• Geomechanics and Engineering
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• 제29권3호
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• pp.219-228
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• 2022

The most widely used parameter to represent rock abrasiveness is the Cerchar abrasivity index (CAI). The CAI value can be applied to predict wear in TBM cutters. It has been extensively demonstrated that the CAI is affected significantly by cementation degree, strength, and amount of abrasive minerals, i.e., the quartz content or equivalent quartz content in rocks. The relationship between the properties of rocks and the CAI is investigated in this study. A database comprising 223 observations that includes rock types, uniaxial compressive strengths, Brazilian tensile strengths, equivalent quartz contents, quartz contents, brittleness indices, and CAIs is constructed. A linear model is developed by selecting independent variables while considering multicollinearity after performing multiple regression analyses. Machine learning-based regression methods including support vector regression, regression tree regression, k-nearest neighbors regression, random forest regression, and artificial neural network regression are used in addition to multiple linear regression. The results of the random forest regression model show that it yields the best prediction performance.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 가을 학술논문 발표대회
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• pp.50-51
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• 2021

Recently, as concrete is used in many construction works in Korea, the use of aggregates is also increasing. However, the depletion of aggregate resources is making it difficult to supply and demand high-quality aggregates, and the use of defective aggregates is causing problems such as poor performance such as the liquidity and strength of concrete pouring out in the field. As a result, quality tests such as sieve analysis test is conducted on their own, but this study was conducted to improve time and manpower by using the CNN-based Deep Learning Model for the fineness modulus.

• 의학교육논단
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• 제17권3호
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• pp.110-121
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• 2015

Competency-based medical education (CBME) is an outcome-oriented curriculum model for medical education that organizes learning activities and assessment methods according to defined competencies as the learning outcomes of a given curriculum. CBME emerged to address the accountability of medical education in response to growing concerns about the patient safety in North America in the 1970s, and the number of medical schools adopting CBME has dramatically increased since 1990. In Korea, CBME has been under consideration as an alternative curriculum model to reform medical education since 2006. The purpose of this paper is three-fold: (1) to review the literature on CBME to identify the challenges and benefits reported in North America, (2) to summarize the process and experiences of planning and implementing CBME at Inje University College of Medicine, and finally (3) to provide recommendations for Korean medical schools to be better prepared for the successful adoption of CBME. In conclusion, one of the key factors for successful CBME implementation in Korea is how well an individual school can modify the current curriculum and rearrange the existing resources in a way that will enhance students' competencies while maximizing the strengths of the school's existing curriculum.

• 한국수학사학회지
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• 제14권2호
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• pp.125-148
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• 2001

For this purpose, most of all, we thought over the theoretical background for the application of Web-resources to the teaching and learning program at the school mathematics. Second, we looked into the applied class and the class pattern with the internet. And then, we arranged the cases using the internet web materials. The last, we mentioned what the matters of learning based on the web are and what the teacher's roles are.

• 한국과학교육학회지
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• 제35권6호
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• pp.971-984
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• 2015

본 연구의 목적은 두 가지였다. 첫째는 대안적인 인지 이론으로서 D. Hammer와 그의 동료들이 발전시켜 온 '자원 기반의 관점(RBV)'을 이론적으로 고찰하는 것이고, 둘째는 그것을 대학생들이 계절 변화에 관한 모델을 구성하는 학습 활동을 해석하는 데 적용하여 이론의 유용성을 예시적으로 보이는 것이었다. 이론적인 고찰은 관련 문헌을 탐색하여 이루어졌으며, 그 결과를 세 가지 유형의 자원들-개념적, 인식론적, 실천적 자원-을 중심으로 정리하였다. 시험 적용을 통해 과학 모델은 하나의 전체로서 제안되기보다 참여자들에게서 활성화된 여러 가지 자원들이 결합하는 과정을 통해 구성된다는 것을 알 수 있었다. 하지만 활성화된 자원들이 모두 모델에 포함되는 것은 아니었으며, 어떤 개념적 자원들은 과학적인 모델을 구성하는 데 제한점으로 작용하기도 하였다. 과학 교육자들은 학생들이 가지고 있는 자원들에 주의를 기울이고 그에 반응적이어야 하며, 학생들이 자신의 자원을 생산적으로 활용하여 과학을 배울 수 있도록 도와야 한다는 것을 시사점으로 제안하였다.

• 대한안전경영과학회지
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• 제12권1호
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• pp.83-96
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• 2010

Learning Organization is a learning based community to make the most important value in the era of Knowledge Economy, Creation. That's why people share, facilitate personal, individual's knowledge & experience systems each other and make good thoughts & ideas in the organization. This study measures the building practices having conducted the F team in Samsung electronics using DLOQ that indicates the activate degree of Learning Organization and the quantitative degrees of Learning Organization through comparing the cross-evaluation between the already measured companies in addition to analyzing the F team's success factors. Learning Organization requires sustainable and continuous activity, not completes by changing many factors with human resources. The study will have the achievement if we measure the successful activity through global companies built a Learning Organization and facilitate the improvement activity sustainably.

• 한국품질경영학회:학술대회논문집
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• 한국품질경영학회 2010년도 춘계학술대회
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• pp.218-225
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• 2010

Learning Organization is a learning based community to make the most important value in the era of Knowledge Economy, Creation. That's why people share, facilitate personal, individual's knowledge & experience systems each other and make good thoughts & ideas in the organization. This study measures the building practices having conducted the F team in Samsung electronics using DLOQ that indicates the activate degree of Learning Organization and the quantitative degrees of Learning Organization through comparing the cross-evaluation between the already measured companies in addition to analyzing the F team's success factors. Learning Organization requires sustainable and continuous activity, nor completes by changing many factors with human resources. The study will have the achievement if we measure the successful activity through global companies built a Learning Organization and facilitate the improvement activity sustainably.

• 한국해양공학회지
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• 제34권2호
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• pp.147-154
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• 2020

Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical background of several related machine learning techniques is introduced in this paper.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.