• Journal of Korean Society of Forest Science
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• v.109 no.4
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• pp.484-491
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• 2020

In this study, to solve the existing inefficient stem analysis process and expensive equipment cost problems, a method for detecting and analyzing tree rings using smartphone images was proposed and a semi-automated computer program (TRIO, Tree Ring Information) was developed. TRIO can measure the annual ring radius and save the results to Excel. Since TRIO uses smartphone images, the results may vary depending on the quality of the smartphone camera. Therefore, using the Samsung Galaxy S10 and Tap 2, 30 dics images of Pinus rigida were acquired and analyzed, and these were compared with WinDENDROTM. As a result of the study, both Samsung Galaxy S10 and S2 showed significant results with WinDENDROTM, and the R2 value of S10 had a high correlation as 0.976, and RMSE was analyzed as 0.4199, and very similar results were output. The R2 value of S2 was 0.975 and the RMSE was 0.4232, showing no significant difference from S10. Accordingly, the TRIO developed in this study analyzed the annual radius value very similar to WinDENDROTM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of the Korean Geosynthetics Society
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• v.21 no.4
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• pp.1-12
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• 2022

Generally, the plate load test and the field density test are conducted for compaction quality control in earthwork, and then additional analysis. Recently developed that the DCPT (Dynamic Cone Penetration Test) equipment for smart compaction quality control its the system are able to get location and real-time information about worker history management. The IoT-based the DCPT system improved the time-cost in the field compared traditional test, and the functions recording and storage of the DPI (Dynamic Cone Penetration Index) were automated. This paper describes using these DCPT equipment on in-situ and compared to the standards of the DCPT, and the compaction trend had be confirmed with DPI as the field test data. As a result, the DPI of the final compaction decreased by 1.4 times compared to the initial compaction, confirming the increase in the compaction strength of the subgrade compaction layer 10 to 14 cm deep from the surface. A trend of increasing compaction strength was observed. This showed a tendency to increase the compaction strength of the target DPI proposed by MnDOT and the results of the existing plate load test, but there was a difference in the increase rate. Therefore, additional studies are needed on domestic compaction materials and laboratory conditions for target DPI and correlation studies with the plate load tests. If this is reflected, it is suggested that DCPT will be widely used as smart construction equipment in earthworks.

• Journal of KIISE
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• v.44 no.2
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• pp.139-147
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• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• Journal of Korean Library and Information Science Society
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• v.10
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• pp.193-228
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• 1983

The purposes of this study are; 1) to make a comparative analysis of the background factors helping the development of the library automation among Korea, Taiwan and Japan. 2) and on the basis of the analysis results, to make some suggestions on improving the variables which are likely to have influence on the library automation development. To accomplish the purpose of the study; 1) the use of computer and data communication technology for the library operations and the science policies among three countries were discussed as the background factors, 2) the pattern of library automation among three countries were presented, 3) the process of MARC development of three countries were compared with, 4) and three countries# information processing systems were discussed. The results of the study can be summarized as follows; 1) The a n.0, pplication of a computer into the library operations in Korea began much later than the other countries. 2) Korea has not have any independent department in the government organization taking charge of the policy making about the library automation in relation to the science policy. 3) Speaking of the national scientific and technological information center, JICST was founded in 1952, 국과회과중심 in 1958, and KORS TIC in 1962. 4) On the one hand, in both Taiwan and Japan, the circulation and acquisition systems tended to be first automated, on the other hand, in Korea the developments of MARC and bibliographies through the production of database were laid the emphasis. 5) The introduction and dissemination of LC MARC Tapes were implemented in Japan in 1972. Taiwan made various tests in the Tape in 1974. Korea planned to introduce and make tests on the Tape in 1983. 6) For the input system, Korea has developed KIPS series, 1,2,3. Chinas# 삼각호마법 and Japan#s 병음한자변환방식 have prospect to be prevalent. The following suggestions can be made from these results, 1) All the information infrastructures should be established in a desirable way. 2) The communication window for the information users is recommended to be set up. 3) The su n.0, pport for the physical environment such as library building, computer facilities, etc. should be provided. 4) The facilities necessary for the education of professionals in the library science department should be su n.0, pported. 5) A department of the government organization exclusively responsible for the development of library automation should be founded.

• Journal of Korea Water Resources Association
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• v.51 no.7
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• pp.617-627
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• 2018

In this study, CMIP5 GCMs rainfall data (2011~2099) based on RCP scenarios were used to analyze the extreme drought evaluation for the future period. For prospective drought assessment, historical observations were used based on the Automated Surface Observing System (ASOS) data (1976~2010) of the Korea Meteorological Administration. Through the analysis of various indicators, such as average annual rainfall, rainy days, drought spell, and average drought severity was carried out for the drought evaluation of the five major river basins (Han river, Nakdong river, Geum river, Sumjin river, and Youngsan river) over the Korean peninsula. The GCMs that predicted the most severe future droughts are CMCC-CMS, IPSL-CM5A-LR and IPSL-CM5A-MR. Moderate future droughts were predicted from HadGEM2-CC, CMCC-CM and HadGEM2-ES. GCMs with relatively weak future drought forecasts were selected as CESM1-CAM5, MIROC-ESM-CHEM and CanESM2. The results of this study might be used as a fundamental data to choose a reasonable climate change scenario in future extreme drought evaluation.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.85-101
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• 2019

Recently, thank to the popularization of light-weight drone through the significant developments in computer technologies as well as the advanced automated procedures in photogrammetry, Unmanned Aircraft Systems have led to a growing interest in industry as a whole. Documentation, maintenance, and restoration projects of large scaled cultural property would required accurate 3D phenomenon modeling and efficient visual inspection methods. The object of this study verify on the accuracies achieved of 3D phenomenon reconstruction as well as on the validity of the preservation, maintenance and restoration of large scaled cultural property by UAS photogrammetry. The test object is cltural heritage(treasure 1324) that is the rock-carved standing Bodhisattva in Soraesan Mountain, Siheung, documented in Goryeo Period(918-1392). This standing Bodhisattva has of particular interests since it's size is largest stone Buddha carved in a rock wall and is wearing a lotus shaped crown that is decorated with arabesque patterns. The positioning accuracy of UAS photogrammetry were compared with non-target total station survey results on the check points after creating 3D phenomenal models in real world coordinates system from photos, and also the quantified informations documented by Culture Heritage Administration were compared with UAS on the bodhisattva image of thin lines. Especially, tests the validity of UAS photogrammetry as a alternative method of visual inspection methods. In particular, we examined the effectiveness of the two techniques as well as the relative fluctuation of rock surface for about 2 years through superposition analysis of 3D points cloud models produced by both UAS image analysis and ground laser scanning techniques. Comparison studies and experimental results prove the accuracy and efficient of UAS photogrammetry in 3D phenomenon modeling, maintenance and restoration for various large-sized Cultural Heritage.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.11
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• pp.109-119
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• 2018

This study investigates the effect of urban and architectural forms on the microclimate in urban areas. It applies urban and architectural elements such as urban form and tissue and building form and characteristics as the main influences on the microclimate within urban area. Among the 23 Automated Weather Stations (AWS) installed within Seoul city by the Korea Meteorological Administration, 6 sites were selected for the analysis, based on their different urban and architectural characteristics, and actual measurements were conducted in August 2017 using individual AWS equipment. Also, the measurements of microclimate and urban and architectural elements within a 500m radius of the AWS measurement points were collected and analyzed. The result of the analysis shows that the microclimate elements, such as wind speed, solar radiation, and temperature, were affected by the direction of the streets, the width, depth, and height of the buildings, the topographic elevation and direction and the traffic volume. This study is expected to contribute to mitigating urban heat island effect and setting the foundation for sustainable cities through development of urban management methods and techniques including the relationship between built environment elements and microclimate.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• International Journal of Highway Engineering
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• v.13 no.1
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• pp.157-168
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• 2011

Recently, the cases of freeze-thaw distress on concrete pavement in domestic have been reported periodically. Hereupon, the necessity to establish the standard of spacing factor came to the fore. The test method for analyzing spacing factor is provided in the standard of ASTM C 457. Since researchers tend to judge study results subjectively, the results should be revised throughly. Image analysis program for determining the characteristics of air-void system on hardened concrete identify air void through the difference of a color. The pixel intensity values used in this program have a significant effect on the analysis results. This study compared the automated void count by varying pixel intensity values with the manual void count in order to determine the optimum range of pixel intensity values. Also, this study analyzed the air-void characteristics on eight kinds of concrete mixtures. In this study, it was confirmed that the variation of void counted manually was around 10% from the results of round robin test, and that the optimum range of pixel intensity values is around 80~90. And it was also confirmed that air content (as a whole) was increased generally and spacing factor was decreased by increasing air-entrainer content. But some concrete mixtures showed a tendency that air content was constant and spacing factor was decreased by increasing air-entrainer content. This causes the air entrained by air-entrainer has more influence on spacing factor than air content. Also, the deviation of spacing factor by cutting position of concrete specimen was about 30~100${\mu}m$ because of the limit of 2-D image analysis. The additional study about variation of spacing factor by cutting position of concrete specimen will be performed later.