• Journal of Korean Society of Forest Science
• /
• v.99 no.6
• /
• pp.929-933
• /
• 2010

According to the IPCC guideline (2006), uncertainty assessment is very important in terms of the greenhouse gas inventory. Therefore, the purpose of this study is to estimate the basic wood density (BWD) and its uncertainty for Pinus densiflora in Korea. In this study, Pinus densiflora forests were divided into two ecotypes which were Gangwon and Jungbu regions. A total of 33 representative sampling plots was selected to collect sample trees after considering the tree ages and DBH distributions. The BWD showed statistically no difference between age classes based on IPCC's classification. While, it showed statistically difference(pvalue=0.0017) between eco-types. The BWD and uncertainty was 0.396(g/$cm^3$) and 12.9(%) for Pinus densiflora in Gangwon, while it was 0.470(g/$cm^3$) and 3.8(%) for Pinus densiflora in Jungbu. The values of the BWD uncertainty for Pinus densiflora were more precised than the values given by the IPCC guideline.

• Journal of the Korea Concrete Institute
• /
• v.22 no.1
• /
• pp.113-122
• /
• 2010

Corrosion of prestressing tendons and wire fractures in grouted post-tensioned prestressed concrete bridges have been considered as a serious safety problem. In bridge evaluation the condition of prestressing tendons should be inspected, and if corroded tendons are found, the loss of tendon area should be included when we calculate the ultimate strength. In the previous study, it was evaluated that continuous acoustic monitoring techniques could be considered as a reliable non-destructive method for detecting wire fractures of fully grouted post-tensioned prestressing tendons. In the present study, an experimental test was performed for detecting wire fractures of post-tensioned prestressing tendons which are prestressed lower than current design level. A 10 m prestressed concrete beam was fabricated, which included two tendons prestressed 66 percentage and 40 percentage of tensile strength, respectively. The corrosion of two tendons was induced by an accelerated corrosion equipment and the test beam was monitored by using seven acoustic sensors and a continuous acoustic monitoring system. From each prestressing tendon, two acoustic signals of wire fractures were successfully detected and source locations were estimated within 20 mm error. Based on the test results, it is considered that continuous acoustic monitoring techniques can be applied to detect low-prestressed wire fracture in fully grouted post-tensioned prestressed concrete beams.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.4 s.310
• /
• pp.37-45
• /
• 2006

In this paper, we propose the development of MHLA(Modulatory Hippocampus Learning Algorithm) which remodel a principle of brain of hippocampus. Hippocampus takes charge auto-associative memory and controlling functions of long-term or short-term memory strengthening. We organize auto-associative memory based 3 steps system(DG, CA3, CAl) and improve speed of learning by addition of modulator to long-term memory learning. In hippocampal system, according to the 3 steps order, information applies statistical deviation on Dentate Gyrus region and is labelled to responsive pattern by adjustment of a good impression. In CA3 region, pattern is reorganized by auto-associative memory. In CAI region, convergence of connection weight which is used long-term memory is learned fast by neural networks which is applied modulator. To measure performance of MHLA, PCA(Principal Component Analysis) is applied to face images which are classified by pose, expression and picture quality. Next, we calculate feature vectors and learn by MHLA. Finally, we confirm cognitive rate. The results of experiments, we can compare a proposed method of other methods, and we can confirm that the proposed method is superior to the existing method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.4
• /
• pp.363-373
• /
• 2020

During last decades numerous studies generating orthoimage have been carried out. Traditional methods require exterior orientation parameters of aerial images and precise 3D object modeling data and DTM (Digital Terrain Model) to detect and recover occlusion areas. Furthermore, it is challenging task to automate the complicated process. In this paper, we proposed a new concept of true orthoimage generation using DL (Deep Learning). DL is rapidly used in wide range of fields. In particular, GAN (Generative Adversarial Network) is one of the DL models for various tasks in imaging processing and computer vision. The generator tries to produce results similar to the real images, while discriminator judges fake and real images until the results are satisfied. Such mutually adversarial mechanism improves quality of the results. Experiments were performed using GAN-based Pix2Pix model by utilizing IR (Infrared) orthoimages, intensity from LiDAR data provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the ISPRS (International Society for Photogrammetry and Remote Sensing). Two approaches were implemented: (1) One-step training with intensity data and high resolution orthoimages, (2) Recursive training with intensity data and color-coded low resolution intensity images for progressive enhancement of the results. Two methods provided similar quality based on FID (Fréchet Inception Distance) measures. However, if quality of the input data is close to the target image, better results could be obtained by increasing epoch. This paper is an early experimental study for feasibility of DL-based true orthoimage generation and further improvement would be necessary.

• Korean Journal of Remote Sensing
• /
• v.34 no.2_1
• /
• pp.203-211
• /
• 2018

Dam discharge is carried out for the management of rivers and area around rivers due to rainy season or drought. Dam discharge should be based on an accurate understanding of the flow rate that can be accommodated in the river. Therefore, understanding the allowable storage capacity of river is an important factor in the management of the environment around the river. However, the methods using water level meters and images, which are currently used to determine the allowable flow rate of rivers, show limitations in terms of accuracy and efficiency. In order to solve these problems, this paper proposes a method to automatically calculate the allowable storage capacity of river based on the images taken by drone. In the first step, we create a 3D model of the river by using the drone images. This generation process consists of tiepoint extraction, image orientation, and image matching. In the second step, the allowable storage capacity is calculated by cross section analysis of the river using the generated river 3D model and the road and river layers in the target area. In this step, we determine the maximum water level of the river, extract the cross-sectional profile along the river, and use the 3D model to calculate the allowable storage capacity for the area. To prove our method, we used Bukhan river's data and as a result, the allowable storage volume was automatically extracted. It is expected that the proposed method will be useful for real - time management of rivers and surrounding areas and 3D models using drone.

• The Journal of Dong Guk Oriental Medicine
• /
• v.8 no.2
• /
• pp.25-46
• /
• 2000

Pain can be evaluated by experimental methods and clinical methods, but due to subjective characteristics of pain, clinical methods are generally used. The clinical pain measurement tools are divided into unidimensional and multidimensional assessment tools. The former include Visual Analogue Scale, Verbal Rating Scale, Numerical Rating Scale, Pain Faces Scale, and Poker Chip Tool and the latter include McGill Pain Questionnaire, MMPI, Pain Behavior Scale, Pain disability index, and Pain Rating Scale. Unidimensional pain scales mainly measure the intensity of pain on the basis of the patient's self report and their simple construction and ease of use enable the invesgator to assess acute pain. Multidimensional pain scales are used to evaluate subjective, psychological and behavioral aspects of pain and because of its comprehensive and confidential properties they are applied to chronic pain. Patient's linguistic and cognitive abilities are major factors to restrain accurate assessment of pain. Although behavioral patterns and vital sign are inferior to self-report in the measurement of pain, they can be useful indexes in those situations. When deciding on a pain-assessment tool, the investigator must determine which aspect of pain he or she wishes to evaluate on the characteristics of the group of patients, their backgrounds, and their communication skills. Making the proper choice will facilitate the acquisition of meaningful data and the formulation of valid conclusions.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.1
• /
• pp.267-274
• /
• 2021

The purpose of this study is to investigate differences in learning flow and teaching presence according to the lecturer's appearance and the lecturer's speech rate. For this experiment, 183 freshman students from Xingtai University in China were selected as subjects of the experiment, and a total of four types of lecture videos were developed to test the lecturer's appearance and their speech rates. Data was analyzed through multivariate analysis of variance. According to the results of the analysis, first, learning flow and teaching presence of groups who experienced the presence of the lecturer appeared were significantly higher than the groups who learned without the appearance of the lecturer. Second, the groups who learned from videos with a fast speech rate showed higher learning flow and teaching presence than the group who learned at a slow speech rate. Third, there were no significant differences in both learning flow and teaching presence according to the lecturer's appearance and speech rate. This result provides a theoretical and practical basis for developing customized videos according to learners' characteristics.