• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Tuberculosis and Respiratory Diseases
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• v.86 no.4
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• pp.251-263
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• 2023

The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.43-48
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• 2023

Globally, there is a collaborative effort to achieve global carbon neutrality in response to climate change. In the case of South Korea, greenhouse gas emissions are rapidly increasing, presenting an urgent situation that requires resolution. In this context, this study developed a thermal energy collection device named a 'steam trap' and created an AI model capable of predicting future electricity usage by collecting energy usage data through steam traps. The average accuracy of electricity usage prediction with this AI model was 96.7%, demonstrating high precision. Consequently, the AI model enables the prediction and management of days with high electricity consumption and identifies which facilities contribute to elevated power usage. Future research aims to optimize energy consumption efficiency through efficient equipment operation using anomaly detection in steam traps and standardizing energy management systems, with the ultimate goal of reducing greenhouse gas emissions.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.1-14
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• 2019

The 4th Industrial Revolution, intelligent automobile application technology is evolving beyond the limit of the mobile device to a variety of application software and multi-media collective technology with big data-based AI(artificial intelligence) technology. with the recent commercialization of 5G mobile communication service, artificial intelligent automobile technology, which is a fusion of automobile and IT technology, is evolving into more intelligent automobile service technology, and each multimedia platform service and application developed in such distributed environment is being developed Accordingly, application software technology developed with a single system SoC of a portable terminal device through various service technologies is absolutely required. In this paper, smart device design for ontology design of intelligent automobile open platform enables to design intelligent automobile middleware software design technology such as Android based SVC Codec and real time video and graphics processing that is not expressed in single ASIC application software technology as SoC based application designWe have experimented in smart device environment through researches, and newly designed service functions of various terminal devices provided as open platforms and application solutions in SoC environment and applied standardized interface analysis technique and proved this experiment.

• Journal of Society of Preventive Korean Medicine
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• v.28 no.1
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• pp.31-42
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• 2024

Objective : In order to understand the changes in domestic approval regulations applicable to traditional Korean medical device companies, this article will explain the major amendments 「Regulation on Medical Device Approval Report Review, etc.」 from 2005 to the present on a year-by-year basis, and provide a counter plan to the recent changes in approval regulations. Methods : We analysed the changes in approval regulatory amendments related to the traditional Korean medical devices from 2005 to the present. Results : The Ministry of Food and Drug Safety is continuously improving medical device approval regulations to ensure the global competitiveness of domestic medical devices and contribute to the improvement of public health. Recent major approval regulatory amendments include the establishment of a review system for software medical devices and digital therapeutics, the recognition of real world evidence materials, the introduction of a biological evaluation of medical devices within a risk management process and a medical device approval licence renewal system. Conclusions : It is expected that the range of medical devices available to Korean medicine doctors will continue to expand in the future through the provision of non-face-to-face medical services and the development of advanced and new medical devices, as well as wearable medical devices and digital therapeutics. In order to increase the market entry potential of traditional Korean medical devices that incorporate advanced technologies such as digital technology and AI-based diagnosis and prediction technology, it is urgent that the government provide significant support to traditional Korean medical device companies to improve approval regulatory compliance.

• Smart Media Journal
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• v.11 no.9
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• pp.56-63
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• 2022

Recently, the frequency and range of algae occurrence in major rivers and lakes are increasing due to the increase in water temperature due to climate change, the inflow of excessive nutrients, and changes in the river environment. Abnormal algae include green algae and red algae. Green algae is a phenomenon in which blue-green algae such as chlorophyll (Chl-a) in the water grow excessively and the color of the water changes to dark green. In this study, a 3D virtual world of digital twin was built to monitor and control water quality information measured in ecological rivers and lakes in the living environment in real time from a remote location, and a sensor measuring device for water quality information based on the Internet of Things (IOT) sensor. We propose to build a smart water environment service platform that can provide algae warning and water quality forecasting by predicting the causes and spread patterns of water pollution such as algae based on AI machine learning-based collected data analysis.

• Journal of Wellbeing Management and Applied Psychology
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• v.6 no.2
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• pp.27-31
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• 2023

Purpose: Establishment of a real-time monitoring system for odor control in traditional markets in Gangwon-do and a system for linking prevention facilities. Research design, data and methodology: Build server and system logic based on data through real-time monitoring device (sensor-based). A temporary data generation program for deep learning is developed to develop a model for odor data. Results: A REST API was developed for using the model prediction service, and a test was performed to find an algorithm with high prediction probability and parameter values optimized for learning. In the deep learning algorithm for AI modeling development, Pandas was used for data analysis and processing, and TensorFlow V2 (keras) was used as the deep learning library. The activation function was swish, the performance of the model was optimized for Adam, the performance was measured with MSE, the model method was Functional API, and the model storage format was Sequential API (LSTM)/HDF5. Conclusions: The developed system has the potential to effectively monitor and manage odors in traditional markets. By utilizing real-time data, the system can provide timely alerts and facilitate preventive measures to control and mitigate odors. The AI modeling component enhances the system's predictive capabilities, allowing for proactive odor management.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.115-122
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• 2022

With the outbreak of COVID-19 a few years ago, video conferencing and electronic document work have increased, and for this reason, the proportion of computer work among modern people's daily routines is increasing. However, as more and more people work on computers in the wrong posture for a long time, the number of patients with poor eyesight and text neck is increasing. Until recently, many studies have been published to correct posture, but most of them have limitations that users may experience discomfort because they have to correct posture by wearing equipment. A posture correction sensor algorithm is proposed to prevent access to the minimum distance between a computer monitor and a person using an ultrasonic sensor device. At this time, an algorithm for minimizing false alarms among warning alarms that sound at the minimum distance is also proposed. Because the ultrasonic sensor device is used, posture correction can be performed without attaching a device to the body, and the user can relieve discomfort. In addition, experimental results showed that accuracy can be improved by reducing false alarms by removing more than half of the noise generated during distance measurement.

• Asia Pacific Journal of Corpus Research
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• v.1 no.1
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• pp.95-125
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• 2020

This paper aims to inform people how to support each other on social media. It alludes to an architecture for social media discourse and proposes a novel theory of support in social media discourse. It makes a methodological contribution. It combines predominately artificial intelligence with corpus linguistics analysis. It is on a large-scale dataset of anonymised diabetes-related user's posts from the Facebook platform. Log-likelihood and precision measures help with validation. A multi-method approach with Discourse Analysis helps in understanding any potential patterns. People living with Diabetes are found to employ sophisticated high-frequency patterns of device-enabled categories of purpose and content. It is with, for example, linguistic forms of Advice with stance-taking and targets such as Diabetes amongst other interactional ways. There can be uncertainty and variation of effect displayed when sharing information for support. The implications of the new theory aim at healthcare communicators, corpus linguists and with preliminary work for AI support-bots. These bots may be programmed to utilise the language patterns to support people who need them automatically.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.2
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• pp.66-75
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• 2010

This paper proposes an efficient method for design and implementation of the artificial intelligence (AI) of 'omok' game on the GPU. The proposed AI is designed on a cooperative structure using min-max game tree and genetic algorithm. Since the evaluation function needs intensive computation but is independently performed on a lot of candidates in the solution space, it is computed on the GPU in a massive parallel way. The implementation on NVIDIA CUDA and the experimental results show that it outperforms significantly over the CPU, in which parallel game tree and genetic algorithm on the GPU runs more than 400 times and 300 times faster than on the CPU. In the proposed cooperative AI, selective search using genetic algorithm is performed subsequently after the full search using game tree to search the solution space more efficiently as well as to avoid the thread overflow. Experimental results show that the proposed algorithm enhances the AI significantly and makes it run within the time limit given by the game's rule.