• Korean Journal of Soil Science and Fertilizer
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• v.8 no.2
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• pp.69-80
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• 1975

Relationships between yield and various nitrogen efficiencies, between efficiencies and between efficiency and nitrogen uptake amount of rice plant were proposed and tested using data from N.P.K simple trials about 30 to 50 locations, for three years. Established relationships are well in accordance with experimental results by showing highly significant correlations between them. The overall indications are that high yielding capacity of fields with fertilizer application, depends primarily on high fertilizer nitrogen uptake by increasing fertilizer use efficiency (Eu), secondly the efficiency (Ef) of absorbed fertilizer nitrogen (Nf) and fertilization efficiency (Fe) and also depends much on nitrogen efficiency for grain yield (E) to great extend and that the efficiency (Es) of soil nitrogen (Ns) contributes to E more than Ef does. All nitrogen efficiencies are negatively correlated with the uptake amount of corresponding nitrogen and counterpart efficiency. Es and Ef could be determined firstly by difference method and secondly E versus Cs (Cs=Ns/Ns+Nf) plotting and thirdly E-Cs plotting with labelled fertilizermethod using the equation E=Es Cs+B where B=Ef Cf but a constant under the given condition and at last Y-Ns plotting with labelled fertilizer using Eq Y=$Es{\cdot}Ns+B$ where B=$Ef{\cdot}Nf$. Es which seems not much variable from field to field is mostly greater (about 80% of tested fields) than Ef which is much variable and depends much on fertilizer form. The relationships tested and well agreed are as follows: 1. Y=$Es{\cdot}Ns+Ef{\cdot}Nf$ (Y is yield) 2. E=$Es{\cdot}Cs+Ef{\cdot}Cf$ where Cf=Nf/Nf+Ns 3. E=b-aN where E=E, Es or Ef and N=N, Ns or Nf respectively, (E=Y/N, N=Nf+Ns), b is theoretical maximum under the given system and a is tangent at N=O of the curve, Y=EN. 4. Fe=Ef Eu and Se=$Es{\cdot}Eu$ where Se is efficiency of soil available nitrogen. 5. E=$(Se{\cdot}Cs+Fe{\cdot}Cf)/Eu$ 6. Y=$Es{\cdot}Eu{\cdot}Sf+Ef{\cdot}Eu{\cdot}Fn$or Y=$Es{\cdot}Eu{\cdot}Ea{\cdot}Sn+Ef{\cdot}Eu{\cdot}Fn $where Sf=$Ea{\cdot}Sn$, Ea is soil available nitrogen equivalent to fertilizer(Sf) divided by total soil nitrogen (Sn).

• Journal of the Korean Institute of Gas
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• v.16 no.5
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• pp.1-6
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• 2012

It becomes a more and more common practice to build facilities bigger and more integrated in an effort to optimize the process within limited resources and spaces. As the capacity of facilities increases, so does the flow rate and pressure. This in turn leads to a high consequence of accident. Not only are these facilities vulnerable to leakage because of their high pressure, but also subsequent fire and explosion can be threatening. For these reasons, there is an urgent need to come up with solutions to assess and minimize the damage from an accident. The Quantitative Risk Assessment(QRA) is one of the most efficient ways to solve problems on pressurized pipelines. The QRA can be re-enforced by applying reduction factors. In this study various types of accidents in a pressurized pipeline were evaluated. The damage from accidents were computed, then. Finally the reduction factors were very effective to decrease consequences of high pressure pipeline accidents.

• Journal of the Society of Naval Architects of Korea
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• v.44 no.5
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• pp.542-550
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• 2007

A geometry modification is one of main keys in achieving a successful optimization. The optimized hull form generated from the geometry modification should be a realistic, faired form from the ship manufacturing point of view. This paper presents a practical hull optimization procedure using a parametric modification function. In the parametric modification function method, the initial ship geometry was easily deformed according to the variations of design parameters. For example, bulbous bow can be modified with several parameters such as bulb area, bulb length, bulb height etc. Design parameters are considered as design variables to modify hull form, which can reduce the number of design variables in optimization process and hence reduce its time cost. To verify the use of the parametric modification function, optimization for KCS was performed at its design speed (FN=0.26) and the wave making resistance is calculated using a well proven potential code with fully nonlinear free surface conditions. The design variables used are key design parameters such as Cp curve, section shape and bulb shape. This study shows that the hull form optimized by the parametric modification function brings 7.6% reduction in wave making resistance. In addition, for verification and comparison purpose, a direct geometry variation method using a bell-shape modification function is used. It is shown that the optimal hull form generated by the bell-shaped modification function is very similar to that produced by the parametric modification function. However, the total running time of the parametric optimization is six times shorter than that of the bell shape modification method, showing the effectiveness and practicalness from a designer point of view in ship yards.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.195-201
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• 2015

For median filtering (MF) detection in altered digital images, this paper presents a new feature vector that is formed from autoregressive (AR) coefficients via an AR model of the gradients between the neighboring row and column lines in an image. Subsequently, the defined 10-D feature vector is trained in a support vector machine (SVM) for MF detection among forged images. The MF classification is compared to the median filter residual (MFR) scheme that had the same 10-D feature vector. In the experiment, three kinds of test items are area under receiver operating characteristic (ROC) curve (AUC), classification ratio, and minimal average decision error. The performance is excellent for unaltered (ORI) or once-altered images, such as $3{\times}3$ average filtering (AVE3), QF=90 JPEG (JPG90), 90% down, and 110% up to scale (DN0.9 and Up1.1) images, versus $3{\times}3$ and $5{\times}5$ median filtering (MF3 and MF5, respectively) and MF3 and MF5 composite images (MF35). When the forged image was post-altered with AVE3, DN0.9, UP1.1 and JPG70 after MF3, MF5 and MF35, the performance of the proposed scheme is lower than the MFR scheme. In particular, the feature vector in this paper has a superior classification ratio compared to AVE3. However, in the measured performances with unaltered, once-altered and post-altered images versus MF3, MF5 and MF35, the resultant AUC by 'sensitivity' (TP: true positive rate) and '1-specificity' (FN: false negative rate) is achieved closer to 1. Thus, it is confirmed that the grade evaluation of the proposed scheme can be rated as 'Excellent (A)'.