• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.419-430
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• 2021

Remote sensing techniques using drone-based multispectral image were studied for fast and two-dimensional monitoring of algal blooms in the river. Drone is anticipated to be useful for algal bloom monitoring because of easy access to the field, high spatial resolution, and lowering atmospheric light scattering. In addition, application of multispectral sensors could make image processing and analysis procedures simple, fast, and standardized. Spectral indices derived from the active spectrum of photosynthetic pigments in terrestrial plants and phytoplankton were tested for estimating chlorophyll-a concentrations (Chl-a conc.) from drone-based multispectral image. Spectral indices containing the red-edge band showed high relationships with Chl-a conc. and especially, 3-band model (3BM) and normalized difference chlorophyll index (NDCI) were performed well (R²=0.86, RMSE=7.5). NDCI uses just two spectral bands, red and red-edge, and provides normalized values, so that data processing becomes simple and rapid. The 3BM which was tuned for accurate prediction of Chl-a conc. in productive water bodies adopts originally two spectral bands in the red-edge range, 720 and 760 nm, but here, the near-infrared band replaced the longer red-edge band because the multispectral sensor in this study had only one shorter red-edge band. This index is expected to predict more accurately Chl-a conc. using the sensor specialized with the red-edge range.

• (The)Study of the Eastern Classic
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• no.69
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• pp.45-82
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• 2017

This paper aims at concrete examination of the 'Official Road(官路)' of the late Joseon Dynasty through government administration of the 17th - 18th century 'Junsangseo(典牲署)'. Until now, the study of the central political system in the Joseon Dynasty was mainly studied by the politically important bureaucrat 'Dangsanggwan(堂上官)', and even if he studied the 'Official Road(官路)', he was a student from the a graduate of Mungwa(文科) and the 'Clean and imfortant Official(淸要職)' connected with it It was examined mainly. As a result, this research attempts to elucidate the routes of 'non - Clean and imfortant Official(非淸要職)' who have not been studied so far. However, it is difficult to deal with all the 'lower officials(參下官)' reaching 263 in total, so it was targeted at the 'Junsangseo(典牲署)' where the 'List of official(先生案)' exists in the 17th - 18th century. In chapter 2, we examined the historical value of 'List of official(先生案)' and were able to secure the confidence of the materials. In Chapter 3, we specifically examined the origins of officials from the 'Junsangseo(典牲署)', the official route, and the occupation. As a result, the 'Junsangseo(典牲署)' 'lower officials(參下官)' was predominantly from the 'Munum(門蔭)'. In addition, I confirmed that I was stepping on a public road that roughly promoted one rank. The number of days in office has also been promoted satisfying the court occupation days. Although this is an analysis limited to 'Junsangseo(典牲署)', it seems that 'lower officials(參下官)' of 'Junsangseo(典牲署)' had gone through routes and routes that were roughly similar to the 'lower officials' of the main office. If we can assume this, we can understand the character of the late Joseon Dynasty 'lower officials(參下官)' by understanding the character of 'lower officials(參下官)' of 'Junsangseo(典牲署)'. To declare this, more case analysis is necessary, and it is necessary to convert a lot of 'List of official(先生案)' data scattered nationwide into DB.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.49-71
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• 2021

Users who intend to utilize knowledge to actively solve given problems proceed their jobs with cross- and sequential exploration of associated knowledge related each other in terms of certain criteria, such as content relevance. A knowledge map is the diagram or taxonomy overviewing status of currently managed knowledge in a knowledge-base, and supports users' knowledge exploration based on certain relationships between knowledge. A knowledge map, therefore, must be expressed in a networked form by linking related knowledge based on certain types of relationships, and should be implemented by deploying proper technologies or tools specialized in defining and inferring them. To meet this end, this study suggests a methodology for developing the knowledge graph-based knowledge map using the Graph DB known to exhibit proper functionality in expressing and inferring relationships between entities and their relationships stored in a knowledge-base. Procedures of the proposed methodology are modeling graph data, creating nodes, properties, relationships, and composing knowledge networks by combining identified links between knowledge. Among various Graph DBs, the Neo4j is used in this study for its high credibility and applicability through wide and various application cases. To examine the validity of the proposed methodology, a knowledge graph-based knowledge map is implemented deploying the Graph DB, and a performance comparison test is performed, by applying previous research's data to check whether this study's knowledge map can yield the same level of performance as the previous one did. Previous research's case is concerned with building a process-based knowledge map using the ontology technology, which identifies links between related knowledge based on the sequences of tasks producing or being activated by knowledge. In other words, since a task not only is activated by knowledge as an input but also produces knowledge as an output, input and output knowledge are linked as a flow by the task. Also since a business process is composed of affiliated tasks to fulfill the purpose of the process, the knowledge networks within a business process can be concluded by the sequences of the tasks composing the process. Therefore, using the Neo4j, considered process, task, and knowledge as well as the relationships among them are defined as nodes and relationships so that knowledge links can be identified based on the sequences of tasks. The resultant knowledge network by aggregating identified knowledge links is the knowledge map equipping functionality as a knowledge graph, and therefore its performance needs to be tested whether it meets the level of previous research's validation results. The performance test examines two aspects, the correctness of knowledge links and the possibility of inferring new types of knowledge: the former is examined using 7 questions, and the latter is checked by extracting two new-typed knowledge. As a result, the knowledge map constructed through the proposed methodology has showed the same level of performance as the previous one, and processed knowledge definition as well as knowledge relationship inference in a more efficient manner. Furthermore, comparing to the previous research's ontology-based approach, this study's Graph DB-based approach has also showed more beneficial functionality in intensively managing only the knowledge of interest, dynamically defining knowledge and relationships by reflecting various meanings from situations to purposes, agilely inferring knowledge and relationships through Cypher-based query, and easily creating a new relationship by aggregating existing ones, etc. This study's artifacts can be applied to implement the user-friendly function of knowledge exploration reflecting user's cognitive process toward associated knowledge, and can further underpin the development of an intelligent knowledge-base expanding autonomously through the discovery of new knowledge and their relationships by inference. This study, moreover than these, has an instant effect on implementing the networked knowledge map essential to satisfying contemporary users eagerly excavating the way to find proper knowledge to use.

• Korean Journal of Heritage: History & Science
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• v.55 no.1
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• pp.175-198
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• 2022

With the development of technology, the methods of digitally converting various forms of analog information have become common. As a result, the concept of recording, building, and reproducing data in a virtual space, such as digital heritage and digital reconstruction, has been actively used in the preservation and research of various cultural heritages. However, there are few existing research results that suggest optimal scanners for small and medium-sized relics. In addition, scanner prices are not cheap for researchers to use, so there are not many related studies. The 3D scanner specifications have a great influence on the quality of the 3D model. In particular, since the state of light reflected on the surface of the object varies depending on the type of light source used in the scanner, using a scanner suitable for the characteristics of the object is the way to increase the efficiency of the work. Therefore, this paper conducted a study on nine small and medium-sized buried cultural properties of various materials, including earthenware and porcelain, by period, to examine the differences in quality of the four types of 3D scanners. As a result of the study, optical scanners and small and medium-sized object scanners were the most suitable digital records of the small and medium-sized relics. Optical scanners are excellent in both mesh and texture but have the disadvantage of being very expensive and not portable. The handheld method had the advantage of excellent portability and speed. When considering the results compared to the price, the small and medium-sized object scanner was the best. It was the photo room measurement that was able to obtain the 3D model at the lowest cost. 3D scanning technology can be largely used to produce digital drawings of relics, restore and duplicate cultural properties, and build databases. This study is meaningful in that it contributed to the use of scanners most suitable for buried cultural properties by material and period for the active use of 3D scanning technology in cultural heritage.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.51-75
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• 2022

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation that is able to alter neuronal activity in particular brain regions. Many studies have researched how tDCS modulates neuronal activity and reorganizes neural networks. However it is difficult to conclude the effect of brain stimulation because the studies are heterogeneous with respect to the stimulation parameter as well as individual difference. It is not fully in agreement with the effects of brain stimulation. In particular few studies have researched the reason of variability of brain stimulation in response to time so far. The study investigated individual variability of brain stimulation based on circadian rhythm and chronotype. Participants were divided into two groups which are morning type and evening type. The experiment was conducted by Zoom meeting which is video meeting programs. Participants were sent experiment tool which are Muse(EEG device), tdcs device, cell phone and cell phone holder after manuals for experimental equipment were explained. Participants were required to make a phone in frount of a camera so that experimenter can monitor online EEG data. Two participants who was difficult to use experimental devices experimented in a laboratory setting where experimenter set up devices. For all participants the accuracy of 98% was achieved by SVM using leave one out cross validation in classification in the the effects of morning stimulation and the evening stimulation. For morning type, the accuracy of 92% and 96% was achieved in classification in the morning stimulation and the evening stimulation. For evening type, it was 94% accuracy in classification for the effect of brain stimulation in the morning and the evening. Feature importance was different both in classification in the morning stimulation and the evening stimulation for morning type and evening type. Results indicated that the effect of brain stimulation can be explained with brain state and trait. Our study results noted that the tDCS protocol for target state is manipulated by individual differences as well as target state.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• 한국노년학
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• v.41 no.4
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• pp.547-566
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• 2021

The purpose of this study is to analyze the effect of social activities on healthy life expectancy (HLE) by gender difference. HLE implies an estimate of how long an individual can expect to live in full health or without disease and/or disability. Morbidity, mortality, and functional health status usually have been known as key variables. Many researchers have tried to investigate factors affecting HLE in countries level by performing comparative analyses. In micro level, there have been some studies about social factors affecting HLE in individual level. However, few studies are found focusing on the relationship between HLE and social activities. This study anlayzes 4,029 over 65 years of age from the first wave (2006) to the seventh wave (2018) of the Korean Longitudinal Study of Ageing (KLoSA), which is a national panel data collected by Korea Employment Information Service. The data has been collected as a part of social and economic policies planning for Korean government. HLE was measured by life period without disease or disability. One of findings is that male older adults (76.9 yrs) show higher HLE in comparing to female group (75.3 yrs). Female group appeared to be more likely to have higher incidence rate and disorders. Another finding indicates that age, number of chronic diseases, and subjective health status affect HLE of both groups. Finally, regarding social activities, religion affiliated activities appear to significantly affect HLE of both groups. In case of male older adults, alumni or hometown gathering also appeared another activities affecting HLE. This study indicates that the effect of social activities types on HLE among older adults appears differently by gender. Further, unlikely of longer life expectancy among female older adults as known, HLE shows a reverse estimate, longer healthy life expectancy among male older adults. This finding may imply that later life of female older adults shows lower quality of life in comparing to that of male group, even if female life expectancy has been higher. This study encourages to develop more social activity programs for older adults in community level. Specifically, more attention is required to planning for programs targeting female older adults.

• 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.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.