature compensation, sugar content predictions became close to the measured values. Since using many data which had wide range of surface temperatures, PLS model II and III were able to predict sugar content of pears without additional temperature compensation. PLS model IV, which included the surface temperatures as an independent variable. showed slightly improved performance(R$^2$=0.73). Performance of the model could be enhanced by using samples with more wide range of temperatures and sugar contents.

• Korean Journal of Radiology
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• 제22권3호
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• pp.334-343
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• 2021

Objective: We aimed to develop a prediction model for diagnosing severe aortic stenosis (AS) using computed tomography (CT) radiomics features of aortic valve calcium (AVC) and machine learning (ML) algorithms. Materials and Methods: We retrospectively enrolled 408 patients who underwent cardiac CT between March 2010 and August 2017 and had echocardiographic examinations (240 patients with severe AS on echocardiography [the severe AS group] and 168 patients without severe AS [the non-severe AS group]). Data were divided into a training set (312 patients) and a validation set (96 patients). Using non-contrast-enhanced cardiac CT scans, AVC was segmented, and 128 radiomics features for AVC were extracted. After feature selection was performed with three ML algorithms (least absolute shrinkage and selection operator [LASSO], random forests [RFs], and eXtreme Gradient Boosting [XGBoost]), model classifiers for diagnosing severe AS on echocardiography were developed in combination with three different model classifier methods (logistic regression, RF, and XGBoost). The performance (c-index) of each radiomics prediction model was compared with predictions based on AVC volume and score. Results: The radiomics scores derived from LASSO were significantly different between the severe AS and non-severe AS groups in the validation set (median, 1.563 vs. 0.197, respectively, p < 0.001). A radiomics prediction model based on feature selection by LASSO + model classifier by XGBoost showed the highest c-index of 0.921 (95% confidence interval [CI], 0.869-0.973) in the validation set. Compared to prediction models based on AVC volume and score (c-indexes of 0.894 [95% CI, 0.815-0.948] and 0.899 [95% CI, 0.820-0.951], respectively), eight and three of the nine radiomics prediction models showed higher discrimination abilities for severe AS. However, the differences were not statistically significant (p > 0.05 for all). Conclusion: Models based on the radiomics features of AVC and ML algorithms may perform well for diagnosing severe AS, but the added value compared to AVC volume and score should be investigated further.

• 정보과학회 논문지
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• 제44권4호
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• pp.352-362
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• 2017

본 논문에서는 루팅된 단말 환경에서 SEAndroid의 보안 위협을 체계적으로 분석하고, 효과적으로 앱 데이터를 보호하는 기법을 제안한다. 루팅되지 않은 안드로이드 단말의 경우, 접근제어 모델에 의해 한 앱의 데이터는 해당 앱만이 접근할 수 있다. 하지만, 루팅된 단말의 경우 접근제어 모델이 무력화되어, 루트 권한 쉘이 임의로 다른 앱의 민감한 데이터에 접근하거나 악성 앱이 다른 앱의 데이터를 외부로 유출할 수도 있다. 이를 방어하기 위해, 본 논문에서는 기존 SEAndroid의 LSM(Linux Security Module) Hook 함수를 수정하여 제한된 프로세스만이 특정 앱 데이터를 접근할 수 있도록 하였다. 또한 새로운 도메인 타입의 관리 프로세스를 추가하였고, 해당 프로세스로 하여금 새로 설치되는 앱의 디렉토리 타입을 분리하여 관리하게 하였다. 실험을 통해, 제안 기법이 앱 데이터를 효과적으로 보호함과 성능 오버헤드가 2초 이내임을 보인다.