• 한국생산제조학회지
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• 제18권6호
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• pp.681-689
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• 2009

Disk grinding was often applied to deburring process in order to enhance the final product quality. Inherent chamfering capability of the flexible disk grinding process in the early stage was analyzed with respect to various process parameters including workpiece length, wheel speed, depth of cut and feed. Initial chamfered edge defined as arc zone was characterized with local radius of curvature. Averaged radius and arc zone ratio was well evaluated using neural network system. Additional neural network analysis adding workpiece length showed enhance performance in predicting arc zone ratio and curvature radius with reduced error rate. A process condition design parameter was estimated using remaining input and output parameters with the prediction error rate lower than 2.0% depending on the relevant input parameter combination and neural network structure composition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권7호
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• 한국정보통신학회논문지
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• 제14권2호
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• pp.534-539
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• 2010

게놈 연구에서 수천 개의 특징들은 비교적 작은 샘플들로부터 모아진다. 게놈 연구의 목적은 미래 관찰들의 결과를 예측하는 분류기를 만드는 것이다. 분류기를 만들기 위해서는 특징 선택, 모델 선택 그리고 예측 평가 등의 3단계 과정을 거친다. 본 논문은 예측 평가에 초점을 맞추고 모든 슬라이드의 사분위수를 똑같게 맞추는 quantilenormalization 적용하여 마이크로어레이 데이터를 표준화 한 후 특징 선택에 앞서 예측 모델의 '진짜' 예측 에러를 평가하기 위해 몇 개의 방법들을 비교하는 시스템을 고안하고 방법들의 예측 에러를 비교 분석 하였다. LOOCV는 전체적으로 작은 MSE와 bias를 나타내었고, 크기가 작은 샘플에서 split 방법과 2-fold CV는 매우 좋지 않는 결과를 보였다. 계산적으로 번거로운 분석에 대해서는 10-fold CV가 LOOCV보다 오히려 더 낳은 경향을 보였다.

• 산업기술연구
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• 제28권A호
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• pp.11-17
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• 2008

Applicability of hyperbolic settlement prediction method to consolidation settlement in the dredged and reclaimed ground was assessed by analyzing results of centrifuge tests modelling self-weight consolidation of soft marine clay. From literature review about self-weight consolidation of soft marine clays located in southern coast in Korea, constitutive relationships of void ratio - effective stress - permeability and typical self-weight consolidation curves with time were obtained by analyzing centrifuge model experiments. For the condition of surcharge loading, exact solution of consolidation settlement curve obtained by using Terzaghi's consolidation theory was compared with results predicted by the hyperbolic method. It was found to have its own inherent error to predict final consolidation settlement. From results of analyzing thc self-weight consolidation with time by using this method, it predicted relatively well in error range of 0.04~18% for the case of showing the linearity in the relationship between T vs T/S in the stage of consolidation degree of 60~90 %. However, it overestimated the final settlement with large errors if those relation curves were nonlinear.

• Journal of Ship and Ocean Technology
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• 제7권4호
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• pp.41-49
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• 2003

Welding deformation reduces the accuracy of ship hull blocks and decreases productivity due to the need for correction work. Preparing an error-minimizing guide at the design stage will lead to higher quality as well as higher productivity. Therefore, developing a precise method to predict the weld deformation is an essential part of it. This paper proposes an efficient method for predicting the weld deformation of complicated structures based on the inherent strain theory combined with the finite element method. A simulation of a stiffened panel confirmed the applicability of this method to simple ship hull blocks.

• 대한조선학회논문집
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• 제37권2호
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• pp.127-136
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• 2000

선박 건조 시 발생하는 용접변형은 블록의 정도를 떨어뜨리고 교정작업으로 인한 생산성 저하의 요인이 되고 있다. 따라서 설계 단계에서 변형을 최소화 할 수 있는 작업기준을 마련한다면 생산성 증대는 물론 품질의 향상을 가져올 수 있을 것이다. 여기에는 먼저 블록의 조립과정에 따른 변형을 예측할 수 있는 정확하고 효율적인 방법이 마련되어야 한다. 본 논문에서는 고유변형도 이론과 유한요소 해석을 결합한 효율적인 변형예측 기법을 제안하였다. 고유변형도는 간이 열탄소성 해석 결과 최고온도 분포와 구속도에 의해 결정된다. 따라서 용접 열전도 해석과 구조물의 조립과정에 따른 구속도 계산을 수행하여 실제 구조물에 발생하는 고유변형도를 정확히 구하고자 하였다. 이를 이용하여 보강판의 변형 예측을 구현하였고 간단한 선체 블록에 적용할 수 있음을 확인하였다.

• 한국지반공학회논문집
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• 제35권3호
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• pp.37-46
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• 2019

최근 다른 현장시험에 비하여 비교적 정확성이 높은 CPT 기반 액상화 평가법의 사용이 증가하고 있다. CPT 기반 액상화 평가는 다양한 흙의 특성을 예측하고 이를 액상화 평가에 활용할 수 있다. 특히, 세립분 함량은 CPT 기반 액상화 평가에서 중요한 입력 변수 중 하나로 이에 대한 정확한 예측식의 사용 및 예측 변동성을 정량적으로 파악하는 것은 매우 중요하다. 본 연구에서는 2017년 포항지진 시 액상화 현상이 관측된 지점에서 수행된 CPT 자료를 이용하여 기존 세립분 함량 예측식들의 오차를 분석하고 포항지역에 적합한 세립분 함량 예측식을 선정하였다. 또한, 지반의 고유한 변동성을 분석하고 CPT의 측정오차, 선정된 예측식에 대한 변환 불확실성을 고려한 세립분 함량의 예측 변동성을 정량적으로 평가하였다.

• KEPCO Journal on Electric Power and Energy
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• 제7권1호
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• Communications for Statistical Applications and Methods
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• 제13권1호
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• pp.49-60
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• 2006

There are six approaches to constructing standardized coefficient for logistic regression. The standardized coefficient based on Kruskal's information theory is known to be the best from a conceptual standpoint. In order to calculate this standardized coefficient, the coefficient of determination based on entropy loss is used among many kinds of coefficients of determination for logistic regression. In this paper, this standardized coefficient is obtained by using four kinds of coefficients of determination which have the most intuitively reasonable interpretation as a proportional reduction in error measure for logistic regression. These four kinds of the sixth standardized coefficient are compared with other kinds of standardized coefficients.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2013년도 제45회 하계 정기학술대회 초록집
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• pp.127.1-127.1
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• 2013

Recent experimental evidence that fluorinated graphene creates local magnetic moments around F adatoms has not been supported by semilocal density-functional theory (DFT) calculations where the adsorption of an F adatom induces no magnetic moment in graphene. Here, we show that such an incorrect prediction of the nonmagnetic ground state is due to the self-interaction error inherent in semilocal exchange-correlation functionals. The present hybrid DFT calculation for an F adatom on graphene predicts not only a spin-polarized ground state with a spin moment of ${\sim}1{\mu}_B$, but also a long-range spin polarization caused by the bipartite nature of the graphene lattice as well as the induced spin polarization of the graphene states. The results provide support for the experimental observations of local magnetic moments in fluorinated graphene.