• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.29-41
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• 2022

Alien species cause problems in various ecosystems, reduce biodiversity, and destroy ecosystems. Due to these problems, the problem of a management plan is increasing, and it is difficult to accurately identify each individual and calculate the number of individuals, especially when researching alien turtle species such as GPS and PIT based on capture. this study intends to conduct an individual recognition study using a UAV. Recently, UAVs can take various sensor-based photos and easily obtain high-definition image data at low altitudes. Therefore, based on previous studies, this study investigated five variables to be considered in UAV flights and produced a test paper using them. OCR was used to monitor the displayed turtles using the manufactured test paper, and this confirmed the recognition rate. As a result, the use of yellow numbers showed the highest recognition rate. In addition, the minimum threat distance was confirmed to be 3 to 6m, and turtles with a shell size of 6 to 8cm were also identified during the flight. Therefore, we tried to propose an object recognition methodology for turtle display text using OCR, and it is expected to be used as a new turtle monitoring technique.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.4
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• pp.425-434
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• 2022

Objectives: This study analyzes TVOC concentrations in root industry workplaces in order to prevent probable occupational disease among workers. Root industry includes all the infrastructure of manufacturing, such as casting and molding. Methods: Real-time TVOC sensors were deployed in three root industry workplaces. We measured TVOC concentrations with these sensors and analyzed the results using a data-analysis tool developed with Python 3.9. Results: During the study period, the mean of the TVOC concentrations remained in an acceptable range, 0.30, 2.15, and 1.63 ppm across three workplaces. However, TVOC concentrations increased significantly at specific times, with respective maximum values of 4.98, 28.35, and 26.65 ppm for the three workplaces. Moreover, the analysis of hourly TVOC concentrations showed that during working hours or night shifts TVOC concentrations increased significantly to higher than twice the daily mean values. These results were scrutinized through classical decomposition results and autocorrelation indices, where seasonal graphs of the corresponding classical decomposition results showed that TVOC concentrations increased at a specific time. Trend graphs showed that TVOC concentrations vary by day. Conclusions: Deploying a real-time TVOC sensor should be considered to reflect irregularly high TVOC concentrations in workplaces in the root industry. It is expected that the real-time TVOC sensor with the presented data analysis methodology can eradicate probable occupational diseases caused by detrimental gases.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.147-149
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• 2022

This paper proposes a state machine design methodology in which an interactive robot that mimics discrete trial training (DTT protocol) can support social interaction training for children with autism. The robot applied to social interaction training uses the response to the provided training stimulus as a quantitative indicator by processing the data received from the sensors measuring the behavioral response of the child. In this process, the state machine is used as information that classifies the state of the acquired data and provides the subsequent stimulus for DTT protocol. Through the joint attentional training, it can be used as evidence-based treatment information by quantitatively classifying the data on the number of sustainable and DTT protocol and the child's response, as well as the current reaction status of the child to the observer performing remote monitoring. At the same time, it was confirmed that it is possible to properly respond to misrecognition situations.

• Journal of Korean Clinical Nursing Research
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• v.15 no.1
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• pp.107-122
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• 2009

Purpose: The purpose of this study was to identify nursing interventions frequently used with cancer patients. Nursing records from 5 general hospitals for patients with stomach, liver, lung or colon cancer were analyzed. Method: A descriptive study methodology was used and nursing records for 15 patients in each disease category at each hospital, who were admitted and discharged during June 2007 were analyzed. Results: Five domains of NIC were found and the physiological(basic) domain was most frequent (31.52%). Twenty two classes of NIC were identified with risk management for safety being most frequent (22.49%). For the 119 nursing interventions identified, the most frequent was pain management with 7,827 (12.31%), followed by prevention of falls (11.76%), surveillance (6.79%) and wound care (5.12%). Nursing activities of pain management and prevention of falls were comparable to activities listed in literature on guidelines for evidence based and best practices in nursing care. Eight of the 17 nursing activities for pain management, and 9 of 14 for fall prevention were consistent with these guidelines. Conclusion: In this study, nursing interventions were found to be focused on physical care, monitoring patients' condition and education. We have to develop diverse nursing interventions and a convenient recording process.

• Journal of Korea Trade
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• v.26 no.3
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• pp.117-136
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• 2022

Purpose - Port is vital for international trade accounting for approximately 80% of world cargo transportation in the global trade sector. Air pollutants emitted owing to the related industry interfaces developed around the port spread throughout the dense population region can have harmful effects on the nearby residents. This study aims to analyze high-concentration diffusion pattern by air pollutants, considering the main management periods by air pollutants. Design/methodology - Employing the concentration criteria per main air pollutant, the analyses of concentration change patterns per air pollutant, wind characteristics that directly affected the air pollutant diffusion, distribution types per air pollutant, and high-concentration diffusion patterns by season according to time changes were conducted. Findings - The substances that caused harmful levels of air pollution in the hinterland living zone of the Busan New Port were PM_10, PM_2.5, and NO_2. Furthermore, the intensive management periods were as follows: For PM_10, 24-h (spring), 12:00-16:00 (summer), 12:00-16:00 (summer), 20:00-12:00 (fall), and 24:00-20:00 (winter), and for PM_2.5, 24-h (all four seasons), and for NO_2, 23:00-04:00 (spring), 23:00-08:00 (summer), and 20:00-08:00 (fall), and 23:00-04:00 (winter). Originality/value - Research finding indicates that regular monitoring and countermeasures to reduce air pollution for each air pollutant makes it possible to achieve effective air quality control in the port and hinterland living zones.

• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.151-161
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• 2022

Purpose - The purpose of this study is to redefine imperial cultural properties and pass on and provide the elegant value of imperial cultural properties with the Allen Collection's promotion to retrieve imperial cultural properties. Design/methodology/approach - This study was approached to deal with the essential interpretation of cultural improvement through discussion in the cultural science approach Findings - As a result of examining ways to revitalize tourism resources using Allen Collection, it is necessary to cultivate international manners and knowledge levels in strengthening imperial awareness through the establishment of imperial museums, enacting laws on standards for designation of imperial cultural assets, and promoting them. In addition, policy needs such as the development plan of the imperial cultural festival, re-establishment, application, reuse, re-establishment, and reproduction according to environmental changes, and technical support and monitoring systems for investigating and preserving imperial cultural assets are needed. Research implications or Originality - The study on the device imperial cultural assets as tour resources the cultural assets mate be not only preserved and inherited to the descendant but also useful in contemporary national emotion positively. for we could obtain wide national support and co-operation in the protection work of cultural assets. What makes our cultural assets leaved indifferent before destruction like this most of all, it is form indifference of the people, we must make an effort to meet with recognizing the value of useful cultural assets by mean of utilizing cultural assets as tour resources to inhibit more damage of destruction of cultural assets.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.344-352
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• 2022

Accurate indoor localization of construction workers and mobile assets is essential in safety management. Existing positioning methods based on GPS, wireless, vision, or sensor based RTLS are erroneous or expensive in large-scale indoor environments. Tightly coupled sensor fusion mitigates these limitations. This research paper proposes a state-of-the-art positioning methodology, addressing the existing limitations, by integrating Stereo Visual Inertial Odometry (SVIO) with fiducial landmarks called AprilTags. SVIO determines the relative position of the moving assets or workers from the initial starting point. This relative position is transformed to an absolute position when AprilTag placed at various entry points is decoded. The proposed solution is tested on the NVIDIA ISAAC SIM virtual environment, where the trajectory of the indoor moving forklift is estimated. The results show accurate localization of the moving asset within any indoor or underground environment. The system can be utilized in various use cases to increase productivity and improve safety at construction sites, contributing towards 1) indoor monitoring of man machinery coactivity for collision avoidance and 2) precise real-time knowledge of who is doing what and where.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.57-69
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• 2024

Crop classification plays a vitalrole in monitoring agricultural landscapes and enhancing food production. In this study, we explore the effectiveness of Long Short-Term Memory (LSTM) models for crop classification, focusing on distinguishing between apple and rice crops. The aim wasto overcome the challenges associatedwith finding phenology-based classification thresholds by utilizing LSTM to capture the entire Normalized Difference Vegetation Index (NDVI)trend. Our methodology involvestraining the LSTM model using a reference site and applying it to three separate three test sites. Firstly, we generated 25 NDVI imagesfrom the Sentinel-2A data. Aftersegmenting study areas, we calculated the mean NDVI values for each segment. For the reference area, employed a training approach utilizing the NDVI trend line. This trend line served as the basis for training our crop classification model. Following the training phase, we applied the trained model to three separate test sites. The results demonstrated a high overall accuracy of 0.92 and a kappa coefficient of 0.85 for the reference site. The overall accuracies for the test sites were also favorable, ranging from 0.88 to 0.92, indicating successful classification outcomes. We also found that certain phenological metrics can be less effective in crop classification therefore limitations of relying solely on phenological map thresholds and emphasizes the challenges in detecting phenology in real-time, particularly in the early stages of crops. Our study demonstrates the potential of LSTM models in crop classification tasks, showcasing their ability to capture temporal dependencies and analyze timeseriesremote sensing data.While limitations exist in capturing specific phenological events, the integration of alternative approaches holds promise for enhancing classification accuracy. By leveraging advanced techniques and considering the specific challenges of agricultural landscapes, we can continue to refine crop classification models and support agricultural management practices.

• Convergence Security Journal
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• v.23 no.5
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• pp.81-89
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• 2023

Kubernetes is a representative open-source software for container orchestration, playing a crucial role in monitoring and managing resources allocated to containers. As container environments become prevalent, security threats targeting containers continue to rise, with resource exhaustion attacks being a prominent example. These attacks involve distributing malicious crypto-mining software in containerized form to hijack computing resources, thereby affecting the operation of the host and other containers that share resources. Previous research has focused on detecting resource depletion attacks, so technology to respond when attacks occur is lacking. This paper proposes a reinforcement learning-based dynamic resource management framework for detecting and responding to resource exhaustion attacks and malicious containers running in Kubernetes environments. To achieve this, we define the environment's state, actions, and rewards from the perspective of responding to resource exhaustion attacks using reinforcement learning. It is expected that the proposed methodology will contribute to establishing a robust defense against resource exhaustion attacks in container environments