• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.671-672
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• 2015

Due to harsh conditions of disaster areas, understanding of current feature of collapsed buildings, terrain, and other infrastructures is critical issue for disaster managers. However, because of difficulties in acquiring the geographical information of the disaster site such as large disaster site and limited capability of rescue workers, comprehensive site investigation of current location of survivors buried under the remains of the building is not an easy task for disaster managers. To overcome these circumstances of disaster site, this study makes use of an unmanned aerial vehicle, commonly known as a drone to effectively acquire current image data from the large disaster areas. The framework of 3D model reconstruction of disaster site using aerial imagery acquired by drones was also presented. The proposed methodology is expected to assist rescue workers and disaster managers in achieving a rapid and accurate identification of survivors under the collapsed building.

• 품질경영학회지
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• 제48권4호
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• pp.597-608
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• 2020

Purpose: The purpose of this paper is to present a probability distribution of the burst pressure of API 5L X65 pipes for the reliability assessment of corroded gas pipelines. Methods: Corrosion is a major cause of weakening the residual strength of the pipe. The mean residual strength on the corrosion defect can be obtained using the burst pressure code. However, in order to obtain the pipe reliability, a probability distribution of the burst pressure should be provided. This study is concerned with estimating the burst pressure distribution using Monte Carlo simulation. A response surface method is employed to represent the distribution parameter as a model of the corrosion defect size. Results: The experimental results suggest that the normal or Weibull distribution should be suitable as the probability distribution of the burst pressure. In particular, it was shown that the probability distribution parameters can be well predicted by using the depth and length of the corrosion defect. Conclusion: Given a corrosion defect on the pipe, its corresponding burst pressure distribution can be provided at instant. Subsequently, a reliability assessment of the pipe is conducted as well.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2018년도 전력전자학술대회
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• pp.96-98
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• 2018

본 논문에서는 800V 배터리 전기자동차 LDC용 낮은 스위치 전압정격을 갖는 새로운 소프트 스위칭 하프브리지 컨버터를 제안한다. 제안하는 컨버터는 입력이 직렬구조로써 입력전압의 절반으로 낮은 스위치의 전압정격을 갖기 때문에 600V의 Si-MOSFET를 사용할 수 있어 도통손실을 줄일 수 있으며 부분공진 동작으로 스위칭 손실 저감 효과를 갖고, 넓은 입력전압 및 부하영역에서 소프트 스위칭을 성취하여 높은 효율을 달성할 수 있으며 변압기의 직렬연결로 된 커패시터로 인해 자화 전류의 오프셋이 없다. 제안하는 소프트 스위칭 컨버터의 동작원리를 제시하고 시작품을 통해 본 논문의 타당성을 검증하였다.

• Asia pacific journal of information systems
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• 제33권3호
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• 한국지능시스템학회논문지
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• 제26권5호
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• pp.343-350
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• 2016

Remaining lifetime prediction of the underground gas pipeline plays a key role in maintenance planning and public safety. One of main causes in the pipeline failure is metal corrosion. This paper deals with estimating the pipeline reliability in the presence of corrosion defects. Because a pipeline has uncertainty and variability in its operation, probabilistic approximation approaches such as first order second moment (FOSM), first order reliability method (FORM), second order reliability method (SORM), and Monte Carlo simulation (MCS) are widely employed for pipeline reliability predictions. This paper presents a fuzzy inference based reliability method (FIRM). Compared with existing methods, a distinction of our method is to incorporate a fuzzy inference into quantifying degrees of variability in corrosion defects. As metal corrosion depends on the service environment, this feature makes it easier to obtain practical predictions. Numerical experiments are conducted by using a field dataset. The result indicates that the proposed method works well and, in particular, it provides more advisory estimations of the remaining lifetime of the gas pipeline.

• 품질경영학회지
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• 제45권4호
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• pp.649-664
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• 2017

Purpose: This paper introduces the technology of prognostics for Industry 4.0 and presents its application procedure for fitness-for-service assessment of natural gas pipelines according to ISO 13374 framework. Methods: Combining data-driven approach with pipe failure models, we present a hybrid scheme for the gas pipeline prognostics. The probability of pipe failure is obtained by using the PCORRC burst pressure model and First Order Second Moment (FOSM) method. A fuzzy inference system is also employed to accommodate uncertainty due to corrosion growth and defect occurrence. Results: With a modified field dataset, the probability of failure on the pipeline is calculated. Then, its residual useful life (RUL) is predicted according to ISO 16708 standard. As a result, the fitness-for-service of the test pipeline is well-confirmed. Conclusion: The framework described in ISO 13374 is applicable to the RUL prediction and the fitness-for-service assessment for gas pipelines. Therefore, the technology of prognostics is helpful for safe and efficient management of gas pipelines in Industry 4.0.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3966-3991
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• 2018

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.

• 한국지하수토양환경학회지:지하수토양환경
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• 제26권4호
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• pp.20-26
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• 2021

In this work, an indigenous microbial consortium was obtained by selectively cultivating microbes using a long-aged petroleum-contaminated soil (Kuwait) containing recalcitrant petroleum hydrocarbons. The obtained microbial consortium was able to grow on and degrade the remaining petroleum hydrocarbons which could not have been utilized by the indigenous microbes in the original Kuwait soil. The following microbial community analysis using 16S rRNA gene sequencing suggested that the enhanced degradation of the remaining recalcitrant petroleum hydrocarbons by the novel microbial consortium may have been attributed to the selected bacterial populations belonging to Bacillus, Burkholderia, Sphingobacterium, Lachnospiraceae, Prevotella, Haemophilus, Pseudomonas, and Neisseria.

• 정보보호학회논문지
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• 제27권4호
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• pp.721-729
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• 2017

악성코드에 대한 빠른 대응을 위해서는 악성코드에 대한 효율적인 분석이 필요하다. 그 중 하나로, 오픈 소스 함수들과 같이 안전한 것으로 확인된 부분을 분석 대상에서 제외하여 방대한 분석 대상을 줄이는 방법이 도움이 될 수 있다. 본 논문은 여러 오픈소스의 동적 링크 라이브러리 파일을 윈도우 환경에서 생성하여 오픈소스의 함수 정보들을 버전별, 컴파일러별로 시그니처 정보를 추출하고 비교하여 변경이 의심스러운 함수를 찾을 수 있는 자동화 도구를 제시한다. 또한 해당 도구는 비교에 사용된 정보들을 DB에 저장, 추후에 사용할 수 있어 분석 시간 오버헤드를 줄일 수 있다.

• JSTS:Journal of Semiconductor Technology and Science
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• 제1권2호
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• pp.103-110
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• 2001

Gate oxide scaling for sub-l00nm CMOS devices has been studied. Issues on the gate oxide scaling are reviewed, which are boron penetration, reliability, and direct tunneling leakage currents. Reliability of Sub-2.0nm oxides and the device performance degradation due to boron penetration are investigated. Especially, the effect of gate leakage currents on the transistor characteristics is studied. As a result, it is proposed that thinner oxides than previous expectations may be usable as scaling proceeds. Based on the gate oxide thickness optimization process we have established, high performance CMOS transistors of $L_{gate}=70nm$ and $T_{ox}=1.4nm$ were fabricated, which showed excellent current drives of $860\mu\textrm{A}/\mu\textrm{m}$ (NMOS) and $350\mu\textrm{A}/\mu\textrm{m}$ (PMOS) at $I_{off}=10\mu\textrm{A}/\mu\textrm{m}$ and $V_dd=1.2V$, and CV/I of 1.60ps (NMOS) and 3.32ps(PMOS).