• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1763-1770
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• 2023

The availability of high-resolution satellite image time series data has led to an increase in change detection research. Various methods are being studied, such as satellite image pixel and object-level change detection algorithms, as well as algorithms that apply deep learning technology. In this paper, we propose a QGIS plugin-based system to enhance the utilization of these useful results and present an actual implementation case. The proposed system is a system for intensive change detection and monitoring of areas of interest, and we propose a convenient system expansion method for algorithms to be developed in the future. Furthermore, it is expected to contribute to the construction of satellite image utilization systems by presenting the basic structure of commercialization of change detection research.

• Journal of Society of Preventive Korean Medicine
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• v.27 no.3
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• pp.1-12
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• 2023

Objectives : The aim of this study is to analyze the news articles related to Korean Medicine(KM) and compare trends in news reports from 2018 to 2022. Method : News articles related to KM were collected through the BigKinds, the news bigdata service of the Korea Press Foundation. News reports from 1 January 2018 to 31 December 2022 were searched. 2,950 news articles out of a total of 12,497 met the inclusion criteria. First, quantitative changes in media coverage were analyzed by year, media outlet, and month. For qualitative analysis, two authors independently coded the content of news articles, discussed them until consensus, and consulted with a third researcher to classify them. In addition, keywords extracted by the BigKind's Topic Rank algorithm were compared and analyzed in each year. Results : The number of news articles on KM decreased by 42% in 2022 compared to 2018. Over a fiveyear period, the Naeil Shinmun reported the most on KM among newspapers, while the Hankyoreh did the least. Among broadcasters, YTN reported the most and SBS did the least. When analyzing the reports by category, the most common was 'treatment', followed by 'prevention' and 'scientification'. As a result of extracting keywords with high weight and frequency, 'immunity' and 'immune system' ranked the first and second in 2018, while 'COVID 19' and 'medical law violation' did in 2022. Conclusion : The decrease in media reports on KM during the COVID-19 epidemic period seems to be due to the limited role of KM in responding to infectious diseases, and efforts to expand the scope of KM can induce increased media reports and social interest.

• Korean Journal of Radiology
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• v.21 no.6
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.3
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• pp.170-184
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• 2024

With the advancement of autonomous navigation technology in maritime domain, there is an active research on swarming Unmanned Surface Vehicles (USVs) that can fulfill missions with low cost and high efficiency. In this study, we propose a formation control algorithm that maintains a certain shape when multiple unmanned surface vehicles operate in a swarm. In the case of swarming, individual USVs need to be able to accurately follow the target state and avoid collisions with obstacles or other vessels in the swarm. In order to generate guidance commands for swarm formation control, the potential field method has been a major focus of swarm control research, but the method using the potential field only uses the position information of obstacles or other ships, so it cannot effectively respond to moving targets and obstacles. In situations such as the formation change of a swarm of ships, the formation control is performed in a dense environment, so the position and velocity information of the target and nearby obstacles must be considered to effectively change the formation. In order to overcome these limitations, this paper applies a method that considers relative velocity to the potential field-based guidance law to improve target following and collision avoidance performance. Considering the relative velocity of the moving target, the potential field for nearby obstacles is newly defined by utilizing the concept of Velocity Obstacle (VO), and the effectiveness and efficiency of the proposed method is verified through swarm control simulation, and swarm control experiments using a small scaled unmanned surface vehicle platform.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.1-14
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• 2024

Global navigation satellite system (GNSS) providing position, navigation and timing (PNT) services consist of satellite, ground, and user systems. Monitoring stations, a key element of the ground segment, play a crucial role in continuously collecting satellite navigation signals for service provision and fault detection. These stations detect anomalies such as threats to the signal-in-space (SIS) of satellites, receiver issues, and local threats. They deliver received data and detection results to the master station. This paper introduces the main monitoring algorithms and measurement pre-processing processes for quality assessment and fault detection of received satellite signals in current satellite navigation system monitoring stations. Furthermore, it proposes a strategy for the development of components, architecture, and algorithms for the new regional navigation satellite system (RNSS) monitoring stations.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.37 no.2
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• pp.175-181
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• 2024

In electrical power substations, bulky iron-core potential transformers (PTs) are installed in a tank of gas-insulated switchgear (GIS) to measure system voltages. This paper proposed a low-power voltage transformer (LPVT) that can replace the conventional iron-core PTs in response to the demand for the digitalization of substations. The prototype LPVT consists of a capacitive voltage divider (CVD) which is embedded in a spacer and an impedance matching circuit using passive components. The CVD was fabricated with a flexible PCB to acquire enough insulation performance and withstand vibration and shock during operation. The performance of the LPVT was evaluated at 80%, 100%, and 120% of the rated voltage (38.1 kV) according to IEC 61869-11. An accuracy correction algorithm based on LabVIEW was applied to correct the voltage ratio and phase error. The corrected voltage ratio and phase error were +0.134% and +0.079 min., respectively, which satisfies the accuracy CL 0.2. In addition, the voltage ratio of LPVT was analyzed in ranges of -40~+40℃, and a temperature correction coefficient was applied to maintain the accuracy CL 0.2. By applying the LPVT proposed in this paper to the same rating GIS, it can be reduced the length per GIS bay by 11%, and the amount of SF6 by 5~7%.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.131-135
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• 2024

In the MQTT protocol, if the retained flag of a message published by a publisher is set, the message is stored in the broker as a retained message. When a new subscriber performs a subscribe, the broker immediately sends the retained message. This allows the new subscriber to perform updates on the current state via the retained message without waiting for new messages from the publisher. However, sending retained messages can become a traffic overhead if new messages are frequently published by the publisher. This situation could be considered an overhead when new subscribers frequently subscribe. Therefore, in this paper, we propose a retained message delivery scheme by considering the characteristics of the published messages. We model the delivery and waiting actions to new subscribers from the perspective of the broker using reinforcement learning, and determine the optimal policy through Q learning algorithm. Through performance analysis, we confirm that the proposed method shows improved performance compared to existing methods.

• Journal of Korean Academy of Nursing
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• v.54 no.2
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• pp.193-210
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• 2024

Purpose: The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance." Methods: The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix. Results: The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol. Conclusion: Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.6
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• pp.469-477
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• 2008

Purpose: The goal of this paper is to present the design and performance of a position encoding circuit for $16{\times}16$ array of position sensitive multi-anode photomultiplier tube for small animal PET scanners. This circuit which reduces the number of readout channels from 256 to 4 channels is based on a charge division method utilizing a resistor array. Materials and Methods: The position encoding circuit was simulated with PSpice before fabrication. The position encoding circuit reads out the signals from H9500 flat panel PMTs (Hamamatsu Photonics K.K., Japan) on which $1.5{\times}1.5{\times}7.0\;mm^3$ $L_{0.9}GSO$ ($Lu_{1.8}Gd_{0.2}SiO_{5}:Ce$) crystals were mounted. For coincidence detection, two different PET modules were used. One PET module consisted of a $29{\times}29\;L_{0.9}GSO$ crystal layer, and the other PET module two $28{\times}28$ and $29{\times}29\;L_{0.9}GSO$ crystal layers which have relative offsets by half a crystal pitch in x- and y-directions. The crystal mapping algorithm was also developed to identify crystals. Results: Each crystal was clearly visible in flood images. The crystal identification capability was enhanced further by changing the values of resistors near the edge of the resistor array. Energy resolutions of individual crystal were about 11.6%(SD 1.6). The flood images were segmented well with the proposed crystal mapping algorithm. Conclusion: The position encoding circuit resulted in a clear separation of crystals and sufficient energy resolutions with H9500 flat-panel PMT and $L_{0.9}GSO$ crystals. This circuit is good enough for use in small animal PET scanners.