• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.329-330
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• 2020

본 논문에서는 Azure 클라우드 플랫폼의 가상서버 호스팅을 이용해 데이터 수집 환경을 구축하고, Azure에서 제공하는 자동화된 기계학습(Automated Machine Learning, AutoML)을 기반으로 데이터 분석 방법에 관한 연구를 수행했다. 가상 서버 호스팅 환경에 LAMP(Linux, Apache, MySQL, PHP)를 설치하여 데이터 수집환경을 구축했으며, 수집된 데이터를 Azure AutoML에 적용하여 자동화된 기계학습을 수행했다. Azure AutoML은 소모적이고 반복적인 기계학습 모델 개발을 자동화하는 프로세스로써 기계학습 솔루션 구현하는데 시간과 자원(Resource)를 절약할 수 있다. 특히, AutoML은 수집된 데이터를 분류와 회귀 및 예측하는데 있어서 학습점수(Training Score)를 기반으로 보유한 데이터에 가장 적합한 기계학습 모델의 순위를 제공한다. 이는 데이터 분석에 필요한 기계학습 모델을 개발하는데 있어서 개발 초기 단계부터 코드를 설계하지 않아도 되며, 전체 기계학습 시스템을 개발 및 구현하기 전에 모델의 구성과 시스템을 설계해볼 수 있기 때문에 매우 효율적으로 활용될 수 있다. 본 논문에서는 NPU(Neural Processing Unit) 학습에 필요한 데이터 수집 환경에 관한 연구를 수행했으며, Azure AutoML을 기반으로 데이터 분류와 회귀 등 가장 효율적인 알고리즘 선정에 관한 연구를 수행했다.

• Journal of Engineering Education Research
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• v.22 no.1
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• pp.55-60
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• 2019

This paper deals with the process and requirements of a capstone design project performed by undergraduate students. We discuss about the preliminary study for the capstone design, the derivation of a subject, the level descriptor of the subject, and the system requirements. And then, we summarize the results of the capstone design project entitled as Microsoft Azure based IoT (Internet of Things) system which is performed by 4 undergraduate students during 10 months. This system is composed of a Zigbee sensor network, the TCP/IP Internet, an IoT server, and a smartphone application program, with which we can gather the sensor data and control actuators at the far away area.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.949-951
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• 2022

최근 키오스크의 사용률이 증가함에 따라 키오스크 사용의 어려움을 겪는 정보 취약계층이 존재한다. 키오스크 사용시 메뉴 선택을 키오스크 앞에서 하며, 절차 또한 복잡하다. 또한 키오스크의 높이가 고정되어 있어 휠체어를 타신분, 어린이 등 고정된 높이에 맞지 않는 사람은 사용이 어렵다. 이를 해결하기 위해 맞춤형 추천과 자동 높낮이 조절 키오스트에 대한 연구가 활발하다. 본 논문에서는 사용자 맞춤형 키오스크를 위한 얼굴 분석 기법의 성능 연구 결과를 제시하고 있다. 가장 대표적인 얼굴 분석 알고리즘들로 알려진 MS Azure 얼굴 분석 기법과 네이버 클로바 얼굴 인식 기법에 대한 비교 실험 결과 성별 인식의 경우 MS Azure 기법이 조금 우수했고 나이 분류의 경우에는 비슷한 성능을 보이는 것을 확인할 수 있었다.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.1-10
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• 2020

The performance of the personalized product recommendation system for offline shopping malls is poor compared with the one using online environment information since it is difficult to obtain visitors' characteristic information. In this paper, a mobile robot platform is suggested capable of recommending personalized advertisement using customers' sex and age information provided by Face API of MS Azure Cloud service. The performance of the developed robot is verified through locomotion experiments, and the performance of API used for our robot is tested using sampled images from open Asian FAce Dataset (AFAD). The developed robot could be effective in marketing by providing personalized advertisements at offline shopping malls.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.354-357
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• 2019

최근 맞벌이 가정이 많아지면서 베이비 시터를 고용해 영아를 양육하는 경우가 많아지고 있는 추세이다. 본 논문에서는 영유아 상태분석에 따른 인공지능 베이비시터 시스템에 대하여 기술하였다. 보다 상세하게는 얼굴인식을 위한 Opencv 영상처리 기법, MS(azure)API 를 이용한 머신러닝 기반의 감정분석과 악취 센서(MQ-135 Sensor)를 이용하여 영유아의 상태를 파악한다. 파악한 영유아의 상태를 바탕으로 스스로 학습하여 요람을 제어하고 어플리케이션을 통해 원격제어를 할 수 있도록 제작한 스마트 베이비시터 시스템에 관한 것이다. 이에 따라 양육에 대한 부담감이 줄어들 것으로 기대하고 양육에 대한 부담감을 조금이나마 경감 시켜 주어 저출산과 양육 지출 비용 절약으로 사회적 측면, 경제적 측면 모두에 기여할 것을 기대한다.

• Korean Journal of Artificial Intelligence
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• v.10 no.1
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• pp.27-32
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• 2022

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.1-6
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• 2023

This study developed models using decision forest, support vector machine, and logistic regression methods to predict and prevent suicidal ideation among Korean adolescents. The study sample consisted of 51,407 individuals after removing missing data from the raw data of the 18th (2022) Youth Health Behavior Survey conducted by the Korea Centers for Disease Control and Prevention. Analysis was performed using the MS Azure program with Two-Class Decision Forest, Two-Class Support Vector Machine, and Two-Class Logistic Regression. The results of the study showed that the decision forest model achieved an accuracy of 84.8% and an F1-score of 36.7%. The support vector machine model achieved an accuracy of 86.3% and an F1-score of 24.5%. The logistic regression model achieved an accuracy of 87.2% and an F1-score of 40.1%. Applying the logistic regression model with SMOTE to address data imbalance resulted in an accuracy of 81.7% and an F1-score of 57.7%. Although the accuracy slightly decreased, the recall, precision, and F1-score improved, demonstrating excellent performance. These findings have significant implications for the development of prediction models for suicidal ideation among Korean adolescents and can contribute to the prevention and improvement of youth suicide.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1321-1322
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• 2012

With the advance of Information Technology (IT) and dynamic requirements, diverse application services have been provided for end users. With huge volume of these services and information, users are required to acquire customized services that provide personalized information and decision at particular extent of time. The case is more appealing in healthcare, where patients wish to have access to their medical record where they have control and provided with recommendation on the medical information. PHR (Personal Health Record) is most prevailing initiative that gives secure access on patient record at anytime and anywhere. PHR should also incorporate decision support to help patients in self-management of their diseases. Available PHR system incorporates basic recommendations based on patient routine data. We have proposed decision support service called "Smart CDSS" that provides recommendations on PHR data for diabetic patients. Smart CDSS follows HL7 vMR (Virtual Medical Record) to help in integration with diverse application including PHR. PHR shares patient data with Smart CDSS through standard interfaces that pass through Adaptability Engine (AE). AE transforms the PHR CCR/CCD (Continuity of Care Record/Document) into standard HL7 vMR format. Smart CDSS produces recommendation on PHR datasets based on diabetic knowledge base represented in shareable HL7 Arden Syntax format. The Smart CDSS service is deployed on public cloud over MS Azure environment and PHR is maintaining on private cloud. The system has been evaluated for recommendation for 100 diabetic patients from Saint's Mary Hospital. The recommendations were compared with physicians' guidelines which complement the self-management of the patient.