• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.117-124
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• 2021

This study is a study to recognize hand movements using a neural network for wrist rehabilitation. The rehabilitation of the hand aims to restore the function of the injured hand to the maximum and enable daily life, occupation, and hobby. It is common for a physical therapist, an occupational therapist, and a security tool maker to form a team and approach a doctor for a hand rehabilitation. However, it is very inefficient economically and temporally to find a place for treatment. In order to solve this problem, in this study, patients directly use smart devices to perform rehabilitation treatment. Using this will be very helpful in terms of cost and time. In this study, a wrist rehabilitation dataset was created by collecting data on 4 types of rehabilitation exercises from 10 persons. Hand gesture recognition was constructed using a neural network. As a result, the accuracy of 93% was obtained, and the usefulness of this system was verified.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.6
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• pp.121-130
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• 2007

In this paper, we propose a reversible watermarking scheme in the JPEG compression domain. The reversible watermarking is useful to authenticate the content without the quality loss because it preserves the original content when embed the watermark information. In the internet, for the purpose to save the storage space and improve the efficiency of communication, digital image is usually compressed by JPEG or GIF. Therefore, it is necessary to develop a reversible watermarking in the JPEG compression domain. When the watermark is embedded, the lossless compression was used and the original image is recovered during the watermark extracting process. The test results show that PSNRs are distributed from 38dB to 42dB and the payload is from 2.5Kbits to 3.4Kbits where the QF is 75. Where the QF of the Lena image is varied from 10 to 99, the PSNR is directly proportional to the QF and the payload is around $1.6{\sim}2.8Kbits$.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.271-282
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• 2021

This study was conducted to develop a model for predicting the length of stay for premature infants through machine learning. For the development of this model, 6,149 cases of premature infants discharged from the hospital from 2011 to 2016 of the discharge injury in-depth survey data collected by the Korea Centers for Disease Control and Prevention were used. The neural network model of the initial hospitalization was superior to other models with an explanatory power (R²) of 0.75. In the model added by converting the clinical diagnosis to CCS(Clinical class ification software), the explanatory power (R²) of the cubist model was 0.81, which was superior to the random forest, gradient boost, neural network, and penalty regression models. In this study, using national data, a model for predicting the length of stay for premature infants was presented through machine learning and its applicability was confirmed. However, due to the lack of clinical information and parental information, additional research is needed to improve future performance.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.146-152
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• 2020

This study was initiated to develop an effective inspection method to detect defects such as corrosion in closed-cell steel members in steel-box girder bridges. The ultrasonic velocity method among various non-destructive method was selected as a rapid and effective method to derive the average propagation velocity in the medium by using the ultrasonic wave velocity method for specimens of different thickness. The regression analysis was performed based on the experimental results, and the results was interpolated to evaluate the prediction accuracy. If the material properties are identical, this ultrasonic velocity method can predict the thickness using the averaged transmitted velocity. In addition, a continuous scanning method moving at 200 mm/s was tested for scanning a wide area of a bridge. The results exhibited that the continuous scanning method was able to effectively scan the different thickness of a bridge.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1506-1513
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• 2019

Recently, as the industrial scope of multi-rotor unmanned aerial vehicles(UAV) is greatly expanded, the demands for data collection, processing, and analysis using UAV are also increasing. However, the acoustic data collected by using the UAV is greatly corrupted by the UAV's motor noise and wind noise, which makes it difficult to process and analyze the acoustic data. Therefore, we have studied a method to enhance the target sound from the acoustic signal received through microphones connected to UAV. In this paper, we have extended the densely connected dilated convolutional network, one of the existing single channel acoustic enhancement technique, to consider the inter-channel characteristics of the acoustic signal. As a result, the extended model performed better than the existed model in all evaluation measures such as SDR, PESQ, and STOI.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.1-9
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• 2021

In actual battlefield environment, the naval ships which have specific missions have to respond to the attack of hostile forces. Especially, in modern warfare, the importance of the survivability of naval ships are increasing due to the high lethality of armaments. Naval ship survivability is generally considered to encompass three constituents, susceptibility, vulnerability and recoverability. Recently, among these three constituents, many researches on vulnerability have been conducted. However, for the vulnerability of naval ships, most of researches are aimed towards the detailed design stages where implementing changes is heavily constrained or even impractical. In this paper, vulnerability assessment model for naval ships on a theater engagement is developed by using M&S technique. By using this model, the characteristics of platform and armaments are reflected on the damage of naval ship. The basic logic of damage assessment is also considered in detail. The damage status of the naval ship is quantified by defining a representative state index of onboard equipment for each system.

• Journal of the Korean Orthopaedic Association
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• v.55 no.4
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• pp.294-304
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• 2020

Anterior cruciate ligament (ACL) reconstruction is a successful procedure independently by patient selection, timing of surgery, surgical technique, choice of graft, and fixation methods. Among these factors, graft selection and fixation methods might be the most critical yet controversial questions for surgeons. Although recent studies showed that grafts have advantages and drawbacks, there is still no ideal graft. Similarly, many fixation methods of femoral and tibial tunnels have been proposed over the last few decades, with no clear superiority of one technique over another. Surgeons should be familiar with a variety of grafts, fixation techniques, and their specific associated surgical procedures as well as the advantages and disadvantages of each. Therefore, this article summarizes the current literature and discusses the current state of graft selection and fixation methods in the treatment of an ACL injury.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.2
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• pp.405-416
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• 2022

Current AI technology is improving the quality of life by using machine learning based on data. When using machine learning, transmitting distributed data and collecting it in one place goes through a de-identification process because there is a risk of privacy infringement. De-identification data causes information damage and omission, which degrades the performance of the machine learning process and complicates the preprocessing process. Accordingly, Google announced joint learning in 2016, a method of de-identifying data and learning without the process of collecting data into one server. This paper analyzed the effectiveness by comparing the difference between the learning performance of data that went through the de-identification process of K anonymity and differential privacy reproduction data using actual financial data. As a result of the experiment, the accuracy of original data learning was 79% for k=2, 76% for k=5, 52% for k=7, 50% for 𝜖=1, and 82% for 𝜖=0.1, and 86% for Federated learning.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.21-21
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• 2023

현재 구축되어있는 방재시설의 능력은 기후위기로 인해 수용가능한 극한강우량의 범위를 넘어서고 있어 대형화된 홍수로 인한 피해가 꾸준히 발생하고 있다. 이로 인해 잠재적 홍수로 인한 도시회복도 관리와 홍수로 수반되는 피해에 대한 복구의 중요도가 높아지고 있다. 회복도는 도시의 재해 취약성, 저항, 적응, 복구, 완화에 대한 능력을 포괄하는 개념으로써 최근 주목받고 있는 개념이지만 대부분의 연구는 주로 시설에 대한 회복도 평가가 이루어지고 있다 (Sen et al.,2021). 또한 재해 후 도시복구에 관한 연구는 다수 존재하지만 복구에 따른 지역의 회복도 변화와 라이프라인과 같은 주요 시설의 복구에 따른 회복도 차이를 고려한 연구는 미비한 실정이다. 따라서 본 연구에서는 도시침수 발생 후 라이프라인을 고려한 도시복구 우선순위 산정모델을 개발하고 재해관리의 효율성 향상측면에서 도시의 기능적 회복도를 평가하였다. 이를 위해 라이프라인 중 도로 복구결과의 평가를 위하여 리스크 매트릭스 기법을 이용한 도로위험도평가를 수행하였으며 도시의 회복도를 측정하였다. 회복도를 크게 홍수로부터 도시가 받은 영향과 재해복구역량으로 구성하였으며 정량적인 평가를 위해 각각 손상함수와 재해재난목적예비비를 활용하여 산정하였다. 이후 복구우선순위를 산정하였으며 복구와 도시회복도와의 관계를 분석하기 위하여 재해연보 자료를 기초로 회귀분석을 통해 복구비용을 추정하였다 (유순영 등.,2014). 시범지역에 적용한 결과 시설 및 도로 복구에 따른 도시영향의 변화보다 복구비사용으로 인한 재해복구역량의 변화가 더욱 크다는 것을 확인하였다. 이는 재해재난목적예비비의 중요성이 크다는 것을 의미하며 향후 추가적인 인문학적, 법제적 요소가 회복도에 미치는 영향을 연구한다면 도시회복도 향상 및 도시복구에 관한 정책적 의사결정에 큰 도움이 될 것이다.

• Journal of the Korea Institute of Building Construction
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• v.23 no.2
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• pp.197-207
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• 2023

Concrete is a widely used material due to its excellent compressive strength and durability. However, depending on the surrounding environment and the characteristics of the materials used in the construction, various defects may occur, such as cracks on the surface and subsidence of the structure. The detects on the surface of the concrete structure occur after completion or over time. Neglecting these cracks may lead to severe structural damage, necessitating regular safety inspections. Traditional visual inspections of concrete walls are labor-intensive and expensive. This research presents a deep learning-based semantic segmentation model designed to detect cracks in concrete walls. The model addresses surface defects that arise from aging, and an image augmentation technique is employed to enhance feature extraction and generalization performance. A dataset for semantic segmentation was created by combining publicly available and self-generated datasets, and notable semantic segmentation models were evaluated and tested. The model, specifically trained for concrete wall fracture detection, achieved an extraction performance of 81.4%. Moreover, a 3% performance improvement was observed when applying the developed augmentation technique.