Comparative Analysis on Mean Life Reliability with Functionally Classified Pavement Sections

도명식 대한토목학회 2011 KSCE JOURNAL OF CIVIL ENGINEERING Vol.15 No.2

The estimation of mean life reliability of highway pavement plays a central role in road maintenance and pavement management. In this paper, a methodology to estimate the mean life and failure probability in consideration of road functional characteristics based on parametric and non-parametric estimation models are presented. Based on the three types of functionally classified roads: urban,rural and recreation roads, five different lifetime distributions were tested: Normal, lognormal, exponential, Weibull, and loglogistic to select the appropriate probability distribution and to estimate mean life and failure rates. For functional classification of roads, the permanent traffic counters located along the national highway in 2007 are used. Furthermore, national highway pavement databases from 1999 to 2008 are also used for selection of optimal probability distribution and estimation of mean life for pavement. The goodness-of-fit test, such as the Anderson-Darling test, was performed to select optimal probability distribution. As a result, an appropriate distribution of each case was selected: lognormal distribution for rural roads and Weibull distribution for recreation roads. The non-parametric estimation method for rural roads was applied because there is no appropriate probability distribution for rural roads. Furthermore, in order to verify the validity of the proposed parametric and non-parametric estimation models, the applicability of the estimation methodology presented in this paper is investigated by using the empirical lifetime data of the national highway in Korea.

Single Image Depth Estimation With Integr ation of Parametr ic Learning and Non-Parametr ic Sampling

정형주,손광훈 한국멀티미디어학회 2016 멀티미디어학회논문지 Vol.19 No.9

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling

Jung, Hyungjoo,Sohn, Kwanghoon Korea Multimedia Society 2016 멀티미디어학회논문지 Vol.19 No.9

Understanding 3D structure of scenes is of a great interest in various vision-related tasks. In this paper, we present a unified approach for estimating depth from a single monocular image. The key idea of our approach is to take advantages both of parametric learning and non-parametric sampling method. Using a parametric convolutional network, our approach learns the relation of various monocular cues, which make a coarse global prediction. We also leverage the local prediction to refine the global prediction. It is practically estimated in a non-parametric framework. The integration of local and global predictions is accomplished by concatenating the feature maps of the global prediction with those from local ones. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods both qualitatively and quantitatively.

조건부 가치측정법에서 영(0)의 응답처리를 위한 모수적 추정법과 비모수적 추정법의 비교연구

이주석 ( Joo Suk Lee ),최은철 ( Eun Chul Choi ) 한국환경경제학회·한국자원경제학회(구 한국환경경제학회) 2013 자원·환경경제연구 Vol.22 No.2

조건부 가치측정법 연구에서 제시금액에 대한 지불의사가 없다는 영(0)의 지불의사 비중이 높을 경우 영의 지불의사를 밝히는 응답 자료들을 어떻게 처리해야 하는가를 두고 논란이 있다. 이에 본 연구에서는 이산화탄소 저감정책에 대한 설문조사결과를 활용하여 보다 합리적으로 영의 지불의사를 밝히는 응답 자료들을 처리할 수 있는 모형들을 비교 분석함으로써 학술적 시사점을 제공하고자 한다. 이를 위하여 본 연구에서는 스파이크모형을 포함한 혼합모형 등 모수적 추정법 뿐만 아니라 다양한 비모수적 추정법의 추정결과를 비교분석하고자 하였다. 분석결과, 모형에 따라서 다른 값들이 도출되었으며, 각각의 모형들의 한계점도 확인할 수 있었다. 이러한 점을 볼 때, 향후 CVM 연구에서는 특정 방법론을 이용하는 것 보다는 보다 보수적인 추정치를 제공하는 방법론을 이용하는 것이 적절한 것으로 판단된다. There has been some debates about zero willingness to pay in contingent valuation method research. Therefore, this paper tries to estimate and compare the results of various models to handle zero willingness to pay responses. For this purpose, we have employed parametric estimation such as the mixed model and the spike model, as well as non-parametric estimations. As a result, these models derived WTP estimate different from conventional model, but they also show some weakness. Therefore, in future research, more conservative estimate of the model should be to use rather than specific model.

Development of a predictive model for study of skin-core phenomenon in stabilization process of PAN precursor

Gelayol Golkarnarenji,MINOO NAEBE,Jeffrey S. Church,Khashayar Badii,Alireza Bab-Hadiashar,Stephen Atkiss,Hamid Khayyam 한국공업화학회 2017 Journal of Industrial and Engineering Chemistry Vol.49 No.-

Studying the presence and progress offiber defects, such as skin-core structure, is an important tool foranalysis of a chemical process. In this article, the skin core morphology has been analyzed by opticalmicroscopic (OM) images and Fourier transform infrared attenuated total reflectance mapping (FTIR-ATRmapping). The results of FTIR-ATR mapping showed that thefiber is almost uniform in the core area whileOM images are accurate enough to be used for skin-core analysis. Using OM images, the core ratio ofsamples were measured to quantify the skin-core structure. Non-parametric kernel density estimationmethods have then been compared with conventional parametric distribution models using these data. The results reveal that the parametric methods cannot adequately describe the skin-core phenomenonand that the non-parametric distributions are more appropriate for the quantification of skin-coremorphology. By applying the non-parametric distributions, a model has been developed, which describesthe relationship between the skin-core defect and the operation parameters of thefiber production. Thisapproach can be used to predict the probability of skin-core occurrence and can be used to decrease thepresence of this phenomenon in the carbonfibers production industry. Our results show thattemperature is one of the most significant operational parameter at a typical oxygen concentration (in airat atmospheric pressure) governing the skin-core formation.

Prediction Intervals for Day-Ahead Photovoltaic Power Forecasts with Non-Parametric and Parametric Distributions

Fonseca, Joao Gari da Silva Junior,Ohtake, Hideaki,Oozeki, Takashi,Ogimoto, Kazuhiko The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.4

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%.

Prediction Intervals for Day-Ahead Photovoltaic Power Forecasts with Non-Parametric and Parametric Distributions

Joao Gari da Silva Fonseca Jun,Hideaki Ohtake,Takashi Oozeki,Kazuhiko Ogimoto 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.4

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%.

Estimating false discovery rate and false non-discovery rate using the empirical cumulative distribution function of p-values in ‘omics’ studies

Robert R. Delongchamp,Mehdi Razzaghi,이태원 한국유전학회 2011 Genes & Genomics Vol.33 No.5

Large numbers of mRNA transcripts, proteins, metabolites,and single nucleotide polymorphisms can be measured in a single tissue sample using new molecular biological techniques. Accordingly, the interpretation of ensuing hypothesis tests should manage the number of comparisons. For example,cDNA microarray experiments generate large multiplicity problems in which thousands of hypotheses are tested simultaneously. In this context, the false discovery rate (FDR)and false non-discovery rate (FNR) are used to account for multiple comparisons. In this study, we propose non-parametric estimates of FDR and FNR that are conceptually and computationally straightforward. Additionally, to illustrate their properties and use in a procedure for an optimum subset of significant tests, an example from a functional genomics study is presented.

Estimating monotone convex functions via sequential shape modification

Lee, Sanghan,Lim, Johan,Kim, Seung-Jean,Joo, Yongsung Taylor Francis 2009 JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION Vol.79 No.8

<P> We propose a sequential method to estimate monotone convex functions that consists of: (i) monotone regression via solving a constrained least square (LS) problem and (ii) convexification of the monotone regression estimate via solving a uniform approximation problem with associated constraints. We show that this method is faster than the constrained LS method. The ratio of computation time increases as data size increases. Moreover, we show that, under an appropriate smoothness condition, the uniform convergence rate achieved by the proposed method is nearly comparable to the best achievable rate for a non-parametric estimate which ignores the shape constraint. Simulation studies show that our method is comparable to the constrained LS method in estimation error. We illustrate our method by analysing ground water level data of wells in Korea.</P>

Single Image Depth Estimation With Integr ation of Parametr ic Learning and Non-Parametr ic Sampling

Hyungjoo Jung,Kwanghoon Sohn 한국멀티미디어학회 2016 멀티미디어학회논문지 Vol.19 No.9