• Journal of Digital Convergence
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• v.12 no.2
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• pp.237-242
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• 2014

Integrated health care system, which is one of the developing solution technologies of IT, BT and NT, could give us new medical environments in future. Health care is one of the most concerned fields in convergence environments. Many studies on the development and application related with health care industry in recent years has been actively. Therefore, in this paper, we described current integrated medical system trends and future works.

• Progress in Medical Physics
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• v.28 no.3
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• pp.83-91
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• 2017

In this work, we investigated the recently proposed phase-contrast x-ray imaging (PCXI) technique, the so-called single grid-based PCXI, which has great simplicity and minimal requirements on the setup alignment. It allows for imaging of smaller features and variations in the examined sample than conventional attenuation-based x-ray imaging with lower x-ray dose. We performed a systematic simulation using a simulation platform developed by us to investigate the image characteristics. We also performed a preliminary PCXI experiment using an established a table-top setup to demonstrate the performance of the simulation platform. The system consists of an x-ray tube ($50kV_p$, 5 mAs), a focused-linear grid (200-lines/inch), and a flat-panel detector ($48-{\mu}m$ pixel size). According to our results, the simulated contrast of phase images was much enhanced, compared to that of the absorption images. The scattering length scale estimated for a given simulation condition was about 117 nm. It was very similar, at least qualitatively, to the experimental contrast, which demonstrates the performance of the simulation platform. We also found that the level of the phase gradient of oriented structures strongly depended on the orientation of the structure relative to that of linear grids.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1080-1081
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• 2023

항만 사고 안전을 예방하기 위해 본 챗봇을 만들었다. 다양한 기술들을 사용하여 사용자들이 항만과 관련된 폭넓은 지식을 제공하기 위해 노력했고, 사람들이 안전 수칙을 최대한 지켰으면 하는 바람에서 안전 수칙들을 지속해 환기해 주는 기능들을 첨가했다.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.17-24
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• 2017

In the medical field, augmented reality is being used for surgical and medical education. However, augmented reality technology is not applied to emergency patients. In this paper, we propose a medical service support model that can support rapid medical service to emergency patients through an augmented reality - based IT device. The proposed model has the function of collecting the information necessary for the first aid simply through the IT equipment based on the reality of reality, and also receiving the first aid method appropriate for the emergency situation to the medical staff and supporting the service. In addition, the proposed model hierarchically collects information related to emergency patient information inquiry, emergency patient status and emergency treatment based on Analytic Hierarchy Process (AHP). The collected information uses a pair of comparison matrices to compensate for the ambiguity between the information. In particular, the collected information is stored in the server of the medical staff, and in addition to the unique information of the collected information, the collected information can be reflected in the medical service of the medical staff.

• Journal of Institute of Convergence Technology
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• v.11 no.1
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• pp.1-5
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• 2021

Deep learning based methods achieve state-of-the-art accuracy, however, they typically rely on supervised training with large labeled datasets. It is known in many medical applications that labeling medical images requires significant expertise and much time, and typical hand-tuned approaches for data augmentation fail to capture the complex variations in such images. This paper proposes a 3D image augmentation method to overcome these difficulties. It allows us to enrich diversity of training data samples that is essential in medical image segmentation tasks, thus reducing the data overfitting problem caused by the fact the scale of medical image dataset is typically smaller. Our numerical experiments demonstrate that the proposed approach provides significant improvements over state-of-the-art methods for 3D medical image segmentation.

• Journal of Microbiology and Biotechnology
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• v.23 no.2
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• pp.274-277
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• 2013

The unique properties of graphene have earned much interest in the fields of materials science and condensedmatter physics in recent years. However, the biological applications of graphene remain largely unexplored. In this study, we investigate the cell culture conditions, which are exposed to graphene onto glass and $SiO_2$/Si using human nerve cell line, SH-SY5Y. Cell viability was 84% when cultured on glass and $SiO_2$/Si coated with graphene as compared to culturing on polystyrene surface. Fluorescence data showed that the presence of graphene did not influence cell morphology. These findings suggest that graphene may be used for biological applications.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.463-470
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• 2019

Currently, the consumption process of Medical Marijuana is complicated, so it is necessary to provide patient-centered services. This study defined the consumption process of Medical Marijuana as three steps and five procedures for the user-centered consumption process direction study, and created a consumption journey map through user interviews with experience of purchasing Medical Marijuana to find the pain point in the consumption process. Then, based on the case studies of the United States and Canada, three scenarios applicable to Korea were presented according to the scope of implementation. It is meaningful that it is a user-centered study based on the experience of Medical Marijuana consumers and it is expected to be used as reference data in improving the consumption process in the future.

• Journal of Information Technology Applications and Management
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• v.28 no.1
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• pp.63-81
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• 2021

The advent of the convergence era led to the convergence of industries while increasing the uncertainty of R&D. R&D uncertainty can be addressed by identifying and addressing industrial innovation patterns, which Neo-Schumpeterian suggested can be identified through the process of identifying the technical characteristics of a particular industry, which can be embodied in the concept of technology trajectory. Thus, this study considered and proposed a method to track the technology trajectory of the convergence industry through topic modeling and patent citation network analysis, and applied it to the micro medical robot industry, which is a representative convergence industry, to track the technology trajectory of active catheter. In particular, it is intended to identify the unique characteristics of the industry by identifying the industry before the promotion of the national-led medical robot industry support policy. Therefore, we tried to understand the innovation pattern of the industry by tracking the technology trajectory of the industry before 2017, the time of full-scale support for the medical robot industry in the United States. Through tracking technology trajectories, the role of each technology classification, the development path, and the knowledge flow between applicants were analyzed empirically. The results of this study are expected to contribute to resolving the remaining uncertainties in the process of establishing an active catheter R&D strategy, one of the leading convergence industries, and furthermore, it is expected to be available for tracking technology trajectories in other industries.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.207-220
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• 2021

Industrial convergence is manifested in various forms by various drivers, and understanding and categorizing the direction of convergence according to the factors in which the convergence occurs is an essential requirement for the establishment of a company's customized convergence strategy and the government's corporate support policy. In this study, the type of convergence is analyzed from the perspective of knowledge flow between heterogeneous technologies, and for this purpose, the result of topic modeling of the text information of the patent and the citation information of the corresponding patent allocated for each topic are used. The methodology presented through case studies in the medical device field is verified. Through the proposed methodology, companies can predict the flow of convergence and use it as decision-making data to create new business opportunities. It is expected that the government and research institutions will be usefully used as basic data for policy preparation.

• Journal of Biomedical Engineering Research
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• v.40 no.6
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• pp.242-249
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• 2019

Forearm electromyography (EMG) generated by wrist movements has been widely used to develop an electrical prosthetic hand, but EMG generated by finger movements has been rarely used even though 20% of amputees lose fingers. The goal of this study is to improve the classification performance of different finger movements using a deep learning algorithm, and thereby contributing to the development of a high-performance finger-based prosthetic hand. Ten participants took part in this study, and they performed seven different finger movements forty times each (thumb, index, middle, ring, little, fist and rest) during which EMG was measured from the back of the right hand using four bipolar electrodes. We extracted mean absolute value (MAV), root mean square (RMS), and mean (MEAN) from the measured EMGs for each trial as features, and a 5x5-fold cross-validation was performed to estimate the classification performance of seven different finger movements. A long short-term memory (LSTM) model was used as a classifier, and linear discriminant analysis (LDA) that is a widely used classifier in previous studies was also used for comparison. The best performance of the LSTM model (sensitivity: 91.46 ± 6.72%; specificity: 91.27 ± 4.18%; accuracy: 91.26 ± 4.09%) significantly outperformed that of LDA (sensitivity: 84.55 ± 9.61%; specificity: 84.02 ± 6.00%; accuracy: 84.00 ± 5.87%). Our result demonstrates the feasibility of a deep learning algorithm (LSTM) to improve the performance of classifying different finger movements using EMG.