• International journal of advanced smart convergence
• /
• 제11권4호
• /
• pp.68-80
• /
• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• International Journal of Computer Science & Network Security
• /
• 제21권7호
• /
• pp.63-69
• /
• 2021

Flipped learning is necessary for modern education but quite difficult to implement. In pedagogical science, the question remains to what extent the practical work of the teacher in combination with the technologies of flipped learning will improve the quality of higher education. The aim of this article is to study the effectiveness and feasibility of using flipped learning technologies, assessing their perception by students (advantages and problems), identified an algorithm for introducing flipped learning technology in higher education institutions. Research methods. The main method is an experiment. An evaluation of the effectiveness of the study was conducted using a questionnaire and observation method. Statistical methods were used to evaluate the results of the experiment. The research hypothesis is that flipped learning allows the teacher to spend more time on an individual approach, to understand the real needs of students, and provide effective feedback, thereby improving the quality of learning and motivation of students, especially while studying complex material. The results of the study are to prove the effectiveness of the technology of flipped education in the study of complex disciplines, courses, topics. The use of flipped learning strategies improves the self-regulation of the educational process, group work skills, improves students' ability to learn, overcome difficulties. The technology of flipped learning in the presence of modern technical means and constant work on improving the level of digital literacy is an effective means for students to master complex topics and problematic issues that require additional consideration and discussion. The perspective of further research is the consideration of integrated approaches to the application of flipped learning technologies to the principles of STEAM-education, multilingual and multicultural programs, etc. It is also worth continuing to develop a set of methods aimed at enhancing the student's learning activities, the formation of group work skills, direct participation in creating the foundations of higher education.

• Nuclear Engineering and Technology
• /
• 제54권1호
• /
• pp.310-317
• /
• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• Structural Engineering and Mechanics
• /
• 제47권2호
• /
• pp.209-225
• /
• 2013

A procedure employing a Teaching-Learning Based Optimization (TLBO) method is developed to design discrete pin jointed structures. TLBO process consists of two parts: the first part represents learning from teacher and the second part illustrates learning by interaction among the learners. The results are compared with those obtained using other various evolutionary optimization methods considering the best solution, average solution, and computational effort. Consequently, the TLBO algorithm works effectively and demonstrates remarkable performance for the optimization of engineering design applications.

• International Journal of Computer Science & Network Security
• /
• 제22권5호
• /
• pp.327-329
• /
• 2022

Education is affected by technical and scientific developments. Progress in one of these areas leads give way to new educational methods and strategies. One of these advanced learning modes is what has been conventionally termed as Micro-learning (ML). It has emerged in educational technology as a result of advances in information technology as well as advances in research in memory, brain, and social-cognitive processes.In this paper, the researcher discusses micro-learning in terms of its concepts, tools, and associated concepts, advantages and disadvantages.

• 한국간호교육학회지
• /
• 제14권1호
• /
• pp.63-72
• /
• 2008

Purpose: This study focused on two aspects: 1) how to design and implement a mobile learning course which is facilitated by a PDA with a web-based class homepage as a tool for mobile learning; 2) how to increase and enhance interactive activities among and between the students and the faculty members by utilizing a PDA as a tool for communication as well as collaboration. Method: To analyze the results of the m-Learning course, data was collected from interviews with the involved two faculty members and a survey from 27 students. Result: The results showed a positive outcome of the m-Learning approach in terms of a more collaborative learning environment in a clinical course where the students practice their clinical activities out of the classroom, far from their faculty members. On the other hand, the problems of the m-Learning approach were that more thorough preparation was needed for the new tools from both the students and the faculty members in preparation in social, cultural, and mental aspects, not withstanding the assumed technical limits of a PDA. Conclusion: m-Learning must be more actively implemented in classes, even though several problems were noticed in terms of both technical aspects of the tools, and social and cultural aspects from the users.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2015년도 제51차 동계학술대회논문집 23권1호
• /
• pp.135-136
• /
• 2015

본 논문에서는 무선 랜(Wi-Fi)을 통하여 학습 환경 구축을 위해 LED 조명 색 온도를 제어하는 방법을 설명 하고, LED 조명을 위한 Android App 개발 과정을 설명한다. 스마트폰과 LED 조명은 Wi-Fi를 통해 통신하며, JAVA언어로 개발한 Android App을 이용하여 학습 주제에 따른 색온도(Color Tempeaure)로 제어하게 되면 LED 조명 색온도가 변경되어 학습 환경 구축이 완료되어, 집중력 향상으로 인한 학습 능률이 증가된다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2008년도 하계종합학술대회
• /
• pp.1187-1188
• /
• 2008

An area of research called evolvable hardware has recently emerged which combines aspects of evolutionary computation with hardware design and synthesis. Evolvable hardware (EHW) is hardware that can change its own circuit structure by genetic learning to achieve maximum adaptation to the environment. In conventional EHW, the learning is executed by software on a computer. In this paper, we have studied and surveyed a gate-level evolvable hardware chip, by integrating both GA hardware and reconfigurable hardware within a single LSI chip. The chip consists of genetic algorithm(GA) hardware, reconfigurable hardware logic, and the control logic. In this paper, we describe the architecture, functions of the chip.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2018년도 제58차 하계학술대회논문집 26권2호
• /
• pp.25-26
• /
• 2018

이 논문에서는 고소 작업자의 위험 행동 분석을 위해 딥러닝 기법 중 연속적인 데이터 분석에 적합하며 매우 뛰어난 성능을 보여주는 LSTM 알고리즘을 이용해 모니터링 하는 시스템을 개발하였다. 모델을 위해 학습 데이터는 안전벨트에 자이로센서 등을 부착해서 실험하였다. 시스템은 작업자의 5가지의 행동 패턴을 분석할 수 있으며, 96%의 정확도를 얻었다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2007년도 하계종합학술대회 논문집
• /
• pp.449-450
• /
• 2007

Evolvable hardware(EHW) is a dynamic field that brings together reconfigurable hardware, artificial intelligence, fault tolerance and autonomous systems. This paper gives an introduction to the field. The features that can be used to identify and classify evolvable hardware are the evolutionary algorithm, the implementation and the genotype representation. Evolvable hardware (EHW) is hardware that can change its own circuit structure by genetic learning to achieve maximum adaptation to the environment. In conventional EHW, the learning is executed by software on a computer.