• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.333-338
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• 2019

Satellite communication is not used by many people like mobile communication, but it is a necessary technology for public service and communication services, such as providing the Internet in military, disaster, remote education and medical services, island areas, and infrastructure vulnerable areas. However, most communication modems have problems with two-way communication with server as IP addresses are assigned to floating IP that change every time they communicate with the network. In this paper, we used the Raspberry Pi as the controller of the terminal device to communicate the network through the satellite modem and the PPP protocol, and to solve the problem of the modem with the floating IP, we used the text message function of the satellite modem. Through this process, two-way data links using the Thuraya geostationary orbit satellite were implemented.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.101-108
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• 2021

In this paper, we analyze Shin's proposed dynamic ID-based user authentication scheme for TMIS(Telecare Medicine Information System), and Shin's authentication scheme is vulnerable to smart card loss attacks, allowing attackers to acquire user IDs, which enables user impersonation attack. In 2019, Shin's proposed authentication scheme attempted to generate a strong random number using ECC, claiming that it is safe to lose a smart card because it is impossible to calculate random number r'i due to the difficulty of the ECC algorithm without knowing random number ri. However, after analyzing Shin's authentication scheme in this paper, the use of transmission messages and smart cards makes it easy to calculate random numbers r'i, which also enables attackers to generate session keys. In addition, Shin's authentication scheme were analyzed to have significantly greater overhead than other authentication scheme, including vulnerabilities to safety analysis, the lack of a way to pass the server's ID to users, and the lack of biometric characteristics with slightly different templates.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.195-200
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• 2021

Seafarers play an important role in shipping and logistics. However, seafarers are relatively vulnerable to mental illness because they have to board ships for a considerable period of time and work in isolation. In particular, in the pandemic situation caused by COVID-19, the crew change is delayed due to the closure of many ports around the world, increasing the mental burden on seafarers. The mental health management of the crew is important because these mental problems can lead to major accidents of lives and ships. This paper identified the necessity of mental health management of seafarers through a survey and identified problems with the currently operated mental health management program and curriculum. Especially, this study proposed VR-based programs to help crews receive mentally counseling treatment in a timely manner and to reduce the mental burden on them by preventing sensitive personal information exposure. Through this, it is expected to contribute to the stable development of the logistics industry by establishing a safe seafarers working environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.153-159
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• 2019

Respiration is one of the important vital signs to determine the condition of the patient. Especially during deep sedation, since the patient's apnea and hypopnea are difficult to detect without continuous monitoring, there is a need for a continuous respiration monitoring method that can accurately and simply determine the patient's respiratory condition. Currently, respiration monitoring methods using various devices have been developed, but these methods have not only late response time but also low reliability at the clinical stage. In this study, attachable sensor using PVDF(polyvinylidene fluoride) film and a monitoring device which could detect abnormal symptoms of breathing in early stage during deep sedation. The results of this study can be used in various medical fields including not only in the area of remote monitoring for respiration related sleep monitoring but also in routine monitoring during deep sedation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.339-345
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• 2022

Cardiovascular diseases is one of the leading causes of death in the world. The objectives of this study were to build various models using sociodemographic variables based on three variable selection methods and seven machine learning algorithms for the identification of hypertension and dyslipidemia and to evaluate predictive powers of the models. In experiments based on full variables and correlation-based feature subset selection methods, our results showed that performance of models using naive Bayes was better than those of models using other machine learning algorithms in both two diseases. In wrapper-based feature subset selection method, performance of models using logistic regression was higher than those of models using other algorithms. Our finding may provide basic data for public health and machine learning fields.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.555-564
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• 2022

Recently, cyberattacks are using hacking techniques utilizing intelligent and advanced malicious codes for non-face-to-face environments such as telecommuting, telemedicine, and automatic industrial facilities, and the damage is increasing. Traditional information protection systems, such as anti-virus, are a method of detecting known malicious URLs based on signature patterns, so unknown malicious URLs cannot be detected. In addition, the conventional static analysis-based malicious URL detection method is vulnerable to dynamic loading and cryptographic attacks. This study proposes a technique for efficiently detecting malicious URLs by dynamically learning malicious URL data. In the proposed detection technique, malicious codes are classified using machine learning-based feature selection algorithms, and the accuracy is improved by removing obfuscation elements after preprocessing using Weighted Euclidean Distance(WED). According to the experimental results, the proposed machine learning-based malicious URL detection technique shows an accuracy of 89.17%, which is improved by 2.82% compared to the conventional method.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.101-106
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• 2022

Recently, virtual human has been widely used in various fields such as education, training, information guide. In addition, it is expected to be applied to services that interact with remote users in metaverse. In this paper, we propose a novel method to make a virtual human' interaction to perceive the user's surroundings. We use the editing authoring tool to apply user's interaction for providing the virtual human's response. The virtual human can recognize users' situations based on fuzzy, present optimal response to users. With our interaction method by context awareness to address our paper, the virtual human can provide interaction suitable for the surrounding environment based on automatic adaptation.

• 한국벤처창업학회:학술대회논문집
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• 2022.11a
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• pp.95-104
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• 2022

근래 지구온난화에 따른 자연재해의 증가와 장기 코로나19의 전염으로 사회적 비대면 필요성이 증대되면서 온라인을 통한 건강관리 및 의료 진단·처방 등 디지털 헬스케어의 필요성이 증대되고 있다. 디지털 헬스케어로 기존 병원 진료의 온라인 원격 진료/처방이 지속 증가하고 있을 뿐만 아니라 관련 빅데이터를 모아 개인 건강과 질병 상태 정보를 취합하여 건강 관리 및 치료를 하는 디지털 치료제 개발이 급속 진행되고 있으며 관련 벤처 창업도 활발히 진행되고 있다. 이러한 디지털 헬스케어, 디지털 치료제 산업의 활성화는 각 개인의 신체 상태를 상시 측정하고 이 정보를 관련 시스템과 연동 할 수 있는 웨어러블 디바이스, 특히 스마트워치의 보급 증대에 힘입은 바가 크다. 본 연구에서는 스마트워치의 기술혁신이 디지털 헬스케어의 수용성에 어떻게 영향을 미치는지 확장된 통합기술수용모델을 적용하여 분석하고, 혁신 사례로 스마트워치를 활용한 디지털 수면 치료제 벤처 개발 현황을 제시하였다. 본 연구를 통해 확인한 결과는 다음과 같다. 첫째 디지털 헬스케어 스마트워치의 개인혁신성, 효용가치, 사용편의 등 ICT 변인들에 대한 기술발전의 매개 영향은 유의한 것으로 나타났다. 둘째 ICT 변인들과 기술발전 매개변수는 디지털 헬스케에 스마트워치 수용의도에 대부분 정(+)의 영향을 미치는 것으로 확인되었다. 단 기술발전은 개인혁신성에는 크게 매개하지 않는 것으로 나타났다. 이러한 혁신기술의 디지털 헬스케어 스마트워치 수용의도 영향 평가 결과는 스마트워치 각종 서비스 상품기획과 마케팅에 유효하게 참조 할 수 있을 것으로 보이며 추후 세분화 연구를 통하여 더욱 소비자 특화된 제품과 서비스를 창출하는데 기여 할 수 있을 것으로 사료 된다.

• Journal of Korean Academy of Nursing
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• v.53 no.1
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• pp.69-86
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• 2023

Purpose: This study aimed to evaluate the effects of digital health interventions on the psychotic symptoms among people with severe mental illness in the community. Methods: A systematic review and meta-analysis were conducted in accordance with the Cochrane Intervention Research Systematic Review Manual and PRISMA. A literature search was conducted of published randomized controlled trials (RCTs) for digital health interventions from January 2022 to April 2022. RevMan software 5.3 was used for quality assessment and meta-analysis. Results: A total 14 studies out of 9,864 studies were included in the review, and 13 were included in meta-analysis. The overall effect size of digital health interventions on psychotic symptoms was - 0.21 (95% CI = - 0.32 to - 0.10). Sub-analysis showed that the reduction of the psychotic symptoms was effective in the schizophrenia spectrum group (SMD = - 0.22; 95% CI = - 0.36 to - 0.09), web (SMD = - 0.41; 95% CI = - 0.82 to 0.01), virtual reality (SMD = - 0.33; 95% CI = - 0.56 to - 0.10), mobile (SMD = - 0.15; 95% CI = - 0.28 to - 0.03), intervention period of less than 3 months (SMD = - 0.23; 95% CI = - 0.35 to - 0.11), and non-treatment group (SMD = - 0.23; 95% CI = - 0.36 to - 0.11). Conclusion: These findings suggest that digital health interventions alleviate psychotic symptoms in patients with severe mental illnesses. However, well-designed digital health studies should be conducted in the future.