• Korean Journal of Poultry Science
• /
• v.9 no.2
• /
• pp.19-62
• /
• 1982

1. The primary purpose of this study was to analyse the current status of layer operations in Korea related to management practices and input and output relationship in egg production by surveying 150 egg producers throughout the country. Based on this primary information, this study attempted to illustrate a model layer farming budget. 2. The average size of the layer operations included in this survey was 7,969 hens per farm during the period from September 1, 1980 to August 31, 1981. However, about 80% of the producers started the layer farming with smaller scales than 3,000 layers and less funds than 10 million won during the later half of 1960s and the early half of 1970s. About 72% of the farmers were graduates from high school or college. These egg producers listed that lack of funds and poor production and management skills are the most important problems in the operation. 3. The farmers used to purchase baby chicks from the well-known hatcheries and commercial mixed feeds on one or two months' credit. While the eggs were sold to wholesalers and/or assemblers. Few of the producers market their products directly or cooperatively through the industry organization.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2002.11a
• /
• pp.103-106
• /
• 2002

We prepared $Co_{77}Cr_{20}Ta_{3}$ thin film with Facing Targets Sputtering Apparatus. which can deposit a high quality thin film CoCrTa magnetic layer for Perpendicular magnetic recording media. In order to obtain Good Crystal orientation of CoCrTa thin films. We prepared Thin Films on slide glass substrate. The thickness of Buffer-layer were varied from 10 to 50 nm and Magnetic layer thickness fixed 100[nm]. input current was varied from 0.2[A] to 0.5[A]. Substrate temperature was varied from room temperature to ${250^{\circ}C}$ respectively. The crystal orientation of the CoCrTa film were examined with XRD. Introduce Buffer-layer thin films showed improvement of dispersion angle of c-axis orientation (${\Delta\theta}_{50}$).

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.64 no.10
• /
• pp.1469-1478
• /
• 2015

In this paper, The methodology of Type-2 fuzzy set-based Radial Basis Function Neural Network(T2RBFNN) is proposed for Sewage Treatment Process and the simulator is developed for application to the real-world sewage treatment plant by using the proposed model. The proposed model has robust characteristic than conventional RBFNN. architecture of network consist of three layers such as input layer, hidden layer and output layer of RBFNN, and Type-2 fuzzy set is applied to receptive field in contrast with conventional radial basis function. In addition, the connection weights of the proposed model are defined as linear polynomial function, and then are learned through Back-Propagation(BP). Type reduction is carried out by using Karnik and Mendel(KM) algorithm between hidden layer and output layer. Sewage treatment data obtained from real-world sewage treatment plant is employed to evaluate performance of the proposed model, and their results are analyzed as well as compared with those of conventional RBFNN.

• Journal of Industrial Technology
• /
• v.41 no.1
• /
• pp.1-6
• /
• 2021

Fly ash and silica fume belong to industry by-products that can be used to produce concrete. This study shows the model of a neural network to evaluate the strength development of blended concrete containing fly ash and silica fume. The neural network model has four input parameters, such as fly ash replacement content, silica fume replacement content, water/binder ratio, and ages. Strength is the output variable of neural network. Based on the backpropagation algorithm, the values of elements in the hidden layer of neural network are determined. The number of neurons in the hidden layer is confirmed based on trial calculations. We find (1) neural network can give a reasonable evaluation of the strength development of composite concrete. Neural network can reflect the improvement of strength due to silica fume additions and can consider the reductions of strength as water/binder increases. (2) When the number of neurons in the hidden layer is five, the prediction results show more accuracy than four neurons in the hidden layer. Moreover, five neurons in the hidden layer can reproduce the strength crossover between fly ash concrete and plain concrete. Summarily, the neural network-based model is valuable for design sustainable composite concrete containing silica fume and fly ash.

• Korean Journal of Soil Science and Fertilizer
• /
• v.41 no.1
• /
• pp.1-8
• /
• 2008

The objectives of this study were to compare nutrient natural input between thinned and unthinned natural hardwood stands at Mt. Joongwang, Pyongchang-gun, Gangwon-do. Throughfall, stemflow, A-layer and B-layer soil water as well as litterfall were sampled at two-week intervals during the period of June to October from 2002 to 2004. The amount of rainfall interception in thinned and unthinned natural hardwood stands was as 12% and 18%, respectively. The results indicated that there was no difference in annual nutrient input by rainfall between thinned and unthinned stands. $Na^+$, $Cl^-$ and $SO_4{^{2-}}$ concentrations of A-layer soil water in the unthinned stand were higher than those in the thinned stand. In the B-layer soil water, $Ca^{2+}$, $Cl^-$, $NO_3{^-}$ and $SO_4{^{2-}}$ concentrations in the unthinned stand were higher than those in thinned stand. Mean annual litterfall input was $2,706kg\;ha^{-1}$ in unthinned stand and $2,589kg\;ha^{-1}$ in thinned stand. Total-N input from litterfall was $50.28kg\;ha^{-1}yr^{-1}$ in the unthinned stand and $36.81kg\;ha^{-1}yr^{-1}$ in the thinned stand, while there was no difference in exchangeable cation input from litterfall between thinned and unthinned stands. Thus, the difference in nutrient inputs except for N by throughfall, stemflow and litterfall between the two stands was not influenced by thinning.

• Journal of Intelligence and Information Systems
• /
• v.15 no.2
• /
• pp.49-59
• /
• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• Proceedings of the IEEK Conference
• /
• 2003.07d
• /
• pp.1565-1568
• /
• 2003

In this paper, we propose modular network to solve difficult and complex problems that are seldom solved with multi-layer neural network. The structure of modular neural network in researched by Jacobs and Jordan is selected in this paper. Modular network consists of several expert networks and a gating network which is composed of single-layer neural network or multi-layer neural network. We propose modular network structure using recurrent neural network, since the state of the whole network at a particular time depends on an aggregate of previous states as well as on the current input. Finally, we show excellence of the proposed network compared with modular network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.694-697
• /
• 2003

In this paper, we propose modular network to solve difficult and complex problems that are seldom solved with Multi-Layer Neural Network(MLNN). The structure of Modular Neural Network(MNN) in researched by Jacobs and jordan is selected in this paper. Modular network consists of several Expert Networks(EN) and a Gating Network(CN) which is composed of single-layer neural network(SLNN) or multi-layer neural network. We propose modular network structure using Recurrent Neural Network(RNN), since the state of the whole network at a particular time depends on aggregate of previous states as well as on the current input. Finally, we show excellence of the proposed network compared with modular network.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.4
• /
• pp.635-641
• /
• 2016

In many industrial applications that use the electric motors robust controllers are needed. The method using a neural network in order to design a robust controller when a disturbance occurs is studied. Backpropagation algorithm, which is used in a conventional neural network controller is used in many areas, but when the number of neurons in the input layer, hidden layer and output layer of the neural network increases the processing speed of the learning process is slow. In this paper an adaptive PI(Proportional and Integral) controller based on CTRNN(Continuous Time Recurrent Neural Network) for permanent magnet synchronous motors is presented. By varying the load and the speed the validity of the proposed method is verified through simulation and experiments.