• Korean Journal of Artificial Intelligence
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• v.10 no.2
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• pp.1-6
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• 2022

In this paper, the research and development aim to strengthen the digital accessibility of the elderly by developing a kiosk incorporating AI voice recognition technology that can replace the promotional signage currently being installed and spread in the elderly and social welfare centers most frequently used by the digital underprivileged. It was intended to develop a converged system for the use of bulletin board functions, educational functions, and welfare center facilities, and to seek ways to increase the user's digital device experience through direct experience and education. Through interviews and surveys of senior citizens and social welfare centers, it was intended to collect problems and pain Points that the elderly currently experience in the process of using kiosks and apply them to the development process, and improve problems through pilot services. Through this study, it was confirmed that voice recognition technology is 2 to 6 times faster than keyboard input, so it is helpful for the elderly who are not familiar with device operation. However, it is necessary to improve the problem that there is a difference in the accuracy of the recognition rate according to the surrounding environment with noise. Through small efforts such as this study, we hope that the elderly will be a little free from digital alienation.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.123-133
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• 2001

Medical domain is very applicable for multi-agent system because medical information systems need much knowledge and close relationship with medical staff, In this paper, we describe design and implementation of an intelligent medical multi-agent platform that provides medical images'information services. This platform supports a physical environment that medical agents can be deployed following FIPA(Foundation for Intelligent Physical Agent)\`s agent management reference model. To use a variety of components on Windows, COM(Common Object Model) interfaces and XML(extensible Markup Language) for encoding ACL(Agent Communication Language) are used for multi-agent communications. Since many kinds of diverse and close relationships with medical staff) are essential, a medical staff is conceptualized as an agent and integrated with multi-agent systems. Also it provides an infrastructure applicable to share necessary knowledge between human agents and software agents in order to make intelligent medical information services easier.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.111-114
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• 2004

Management of human knowledge is an interesting concept that has attracted the attention of philosophers for thousands of years. Artificial intelligence and knowledge engineering has provided some degree of rigor to the study of knowledge systems and expert systems(ES) re able to use knowledge to solve the problems and answer questions. Therefore, the process of conceptualization and inference of knowledge are fundamental problem solving activities and hence, are essential activities for solving the problem of software ES construction Especially, the access to relevant, up-to-date and reliable knowledge is very important task in the daily work of physicians and nurses. In this study, we propose the conceptualization and inference mechanism for implicit knowledge management in medical diagnosis area. To this purpose, we combined the dynamic knowledge map(KM) and relational database(RDB) into a dynamic knowledge map(DKM). A graphical user-interface of DKM allows the conceptualization of the implicit knowledge of medical experts. After the conceptualization of implicit knowledge, we developed an RDB-based inference mechanism and prototype software ES to access and retrieve the implicit knowledge stored in RDB. Our proposed system allows the fast comfortable access to relevant knowledge fitting to the demands of the current task.

• Korean Journal of Artificial Intelligence
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• v.5 no.2
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• pp.26-42
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• 2017

This study perceived that there are limits to prompt and accurate monitoring when an accident occurs and the correct information of egg production stage, such as the date of spawning, cleaning, and refrigerating cannot be identified, since eggshell codes using barcode only show numbers identifying a city and province and the name of producers. To fix this problem, this study partially suggested the RFID (Radio Frequency Identification) technology and IoT-based Connected System. The proposed system in this study shares data with related agencies as the system of agricultural and livestock product information runs as the main server, and the database information of the proposed system is provided by farmhouses, distributors, and sellers. Through various media such as a webpage or mobile application built to provide the relevant information, customers can search and obtain information about agricultural and livestock products they want. Since the information on an entire process is open to the public, information ranging from simple to clear, additional ones such as hazardous elements can be viewed.

• Korean Journal of Artificial Intelligence
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• v.2 no.1
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• pp.1-3
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• 2014

These days, interest and demand on smart learning has rapidly increased. Video English and mobile system based English speaking service have become popular. This study gave prototype of application to give and take voice message with world people and to give new concept of voice pen-pal beyond exchange of text messages. In modern society having rapidly increasing demand on smart learning, you can study foreign language by smart phone and communicate with foreigners by voice anytime and anywhere. The app allows global exchange to learn conversation. Recruitment of initial users and profit model have problems. We shall develop to improve problems and to solve difficulty.

• Korean Journal of Artificial Intelligence
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• v.6 no.1
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• pp.11-15
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• 2018

The purpose of this study is to develop a pulsatile drowsiness detection system that can compensate the limitations of existing camera - based or breathing pressure sensor based Drowsiness driving prevention systems. A heart rate sensor mounted on the driver's finger and an alarm system that sounds when drowsiness is detected. The heart rate sensor was used to measure pulse changes in the wrist, and an alarm system based on the Arduino, which works in conjunction with the laptop, generates an audible alarm in the event of drowsiness. In this paper, we assume that the pulse rate of the drowsy state is 60 ~ 65 times / minute, which is the middle between the awake state and the sleep state. As a result of the experiment, the alarm sounded when the driver's pulse rate was in the drowsy pulse rate range. Based on these experiments, the drowsiness detection system was able to detect the drowsiness of the driver successfully in real time. A more effective drowsiness prevention system can be developed in the future by incorporating the results of the present study on a pulse-based drowsiness prevention system in an existing drowsiness prevention system.

• Korean Journal of Artificial Intelligence
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• v.9 no.1
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• pp.15-20
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• 2021

This paper was conducted to prevent and respond to crimes by predicting crimes based on artificial intelligence. While the quality of life is improving with the recent development of science and technology, various problems such as poverty, unemployment, and crime occur. Among them, in the case of crime problems, the importance of crime prediction increases as they become more intelligent, advanced, and diversified. For all crimes, it is more critical to predict and prevent crimes in advance than to deal with them well after they occur. Therefore, in this paper, we predicted crime types and crime tools using the Multiclass Logistic Regression algorithm and Multiclass Neural Network algorithm of machine learning. Multiclass Logistic Regression algorithm showed higher accuracy, precision, and recall for analysis and prediction than Multiclass Neural Network algorithm. Through these analysis results, it is expected to contribute to a more pleasant and safe life by implementing a crime prediction system that predicts and prevents various crimes. Through further research, this researcher plans to create a model that predicts the probability of a criminal committing a crime again according to the type of offense and deploy it to a web service.

• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.585-589
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• 2007

This study proposes an automated pegboard utilizing the RFID system with multiple reader antennas for the rehabilitation services and the occupational therapy. The system automates the scoring by detecting the plugging correctness as well as the plugging status. It also aims to increase the patient's interest and the functional intelligence. The system was prototyped and tested for the automatic capability of the scoring the session time and success rate. The proposed system will be served as the typical example for the ubiquitous rehabilitation devices.

• Annual Conference of KIPS
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• 2019.10a
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• pp.40-43
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• 2019

본 논문은 국제표준화인 OHDSI OMOP-CDM 의 확장으로 의료영상 표준기반으로 한 관리시스템에 대해 기술한다. 이를 위해 기존 공통데이터모델과 연계에 중점을 두어 DICOM 메타태그정보 기반의 의료영상 표준 모델의 스키마를 제시한다. 이를 기반으로 머신러닝 기술개발을 위한 데이터 셋 생성과 관리를 위한 웹 기반 시스템 구조와 기능에 대해서 기술한다. 끝으로 구현된 시스템에서 제공하는 웹 서비스 수행 결과를 보인다.

• Journal of Biomedical Engineering Research
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• v.45 no.4
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• pp.139-147
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• 2024

This study enhances AI algorithms for extracting spinal regions from Whole Spine X-rays, aiming for higher accuracy while minimizing learning and detection times. Whole Spine X-rays, critical for diagnosing conditions such as scoliosis and kyphosis, necessitate precise differentiation of spinal contours. The conventional manual methodology encounters challenge due to the overlap of anatomical structures, prompting the integration of AI to overcome these limitations and enhance diagnostic precision. In this study, 1204 AP and 500 LAT Whole Spine X-ray images were meticulously labeled, spanning the third cervical to the fifth lumbar vertebrae. We based our efforts on the HR-Net algorithm, which exhibited the highest accuracy, and proceeded to simplify its network architecture and enhance the block structure for optimization. The optimized HR-Net algorithm demonstrates an improvement, increasing accuracy by 2.98% for the AP dataset and 1.59% for the LAT dataset compared to its original formulation. Additionally, the modification resulted in a substantial reduction in learning time by 70.06% for AP images and 68.43% for LAT images, along with a decrease in detection time by 47.18% for AP and 43.07% for LAT images. The time taken per image for detection was also reduced by 47.09% for AP and 43.07% for LAT images. We suggest that the application of the proposed HR-Net in this study can lead to more accurate and efficient extraction of spinal regions in Whole Spine X-ray images. This can become a crucial tool for medical professionals in the diagnosis and treatment of spinal-related conditions, and it will serve as a foundation for future research aimed at further improving the accuracy and speed of spinal region segmentation.