• 대한조선학회논문집
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• 제58권6호
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• pp.382-390
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• 2021

The present study suggests the application of a depth camera for wave height field measurement, focusing on the calibration procedure and test setup. Azure Kinect system is used to measure the water surface elevation, with a field of view of 800 mm × 800 mm and repetition rate of 30 Hz. In the optimal optical setup, the spatial resolution of the field of view is 288 × 320 pixels. To detect the water surface by the depth camera, tracer particles that float on the water and reflects infrared is added. The calibration consists of wave height scaling and correction of the barrel distortion. A polynomial regression model of image correction is established using machine learning. The measurement results by the depth camera are compared with capacitance type wave height gauge measurement, to show good agreement.

• 한국인공지능학회지
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• 제9권1호
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• pp.9-14
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• 2021

This paper predicted a model that indicates whether to buy a car based on primary health insurance customer data. Currently, automobiles are being used to land transportation and living, and the scope of use and equipment is expanding. This rapid increase in automobiles has caused automobile insurance to emerge as an essential business target for insurance companies. Therefore, if the car insurance sales are predicted and sold using the information of existing health insurance customers, it can generate continuous profits in the insurance company's operating performance. Therefore, this paper aims to analyze existing customer characteristics and implement a predictive model to activate advertisements for customers interested in such auto insurance. The goal of this study is to maximize the profits of insurance companies by devising communication strategies that can optimize business models and profits for customers. This study was conducted through the Microsoft Azure program, and an automobile insurance purchase prediction model was implemented using Health Insurance Cross-sell Prediction data. The program algorithm uses Two-Class Logistic Regression and Two-Class Boosted Decision Tree at the same time to compare two models and predict and compare the results. According to the results of this study, when the Threshold is 0.3, the AUC is 0.837, and the accuracy is 0.833, which has high accuracy. Therefore, the result was that customers with health insurance could induce a positive reaction to auto insurance purchases.

• 한국인공지능학회지
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• 제9권1호
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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.

• 한국인공지능학회지
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• 제10권1호
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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.

• 한국인공지능학회지
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• 제11권1호
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• pp.1-7
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• 2023

In this paper, a study was conducted to compare the prediction model of cardiovascular disease occurrence. It is the No.1 disease that accounts for 1/3 of the world's causes of death, and it is also the No. 2 cause of death in Korea. Primary prevention is the most important factor in preventing cardiovascular diseases before they occur. Early diagnosis and treatment are also more important, as they play a role in reducing mortality and morbidity. The Results of an experiment using Azure ML, Logistic Regression showed 88.6% accuracy, Decision Tree showed 86.4% accuracy, and Support Vector Machine (SVM) showed 83.7% accuracy. In addition to the accuracy of the ROC curve, AUC is 94.5%, 93%, and 92.4%, indicating that the performance of the machine learning algorithm model is suitable, and among them, the results of applying the logistic regression algorithm model are the most accurate. Through this paper, visualization by comparing the algorithms can serve as an objective assistant for diagnosis and guide the direction of diagnosis made by doctors in the actual medical field.

• Clinics in Shoulder and Elbow
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• 제26권2호
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• pp.126-130
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• 2023

Background: Arm swing plays a role in gait by accommodating forward movement through trunk balance. This study evaluates the biomechanical characteristics of arm swing during gait. Methods: The study performed computational musculoskeletal modeling based on motion tracking in 15 participants without musculoskeletal or gait disorder. A three-dimensional (3D) motion tracking system using three Azure Kinect (Microsoft) modules was used to obtain information in the 3D location of shoulder and elbow joints. Computational modeling using AnyBody Modeling System was performed to calculate the joint moment and range of motion (ROM) during arm swing. Results: Mean ROM of the dominant elbow was 29.7°±10.2° and 14.2°±3.2° in flexion-extension and pronation-supination, respectively. Mean joint moment of the dominant elbow was 56.4±12.7 Nm, 25.6±5.2 Nm, and 19.8±4.6 Nm in flexion-extension, rotation, and abduction-adduction, respectively. Conclusions: The elbow bears the load created by gravity and muscle contracture in dynamic arm swing movement.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.320-322
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• 2021

무선 인터넷 기술이 발달하고 게임 시장이 확대됨에 따라 모바일 플랫폼을 포함한 다양한 플랫폼에 탑재되는 다양한 형태의 게임이 개발되고 있다. 이러한 환경에서 게임 사용자 관점의 몰입감을 보장하는 것이 게임의 경쟁력을 확보할 수 있게 되므로 HCI(Human Computer Interaction) 이론에서 제시하는 각 영역을 충족시켜 몰입감을 늘리는 것이 필요하다. 이를 위하여 본 고에서는 게임 사용자의 게임 진행의 자유도를 확보하고 몰입감을 확보하기 위한 방안으로써 인체의 동작을 인식하는 방식의 인터페이스를 적용하여 활강 게임을 구현하고 키오스크에 탑재하여 실험하였다.

• 한국임상수의학회지
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• 제40권3호
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• pp.189-196
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• 2023

This study was conducted to determine genetic diversities of Eimeria spp. detected from wildlife. From January 2020 to December 2021, molecular analysis was conducted for Eimeria spp. detected from wildlife rescued in Jeonbuk province, Korea. Polymerase chain reaction targeting 18 s rRNA gene for Eimeria spp. detected from 8 domestic pigeon (Columba livia domestica), 1 Oriental turtle dove (Streptopelia orientalis), 1 Eurasian eagle owl (Bubo bubo), 1 Azure-winged magpie (Cyanopica cyanus), 1 Moorhen (Gallinula chloropus), and 1 raccoon dog (Nyctereutes procyonoides) was conducted for phylogenetic analysis. Domestic pigeon and Oriental turtle dove were bound to the same cluster. In addition, carnivorous Eurasian eagle owl and poultry were bound to the same cluster. These results suggest that Eimeria spp. can be shared between similar species regardless of species along the food chain, suggesting that wild birds could be carriers of Eimeria spp. in Korea.

• Journal of Korea Artificial Intelligence Association
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• 제1권1호
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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.

• 응용통계연구
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• 제31권5호
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• pp.539-554
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• 2018

최근 등장하는 다양한 사물인터넷 기기 혹은 상황인식 기반의 인공지능에서는 사용자와 기기의 상호작용이 중요시 된다. 특히 인간을 대상으로 상황에 맞는 대응을 하기 위해서는 인간의 표정을 실시간으로 인식하여 빠르고 정확한 판단을 내리는 것이 필요하다. 따라서, 보다 빠르고 정확하게 표정을 인식하는 시스템을 구축하기 위해 얼굴 이미지 분석에 대한 많은 연구들이 선행되어 왔다. 본 연구에서는 웹사이트 Kaggle에서 제공한 48*48 8-bit grayscale 이미지 데이터셋을 사용하여 얼굴인식과 표정분류로 구분된 두 단계를 거치는 얼굴표정 자동 인식 시스템을 구축하였고, 이를 기존의 연구와 비교하여 자료 및 방법론의 특징을 고찰하였다. 분석 결과, Face landmark 정보에 주성분분석을 적용하여 단 30개의 주성분만으로도 빠르고 효율적인 예측모형을 얻을 수 있음이 밝혀졌다. LDA, Random forest, SVM, Bagging 중 SVM방법을 적용했을 때 가장 높은 정확도를 보이며, LDA방법을 적용하는 경우는 SVM 다음으로 높은 정확도를 보이며, 매우 빠르게 적합하고 예측하는 것이 가능하다.