• Journal of Radiation Protection and Research
• /
• v.46 no.4
• /
• pp.184-193
• /
• 2021

Background: The radionuclides released by the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident 9 years ago are still being monitored by various research teams and the Japanese government. Comparison of different surveys' results could help evaluate the exposure doses and the mechanism of radiocesium behavior in the urban environment in the area. In this study, we clarified the relationship between land use and temporal changes in the ambient dose rates (air dose rates) using big data. Materials and Methods: We set a series of 1 × 1 km² meshes within the 80 km zone of the FDNPP to compare the different survey results. We then prepared an analysis dataset from all survey meshes to analyze the temporal change in the air dose rate. The selected meshes included data from all survey types (airborne, fixed point, backpack, and carborne) obtained through the all-time survey campaigns. Results and Discussion: The characteristics of each survey's results were then evaluated using this dataset, as they depended on the measurement object. The dataset analysis revealed that, for example, the results of the carborne survey were smaller than those of the other surveys because the field of view of the carborne survey was limited to paved roads. The location factor of different land uses was also evaluated considering the characteristics of the four survey methods. Nine years after the FDNPP accident, the location factor ranged from 0.26 to 0.49, while the half-life of the air dose rate ranged from 1.2 to 1.6. Conclusion: We found that the decreasing trend in the air dose rate of the FDNPP accident was similar to the results obtained after the Chernobyl accident. These parameters will be useful for the prediction of the future exposure dose at the post-accident.

• Korean Journal of Remote Sensing
• /
• v.38 no.1
• /
• pp.111-126
• /
• 2022

Land use and land cover (LULC) mapping is an important factor in geospatial analysis. Although highly precise ground-based LULC monitoring is possible, it is time consuming and costly. Conversely, because the synthetic aperture radar (SAR) sensor is an all-weather sensor with high resolution, it could replace field-based LULC monitoring systems with low cost and less time requirement. Thus, LULC is one of the major areas in SAR applications. We developed a LULC model using only KOMPSAT-5 single co-polarized data and digital elevation model (DEM) data. Twelve HH-polarized images and 18 VV-polarized images were collected, and two HH-polarized images and four VV-polarized images were selected for the model testing. To train the LULC model, we applied the conditional generative adversarial network (cGAN) method. We used U-Net combined with the residual unit (ResUNet) model to generate the cGAN method. When analyzing the training history at 1732 epochs, the ResUNet model showed a maximum overall accuracy (OA) of 93.89 and a Kappa coefficient of 0.91. The model exhibited high performance in the test datasets with an OA greater than 90. The model accurately distinguished water body areas and showed lower accuracy in wetlands than in the other LULC types. The effect of the DEM on the accuracy of LULC was analyzed. When assessing the accuracy with respect to the incidence angle, owing to the radar shadow caused by the side-looking system of the SAR sensor, the OA tended to decrease as the incidence angle increased. This study is the first to use only KOMPSAT-5 single co-polarized data and deep learning methods to demonstrate the possibility of high-performance LULC monitoring. This study contributes to Earth surface monitoring and the development of deep learning approaches using the KOMPSAT-5 data.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.3
• /
• pp.31-39
• /
• 2022

The Army, Navy, and Air Force are currently making various efforts to apply defense IoT technology by reflecting the characteristics of each military. However, there are concerns that comprehensive collection, management, and analysis may be limited because each military has no overlapping system or guaranteed interoperability of data, and there are inefficient aspects in terms of using and investing a limited defense budget. In this study, evaluation items and priorities for pilot projects were selected by applying Delphi technique and AHP method to determine which projects are preferentially effective when commonly applied in terms of Army, Navy, and Air force. As a result of the analysis, the project basis and driving force, project feasibility, performance and ripple effect, redundancy and connection were identified in the order of IoT-based small detachment units and unmanned base construction, intelligent smart unit, smart ground ammunition depot, and smart training management system. According to the comprehensive results reflecting the weights of each evaluation item, the fact that small detachment units and unmanned base construction project are recognized as the first project to be applied to the Army, Navy, and Air Force is a valid approach to be effective while each military is burdened with personnel management because of the reduction of manpower. Through the research results, it is expected that the direction of application to the policy of the defense IoT project can be confirmed in terms of efficient use of limited budgets.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.365-381
• /
• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.8
• /
• pp.243-252
• /
• 2023

Satellite imageries are at a greatest importance for land cover examining. Numerous studies have been conducted with satellite images and uses semantic segmentation techniques to extract information which has higher altitude viewpoint. The device which is taking these images must employee wireless communication links to send them to receiving ground stations. Wireless communications from a satellite are inevitably affected due to transmission errors. Evidently images which are being transmitted are distorted because of the information loss. Current semantic segmentation techniques are not made for segmenting distorted images. Traditional image enhancement methods have their own limitations when they are used for satellite images enhancement. This paper proposes an auto-encoder based image pre-enhancing method for satellite images. As a distorted satellite images dataset, images received from a real radio transmitter were used. Training process of the proposed auto-encoder was done by letting it learn to produce a proper approximation of the source image which was sent by the image transmitter. Unlike traditional image enhancing methods, the proposed method was able to provide more applicable image to a segmentation model. Results showed that by using the proposed pre-enhancing technique, segmentation results have been greatly improved. Enhancements made to the aerial images are contributed the correct assessment of land resources.

• Journal of Internet Computing and Services
• /
• v.24 no.4
• /
• pp.85-92
• /
• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Journal of Veterinary Clinics
• /
• v.40 no.3
• /
• pp.215-220
• /
• 2023

A 3-month-old Korean native cattle (Hanwoo) calf with difficulty taking normal posture and an inability to rise was referred for a definite diagnosis and active treatment, including surgery. The calf had a history of an accident in which both hind limbs were trapped in a barn structure. After admission, a "rabbit leg" posture was observed, a typical sign of gastrocnemius muscle rupture, and both digits were knuckled downward like they were trying to grip the ground. This was considered to be a result of the superficial digital flexor not rupturing but only the gastrocnemius muscle rupturing. Physical examination revealed laceration of the metatarsus and firmness behind both stifle joints which were presumed to be the sites of gastrocnemius muscle rupture. Skeletal abnormalities, including fractures, were ruled out by radiography. Based on these findings, the patient was diagnosed with bilateral gastrocnemius muscle rupture, and surgery was performed to reconnect the head of the ruptured muscle. Because the rupture occurred perpendicular to the muscle direction, the locking loop technique, a method of suturing severed tendons, was used to reduce the tension. After surgery, the cast was used to prevent further injuries and promote voluntary rehabilitation. Follow-up was completed, with the calf showing normal posture and gait 112 days after surgery. This is the first case report in the Republic of Korea describing the successful diagnosis and treatment of bilateral gastrocnemius muscle rupture in a calf.

• Geomechanics and Engineering
• /
• v.34 no.3
• /
• pp.267-284
• /
• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• Journal of architectural history
• /
• v.31 no.6
• /
• pp.17-32
• /
• 2022

Ijidang(二止堂) is a private village school (seodang, 書堂) established in Okcheon, north Chungcheong province where Jungbong Jo, Heon(重峯 趙憲, 1544-1592 AD) had trained for his sound mind and body(yusangcheo, 遊賞處). Jo, Heon was a notable Neo-Confucian scholar of the Kiho School and also a righteous general leading soldiers in Joseon Dynasty. According to Ijidanggi(二止堂記), Ijidang was constructed to honor Jo, Heon and to train local talents in 1647 with the support of Song, Si-yeol(宋時烈, 1607-1689 AD),a representative scholar belong to Kiho School and other local Confucian scholars. Ijidang currently consists of Main Body(本體) in the middle along with the East(Dong-lu 東樓) and the West Pavilions(Seo-lu 西樓) attached to each side. The Main Body and the Dong-lu firstly constructed were to give lectures and to rest within. Ijidang has undergone several changes in its form so far. The surrounding nearby Ijidang shows characteristics of the Confucian architecture for training(J eong-sa, 精舍) and the building itself was to be built in a scenic place apart from the secular world in which scholars stayed, cultivated their body and mind or taught disciples within. The lecture space of Ijidang is positioned next to the main hall(Dae-cheong 大廳) unlike other typical forms of a three-bay building(samganjije 三間之制) at that period. West lu, a two-story building added afterwards in the early 18th century representing characteristics of the Gate Pavilion(Mullu, 門樓) of Neo-Confucian Academies(Seo-won, 書院) in Joseon Dynasty was typically located where the entrance and the ground floor of the main building are visible simultaneously.

• Korean Journal of Applied Biomechanics
• /
• v.34 no.1
• /
• pp.25-33
• /
• 2024

Objective: The purpose of this study was to compare the lower extremity muscle activity and knee joint load according to movement speed conditions during the barbell back squat. Method: Nine males with resistance training experience participated in this study. Participants performed the barbell back squat in three conditions (Standard, Fast, and Slow) differing movement speed. During the barbell back squat, muscle activity of the rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris long head (BFL), semitendinosus (ST), gluteus maximus (GM), gastrocnemius (GCN), and tibialis anterior (TA) was collected using an 8 channel wireless EMG system. The peak flexion angle of the lower extremity joints and the peak resultant joint force in each direction of the knee joint were calculated using eight motion capture cameras and ground reaction force plates. This study was to used the Friedman test and the Wilcoxon signed rank test, to compare lower extremity muscle activity and peak resultant joint force at knee joint according to movement speed conditions during the barbell back squat, and the statistical significance level was set at .01. Results: In the downward phase of the barbell back squat, the RF and TA showed the higher muscle activity in the fast condition, and in the upward phase, RF, VL, VM, BFL, ST, GM, and TA showed the higher muscle activity in the fast condition. As a results, analyzing of the load on the knee joint, in the downward phase, and in the upward phase, the higher peak compressive force of the knee joint was showed in the fast condition. Conclusion: The barbell back squat with fast movement speed was more effective due to increased muscle activity of lower extremity, but one must be careful of knee joint injuries because the load on the knee joint may increase during the barbell back squat with fast movement speed.