• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.171-178
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• 2021

Caravans are easily affected by external physical factors and often cause dangerous situations for passengers. Therefore, in order to secure the stability of the passenger, there is a need to develop a sway reduction device capable of preventing the sway phenomenon in advance. This paper aims to minimize the hitch angle between the tow vehicle and the caravan. Specifically, the initial instability of the caravan is detected through an IMU sensor mounted on each of the tow vehicle and the caravan, and a control value is calculated to reduce errors from the Hitch angle and Hitch yaw rate using a PID controller. Different braking torques are generated, distributed, and controlled on the left and right brakes of the caravan according to the calculated control value. It could be verified through the driving experiment that the hitch angle was decreased compared to the case where the performance of the sway reduction device was not controlled, and the transverse stability improvement rate was improved by 94.49% compared to before control.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.173-182
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• 2022

In this paper, we propose a machine learning model that can prevent fraudulent transactions in advance and interpret them using the XAI approach. For the experiment, we collected a real data set of 12,258 mobile phone sales posts from Joonggonara, a major domestic online C2C resale trading platform. Characteristics of the text corresponding to the post body were extracted using Doc2vec, dimensionality was reduced through PCA, and various derived variables were created based on previous research. To mitigate the data imbalance problem in the preprocessing stage, a complex sampling method that combines oversampling and undersampling was applied. Then, various machine learning models were built to detect fraudulent postings. As a result of the analysis, LightGBM showed the best performance compared to other machine learning models. And as a result of SHAP, if the price is unreasonably low compared to the market price and if there is no indication of the transaction area, there was a high probability that it was a fraudulent post. Also, high price, no safe transaction, the more the courier transaction, and the higher the ratio of 0 in the price also led to fraud.

• Journal of Industrial Convergence
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• v.19 no.6
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• pp.155-164
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• 2021

The purpose of this study was to identify the individual, emotional, parent, and social environmental factors on career decision-making in multicultural youth. To this end, 1,146 multicultural adolescents who were enrolled in high school and whose fathers were Korean were selected as subjects of analysis as data for the 7th year of the Multicultural Youth Panel (MCAPS). As a result, first, it was found that self-esteem, which was an individual factor, and adaptation to school life and multicultural acceptance, which are social and environmental factors, positive effect career decision-making. Second, it was found that depression as an emotional factor and neglect as a parent factor had a negative effect on career decision-making. However, it was confirmed that stress as an individual factor, and parent-child communication as a parent factor did not affect career decision-making. These results are meaningful in that they provided basic data on how to deal with each factor and prevent multicultural youths from wandering in advance without deciding their career paths.

• Smart Media Journal
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• v.11 no.5
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• pp.48-55
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• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.147-157
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• 2022

The purpose of this study is to establish policy recommendations for the promotion of AI service impact assessment based on the definition of impact assessment and analysis of domestic and foreign AI service impact assessment cases. The direction of implementation was analyzed based on the case of impact evaluation promoted in various fields at home and abroad and the case of impact evaluation at home and abroad of artificial intelligence services. As a step-by-step implementation plan, in the first stage, quantitative indicators such as AI level survey-based economic effects are developed, and in the second stage, information culture such as safety and reliability and artificial intelligence ethics described in the Framework Act on Intelligence Information, social, economic, information protection, and people's daily lives are prepared. In the third stage, discussion on detailed metrics and methods will be expanded and impact assessment results will be evaluated. This study requires analysis through various participants such as policy designers, artificial intelligence service developers, and civic groups in the future.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.104-112
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• 2022

The pandemic caused by the COVID-19 virus, which has lasted for the past three years, has changed society and the way people live in many ways. These changes also affect cyberspace, so the pre-pandemic information security model and standards have limitations when applied to the current situation. In this paper, a new method to improve the information security model of public institutions was proposed in consideration of various situations in the new normal era. In other words, through the proposed information security model, the possibility of external intrusion is blocked in advance through the policy and technical supplementation of remote work, which is a weakness of the existing information security operation of public institutions. Also, how to prevent abnormal authentication attempts by building a secure VPN environment, how to prevent social engineering cyber attacks targeting fear and uncertainty caused by COVID-19, and how to use a smooth network and create a remote work environment. For this purpose, methods for securing service availability were additionally presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1259-1265
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• 2022

Recently, AI, big data, and IoT technologies are being used in various solutions such as fire detection and gas or dangerous substance detection for safety accident prevention. According to the status of occupational accidents published by the Ministry of Employment and Labor in 2021, the accident rate, the number of injured, and the number of deaths have increased compared to 2020. In this paper, referring to the dataset construction guidelines provided by the National Intelligence Service Agency(NIA), the dataset is directly collected from the field and learned with YOLOv4 to propose a collision risk object detection system through object detection. The accuracy of the dangerous situation rule violation was 88% indoors and 92% outdoors. Through this system, it is thought that it will be possible to analyze safety accidents that occur in industrial sites in advance and use them to intelligent platforms research.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.523-529
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• 2022

This study looked at how children's mental health is affected by Domestic violence and school violence at home and school after Covid-19, as well as how these victim experiences relate to children's mental health. This study surveyed 650 students from 14 middle schools in Wanju, Korea. The moderating effect of smartphone overdependence in the experience of violence and mental health was investigated. Major research findings: First, children's exposure to domestic violence and school violence has a significant impact on smartphone dependence. Second, children's exposure to domestic and school violence has a significant impact on their mental health. Third, it has been demonstrated that smartphone dependence is statistically significant in the relationship between domestic and school violence. Based on these findings, we present a convergent intervention and practice strategy for children in the Covid-19 era to cope with mental health problems and expand the support system.

• Journal of Platform Technology
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• v.11 no.4
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• pp.3-12
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• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.118-121
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• 2022

국내 산업재해 사고 사망자의 상당수가 건설업에서 발생하고 있다. 건설 현장에는 굴삭기, 크레인과 같은 중장비가 많고 높은 곳에서 작업하는 경우가 흔해 위험 요소에 노출될 가능성이 높다. 물리적 사고 외에도 작업 중 발생하는 미세먼지에는 여러 유해 인자가 존재하여 건설근로자들에게 호흡기질환과 같은 직업병을 유발한다. 정부에서는 산업현장 안전 관리의 중요성이 증가함에 따라 각종 산업재해로부터 근로자를 보호하기 위한 법안을 마련하였다. 따라서 건설 현장의 경우 산업재해를 방지하기 위해서 위험요소를 사전에 인지하고 즉각 대응할 수 있는 기술이 필요하다. 본 연구에서는 인공지능(AI)과 사물인터넷(IoT)을 통한 자동화 기술을 활용하여 24시간 안전 관리 시스템을 제안한다. 제안하는 IoT 기반 통합안전 관리 시스템은 AI를 적용한 CCTV를 통해 산업 현장을 모니터링하고, 다수의 IoT 센서가 측정한 수치를 근로자 및 관리자가 실시간으로 확인할 수 있게 하여 산업 현장 내 안전사고를 예방한다. 구체적으로 어플리케이션을 통해 미세먼지 농도, 가스 농도, 온도, 습도, 안전모 착용 여부 등을 모니터링할 수 있다. 모니터링 중에 유해물질의 농도가 일정 수치를 넘기거나 안전모를 착용하지 않은 근로자가 발견될 경우 근로자 및 관리자에게 경고 알림을 발송한다. 유해물질 농도는 IoT 센서를 통해 측정하며 안전모 착용 여부는 카메라 센서에 딥러닝 모델을 적용하여 인식하였다. 본 연구에서 제시한 통합안전관리시스템을 통해 건설현장을 비롯한 산업현장의 산업재해 감소와 근로자 안전 증진에 기여할 수 있을 것으로 기대한다.