• 제목/요약/키워드: e-Learning 2.0

검색결과 208건 처리시간 0.043초

A Single-Center Experience of Robotic-Assisted Spine Surgery in Korea : Analysis of Screw Accuracy, Potential Risk Factor of Screw Malposition and Learning Curve

  • Bu Kwang Oh;Dong Wuk Son;Jun Seok Lee;Su Hun Lee;Young Ha Kim;Soon Ki Sung;Sang Weon Lee;Geun Sung Song;Seong Yi
    • Journal of Korean Neurosurgical Society
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    • 제67권1호
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    • pp.60-72
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    • 2024
  • Objective : Recently, robotic-assisted spine surgery (RASS) has been considered a minimally invasive and relatively accurate method. In total, 495 robotic-assisted pedicle screw fixation (RAPSF) procedures were attempted on 100 patients during a 14-month period. The current study aimed to analyze the accuracy, potential risk factors, and learning curve of RAPSF. Methods : This retrospective study evaluated the position of RAPSF using the Gertzbein and Robbins scale (GRS). The accuracy was analyzed using the ratio of the clinically acceptable group (GRS grades A and B), the dissatisfying group (GRS grades C, D, and E), and the Surgical Evaluation Assistant program. The RAPSF was divided into the no-breached group (GRS grade A) and breached group (GRS grades B, C, D, and E), and the potential risk factors of RAPSF were evaluated. The learning curve was analyzed by changes in robot-used time per screw and the occurrence tendency of breached and failed screws according to case accumulation. Results : The clinically acceptable group in RAPSF was 98.12%. In the analysis using the Surgical Evaluation Assistant program, the tip offset was 2.37±1.89 mm, the tail offset was 3.09±1.90 mm, and the angular offset was 3.72°±2.72°. In the analysis of potential risk factors, the difference in screw fixation level (p=0.009) and segmental distance between the tracker and the instrumented level (p=0.001) between the no-breached and breached group were statistically significant, but not for the other factors. The mean difference between the no-breach and breach groups was statistically significant in terms of pedicle width (p<0.001) and tail offset (p=0.042). In the learning curve analysis, the occurrence of breached and failed screws and the robot-used time per screw screws showed a significant decreasing trend. Conclusion : In the current study, RAPSF was highly accurate and the specific potential risk factors were not identified. However, pedicle width was presumed to be related to breached screw. Meanwhile, the robot-used time per screw and the incidence of breached and failed screws decreased with the learning curve.

Selection of Three (E)UV Channels for Solar Satellite Missions by Deep Learning

  • Lim, Daye;Moon, Yong-Jae;Park, Eunsu;Lee, Jin-Yi
    • 천문학회보
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    • 제46권1호
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    • pp.42.2-43
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    • 2021
  • We address a question of what are three main channels that can best translate other channels in ultraviolet (UV) and extreme UV (EUV) observations. For this, we compare the image translations among the nine channels of the Atmospheric Imaging Assembly on the Solar Dynamics Observatory using a deep learning model based on conditional generative adversarial networks. In this study, we develop 170 deep learning models: 72 models for single-channel input, 56 models for double-channel input, and 42 models for triple-channel input. All models have a single-channel output. Then we evaluate the model results by pixel-to-pixel correlation coefficients (CCs) within the solar disk. Major results from this study are as follows. First, the model with 131 Å shows the best performance (average CC = 0.84) among single-channel models. Second, the model with 131 and 1600 Å shows the best translation (average CC = 0.95) among double-channel models. Third, among the triple-channel models with the highest average CC (0.97), the model with 131, 1600, and 304 Å is suggested in that the minimum CC (0.96) is the highest. Interestingly they are representative coronal, photospheric, and chromospheric lines, respectively. Our results may be used as a secondary perspective in addition to primary scientific purposes in selecting a few channels of an UV/EUV imaging instrument for future solar satellite missions.

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뇌조직의 리포푸신, 아세틸콜린 및 그 관련효소 활성에 미치는 실크 피브로인의 영향 (Effects of Silk Fibroin Powder on Lipofuscin, Acetylcholine and Its Related Enzyme Activities in Brain of SD Rats)

  • 최진호;김대익;박수현;김동우;이광길;여주홍;김정민;이용우
    • 한국잠사곤충학회지
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    • 제42권2호
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    • pp.120-125
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    • 2000
  • This study was designed to investigate the effects of silk fibroin (Mw 500) powder (SFP) on lipofuscin, acetylcholine (ACh) and its related enzyme activities in brain of rats. Sprague-Dawley (SD) male rats (160$\pm$10 g) were fed basic diet (control group), and experimental diets (SFP-2.5 and SFp-5.0 groups) added 2.5 and 5.0 g/kg BW/day for 6 weeks. In case of liver membranes, lipofuscin (LF) levels resulted in a considerable decreases (11.5% and 13.8%, respectively) in SFP-2.5 and SFP-5.0 groups compared with control group. But in case of brain as the most sensitive organ, LF levels were remarkably inhibited about 18.3% and 21.7% in SFP-2.5 and SFP-5.0 groups compared with control group. Acetylcholine (ACh) levels were considerable decrease (3.0% and 9.2%, respectively) in brain membranes of SFP-2.5 and SFP-5.0 groups compared with control group. choine acetyltranferase (ChAT) activities as a synthesis enzyme of ACh, and acetylcholinesterase (AChE) activities as a hydrolysis enzyme resulted in a slight increases (2.4% and 3.0%, 4.6% and 6.3%, respectively), but significance difference between ChAT and AChE activities by SFP administration could be not obtained. Monoamine oxidase-B (MAO-B) activities were significantly inhibited (9.5% and 12.6%, respectively) in brain of SEP-2.5 and SFP-5.0 groups compared with control group. These results suggest that inhibiting effects of LF accumulation and MAO-B activity of silk fibroin(SFP) may play a pivotal role in protecting learning memory impairments by attenuating a various age-related changes for improvement of brain function.

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Laparoscopic Gastrectomy Performed by an Expert in Open Gastrectomy

  • Chi, Kyong-Choun;Park, Joong-Min
    • Journal of Gastric Cancer
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    • 제17권3호
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    • pp.237-245
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    • 2017
  • Purpose: Senior surgeons prefer open gastrectomy (OG), while young surgeons prefer laparoscopic gastrectomy (LG). The purpose of this study was to evaluate the surgical outcomes of LG performed by a senior surgeon who was an expert in OG during his learning period, by comparing them with LGs performed by a young surgeon. Materials and Methods: A senior surgeon performed 50 curative gastrectomies with laparoscopy (LG-S group) from March 2015 to August 2016. A young surgeon's initial 50 LGs comprised the LG-Y group. Clinicopathological characteristics and surgical outcomes were compared between the LG-S and LG-Y groups. Results: D2 lymphadenectomy was more frequently performed in the LG-S group than in the LG-Y group (P=0.029). The operation time and number of retrieved lymph nodes did not significantly differ between the 2 surgeons (P=0.258 and P=0.410, respectively). Postoperative hospital stay and postoperative complication rate were similar between 2 groups (P=0.234 and P=1.000, respectively). Similarly, significant decreases in operation time with increasing case numbers were observed for both surgeons, whereas the number of retrieved lymph nodes increased significantly in the LG-Y group but not in the LG-S group. Conclusions: The LG outcomes when performed by the senior surgeon were comparable to those when performed by the young surgeon, despite performing more extended lymphadenectomies. Senior surgeons who are experts in OG should not refrain from performing LG.

자연상수 e에 대한 이해를 기반으로 지수함수 y=2x의 x=0에서의 순간변화율 구성에 관한 연구 (A Study on the Process of Constructing the Instantaneous Rate of Change of Exponential Function y=2x at x=0 Based on Understanding of the Natural Constant e)

  • 이동근;양성현;신재홍
    • 대한수학교육학회지:학교수학
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    • 제19권1호
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    • pp.95-116
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    • 2017
  • 지수함수의 미분에 대한 경험이 없는 학생들을 대상으로 자연상수 e를 구성하는 과정과 자연상수 e에 대한 이해를 기반으로 지수함수 $y=2^x$의 x=0에서 미분계수를 구하는 일련의 과정을 교수실험을 통하여 관찰하였다. 본 연구의 목적은 학생들의 반응을 일반화하는 것에 있는 것이 아니라, 실험에 참여한 학생들의 다양한 반응 분석을 통하여 미적분 관련 수학적 개념 지도에 대한 시사점을 찾아 제시하고자 하였다. 본 연구와 같이 학습자의 이해 방식과 구성 방식에 대한 연구 자료의 축적은 이후 미적분 관련 학습 모델을 제시하는데 중요한 기초 자료가 될 것으로 기대된다.

A study of glass and carbon fibers in FRAC utilizing machine learning approach

  • Ankita Upadhya;M. S. Thakur;Nitisha Sharma;Fadi H. Almohammed;Parveen Sihag
    • Advances in materials Research
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    • 제13권1호
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    • pp.63-86
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    • 2024
  • Asphalt concrete (AC), is a mixture of bitumen and aggregates, which is very sensitive in the design of flexible pavement. In this study, the Marshall stability of the glass and carbon fiber bituminous concrete was predicted by using Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and M5P Tree machine learning algorithms. To predict the Marshall stability, nine inputs parameters i.e., Bitumen, Glass and Carbon fibers mixed in 100:0, 75:25, 50:50, 25:75, 0:100 percentage (designated as 100GF:0CF, 75GF:25CF, 50GF:50 CF, 25GF:75CF, 0GF:100CF), Bitumen grade (VG), Fiber length (FL), and Fiber diameter (FD) were utilized from the experimental and literary data. Seven statistical indices i.e., coefficient of correlation (CC), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Scattering index (SI), and BIAS were applied to assess the effectiveness of the developed models. According to the performance evaluation results, Artificial neural network (ANN) was outperforming among other models with CC values as 0.9147 and 0.8648, MAE values as 1.3757 and 1.978, RMSE values as 1.843 and 2.6951, RAE values as 39.88 and 49.31, RRSE values as 40.62 and 50.50, SI values as 0.1379 and 0.2027 and BIAS value as -0.1 290 and -0.2357 in training and testing stage respectively. The Taylor diagram (testing stage) also confirmed that the ANN-based model outperforms the other models. Results of sensitivity analysis showed that the fiber length is the most influential in all nine input parameters whereas the fiber combination of 25GF:75CF was the most effective among all the fiber mixes in Marshall stability.

머신 러닝 알고리즘을 이용한 역방향 깃발의 에너지 하베스팅 효율 예측 (Prediction of Energy Harvesting Efficiency of an Inverted Flag Using Machine Learning Algorithms)

  • 임세환;박성군
    • 한국가시화정보학회지
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    • 제19권3호
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    • pp.31-38
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    • 2021
  • The energy harvesting system using an inverted flag is analyzed by using an immersed boundary method to consider the fluid and solid interaction. The inverted flag flutters at a lower critical velocity than a conventional flag. A fluttering motion is classified into straight, symmetric, asymmetric, biased, and over flapping modes. The optimal energy harvesting efficiency is observed at the biased flapping mode. Using the three different machine learning algorithms, i.e., artificial neural network, random forest, support vector regression, the energy harvesting efficiency is predicted by taking bending rigidity, inclination angle, and flapping frequency as input variables. The R2 value of the artificial neural network and random forest algorithms is observed to be more than 0.9.

Applying the Multiple Cue Probability Learning to Consumer Learning

  • Ahn, Sowon;Kim, Juyoung;Ha, Young-Won
    • Asia Marketing Journal
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    • 제15권3호
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    • pp.159-172
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    • 2013
  • In the present study, we apply the multiple cue probability learning (MCPL) paradigm to examine consumer learning from feedback in repeated trials. This paradigm is useful in investigating consumer learning, especially learning the relationships between the overall quality and attributes. With this paradigm, we can analyze what people learn from repeated trials by using the lens model, i.e., whether it is knowledge or consistency. In addition to introducing this paradigm, we aim to demonstrate that knowledge people gain from repeated trials with feedback is robust enough to weaken one of the most often examined contextual effects, the asymmetric dominance effect. The experiment consists of learning session and a choice task and stimuli are sport rafting boats with motor engines. During the learning session, the participants are shown an option with three attributes and are asked to evaluate its overall quality and type in a number between 0 and 100. Then an expert's evaluation, a number between 0 and 100, is provided as feedback. This trial is repeated fifteen times with different sets of attributes, which comprises one learning session. Depending on the conditions, the participants do one (low) or three (high) learning sessions or do not go through any learning session (no learning). After learning session, the participants then are provided with either a core or an extended choice set to make a choice to examine if learning from feedback would weaken the asymmetric dominance effect. The experiment uses a between-subjects experimental design (2 × 3; core set vs. extended set; no vs. low vs. high learning). The results show that the participants evaluate the overall qualities more accurately with learning. They learn the true trade-off rule between attributes (increase in knowledge) and become more consistent in their evaluations. Regarding the choice task, there is a significant decrease in the percentage of choosing the target option in the extended sets with learning, which clearly demonstrates that learning decreases the magnitude of the asymmetric dominance effect. However, these results are significant only when no learning condition is compared either to low or high learning condition. There is no significant result between low and high learning conditions, which may be due to fatigue or reflect the characteristics of learning curve. The present study introduces the MCPL paradigm in examining consumer learning and demonstrates that learning from feedback increases both knowledge and consistency and weakens the asymmetric dominance effect. The latter result may suggest that the previous demonstrations of the asymmetric dominance effect are somewhat exaggerated. In a single choice setting, people do not have enough information or experience about the stimuli, which may lead them to depend mostly on the contextual structure among options. In the future, more realistic stimuli and real experts' judgments can be used to increase the external validity of study results. In addition, consumers often learn through repeated choices in real consumer settings. Therefore, what consumers learn from feedback in repeated choices would be an interesting topic to investigate.

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Developing a Web-Based Knowledge Product Outsourcing System at a University

  • Onte, Mark B.;Marcial, Dave E.
    • Journal of Information Processing Systems
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    • 제9권4호
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    • pp.548-566
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    • 2013
  • The availability of technology and the abundance of experts in universities create an ample opportunity to provide a venue that allows a knowledge seeker to easily connect with and request advice from university experts. On the other hand, outsourcing provides opportunities and remains one of the emerging trends in organizations, and can very clearly observed in the Philippines. This paper describes the development of a reliable web-based approach to Knowledge Product Outsourcing (KPO) services in the Silliman Online University Learning system. The system is called an "e-Knowledge Box."It integrates Web 2.0 technologies and mechanisms, such as instant messaging, private messaging, document forwarding, video conferencing, online payments, net meetings, and social collaboration together into one system. Among the tools used are WAMP Server 2.0, PHP, BlabIM, Wordpress 3.0, Video Whisper, Red5, Adobe Dreamweaver CS4, and Virtual Box. The proposed system is integrated with the search engine in URLs, Web feeds, email links, social bookmarking, search engine sitemaps, and Web Analytics Direct Visitor Reports. The site demonstrates great web usability and has an excellent rating in functionality, language and content, online help and user guides, system and user feedback, consistency, and architectural and visual clarity. Likewise, the site was was rated as being very good for the following items: navigation navigation, user control, and error prevention and correction.

조경실무 교육수요 수준별 이러닝 콘텐츠 개발 방법론 - 모듈형 학습객체 개발과 재사용을 중심으로 - (A Study on the Development Method of e-Learning Contents by the Level of Demand for Landscaping Practical Education - Development and Reuse of Modular Learning Objects -)

  • 최자호
    • 한국조경학회지
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    • 제46권3호
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    • pp.1-13
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    • 2018
  • 조경은 폭넓은 지식과 경험이 요구되는 소수인력분야로 타 분야에 비해 서비스 시장이 협소하여, 실무자를 위한 교육 서비스가 부족한 실정이다. 이에 본 연구는 교육수요 수준별로 맞춤형 조경실무교육이 가능함과 동시에 개발 과정의 경제적 효율성을 높이는 이러닝 콘텐츠 개발 방법론을 제시하고자 수행하였다. 먼저, 이론 고찰에서 ADDIE 모형을 변형해 효율성을 추구한 교육 과정 개발 모형을 선정하였으며, 스콤 기반 모형의 학습객체 재사용 개념을 도입하였다. 특히, 선행연구에서 나타난 문제점을 보완하기 위해 분석, 설계 단계를 강화하였으며, 조경과 ICT에 대한 융복합 지식을 지닌 교수설계자가 전반적 단계를 주도하도록 하였다. 실제적 개발 과정은 단계별 절차에 의해 조경실무자 요구, 환경 등의 '분석', 콘텐츠 재사용성을 고려한 교수학습 절차, 활동 등의 '설계', 실제 촬영, 편집 등의 1차 개발, 1차 개발 콘텐츠를 재사용하는 2차 개발 등의 '개발', 전문성, 만족도 등에 대한 '평가 및 수정' 단계 순으로 진행하였다. 연구결과, 모듈형 학습객체로 구성된 공간별 과정이 총 8과목 216차시로 1차 개발되었으며, 모듈화된 학습객체를 단위별로 교차 조합한 분야별 과정이 총 3과목 208차시, 난이도별 과정이 총 3과목 216차시로 2차 개발되었다. 이에 대한 '평가'로 만족도 평가는 전반적 만족도 4.02, 8개 척도의 평균값은 3.97로 둘 다 4.0에 근접하였다. 전문성 평가는 8개 과목의 평가점수가 84.8~99.0으로 매우 높게 집계되었으며, 내용적으로는 5개 평가항목의 점수가 89.9~96.4점으로 비교적 균등하게 나타났다. 결론적으로 이러닝 콘텐츠의 디지털적 특성과 조경산업의 일반적 특성에 대한 명확한 이해를 바탕으로 연구를 수행함에 따라 모듈형 학습객체로 구성된 교육 과정 개발과 단위별 학습객체의 재사용에 의한 교육 과정 개발이 가능하였다. 특히, 보편적 절차에 의한 전문적 지식과 경험을 전달하는데 효과적임이 검증되었으며, 오프라인 교육에서 발생하는 아날로그적 문제점을 일부 극복할 수 있는 계기를 마련하였다. 향후, 콘텐츠 확충에 의한 추가연구와 세분화된 주제를 대상으로 연구할 필요가 있다.