• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.67-75
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• 2022

Medical information is variously generated not only from medical devices but also from electronic devices. Recently, related convergence technologies from big data collection in healthcare to medical AI products for patient's condition analysis are rapidly increasing. However, there are difficulties in applying them because of independent developmental procedures. In this paper, we propose an intelligent hospital information system (iHIS) model to simplify and integrate research, development and application of medical AI technology. The proposed model includes (1) real-time patient data management, (2) specialized data management for medical AI development, and (3) real-time monitoring for patient. Using this, real-time biometric data collection and medical AI specialized data generation from patient monitoring devices, as well as specific AI applications of camera-based patient gait analysis and brain MRA-based cerebrovascular disease analysis will be introduced. Based on the proposed model, it is expected that it will be used to improve the HIS by increasing security of data management and improving practical use through consistent interface platformization.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.553-558
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• 2022

The purpose of this work was a simulation study to evaluate the virtual monochromatic (VM) image quality of blood vessels compared to the monochromatic image. Dual-energy images were obtained based on the linear attenuation coefficients of five materials at 50 keV and 80 keV at low- and high-energies, respectively. A weighting factor is required to synthesize the VM image, and the liver and bone were used as basis materials to obtain the weighting factor. VM images were synthesized at energies ranging from 30 keV to 100 keV. Image quality was evaluated by Contrast to noise ratio (CNR) and noise by setting calcium and contrast medium as signals and blood as background. According to the results, the energies with the maximum CNR were 50 keV and 60 keV for calcium and contrast medium, respectively. The energies showing the minimum noise were 70 keV, 70 keV, and 60 keV in calcium, iodine contrast medium, and blood, respectively. The VM image can contribute to the improvement of diagnostic performance in CT examination because it can implement an image at the optimal energy that minimize noise and maximize CNR.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.537-543
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• 2022

The purpose of this simulation study was to evaluate the possibility of pancreas detection through effective atomic number information using dual-energy computed tomography(CT). The effective atomic number of 10 tissue-equivalent materials were estimated through stoichiometric calibration. For stoichiometric calibration, HU values at low-energy (80 kV) and high-energy (140 kV) for 10 tissue-equivalent materials were used. Based on this method, the effective atomic number image of the tissue-equivalent material was extracted through an iterative algorithm. According to the results, the attenuation ratio in accordance with the effective atomic number was estimated to have an R² value of 0.9999, and the effective atomic number of Pancreas, Water, Liver, Blood, Spongiosa, and Cortical bone was overall within 1% accuracy compared to the theoretical value. Conventional pancreatic cancer examination uses a contrast medium, so there is a possibility of potential side effects of the contrast medium. In order to solve this problem, it is thought that it will be possible to contribute to an accurate and safe examination by extracting the effective atomic number using dual-energy CT without contrast enhancement. Based on this study, future research will be conducted on the detection of pancreatic cancer using the HU value of pancreatic cancer based on clinical images.

• Korean small business review
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• v.42 no.1
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• pp.43-56
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• 2020

The technology conflict between the U.S. and China is deepening recently. The U.S.-China battle began as a national security issue but is comprehending as a U.S.'s check for China's rapid technological advancement. China is rapidly growing in several indexes including R&D expenditure, patent application, and publications, and is challenging the U.S. in 5G and Artificial Intelligence. In 2018, Huawei became the largest 5G network/equipment provider and second largest smart phone manufacturer in the world. Now, Huawei is outperforming at AI chipset manufacturing, Bigdata analysis and cloud, positioning to become a critical player in the 4th industrial revolution. The purpose of this research is to analyze the effect of recent Huawei issues to Korean SMEs focusing on the relation between Huawei and Korean companies; the cooperation status from the Global Value Chain (GVC) perpsective, and Korean government's policies related to Huawei's information security issues will be the three main frames for the analysis. Then, this research proposes policy implications such as increasing Korea's competitiveness in manufacturing and information security.

• BMB Reports
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• v.55 no.9
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• pp.465-471
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• 2022

Understanding and monitoring virus-mediated infections has gained importance since the global outbreak of the coronavirus disease 2019 (COVID-19) pandemic. Studies of high-throughput omics-based immune profiling of COVID-19 patients can help manage the current pandemic and future virus-mediated pandemics. Although COVID-19 is being studied since past 2 years, detailed mechanisms of the initial induction of dynamic immune responses or the molecular mechanisms that characterize disease progression remains unclear. This study involved comprehensively collected biospecimens and longitudinal multi-omics data of 300 COVID-19 patients and 120 healthy controls, including whole genome sequencing (WGS), single-cell RNA sequencing combined with T cell receptor (TCR) and B cell receptor (BCR) sequencing (scRNA(+scTCR/BCR)-seq), bulk BCR and TCR sequencing (bulk TCR/BCR-seq), and cytokine profiling. Clinical data were also collected from hospitalized COVID-19 patients, and HLA typing, laboratory characteristics, and COVID-19 viral genome sequencing were performed during the initial diagnosis. The entire set of biospecimens and multi-omics data generated in this project can be accessed by researchers from the National Biobank of Korea with prior approval. This distribution of large-scale multi-omics data of COVID-19 patients can facilitate the understanding of biological crosstalk involved in COVID-19 infection and contribute to the development of potential methodologies for its diagnosis and treatment.

• Journal of Service Research and Studies
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• v.12 no.2
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• pp.80-93
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• 2022

This study is a study on a methodology that can extract various factors that affect purchase and use of products/services from the consumer's point of view through previous studies, and analyze the types and tendencies of consumers according to age and gender. To this end, we quantify factors in terms of general personal propensity, consumption influence, consumption decision, etc. to check the consistency of data, and based on these studies, we conduct research to suggest and prove data analysis methodologies of consumer types that are meaningful from the perspectives of startups and SMEs. did As a result, it was confirmed through cross-validation that there is a correlation between the three main factors assumed for data analysis from the consumer's point of view, the general tendency, the general consumption tendency, and the factors influencing the consumption decision. verified. This study presented a data analysis methodology and a framework for consumer data analysis from the consumer's point of view. In the current data analysis trend, where digital infrastructure develops exponentially and seeks ways to project individual preferences, this data analysis perspective can be a valid insight.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.125-144
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• 2023

Recently, the use of Large Language Models (LLMs) such as ChatGPT has been increasing in various fields such as interactive commerce and mobile financial services. However, LMMs, which are mainly created by learning existing documents, can also learn various human biases inherent in documents. Nevertheless, there have been few comparative studies on the aspects of bias and discrimination in LLMs. The purpose of this study is to examine the existence and extent of nine types of discrimination (Age, Disability status, Gender identity, Nationality, Physical appearance, Race ethnicity, Religion, Socio-economic status, Sexual orientation) in LLMs and suggest ways to improve them. For this purpose, we utilized BBQ (Bias Benchmark for QA), a tool for identifying discrimination, to compare three large-scale language models including ChatGPT, GPT-3, and Bing Chat. As a result of the evaluation, a large number of discriminatory responses were observed in the mega-language models, and the patterns differed depending on the mega-language model. In particular, problems were exposed in elder discrimination and disability discrimination, which are not traditional AI ethics issues such as sexism, racism, and economic inequality, and a new perspective on AI ethics was found. Based on the results of the comparison, this paper describes how to improve and develop large-scale language models in the future.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.2
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• pp.229-238
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• 2023

The objective of this study was to investigate trends in ankyloglossia and its surgical treatment among pediatric patients in South Korea from 2011 to 2020. Data from Health Insurance Review and Assessment Service (HIRA)'s Healthcare Bigdata Hub were used for analysis of the ankyloglossia diagnosis rate and frenum surgery rate. Considering annual population change, crude rates per 100,000 were calculated and analyzed. To investigate other factors of frenum surgery incidence besides gender and age, pediatric patient sample data from HIRA were used. The diagnosis rate of ankyloglossia increased from 204.4 in 2011 to 356.6 per 100,000 people in 2020, while the frenum surgery rate increased from 26.8 to 34.3 per 100,000 people. Males were more likely to receive frenum surgery than females. Surgeries were more likely to be done at a hospital instead of a clinic or a general hospital. In the age group of 0 - 4 years, the largest number of frenum surgeries were performed in pediatrics, and in the age group of 5 - 9 years, the largest number of surgeries were conducted in pediatric dentistry. In the older age groups, the largest proportion of frenum surgeries were performed in the departments of conservative dentistry and oral and maxillofacial surgery. The diagnosis of ankyloglossia and the operation of frenum surgery among South Korean children increased during the last decade. Since the function of the tongue can affect maxillofacial development in many aspects, pediatric dentists should pay more attention to the functional management of intraoral soft tissue in growing children.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• Journal of Internet of Things and Convergence
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• v.9 no.2
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• pp.61-69
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• 2023

In recent years, due to heightened environmental awareness, Electric Power Steering (EPS) has been increasingly adopted as the steering control unit in manufactured vehicles. This has had numerous benefits, such as improved steering power, elimination of hydraulic hose leaks and reduced fuel consumption. However, for EPS systems to respond to actions, sensors must be employed; this means that the consistency of the sensor's linear variation is integral to the stability of the steering response. To ensure quality control, a reliable method for detecting defects and assessing linearity is required to assess the sensitivity of the EPS sensor to changes in the internal design characters. This paper proposes a data-driven defect and linearity assessment monitoring system, which can be used to analyze EPS component defects and linearity based on vehicle speed interval division. The approach is validated experimentally using data collected from an EPS test jig and is further enhanced by the inclusion of a Graphical User Interface (GUI). Based on the design, the developed system effectively performs defect detection with an accuracy of 0.99 percent and obtains a linearity assessment score at varying vehicle speeds.