• Journal of Korean Society of Forest Science
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• v.107 no.2
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• pp.184-192
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• 2018

In this study was implemented to develop tree stem weight prediction equation of Larix kaempferi in central region by selecting a standard site, taking into account of diameter and position of the local trees. Fifty five sample trees were selected in total. By utilizing actual data of the sample trees, 11 models were compared and analyzed in order to estimate four different kinds of weights which include fresh weight, ovendry outside bark weight, ovendry inside bark weight and merchantable weight. As to estimate its weight, the study has classified its model according to three parameters: DBH, DBH and height, and volume. The optimal model was chosen by comparing the performance of model using the fit index and standard error of estimate and residual distribution. As a result, the formula utilizing DBH (Variable 1) is $W=a+bD+cD^2$ (3) and its fit index was 90~92%. The formula for DBH and height (Variable 2) is $W=aD^bH^C$ (8) and its fit index was 97~98%. In summation, Variable 2 model showed higher fitness than Variable 1 model. Moreover, fit index of formula for total volume and merchantable volume (W=aV) showed high rate of 98~99%, as well as resulting 7.7-17.5 with SEE and 8.0-10.0 with CV(%) which lead to predominately high fitness in conclusion. This study is expected to provide information on weights for single trees and furthermore, to be used as a basic study for weight of stand unit and biomass estimation equations.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.5
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• pp.277-283
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• 2016

Tetrapod is one of the most widely used concrete armor units for rubble mound breakwaters. The calculation of the stability number of Tetrapods is necessary to determine the optimal weight of Tetrapods. Many empirical formulas have been developed to calculate the stability number of Tetrapods, from the Hudson formula in 1950s to the recent one developed by Suh and Kang. They were developed by using the regression analysis to determine the coefficients of an assumed formula using the experimental data. Recently, software engineering (or machine learning) methods are introduced as a large amount of experimental data becomes available, e.g. artificial neural network (ANN) models for rock armors. However, these methods are seldom used probably because they did not significantly improve the accuracy compared with the empirical formula and/or the engineers are not familiar with them. In this study, we propose an explicit method to calculate the stability number of Tetrapods using the weights and biases of an ANN model. This method can be used by an engineer who has basic knowledge of matrix operation without requiring knowledge of ANN, and it is more accurate than previous empirical formulas.

• Journal of Life Science
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• v.19 no.7
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• pp.845-851
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• 2009

A vector harboring double cassettes; a heterologous gene expression cassette of pHCE-InaN-antigen and a ghost formation cassette of pAPR-cI-E lysis 37 SDM was constructed and introduced to E. coli DH5a. For the production of a bacterial ghost vaccine, bacterial ghosts from E. coli / Streptococcus iniae with four different types of antigens - enolase, GAPDH, sagA and piaA - were produced by the optimization of fermentation parameters such as a glucose concentration of 1 g/l, agitation of 300 rpm and aeration of 1 vvm. Efficiency of ghost bacteria formation was evaluated with cultures of OD$_{600}$=1.0, 2.0 and 3.0. The efficiency of the ghost bacteria formation was 99.54, 99.67, 99.99 and 99.99% with inductions at OD$_{600}$=3.0, 1.0, 2.0 and 1.0 for E. coli/S. iniae antigens enolase, piaA, GAPDH and sagA, respectively. Ghost bacteria as a vaccine was harvested by centrifugation. The antigen protein expressions were analyzed by SDS-PAGE and western blot analysis, and the molecular weights of the enolase, piaA, GAPDH and sagA were 78, 26, 67 and 26 kDa, respectively. The molecular weights of the expressed antigens were consistent with theoretical sizes obtained from the amino acid sequences.

• Journal of Chest Surgery
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• v.30 no.6
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• pp.585-590
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• 1997

Between December 1994 and October 1996, 57 premature infants with evidence of a hemodynamically significant PDA associated with cardiopulmonary compromise underwent indomethcin therapy(Group I, n=48) or surgical ligation(Group II, n=9) because of indomethacin failure. The gestational alee(29.6$\pm$ 3.1weeks vs. 28.1 $\pm$ 1.6weeks) and birth weight(1,413 $\pm$ 580gm, ,098 $\pm$ 235gm) showed no significant differences between the two groups. Medical management included fluid restriction, diuretics, and indomethacin therapy(one or two cycles). Surgical libation was done at the neonatal intensive care unit(NICU) without moving the patient to the operation room. There was no complication associated with the operation. There were 9 deaths in Group I(19%, 9/48) and 2 deaths in Group II(22% , 219). The main causes of deaths were persistent bronchopulmonary dysplasia with sepsis(n=8) and intrapulmonary hemorrhage(n=3). The rate of medical treatment failure including death and complication in premature infants whose body weights were less than 1500gm was higher(41%, 15/38) than in premature infants whose body weights were more than 1500gm(16%, 3/19). Early surgical ligation of PDA may be applicable in the premature infant with a large size, low birth weight(<1500 gm), or associated intracardiac anomalies. Perfoming the operation in the NICU may be safe in s ead of moving the patient to the operating room.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.367-374
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• 2022

This papter presents the use of the automatic differential method based on the backpropagation method to obtain the design sensitivity and its application to topology optimization considering the stress constraints. Solving topology optimization problems with stress constraints is difficult owing to singularities, the local nature of stress constraints, and nonlinearity with respect to design variables. To solve the singularity problem, the stress relaxation technique is used, and p-norm for stress constraints is applied instead of local stresses for global stress measures. To overcome the nonlinearity of the design variables in stress constraint problems, it is important to analytically obtain the exact design sensitivity. In conventional topology optimization, design sensitivity is obtained efficiently and accurately using the adjoint variable method; however, obtaining the design sensitivity analytically and additionally solving the adjoint equation is difficult. To address this problem, the design sensitivity is obtained using a backpropagation technique that is used to determine optimal weights and biases in the artificial neural network, and it is applied to the topology optimization with the stress constraints. The backpropagation technique is used in automatic differentiation and can simplify the calculation of the design sensitivity for the objectives or constraint functions without complicated analytical derivations. In addition, the backpropagation process is more computationally efficient than solving adjoint equations in sensitivity calculations.

• Journal of Mushroom
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• v.6 no.1
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• pp.27-31
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• 2008

The optimization of submerged culture conditions for mycelial growth and exopolysaccharide (EPS) production in an edible mushroom Tremella fuciformis were studied in shake flasks and bioreactors. The temperature of $28^{\circ}C$ and pH 8 in the beginning of fermentation in agitated flasks was the most efficient condition to obtain maximum mycelial biomass and EPS. The optimal medium constituents were as follows (g l-1): glucose 20, tryptone 2, $KH_2PO_4$ 0.46, $K_2HPO_4$ 1 and $MgSO_4H_2O$ 0.5. The fungus was cultivated under various agitation and aeration conditions in a 5L stirred-tank bioreactor. The maximum cell mass and EPS production were obtained at a relatively high agitation speed of 200 rpm and at an aeration rate of 2 vvm. The flow behavior of the fermentation broth was Newtonian and the maximum apparent viscosity (35 cP) was observed at a highly aerated condition (2 vvm). The EPS productivity in an airlift reactor was higher than that in the stirred-tank reactor. The EPS was protein-bound polysaccharides consisted of mainly mannose, xylose, and fructose. The molecular weights of EPS were determined to be $1.3{\sim}1.5{\times}10^6$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2352-2360
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• 2011

In this paper, we introduce a comprehensive design methodology of Radial Basis Function Neural Networks (RBFNN) that is based on mechanism of clustering and optimization algorithm. We can divide some clusters based on similarity of input dataset by using clustering algorithm. As a result, the number of clusters is equal to the number of nodes in the hidden layer. Moreover, the centers of each cluster are used into the centers of each receptive field in the hidden layer. In this study, we have applied Fuzzy-C Means(FCM) and K-Means(KM) clustering algorithm, respectively and compared between them. The weight connections of model are expanded into the type of polynomial functions such as linear and quadratic. In this reason, the output of model consists of relation between input and output. In order to get the optimal structure and better performance, Particle Swarm Optimization(PSO) is used. We can obtain optimized parameters such as both the number of clusters and the polynomial order of weights connection through structural optimization as well as the widths of receptive fields through parametric optimization. To evaluate the performance of proposed model, NXT equipment offered by National Instrument(NI) is exploited. The situation awareness system-related intelligent model was built up by the experimental dataset of distance information measured between object and diverse sensor such as sound sensor, light sensor, and ultrasonic sensor of NXT equipment.

• Journal of Distribution Science
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• v.14 no.11
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• pp.83-90
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• 2016

Purpose - During the last two years, convenient stores (CS) are emerging as one of the most fast-growing retail trades in Korea. The goal of this work is to forecast and to analyze sales at CS using ARIMA-Intervention model (IM) and exponential smoothing method (ESM), together with sales at supermarkets in South Korea. Considering that two retail trades above are homogeneous and comparable in size and purchasing items on off-line distribution channel, individual behavior and characteristic can be detected and also relative superiority of future growth can be forecasted. In particular, the rapid growth of sales at CS is regarded as an everlasting external event, or step intervention, so that IM with season variation can be examined. At the same time, Winters ESM can be investigated as an alternative to seasonal ARIMA-IM, on the assumption that the underlying series shows exponentially decreasing weights over time. In case of sales at supermarkets, the marked intervention could not be found over the underlying periods, so that only Winters ESM is considered. Research Design, Data, and Methodology - The dataset of this research is obtained from Korean Statistical Information Service (1/2010~7/2016) and Survey of Service Trend of Korea Statistics Administration. This work is exploited time series analyses such as IM, ESM and model-fitting statistics by using TSPLOT, TSMODEL, EXSMOOTH, ARIMA and MODELFIT procedures in SPSS 23.0. Results - By applying seasonal ARIMA-Intervention model to sales at CS, the steep and persisting increase can be expected over the next one year. On the other hand, we expect the rate of sales growth of supermarkets to be lagging and tied up constantly in the next 2016 year. Conclusions - Based on 2017 one-year sales forecasts for CS and supermarkets, we can yield the useful information for the development of CS and also for all retail trades. Future study is needed to analyze sales of popular items individually such as tobacco, banana milk, soju and so on and to get segmented results. Furthermore, we can expand sales forecasts to other retail trades such as department stores, hypermarkets, non-store retailing, so that comprehensive diagnostics can be delivered in the future.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.158-161
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• 2003

A cascade structured neural network called Sigma-Pi$_{t}$ Cascaded Hybrid Neural Network ($\sigma$$\pi$$_{t}$-CHNN) is Proposed. It is an extended version of the Sigma-Pi Cascaded extended Hybrid Neural Network ($\sigma$$\pi$-CHNN), where the classical multiplicative neuron ($\pi$-neuron) is replaced by the translated multiplicative ($\pi$$_{t}$-neuron) model. The learning algorithm of $\sigma$$\pi$$_{t}$-CHNN is composed of an evolutionary programming method, responsible for determining the network architecture, and of a Levenberg-Marquadt algorithm, responsible for tuning the weights of the network. The $\sigma$$\pi$$_{t}$-CHNN is evaluated in 2 pattern classification problems: the 2 spirals and the sonar problems. In the 2 spirals problem, $\sigma$$\pi$$_{t}$-CHNN can generate neural networks with 10％ less hidden neurons than that in previous neural models. In the sonar problem, $\sigma$$\pi$$_{t}$-CHNN can find the optimal solution for the problem i.e., a network with no hidden neurons. These results confirm the expanded information processing capabilities of $\sigma$$\pi$$_{t}$-CHNN, when compared to previous neural network models. network models.