• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1475-1476
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• 2013

• International conference on construction engineering and project management
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• 2015.10a
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• pp.333-335
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• 2015

A computer vision-based scene recognition algorithm is proposed for monitoring construction sites. The system analyzes images acquired from a surveillance camera to separate regions and classify them as building, ground, and hole. Mean shift image segmentation algorithm is tested for separating meaningful regions of construction site images. The system would benefit current monitoring practices in that information extracted from images could embrace an environmental context.

• Journal of the Korean Home Economics Association
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• v.40 no.5
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• pp.147-158
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• 2002

This study was intended to make an analysis of the classification of Internet consumer information provided on the Internet and its realities with a focus on the realities of information characteristics and technological level to provide systematized consumer information for consumers. As a result, the following findings were obtained: First, it was found that information on consumer panel research, anti-site and consumerism is lacking in terms of the type of information currently provided in the Internet consumer information classification. Second, it was found that up-to-datensess, communically and connectivity were high in the level of Internet consumer information characteristics. Third, it was found that publicity was realities well carried out in terms of the technological level of Internet consumer information but that the realities of publicity was still tacking. Fourth, it is thought that it is necessary to construct the Internet consumer information provision site through several eve-level programs for elementary, middle and high school students in a systematic fashion in order to provide diverse consumers with Internet consumer information at several levels.

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.55-65
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• 2020

Image classification needs the spectral similarity comparison between spectral features of each pixel and the representative spectral features of each class. The spectral similarity is obtained by computing the spectral feature vector distance between the pixel and the class. Each spectral feature contributes differently in the image classification depending on the class separability of the spectral feature, which is computed using a suitable vector distance measure such as the Bhattacharyya distance. We propose a method to determine the weight value of each spectral feature in the computation of feature vector distance for the similarity measurement. The weight value is determined by the ratio between each feature separability value to the total separability values of all the spectral features. We created ten spectral features consisting of seven bands of Landsat-8 OLI image and three indices, NDVI, NDWI and NDBI. For three experimental test sites, we obtained the overall accuracies between 95.0% and 97.5% and the kappa coefficients between 90.43% and 94.47%.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.426-433
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• 2013

This study was aimed to classify soil desalination area for cultivation using NDVI (Normalized difference vegetation index) of high-resolution satellite image because the soil salinity affects the change of plant community in reclaimed lands. We measured the soil salinity and NDVI at 28 sites in the Saemangeum reclaimed land in June 2013. In halophyte and non-vegetation sites, no relation was found between NDVI and soil salinity. In glycophyte sites, however, we found that the soil salinity was below 0.1% and NDVI ranged from 0.11 to 0.57 which was greater than the other sites. So, we could distinguish the glycophyte sites from the halophyte sites and non-vegetation, and classify the area that soil salinty was below 0.1%. This technique could save the time and labor to measure the soil salinity in large area for agricultural utilization.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.1466-1483
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• 2008

This paper discusses the development of an up-to-date computerized CPT (Cone Penetration Test) based soil engineering classification system to provide geotechnical engineers with a handy tool for their daily design activities. Five CPT soil engineering classification systems are incorporated in this effort. They include the probabilistic region estimation and fuzzy classification methods, both developed by Zhang and Tumay, the Schmertmann, the Douglas and Olsen, and the Robertson et al. methods. In the probabilistic region estimation method, a conformal transformation is used to determine the soil classification index, U, from CPT cone tip resistance and friction ratio. A statistical correlation is established between U and the compositional soil type given by the Unified Soil Classification System (USCS). The soil classification index, U, provides a soil profile over depth with the probability of belonging to different soil types, which more realistically and continuously reflects the in-situ soil characterization, which includes the spatial variation of soil types. The CPT fuzzy classification on the other hand emphasizes the certainty of soil behavior. The advantage of combining these two classification methods is realized through implementing them into visual basic software with three other CPT soil classification methods for friendly use by geotechnical engineers. Three sites in Louisiana were selected for this study. For each site, CPT tests and the corresponding soil boring results were correlated. The soil classification results obtained using the probabilistic region estimation and fuzzy classification methods are cross-correlated with conventional soil classification from borings logs and three other established CPT soil classification methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.887-892
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• 2019

Many researchers have developed a series of vision-based technologies to monitor construction sites automatically. To achieve high performance of vision-based technologies, it is essential to build a large amount and high quality of training image database (DB). To do that, researchers usually visit construction sites, install cameras at the jobsites, and collect images for training DB. However, such human and site-dependent approach requires a huge amount of time and costs, and it would be difficult to represent a range of characteristics of different construction sites and resources. To address these problems, this paper proposes a framework that automatically constructs a training image DB using web crawling techniques. For the validation, the authors conducted two different experiments with the automatically generated DB: construction work type classification and equipment classification. The results showed that the method could successfully build the training image DB for the two classification problems, and the findings of this study can be used to reduce the time and efforts for developing a vision-based technology on construction sites.

• Convergence Security Journal
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• v.23 no.2
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• pp.47-54
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• 2023

The global popularity of K-content(Korean Wave) has led to a continuous increase in copyright infringement cases involving domestic works, not only within the country but also overseas. In response to this trend, there is active research on technologies for detecting illegal distribution sites of domestic copyrighted materials, with recent studies utilizing the characteristics of domestic illegal distribution sites that often include a significant number of advertising banners. However, the application of detection techniques similar to those used domestically is limited for overseas illegal distribution sites. These sites may not include advertising banners or may have significantly fewer ads compared to domestic sites, making the application of detection technologies used domestically challenging. In this study, we propose a detection technique based on the similarity comparison of links and text trees, leveraging the characteristic of including illegal sharing posts and images of copyrighted materials in a similar hierarchical structure. Additionally, to accurately compare the similarity of large-scale trees composed of a massive number of links, we utilize Graph Neural Network (GNN). The experiments conducted in this study demonstrated a high accuracy rate of over 95% in classifying regular sites and sites involved in the illegal distribution of copyrighted materials. Applying this algorithm to automate the detection of illegal distribution sites is expected to enable swift responses to copyright infringements.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.43 no.6
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• pp.395-400
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• 2017

Objectives: The aim of this study was to evaluate different anatomical variants of the anterior loop of the inferior alveolar nerve (IAN) via cone-beam computed tomography (CBCT). Materials and Methods: CBCT images of 71 patients (36 males and 35 females) were evaluated. We used the classification described by Solar for IAN evaluation. In this classification, three different types of IAN loops were introduced prior to emerging from the mental foramen. We classified patients according to this system and introduced a new, fourth type. Results: Type I was seen in 15 sites (10.6%), type II in 39 sites (27.5%), and type III in 50 sites (35.2%). We found a new type in 38 sites (26.8%) that constituted a fourth type. Conclusion: We found that type III was the most common variant. In the fourth type, the IAN was not detectable because the main nerve was adjacent to the cortical plate and the incisive branch was thinner than the main branch and alongside it. In this type, more care is needed for surgeries including inferior alveolar and mental nerve transposition.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.619-628
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• 2021

Background and objective: Native to the limestone zones of the Korean Peninsula, Zabelia tyaihyonii is a popular plant for landscaping. As it is now classified as a rare species, the conservation of its genetic resources is necessary. Methods: In this study, which aimed to understand the morphological variation of Z. tyaihyonii, 18 characteristics of Z. tyaihyonii from five habitats were examined. Results: Of these 18 characteristics, 16 characteristics showed significant differences among sites, and the coefficient of variation ranged from 5.4% (for corolla lobe number) to 31.3% (for flower number). Notable variations were observed in the size of flower and calyx lobe. When the corolla length and calyx lobe length were used as the classification key of Z. tyaihyonii, the sites were divided into those with small, intermediate, and large values. Hair was observed on the filament of all samples, a finding which conflicts with an earlier report. Rather than classifying Z. tyaihyonii into different species on the basis of corolla length (COL) and calyx lobe length (CALL) values, we recommend modifying the species description to incorporate the variation in these characteristics of interest. Principal component analysis results showed that the first main component was highly correlated with the traits related to the size of the calyx lobe (length: 0.819, width: 0.758), and the second main component was highly correlated with the traits related with the size of the inflorescence (length: 0.790, width: 0.626). Conclusion: Several notable variations were identified among the characteristics related to inflorescence and calyx lobe. There is little genetic exchange among groups, or each group is influenced by micro environmental factors, because sites that are located nearby. In addition, the difference between COL and CALL, which is used as the classification key for Z. tyaihyonii, was divided into small group, large group, and intermediate group, regardless of the sites' geographical distance.