• 기록학연구
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• 제12호
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• pp.3-35
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• 2005

There have been growing interests in work process analysis methodology, ever since ISO 15489 and DIRKS were introduced to the Korean archival and records management community. It is because the methodology has been expected to provide with much needed information, with which records management programs could be designed. The information encompass from those needed in developing macro-organizational framework such as business classification schemes, to those essential to identify exact points of time, at which records management systems should capture records during each phase of business process. The Korean community, however, currently possesses a surprisingly scanty understanding of the methodology. The present paper aims to promote its understanding among Korean archivists and records managers, by reviewing an Australian standard of Work Process Analysis for Recordkeeping(AS 5090). It begins with drawing attention to the fact that the work process analysis is utilized in diverse areas, encompassing systems development, organizational re-engineering, and human resources management in business settings. When the methodology is implemented in records management settings, therefore, focus should be lied in finding out ways of creating and capturing records from the routine business activities. The paper then reviews the relationship among the three standards, AS 5090, DIRKS, and ISO 15489. Most parts of the paper are dedicated to review AS 5090 in detail, with examples. The paper also provides with output forms to be used to organize and represent results from the analysis.

• 융합정보논문지
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• 제8권6호
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• pp.165-171
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• 2018

최근 IT보안의 화두가 되고 있는 가장 위협적인 공격은 APT공격이다. APT공격에 대한 대응은 인공지능기법을 활용한 대응이외에는 방법이 없다는 것이 현재까지의 결론이다. 여기서는 머신러닝 기법을 활용한 사이버위협 데이터를 분석하는 방법, 그 중에서도 빅데이터 머신러닝 프레임웍인 Scikit Learn를 활용하여 사이버공격 사례를 수집한 데이터셋을 이용하여 사이버공격을 분석하는 머신러닝 알고리즘을 구현하였다. 이 결과 70%에 육박하는 공격 분류 정확도를 보였다. 이 결과는 향후 보안관제 시스템의 알고리즘으로 발전가능하다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3199-3218
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• 2019

Location-based services (LBSs) have become popular in recent years due to the ever-increasing usage of smart mobile devices and mobile applications through networks. Although LBS application provides great benefits to mobile users, it also raises a sever privacy concern of users due to the untrusted service providers. In the lack of privacy enhancing mechanisms, most applications of the LBS may discourage the user's acceptance of location services in general, and endanger the user's privacy in particular. Therefore, it is a great interest to discuss on the recent privacy-preserving mechanisms in LBSs. Many existing location-privacy protection-mechanisms (LPPMs) make great efforts to increase the attacker's uncertainty on the user's actual whereabouts by generating a multiple of fake-locations together with user's actual positions. In this survey, we present a study and analysis of existing LPPMs and the state-of-art privacy measures in service quality aware LBS applications. We first study the general architecture of privacy qualification system for LBSs by surveying the existing framework and outlining its main feature components. We then give an overview of the basic privacy requirements to be considered in the design and evaluation of LPPMs. Furthermore, we discuss the classification and countermeasure solutions of existing LPPMs for mitigating the current LBS privacy protection challenges. These classifications include anonymization, obfuscation, and an encryption-based technique, as well as the combination of them is called a hybrid mechanism. Finally, we discuss several open issues and research challenges based on the latest progresses for on-going LBS and location privacy research.

• 한국항해항만학회지
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• 제43권3호
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• pp.160-165
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• 2019

선원의 행동은 해양사고에 있어서 주요한 원인이다. 본 연구에서는 은닉 마르코프 모델(Hidden Markov Model)에 기반하여 선원의 행동을 모델링하였다. 그런 후, 모델에서 추정한 행동의 경로분석을 통하여 어떠한 상황과 절차 그리고 오류에 의해서 해양사고가 발생되는지를 해석하였다. 모델 구현을 위하여, 선원의 행동을 해양안전심판원에서 간행된 재결 요약서에서 관측하였고, 관측한 결과는 SRKBB(Skill-, Rule-, and Knowledge-Based Behavior)를 기반으로 한 행동분류 프레임워크를 이용하여 HMM 학습에 적합한 행동 데이터로 변환하였다. 선박유형별 선원의 행동을 모델링한 결과, 선박 유형별로 차별성이 있음을 확인하였고, 선원이 우선적으로 행한 행동경로의 식별이 가능하였다. 연구 결과, 본 연구에서 제안한 모델링 기법은 선원의 행동경로 예측에 적용 가능할 뿐만 아니라 해양사고 예방에 필요한 선원 행동 보정을 위한 우선순위 결정에 기여할 수 있을 것으로 기대된다.

• 지역사회간호학회지
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• 제30권3호
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• pp.307-323
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• 2019

Purpose: The purpose of this study is to analyze the contents of disaster nursing education at universities in Korea and Japan, with focus on textbooks. Methods: Disaster nursing contents from 11 Korean community health nursing textbooks and 3 Japanese disaster nursing textbooks were analysed. Results: Three themes and 8 categories of disaster nursing content in the selected textbooks were analyzed. The themes classified include 'understanding of disaster', 'disaster management' and 'disaster management system'. The theme of 'understanding of disaster' consists of such categories as 'disaster concept/outline', 'classification of disaster', 'disaster management step' and 'disaster impact'. The theme of 'disaster management activities' consists of categories such as 'disaster management activities' and 'disaster nursing'. The theme of 'national disaster management system' consists of categories like 'national disaster management system' and 'international disaster relief'. From the comparison of disaster nursing education in the two countries, we found that themes were similar but there were differences between the two countries in content configuration. Korea and Japan have adopted the framework of International Council of Nurses for disaster nursing education. Korea stressed legal and ethical capabilities, while Japan included psychological support for disaster management service providers. Conclusion: Disaster education is an important factor in a nurse's ability for a disaster management. Development of a comprehensive disaster education program is necessary to enhance disaster care capacities.

• 대한통합의학회지
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• 제7권3호
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• pp.11-19
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• 2019

Purpose : The purpose of this study is to examine the effect of instrumental-activity of daily living program on a patient with subacute stroke in the view of self-efficiency, motivation for rehabilitation, social support. Methods : Eight stroke patients who attended instrumental-activity of daily living program at P hospital in Busan Metropolitan City from march 2018 to January 2019 were recruited. Instrumental-activity of daily living program was offered to 8 stroke patients 1 session (at least 1 hours) a day, 5 times a week, for 4~5 weeks. The instrumental-activity of daily living program was based on occupational therapy practice framework (OTPF) and international classification of functioning, disability and health (ICF). We evaluated self-efficacy, rehabilitation motivation, social support before and after intervention. Self-efficacy was evaluated using the general self-efficacy scale. The collected data was processed using SPSS 20.0 and were analyzed using descriptive statistics, Wilcoxon signed rank test, Pearson's correlation coefficient. Results : There were statistically significant differences in self-efficiency, rehabilitation motivation, social support between before and after instrumental-activity of daily living program (p<.05). Examining the correlation between the self-efficiency, rehabilitation motivation and social support of the stroke patient, there was a correlation between the self-efficiency and social support (p<.05). Conclusion : This study showed that the instrumental-activity of daily living program have positive effects on self-efficacy, rehabilitation motivation, social support. When considering the instrumental-activity of daily living program with improved self-efficacy, rehabilitation motivation, social support of the participants, we suggest that further studies will be needed to examine more extensive instrumental-activity of daily living and rehabilitation to the society with a larger sample size.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3708-3728
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• 2021

Indian herbal plants are used in agriculture and in the food, cosmetics, and pharmaceutical industries. Laboratory-based tests are routinely used to identify and classify similar herb species by analyzing their internal cell structures. In this paper, we have applied computer vision techniques to do the same. The original leaf image was preprocessed using the Chan-Vese active contour segmentation algorithm to efface the background from the image by setting the contraction bias as (v) -1 and smoothing factor (µ) as 0.5, and bringing the initial contour close to the image boundary. Thereafter the segmented grayscale image was fed to a leaky capacitance fired neuron model (LCFN), which differentiates between similar herbs by combining different groups of pixels in the leaf image. The LFCN's decay constant (f), decay constant (g) and threshold (h) parameters were empirically assigned as 0.7, 0.6 and h=18 to generate the 1D feature vector. The LCFN time sequence identified the internal leaf structure at different iterations. Our proposed framework was tested against newly collected herbal species of natural images, geometrically variant images in terms of size, orientation and position. The 1D sequence and shape features of aloe, betel, Indian borage, bittergourd, grape, insulin herb, guava, mango, nilavembu, nithiyakalyani, sweet basil and pomegranate were fed into the 5-fold Bayesian regularization neural network (BRNN), K-nearest neighbors (KNN), support vector machine (SVM), and ensemble classifier to obtain the highest classification accuracy of 91.19%.

• 한국전산구조공학회논문집
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• 제33권6호
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• pp.411-417
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• 2020

Building Information Modeling(BIM)기술을 유지관리 단계에서 활용하기 위해서는 상당량의 유지관리 데이터와 BIM기반 정보모델 객체들이 연계되어 운용되어야 한다. 본 연구에서는 교량 점검데이터를 표현하기 위해 확장된 IFC기반의 BIM모델과 온톨로지를 연계하여 정보를 관리하는 방법을 제시하였다. 이를 위해 현재의 IFC버전은 교량 객체를 제대로 표현할 수 없기 때문에 교량을 위한 IFC엔티티를 확장하였으며, 확장된 IFC기반의 정보모델을 생성하는 방법을 제시하였다. 또한, 교량 점검데이터에 대한 기본 개념을 추출하고, 교량 점검데이터를 위한 온톨로지(Ontology)를 생성하였다. 추출된 기본 개념들은 제시된 온톨로지에서 시멘틱 웹의 트리플(Triple) 방식으로 관계를 형성되었다. 마지막으로, 생성된 IFC기반의 BIM모델은 제시된 온톨로지와의 통합을 위하여 시멘틱 데이터 형식으로 변환되었다. 확장된 IFC기반 BIM모델은 제시된 교량 점검데이터 관리를 위한 온톨로지와 통합되었고, 실제 교량 점검데이터를 기반으로 테스트모델을 생성하였다. SPARQL query를 통해 목적에 맞는 교량 점검데이터가 추출됨을 확인하여 실효성을 검증하였다.

• 한국융합학회논문지
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• 제12권6호
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• pp.173-181
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• 2021

그동안은 규모가 큰 기업 위주의 분석과 과거 경영실적과 특허권의 관계위주의 분석을 했으나, 본 연구에서는 실증분석을 통해 중소기업을 대상으로 특허를 보유한 기업의 미래 매출액 변동성에 대해 살펴보았다. 본 연구에서는 지식재산권인 특허권의 양적가치와 질적가치가 기업경영성과에 미치는 영향을 분석하였다. 특허의 양적가치로는 특허의 수를, 질적가치는 특허평균점수를 기업경영성과로는 평균매출액증가율을 사용하였다. SPSS를 이용한 판별분석을 통해 2가지 독립변수 모두 평균매출액 증가율이 2배 이상인 기업과 2배 미만인 기업을 구분하는데 유의적인 변수임을 확인하였다. 따라서 특허권 보유 중소기업에 대해 보증심사나 여신심사시 본 연구결과를 이용하여 향후 매출액이 어떻게 변경될지에 대한 분석의 틀을 이해관계자에게 제공한다는데 의의가 있다.

• 융합정보논문지
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• 제12권1호
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• pp.31-38
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• 2022

본 논문은 전문가 독립적 비지도 신경망 학습 기반 다변량 시계열 데이터 분석 모델인 MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder)의 실제 현장에서의 적용과 Auto-encoder 기반인 MSCRED 모델의 한계인, 학습 데이터가 오염되지 않아야 된다는 점을 극복하기 위한 학습 데이터 샘플링 기법인 Subset Sampling Validation을 제시한다. 라벨 분류가 되어있는 발전소 장비의 진동 데이터를 이용하여 1) 학습 데이터에 비정상 데이터가 섞여 있는 상황을 재현하고, 이를 학습한 경우 2) 1과 같은 상황에서 Subset Sampling Validation 기법을 통해 학습 데이터에서 비정상 데이터를 제거한 경우의 Anomaly Score를 비교하여 MSCRED와 Subset Sampling Validation 기법을 유효성을 평가한다. 이를 통해 본 논문은 전문가 독립적이며 오류 데이터에 강한 이상 진단 프레임워크를 제시해, 다양한 다변량 시계열 데이터 분야에서의 간결하고 정확한 해결 방법을 제시한다.