• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.275-284
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• 2023

Land monitoring involves systematically understanding changes in land use, leveraging spatial information such as satellite imagery and aerial photographs. Recently, the integration of deep learning technologies, notably object detection and semantic segmentation, into land monitoring has spurred active research. This study developed a web service to facilitate such integrations, allowing users to analyze aerial and drone images using CNN models. The web service architecture comprises AI, WEB/WAS, and DB servers and employs three primary deep learning models: DeepLab V3, YOLO, and Rotated Mask R-CNN. Specifically, YOLO offers rapid detection capabilities, Rotated Mask R-CNN excels in detecting rotated objects, while DeepLab V3 provides pixel-wise image classification. The performance of these models fluctuates depending on the quantity and quality of the training data. Anticipated to be integrated into the LX Corporation's operational network and the Land-XI system, this service is expected to enhance the accuracy and efficiency of land monitoring.

• The Journal of Information Systems
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• v.32 no.4
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• pp.149-162
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• 2023

Purpose In the textile industry, fabric defects significantly impact product quality and consumer satisfaction. This research seeks to enhance defect detection by developing a transformer-based deep learning image segmentation model for learning high-dimensional image features, overcoming the limitations of traditional image classification methods. Design/methodology/approach This study utilizes the ZJU-Leaper dataset to develop a model for detecting defects in fabrics. The ZJU-Leaper dataset includes defects such as presses, stains, warps, and scratches across various fabric patterns. The dataset was built using the defect labeling and image files from ZJU-Leaper, and experiments were conducted with deep learning image segmentation models including Deeplabv3, SegformerB0, SegformerB1, and Dinov2. Findings The experimental results of this study indicate that the SegformerB1 model achieved the highest performance with an mIOU of 83.61% and a Pixel F1 Score of 81.84%. The SegformerB1 model excelled in sensitivity for detecting fabric defect areas compared to other models. Detailed analysis of its inferences showed accurate predictions of diverse defects, such as stains and fine scratches, within intricated fabric designs.

• Annual Conference of KIPS
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• 2023.11a
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• pp.641-643
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• 2023

Deep Learning(DL)을 사용한 호흡음의 자동 분석은 폐 질환의 조기 진단에 중추적인 역할을 한다. 그러나 현재의 DL 방법은 종종 호흡음의 공간적 및 시간적 특성을 분리하여 검사하기 때문에 한계가 있다. 본 연구는 컨볼루션 연산을 통해 공간적 특징을 캡처하고 시간 컨볼루션 네트워크를 사용하여 이러한 특징의 공간적-시간적 상관 관계를 활용하는 새로운 DL 프레임워크를 제한한다. 제안된 프레임워크는 앙상블 학습 접근법 내에 컨볼루션 네트워크를 통합하여 폐음 녹음에서 호흡 이상 및 질병을 검출하는 정확도를 크게 향상시킨다. 잘 알려진 ICBHI 2017 챌린지 데이터 세트에 대한 실험은 제안된 프레임워크가 호흡 이상 및 질병 검출을 위한 4-Class 작업에서 비교모델 성능보다 우수함을 보여준다. 특히 민감도와 특이도를 나타내는 점수 메트릭 측면에서 최대 45.91%와 14.1%의 개선이 이진 및 다중 클래스 호흡 이상 감지 작업에서 각각 보여준다. 이러한 결과는 기존 기술보다 우리 방법의 두드러진 이점을 강조하여 호흡기 의료 기술의 미래 혁신을 주도할 수 있는 잠재력을 보여준다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.763-765
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• 2023

본 논문에서는 저성능 컴퓨터나 스마트폰의 카메라를 통해 입력받은 영상을 기반으로 사용자의 포즈를 추정하고, 실시간으로 사용자의 포즈에 따라 3D 모델의 모션이 제어되어 가시화 될 수 있는 클라이어트-서버 구조의 "자세추정 및 3D 모델 모션 제어 시스템"을 제안한다. 제안 시스템은 소켓통신 기반의 클라이언트-서버구조로 구성되어, 서버에서는 실시간 자세 추정을 위한 딥러닝 모델이 수행되고, 저성능 클라이언트에서는 실시간으로 카메라 영상을 획득하여 영상을 서버에 전송하고, 서버로부터 자세 추정 정보를 받아 이를 3D 모델에 반영하고 렌더링 함으로써 사용자와 함께 3D 모델이 같은 동작을 수행하는 증강현실 화면을 생성한다. 고성능을 요구하는 객체 자세 추정 모듈은 서버에서 실행하고, 클라이언트에서는 영상 획득 및 렌더링만을 실행하기 때문에, 모바일 앱에서의 실시간 증강현실을 위한 자세 추정 및 3D 모델 모션 제어가 가능하다. 제안 시스템은 "증강현실 기반 영상 찍기 앱" 에 반영되어 사용자의 움직임을 따라하는 3D 캐릭터들의 영상을 쉽게 생성할 수 있도록 할 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.89-95
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• 2023

In recent decades, human action recognition (HAR) has demonstrated potential applications in sports analysis, human-robot interaction, and large-scale signage content. In this paper, spatial temporal attention graph convolutional network (STAGCN)-based HAR system is proposed. Spatioal-temmporal features of skeleton sequences are assigned different weights by STAGCN, enabling the consideration of key joints and viewpoints. From simulation results, it has been shown that the performance of the proposed model can be improved in terms of classification accuracy in the NTU RGB+D dataset.

• Convergence Security Journal
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• v.24 no.3
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• pp.13-21
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• 2024

Recently, with the rapid expansion of networks driven by the advancements of the Fourth Industrial Revolution, cybersecurity threats are becoming increasingly severe. Traditional signature-based Network Intrusion Detection Systems (NIDS) are effective in detecting known attacks but show limitations when faced with new threats such as Advanced Persistent Threats (APT). Additionally, deep learning models based on supervised learning can lead to biased decision boundaries due to the imbalanced nature of network traffic data, where normal traffic vastly outnumbers malicious traffic. To address these challenges, this paper proposes a network intrusion detection method based on one-class models that learn only from normal data to identify abnormal traffic. The effectiveness of this approach is validated through experiments using the Deep SVDD and MemAE models on the NSL-KDD dataset. Comparative analysis with supervised learning models demonstrates that the proposed method offers superior adaptability and performance in real-world scenarios.

• Annual Conference of KIPS
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• 2017.11a
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• pp.1037-1040
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• 2017

현재 머신러닝과 딥러닝의 기술이 빠른 속도로 발전하면서 수많은 인공지능 음성 비서가 출시되고 있지만, 발화자의 문장 내 존재하는 단어만 분석하여 결과를 반환할 뿐, 비언어적 요소는 인식할 수 없기 때문에 결과의 구조적인 한계가 존재한다. 따라서 본 연구에서는 인간의 의사소통 내 존재하는 비언어적 요소인 말의 빠르기, 성조의 변화 등을 수치 데이터로 변환한 후, "플루칙의 감정 쳇바퀴"를 기초로 지도학습 시키고, 이후 입력되는 음성 데이터를 사전 기계학습 된 데이터를 기초로 kNN 알고리즘을 이용하여 분석한다.

• Annual Conference of KIPS
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• 2022.11a
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• pp.535-537
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• 2022

AI 가속기는 머신 러닝 및 딥 러닝을 포함한 인공 지능 및 기계 학습 응용 프로그램의 연산을 더 빠르게 수행하도록 설계된 일종의 하드웨어 가속기 또는 컴퓨터 시스템이다. 가속기를 설계하기 위해선 설계 영역 탐색(Design Space Exploration)을 하여야 하고 여러 인공지능 중에서도 합성 곱 신경망(CNN)에 대한 설계 영역 탐색을 소개한다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.82-89
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• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.