• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.674-677
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• 2020

The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.

• Journal of Platform Technology
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• v.11 no.2
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• pp.3-14
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• 2023

This paper proposes a big data sentiment analysis method and deep learning implementation method to provide a webtoon comment analysis web page for convenient comment confirmation and feedback of webtoon writers for the development of the cartoon industry in the video animation field. In order to solve the difficulty of automatic analysis due to the nature of Internet comments and provide various sentiment analysis information, LSTM(Long Short-Term Memory) algorithm, ranking algorithm, and word2vec algorithm are applied in parallel, and actual popular works are used to verify the validity. If the analysis method of this paper is used, it is easy to expand to other domestic and overseas platforms, and it is expected that it can be used in various video animation content fields, not limited to the webtoon field

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.128-134
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• 2022

Nowadays, people consume a lot of content such as web dramas or K-pop videos through mobile devices such as smartphones, and the market for indirect advertisements through these web dramas or K-pop videos is also increasing every year. In order to lead to the immediate purchase of indirect products in web dramas, a system that allows consumers to purchase immediately at the time the products appear in the drama is needed. In this paper, we propose a system to allow viewers to purchase products worn by celebrities immediately when viewers see and click on them. When a user clicks on a video, it recognizes the product worn by the celebrity, and displays information on the screen on the most similar product corresponding to the recognized product, allowing them to go to the seller's site where they can purchase it. In order for such a system to operate stably, a pose estimation and siamese network-based system is proposed. The proposed system will primarily be released as a streaming service in the form of an app or web page that connects the products in web dramas or other K-pop video contents screened on the mobile with e-commerce. Furthermore, in the future, the technology is expected to be used globally in various industries such as smart mobility and display kiosks.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.95-97
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• 2019

본 논문에서는 비디오 데이터를 이용한 감독 학습 프레임 워크를 제안한다. 최근 Deep Convolutional Neural Networks의 성공으로 많은 분야에서 사용되고 있다. DCNNs 모델 성능의 중요한 요소 중 하나는 Large-cale Dataset을 구축하는 것으로 Small-scale Dataset으로 모델을 학습한다면 과적합 및 일반화 오류를 해결하기 어렵다. 이러한 문제점을 해결하는 방법으로 이미지 왜곡을 통한 데이터 셋을 증가 또는 Dropout 기법 등을 사용하였지만 원본 데이터가 적은 경우에는 모델이 일반화 능력을 갖기 어렵다. 따라서 본 논문에서는 이러한 문제점을 보완하고자 Web으로부터 얻은 비디오에서 해당 Class와 관련된 프레임들을 추출하여 보다 쉽게 데이터 셋을 확장하고, 모델의 성능을 향상 시키는 방법을 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1211-1214
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• 2021

기술의 비약전인 발전으로 콘텐츠 분야 역시 최근 상용화되어 새로운 초실감 시대으로 급격한 변화의 국면을 맞이하고 있다. 가상현실, 증강현실, 홀로그램 등 다양한 시각 정보 처리 및 표현 기술 등은 기존에 경험하지 못했던 사용자 실감형 경험을 가능하게 한다. 현재 코로나19로 인해 큰 피해를 입은 문화예술을 WebVR 전시회로 누구든 어디에서나 문화 인프라의 혜택을 받게 하며 또한 기준에 존재하던 예술인들뿐만 아니라 일반인들도 작품 전시할 수 있게 스타일 전이 기능을 넣어 사람들이 문화예술에 대한 관심을 높일 수 있도록 기대한다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.881-891
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• 2023

Recently, various studies on malicious URL detection using artificial intelligence have been conducted, and most of the research have shown great detection performance. However, not only does classical machine learning require a process of analyzing features, but the detection performance of a trained model also depends on the data analyst's ability. In this paper, we propose a DL-ML Fusion Hybrid Model for malicious web site URL detection based on URL lexical features. the propose model combines the automatic feature extraction layer of deep learning and classical machine learning to improve the feature engineering issue. 60,000 malicious and normal URLs were collected for the experiment and the results showed 23.98%p performance improvement in maximum. In addition, it was possible to train a model in an efficient way with the automation of feature engineering.

• Magazine of the Korea Concrete Institute
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• v.11 no.1
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• pp.191-200
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• 1999

The shear behavior of simply supported reinforced concrete deep beams subject to concentrated loads has been scrutinized experimentally to verify the influence of the structural parameters such as concrete strength, shear span-depth ratio, and web reinforcements. A total of 42 reinforced concrete deep beams with compressive strengths of 250 kg/$cm^2$ and 500 kg/$cm^2$ has been tested at the laboratory under one or two-point top loading. The shear span-depth ratio have been taken as three types of 0.4, 0.8 and 1.2, and the horizontal and vertical shear reinforcements ratio, ranging from 0.0 to 0.57 percent respectively. In the tests, the effects of the shear span-depth ratio, concrete strength and web reinforcements on the shear strength and crack initiation and propagation have been carefully checked and analyzed. From the tests, it has been observed that the failures of all specimens were due to shear and the shear behaviors of specimens were greatly affected by inclined cracks from the load application points to the supports in shear span. The load bearing capacities have changed significantly depending on the shear span ratio, and the efficiency of horizontal shear reinforcements were increased as the shear span-depth ratio decreased. The test results have been analyzed and compared with the formulas proposed by previous researchers and the design equation from the code. While the shear strengths obtained from the tests showed around 1.4 and 1.9 times higher than the values calculated by CIRIA guide and the domestic code, they were closely coincident with the formulas given by de Paiva's equation.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.87-94
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• 2022

Recently, with the development of information and communication infrastructure, the number of Internet access devices is rapidly increasing. Smartphones, laptops, computers, and even IoT devices are receiving information and communication services through Internet access. Since most of the device operating environment consists of web (WEB), it is vulnerable to web cyber attacks using web shells. When the web shell is uploaded to the web server, it is confirmed that the attack frequency is high because the control of the web server can be easily performed. As the damage caused by the web shell occurs a lot, each company is responding to attacks with various security devices such as intrusion prevention systems, firewalls, and web firewalls. In this case, it is difficult to detect, and in order to prevent and cope with web shell attacks due to these characteristics, it is difficult to respond only with the existing system and security software. Therefore, it is an automated defense system through the collection and analysis of web shells based on artificial intelligence machine learning that can cope with new cyber attacks such as detecting unknown web shells in advance by using artificial intelligence machine learning and deep learning techniques in existing security software. We would like to propose about. The machine learning-based web shell defense system model proposed in this paper quickly collects, analyzes, and detects malicious web shells, one of the cyberattacks on the web environment. I think it will be very helpful in designing and building a security system.

• Journal of the Korean Wood Science and Technology
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• v.19 no.3
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• pp.62-71
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• 1991

An evaluation of bending test of composite I and Box beams for determining the ultimate strength limit design criteria was presented. Maxium loads of composite I beams were found in beams composed of thicker upper flanges and/or vertical LVL flanges. These loads of plywood web beams were greater than those of PB web beams. Maximum loads of unsymmetrical box beams were less than those of symmetrical box beams. Thus, it took on different phase in box type beams. Ultimate loads of composite beams were greater than those of solid. The failure of composite beams were abrupt and failure mode was classified into following categories; Edgewise shear failure in web, delamination in flange-web joint, tension failure and tearing in LVL flanges, and web delamination. These failures of composite beams were appeared at the mixed mode. The influence factor affecting the performance of tested composite beams was shear strength of PB-web composite beams and compressive strength in plywood-web composite beams. It was also assumed that the influence factors on structural performance on composite beams were flange quality, web material and geometry of cross section. As one of the design methods resisting to compressive stress that was required in the case of small span to depth ratio and deep beams. composite I-beams composed of thicker upper flanges comparing to lower flanges were very effective in structural performance.