• Journal of Soil and Groundwater Environment
• /
• v.14 no.1
• /
• pp.1-10
• /
• 2009

Groundwater flow due to intake of horizontal collector well in riverbank filtration site was analyzed by use of numerical groundwater modeling program (FEFLOW 5.1). Drawdowns of groundwater table nearby collector well were evaluated according to variations of several conditions; pumping rate, thickness of aquifer, offset distance from well to shore line of stream, conductance of streambed. It is observed that the drawdowns of groundwater table are clearly changed according to the variations of these conditions. The results of sensitive analysis shows that the thickness of alluvial aquifer and the offset distance are more sensitive than the conductance of streambed in evaluation of drawdown. This result implies that hydrogeological conditions, as like thickness of aquifer and its distribution in the site are important factors in site selection and evaluating the availability of riverbank filtration intake using horizontal collector well system. It is also revealed that numerical modeling using FEFLOW with 1-D discrete element feature can give efficient quantitative evaluation of horizontal collector well and estimation of availability of riverbank filtration site.

• Maxillofacial Plastic and Reconstructive Surgery
• /
• v.41
• /
• pp.47.1-47.10
• /
• 2019

Background: Hyaluronic acid (HA) has been applied as a primary biomaterial for temporary soft tissue augmentation and as a carrier for cells and the delivery of growth factors to promote tissue regeneration. Although HA derivatives are the most versatile soft tissue fillers on the market, they are resorbed early, within 3 to 12 months. To overcome their short duration, they can be combined with cells or growth factors. The purpose of this study was to investigate the stimulating effects of human fibroblasts and basic fibroblast growth factors (bFGF) on collagen synthesis during soft tissue augmentation by HA hydrogels and to compare these with the effects of a commercial HA derivative (Restylane^®). Methods: The hydrogel group included four conditions. The first condition consisted of hydrogel (H) alone as a negative control, and the other three conditions were bFGF-containing hydrogel (HB), human fibroblast-containing hydrogel (HF), and human fibroblast/bFGF-containing hydrogel (HBF). In the Restylane^® group (HGF), the hydrogel was replaced with Restylane^® (R, RB, RF, RBF). The gels were implanted subdermally into the back of each nude mouse at four separate sites. Twelve nude mice were used for the hydrogel (n = 6) and Restylane^® groups (n = 6). The specimens were harvested 8 weeks after implantation and assessed histomorphometrically, and collagen synthesis was evaluated by RT-PCR. Results: The hydrogel group showed good biocompatibility with the surrounding tissues and stimulated the formation of a fibrous matrix. HBF and HF showed significantly higher soft tissue synthesis compared to H (p < 0.05), and human collagen type I was well expressed in HB, HF, and HBF; HBF showed the strongest expression. The Restylane^® filler was surrounded by a fibrous capsule without any soft tissue infiltration from the neighboring tissue, and collagen synthesis within the Restylane^® filler could not be observed, even though no inflammatory reactions were observed. Conclusion: This study revealed that HA-based hydrogel alone or hydrogel combined with fibroblasts and/or bFGF can be effectively used for soft tissue augmentation.

• Journal of the Society of Naval Architects of Korea
• /
• v.55 no.1
• /
• pp.66-74
• /
• 2018

In order to evaluate the precision degree of the blocks on the dock, the shipyards recently started to use the point cloud approaches using the 3D scanners. However, they hesitate to use it due to the limited time, cost, and elaborative effects for the post-works. Although it is somewhat traditional instead, they have still used the electro-optical wave devices which have a characteristic of having less dense point set (usually 1 point per meter) around the contact section of two blocks. This paper tried to expand the usage of point sets. Our approach can estimate the rework time to weld between the Pre-Erected(PE) Block and Erected(ER) block as well as the precision of block construction. In detail, two algorithms were applied to increase the efficiency of estimation process. The first one is K-mean clustering algorithm which is used to separate only the related contact point set from others not related with welding sections. The second one is the Concave hull algorithm which also separates the inner point of the contact section used for the delayed outfitting and stiffeners section, and constructs the concave outline of contact section as the primary objects to estimate the rework time of welding. The main purpose of this paper is that the rework cost for welding is able to be obtained easily and precisely with the defective point set. The point set on the blocks' outline are challenging to get the approximated mathematical curves, owing to the lots of orthogonal parts and lack of number of point. To solve this problems we compared the Radial based function-Multi-Layer(RBF-ML) and Akima interpolation method. Collecting the proposed methods, the paper suggested the noble point matching method for minimizing the rework time of block-welding on the dock, differently the previous approach which had paid the attention of only the degree of accuracy.

• Journal of Korean Society of Rural Planning
• /
• v.30 no.1
• /
• pp.57-66
• /
• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

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