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이용원 팬코리아영어교육학회 2016 영어교육연구 Vol.28 No.1
The current study reports the findings from Phase 2 of a larger research study undertaken toinvestigate the feasibility of using generic scoring models for e-rater in the context of scoringessays for independent writing tasks for TOEFL CBT and TOEFL iBT. In Phase 1, sixdifferent variants of generic and hybrid scoring models of e-rater were created based ontransformed writing data from three different samples of TOEFL CBT prompts (n1=20, n2=20,n3= 40) with the help of ETS (Educational Testing Service) staff and then evaluated on aseparate sample of seven TOEFL CBT prompts (Lee, 2016). In the present investigation, thesesix generic/hybrid models were used, along with prompt-specific models, to score a total of3,126 essays written for two TOEFL iBT independent writing tasks from a field study and theirperformance was evaluated. Results of the analysis showed that (a) there were relatively smallscore variations among different automated scoring models and (b) similar levels of scoreagreement were achieved between the human-human rater pair and various human-automatedrater pairs, although the prompt-specific model behaved most similarly to the human raters. Interms of criterion-related validity of scores, the human rater scores turned out to be somewhatbetter indicators of test-takers’ overall ESL (English as a Second Language) languageproficiency than the automated scores in general. Nevertheless, the comparative advantage invalidity of human rater scores (over automated scores) seemed to diminish significantly, whenmore direct writing measures, such as scores for TOEFL CBT independent writing tasks, wereused as criterion measures.
이용원,성연선,Edward Jeong Bae,오성훈,배인수,강효진 생화학분자생물학회 2015 Experimental and molecular medicine Vol.47 No.-
An F-box protein, β-TrCP recognizes substrate proteins and destabilizes them through ubiquitin-dependent proteolysis. It regulates the stability of diverse proteins and functions as either a tumor suppressor or an oncogene. Although the regulationby β-TrCP has been widely studied, the regulation of β-TrCP itself is not well understood yet. In this study, we found that the level of β-TrCP1 is downregulated by various protein kinase inhibitors in triple-negative breast cancer (TNBC) cells. A PI3K/mTOR inhibitor PI-103 reduced the level of β-TrCP1 in a wide range of TNBC cells in a proteasome-dependent manner. Concomitantly, the levels of c-Myc and cyclin E were also downregulated by PI-103. PI-103 reduced the phosphorylation of β-TrCP1 prior to its degradation. In addition, knockdown of β-TrCP1 inhibited the proliferation of TNBC cells. We furtheridentified that pharmacological inhibition of mTORC2 was sufficient to reduce the β-TrCP1 and c-Myc levels. These results suggest that mTORC2 regulates the stability of β-TrCP1 in TNBC cells and targeting β-TrCP1 is a potential approach to treathuman TNBC.
CFIT 자율 회피를 위한 심층강화학습 기반 에이전트 연구
이용원,유재림 한국항공운항학회 2022 한국항공운항학회지 Vol.30 No.2
In Efforts to prevent CFIT accidents so far, have been emphasizing various education measures to minimize the occurrence of human errors, as well as enforcement measures. However, current engineering measures remain in a system (TAWS) that gives warnings before colliding with ground or obstacles, and even actual automatic avoidance maneuvers are not implemented, which has limitations that cannot prevent accidents caused by human error. Currently, various attempts are being made to apply machine learning-based artificial intelligence agent technologies to the aviation safety field. In this paper, we propose a deep reinforcement learning-based artificial intelligence agent that can recognize CFIT situations and control aircraft to avoid them in the simulation environment. It also describes the composition of the learning environment, process, and results, and finally the experimental results using the learned agent. In the future, if the results of this study are expanded to learn the horizontal and vertical terrain radar detection information and camera image information of radar in addition to the terrain database, it is expected that it will become an agent capable of performing more robust CFIT autonomous avoidance.