• The Korean Journal of Archival Studies
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• no.70
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• pp.5-40
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• 2021

Administrative information dataset records produced by various systems designed for work are difficult to manage on a case-by-case basis, requiring separate procedures to identify and evaluate data-sets. Identified data set records are apprasal and transferred to the records management system or disposed of. In this process, sufficient records management elements must be reflected in the production system itself in order to adhere to the principles of record management. In this paper, the functional requirements of the production system to accurately identify and safely manage data-sets were derived and applied based on the case of the KR property management system. It is hoped that this research on functional requirements of production systems will be added to lead to the creation of standards for functional requirements of data set production systems.

• Journal of the Korean Society for information Management
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• v.37 no.2
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• pp.251-284
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• 2020

As the use of information has emerged as the core of national competitiveness, major developed countries and the Korean government have realized the importance of data. They have pursued technical research and standard establishment for long-term preservation and continuously strived for systematic management and preservation of data. However, although various types of data are specified for the purpose of record management in the law, there is no specific method on how to collect, manage and preserve them, except standard electronic documents. In particular, management and preservation of huge datasets from the administrative information system have been strongly demanded above all. Any guidelines for datasets do not have been properly provided. After the framework for selecting preservation format must be prepared, the system can be supplemented and built. The framework considering the characteristics of the dataset should be specified more concretely, and empirical verification of the conversion and restoration for the dataset preservation format derived according to the selection criteria is necessary. Therefore, this study intends to propose a method for long-term preservation through empirical verification of the preservation format after deriving an evaluation the framework for the preservation format selection criteria considering the characteristics of the dataset.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2022.10a
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• pp.95-96
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• 2022

본 논문에서는 충청남도 지자체 최초로 시행하고 있는 영유아 교통안전용품 지원사업에 대한 목적과 운영방법에 대해 소개하고 있다. 영유아 교통안전용품 보급사업에 대한 기대효과와 본 사업이 전국적으로 확산되는 것을 제언하고자 한다.

• Food Science and Preservation
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• v.30 no.3
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• pp.459-471
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• 2023

The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.

• Korean Journal of Culture and Social Issue
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• v.8 no.1
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• pp.61-77
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• 2002

This study focuses on drivers' risk perception & construct in risk scene. The measures used were the scores of hazard perception, namely the subjects' evaluation of the degree of risk through the 'Q-sorting' with 30 drivers. The subjects were divided into 3 groups according to their evaluating score, Z-score, road users' hazard(type 1), environmental hazard(type 2), situational hazard(type 3). And ten constructs derived from Q-sorting were compared through 'consensus item analysis'. It suggest that there are different in constructs for risk perception. Then those results are discussed in terms of theoretical and practical implication of traffic safety including accidents analysis and drivers' education.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.443-446
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• 2014

Code reuse attack (CRA)는 기존의 코드 내에서 필요한 코드 조각들 (gadgets)을 모아 indirect branch 명령어들로 잇는 방식으로 공격자가 원하는 악성 프로그램을 구성할 수 있는 강력한 공격 방법이다. 공격자는 자신의 코드를 대상 시스템에 심는 대신 기존의 코드를 이용하기 때문에, 대부분의 범용 운영체제 (OS)가 강제하는 W^X protection 을 무력화할 수 있다. 이러한 CRA 에 대응하기 위하여 다수의 연구들에서 branch 의 trace 를 분석하여 CRA 고유의 특성을 찾아내는 Signature 기반 탐지 기술을 제안하였다. 본 논문에서는 ARM 프로세서 상에서의 CRA 를 대응하기 위한 Signature 기반 탐지 기술을 효율적으로 도울 수 있는 binary 분석 및 meta-data 생성 기술을 제안한다. 특히, 본 논문은 우리의 이전 논문에서 고려 되지 못했던 ARM 의 두 가지 명령어 세트의 특성을 고려하여, 공격자가 어느 명령어 세트를 이용하여 CRA 를 시도하더라도 막아낼 수 있도록 meta-data 를 두 가지 mode 에 대해서 생성하였다. 실험 결과, meta-data 는 본래 바이너리 코드 대비 20.8% 정도의 크기 증가를 일으키는 것으로 나타났다.

• The Optical Journal
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• s.108
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• pp.50-51
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• 2007

진공증착장비 전문 제조업체인 (주)오리진(대표.방기선, www.mmkorea.com)이 지난 한 해에만 진공증착장비를 50세트 이상 납품하며 시장의 '다크호스'로 주목받고 있다. 이미 10여 년 전 국내에 처음 진공증착장비에 들어가는 전자빔파워를 개발하여 시장에 공급한 이력을 갖고 있는 오리진은 전자측정분야, 제어분야, 안전분야 등에서 오랜 기간 쌓은 노하우를 바탕으로 진공증착장비 수출업체로 거듭나겠다는 전략이다.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.37-44
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• 2023

As a primary technology of Industry 4.0, human-robot collaboration (HRC) requires additional measures to ensure worker safety. Previous studies on avoiding collisions between collaborative robots and workers mainly detect collisions based on sensors and cameras attached to the robot. This method requires complex algorithms to continuously track robots, people, and objects and has the disadvantage of not being able to respond quickly to changes in the work environment. The present study was conducted to implement a web-based platform that manages collaborative robots by recognizing the emotions of workers - specifically their perception of danger - in the collaborative process. To this end, we developed a web-based application that collects and stores emotion-related brain waves via a wearable device; a deep-learning model that extracts and classifies the characteristics of neutral, positive, and negative emotions; and an Internet-of-things (IoT) interface program that controls motor operation according to classified emotions. We conducted a comparative analysis of our system's performance using a public open dataset and a dataset collected through actual measurement, achieving validation accuracies of 96.8% and 70.7%, respectively.

• Journal of the Korean Geotechnical Society
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• v.39 no.7
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• pp.31-37
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• 2023

Since tunnels are built underground, it is impossible to check visually the location and degree of deterioration of steel ribs. Therefore, in tunnel maintenance, GPR images are generally used to detect steel ribs. While research on GPR image analysis employing artificial neural networks has primarily focused on detecting underground pipes and road damage, there have been limited applications for analyzing tunnel GPR data, specifically for steel rib detection, both internationally and domestically. In this study, a one-step object detection algorithm called YOLO, based on a convolutional neural network, was utilized to automate the localization of steel ribs using GPR data. The performance of the algorithm is then analyzed. Two datasets were employed for the analysis. A dataset comprising 512 original images and another dataset consisting of 2,048 augmented images. The omission rate, which represents the ratio of undetected steel ribs to the total number of steel ribs, was 0.38% for the model using the augmented data, whereas the omission rate for the model using only the original data was 7.18%. Thus, from an automation standpoint, it is more practical to employ an augmented dataset.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.515-515
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• 2023

팔당호는 북한강과 남한강이 합류하여 생성된 호소로 수도인 서울과 수도권인 경기도 동부지역의 물 공급을 담당하는 중요한 상수원이다. 이러한 팔당호에서 유해남조 발생은 상수원수 활용과 직접적으로 연관되어 있어 신속하고 정확한 관리 및 예측이 필요하다. 본 연구에서는 안전한 상수원 활용을 위해, 딥러닝 기법을 이용하여 유해남조 사전 예측 모델을 구축하고자 하였다. 모델 입력 변수는 2012년부터 2021년까지 10년 동안의 주간 팔당호 수질(수온, DO, BOD, COD, Chl-a, TN, TP, pH, 전기전도도, TDN, NH4N, NO3N, TDP, PO4P, 부유물질)과 수문(유입량, 총방류량), 기상 정보(평균기온, 최저기온, 최고기온, 일 강수량, 평균풍속, 평균 상대습도, 합계일조량), 그리고 북한강과 남한강 유입지점의 남조 세포 수를 사용하였다. 모델 출력 변수는 수질, 수문, 기상 요인으로 인한 남조의 성장 발현 시기를 고려하여 1주 후의 댐앞 남조 세포수를 사용하였다. 사용한 딥러닝 기법은 최근 주목받고 있는 Temporal Fusion Transformer (TFT)를 사용하였다. 모델 훈련용 데이터와 테스트용 데이터는 각각 8:2의 비율로 나누었으며, 검증용 데이터는 훈련용 데이터 내에서 훈련 데이터와 검증 데이터를 6:4 비율로 분배하였다. Lookback은 5로 설정하였고, 이는 주단위 데이터로 구성된 데이터세트의 특성을 반영한 것이다. 모델의 성능은 실측값과 예측값을 토대로 R-square와 Root Mean Squared Error (RMSE)를 계산하여 평가하였다. 모델학습은 총 154번 반복 진행되었으며, 이 중 성능이 가장 준수한 시점은 54번째 반복 시점으로 훈련손실 대비 검증손실이 가장 양호한 값을 나타냈다(훈련손실:0.443, 검증손실 0.380). R-square는 훈련단계에서 0.681, 검증단계에서 0.654였고, 테스트 단계에서 0.606으로 산출되었다. RMSE는 훈련단계에서 0.614(㎍/L), 검증단계에서 0.617(㎍/L), 테스트 단계에서 0.773(㎍/L)였다. 모델에 사용한 데이터세트가 주간 데이터라는 특성을 고려하면, 소규모 데이터를 사용하였음에도 본 연구에서 구축한 모델의 성능은 양호하다고 평가할 수 있다. 향후 연구에서 데이터세트를 보강하고 모델을 업데이트한다면, 모델의 성능을 더욱더 개선할 수 있을 것으로 기대된다.