• Smart Media Journal
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• v.13 no.2
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• pp.52-61
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• 2024

In the shipyard, aerial images are acquired at regular intervals using Unmanned Aerial Vehicles (UAVs) for the management of external storage yards. These images are then investigated by humans to manage the status of the storage yards. This method requires a significant amount of time and manpower especially for large areas. In this paper, we propose an automated management technology based on a semantic segmentation foundation model to address these challenges and accurately assess the status of external storage yards. In addition, as there is insufficient publicly available dataset for external storage yards, we collected a small-scale dataset for external storage yards objects and equipment. Using this dataset, we fine-tune an object detector and extract initial object candidates. They are utilized as prompts for the Segment Anything Model(SAM) to obtain precise semantic segmentation results. Furthermore, to facilitate continuous storage yards dataset collection, we propose a training data generation pipeline using SAM. Our proposed method has achieved 4.00%p higher performance compared to those of previous semantic segmentation methods on average. Specifically, our method has achieved 5.08% higher performance than that of SegFormer.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.115-127
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• 2024

This research aimed to construct models with various structures based on the Transformer module and to perform land cover classification, thereby examining the applicability of the Transformer module. For the classification of land cover, the Unet model, which has a CNN structure, was selected as the base model, and a total of four deep learning models were constructed by combining both the encoder and decoder parts with the Transformer module. During the training process of the deep learning models, the training was repeated 10 times under the same conditions to evaluate the generalization performance. The evaluation of the classification accuracy of the deep learning models showed that the Model D, which utilized the Transformer module in both the encoder and decoder structures, achieved the highest overall accuracy with an average of approximately 89.4% and a Kappa coefficient average of about 73.2%. In terms of training time, models based on CNN were the most efficient. however, the use of Transformer-based models resulted in an average improvement of 0.5% in classification accuracy based on the Kappa coefficient. It is considered necessary to refine the model by considering various variables such as adjusting hyperparameters and image patch sizes during the integration process with CNN models. A common issue identified in all models during the land cover classification process was the difficulty in detecting small-scale objects. To improve this misclassification phenomenon, it is deemed necessary to explore the use of high-resolution input data and integrate multidimensional data that includes terrain and texture information.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.205-212
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• 2023

Among cardiac diseases, arrhythmias can lead to serious complications such as stroke, heart attack, and heart failure if left untreated, so continuous and accurate ECG monitoring is crucial for clinical care. However, the accurate interpretation of electrocardiogram (ECG) data is entirely dependent on medical doctors, which requires additional time and cost. Therefore, this paper proposes an arrhythmia recognition module for the purpose of developing a medical platform through the analysis of abnormal pulse waveforms based on Lifelogs. The proposed method is to convert ECG data into image format instead of time series data, apply visual pattern recognition technology, and then detect arrhythmia using CNN model. In order to validate the arrhythmia classification of the CNN model by image type conversion of ECG data proposed in this paper, the MIT-BIH arrhythmia dataset was used, and the result showed an accuracy of 97%.

• The Journal of Bigdata
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• v.8 no.2
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• pp.1-13
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• 2023

Current analysis of air passengers mainly relies on statistical methods, but there are limitations in analyzing detailed aspects such as travel routes, number of regional passengers and airport access times. However, with the advancement of big data technology and revised three data acts, big data-based transportation analysis has become more active. Mobile communication data, which can precisely track the location of mobile phone terminals, can serve as valuable analytical data for transportation analysis. In this paper, we propose a air passenger Origin/Destination (O/D) extraction algorithm based on mobile communication data that overcomes the limitations of existing air transportation user analysis methods. The algorithm involves setting airport signal detection zones at each airport and extracting air passenger based on their base station connection history within these zones. By analyzing the base station connection data along the passenger's origin-destination paths, we estimate the entire travel route. For this paper, we extracted O/D information for both domestic and international air passengers at all domestic airports from January 2019 to December 2020. To compensate for errors caused by mobile communication service provider market shares, we applied a adjustment to correct the travel volume at a nationwide citizen level. Furthermore correlation analysis was performed on O/D data and aviation statistics data for air traffic users based on mobile communication data to verify the extracted data. Through this, there is a difference in the total amount (4.1 for domestic and 4.6 for international), but the correlation is high at 0.99, which is judged to be useful. The proposed algorithm in this paper enables a comprehensive and detailed analysis of air transportation users' travel behavior, regional/age group ratios, and can be utilized in various fields such as formulating airport-related policies and conducting regional market analysis.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.63-75
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• 2016

Aquaculture has historically delivered marine products because the country is surrounded by ocean on three sides. Surveys on production have been conducted recently to systematically manage aquaculture facilities. Based on survey results, pricing controls on marine products has been implemented to stabilize local fishery resources and to ensure minimum income for fishermen. Such surveys on aquaculture facilities depend on manual digitization of aerial photographs each year. These surveys that incorporate manual digitization using high-resolution aerial photographs can accurately evaluate aquaculture with the knowledge of experts, who are aware of each aquaculture facility's characteristics and deployment of those facilities. However, using aerial photographs has monetary and time limitations for monitoring aquaculture resources with different life cycles, and also requires a number of experts. Therefore, in this study, we investigated an automatic prototype system for detecting boundary information and monitoring aquaculture facilities based on satellite images. KOMPSAT-3 (13 Scene), a local high-resolution satellite provided the satellite imagery collected between October and April, a time period in which many aquaculture facilities were operating. The ANN classification method was used for automatic detecting such as cage, longline and buoy type. Furthermore, shape files were generated using a digitizing image processing method that incorporates polygon generation techniques. In this study, our newly developed prototype method detected aquaculture facilities at a rate of 93%. The suggested method overcomes the limits of existing monitoring method using aerial photographs, but also assists experts in detecting aquaculture facilities. Aquaculture facility detection systems must be developed in the future through application of image processing techniques and classification of aquaculture facilities. Such systems will assist in related decision-making through aquaculture facility monitoring.

• Atmosphere
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• v.12 no.4
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• pp.20-42
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• 2002

Based on the "Mid to Long Term Plan for Space Development", a project to launch COMeS (Communication, Oceanography, and Meteorological Satellite) into the geostationary orbit is undergoing. Accordingly, KMA (Korea Meteorological Administration) has defined the meteorological missions and prepared the user requirements to fulfill the missions. To make a realistic user requirements, we prepared a first draft based on the ideal meteorological products derivable from a geostationary platform and sent the RFI (request for information) to the sensor manufacturers. Based on the responses to the RFI and other considerations, we revised the user requirement to be a realistic plan for the 2008 launch of the satellite. This manuscript introduces the revised user requirements briefly. The major mission defined in the revised user requirement is the augmentation of the detection and prediction ability of the severe weather phenomena, especially around the Korean Peninsula. The required payload is an enhanced Imager, which includes the major observation channels of the current geostationary sounder. To derive the required meteorological products from the Imager, at least 12 channels are required with the optimum of 16 channels. The minimum 12 channels are 6 wavelength bands used for current geostationary satellite, and additional channels in two visible bands, a near infrared band, two water vapor bands and one ozone absorption band. From these enhanced channel observation, we are going to derive and utilize the information of water vapor, stability index, wind field, and analysis of special weather phenomena such as the yellow sand event in addition to the standard derived products from the current geostationary Imager data. For a better temporal coverage, the Imager is required to acquire the full disk data within 15 minutes and to have the rapid scan mode for the limited area coverage. The required thresholds of spatial resolutions are 1 km and 2 km for visible and infrared channels, respectively, while the target resolutions are 0.5 km and 1 km.

• The Journal of Korean Orthopaedic Ultrasound Society
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• v.7 no.2
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• pp.105-112
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• 2014

Purpose: Ultrasound-guided epidural caudal block for low back pain and radiating pain is often performed in the treatment of outpatients. However, this procedure has a failure rate of up to 25% even when it performed by an experienced physician. The authors investigate the effectiveness of Ultrasound-guided epidural caudal block in patients related to disc herniation or spinal stenosis. Materials and Methods: Ultrasound-guided caudal epidural block was performed in 55 outpatients with LBP and radiating pain. Patient was placed in the prone position and sonographic image of sacral hiatus was obtained using linear probe. A 22-gauge needle was advanced into the sacrococcygeal membrane under ultrasound guidance and then medication was injected into the caudal epidural space. There were 31 cases of disc herniation, and 24 cases of spinal stenosis. Patients were evaluated by Visual Analog Scale (VAS) pain score at pre-treatment, post-treatment, 2 weeks and 4 weeks by telephone interviews. Results: 53 of the 55 cases (96.4%) of needle insertion into the sacral canal under ultrasound guidance were successful. Gender was not significantly different between disc herniation group and spinal stenosis group. But there was a significant age difference between disc herniation group ($42.3{\pm}10.8$), and spinal stenosis group ($62.8{\pm}15.1$) [p<0.001]. The VAS score at pre-treatment, post-treatment, 2 weeks, 4 weeks in disc group were 6.84, 3.1, 1.8 & 1.77. The VAS score at pre-treatment, post-treatment, 2 weeks, 4 weeks in spinal stenosis group were 6.88, 3.58, 4.33 & 4.88. The VAS score in both groups was significantly improved after the procedure (p<0.001). Over time, the two groups were statistically significant differences in VAS score after adjusting for age (p<0.001). Conclusion: Ultrasound-guided caudal epidural block seems to provide a high success rate and a significantly better response in disc group than spinal stenosis group.

• Journal of the Korean earth science society
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• v.32 no.4
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• pp.388-401
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• 2011

Asian dust was observed a total of 66 times in the springtime during the period from 2002 to 2010, with 26 cases in March, 23 cases in April and 17 cases in May. This study investigates a Asian dust episode that occurred during the period from 22 to 25 May 2010, based on synoptic weather patterns, wind vector at 850 hPa, relative humidity at 1000 hPa, Jet streams and wind vector at 300 hPa, PM10 concentration in Korea and satellite imagery. In this case, Asian dust originated on 22 May along the rear of a developing low pressure system in Mongolia. The Asian dust was then transported southeastward and bypassed the Korea peninsula from 23 to 24 May, before reaching Japan on 25 May. Jet streams on 24 May bypassed the Korean peninsula and induced the development of a surface low pressure centered over the peninsula. The resulting air flow was critical to the trajectory of the Asian dust, which likewise bypassed the Korean peninsula. 72-hour backward trajectory data reveal that the Shandong Peninsula and the East China Sea were the points of origin for the air flows that swept through the Japanese sites where Asian dust was observable to the naked eay. The Asian dust pathway is ascertained by horizontal distribution of the Asian dust of RGB imagery from MODIS satellites which captured the Asian dust moving over the Shandong Peninsula, the East China Sea, and northwest of the Kyushu region in Japan. Since the synoptic pattern and the transport way of the Asian dust case are far from typical ones, which Asian dust forecasting technique has long been based on, this study can be good example of exceptional Asian dust pattern and it will be used for more accurate Asian dust forecasting.

• Economic and Environmental Geology
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• v.30 no.2
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• pp.137-142
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• 1997

At the tailings dam of the disused Brukunga pyrite mine in South Australia, reaction of groundwater with the tailings causes the formation and discharge of sulphuric acid. There is a need to improve remediation efforts by decreasing groundwater flow through the tailings dam. Geophysical methods have been investigated to determine whether they can be used to characterise variations in depth to watertable and map preferred groundwater flow paths. Three methods were used: transient electromagnetic (TEM) soundings, direct current (DC) soundings and profiling, and self potential (SP) profiling. The profiling methods were used to map the areal extent of a given response, while soundings was used to determine the variation in response with depth. The results of the geophysical surveys show that the voltages measured with SP profiling are small and it is hard to determine any preferred channels of groundwater flow from SP data alone. Results obtained from TEM and DC soundings, show that the DC method is useful for determining layer boundaries at shallow depths (less than about 10 m), while the TEM method can resolve deeper structures. Joint use of TEM and DC data gives a more complete and accurate geoelectric section. The TEM and DC measurements have enabled accurate determination of depth to groundwater. For soundings centred at piezometers, this depth is consistent with the measured watertable level in the corresponding piezometer. A map of the watertable level produced from all the TEM and DC soundings at the site shows that the shallowest level is at a depth of about 1 m, and occurs at the southeast of the site, while the deepest watertable level (about 17 m) occurs at the northwest part of the site. The results indicate that a possible source of groundwater occurs at the southeast area of the dam, and the aquifer thickness varies between 6 and 13 m. A map of the variation of resistivity of the aquifer has also been produced from the TEM and DC data. This map shows that the least resistive (i.e., most conductive) section of the aquifer occurs in the northeast of the site, while the most resistive part of the aquifer occurs in the southeast. These results are interpreted to indicate a source of fresh (resistive) groundwater in the southeast of the site, with a possible further source of conductive groundwater in the northeast.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.652-657
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• 2018

Breeding control in a farm is a very important factor affecting milk productivity. Breeding management is important for the early detection of estrus, and reliable, automatic, more accurate, and faster monitoring of the timing of dairy cows is essential for farmers. This study measured the accuracy of estrus using the estrus indications, changes in activities, rumination activities, ruminal temperature, and pH. The biomedical information device S1 used in this study provided an estrus notice using the rumen temperature, pH, cow activities, and number of drinking estimations, which were inserted in the rumen through the oral route. The S2 device was used in the estrus notice for the rumen activities and cow activities. The data collected on the instrument were collected at intervals of 2 hours per day at the reference days (RD: -7~-3, +7~+ 3) +2), 7 days before insemination, and 7 days after insemination. The activities of the S1 device used in this paper increased with increasing number of insemination days (-1: $12.5{\pm}1.03/day$; 0: $12.9{\pm}1.73/day$) compared to the reference day (RD: $10.2{\pm}1.0/day$). The activities of the S2 device was also found to increase from the reference day to the insemination day (0: $63.0{\pm}3.66$) compared to the reference day (RD: $40.3{\pm}2.68$). The number of daily drinks in S1 decreased from the reference day (RD: $5.9{\pm}0.89/day$) to before the insemination day (-2: $5.6{\pm}0.98$; -1: $5.7{\pm}0.96$); +2: $6.0{\pm}0.73$). The number of daily drinks on the insemination day (0: $6.3{\pm}0.86$; +2: $6.0{\pm}0.73$) was similar to the reference day. The number of daily rumination in S2 decreased from the reference day (RD: $493.8{\pm}10.92$) to the insemination day (-1: $390.2{\pm}13.36$; 0: $354.1{\pm}16.71$).