• Asian-Australasian Journal of Animal Sciences
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• v.28 no.5
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• pp.703-715
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• 2015

One of the challenges to increase milk production in a large pasture-based herd with an automatic milking system (AMS) is to grow forages within a 1- km radius, as increases in walking distance increases milking interval and reduces yield. The main objective of this study was to explore sustainable forage option technologies that can supply high amount of grazeable forages for AMS herds using the Agricultural Production Systems Simulator (APSIM) model. Three different basic simulation scenarios (with irrigation) were carried out using forage crops (namely maize, soybean and sorghum) for the spring-summer period. Subsequent crops in the three scenarios were forage rape over-sown with ryegrass. Each individual simulation was run using actual climatic records for the period from 1900 to 2010. Simulated highest forage yields in maize, soybean and sorghum- (each followed by forage rape-ryegrass) based rotations were 28.2, 22.9, and 19.3 t dry matter/ha, respectively. The simulations suggested that the irrigation requirement could increase by up to 18%, 16%, and 17% respectively in those rotations in El-Nino years compared to neutral years. On the other hand, irrigation requirement could increase by up to 25%, 23%, and 32% in maize, soybean and sorghum based rotations in El-Nino years compared to La-Nina years. However, irrigation requirement could decrease by up to 8%, 7%, and 13% in maize, soybean and sorghum based rotations in La-Nina years compared to neutral years. The major implication of this study is that APSIM models have potentials in devising preferred forage options to maximise grazeable forage yield which may create the opportunity to grow more forage in small areas around the AMS which in turn will minimise walking distance and milking interval and thus increase milk production. Our analyses also suggest that simulation analysis may provide decision support during climatic uncertainty.

• Steel and Composite Structures
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• v.41 no.6
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• pp.831-850
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• 2021

Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winklersoil model, analytical equations for the moment-rotation response ofsoil during mining induced ground movements are developed. To define the full static moment-rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment-rotation curve. The maximal moment-rotation response of the footing is defined by X = 0:6. Despite the use of Winkler model, the computed moment-rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.64-70
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• 2013

Estimation approaches for casual relation model with high-order factors have strict restrictions or limits. In the case of ML (Maximum Likelihood), a strong assumption which data must show a normal distribution is required and factors of exponentiation is impossible due to the uncertainty of factors. To overcome this limitation many PLS (Partial Least Squares) approaches are introduced to estimate the structural equation model including high-order factors. However, it is possible to yield biased estimates if there are some differences in the number of measurement variables connected to each latent variable. In addition, any approach does not exist to deal with general cases not having any measurement variable of high-order factors. This study compare several approaches including the repeated measures approach which are used to estimate the casual relation model including high-order factors by using PLS (Partial Least Squares), and suggest the best estimation approach. In other words, the study proposes the best approach through the research on the existing studies related to the casual relation model including high-order factors by using PLS and approach comparison using a virtual model.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.113-121
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• 2014

In this work, an integrated model including molecular dynamics and chemical rate theory was implemented to calculate the growth of point defect clusters(PDC) and copper-rich precipitates(CRP) which could change the mechanical properties of reactor pressure vessel(RPV) steels in a nuclear power plant. A number of time-dependent differential equations were established and numerically integrated to estimate the evolution of irradiation defects. The calculation showed that the concentration of the vacancies was higher than that of the self-interstitial atoms. The higher concentration of vacancies induced a formation of the CRPs in the later stage. The size of the CRPs was used to estimate the mechanical property changes in RPV steels, as is the same case with the PDCs. The calculation results were compared with the measured values of yield strength change and Charpy V-notch transition temperature shift, which were obtained from the surveillance test data of Korean light water reactors(LWRs). The estimated values were in fair agreement with the experimental results in spite of the uncertainty of the modeling parameters.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.20-27
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• 2020

Several uncertainties in the landing environment of an aircraft are not considered, such as the falling speed, ambient temperature, and sensor noise. These uncertainties negatively affect the performance of the controller applied to a landing gear. The sliding mode control (SMC) method, which maintains the optimal performance of a controller under uncertainties, is used in this study. The landing gear is equipped with a magnetorheological damper that changes the yield shear stress according to the applied magnetic field. The applied controller employs a hybrid control combining Skyhook control and force control. The SMC maintains the optimal performance of the hybrid control by minimizing the tracking error of the damper force, even in various landing environments where parameter uncertainties are applied. The effect of SMC is verified through co-simulation results from Simscape and Simulink.

• 한국지구물리탐사학회:학술대회논문집
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• 2003.11a
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• pp.684-689
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• 2003

One field identified by an inverse method is one of multiple candidate solutions those are independently obtained through a specific estimation technique. While averaging of optimized fields can provide a better description of the spatial feature of an unknown field, it deteriorates the flow and transport characteristics of the optimized fields. As a result, the averaged field is not suited for modeling aquifer performances. Based on genetic algorithm, an optimal field synthesis technique is developed, which combines diversely optimized fields into a refined group of fields. Each field in the population is paired, and a sub-region of each field is exchanged by crossover operation to create a group of synthesized fields of enhanced modeling capability. The population of the fields is evolved till the synthesized fields become sufficiently similar. Applications of the optimal field synthesis to synthetic cases indicate that the objective functions of the fields assessing the modeling capabilities are further reduced after the optimal field synthesis. The identified fields from various inverse techniques may yield a range of modeling results under varied flow situations. The uncertainty is narrowed down through the optimal field synthesis and the associated modeling results converge on that of the reference field. The developed inverse modeling facilitates the construction of a reliable simulation model and hence trustworthy predictions of the future performances.

• The Plant Pathology Journal
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• v.33 no.3
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• pp.238-248
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• 2017

Pokkah Boeng is a serious disease of sugarcane, which can lead to devastating yield losses in crop-producing regions, including southern China. However, there is still uncertainty about the causal agent of the disease. Our aim was to isolate and characterize the pathogen through morphological, physiological, and molecular analyses. We isolated sugarcane-colonizing fungi in Fujian, China. Isolated fungi were first assessed for their cell wall degrading enzyme capabilities, and five isolates were identified for further analysis. Internal transcribed spacer sequencing revealed that these five strains are Fusarium, Alternaria, Phoma, Phomopsis, and Epicoccum. The Fusarium isolate was further identified as F. verticillioides after Calmodulin and EF-$1{\alpha}$ gene sequencing and microscopic morphology study. Pathogenicity assay confirmed that F. verticillioides was directly responsible for disease on sugarcane. Co-inoculation of F. verticillioides with other isolated fungi did not lead to a significant difference in disease severity, refuting the idea that other cellulolytic fungi can increase disease severity as an endophyte. This is the first report characterizing pathogenic F. verticillioides on sugarcane in southern China.

• Earthquakes and Structures
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• v.15 no.4
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• pp.431-441
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• 2018

This study provides fragility-based assessment of seismic performance of reinforced concrete bridges. Seismic fragility curves were created using nonlinear analysis (NA) and artificial neural networks (ANNs). Nonlinear response history analyses were performed, in order to calculate the seismic performances of the bridges. To this end, 306 bridge-earthquake cases were considered. A multi-layered perceptron (MLP) neural network was implemented to predict the seismic performances of the selected bridges. The MLP neural networks considered herein consist of an input layer with four input vectors; two hidden layers and an output vector. In order to train ANNs, 70% of the numerical results were selected, and the remained 30% were employed for testing the reliability and validation of ANNs. Several structures of MLP neural networks were examined in order to obtain suitable neural networks. After achieving the most proper structure of neural network, it was used for generating new data. A total number of 600 new bridge-earthquake cases were generated based on neural simulation. Finally, probabilistic seismic safety analyses were conducted. Herein, fragility curves were developed using numerical results, neural predictions and the combination of numerical and neural data. Results of this study revealed that ANNs are suitable tools for predicting seismic performances of RC bridges. It was also shown that yield stresses of the reinforcements is one of the important sources of uncertainty in fragility analysis of RC bridges.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.186-199
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• 2013

We pose pattern classification as a density estimation problem where we consider mixtures of generative models under partially labeled data setups. Unlike traditional approaches that estimate density everywhere in data space, we focus on the density along the decision boundary that can yield more discriminative models with superior classification performance. We extend our earlier work on the recursive estimation method for discriminative mixture models to semi-supervised learning setups where some of the data points lack class labels. Our model exploits the mixture structure in the functional gradient framework: it searches for the base mixture component model in a greedy fashion, maximizing the conditional class likelihoods for the labeled data and at the same time minimizing the uncertainty of class label prediction for unlabeled data points. The objective can be effectively imposed as individual mixture component learning on weighted data, hence our mixture learning typically becomes highly efficient for popular base generative models like Gaussians or hidden Markov models. Moreover, apart from the expectation-maximization algorithm, the proposed recursive estimation has several advantages including the lack of need for a pre-determined mixture order and robustness to the choice of initial parameters. We demonstrate the benefits of the proposed approach on a comprehensive set of evaluations consisting of diverse time-series classification problems in semi-supervised scenarios.

• Journal of Korean Society of Steel Construction
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• v.9 no.4 s.33
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• pp.523-533
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• 1997

Current limit state design method for encased composite columns contains irrational and uncertain design equations in defining section and material properties of composite members. Through investigating previous research used in formulating the design equation, this paper explores the irrationality and uncertainty such as 1) transformation of yield stress and elastic modulus for composite section, 2) an equation influencing buckling strength in terms of area rather than moment of inertia, and 3) selection of larger radius of gyration between steel and concrete sections. Improving the design equations this paper proposes two design methods which can be directly used in practical design.