• Journal of KIISE:Computing Practices and Letters
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• v.15 no.10
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• pp.724-739
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• 2009

This paper suggests the energy-efficient message delivery scheme and the software platform which exploits the multiple network interfaces of the mobile terminals and GPS in the current mobile devices. The mobile terminals determine the delivery method among 'direct', 'indirect', and 'WAN' based on the position information of itself and other terminals. 'Direct' method sends a message directly to the target terminal using local RAT. 'Indirect' method extends the service area by exploiting intermediate terminals as relay node. If the target terminal is too far to reach through 'direct' or 'indirect' method, the message is sent using wireless WAN technology. Our proposed scheme exploits the position information and, thus, power consumption is drastically reduced in determining handover time and direction. Network simulation results show that our proposed delivery scheme improves the message transfer efficiency and the handover detection latency. We implemented a message platform in a smart phone realizing the proposed delivery scheme. We compared our platform with other typical message platforms from energy efficiency aspect by observing the real power consumption and applying the mathematical modeling. The comparison results show that our platform requires significantly less power.

• Journal of the Korea Society of Computer and Information
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• v.10 no.2 s.34
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• pp.113-121
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• 2005

In this paper, we design the hybrid learning algorithm of LVQ which is to perform EMG pattern recognition. The proposed hybrid LVQ learning algorithm is the modified Counter Propagation Networks(C.p Net. ) which is use SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of LVa. The weights of the proposed C.p. Net. which is between input layer and subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVd algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights which is between subclass layer and class layer of C.p. Net. is learned to classify the classified subclass. which is enclosed a class . To classify the pattern vectors of EMG. the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

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

• Journal of the Korean Data and Information Science Society
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• v.11 no.2
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• pp.157-172
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• 2000

Metal containers represent a situation where a specific metal is exposed to a wide variety of electrolytes of varying degrees of corrosivity. For example, hundreds, if not thousands of different products are packaged in an aluminum beverage can. These products vary in pH, chloride concentration and other natural or artificial ingredients which can effect the type and severity of potential corrosion. Both localized (perforation) and uniform corrosion (metal dissolution without the onset of pitting) may occur in the can. A quick test or series of tests which could predict the propensity towards both types of corrosion would be useful to the manufacturer. Electrochemical noise data is used to detect the onset and continuation of pitting corrosion. Specific noise parameters such as the noise resistance (the potential noise divided by the current noise) have been used to both detect pitting corrosion and also to estimate the pitting severity. The utility of noise resistance and other electrochemical parameters has been explored through the application of artificial neural networks. The versatility of artificial neural networks is further demonstrated by combing electrochemical data with electrolyte properties such as pH and chloride concentration to predict both the severity of both localized and uniform corrosion.

• Journal of IKEEE
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• v.15 no.4
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• pp.354-362
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• 2011

One of the unsolved problems in Artificial Neural Networks is related to the capacity of a neural network. This paper presents a CoreNet which has a multi-leveled input and a multi-leveled output as a 2-layered artificial neural network. I have suggested an equation for calculating the capacity of the CoreNet, which has a p-leveled input and a q-leveled output, as $a_{p,q}=\frac{1}{2}p(p-1)q^2-\frac{1}{2}(p-2)(3p-1)q+(p-1)(p-2)$. With an odd value of p and an even value of q, (p-1)(p-2)(q-2)/2 needs to be subtracted further from the above equation. The simulation model 1(3)-1(6) has 3 levels of an input and 6 levels of an output with no hidden layer. The simulation result of this model gives, out of 216 possible functions, 80 convergences for the number of implementable function using the cot(x) input leveling method. I have also shown that, from the simulation result, the two diverged functions become implementable by precalculating the weight values. The simulation result and the precalculation of the weight values give the same result as the above equation in the total number of implementable functions.

• Convergence Security Journal
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• v.21 no.1
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• pp.121-127
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• 2021

MANET, which does not have any infrastructure other than wireless nodes, has the advantage of being able to construct a fast network. However, the movement of nodes and wireless media are also the causes of security vulnerabilities of MANET. In particular, the damage caused by the attacking nodes existing on the network is considerably greater than that of other networks. Therefore, it is necessary to detection technique for attacking nodes and techniques to reduce damage caused by attacks. In this paper, we proposed a hierarchical structure technique to increase the efficiency of intrusion detection and collaboration-based intrusion detection technique applying a P2P mesh network configuration technique to reduce damage caused by attacks. There was excluded the network participation of the attacking node in advance through the reliability evaluation of the nodes in the cluster. In addition, when an attack by an attacking node is detected, this paper was applied a method of minimizing the damage of the attacking node by transmitting quickly the attack node information to the global network through the P2P mesh network between cluster heads. The ns-2 simulator was used to evaluate the performance of the proposed technique, and the excellent performance of the proposed technique was confirmed through comparative experiments.

• Journal of the Korean Ceramic Society
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• v.38 no.6
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• pp.593-595
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• 2001

Zinc-phosphate glasses에 B$_2$O$_3$를 도입하여 borate, phosphate, boro-phosphate networks가 혼재되어 있는 highly cross-linked structures를 형성시켜 Phosphate glasses의 화학적 안정화를 검토하였다. Raman 측정 결과 B$_2$O$_3$와 P$_2$O$_{5}$는 잘 혼화된 polynary networks를 이루고 있으며, 이렇게 하여 증진된 구조적 cross-linking에 의하여 xB$_2$O$_3$.(1-x)Zn$_2$P$_2$O$_{7}$ glasses의 T$_{g}$, T$_{d}$는 증가하였고 동시에 CTE는 감소하는 결과를 가져왔다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.195-201
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• 2014

In this study, face recognition system was designed based on polynomial Radial Basis Function Neural Networks(pRBFNNs) pattern classifier using 2-directional 2-dimensional principal component analysis algorithm. Existing one dimensional PCA leads to the reduction of dimension of image expressed by the multiplication of rows and columns. However $(2D)^2PCA$(2-Directional 2-Dimensional Principal Components Analysis) is conducted to reduce dimension to each row and column of image. and then the proposed intelligent pattern classifier evaluates performance using reduced images. The proposed pRBFNNs consist of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with the aid of fuzzy c-means clustering. In the conclusion part of rules. the connection weight of RBFNNs is represented as the linear type of polynomial. The essential design parameters (including the number of inputs and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. Using Yale and AT&T dataset widely used in face recognition, the recognition rate is obtained and evaluated. Additionally IC&CI Lab dataset is experimented with for performance evaluation.

• Journal of Communications and Networks
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• v.9 no.2
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• pp.185-191
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• 2007

Alpha stable distribution is better for modeling impulsive noises than Gaussian distribution in signal processing. This class of process has no closed form of probability density function and finite second order moments. In general, Wiener filter theory is not meaningful in S$\alpha$SG environments because the expectations may be unbounded. We proposed a new adaptive recursive least p-norm Kalman filtering algorithm based on least p-norm of innovation process with infinite variances, and a new robust multiuser detection method based on least p-norm Kalman filtering. The simulation experiments show that the proposed new algorithm is more robust than the conventional Kalman filtering multiuser detection algorithm.

• Journal of muscle and joint health
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• v.28 no.2
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• pp.174-182
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• 2021

Purpose: This study is a secondary data analysis study to identify factors related to the quality of life of people aged 65 y or older, according household type. Methods: In 2019, the study extracted the data from the elderly participants (65 y of age or older) from G province Community Health Survey. The data were compiled and analyzed in a composite sample. Results: The quality of life was lowest among single people, grandparents-grandchildren (F=39.88, p<.001). Variables that significantly influenced quality of life in single-person households were basic security(β=-.03, p=.002), high-risk drinking (β=.04, p=.002), number of day to walk (β=.01, p<.001), diabetes mellitus (β=-.03, p<.001), depression (β=-.02, p<.001), and contact frequency (β=.00, p<.001). Variables that significantly influenced grandparents-grandchildren households were basic security (β=.03, p<.001), smoking(β=-.02, p<.001), number of day to walk (β=.00, p<.001), hypertension (β=-.01, p=.009), diabetes mellitus (β=-.04, p<.001), cognitive impairment (β=-.02, p<.001), depression(β=-.02, p<.001), contact frequency (β=.01, p<.001), and neighborhood trust (β=.02, p<.001). Conclusion: In this study, there were differences in health-related quality of life for each type of household, and various support systems are needed in their social networks to suit their characteristics.