• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.899-902
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• 2002

In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state variable in the SC-CNN.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.7
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• pp.1064-1068
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• 2002

In this paper, we introduce a hyper-chaos synchronization method using State-Controlled Cellular Neural Network(SC-CNN). We make a hyper-chaos circuit using SC-CNN with the n-double scroll. A hyper-chaos circuit is created by applying identical n-double scrolls with weak coupled method, to each cell. Hyper-chaos synchronization was achieved using drive response synchronization between the transmitter and receiver about each state in the SC-CNN. From the result of the recovery signal through the demodulation method in the receiver, We shown that recovery quality of state variable $$\chi$_3$ is superior to that of ${$\chi$_2}, {$\chi$_1}$ in secure communication.

• ETRI Journal
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• v.38 no.5
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• pp.885-895
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• 2016

The lowest power mode (passive-standby mode) was proposed for reducing the power consumption of set-top boxes in a standby state when not receiving content. However, low-power set-top boxes equipped with the lowest power mode have been rarely commercialized because of their low-quality user experience. In the lowest power mode, they deactivates almost all of operational modules and processes, and thus require dozens of seconds for activation latency (that is, the latency for activating all modules of the set-top boxes in a standby state). They are not even updated in a standby state because they deactivate their network interfaces in a standby state. This paper proposes an adaptive standby mode scheduling method for improving the user experience of such boxes. Set-top boxes using the proposed method can analyze the activation pattern and find the frequently used time period (that is, when the set-top boxes are frequently activated). They prepare for their activation during this frequently used time period, thereby reducing the activation latency and enabling their update in a standby state.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.83-90
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• 2020

Prototype selection offers the advantage of ensuring low learning time and storage space by selecting the minimum data representative of in-class partitions from the training data. This paper designs a new training data generation method using hyper-rectangles that can be applied to general classification algorithms. Hyper-rectangular regions do not contain different class data and divide the same class space. The median value of the data within a hyper-rectangle is selected as a prototype to form new training data, and the size of the hyper-rectangle is adjusted to reflect the data distribution in the class area. A set cover optimization algorithm is proposed to select the minimum prototype set that represents the whole training data. The proposed method reduces the time complexity that requires the polynomial time of the set cover optimization algorithm by using the greedy algorithm and the distance equation without multiplication. In experimented comparison with hyper-sphere prototype selections, the proposed method is superior in terms of prototype rate and generalization performance.

• ETRI Journal
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• v.40 no.3
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• pp.337-346
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• 2018

In this paper, we propose a grant-aware (GA) scheduling algorithm that can provide higher throughput and lower latency than a conventional dual round-robin matching (DRRM) method. In our proposed GA algorithm, when an output receives requests from different inputs, the output not only sends a grant to the selected input, but also sends a grant indicator to all the other inputs to share the grant information. This allows the inputs to skip the granted outputs in their input arbiters in the next iteration. Simulation results using OPNET show that the proposed algorithm provides a maximum 3% higher throughput with approximately 31% less queuing delay than DRRM.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.177-183
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• 2011

Hidden Markov random eld(HMRF) is one of the most common model for image segmentation which is an important preprocessing in many imaging devices. The HMRF has unknown hyper-parameters on Markov random field to be estimated in segmenting testing images. However, in practice, due to computational complexity, it is often assumed to be a fixed constant. In this paper, we numerically show that the segmentation results very depending on the fixed hyper-parameter, and, if the parameter is misspecified, they further depend on the choice of the class-labelling algorithm. In contrast, the HMRF with estimated hyper-parameter provides consistent segmentation results regardless of the choice of class labelling and the estimation method. Thus, we recommend practitioners estimate the hyper-parameter even though it is computationally complex.

• Smart Media Journal
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• v.9 no.4
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• pp.36-43
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• 2020

The Monte Carlo tree search (MCTS) is a popular method for implementing an intelligent game program. It has several hyper-parameters that require an optimization for showing the best performance. Due to the stochastic nature of the MCTS, the hyper-parameter optimization is difficult to solve. This paper uses the self-playing capability of the MCTS-based game program for optimizing the hyper-parameters. It seeks a winner path over the hyper-parameter space while performing the self-play. The top-q longest winners in the winner path compete for the final winner. The experiment using the 15-15-5 game (Omok in Korean name) showed a promising result.

• Journal of Sensor Science and Technology
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• v.31 no.4
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• pp.260-265
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• 2022

In this study, we establish hyper-elastic haptic feedback in a virtual environment using finite element analysis techniques and develop a Force Torque (FT) sensor utilization method for application in tele-operation environments. In general, regarding haptic feedback data, in a tele-operation environment, the user is provided with feedback according to the measured force data when the model is inserted through an FT sensor. Conversely, in a virtual environment, the press-fitting model can be expressed through the spring-damper system rather than an FT sensor to provide feedback. However, unlike rigid and the elastic bodies, the hyper-elastic body represented by a spring-damper system in a virtual environment is a simple impedance model using stiffness and damping coefficients; it is limited in terms of providing actual feedback. Thus, in this study, haptic feedback was implemented using the data obtained from POD-RBF analysis results during hyper-elastic press-fitting experiments. The haptic feedback mechanism developed in this study was verified by comparing the FT sensor feedback data measured and calculated through hyper-elastic press-fitting experiments with spring-damper feedback data. Subsequently, the POD-RBF analysis feedback was compared and evaluated against the feedback mechanism of each environment through the test subject, and the similarities between the POD-RBF analysis feedback and FT sensor data feedback were verified.

• Polymer(Korea)
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• v.35 no.1
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• pp.66-71
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• 2011

With the synthesis of hyper crosslinked polymer particle (HCPP), having microporous structure with hydroxyl functional group, synthesized via polymerization reaction consists of three stepssuspension polymerization, hyper crosslinking by Friedel-Craft catalysis and hydrolysis reaction, the effects of the ratio of each monomer, hyper crosslinking conditions and $CO_2$ supercritical drying on the variations of surface morphology, pore size & distribution and BET surface area of HCPP have been investigated. It was observed that the formation of surface crack or fracture of HCPP was intimately related with the degree of hyper crosslinking reaction between microphase separated domains. And the value of BET surface area of HCPP increased with the increase of reaction temperature, time and the amounts of solvent used in hyper crosslinking step. Moreover, $CO_2$ supercritical drying was proven to be a very effective method for removing stabilizer, unreacted monomers and oligomers from HCPP but needed to add methanol as a co-solvent for efficient removing of residual catalyst.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.949-954
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• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.