• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Animal Bioscience
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• 제34권6호
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• pp.967-974
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• 2021

Objective: The objective of this study was to estimate the genetic correlation (rpc) of growth performance between purebred (Duroc and Korean native) and synthetic (WooriHeukDon) pigs using a single-step method. Methods: Phenotypes of 15,902 pigs with genotyped data from 1,792 pigs from a nucleus farm were used for this study. We estimated the rpc of several performance traits between WooriHeukDon and purebred pigs: day of target weight (DAY), backfat thickness (BF), feed conversion rate (FCR), and residual feed intake (RFI). The variances and covariances of the studied traits were estimated by an animal multi-trait model that applied the Bayesian inference. Results: rpc within traits was lower than 0.1 for DAY and BF, but high for FCR and RFI; in particular, rpc for RFI between Duroc and WooriHeukDon pigs was nearly 1. Comparison between different traits revealed that RFI in Duroc pigs was associated with different traits in WooriHeukDon pigs. However, the most of rpc between different traits were estimated with low or with high standard deviation. Conclusion: The results indicated that there were substantial differences in rpc of traits in the synthetic WooriHeukDon pigs, which could be caused by these pigs having a more complex origin than other crossbred pigs. RFI was strongly correlated between Duroc and WooriHeukDon pigs, and these breeds might have similar single nucleotide polymorphism effects that control RFI. RFI is more essential for metabolism than other growth traits and these metabolic characteristics in purebred pigs, such as nutrient utilization, could significantly affect those in synthetic pigs. The findings of this study can be used to elucidate the genetic architecture of crossbred pigs and help develop new breeds with target traits.

• 한국물환경학회지
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• 제37권3호
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• pp.204-216
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• 2021

It is important to understand the factors influencing the temporal and spatial variability of water quality in order to establish an effective customized management strategy for contaminated aquatic ecosystems. In this study, the spatial diversity of the 5-year (2015 - 2019) average total phosphorus (TP) concentration observed in 40 Total Maximum Daily Loads unit-basins in the Nakdong River watershed was analyzed using 50 predictive variables of watershed characteristics, climate characteristics, land use characteristics, and soil characteristics. Cross-correlation analysis, a two-stage exhaustive search approach, and Bayesian inference were applied to identify predictors that best matched the time-averaged TP. The predictors that were finally identified included watershed altitude, precipitation in fall, precipitation in winter, residential area, public facilities area, paddy field, soil available phosphate, soil magnesium, soil available silicic acid, and soil potassium. Among them, it was found that the most influential factors for the spatial difference of TP were watershed altitude in watershed characteristics, public facilities area in land use characteristics, and soil available silicic acid in soil characteristics. This means that artificial factors have a great influence on the spatial variability of TP. It is expected that the proposed statistical modeling approach can be applied to the identification of major factors affecting the spatial variability of the temporal average state of various water quality parameters.

• Smart Structures and Systems
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• 제31권4호
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Journal of Radiation Protection and Research
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• 제33권4호
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• pp.127-134
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• 2008

울진원전 3,4 호기의 가상적 중대사고로 인한 종합적인 경제적 리스크를 평가하였다. 이 연구의 목적을 위해 방사능 구름이 내륙을 향하는 것으로 가정하였다. 평가과정에서 불확실한 인자의 정량화에는 전문가 판단 및 의견도출에 유용한 것으로 알려진 델파이 기법을 이용하였다. 종합적인 경제적 리스크는 직접영향 비용과 간접영향 비용으로 구분되므로, 먼저 직접영향에 대한 비용을 평가하고, 예측된 가중치들 이용하여 직접영향 대비 간접영향 비용을 평가하였다. 행동학적 접근방법인 델파이 문제점을 보완하기 위해 수학적 접근방법인 베이지안 기법을 자료처리 과정으로 하는 모형을 적용하여 간접영향에 대한 경제적 충격량을 예측하였다. 1D 몬테칼로분석(MCA)으로 평가한 간접피해에 대한 가중치는 평균 2.59, 중앙값 2.08로 OECD/NEA에서 제시하는 가중치 1.25보다 높게 나타났다. 작은 국토나 방사선에 민감한 대중 성향과 같은 인지들이 패널의 판단에 영향을 미쳤을 수 있다. 직접피해 평가모델의 모수를 U형과 V형으로 구분하고 2D MCA를 사용한 종합적 경제적 리스크는 중앙값의 50%ile을 기준으로 2006년 국내총생산의 3.9%에 해당되었으며, 직접피해 영향이 가장 큰 자산 및 전력손실 비용을 제외하면 총 경제적 리스크는 국내총생산의 2.2% 수준이었다. 이 결과는 원전 비상계획과 대응태세 준비에 대한 투자 정당화에 참조 자료로 이용될 수 있다.