• Journal of the Society of Disaster Information
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• v.19 no.2
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• pp.432-438
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• 2023

Purpose: The purpose of this study is to enable users to conveniently report risks by automatically classifying risk categories in real time using AI for images reported in the life safety prevention service app. Method: Through a system consisting of a life safety prevention service platform, life safety prevention service app, AI model serving server and sftp server interconnected through the Internet, the reported life safety images are automatically classified in real time, and the AI model used at this time An AI learning algorithm for generation was also developed. Result: Images can be automatically classified by AI processing in real time, making it easier for reporters to report matters related to life safety.Conclusion: The AI image automatic classification system presented in this paper automatically classifies reported images in real time with a classification accuracy of over 90%, enabling reporters to easily report images related to life safety. It is necessary to develop faster and more accurate AI models and improve system processing capacity.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.305-310
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• 2021

Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changing to web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CT images and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fully-convolutional network-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imaging field. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI web application service. We expect that our proposed AI web application service will be utilized for screening test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1873-1880
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• 2016

Most of the energy used to operate a cellular network is consumed by a base station (BS), and reducing the transmission power of a BS is required for energy-efficient cellular networks. In this paper, a distributed transmit power control (TPC) algorithm is proposed based on the flocking model to improve the energy efficiency of a cellular network. Just as each bird in a flock attempts to match its velocity with the average velocity of adjacent birds, in the proposed algorithm each mobile station (MS) in a cell matches its rate with the average rate of the co-channel MSs in adjacent cells by controlling the transmit power of its serving BS. Simulation results show that the proposed TPC algorithm follows the same convergence properties as the flocking model and also effectively reduces the power consumption at the BSs while maintaining a low outage probability as the inter-cell interference increases. Consequently, it significantly improves the energy efficiency of a cellular network.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.109-116
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• 2021

In this paper, we propose the educational model for a convergence minor by fusing culinary arts with robot technology to develop coding ability for the students in the culinary arts major which is not originally related to software field. It is meaningful that the educational model follows the trend along the development of the fourth industrial revolution technology and has the function to make the students who are not in software major grow as software experts. However there are difficulties in designing the convergence minor because the culinary arts major is distant to the robot technology in the view of technology. To overcome this difficulty the convergence minor is designed to attract the interest for the students in culinary arts major by construct educational subjects systematically such as cooking, dessert making, barista working, autonomous serving and so on based on robots. Also the practices in which various robots are utilized are included in the convergence minor to develop actual coding ability. By comparison to the other models of convergence minors, the proposed model shows enhanced educational effects in 20% than the others.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.454-456
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• 2021

본 논문은 웹의 발달로 인하여 의료 서비스들이 기존의 Client-Server 방식의 제품에서 Web 방식의 제품으로 변경되고 있는 현대 흐름에서 인공지능 어플리케이션 또한 Web 으로 서비스 하기 위한 방법과 구현된 요로결석 AI 어플리케이션에 대해 기술한다. 이를 구현하기 위해 Python 기반의 Flask 라는 마이크로 웹 프레임워크를 사용하여 DICOM 핸들링, Pre-Processing, Mask 를 생성하고 Predict 결과를 Model Serving 을 통하여 Urinary Stone Segmentation Model 이 서비스되는 인공지능 웹 어플리케이션 동작 방식과 수행 결과를 보인다.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.517-524
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• 2024

This study aims to train and implement a deep learning model for the fusion of website creation and artificial intelligence, in the era known as the AI revolution following the launch of the ChatGPT service. The deep learning model was trained using 3,000 collected web page images, processed based on a system of component and layout classification. This process was divided into three stages. First, prior research on AI models was reviewed to select the most appropriate algorithm for the model we intended to implement. Second, suitable web page and paragraph images were collected, categorized, and processed. Third, the deep learning model was trained, and a serving interface was integrated to verify the actual outcomes of the model. This implemented model will be used to detect multiple paragraphs on a web page, analyzing the number of lines, elements, and features in each paragraph, and deriving meaningful data based on the classification system. This process is expected to evolve, enabling more precise analysis of web pages. Furthermore, it is anticipated that the development of precise analysis techniques will lay the groundwork for research into AI's capability to automatically generate perfect web pages.

• Food Science of Animal Resources
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• v.32 no.1
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• pp.91-97
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• 2012

Antimicrobial resistance of foodborne pathogens is an important food safety issue worldwide as well as in Korea. In this study, exposure to antimicrobial resistant (AMR) Stapylococcus aureus was assessed from the consumption of pork based food dishes prepared in food service operations using the Monte Carlo simulation. Thirty five isolates of S. aureus were obtained from 124 semi-processed pork products and their antibiotic resistance patterns were determined. The highest resistance was observed for penicillin (76.7%) followed by ampicillin (70.0%). Two isolates were resistant to oxacillin (6.7%) and no vancomycin resistance was observed. Dietary exposure to penicillin resistant S. aureus as the most frequently observed AMR S. aureus from pork-based dishes was estimated based on contamination data as well as compliance to guidelines for time and temperature controls during food service operations. The mean level of penicillin resistant S. aureus in pork dishes during preparation was below 1 Log CFU/g. As a conservative approach, 95th percentile estimated level of penicillin resistant S. aureus was below the level for toxin production. The estimated probability of staphylococcal intoxication by AMR S. aureus was very low using currently available data.