• Journal of the Korean Institute of Gas
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• v.25 no.5
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• pp.63-69
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• 2021

In this study, we established an algorithm to determine whether the work in the gas facility is a normal work or an abnormal work using the threshold of the reconstruction error of the autoencoder. This algorithm do deep learning the autoencoder only with time-series data of a normal work, and derives the optimized threshold of the reconstruction error of the normal work. We applied this algorithm to the time series data of the new work to get the reconstruction error, and then compare it with the reconstruction error threshold of the normal work to determine whether the work is normal work or abnormal work. In order to train and validate this algorithm, we defined the work in a virtual gas facility, and constructed the training data set consisting only of normal work data and the validation data set including both normal work and abnormal work data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• fall
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• pp.335-337
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• 2021

최근 드론의 용도는 취미, 공연, 농업, 안전, 군사, 연구, 물자수송 등 다양한 분야와 목적으로 활용되고 있다. 더불어 드론의 불법적 활용으로 인한 안전 및 법적 문제 또한 빈번히 발생하고 있어, 이런 문제들을 예방하기 위한 드론의 탐지 기술이 활발히 연구되고 있다. 본 논문은 카메라로 촬영된 영상에서 조류와 같은 다른 객체와 구별하여 드론을 탐지하는 기술과 상공에서 바라본 객체들을 탐지하는 기술을 구현한다. 제안 방법은 딥러닝 기반의 YOLOv4를 사용하였다. UAV_123 데이터세트로 학습한 실험 결과, mAP는 85%, Recall은 85%, Precision은 81%의 정확도를 보였다. 제안 방법은 인명 구조, 배송, 건축 뿐만 아니라 안티 드론 시장에서도 효과적으로 활용될 수 있을 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.709-711
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• 2023

안전관리를 위한 인공지능 기술은 꾸준히 연구되고 있는 분야다. 특히, 컴퓨터 비전 기술을 활용한 CCTV 영상 분석은 군중의 동선과 밀도를 파악하는데 유용하며, 대규모 실내 공간에서 체계적인 안전관리를 가능하게 한다. 그러나 기존의 CCTV 카메라를 사용한 군중 수 추정은 가려짐(occlusion)과 같은 한계가 있다. 본 논문은 무선 랜 신호 데이터 분석 기법을 활용하여 수집한 데이터를 활용하여 실내 환경에서 군중 수를 추정하고자 한다. 본 논문에서는 인원 수 분류 예측을 위해 셰이플릿 확장 변환(Random Dilated Shapelet Transform) 기법을 제안한다. 단일 데이터 세트 내 분류 결과와, TX, RX 배치 방식에 따른 분류 성능의 차이는 모델의 성능 부족보다 데이터의 특성을 고려한 새로운 접근 방법의 필요성을 알려준다.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Tunnel and Underground Space
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• v.30 no.6
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• pp.540-550
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• 2020

Machine learning has been actively used in the field of automation due to the development and establishment of AI technology. The important thing in utilizing machine learning is that appropriate algorithms exist depending on data characteristics, and it is needed to analysis the datasets for applying machine learning techniques. In this study, advance rate is predicted using geotechnical and machine data of TBM tunnel section passing through the soil ground below the stream. Although there were no problems of application of statistical technology in the linear regression model, the coefficient of determination was 0.76. While, the ensemble model and support vector machine showed the predicted performance of 0.88 or higher. it is indicating that the model suitable for predicting advance rate of the EPB Shield TBM was the support vector machine in the analyzed dataset. As a result, it is judged that the suitability of the prediction model using data including mechanical data and ground information is high. In addition, research is needed to increase the diversity of ground conditions and the amount of data.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.427-441
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• 2021

Purpose: This study proposes a method for developing a model that predicts the probability of traffic accidents in advance to prevent the most frequent traffic accidents in the military. Method: For this purpose, CRISP-DM (Cross Industry Standard Process for Data Mining) was applied in this study. The CRISP-DM process consists of 6 stages, and each stage is not unidirectional like the Waterfall Model, but improves the level of completeness through feedback between stages. Results: As a result of modeling the same data set as the previously constructed accident investigation data for the entire group, when the classification criterion was 0.5, Significant results were derived from the accuracy, specificity, sensitivity, and AUC of the model for predicting traffic accidents. Conclusion: In the process of designing the prediction model, it was confirmed that it was difficult to obtain a meaningful prediction value due to the lack of data. The methodology for designing a predictive model using the data set was proposed by reorganizing and expanding a data set capable of rational inference to solve the data shortage.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.1
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• pp.41-56
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• 2021

This study aims to analyze the court ruling on "Inter-Korean Summit Meeting Minutes Deletion," identify how the established requirements, concept, and scope for the records prescribed in the Public Records Management Act are applied in actual cases, and summarize the future tasks. It analyzes the "approval theory" as the point of establishment for records by the ruling means and how the meaning of approval is determined, and examines the difference between the e-jiwon System and the On-Nara System to understand the meaning of ruling clearly. Moreover, it analyzes how the "Invalidity of Public Documents Crime" in Article 141 in the Criminal Act influences record management. Based on such comprehensive case analyses, the study proposes what tasks the administrative agencies such as the National Archives of Korea and the Ministry of the Interior and Safety should perform.

• Journal of Korean Society of Archives and Records Management
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• v.14 no.3
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• pp.7-32
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• 2014

The objective of this research is to seek a preservation method against the set of data. To achieve this purpose, this study analyzed the MIDAS RASIS RI/ RG of the Nuclear Safety and Security Commission, and then followed it with the under analysis of the MIDAS RASIS, thus presenting the transfer process. This was conducted for the records management of the MIDAS RASIS-designed records management modules DB. For the records management of MADIS RASIS, the records management module DB was thus planned, which presented the transfers through the standard records management system.

• Convergence Security Journal
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• v.21 no.3
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• pp.49-56
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• 2021

Recently, artificial intelligence is regarded as an essential technology in our society. In particular, the invasion of privacy in artificial intelligence has become a serious problem in modern society. Split learning, proposed at MIT in 2019 for privacy protection, is a type of federated learning technique that does not share any raw data. In this study, we studied a safe and accurate segmentation learning model using known differential privacy to safely manage data. In addition, we trained SVHN and GTSRB on a split learning model to which 15 different types of differential privacy are applied, and checked whether the learning is stable. By conducting a learning data extraction attack, a differential privacy budget that prevents attacks is quantitatively derived through MSE.

• Journal of Food Hygiene and Safety
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• v.39 no.2
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• pp.109-117
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• 2024

This study investigated the microbial contamination of agricultural, livestock, and marine ingredients in 55 meal kits distributed across Gyeonggi-do, South Korea. Of the 55 meal kits, 48 contained agricultural ingredients, 43 contained livestock ingredients, and 16 contained marine ingredients. The detection rate of the total aerobic bacteria in the agricultural, livestock, and marine products was 100%. The average numbers of the total aerobic bacteria were 6.57 log colony-forming units (CFU)/g in the agricultural products, 4.60 log CFU/g in the livestock products, and 5.47 log CFU/g in the marine products. The coliform detection rates in the agricultural, livestock, and marine products were 81.25%, 69.77%, and 43.75%, respectively. The average numbers of coliforms were 2.83 log CFU/g in the agricultural products, 1.34 log CFU/g in the livestock products, and 1.12 log CFU/g in the marine products. Escherichia coli was detected in 13 livestock products (30.23%), with levels ranging from 0.70 to 2.36 log CFU/g. Contrastingly, E. coli was detected in only one marine product (6.25%) and was not detected in any agricultural products. The detection rates of fungi in agricultural, livestock, and marine products were 97.92%, 93.02%, and 93.75%, respectively. The average numbers of fungi were 3.82 log CFU/g for the agricultural products, 2.92 log CFU/g for the livestock products, and 2.82 log CFU/g for the marine products. The isolation rates of foodborne pathogens from the agricultural, livestock, and marine products were 35.42%, 37.21%, and 31.25%, respectively. Forty-five foodborne pathogens of seven species, including Bacillus cereus and Salmonella spp., were isolated from the raw materials of the agricultural, livestock, and marine products in 55 meal kits. To prevent foodborne diseases caused by meal kits, it is necessary to focus on washing, heating, and preventing cross-contamination during cooking.