• ETRI Journal
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• 제31권1호
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• pp.62-64
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• 2009

We report on the bias stability characteristics of transparent ZnO thin film transistors (TFTs) under visible light illumination. The transfer curve shows virtually no change under positive gate bias stress with light illumination, while it shows dramatic negative shifts under negative gate bias stress. The major mechanism of the bias stability under visible illumination of our ZnO TFTs is thought to be the charge trapping of photo-generated holes at the gate insulator and/or insulator/channel interface.

• ETRI Journal
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• 제31권6호
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• pp.741-748
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• 2009

We present an analysis of microstrip coupled lines (MCLs) used to improve the stability of a 60 GHz narrowband amplifier. The circuit has a 4-stage structure implementing MCLs instead of metal-insulator-metal (MIM) capacitors for the unconditional stability of the amplifier and yield enhancement. The stability parameter, U, is used to compare the stability of MCLs with that of MIM capacitors. Experimental results show that MCLs are more stable than MIM capacitors with the same capacitances as MCLs because the parasitic parallel resistances of MCLs are lower than those of MIM capacitors. Moreover, the bandwidth of an amplifier using MCLs is narrower than one using MIM capacitors because the parasitic series inductances of MCLs are higher than those of MIM capacitors.

• ETRI Journal
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• 제34권6호
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• pp.966-969
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• 2012

From a practical viewpoint, the topic of electrical stability in oxide thin-film transistors (TFTs) has attracted strong interest from researchers. Positive bias stress and constant current stress tests on indium-gallium-zinc-oxide (IGZO)-TFTs have revealed that an IGZO-TFT with a larger Ga portion has stronger stability, which is closely related with the strong binding of O atoms, as determined from an X-ray photoelectron spectroscopy analysis.

• 제어로봇시스템학회논문지
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• 제2권2호
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• pp.108-114
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• 1996

We present proofs of the stability and convergence of Self-organizing feature map (SOFM) neural network with time-invarient learning rate and binary reinforcement function. One of the major problems in Self-organizing feature map neural network concerns with learning rate-"Kalman Filter" gain in stochsatic control field which is monotone decreasing function and converges to 0 for satisfying minimum variance property. In this paper, we show that the stability and convergence of Self-organizing feature map neural network with time-invariant learning rate. The analysis of the proposed algorithm shows that the stability and convergence is guranteed with exponentially stable and weak convergence properties as well.s as well.

• 대한임베디드공학회논문지
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• 제16권6호
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• pp.307-312
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• 2021

This paper proposes an adaptive time-delayed control approach with the integral sliding-mode surface for the fast convergence rate of robot manipulators. Adaptive switching gain aims to guarantee the system stability in such a way as to suppress time-delayed estimation error in the proposed control approach. Moreover, it makes an effort to increase the convergence ability in reaching the phase. An integral sliding-mode surface is employed to achieve a fast convergence rate in the sliding phase. The stability of the proposed one is proved to be asymptotically stable in the Lyapunov stability. The efficiency of the proposed control approach is illustrated with a tutorial example in robot manipulator, which is compared to that of the existing control approach.

• Korean Journal of Mathematics
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• 제31권3호
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• pp.269-294
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• 2023

The purpose of this paper is to introduce a new three-step iterative process and show that our iteration scheme is faster than other existing iteration schemes in the literature. We provide a numerical example supported by graphs and tables to validate our proofs. We also prove convergence and stability results for the approximation of fixed points of the contractive-like mapping in the framework of uniformly convex Banach space. In addition, we have established some weak and strong convergence theorems for nonexpansive mappings.

• KDI Journal of Economic Policy
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• 제39권1호
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• pp.19-40
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• 2017

This paper analyzes the interaction between monetary and macroprudential policies mainly in the context of the non-cooperation among policy authorities. Each policy authority's optimal response is to tighten its policy measures when other authorities' policy measures are loosened. This indicates that the two policies are substitutes for each other. This result still holds when an additional financial stability mandate is assigned to the central bank. The condition for the response functions to converge to a Nash equilibrium state is analyzed along with the speed of convergence, showing that they depend on the authorities' preferences and the number of mandates assigned to policy authorities. If the financial supervisory authority (FSA) assigns greater importance to the output gap or a stronger financial stability mandate is assigned to the central bank (CB), the probability of non-convergence increases and the speed of convergence declines even when the condition of convergence is satisfied. Meanwhile, if the CB considers output stability as an important task, the probability of convergence and the speed of converging to a state of equilibrium are high. Finally, when a single mandate or small number of mandates is/are assigned to each authority, stability is more quickly restored as compared to when many mandates are assigned.

• 스마트미디어저널
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• 제11권5호
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• pp.82-90
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• 2022

Along with the growing popularity of 5G technology, providing flexible and personalized network services suitable for requirements of customers has also become a lucrative venture and business key for network service providers. Therefore, dynamic network provisioning is needed to help network service providers. Moreover, increasing user demand for network services meets specific requirements of users, including location, usage duration, and QoS. In this paper, a routing algorithm, which makes routing decisions using Reinforcement Learning (RL) based on the information about link stability, is proposed and called Link Stability aware Reinforcement Learning (LSRL) routing. To evaluate this algorithm, several mininet-based experiments with various network settings were conducted. As a result, it was observed that the proposed method accepts more requests through the evaluation than the past link annotated shorted path algorithm and it was demonstrated that the proposed approach is an appealing solution for dynamic network provisioning routing.

• 융복합기술연구소 논문집
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• 제10권1호
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• pp.41-45
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• 2020

This paper presents the effects of a virtual mass on the stability boundary of a virtual spring in the haptic system with first-order-hold. The virtual rigid body is modeled as a virtual spring and a virtual mass. When first-order-hold is applied, we analyze the stability boundary of the virtual spring through the simulation according to the virtual mass and the sampling time. As the virtual mass increases, the stability boundary of the virtual spring gradually increases and then decreases after reaching the maximum value. The results are compared with the stability boundary in the haptic system with zero-order-hold. When a virtual mass is small, the stability boundary of a virtual spring in the system with first-order-hold is larger than that in the system with zero-order-hold.

• 대한수학회논문집
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• 제32권4호
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• pp.1009-1024
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• 2017

In this paper, we consider the new faster iterative scheme due to Sintunavarat and Pitea ([32]) for further investigation and we prove its strong and ${\Delta}$-convergence theorems, data dependence and stability results in hyperbolic space. Our results extend, improve and generalize several recent results in CAT(0) space and uniformly convex Banach space.