• Journal of Gastric Cancer
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• v.18 no.2
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• pp.182-188
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• 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.

• Smart Media Journal
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• v.9 no.3
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• pp.25-30
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• 2020

The purpose of this paper is to implement a deep learning-based real-time video analysis algorithm that monitors safety of workers in industrial facilities. The worker's clothes were divided into six classes according to whether workers are wearing a helmet, safety vest, and safety belt, and a total of 5,307 images were used as learning data. The experiment was performed by comparing the mAP when weight was applied according to the number of learning iterations for 645 images, using YOLO v4. It was confirmed that the mAP was the highest with 60.13% when the number of learning iterations was 6,000, and the AP with the most test sets was the highest. In the future, we plan to improve accuracy and speed by optimizing datasets and object detection model.

• The Mathematical Education
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• v.17 no.2
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• pp.1-13
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• 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
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• v.27 no.3
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• pp.19-36
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• 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
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• v.2 no.2
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• pp.130-135
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• 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
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• v.32 no.3
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• pp.263-286
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• 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.

• Korean Journal of Food Science and Technology
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• v.36 no.6
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• pp.974-978
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• 2004

Brown-alga-derived natural agent NX42, mainly composed of algal polysaccharides and phlorotannins, showed mild but dose-dependent inhibition of acetylcholinesterase with $IC_{50}=600-700\;{\mu}g/mL$. Phlorotannin-rich fraction of NX42 showed substantial increase of the activity by more than one order of magnitude ($IC_{50}=54\;{\mu}g/mL$) and significant protection of SK-N-SH cells from oxidative stress by $H_2O_2$. Learning trials of mice for 5 consecutive days revealed electric-shock treatment during learning period significantly retarded learning process, whereas NX42-treated mice showed significant resistance against leaning deficiency possibly mainly due to anticholinesterase and neuroprotective activities of phlorotannin.

• Journal of The Korean Association For Science Education
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• v.28 no.4
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• pp.302-315
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• 2008

Circular motion has been one of the most difficult concepts for students to understand. To facilitate for students to form scientific mental models about circular motion, this study developed 4M learning cycle teaching model based on the integrated mental model theory and strategies. For this study, fifty-three eleventh graders at a technical high school in Inchon were taught for 3 class hours. We conducted tests of basic physics concept and mental model of circular motion before, after, and two months after instruction. In results, we found that there were statistically significant improvement in the test of basic physics concept and mental model related with circular motion after instruction. Especially, this teaching model affected learning effectiveness of Correctness and Coherence of mental model.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.43-57
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• 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
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• v.21 no.9
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• pp.297-302
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• 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.