• Ocean and Polar Research
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• v.34 no.2
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• pp.239-251
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• 2012

The food web dynamics in a coastal ecosystem of Korea were predicted with Ecosim, a trophic flow model, under various scenarios of primary productivity due to ocean warming and ocean acidification. Changes in primary productivity were obtained from an earth system model 2.1 under A1B scenario of IPCC $CO_2$ emission and replaced for forcing functions on the phytoplankton group during the period between 2020 and 2100. Impacts of ocean acidification on species were represented in the model for gastropoda, bivalvia, echinodermata, crustacean and cephalopoda groups with effect sizes of conservative, medium and large. The model results show that the total biomass of invertebrate and fish groups decreases 5%, 11~28% and 14~27%, respectively, depending on primary productivity, ocean acidification and combined effects. In particular, the blenny group shows zero biomass at 2080. The zooplankton group shows a sudden increase at the same time, and finally reaches twice the baseline at 2100. On the other hand, the ecosystem attributes of the mean trophic level of the ecosystem, Shannon's H and Kempton's Q indexes show a similar reduction pattern to biomass change, indicating that total biomass, biodiversity and evenness shrink dynamically by impacts of climate change. It is expected from the model results that, after obtaining more information on climate change impacts on the species level, this study will be helpful for further investigation of the food web dynamics in the open seas around Korea.

• Archives of Plastic Surgery
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• v.32 no.6
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• pp.777-781
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• 2005

Syndactyly and polysyndactyly are one of the most common congenital anomalies of upper limb. Although there are many surgical approaches, most of them require skin graft for covering the raw surfaces. Therefore these methods involve many disadvantages such as grafted skin contracture, web recurrence, skin graft loss and long operation time, and the grafted hyperpigmented skin and donor site of skin graft, which lead to poor results aesthetically. The authors treated seven cases with a Hayashi's new method in four patients. In this method, tissue of interdigital space are regarded as forming 4 facets of a two piled cube. A dorsal rectangular flap on dorsum of interdigital web makes a new interdigital space. One side of divided digit is coverd with lateral based plantar flap and the other side of divided digit is covered with subcutaneous pedicled flap and remnant web skin. The authors could obtain natural deep interdigital space without web recurrence and scar contracture in 7 cases. Moreover this method does not require skin grafting, accordingly produces better aesthetic results without hyperpigmentation and donor site scaring. Therefore we report this operation technique, which might be used as one of the standard in surgical correction of syndactyly and polysyndactyly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.98-101
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• 2011

Recently with the advent of Web 2.0 mashups are Web services, a new concept is in the spotlight. Mashups with Web services, such as a new concept for the purposes of an individual's social networking community type social network service web service coming seeping deep into our lives was a huge pillars. The structure of the network of one-way, tweeter and horizontal moves are also suitable for biased information. People from twitter's vast social network that matches their area of interest and share information with people who are wanted. In this paper, we develop the smart retweet mashup service that matching areas of interest for the purpose of sharing information between people. To selectively share information and consideration, additional information is required. Information derived from user-specified area of interest by region and share information is selectively targeted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1505-1511
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• 2021

Pretrained models with high performance in various tasks within natural language processing have the advantage of learning the linguistic patterns of sentences using large corpus during the training, allowing each token in the input sentence to be represented with appropriate feature vectors. One of the methods of constructing a corpus required for a pre-trained model training is a collection method using web crawler. However, sentences that exist on web may contain unnecessary words in some or all of the sentences because they have various patterns. In this paper, we propose a dataset construction method for filtering sentences containing unnecessary words using neural network models for corpus collected from the web. As a result, we construct a dataset containing a total of 2,330 sentences. We also evaluated the performance of neural network models on the constructed dataset, and the BERT model showed the highest performance with an accuracy of 93.75%.

• Journal of Korean Society of Steel Construction
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• v.27 no.2
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• pp.155-167
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• 2015

If of application of the deep deckting floor in long span more than 6m, the deflection caused by the construction load occurred high. Because the constructing-works and safety by this deflection, take actually supports to laborers working on the deck. However, installed supports are having difficultly such as the restricted passage, deficiency of working space, and lowering of efficiency. And toward-opening deck is seen as local buckling of web plate, flexural-torsional buckling, and gradually opening of corrugated decking. In this study, we will suggest a deep decking floor system that reinforced with both ends cap plates for toward-opneing decking change from opening to closing. The constructing deflection of a deep decking more than 6m must be satified 30mm and L/180 as proposed. Full-scale field tests loading by sand conducted a deep decking reinforced with and without cap plate. In conclusion, the specimen reinforced with cap plates have shown that to ensure the negative moment $wl^2/18$. And constructing-deflection of deep decking shown that to satisfy the evaluation value (L/180 or 30mm).

• Smart Media Journal
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• v.12 no.2
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• pp.66-75
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• 2023

Recently, the number of cloud web applications is increasing owing to the accelerated migration of enterprises and public sector information systems to the cloud. Traditional network attacks on cloud web applications are characterized by Denial of Service (DoS) attacks, which consume network resources with a large number of packets. However, HTTP DoS attacks, which consume application resources, are also increasing recently; as such, developing security technologies to prevent them is necessary. In particular, since low-bandwidth HTTP DoS attacks do not consume network resources, they are difficult to identify using traditional security solutions that monitor network metrics. In this paper, we propose a new detection model for detecting HTTP DoS attacks on cloud web applications by collecting the application metrics of web servers and learning them using machine learning. We collected 18 types of application metrics from an Apache web server and used five machine learning and two deep learning models to train the collected data. Further, we confirmed the superiority of the application metrics-based machine learning model by collecting and training 6 additional network metrics and comparing their performance with the proposed models. Among HTTP DoS attacks, we injected the RUDY and HULK attacks, which are low- and high-bandwidth attacks, respectively. As a result of detecting these two attacks using the proposed model, we found out that the F1 scores of the application metrics-based machine learning model were about 0.3 and 0.1 higher than that of the network metrics-based model, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.962-975
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• 2020

The accurate segmentation of infant brain MR image into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is very important for early studying of brain growing patterns and morphological changes in neurodevelopmental disorders. Because of inherent myelination and maturation process, the WM and GM of babies (between 6 and 9 months of age) exhibit similar intensity levels in both T1-weighted (T1w) and T2-weighted (T2w) MR images in the isointense phase, which makes brain tissue segmentation very difficult. We propose a deep network architecture based on U-Net, called Triple Residual Multiscale Fully Convolutional Network (TRMFCN), whose structure exists three gates of input and inserts two blocks: residual multiscale block and concatenate block. We solved some difficulties and completed the segmentation task with the model. Our model outperforms the U-Net and some cutting-edge deep networks based on U-Net in evaluation of WM, GM and CSF. The data set we used for training and testing comes from iSeg-2017 challenge (http://iseg2017.web.unc.edu).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.201-202
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• 2018

In this paper, a dog breed image provided by Dataset of existing ImageNet and Oxford-IIIT Pet Image is combined with a dog breed image obtained through data mining on Internet and a random-label is added. this paper introduces to recognize 122 classes of dog breeds and 1 class that is not dog breeds. The recognition rate of dog breeds using both conventional DB and collection DB was improved 1.5% over Top-1 compared to recognition rate of dog breeds using only existing DB. The image recognition rate about non-dog image, was 93% recognition rate in case of 10000 random DBs.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.15-21
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• 2017

In this paper, we present a solution to the problems caused by using only text - based information as an index element when a commercialized missing person indexing system indexes missing persons registered in the database. The existing system could not be used for the missing persons inquiry because it could not formalize the image of the missing person registered together when registering the missing person. To solve these problems, we propose a method to extract the similarity of images by using OpenCV image processing and TensorFlow deep - running image processing, and to process images of missing persons to process them into meaningful information. In order to verify the indexing method used in this paper, we constructed a Web server that operates to provide the information that is most likely to be needed to users first, using the image provided in the non - regular environment of the same subject as the search element.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.823-831
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• 2019

In recent years, there has been developed systems such as a surveillance system and access control using a face recognition function instead of a password or an RFID chip, thereby reducing the risk of falsification. Moreover, deep learning technology has been applied to real-time face recognition technology in video, so it makes possible the development of access control system that improves the accuracy of recognition and efficiency of management. In this paper, we propose a real-time access management system based on face recognition using ResNet. The system is based on web server, which make it possible to manage the access by recognizing the person of the image through the camera and access information stored in the database. It can be accessed by a user application to receive various information. The implemented system identifies a person in real time and allows access control by accurately distinguishing whether they are members or not, and the test results can recognize in 0.2 seconds. The accuracy of recognition rate is up to about 97% depending on the experiment environment. With this system, access can be managed quickly and effectively, even many people rush to it.