• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.245-259
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• 2023

The agricultural reservoir is a critical water supply system in South Korea, providing approximately 60% of the agricultural water demand. However, the reservoir faces several issues that jeopardize its efficient operation and management. To address this issues, we propose a novel deep-learning-based water level recognition model that uses CCTV image data to accurately estimate water levels in agricultural reservoirs. The model consists of three main parts: (1) dataset construction, (2) image segmentation using the U-Net algorithm, and (3) CCTV-based water level recognition using either CNN or ResNet. The model has been applied to two reservoirs G-reservoir and M-reservoir with observed CCTV image and water level time series data. The results show that the performance of the image segmentation model is superior, while the performance of the water level recognition model varies from 50 to 80% depending on water level classification criteria (i.e., classification guideline) and complexity of image data (i.e., variability of the image pixels). The performance of the model can be improved if more numbers of data can be collected.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.239-246
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• 2020

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.128-136
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• 2011

To monitor and predict the change of coastal environment according to the construction of Saemangeum sea dyke and the development of land reclamation, we have done real-time and periodic ocean observation and numerical simulation since 2002. Saemangeum coastal environmental data can be largely classified to marine meteorology, ocean physics and circulation, water quality, marine geology and marine ecosystem and each part of data has been generated continuously and accumulated over about 10 years. The collected coastal environmental data are huge amounts of heterogeneous dataset and have some characteristics of multi-dimension, multivariate and spatio-temporal distribution. Thus the implementation of information system possible to data collection, processing, management and service is necessary. In this study, through the implementation of Saemangeum coastal environmental information system using geographic information system, it enables the integral data collection and management and the data querying and analysis of enormous and high-complexity data through the design of intuitive and effective web user interface and scientific data visualization using statistical graphs and thematic cartography. Furthermore, through the quantitative analysis of trend changed over long-term by the geo-spatial analysis with geo- processing, it's being used as a tool for provide a scientific basis for sustainable development and decision support in Saemangeum coast. Moreover, for the effective web-based information service, multi-level map cache, multi-layer architecture and geospatial database were implemented together.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.527-534
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• 2011

This paper describes an analysis of topographical changes to the Eulsuk-Island at the Nakdong River Estuary using a long-term dataset of high resolution aerial images from 1983 to 2007. Ground control surveying was performed at some feature points using GPS(Global Positioning System) to accomplish AT(Aerial Triangulation) for past aerial images. Even if some still existing feature points appeared on old aerial images were used as GCPs(Ground Control Points) for past aerial images in AT, its accuracy reached at 1m level. Since then, a quantitative analysis of topographical changes was conducted on digital orthophotos produced by a series of aerial images taken by different years. The change volume of total area, construction, vegetation, buildings and roads could be extracted per each period in study area. The total area decreased from 1983 to 1992, but it has not almost changed since 1992. According to the continuous development, the area of vegetation has steadily decreased, while that of buildings and roads has generally increased. The result of this study can provide us with invaluable base data for further topographical change monitoring in Eulsuk-Island and Nakdong River estuary caused by continuous development in this area.

• Horticultural Science & Technology
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• v.29 no.2
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• pp.116-122
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• 2011

In this study, we report the generation and analysis of 1,392 expressed sequence tags (ESTs) from Korean Stewartia (Stewartia koreana Nakai). A cDNA library was generated from the young leaf tissue and a total of 1,392 cDNA were partially sequenced. EST and unigene sequence quality were determined by computational filtering, manual review, and BLAST analyses. Finally, 1,301 ESTs were acquired after the removal of the vector sequence and filtering over a minimum length 100 nucleotides. A total of 893 unigene, consisting of 150 contigs and 743 singletons, was identified after assembling. Also, we identified 95 new microsatellite-containing sequences from the unigenes and classified the structure according to their repeat unit. According to homology search with BLASTX against the NCBI database, 65% of ESTs were homologous with known function and 11.6% of ESTs were matched with putative or unknown function. The remaining 23.2% of ESTs showed no significant similarity to any protein sequences found in the public database. Annotation based searches against multiple databases including wine grape and populus sequences helped to identify putative functions of ESTs and unigenes. Gene ontology (GO) classification showed that the most abundant GO terms were transport, nucleotide binding, plastid, in terms biological process, molecular function and cellular component, respectively. The sequence data will be used to characterize potential roles of new genes in Stewartia and provided for the useful tools as a genetic resource.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.55-63
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• 2019

Recently, the use of real-world image data has been increasing to express realistic virtual environments in various application fields such as education, manufacturing, and construction. In particular, with increasing interest in digital twins like smart cities, realistic 3D urban models are being built using real-world images, such as aerial images. However, the captured aerial image includes shadows from the sun, and the 3D city model including the shadows has a problem of distorting and expressing information to the user. Many studies have been conducted to remove the shadow, but it is recognized as a challenging problem that is still difficult to solve. In this paper, we construct a virtual environment dataset including the height map of buildings using 3D spatial information provided by VWorld, and We propose a new shadow detection method using height map and deep learning. According to the experimental results, We can observed that the shadow detection error rate is reduced when using the height map.

• Journal of the Korea Concrete Institute
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• v.22 no.1
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• pp.19-27
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• 2010

Aging and severe environments are major causes of damage in reinforced concrete (RC) structures such as buildings and bridges. Deterioration such as concrete cracks, corrosion of steel, and deformation of structural members can significantly degrade the structural performance and safety. Therefore, effective and easy-to-use methods are desired for repairing and strengthening such concrete structures. Various methods for strengthening and rehabilitation of RC structures have been developed in the past several decades. Recently, FRP composite materials have emerged as a cost-effective alternative to the conventional materials for repairing, strengthening, and retrofitting deteriorating/deficient concrete structures, by externally bonding FRP laminates to concrete structural members. The main purpose of this study is to investigate the effectiveness of adaptive neuro-fuzzy inference system (ANFIS) in predicting behavior of circular type concrete column retrofitted with FRP. To construct training and testing dataset, experiment results for the specimens which have different retrofit profile are used. Retrofit ratio, strength of existing concrete, thickness, number of layer, stiffness, ultimate strength of fiber and size of specimens are selected as input parameters to predict strength, strain, and stiffness of post-yielding modulus. These proposed ANFIS models show reliable increased accuracy in predicting constitutive properties of concrete retrofitted by FRP, compared to the constitutive models suggested by other researchers.

• Journal of Digital Convergence
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• v.17 no.11
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• pp.1-7
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• 2019

This study analyzed the effects of labor aging on labor productivity using panel statistics of 16 local governments from 1995 to 2017. The aging of the labor force, defined as the proportion of workers aged 60 or older in total employment, in the results of the panel regression analysis considering regional fixed effects and various adjustment variables, has a very consistent and significant negative effect on labor productivity. For every 1% increase in aging, labor productivity decreases by about 0.14 ~ 0.20%. In addition, the per capita capital stock and human capital considered as adjustment variables contributed to the increase of labor productivity, and the unemployment rate, which is a proxy variable of the economic fluctuation, has a significant negative effect on labor productivity as expected. The coefficient of the industrial structure, which represents the share of the service industry in the whole industry, was positive, but is not significant. The results of this study suggest that the design and construction of economic and educational policies that can maintain and expand human capital are necessary to curb the reduction in labor productivity expected by the aging workforce.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.4
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• pp.235-243
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• 2021

River surveying is conducted to acquire basic geographic data for river master plans and various river maintenance, and it is also used to predict changes after river maintenance construction. ABL (Airborne Bathymetric LiDAR) system is a cutting-edge surveying technology that can simultaneously observe the water surface and river bed using a green laser, and has many advantages in river surveying. In order to use the ABL data for river surveying, it is prerequisite step to segment and extract the water surface and river bed points from the original point cloud data. In this study, point cloud segmentation was performed by applying the ground filtering technique, ATIN (Adaptive Triangular Irregular Network) to the ABL data and then, the water surface and riverbed point clouds were extracted sequentially. In the Gokgyocheon river area, Chungcheongnam-do, the experiment was conducted with the dataset obtained using the Leica Chiroptera 4X sensor. As a result of the study, the overall classification accuracy for the water surface and riverbed was 88.8%, and the Kappa coefficient was 0.825, confirming that the ABL data can be effectively used for river surveying.