• Journal of Korea Port Economic Association
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• v.22 no.3
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• pp.43-57
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• 2006

Responsiveness to the uncertainty of SCM system shows its competitiveness. In order to secure SCM competitiveness, RFID-related projects aim to enhance both system visibility and process automation. Nowadays, we conduct RFID technology-oriented researches very actively; however, quantitative ROI analysis model from the perspective of SCM does not exist yet, which helps decide the introduction of technology. Therefore, our study suggests a ROI analysis model for the adoption of RFID technology, and we demonstrate its usefulness using the real world data that is taken from one of the government-funded RFID projects in Korea.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.3
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• pp.99-106
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• 2016

In this paper, we will discuss about correction method of missing data on noisy observation matrix and uncertainty analysis for the potential noise. In situations without missing data in an observation matrix, this solution is known to be accurately induced by SVD (Singular Value Decomposition). However, usually the several entries of observation matrix have not been observed and other entries have been perturbed by the influence of noise. In this case, it is difficult to find the solution as well as cause the 3D reconstruction error. Therefore, in order to minimize the 3D reconstruction error, above all things, it is necessary to correct reliably the missing data under noise distribution and to give a quantitative evaluation for the corrected results. This paper focuses on a method for correcting missing data using geometrical properties between 2D projected object and 3D reconstructed shape and for estimating a noise level of the observation matrix using ranks of SVD in order to quantitatively evaluate the performance of the correction algorithm.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.3
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• pp.45-50
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• 2004

The design of reaction wheel control logic is critical to achieve the spacecraft attitude stabilization and performance requirements for the successful mission. Due to various uncertainties on orbit there exist limitation to obtain the model parameters through the ground tests and to design the associated control logic. Thus, the model parameter correction using on-orbit data is essential to the control performance on orbit. This paper performs the system identification using KOMPSAT-1 telemetry data and extracts the model parameters of the reaction wheel. Moreover, the reaction wheel is remodeled and compared with the ground test results.

• Journal of IKEEE
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• v.24 no.2
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• pp.403-408
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• 2020

Photovoltaic (PV) has both intermittent and uncertainty in nature, so it is difficult to accurately predict. Thus anomaly detection technology is important to diagnose real time PV generation. This paper identifies a correlation between various parameters and classifies the PV data applying k-nearest neighbor and dynamic time warpping. Results for the two classifications showed that an outlier detection by a fault of some facilities, and a temporary power loss by partial shading and overall shading occurring during the short period. Based on 100kW plant data, machine learning analysis and test results verified actual outliers and candidates of outlier.

• Journal of Korean Society of Steel Construction
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• v.20 no.1
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• pp.93-104
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• 2008

study deals with the reliability assesment for the 5-year phases of a suspension bridge construction in Korea. The main objectives of this study are; (1) the evaluation of the reliability of a suspension bridge by considering an ultimate limit state for the fracture of main cable wires, (2) the determination of the critical phases among 28 construction stages for the deck erection, and (3) the evaluation of the reliability of the limit state for the erection control during construction stages. The research and the design of the suspension bridge have been focused on the state of construction mainly based on empirical data. Based on the recent survey of the distribution of accidents in Korean railways, over 80% of the accidents related to the uncertainties in human error, planning, design, materials and loads during construction have ben reported before the completion of construction. While many researches have evaluated the safety of bridges, the uncertainties in the construction phases have not been well treated in a guidelines or a specifications. An improved adaptive response surface method is used for the risk assessment in the construction phases of the target suspension bridge.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.5
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• pp.263-270
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• 2021

In this study, a reliability-based design optimization of a 130-m class fixed-type offshore platform, to be installed in the North Sea, was carried out, while considering environmental, material, and manufacturing uncertainties to enhance its structural safety and economic aspects. For the reliability analysis, and reliability-based design optimization of the structural integrity, unity check values (defined as the ratio between working and allowable stress, for axial, bending, and shear stresses), of the members of the offshore platform were considered as constraints. Weight of the supporting jacket structure was minimized to reduce the manufacturing cost of the offshore platform. Statistical characteristics of uncertainties were defined based on observed and measured data references. Reliability analysis and reliability-based design optimization of a jacket-type offshore structure were computationally burdensome due to the large number of members; therefore, we suggested a method for variable screening, based on the importance of their output responses, to reduce the dimension of the problem. Furthermore, a deterministic design optimization was carried out prior to the reliability-based design optimization, to improve overall computational efficiency. Finally, the optimal design obtained was compared with the conventional rule-based offshore platform design in terms of safety and cost.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.291-291
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• 2021

모형이 갖는 불확실성의 정량화나 매개변수의 최적화는 계산시간의 기하급수적인 증가를 가져온다. 계산시간의 효율성을 극대화할 수 있는 기법으로 최근 대체모형이 개발되었으며, 다양한 분야에서 적용되고 있다. 그러나 대체모형은 훈련된 데이터 공간에서 크게 벗어난 극한 사상를 정확하게 모의하기는 어려운 단점이 있다. 본 연구는 이와 같은 대체모형의 단점을 개선할 수 있는 새로운 PCK(polynomial chaos-krigging) 기법을 제시한다. PCK는 PCE(polynomial chaos expansion) 기법과 OK(ordinary krigging) 기법을 결합한 것이며, PCK의 효과는 기존의 PCE 및 OK 모형의 결과와 비교하여 입증하였다. 본 연구의 분석 결과는 다음과 같다. (1) PCK는 더 적은 수의 훈련 샘플만으로도 원래 모형을 더 정확하게 대체할 수 있다. (2) 원래 훈련 샘플보다 약 3배 더 큰 극한사상을 모의했을 때, PCE와 OK는 예측이 실패하였지만, PCK의 예측은 정확하였다. (3) 민감도 분석 결과 PCK의 매개변수 특성과 거동이 PCE 및 OK보다 원래 모형의 특성과 거동에 더 일치한다. 본 연구에서는 3개의 대체모형의 결과를 원래모형의 결과와 비교하였으며 그 적용성을 극한강우에 대해 검토하였다. 일반적으로 훈련 샘플의 범위와 비슷한 강우사상에 대해서는 모든 대체모형의 결과가 우수하였으나, 훈련 샘플의 범위에서 벗어난 극한 사상의 모의는 PCK만 적용이 가능하였다. 제안된 대체모형은 극한사상의 예측에 있어 기존 대체모형보다 매우 향상된 정확도를 제공함을 확인할 수 있었다.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.127-136
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• 2022

As technological development continues, platforms with more diverse structures are emerging. Existing research predicts that a new structure based on technology and innovation will affect the two-sided market. This study evaluated the decentralized identifier (DID) platform, a new platform based on blockchain technology, of the importance of this platform from the perspective of the two-sided market. Using the Analytic Network Process, IT, platform, and blockchain experts conducted a dual comparison survey. Data with a consistency ratio value of 0.1 or less were selected and analyzed for 12 data. The research results showed the importance of service quality, policy support, openness, and uncertainty. This study is expected to be used to support the development of strategic decision-making for blockchain and DID platform-based business companies.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1013-1024
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• 2022

In addition to the assessment measure of electric quality levels, load loss are also a factor in hindering the financial profits of electrical sales companies. Therefore, accurate analysis of load losses generated from distributed power networks is very important. The accurate calculation of load losses in the distribution line has been carried out for a long time in many research institutes as well as power utilities around the world. But it is increasingly difficult to calculate the exact amount of loss due to the increase in the congestion of distribution power network due to the linkage of distributed energy resources(DER). In this paper, we develop smart grid big data infrastructure in order to accurately analyze the load loss of the distribution power network due to the connection of DERs. Through the preprocess of data selected from the smart grid big data, we develop a load loss analysis model that eliminated 'veracity' which is one of the characteristics of smart grid big data. Our analysis results can be used for facility investment plans or network operation plans to maintain stable supply reliability and power quality.