• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.1037-1040
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• 2000

We introduce the forecasting method for a next day electric peak load that uses the optimal combination of two types of neural networks. First network uses learning data that are past 10days of the target day. We name the neural network Short Term Neural Network (STNN). Second network uses those of last year. We name the neural network Long Term Neural Network (LTNN). Then we get the forecasting results that are the linear combination of the forecasting results by STNN and the forecasting results by LTNN. We name the method Combination Forecasting Method (CFM). Then we discuss the optimal combination of STNN and LTNN. Using CFM of the optimal combination of STNN and LTNN, we can reduce the forecasting error.

• ETRI Journal
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• v.31 no.3
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• pp.263-270
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• 2009

This paper investigates linear soft combination schemes for cooperative spectrum sensing in cognitive radio networks. We propose two weight-setting strategies under different basic optimality criteria to improve the overall sensing performance in the network. The corresponding optimal weights are derived, which are determined by the noise power levels and the received primary user signal energies of multiple cooperative secondary users in the network. However, to obtain the instantaneous measurement of these noise power levels and primary user signal energies with high accuracy is extremely challenging. It can even be infeasible in practical implementations under a low signal-to-noise ratio regime. We therefore propose reference data matrices to scavenge the indispensable information of primary user signal energies and noise power levels for setting the proposed combining weights adaptively by keeping records of the most recent spectrum observations. Analyses and simulation results demonstrate that the proposed linear soft combination schemes outperform the conventional maximal ratio combination and equal gain combination schemes and yield significant performance improvements in spectrum sensing.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.160-160
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• 2000

An optimization approach is used to solve the division problem of working area, and a cost function is defined to represent the constraints on the solution, which is then mapped onto the Hopfield neural network for minimization. Each neuron in the network represents a possible combination among many components. Division is achieved by initializing each neuron that represents a possible combination and then allowing the network settle down into a stable state. The network uses the initialized inputs and the compatibility measures among components in order to divide working area.

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.431-438
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• 1999

The environmental control based on interactive thermoregulatory behavior for swine production has many advantages over the conventional temperature-based control methods. Therefore, this study was conducted to compare various feature selection methods using postural images of growing pigs under various environmental conditions. A color CCD camera was used to capture the behavioral images which were then modified to binary images. The binary images were processed by thresholding, edge detection, and thinning techniques to separate the pigs from their background. Following feature were used for the input patterns to the neural network ; \circled1 perimeter, \circled2 area, \circled3 Fourier coefficients (5$\times$5), \circled4 combination of (\circled1 ＋ \circled2), \circled5 combination of (\circled1 ＋ \circled3), \circled6 combination of (\circled2 ＋ \circled3), and \circled7 combination of (\circled1 ＋ \circled2 ＋ \circled3). Using the above each input pattern, the neural network could classify training images with the success rates of 96%, 96%, 96%, 100%, 100%, 96%, 100%, and testing images with those of 88%, 86%, 93%, 96%, 91%, 90%, 98%, respectively. Thus, the combination of perimeter, area and Fourier coefficients of the thinning images as neural network features gave the best performance (98%) in the behavioral classification.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.468-471
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• 1999

In this paper, we present a solution for combining multiple neural networks. Each neural network is trained with different features. And the neural networks are combined by four methods. The recognition rates by four combination methods are compared. The experimental results for handwritten digit recognition shows that the combination at hidden layers by single layer neural network is superior to any other methods. The reasons of the results are explained.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.836-843
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• 2005

The Wireless Internet services widely spread out with the developments of CDMA(Code Division Multiple Access) networks and wireless units. In contrast to the telecommunication network, WPAN (Wireless Personal Area Network) enables to transmit data and voice in personal area. Although WPAN technologies are commercially utilized, the combined services with COMA network are not so poplar up to now. Various services can be provided using the combination between COMA and WPAN. This paper presents the practical and united model between COMA and WPAN. Specially, the main focus of this research lies on the design of the Middle-ware system of a handset which could be managing both COMA and WPAN. This system used Bluethooth by WPAN. For the devices with the proposed WPAN Middle-ware, service areas of the COMA network can be expanded to WPAN, various services can be realized by the transmission of data and voice, and consequently, the user computing environment could be improved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2415-2423
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• 2009

We consider organizational knowledge combination process. Previous literature investigated the issue from either managerial or social aspects, emphasizing on a part of the whole process and limiting to qualitative analyses. Here we propose an integrative and quantitative approach which considers knowledge combination process from engineering perspective. We employ a queueing network model and techniques to capture the process of interactions of entities in the knowledge combination environment. By doing so, we are able to understand the performance of the knowledge combination process. The performance measures derived can provide valuable implications for managerial decisions such as planning and controlling the knowledge combination process.

• Herbal Formula Science
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• v.27 no.1
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• pp.65-71
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• 2019

Objective : This study aims to diversify interpretation of formulas from "Onbyungjobyun" by analyzing various formulas from "Onbyungjobyun" as basic formulas. Method : This study found herbal communities of combination by analyzing herbal combinations based on network analysis of formulas from "Onbyungjobyun", and has analyzed each community of combination as basic formulas. Result : The results of network analysis showed a total of 3 herbal communities of combination; first was medicinal herbs from Eungyo-san(銀翹散), Jeungaek-tang(增液湯), Bokmaek-tang(復脈湯), Gyeji-tang(桂枝湯), Sogeonjung-tang (小建中湯) series; second was medicinal herbs from Angungwoohwang-hwan(安宮牛黃丸); third was medicinal herbs from Baekho-tang(白虎湯), Jaseol-dan(紫雪丹), Sayeok-tang(四逆湯) series. Conclusion : The formulas from "Onbyungjobyun" are consisted of herbal communities of combination; that treat warm-heat pathogen and supplies yin essence or yang qi; treat reverse transmission to the pericardium(逆傳心包); and treat heat in the qi phase in Onbyeong and cold-dampness in the middle energizer.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.177-180
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• 2010

In cadastral field GPS mainly applies to fundamental survey, while there are numerous research about cadastral detail survey using GPS application in order to increase surveying efficiency as survey technology improve. The purpose of this experiment is to analyze the accuracy of position and estimate the efficiency of GPS/GLONASS combination surveying with control points. As the result of this experiment, Network RTK-GPS/GLONASS combination survey is superior to Newtork RTK-GPS with respect to position accuracy and work efficiency.