• Journal of Distribution Science
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• v.21 no.4
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• pp.47-56
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• 2023

Purpose: the research aims to analyze the impact of the distribution of knowledge through online learning on the intellectual potential of PhD students and produce recommendations for policy to improve intellectual capacity. During the literature review, it was determined that a large number of studies examined the impact of online learning on the quality of education at different levels. Research design, data and methodology: the research methodology is based on subjective assessment and studying the students' opinions. The basis of the study was a comprehensive analysis of primary data obtained through a sociological survey of PhD students. 324 respondents from humanitarian, medical and natural faculties participated in the survey. Results: the study revealed that online learning helps increase students' intellectual potential. PhD students had a positive attitude towards the transition from traditional education to online learning. It should be noted that, according to the results, the most popular gadgets were laptops and smartphones, which were characterized by high mobility and ease of use. Based on the obtained results, recommendations were developed for the formation of online learning with a focus on increasing students' intellectual potential. Conclusions: based on the results of the assessment of educational and innovative potential, policy recommendations and further research in this area were proposed.

• Journal of the Korean Society of Visualization
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• v.22 no.2
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• pp.27-34
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• 2024

In this study, we propose a novel approach for rapid and accurate pathogen detection by integrating Polydiacetylene (PDA) hydrogel sensors with advanced deep learning algorithms and visualization techniques. PDA hydrogel sensors exhibit a color transition in the presence of pathogens, enabling straightforward and quick pathogen detection. We developed a reliable pathogen detection system that combines deep neural network algorithms with color quantification technology for image-based analysis. This image-based system retains the ease of pathogen detection offered by PDA sensors while deriving quantified color standards to overcome the limitations of human visual assessment, enhancing reliability. This advancement contributes to public health and the development and application of pathogen detection technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.1-15
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• 2023

The aim of this study is to identify intelligent learning support functions in Learning Management System (LMS) to support university student learning activities during the transition from face-to-face classes to online learning. To accomplish this, we investigated the perceptions of students on the levels of importance and urgency toward learning support functions of LMS powered with Artificial Intelligent (AI) technology and analyzed the differences in perception according to student characteristics. As a result of this study, the function that students considered to be the most important and felt an urgent need to adopt was to give automated grading and feedback for their writing assignments. The functions with the next highest score in importance and urgency were related to receiving customized feedback and help on task performance processed as well as results in the learning progress. In addition, students view a function to receive customized feedback according to their own learning plan and progress and to receive suggestions for improvement by diagnosing their strengths and weaknesses to be both vitally important and urgently needed. On the other hand, the learning support function of LMS, which was ranked as low importance and urgency, was a function that analyzed the interaction between professors and students and between fellow students. It is expected that the results of this student needs analysis will be helpful in deriving the contents of learning support functions that should be developed as well as providing basic information for prioritizing when applying AI technology to implement learner-centered LMS in the future.

• School Mathematics
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• v.10 no.2
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• pp.155-179
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• 2008

Multiplication problems for the 7th curriculum focus on functional realms featuring the memorization and application of the multiplication table, exposing learners only to additive thinking characterized by simple counting and drawing. A diversity of research has yet to be conducted for the transition to multiplicative thinking that highlights the capability to solve problems by using multiplication and division in the expanded number scope like 'prime numbers', 'fractional numbers', and 'ratio/rates' and to describe accurately how they solved. This research was designed to develop and utilize teaching-learning materials for the transition of fifth graders' additive thinking to advanced multiplicative one and to analyze the application results in order to identify validity in material development. The following conclusions were made. First, the development and application of teaching-learning materials for multiplicative thinking cultivation facilitated the transition from additive thinking featuring simple counting and drawing to multiplicative thinking characterized by multiplication and accurate description in a more complicated and expanded number scope. Second, the development of materials featuring 'basic'-'intermediate'-'in-depth' courses by activity enabled learners to benefit from learning by level and expansion in number scope. Third, the use of topics and materials closely connected to daily lives stimulated learners' curiosity, helping them concentrate more on given problems. Fourth, communication between teachers and students or among learners themselves was promoted by continuously encouraging them to explain and by reviewing their documents identifying rules or patterns.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.175-182
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• 1994

In this study, we present unvoiced consonant recognition system using characteristic parameters of the phoneme of the each syllable. For the recognition, the characteristic parameters on the time domain such as ZCR, total energy of the consonant region and half region energy of the consonant region, and those on the frequency domain such as the frequency spectrum of the transition region are used. The objective unvoiced consonants in this study are /ㄱ/,/ㄷ/,/ㅂ/,/ㅈ/,/ㅋ/,/ㅌ/,/ㅍ/ and /ㅊ/. Each characteristic parameter of two regions extracted from these segmented unvoiced consonants are used for each recognition system of the region, independently, And complementing two outputs of each other system, the final output is to be produced. The recognition system is implemented using MLP which has learning ability. The recognition simulation results for 112 unvoiced consonant samples are that average recognition rates are 96.4$\%$ under 80$\%$ learning rates and 93.7$\%$ under 60$\%$ learning rates.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.45-52
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• 2023

With the advent of deep learning, Artificial Intelligence (AI) technology has experienced rapid advancements, extending its application across various industrial sectors. However, the focus has shifted from the independent use of AI technology to its dispersion and proliferation through the open AI ecosystem. This shift signifies the transition from a phase of research and development to an era where AI technology is becoming widely accessible to the general public. However, as this dispersion continues, there is an increasing demand for the verification of outcomes derived from AI technologies. Causal AI applies the traditional concept of causal inference to AI, allowing not only the analysis of data correlations but also the derivation of the causes of the results, thereby obtaining the optimal output values. Causal AI technology addresses these limitations by applying the theory of causal inference to machine learning and deep learning to derive the basis of the analysis results. This paper analyzes recent cases of causal AI technology and presents the major tasks and directions of causal AI, extracting patterns between data using the correlation between them and presenting the results of the analysis.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.592-599
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• 2007

The advancement of wireless technology improves the electronic learning by combining with the mobile function, and promotes the expanded transition to the mobile learning. Basically, the mobile learning provides the usefulness in terms of tile and space to provide learners with the access to the educational contents. However, the small display device and limited memory space of mobile device is limiting the access to the learning contents simply to the text-based transmission. This paper designed and implemented the multimedia mobile learning system that reduces the size of parser by define into MSMIL composed only of needed tag to multimedia contents production in the mobile devices by using the SMIL that supports the multimedia object synchronization reduces the data of multimedia learning data and enhances the transmission efficiency by applying the macro method in creating the contents of learning. The results of implementation indicates that it simplifies the designing language, makes the language learning easy, and saves the CPU resources for the parsing by reducing the size of parser.

• Journal of the Korean Institute of Educational Facilities
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• v.19 no.2
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• pp.3-12
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• 2012

This study is carried out to analyze transition about educational facilities of elementary schools with the change of policy and to find out a developmental direction for qualitative improvement. An analysis of blueprints is done to 34 elementary schools representing periodic characteristics after the 1960's. As a result, various cases were shown in arrangement type, unit space, block planning and plane planning of the schools after a abolition of law about the standardization of educational facilities on 1997, however, they were showing a tendency to simplify after the introduction of BTL on 2005. Spatial composition factors were very various in the schools planned from the middle of 1990's to the early of 2000's. Meanwhile, nearly fifty percent of occupying ratio about learning space in the schools of standard type was gradually decreased by modernization planning on 1990's. However, it was increased a little again after the BTL. In case of living area, the occupying ratio was comparatively high in the schools having characteristics of 1990's and it also tends to increase after the introduction of BTL.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.269-276
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• 2020

As we become an aging society, the number of elderly patients continues to increase. Pressure sores that can easily occur in patients with trauma cause serious socio-economic problems. In general, prevention of bedsores through predicting the patient's posture is being developed. Developed method usually use artificial intelligence techniques to estimate the patient's posture by measured pressure images in the mattress. In this method, it has a problem the reduction of estimation accuracy when posture of patient is changed. Therefore, it is necessary to use the filter of pressure images in the position transition of patient. In this paper, we propose an algorithm to predict the patient's posture, and an algorithm to reduce the ambiguity that can occur in the patient's posture transition section. By obtaining stable data through this algorithm, learning/prediction stability of the neural network can be expected, and prediction performance is improved accordingly. Through experiments, the effectiveness of the algorithm was verified.