• Journal of Gastric Cancer
• /
• 제18권2호
• /
• pp.182-188
• /
• 2018

Purpose: Despite the fact that there are several reports of single-port laparoscopic distal gastrectomy (SPDG), no analysis of its learning curve has been described in the literature. The aim of this study was to investigate the favorable factors for SPDG and to analyze the learning curve of SPDG. Materials and Methods: A total of 125 cases of SPDG performed from November 2011 to December 2015 were enrolled. All operations were performed by 2 surgeons (surgeon A and surgeon B). The moving average method was used for defining the learning curve. All cases were divided into 10 cases in a sequence, and the mean operative time and estimated blood loss data were extracted from each group. Results: Surgeon A performed 68 cases (female-to-male sex ratio, 91.1%:8.82%), and surgeon B performed 57 cases (female-to-male sex ratio, 61.4%:38.5%). The operative time of surgeon B significantly decreased after 30 cases ($157.8{\pm}38.4$ minutes vs. $118.1{\pm}34.5$ minutes, P=0.003); that of surgeon A did not significantly decrease before and after around 30 cases ($160.8{\pm}51.6$ minutes vs. $173.3{\pm}35.2$ minutes, P=0.6). The subgroup analysis showed that the operative time significantly decreased in the patients with body mass index (BMI) of <$25kg/m^2$ (<$25kg/m^2$:${\geq}25kg/m^2$, $159.3{\pm}41.7$ minutes: $194.25{\pm}81.1$ minutes; P=0.001). Conclusions: Although there was no significant decrease in the operative time for surgeon A, surgeon B reached the learning curve upon conducting 30 cases of SPDG. BMI of <$25kg/m^2$ was found to be a favorable factor for SPDG.

• 스마트미디어저널
• /
• 제9권3호
• /
• pp.25-30
• /
• 2020

본 논문에서는 산업 시설에서 작업자의 안전을 실시간으로 감시하는 딥러닝 기반 영상 분석 시스템을 구현하는 데 목적을 둔다. 작업자의 복장을 안전모, 안전조끼, 안전벨트 착용 여부에 따라 총 여섯 가지의 클래스로 나누고, 총 5,307개의 영상을 학습데이터로 이용하였다. 실험은 속도와 정확도가 준수한 YOLO v4를 이용하였으며, 총 645장의 영상에 대해 학습 반복 수에 따른 가중치를 적용했을 때의 mAP를 비교함으로써 수행되었다. 학습 반복 수 6,000에서의 mAP가 60.13%로 제일 높았으며, 테스트셋이 가장 많은 클래스의 AP가 가장 높음을 확인하였다. 추후 데이터셋과 객체 검출 모델을 최적화함으로써, 정확도와 속도를 개선할 예정이다.

• 한국수학교육학회지시리즈A:수학교육
• /
• 제17권2호
• /
• pp.1-13
• /
• 1979

This study aimed at studying the sequence of the Figure Transformation Learning, inquiring relationship among these transformations and then researching whether there is the difference of the learning ability or not between by teaching them as it is independent and by teaching them as it is contains. (Hypothesis 1) It may be more effective to teach The Sequence of Transformation Learning by beginning with peculiar field, ending with general field than vice versa At the result of verification-C $R_{M}$=2.59, 0.005$R_{M}$=5.19, p<0.005-significant difference appeared. It is proved more effective to teach the Figure Transformation Learning the way it contains than the way it is independent. Synthesizing two hypothesises of the above, the conclusion is following The Figure Transformation Learning should be taught by beginning with peculiar field. ending with general field (congruent transformationlongrightarrowsimilar transformationlongrightarrowprojective transformationlongrightarrowtopological transformation). To teach it the way it contains is more effective.ive.

• Journal of Information Technology Applications and Management
• /
• 제27권3호
• /
• pp.19-36
• /
• 2020

The Internet of things (IoT) is a system of interrelated computed devices, digital machines and any physical objects which are provided with unique identifiers and the potential to transmit data to people or machine (M2M) without requiring human interaction. IoT devices can be used to monitor and control the electrical and electronic systems used in different fields like smart home, smart city, smart healthcare and etc. In this study we introduce four imaginary IoT devices as a learning support assistants according to students' dominant learning styles measured by Honey and Mumford Learning Styles: Activists, Reflectors, Theorists and Pragmatists. This research emphasizes the association between students' strong learning styles and a preference to appropriate IoT devices with specific characteristics. Moreover, different levels of IoT devices' architecture are clearly explained in this study where all the artificial devices are designed based on this structure. Data analysis of experiment were measured by the use of chi square test for association and research results showed the statistical significance of the estimated model and the impacts of each category over the model where we finally got accurate estimates for our research variables. This study revealed the importance of considering the students' dominant learning styles before inventing a new IoT device.

• Journal of Semiconductor Engineering
• /
• 제2권2호
• /
• pp.130-135
• /
• 2021

Recently, deep neural networks (DNNs) are actively used for action control so that an autonomous system, such as the robot, can perform human-like behaviors and operations. Unlike recognition tasks, the real-time operation is essential in action control, and it is too slow to use remote learning on a server communicating through a network. New learning techniques, such as reinforcement learning (RL), are needed to determine and select the correct robot behavior locally. In this paper, we propose an energy-efficient DNN processor with a LUT-based processing engine and near-zero skipper. A CNN-based facial emotion recognition and an RNN-based emotional dialogue generation model is integrated for natural HRI system and tested with the proposed processor. It supports 1b to 16b variable weight bit precision with and 57.6% and 28.5% lower energy consumption than conventional MAC arithmetic units for 1b and 16b weight precision. Also, the near-zero skipper reduces 36% of MAC operation and consumes 28% lower energy consumption for facial emotion recognition tasks. Implemented in 65nm CMOS process, the proposed processor occupies 1784×1784 um2 areas and dissipates 0.28 mW and 34.4 mW at 1fps and 30fps facial emotion recognition tasks.

• Computers and Concrete
• /
• 제32권3호
• /
• pp.263-286
• /
• 2023

Experimenting with concrete to determine its compressive and tensile strengths is a laborious and time-consuming operation that requires a lot of attention to detail. Researchers from all around the world have spent the better part of the last several decades attempting to use machine learning algorithms to make accurate predictions about the technical qualities of various kinds of concrete. The research that is currently available on estimating the strength of concrete draws attention to the applicability and precision of the various machine learning techniques. This article provides a summary of the research that has previously been conducted on estimating the strength of concrete by making use of a variety of different machine learning methods. In this work, a classification of the existing body of research literature is presented, with the classification being based on the machine learning technique used by the researchers. The present review work will open the horizon for the researchers working on the machine learning based prediction of the compressive strength of concrete by providing the recommendations and benefits and drawbacks associated with each model as determining the compressive strength of concrete practically is a laborious and time-consuming task.

• 한국식품과학회지
• /
• 제36권6호
• /
• pp.974-978
• /
• 2004

다당류 및 플로로탄닌 등을 주성분으로 하는 갈조추출물 NX42가 인지능력 향상에 미치는 영향을 평가하기 위한 in vitro 및 동물실험을 수행하였다. 그 결과 NX42는 acetylcholinesterase에 대하여 온화하지만 용량의존적인 억제효과($IC_{50}=600-700\;{\mu}g/mL$)를 나타내었다. NX42로부터 추출된 플로로탄닌 분획은 현저히 높은 용량 의존적 억제 효과($IC_{50}=54\;{\mu}g/mL$)를 나타내었다. 반면, 플로로탄닌이 제거된 분획과 푸코이단은 억제효과가 없었다. NX42 및 플로로탄닌 분획은 과산화수소에 의해 유발된 산화스트레스 조건 하에서의 SK-N-SH 세포의 파괴를 유의성 있게 억제한 반면, 플로로탄닌이 제거된 분획과 푸코이단은 보호효과를 나타내지 않았다. 스트레스 조건 하에 있는 마우스의 학습능력에 미치는 효과를 평가한 결과, NX42를 섭취한 마우스의 경우에는 섭취하지 않은 경우에 비하여 유의성 있게 개선된 학습능력을 나타내었으며, 이는 in vitro 실험 결과를 바탕으로 볼 때, NX42에 함유된 플로로탄닌의 acetylcholinesterase 억제 활성 및 신경보호활성에 의한 것으로 해석된다.

• 한국과학교육학회지
• /
• 제28권4호
• /
• pp.302-315
• /
• 2008

원운동은 물리수업에서 중요한 주제 중 하나이다. 특히, 등속 원운동은 우리나라 중등 교육과정에서 반드시 다루는 핵심 내용이다. 학생들은 원운동 학습 시 많은 어려움을 겪으며, 수업 후에도 이러한 어려움들이 잘 해소되지 않아서 다양한 교수 학습 방법이 적용되어 왔다. 그러나 학생들은 수업 전에 이미 가지고 있는 원운동에 대한 선개념을 수업 후에도 지속시킬 뿐 아니라, 원운동 학습 자체에 대한 어려움을 많이 제기하고 있다. 이에 본 연구에서는 원운동 학습 어려움 해소를 위해 통합적 정신모형 이론에 기반한 4M 순환학습 수업모형과 전략을 토대로 수업자료를 개발하였다. 그리고 이를 인천 소재 실업계 고등학생 53명을 대상으로 수업을 실시하여 기초 물리개념과 원운동 정신모형의 변화를 알아보았다. 분석 결과, 학생들의 기초 물리개념과 원운동 정신모형의 Correctness, Coherence, Completeness가 모두 향상되었음을 확인하였다.

• International Journal of Quality Innovation
• /
• 제6권1호
• /
• pp.43-57
• /
• 2005

Action learning motivates students to solve open-ended problems by 'developing skills through doing'. This paper reviews the concept of action learning and discusses the adoption of action learning approach to teach operations management at universities. It presents the design and delivery of an action-learning course at City University of Hong Kong. The course incorporates classroom lectures, tutorials and an action-learning workshop. The experience gained proves that action learning facilitates student participation and teamwork and provides a venue of accelerating learning where enables students to handle dynamic problem situations more effectively. The paper concludes that adopting action-learning approach can help lecturers to enhance quality teaching in operations management courses, and provide an alternate means of effective paradigm other than traditional classroom teaching and/or computer-based training at universities.

• International Journal of Computer Science & Network Security
• /
• 제21권9호
• /
• pp.297-302
• /
• 2021

The development of various phishing websites enables hackers to access confidential personal or financial data, thus, decreasing the trust in e-business. This paper compared the detection techniques utilizing URL-based features. To analyze and compare the performance of supervised machine learning classifiers, the machine learning classifiers were trained by using more than 11,005 phishing and legitimate URLs. 30 features were extracted from the URLs to detect a phishing or legitimate URL. Logistic Regression, Random Forest, and CatBoost classifiers were then analyzed and their performances were evaluated. The results yielded that CatBoost was much better classifier than Random Forest and Logistic Regression with up to 96% of detection accuracy.