• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.177-180
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• 2008

There are many application areas of sensor networks, such as surveillance, hospital monitoring, and home network. These are dependent on the secure operation of networks, and will have serious outcome if the networks is injured. An adversary can inject false data into the network through the compromising node. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false data during forwarding process. In this scheme, it is important that the choice of the threshold value since it trades off security and overhead. This paper presents an adaptive threshold value determining method in the SEF using fuzzy logic. The fuzzy logic determines a security distance value by considering the situation of the network. The Sensor network is divided into several areas by the security distance value, it can each area to uses the different threshold value. The fuzzy based threshold value can reduce the energy consumption in transmitting.

• Journal of Communications and Networks
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• v.12 no.5
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• pp.475-479
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• 2010

The clipping value, defined as the log-likelihood ratio (LLR) in the case wherein all the list of candidates have the same binary value, is investigated, and an effective method to estimate it is presented for iterative tree search detection. The basic principle behind the method is that the clipping value of a channel bit is equal to the LLR of the maximum probability of correct decision of the bit to the corresponding probability of erroneous decision. In conjunction with multilevel bit mappings, the clipping value can be calculated with the parameters of the number of transmit antennas, $N_t$; number of bits per constellation point, $M_c$; and variance of the channel noise, $\sigma^2$, per real dimension in the Rayleigh fading channel. Analyses and simulations show that the bit error performance of the proposed method is better than that of the conventional fixed-value method.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.197-200
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• 2008

In most sensor networks, nodes can be easily compromised by adversaries due to hostile environments. Adversaries may use compromised nodes to inject false reports into the sensor networks. Such false report attacks will cause false alarms that can waste real-world response effort, and draining the finite amount of energy resource in the battery-powered network. A dynamic enroute scheme proposed by Yu and Guan can detect and drop such false reports during the forwarding phase. In this scheme, choosing a threshold value is very important, as it trades off between security power and energy consumption. In this paper, we propose a threshold determining method which uses the fuzzy rule-based system. The base station periodically determines a threshold value though the fuzzy rule-based system. The number of cluster nodes, the value of the key dissemination limit, and the remaining energy of nodes are used to determine the threshold value.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.20 no.3
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• pp.255-264
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• 2002

The topography is recognized as an important factor in determining the streamflow response of watershed to precipitation. In watershed analysis, stream networks are very important parameters. Each DEM grid size and flowaccumulation threshold value of drainage accumulation matrix have influence on stream networks extracted by using grid DEM. Therefore, stream networks extracted from DEM varies with each DEM grid size and flowaccumulation threshold value. Generally, small threshold values will generate more detailed stream network with higher drainage density High threshold values will generate coarser stream networks. In this paper, total stream length in the study area was used to calculate the flowaccumulation threshold value by each DEM grid size. Stream network was derived by each DEM grid size, which is applied flowaccumulation threshold value. Regression equation was derived by correlation between flowaccumulation threshold value and each DEM grid size.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.131-136
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• 1999

This paper proposes the robot inverse calibration method using a neural networks. A highorder networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the difference of joint variables only between measuring value and analytic value about the desired pose(position, orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness, the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from $\pm$2$^{\circ}$to $\pm$ 0.1$^{\circ}$.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.86-94
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• 1997

This paper proposes the robot inverse calibration method using a neural networks. A high-order networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the diff- erence of joint variables only between measuring value and analytic value about the desired pose(position, orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness, the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from .+-. 5 .deg. to .+-. 0.1 .deg. .

• KDI Journal of Economic Policy
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• v.34 no.3
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• pp.133-186
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• 2012

This paper analyzes the effectiveness of social networks in finding jobs and estimates the value of job search network using the Korean Laber and Income Panel Study (KLIPS) dataset and utilizing the Difference-in-Difference Propensity Score Matching (PSM) methodology (Heckman et al., 1997). While the wide use of social networks in the Korean labor market is often perceived as 'nepotism,' this study confirms that social networks, by serving as an effective information transmitter between job search and recruitment, make a significant contribution to improving the adequacy of job matching in the domestic labor market. In order to verify the effectiveness of using social networks for getting jobs, this study looks into the cases of labor turnover using social networks and also not using it. In the aspect of individual satisfaction improvement relating to workplace and job duties, both cases of turnover turn out to experience an increased satisfaction by 2~3 points (on a 100-score scale). Meanwhile, as for the educational and technical adequacy improvement, no positive effects are found in the case of turnover without social networks, whereas the educational and technical adequacy improvement turns out to increase by 2.13 and 2.52 points, respectively, in the case of turnover using social networks. The effect of income increase through turnover using social networks registered 40,074 Korean won per month (as of 2010), which can be considered as the result from the improved educational and technical adequacy. Of all things being considered, the value of job search network per wage worker in the Korean society is estimated to be 18.72 million won in terms of life-cycle wage improvement, and 758.2 scores in terms of the improvement of working life satisfaction. Provided that the cash value of satisfaction score 1 is equivalent to 'n' times 10,000 won, the aggregate value of job search network is estimated to be 18.72+7.582n million won, which means the total amount of costs that a wage worker in the Korean society willingly pays to maintain and manage job networks for lifetime.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.1
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• pp.33-58
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• 2024

This paper reviews basic concepts for Physics-Informed Neural Networks (PINN) applied to the initial value problems for ordinary differential equations. In particular, using only basic calculus, we derive the error estimates where the error functions (the differences between the true solution and the approximations expressed by neural networks) are dominated by training loss functions. Numerical experiments are conducted to validate our error estimates, visualizing the relationship between the error and the training loss for various first-order differential equations and a second-order linear equation.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.2
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• pp.108-115
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• 1999

This paper proposes the robot inverse calibration method using a neural networks. A highorder networks called Pi-Sigma networks has been used. The Pi-Sigma networks uses linear summing units in the hidden layer and product unit in output layer. The inverse calibration model which compensates the difference of joint variables only between measuring value and analytic value about the desired pose(position orientation) of a robot is proposed. The compensated values are determined by using the weights obtained from the learning process of the neural networks previously. To prove the reasonableness the SCARA type direct drive robot(4-DOF) and anthropomorphic robot(6-DOF) are simulated. It shows that the proposed calibration method can reduce the errors of the joint variables from $\pm$3 to $\pm$0.1.

• Journal of the Korean Mathematical Society
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• v.53 no.5
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• pp.1133-1148
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• 2016

In this paper, we first discuss a representation of solutions to the initial value problem and the initial-boundary value problem for discrete evolution equations $${\sum\limits^l_{n=0}}c_n{\partial}^n_tu(x,t)-{\rho}(x){\Delta}_{\omega}u(x,t)=H(x,t)$$, defined on networks, i.e. on weighted graphs. Secondly, we show that the weight of each link of networks can be uniquely identified by using their Dirichlet data and Neumann data on the boundary, under a monotonicity condition on their weights.