• BMB Reports
• /
• v.42 no.5
• /
• pp.271-276
• /
• 2009

Sweetpotato (Ipomoea batatas (L). Lam.) is relatively tolerant to unfavorable growth conditions such as drought, yet has not been exploited to provide a better understanding of the molecular basis of drought stress tolerance. We obtained 983 high-quality expressed sequence tags of 100 bp or longer (average length of 700 bp) from cDNA libraries of detached white fibrous root tissues by subjecting them to dehydration for 6 h. The 431 cDNAs were each assigned a function by alignment using the BLASTX algorithm. Among them, three genes associated with various abiotic stresses and nine genes not previously associated with drought stress were selected for expression pattern analysis through detailed reverse transcription-polymerase chain reaction. The direct and indirect relationships of the 12 genes with drought tolerance mechanisms were ascertained at different developmental stages and under various stress conditions.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.5 no.1
• /
• pp.1-6
• /
• 2004

Air qualify monitoring if a primary activity for industrial and social environment. Especially, the VOCs(Volatile Organic Compounds) are very harmful for human and environment. Throughout this research. we designed sensor array with various kinds of gas sensor, and the recognition algorithm with ANN(Artificial Neural Network : BP), respectively. We have designed system to recognize various kinds and quantities of VOCs, such as benzene, tolylene, and xylene.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.6
• /
• pp.725-735
• /
• 2006

This paper deals with the problem of self-learning cooperative motion control for the pushing task of a humanoid robot in the sagittal plane. A model with 27 linked rigid bodies is developed to simulate the system dynamics. A simple genetic algorithm(SGA) is used to find the cooperative motion, which is to minimize the total energy consumption for the entire humanoid robot body. And the multi-layer neural network based on backpropagation(BP) is also constructed and applied to generalize parameters, which are obtained from the optimization procedure by SGA, in order to control the system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.113-117
• /
• 1998

We propose a way to show the inherent learning complexity for the multilayer perceptron. We display the solution space and the error surfaces on the input space of a single neuron with two inputs. The evolution of its weights will follow one of the two error surfaces. We observe that when we use the back-propagation(BP) learning algorithm (1), the wight cam not jump to the lower error surface due to the implicit continuity constraint on the changes of weight. The self-relaxation approach is to explicity find out the best combination of all neurons' two error surfaces. The time complexity of training a multilayer perceptron by self-relaxationis exponential to the number of neurons.

• Journal of Advanced Marine Engineering and Technology
• /
• v.28 no.6
• /
• pp.1010-1016
• /
• 2004

In this paper, a scheme for estimating the parameters of solar cells and a NN-based emulator for predicting the maximum power point are presented. The diode model with series and shunt resistors is used to estimate parameters highly affecting its V-I characteristic curve and both a real-coded genetic algorithm and the model adjustment technique are employed. For implementing the emulator, a multi-layered neural network incorporating with the BP algorithm is used. A set of simulation works using both field data and generated data are carried out to demonstrate the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
• /
• 2006.07d
• /
• pp.1943-1944
• /
• 2006

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our methodis different from those using supervised learning algorithms, such as the backpropagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin

• Computers and Concrete
• /
• v.12 no.2
• /
• pp.229-241
• /
• 2013

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

• 제어로봇시스템학회:학술대회논문집
• /
• 1993.10a
• /
• pp.772-777
• /
• 1993

This paper presents a neuro-fuzzy modelling method that determines chemicals dosing model based on historical operation data for effective water quality control in water treatment system and calculates automatically the amount of optimum chemicals dosing against the changes of raw water qualities and flow rate. The structure identification in the modelling by means of neuro-fuzzy reasing is performed by Genetic Algorithm(GA) and Complex Method in which the numbers of hidden layer and its hidden nodes, learning rate and connection pattern between input layer and output layer are identified. The learning network is implemented utilizing Back Propagation(BP) algorithm. The effectiveness of the proposed modelling scheme and the feasibility of the acquired neuro-fuzzy network is evaluated through computer simulation for chemicals dosing control in water treatment system.

• Proceedings of the KIEE Conference
• /
• 2004.07d
• /
• pp.2484-2486
• /
• 2004

"Dynamic Neural Unit"(DNU) based upon the topology of a reverberating circuit in a neuronal pool of the central nervous system. In this thesis, we present a genetic DNU-control scheme for unknown nonlinear systems. Our method is different from those using supervised learning algorithms. such as the back propagation (BP) algorithm, that needs training information in each step. The contributions of this thesis are the new approach to constructing neural network architecture and its trainin.

• Proceedings of the KIEE Conference
• /
• 1997.11a
• /
• pp.203-205
• /
• 1997

In this paper, we present a useful method of estimating earth resistivity using BP algorithm in Neural-Networks. From this method, equivalent earth resistivity(EER) can be obtained directly using training data composed of field-measured apparent resistivity and probe distance. This approach can reduce errors which is conventionally raised from manual operation of calculating EER. To evaluate its accuracy and convenience, the result of proposed method is compared to that of conventional methods, graphical($\rho$-a graph) and numerical(CDEGS program), respectively.