• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1872-1879
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• 2022

Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.1-9
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• 2021

Deep learning in computer vision has made accelerated improvement over a short period but large-scale learning data and computing power are still essential that required time-consuming trial and error tasks are involved to derive an optimal network model. In this study, we propose a similar image classification performance improvement method based on CR (Confusion Rate) that considers only the characteristics of the data itself regardless of network optimization or data reinforcement. The proposed method is a technique that improves the performance of the deep learning model by calculating the CRs for images in a dataset with similar characteristics and reflecting it in the weight of the Loss Function. Also, the CR-based recognition method is advantageous for image identification with high similarity because it enables image recognition in consideration of similarity between classes. As a result of applying the proposed method to the Resnet18 model, it showed a performance improvement of 0.22% in HanDB and 3.38% in Animal-10N. The proposed method is expected to be the basis for artificial intelligence research using noisy labeled data accompanying large-scale learning data.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.42-50
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• 2023

The development of technology has achieved digital innovation throughout the media industry, including production techniques and editing technologies, and has brought diversity in the form of consumer viewing through the OTT service and streaming era. The convergence of big data and deep learning networks automatically generated text in format such as news articles, novels, and scripts, but there were insufficient studies that reflected the author's intention and generated story with contextually smooth. In this paper, we describe the flow of pictures in the storyboard with image caption generation techniques, and the automatic generation of story-tailored scenarios through language models. Image caption using CNN and Attention Mechanism, we generate sentences describing pictures on the storyboard, and input the generated sentences into the artificial intelligence natural language processing model KoGPT-2 in order to automatically generate scenarios that meet the planning intention. Through this paper, the author's intention and story customized scenarios are created in large quantities to alleviate the pain of content creation, and artificial intelligence participates in the overall process of digital content production to activate media intelligence.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1435-1440
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• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1053-1065
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• 2020

With the recent development of hardware performance and artificial intelligence technology, sophisticated fake videos that are difficult to distinguish with the human's eye are increasing. Face synthesis technology using artificial intelligence is called Deepfake, and anyone with a little programming skill and deep learning knowledge can produce sophisticated fake videos using Deepfake. A number of indiscriminate fake videos has been increased significantly, which may lead to problems such as privacy violations, fake news and fraud. Therefore, it is necessary to detect fake video clips that cannot be discriminated by a human eyes. Thus, in this paper, we propose a deep-fake detection model applied with Bidirectional Convolution LSTM and Attention Module. Unlike LSTM, which considers only the forward sequential procedure, the model proposed in this paper uses the reverse order procedure. The Attention Module is used with a Convolutional neural network model to use the characteristics of each frame for extraction. Experiments have shown that the model proposed has 93.5% accuracy and AUC is up to 50% higher than the results of pre-existing studies.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.364-366
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• 2018

Visual Question Answering(VQA)은 주어진 이미지와 질문에 대해 알맞은 정답을 찾는 기술이다. VQA는 어린이 학습, 인공지능 비서 등 여러 분야에 활용할 수 있는 중요한 기술이다. 그러나 관련된 한국어 데이터를 확보하기 힘든 이유로 한국어를 이용한 연구는 이루어지지 못하고 있다. 본 논문에서는 기존 영어 VQA 데이터를 한글로 번역하여 한국어 VQA 데이터로 사용하며, 이미지 정보와 질문 정보를 적절히 조절할 수 있는 Gate를 한국어 VQA에 적용한다. 실험 결과, 본 논문에서 제안한 모델이 영어 및 한국어 VQA 데이터에서 다른 모델보다 더 좋은 성능을 보였다.

• Annual Conference of KIPS
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• 2024.05a
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• pp.888-890
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• 2024

딥러닝의 발전으로 인공지능의 실세계 응용이 다방면으로 확대되고 있다. 하지만 학습에 사용된 소스 도메인 데이터와 테스트에 사용된 타겟 도메인 데이터 간의 분포 차이로 인해 모델의 성능이 크게 저하될 수 있다. 이를 극복하기 위해 도메인 적응 방법이 제안되었으나, 소스 도메인 데이터에 접근할 수 없는 경우 적용에 한계가 있다. 이에 대응하여 소스 데이터가 필요 없는 소스-프리 도메인 적응 기술과 실시간으로 적응하는 테스트 시간 적응 방법이 연구되고 있다. 본 논문은 최신 소스-프리 도메인 적응 및 테스트 시간 적응 방법의 동향을 파악하고 각 방법론의 기술적 특징을 분석하고자 한다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.1148-1151
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• 2021

1 인 미디어 산업의 성장으로 다양한 콘텐츠 제작의 증가와 함께 영상의 분위기를 좌우하는 BGM 의 수요도 급증하고 있다. 그러나 무료 음원은 한정되어 있으며 이미 많은 영상에 쓰여 시청자에게 흔한 느낌을 준다. 특히 MCN 에 소속되지 않은 콘텐츠 크리에이터들은 개성 있고 영상에 어울리는 음원 확보에 어려움을 겪고 있다. 본 연구는 이러한 콘텐츠 제작 환경을 개선하기 위해 창작자가 직접 녹음하거나 악보를 스캔해 자신만의 음원을 제작할 수 있는 웹 애플리케이션 '플랫'을 제안한다. 본 연구를 통해 콘텐츠 크리에이터들은 독창적이고 풍성한 콘텐츠를 만들 수 있으며, 음악적 숙련도와 관계없이 쉽게 음원을 만들 수 있어 작곡에 대한 접근성이 좋아질 것으로 보인다. 또한, 딥러닝을 활용해 음악을 창작함으로써 인공지능 작곡 분야를 활성화하고 디지털 음악 시장의 새로운 분야를 개척하는 데 이바지할 것으로 기대한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.70-85
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• 2021

Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.