• Journal of Korean Society of Forest Science
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• v.111 no.2
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• pp.302-310
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• 2022

Here we aimed to classify the major coniferous tree species (Pinus densiflora, Pinus koraiensis, and Larix kaempferi) by tree measurement information and machine learning algorithms to establish an efficient forest management plan. We used national forest monitoring information amassed over nine years for the measurement information of trees, and random forest (RF), XGBoost (XGB), and light GBM (LGBM) as machine learning algorithms. We compared and evaluated the accuracy of the algorithm through performance evaluation using the accuracy, precision, recall, and F1 score of the algorithm. The RF algorithm had the highest performance evaluation score for all tree species, and highest scores for Pinus densiflora, with an accuracy of about 65%, a precision of about 72%, a recall of about 60%, and an F1 score of about 66%. The classification accuracy for the dominant trees was higher than about 80% in the crown classes, but that of the co-dominant trees, the intermediate trees, and the overtopper trees was evaluated as low. We consider that the results of this study can be used as reference data for decision-making in the selection of thinning trees for forest management.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.446-463
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• 2022

This study suggests the need for polar literacy education as an effective conceptual system to explain climate change in terms of science education in line with the common effort of humankind to respond to global environmental changes. To this end, we investigated the status of polar literacy in high school students through quantitative tests and qualitative interviews and discussed the resulting implications. A total of 329 high school sophomore students from two high schools participated in a test consisting of 25 true and false questions developed by referring to the Polar Literacy Principles, while 13 students agreed to be interviewed. The results showed that a somewhat insufficient understanding and conceptual gaps appeared regarding several areas of the Polar Literacy Principles. Knowledge of the geographic features of the polar regions was weak, and little was known about the components and key characteristics of the cryosphere. The lack of understanding of these concepts results in the inability of students to link the operational mechanisms of polar and global climate change sufficiently. While accepting unsatisfactory concepts in the school curriculum without criticism from outside media, students perceived the mechanism of climate change as somewhat monotonous or distorted. Moreover, linguistic information, analogies, and visual observation were used as cognitive strategies to compensate for the ambiguous understanding of polar and climate change. Based on the abovementioned results, we argue that polar literacy education should be introduced as a new knowledge system that can be used to aid a systematic and comprehensive understanding of climate change within the school science curriculum. Additionally, we suggest the following implications: review the consistency of knowledge related to polar literacy in other subjects, provide critical standards for out-of-school media information related to climate change, examine students' misconceptions, and identify improved thinking strategies.

• Tunnel and Underground Space
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• v.32 no.1
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• pp.78-92
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• 2022

In this study, we proposed a diagnostics and assessment model for mining sites that can evaluate the smart mining technology level in a systematic and structured way. For this, the maturity of the smart mining was defined, and detailed assessment items of the diagnostics and assessment model for smart mining were derived by considering the smart factory diagnostics and assessment model (KS X 9001-3) used in the manufacturing industry. While maintaining the existing system, the existing 46 detailed assessment items were modified to be suitable for mining. As a result, a total of 29 detailed assessment items were derived in the areas of promotion strategy, process, information system and automation, and performance. Based on this, a questionnaire was designed to diagnose the level of smart mining technology, and assessment was performed by applying it to domestic iron mines. The level of smart mining technology in the study area was found to be level 2, and it could be inferred that it was about 40% lower than the average smart level of the general manufacturing industry. In addition, by using the developed model, it was possible to recognize the weak points of the mine at each stage of the introduction, operation, and advancement of smart mining, and to suggest investment and improvement directions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.177-185
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• 2022

A method of restoring the occluded area was proposed by referring to images taken with the same types of sensors on high-resolution optical satellite images through deep learning. For the natural continuity of the simulated image with the occlusion region and the surrounding image while maintaining the pixel distribution of the original image as much as possible in the patch segmentation image, CycleGAN (Cycle Generative Adversarial Network) method with ConvNeXt block applied was used to analyze three experimental regions. In addition, We compared the experimental results of a training patch size of 512*512 pixels and a 1024*1024 pixel size that was doubled. As a result of experimenting with three regions with different characteristics,the ConvNeXt CycleGAN methodology showed an improved R² value compared to the existing CycleGAN-applied image and histogram matching image. For the experiment by patch size used for training, an R² value of about 0.98 was generated for a patch of 1024*1024 pixels. Furthermore, As a result of comparing the pixel distribution for each image band, the simulation result trained with a large patch size showed a more similar histogram distribution to the original image. Therefore, by using ConvNeXt CycleGAN, which is more advanced than the image applied with the existing CycleGAN method and the histogram-matching image, it is possible to derive simulation results similar to the original image and perform a successful simulation.

• Composites Research
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• v.35 no.5
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• pp.303-308
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• 2022

PIC (Piecewise Integrated Composite) is a new concept for designing a composite structure with mosaically assigning various types of stacking sequences in order to improve mechanical properties of laminated composites. Also, machine learning is a sub-category of artificial intelligence, that refers to the process by which computers develop the ability to continuously learn from and make predictions based on data, then make adjustments without further programming. In the present study, the tapered box beam type PIC robot arm for carrying and transferring wide and thin LCD display was designed based on the machine learning in order to increase structural stiffness. Essential training data were collected from the reference elements, which were intentionally designated elements among finite element models, during preliminary FE analysis. Additionally, triaxiality values for each finite element were obtained for judging the dominant external loading type, such as tensile, compressive or shear. Training and evaluating machine learning model were conducted using the training data and loading types of elements were predicted in case the level accuracy was fulfilled. Three types of stacking sequences, which were to be known as robust toward specific loading types, were mosaically assigned to the PIC robot arm. Henceforth, the bending type FE analysis was carried out and its result claimed that the PIC robot arm showed increased stiffness compared to conventional uni-stacking sequence type composite robot arm.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1015-1023
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• 2022

The Entry-Descent-Landing process of a lander involves many environmental and technical challenges. To solve these problems, recently, terrestrial relative navigation (TRN) technology has been essential for landers. TRN is a technology for estimating the position and attitude of a lander by comparing Inertial Measurement Unit (IMU) data and image data collected from a descending lander with pre-built reference data. In this paper, we present a method for generating descent dataset and extracting landmarks, which are key elements for developing TRN technologies to be used on Mars. The proposed method generates IMU data of a descending lander using a simulated Mars landing trajectory and generates descent images from high-resolution ortho-map and digital elevation map through a ray tracing technique. Landmark extraction is performed by an area-based extraction method due to the low-textured surfaces on Mars. In addition, search area reduction is carried out to improve matching accuracy and speed. The performance evaluation result for the descent dataset generation method showed that the proposed method can generate images that satisfy the imaging geometry. The performance evaluation result for the landmark extraction method showed that the proposed method ensures several meters of positioning accuracy while ensuring processing speed as fast as the feature-based methods.

• Journal of Naturopathy
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• v.11 no.1
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• pp.68-78
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• 2022

Background: Integrative medical research is needed to explore the development direction of new yoga therapy. Purposes: A systematic literature review is conducted to analyze the current status of clinical research on yoga therapy into scientific categories, and to explore the content and development direction of yoga therapy. Methods: Through electronic databases such as RISS, NDSL, DBpia, e-article, and KISS, 530 domestic academic papers were selected from 2010 to 2018 and the final 28 were extracted based on PRISMA guidelines. Results: As a result of the study, in terms of quality, it remained at 3b of the CEBM level of evidence, and in terms of quantity, the number of experimental groups in the entire clinical study was 288, so domestic clinical studies of yoga therapy are insufficient. It was found that 80% of yoga therapy was exercise therapy focusing on asana movements. This seems to be due to a lot of researchers in the field of physical education. Conclusions: These results indicate that understanding and practice of the Ashtanga-yoga's training system and Pancha-kosha theory from the perspective of integrative medicine are necessary. In other words, yoga therapy is required to develop into an integrated mind-body therapy program that integrates holistic healing yoga based on individual mental and physical constitution, meditation therapy based on Ayurveda, and exercise therapy.

• Korean Journal of Ichthyology
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• v.34 no.1
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• pp.8-15
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• 2022

The appearance of larvae of a rare species, Peristedion liorhynchus, in Korean waters is suggestive of spawning or adult intrusion. We conducted high-throughput sequencing (HTS) on 31,776 pelagic fish eggs collected from 123 stations off the Korean Peninsula during May to August in 2013, 2014 and 2017. A total of 21,621,874 HTS reads were mapped onto the P. liorhynchus COX1 reference sequence. Three consensus sequences (313 bp) were constructed from the three samples, respectively, off Uljin and Goeje Islands in May and off Ulsan in July. These samples were formed a clade with P. liorhynchus in the maximum likelihood tree of Peristedion. The average genetic distance within the P. liorhynchus clade (0.0054±0.0046) was less than that among clades (0.1475±0.0396). The results indicate that the HTS analysis of mixed fish eggs is useful for monitoring the intrusion of rare species such as P. liorhynchus in Korean waters.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.367-380
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• 2022

In this study, the periodic and simplified update and production way of the ECVAM (Environmental Conservation Value Assessment Map) was presented through the classification of environmental values using KOMPSAT-3A satellite imagery and land cover map. ECVAM is a map that evaluates the environmental value of the country in five stages based on 62 legal evaluation items and 8 environmental and ecological evaluation items, and is provided on two scales: 1:25000 and 1:5000. However, the 1:5000 scale environmental assessment map is being produced and serviced with a slow renewal cycle of one year due to various constraints such as the absence of reference materials and different production years. Therefore, in this study, one of the deep learning techniques, KOMPSAT-3A satellite image, SI (Spectral Indices), and land cover map were used to conduct this study to confirm the possibility of establishing an environmental assessment map. As a result, the accuracy was calculated to be 87.25% and 85.88%, respectively. Through the results of the study, it was possible to confirm the possibility of constructing an environmental assessment map using satellite imagery, optical index, and land cover classification.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.765-779
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• 2022

This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.