• Structural Engineering and Mechanics
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• v.64 no.1
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• pp.109-120
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• 2017

The behavior of moment resistant steel structures depends on both the beam-column connections and columns foundations connections. Obviously, if the connections can meet the adequate ductility and resistance against lateral loads, the seismic capacity of these structures will be linked practically to the performance of these connections. The shape memory alloys (SMAs) have been most recently used as a means of energy dissipation in buildings. The main approach adopted by researchers in the use of such alloys is firstly bracing, and secondly connecting the beams to columns. Additionally, the behavior of these alloys is modeled in software applications rarely involving equivalent torsional springs and column-foundation connections. This paper attempts to introduce the shape memory alloys and their applications in steel structural connections, proposing a new steel column-foundation connection, not merely a theoretical model but practically a realistic and applicable model in structures. Moreover, it entails the same functionality as macro modeling software based on real behavior, which can use different materials to establish a connection between the columns and foundations. In this paper, the suggested steel column-foundation connection was introduced. Moreover, exploring the seismic dynamic behavior under cyclic loading protocols and the famous earthquake records with different materials such as steel and interconnection equipment by superelastic shape memory alloys have been investigated. Then, the results were compared to demonstrate that such connections are ideal against the seismic behavior and energy dissipation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.1-7
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• 2006

This paper presents experimental instruction-level current consumption model for low power microcontroller ATmega128. The accessibility of instruction for internal memory decides power consumption of the microcontroller as much as 17% of difference between access instruction and non-access instruction. The power consumption for the given program will be increased in the proportional to the ratio of memory access instruction and lower level memory access in the hierarchy. Throughout the current consumption model, the power consumption can be predicted and optimized in the direction of reducing the frequency memory access. Also, the various optimization methods are introduced in terms of software and hardware viewpoints.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.80-90
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• 2010

In this paper, we introduce some issues to implement an agent system to support service mobility in home network environment, and describe detailed design method in terminal as well as server agent. Specifically, we describe user recognition module, signaling message receiving/parsing module of terminal agent and signaling message receiving/parsing module, multimedia switching module, memory management module of server agent. We define several parameters managed in IP sharing device and design binding table structure to support mobility. And we utilize M/M/1/K queueing theory to obtain relations between memory size, blocking probability and memory utilization. From the obtained results, we show that memory size can be predicted in server agent mounted on IP sharing device.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.279-285
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• 2007

The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.339-344
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• 2011

In the past much effort has been made to utilize advanced computational fluid dynamic (CFD) programs for aeroelastic simulations and analysis. However, it is limited in the field of unsteady aeroelasticity due to enormous size of computer memory and unreasonably long CPU time. Recently, AAEMS(Aerodynamics is Aeroelasticity minus Structure) was developed for linear time-invariant, coupled fluid-structure systems. In this paper, to demonstrate further the efficiency and accuracy of the new model reduction method, we successfully examine AGARD 445.6 wing modeled by FLUENT CFD, FSIPRO3D and NASTRAN FEM(Finite Element Method) programs. Using the ROM(Reduced Order Modeling) one can predict flutter boundary as a function of the dynamic pressure.

• International Journal of CAD/CAM
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• v.8 no.1
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• pp.41-44
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• 2009

This paper describes an implementation of virtual teeth modeling for a haptic dental simulation. The system allows dental students to practice dental procedures with realistic tactual feelings. The system requires fast and stable haptic rendering and volume modeling techniques working on the virtual tooth. In our implementation, a volumetric implicit surface is used for intuitive shape modification without topological constraints and haptic rendering. The volumetric implicit surface is generated from input geometric model by using a closest point transformation algorithm. And for visual rendering, we apply an adaptive polygonization method to convert volumetric teeth model to geometric model. We improve our previous system using new octree design to save memory requirement while increase the performance and visual quality.

• Journal of electromagnetic engineering and science
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• v.8 no.1
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• pp.17-22
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• 2008

It is presented that sampling rates for behavioral modeling of quasi-memory less nonlinear devices can be far less than the Nyquist rate of the input signal. Although it has been believed that the sampling rate of nonlinear device modeling should be at least the Nyquist rate of the output signal, this paper suggests that far less than the Nyquist rate of the input signal can be applied to the modeling of quasi-memoryless nonlinear devices, such as frequency multipliers. To verify, a QPSK signal at 820 MHz were applied to a frequency tripler, whereby the device can be utilized as an up-converting mixer into 2.46 GHz with the aid of digital predistortion. AM-AM, AM-PM and PM-PM can be successfully measured regardless of sampling rates.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.2
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• pp.167-173
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• 2017

In this paper, the characterization of the vertical position of trapped charges in the charge-trap flash (CTF) memory is performed in the novel CTF memory cell with gate-all-around structure using technology computer-aided design (TCAD) simulation. In the CTF memories, injected charges are not stored in the conductive poly-crystalline silicon layer in the trapping layer such as silicon nitride. Thus, a reliable technique for exactly locating the trapped charges is required for making up an accurate macro-models for CTF memory cells. When a programming operation is performed initially, the injected charges are trapped near the interface between tunneling oxide and trapping nitride layers. However, as the program voltage gets higher and a larger threshold voltage shift is resulted, additional charges are trapped near the blocking oxide interface. Intrinsic properties of nitride including trap density and effective capture cross-sectional area substantially affect the position of charge centroid. By exactly locating the charge centroid from the charge distribution in programmed cells under various operation conditions, the relation between charge centroid and program operation condition is closely investigated.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1165-1173
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• 2017

Deep learning method, which is one of the most famous machine learning algorithms, has proven its applicability in various applications and is widely used in digital signal processing. However, it is difficult to apply deep learning technology to IoT devices with limited CPU performance and memory capacity, because a large number of training samples requires a lot of memory and computation time. In particular, if the Arduino with a very small memory capacity of 2K to 8K, is used, there are many limitations in implementing the algorithm. In this paper, we propose a method to optimize the ELM algorithm, which is proved to be accurate and efficient in various fields, on Arduino board. Experiments have shown that multi-class learning is possible up to 15-dimensional data on Arduino UNO with memory capacity of 2KB and possible up to 42-dimensional data on Arduino MEGA with memory capacity of 8KB. To evaluate the experiment, we proved the effectiveness of the proposed algorithm using the data sets generated using gaussian mixture modeling and the public UCI data sets.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.1
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• pp.47-52
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• 2012

A power amplifier is one of important factors for basestation's efficiency and the researches for efficency enhancement focus Doherty amplifier structure with GaN power devices in these days. A memory effect of Doherty amplifier affect operation characteristics for linearity and efficiency. This paper reports on electrothermal nonlinearity modeling and compensation for GaN Doherty amplifier's distortion. Also this paper reports on the dynamic expression of the instantaneous junction temperature as a function of the instantaneous dissipated power. We design distortion model for GaN Doherty amplifier and predistortion compensator for electrothermal memory effect from the proposed behavior model parameters. The simulations was evaluated by ADS Tools and GaN Doherty amplifier with 37dBm. The GaN Doherty amplifier with compensator enhanced about 16dB than without electrothemal memory effect compensator in 2-tone output spectrum.