• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4400-4419
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• 2021

In the 4th Industrial Revolution, the healthcare industry is undergoing a paradigm shift from post-care and management systems based on diagnosis and treatment to disease prevention and management based on personal precision medicine. To optimize medical services for individual patients, an open ecosystem for the healthcare industry that allows the exchange and utilization of personal health records (PHRs) is required. However, under the current system of hospital-centered data management, it is difficult to implement the linking and sharing of PHRs in practice. To address this problem, in this study, we present the design and implementation of a patient-centered PHR platform using blockchain technology. This platform achieved transparency and reliability in information management by eliminating the risk of leakage and tampering/altering personal information, which could occur when using a PHR. In addition, the patient-consent system was applied to a PHR; thus, the patient acted as the user with ownership. The proposed blockchain-based PHR platform enables the integration of personal medical information with scattered distribution across multiple hospitals, and allows patients to freely use their health records in their daily lives and emergencies. The proposed platform is expected to serve as a stepping stone for patient-centered healthcare data management and utilization.

• Journal of Sensor Science and Technology
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• v.31 no.2
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• pp.96-101
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• 2022

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.297-309
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• 2022

Taking into account the globalization of the economy and the intensification of European integration processes, it has been proved that an important part of evaluating the effectiveness of activity of public sector enterprises should be a comparative analysis of the overall operation environment, namely public procurement, economic environment and tax policy, with the relevant systems of other countries of the world. The indicators and components of international ratings "Doing Business", "Paying Taxes", "Benchmarking Public Procurement" have been studied. Different groups of indicators forming "Doing Business" and "Paying Taxes" in the context of the impact on public sector enterprises have been identified: those which fully comply with the need to evaluate the operating environment of public sector enterprises and those which should be used with restrictions, regarding the peculiarities of creating and functioning of public sector enterprises. A comparative analysis of the place of Ukraine, Lithuania and Slovenia among other countries of the world in accordance with the international rankings of "Doing Business", "Paying Taxes", "Benchmarking Public Procurement" have been made. It has been substantiated that the results of such comparative analysis will allow identifying risk areas, and relevant information can be used in developing of recommendations for improving the effectiveness of activity of public sector enterprises.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.291-296
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• 2017

Infrastructure development as national project suffers from social conflict which is one of main risk to be managed. Social conflicts have a negative impact on not only the social integration but also the national economy as they require enormous social costs to be solved. Against this backdrop, this study analyzes social conflict using articles published by online news media based on web-crawling and natural language processing (NLP) techniques. As an illustrative case, the Jeju Naval Base (JNB) project which is one of representative conflict case in South Korea is analyzed. Total of 21,788 articles and representative keywords are identified annually. Additionally, comparative analysis is conducted between the extracted keywords and actual events occurred during the project. The authors explain actual events in the JNB project based on the extracted words by the year. This study contributes to analyze social conflict and to extract meaningful information from unstructured data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2987-3002
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• 2023

In 2015, the number of senior citizens aged 65 and over in Korea was 6,662,400, accounting for 13.1% of the total population. Along with these social phenomena, risk information related to the elderly is increasing every year. In particular, a fall accident caused by a fall can cause serious injury to an elderly person, so special attention is required. Therefore, in this paper, we implemented a system that monitors fall accidents and informs them in real time to minimize damage caused by falls. To this end, beacon-based indoor location positioning was performed and biometric information based on an integrated module was collected using various sensors. In other words, a multi-functional sensor integration module was designed based on Arduino to collect and monitor user's temperature, heart rate, and motion data in real time. Finally, through the analysis and prediction of measurement signals from the integrated module, damage from fall accidents can be reduced and rapid emergency treatment is possible. Through this, it is possible to reduce the damage caused by a fall accident, and rapid emergency treatment will be possible. In addition, it is expected to lead a new paradigm of safety systems through expansion and application to socially vulnerable groups.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.799-808
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• 2023

Globally, abnormal climate phenomena have led to an increase in rainfall intensity, consequently causing a rise in flooding-related damages. Agricultural areas, in particular, experience significant annual losses every year due to a lack of research on flooding in these regions. This study presents a comprehensive analysis of the flood event that occurred on July 16, 2017, in the agricultural area situated in Sindaedong, Heungdeok-gu, Cheongju-si. To achieve this, the EPA (United States Environmental Protection Agency) Storm Water Management Model (SWMM) was employed to generate runoff data by rainfall information. The produced runoff data facilitated the identification of flood occurrence points, and the analysis results exhibited a strong correlation with inundation trace maps provided by the Ministry of the Interior and Safety (MOIS). The detailed output of the SWMM model enabled the extraction of time-specific runoff information at each inundation point, allowing for a detailed understanding of the inundation status in the agricultural area over different time frames. This research underscores the significance of utilizing the SWMM model to simulate inundation in agricultural areas, thereby validating the efficacy of flood alerts and risk management plans. In particular, the integration of rainfall data and the SWMM model in flood prediction methodologies is expected to enhance the formulation of preventative measures and response strategies against flood damages in agricultural areas.

• Journal of Sensor Science and Technology
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• v.33 no.4
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• pp.203-208
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• 2024

Every year, approximately 350 million tons of plastic waste are generated worldwide. This waste, can degrade into microplastics, owing to factors such as temperature changes and UV exposure. These smaller plastic particles are increasingly entering the food chain through marine life, thereby raising concerns about their impact on human health. Consequently, there is an increasing need to measure microplastics. Common methods involve direct collection by using a manta trawl equipped with a 330 ㎛ mesh net or performing spectroscopic and thermal analyses on collected samples. However, these methods require complex pre-processing, which risk sample destruction. In this study, we developed a system to directly sample microplastics in aquatic environments by using laser-induced fluorescence spectroscopy. Through an analysis of the fluorescence spectra as well as, the with gradient and integration at specific points, we successfully distinguished microplastics of 100, 200, 300, and 500 ㎛ in size, and we also differentiated between polyethylene (PE) and polystyrene (PS) types.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2782-2804
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• 2024

The integration of artificial intelligence technology with medicine has rapidly evolved, with increasing demands for quality of life. However, falls remain a significant risk leading to severe injuries and fatalities, especially among the elderly. Therefore, the development and application of computer vision-based fall detection technologies have become increasingly important. In this paper, firstly, the keypoint detection algorithm ViTPose++ is used to obtain the coordinates of human body keypoints from the camera images. Human skeletal feature maps are generated from this keypoint coordinate information. Meanwhile, human dense feature maps are produced based on the DensePose algorithm. Then, these two types of feature maps are confused as dual-channel inputs for the model. The convolutional gated recurrent unit is introduced to extract the frame-to-frame relevance in the process of falling. To further integrate features across three dimensions (spatio-temporal-channel), a dual-channel fall detection algorithm based on video streams is proposed by combining the Convolutional Block Attention Module (CBAM) with the ConvGRU. Finally, experiments on the public UR Fall Detection Dataset demonstrate that the improved ConvGRU-CBAM achieves an F1 score of 92.86% and an AUC of 95.34%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.125-151
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• 2004

This study tests the price discovery from US Treasury bond markets to Korean bond markets using the daily returns of Korean bond data (CD, 3-year T-note, 5-year T-note, 5-year corporate note) and US treasury bond markets (3-month T-bill, 5-year T-note 10-year T-bond) from July 1, 1998 to December 31, 2003. For further research, we divide full data into two sub-samples on the basis of the start-up of bond valuation system in Korean bond market July 1, 2000, employing uni-variate AR(1)-GARCH(1,1)-M model. The main results are as follows. First the volatility spillover effects from US Treasury bond markets (3-month T-bill, 5-year T-note, 10-year T-bond) to Korean Treasury and Corporate bond markets (CD, 3-year T-note, 5-year T-note, 5-year corporate note) are significantly found at 1% confidence level. Second, the price discovery function from US bond markets to Korean bond markets in the sub-data of the pre-bond valuation system exists much stronger and more persistent than those of the post-bond valuation system. In particular, the role of 10-year T-bond compared with 3-month T-bill and 5-year T-note is outstanding. We imply these findings result from the international capital market integration which is accelerated by the broad opening of Korean capital market after 1997 Korean currency crisis and the development of telecommunication skill. In addition, these results are meaningful for bond investors who are in charge of capital asset pricing valuation, risk management, and international portfolio management.