• Journal of the Korean Orthopaedic Association
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• v.56 no.6
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• pp.498-503
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• 2021

Purpose: The aim of this study were to find ideal entry point of straight antegrade humeral intramedullary nail (SAHN) for the treatment of proximal humerus fracture in Korean and to analyze anatomical conformity using computed tomography. Materials and Methods: From May 2014 to October 2016, the study was conducted retrospectively on 74 Korean patients who had taken computed tomography on both normal and affected shoulder joint as result of shoulder injury. The mean age of the patients was 64.5 years (range, 22-95 years). Radiologic evaluation was done using multiplanar reconstruction technique of the computer tomography on normal proximal humerus. We located ideal entry point of SAHN as the point where humerus intramedullary center axis and humeral head meet. Distance between the entry point and local anatomical landmark was measured. We defined the critical distance as the distance between entry point and the most medial point of the supraspinatus attachment site. For adequate fixation and avoidance of injury to rotator cuff, critical distance should be over 8 mm according to Euler, and we defined the critical type when it is less than 8 mm. Critical distance, sex, age, height, body weight, body mass index was evaluated for the statistical significance. Results: The ideal entry point was as follows: the mean anteroposterior distance, the sagittal distance to the lateral margin of bicipital groove, was 11.5 mm and the mean mediolateral distance, the coronal distance to the lateral margin of grater tuberosity, was 20.5 mm. The mean critical distance, distance from the entry point to the just medial to insertion of the supraspinatus tendon, was 8.0 mm. Critical type with critical distance less than 8 mm was found in 41 in 74 patients (55.4%). Conclusion: The ideal entry point of SAHN in Korean was located on 11.5 mm posteriorly from the lateral margin of bicipital groove and 20.5 mm medially from lateral margin of greater tuberosity. More than half of the cases were critical type. Since critical type can possibly cause rotate cuff injury during nail insertion on entry point, surgeon should consider anatomical variance before choosing surgical option.

• Journal of the Korean Orthopaedic Association
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• v.55 no.1
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• pp.46-53
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• 2020

Purpose: Scapular body fractures have generally been treated with non-surgical methods. This study reports the clinical and radiological outcomes after lateral-posterior internal fixation for treating displaced scapular body fractures. Materials and Methods: From March 2007 to May 2017, out of 40 patients who underwent internal fixation for scapular fractures, 13 cases of lateral plate fixation of a scapular body fracture were reviewed retrospectively. Preoperative and postoperative displacement, angulation and glenopolar angle (GPA) were measured. The range of shoulder motion, visual analogue scale (VAS), and disabilities of the arm, shoulder, and hand (DASH), and Constant score were assessed at the last follow-up. Results: The mean follow-up period was 17.7 months (range, 6-45 months). The mean preoperative GPA was 23.3°±3.96° (range, 17.8°-28.1°) and the postoperative GPA was 31.1°±4.75° (range, 22.5°-40.1°). Injury to the suprascapular nerve, nonunion, fracture redisplacement, metallic failure, or infection did not occur. At the last follow-up, the mean range of motion was 150.5°±19.3° in forward flexion, 146.6°±2.34° in lateral abduction, 66.6°±19.1° in external rotation, and 61.6°±18.9° in internal rotation. The VAS, DASH, and Constant scores were 1.7±1.3, 6.2±2.4, and 86±7.9 points, respectively. Conclusion: A scapular body fracture with severe displacement, angulation and marked decreased GPA can be stabilized by lateralposterior plate fixation using the appropriate surgical technique with good functional and radiological results.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.497-510
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• 2022

Agricultural reservoirs are essential structures for water supplies during dry period in the Korean peninsula, where water resources are temporally unequally distributed. For efficient water management, systematic and effective monitoring of medium-small reservoirs is required. Synthetic Aperture Radar (SAR) provides a way for continuous monitoring of those, with its capability of all-weather observation. This study aims to evaluate the applicability of SAR in monitoring medium-small reservoirs using Sentinel-1 (10 m resolution) and Capella X-SAR (1 m resolution), at Chari (CR), Galjeon (GJ), Dwitgol (DG) reservoirs located in Ulsan, Korea. Water detected results applying Z fuzzy function-based threshold (Z-thresh) and Chan-vese (CV), an object detection-based segmentation algorithm, are quantitatively evaluated using UAV-detected water boundary (UWB). Accuracy metrics from Z-thresh were 0.87, 0.89, 0.77 (at CR, GJ, DG, respectively) using Sentinel-1 and 0.78, 0.72, 0.81 using Capella, and improvements were observed when CV was applied (Sentinel-1: 0.94, 0.89, 0.84, Capella: 0.92, 0.89, 0.93). Boundaries of the waterbody detected from Capella agreed relatively well with UWB; however, false- and un-detections occurred from speckle noises, due to its high resolution. When masked with optical sensor-based supplementary images, improvements up to 13% were observed. More effective water resource management is expected to be possible with continuous monitoring of available water quantity, when more accurate and precise SAR-based water detection technique is developed.

• Korean Journal of Environment and Ecology
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• v.36 no.4
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• pp.361-367
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• 2022

This study was conducted to identify annual variation of observation and activity pattern of Korean chipmunk (Tamias sibiricus) using camera traps in the Seoraksan and Jirisan National Parks, South Korea from May 2019 to May 2021. The annual variation was identified based on the observed frequency through weekly observations. Daily activity patterns of the species were also analyzed by season. The daily activity pattern of chipmunk appeared to be constantly diurnal across the years regardless of habitat or season. The Korean chipmunks living in the two different regions were observed in different time periods throughout the year. While the chipmunks inhabiting the Seoraksan were observed from 18^th to 45^th week, the chipmunks inhabiting the Jirisan National Park were observed from 7^th to 48^th week. This may be influenced by the hibernation period of chipmunks in the two different regions. In both regions, chipmunks were most frequently observed in autumn. It is considered that seasonal variation on population dynamic and activity patterns of chipmunks were reflected in the observation frequency. Although the observation frequency of camera trap is an indirect indicator and thus having a limitation that it cannot distinguish the population density and amount of activity for the target species, camera trapping is still an effective survey technique for monitoring mammals due to its high accessibility and easy use.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.8
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• pp.223-230
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• 2021

The lightweight block cipher PIPO announced at ICISC'20 has been effectively implemented by applying the bit slice technique. In this paper, we propose a parallel optimal implementation of PIPO for ARM processors. The proposed implementation enables parallel encryption of 8-plaintexts and 16-plaintexts. The implementation targets the A10x fusion processor. On the target processor, the existing reference PIPO code has performance of 34.6 cpb and 44.7 cpb in 64/128 and 64/256 standards. Among the proposed methods, the general implementation has a performance of 12.0 cpb and 15.6 cpb in the 8-plaintexts 64/128 and 64/256 standards, and 6.3 cpb and 8.1 cpb in the 16-plaintexts 64/128 and 64/256 standards. Compared to the existing reference code implementation, the 8-plaintexts parallel implementation for each standard has about 65.3%, 66.4%, and the 16-plaintexts parallel implementation, about 81.8%, and 82.1% better performance. The register minimum alignment implementation shows performance of 8.2 cpb and 10.2 cpb in the 8-plaintexts 64/128 and 64/256 specifications, and 3.9 cpb and 4.8 cpb in the 16-plaintexts 64/128 and 64/256 specifications. Compared to the existing reference code implementation, the 8-plaintexts parallel implementation has improved performance by about 76.3% and 77.2%, and the 16-plaintext parallel implementation is about 88.7% and 89.3% higher for each standard.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.2
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• pp.167-176
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• 2021

When producing recycled asphalt mix, it is important that the old binder of reclaimed asphalt pavement(RAP) should be well melted during blending in the mixer. The recycled asphalt mix is produced by instant mixing(IM) of all materials(RAP, virgin asphalt and new aggregates) all together in the mixer. However, in the same recycled mix, the binder around RAP aggregate was found to show higher oxidation level than the binder coated around the virgin aggregate because the old binder of RAP was not rejuvenated properly while instant mixing. The partially-rejuvenated RAP binder is assumed to be a high stiffness point in IM recycled mix. In this study, the stage mixing(SM) method was introduced; blending RAP and virgin asphalt for the first stage, and then mixing all together with hot new aggregates for the second stage. To compare the effect of the two mixing methods on moisture resistance of recycled mixes, a statistical t-test was performed between SM and IM using indirect tensile strength(ITS) and tensile strength ratio(TSR). Three conditioning methods were used; a 16-h freezing and then 24-h submerging, 48-h submerging, and 72-h submerging in 60℃ water. It was found that the TSR(=ITS_wet/ITS_dry) values of the mixes prepared by SM was clearly higher than the IM mixes, and coefficients of variation of SM mixes were lower than the IM mixes. It was also observed that the ITS_WET of SM was significantly different from the IM at α=0.05 level by statistical t-test. The ITS_WET of SM mix was reduced less than the IM mix in severer conditioned mixes. Therefore, it was concluded that the stage mixing method was an important blending technique for producing better-quality of recycled asphalt mixes, which would show higher moisture resistance than the recycled mixes produced by conventional instant mixing.

• Korean Journal of Environment and Ecology
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• v.35 no.6
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• pp.594-600
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• 2021

The purpose of this study is to observe the period of mating calls of cicadas in South Korea to identify the emergence period and geographic distribution for each cicada species. The study sites were 19 protection areas nationwide. The mating calls of cicadas were collected over the 12 months of 2019. A bioacoustics measuring device was installed to record the mating calls of cicadas in WAV, 44,100Hz format for 1 minute every hour. The temperature was recorded once or twice every hour using a micro-meteorological measuring device. Nine species of Korean cicadinae were studied. The start and end periods of mating calls were recorded for each cicada species for the subsequent analysis. The analysis results showed that nine cicada species appeared in the 19 protection areas. The chronological order of mating call periods for each species was as follows: Cryptotympana atrata (7/12 - 9/30), Meimuna opalifera (7/27 - 10/20), Hyalessa fuscata (7/25 - 10/9), Graptopsaltria nigrofuscata (7/28 - 9/5), Platypleura kaempferi (7/3 - 9/29), Suisha coreana (9/14 - 10/30), Leptosemia takanonis (6/26 - 8/2), Auritibicen intermedius (7/27 - 9/28), and Meimuna mongolica (8/8 - 9/11). The mating call period was between 35 (Meimuna mongolica) and 89 (Platypleura kaempferi) days, with the average being 62 days. The elevation above sea level for the habitats of each species was as follows: 5 - 386 m for Cryptotympana atrata, 7 - 759 m for Meimuna opalifera, 7 - 967 m for Hyalessa fuscata, 42 - 700m for Graptopsaltria nigrofuscata, 7 - 700 m for Platypleura kaempferi, 5 - 759 m for Suisha coreana, 7 - 759 m for Leptosemia takanonis, 397 - 967 m for Auritibicen intermedius, and 7 - 42 m for Meimuna mongolica. The average temperature of the habitats of each species was as follows: 23.9℃ for Cryptotympana atrata, 21.8℃ for Meimuna opalifera, 22℃ for Hyalessa fuscata, 23℃ for Graptopsaltria nigrofuscata, 22.9℃ for Platypleura kaempferi, 14.6℃ for Suisha coreana, 20.6℃ for Leptosemia takanonis, 19.3℃ for Auritibicen intermedius, and 24.4℃ for Meimuna mongolica. In terms of the habitat distribution of species, Meimuna opalifera, Hyalessa fuscata, and Platypleura kaempferi were distributed in more than 15 protection sites. Cryptotympana atrata was distributed in the lowlands in the southwest. Graptopsaltria nigrofuscata was distributed in the western area of the Korean Peninsula. Suisha coreana was distributed in areas excluding high mountain areas and parts of the southeast area. Leptosemia takanonis was distributed in areas near the mountains. Auritibicen intermedius was distributed locally in the high mountain areas. Meimuna mongolica was distributed locally in flat wetlands.