• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.2824-2837
• /
• 2019

Container technologies are widely used in infrastructures to deploy and manage applications in cloud computing environment. As containers are light-weight software, the cluster of cloud applications can easily scale up or down to provide Internet-based services. Container-based applications can well deal with fluctuate workloads by dynamically adjusting physical resources. Current works of scheduling applications often construct applications' performance models with collected historical training data, but these works with static models cannot self-adjust physical resources to meet the dynamic requirements of cloud computing. Thus, we propose a self-adaptive automatic container scheduling framework AutoScale for cloud applications, which uses a feedback-based approach to adjust physical resources by extending, contracting and migrating containers. First, a queue-based performance model for cloud applications is proposed to correlate performance and workloads. Second, a fuzzy Kalman filter is used to adjust the performance model's parameters to accurately predict applications' response time. Third, extension, contraction and migration strategies based on predicted response time are designed to schedule containers at runtime. Furthermore, we have implemented a framework AutoScale with container scheduling strategies. By comparing with current approaches in an experiment environment deployed with typical applications, we observe that AutoScale has advantages in predicting response time, and scheduling containers to guarantee that response time keeps stable in fluctuant workloads.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.21 no.1
• /
• pp.53-60
• /
• 2010

This paper presents a novel gain control system composed of a feedback circuit, Two stage Low Noise Amplifier (LNA) using 0.18 um CMOS technology for 5.2 GHz. The feedback circuit consists of the seven function blocks: peak detector, comparator, ADC, IVE(Initial Voltage Elimination) circuit, switch, storage, and current controller. We focus on detecting signal and designing storage circuit that store the previous state. The power consumption of the feedback circuit in the system can be reduced without sacrificing the gain by inserting the storage circuit. The adaptive front-end system with the feedback circuit exhibits 11.39~22.74 dB gain, and has excellent noise performance at high gain mode. Variable gain LNA consumes 5.68~6.75 mW from a 1.8 V supply voltage.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.51 no.1
• /
• pp.18-27
• /
• 2002

This paper presents an implementation of digital high-performance speed sensorless control system of an induction motor drives with Direct Torque Control(DTC). The system consists of closed loop stator flux and torque observer, speed and torque estimators, two hysteresis controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320C31 DSP controller board. The stator flux observer is based on the combined current and voltage model with stator flux feedback adaptive control for wide speed range. The speed estimator is using the model reference adaptive system(MRAS) with rotor flux linkages for speed turning signal estimation. In order to prove the suggested speed sensorless control algorithm, and to obtain a high-dynamic robust adaptive performance, we have some simulations and actual experiments at low(20rpm) and high(1000rpm) speed areas. The developed speed sensorless system are shown a good speed control response characteristic, and high performance features using 2.2[kW] general purposed induction motor.

• Smart Structures and Systems
• /
• v.13 no.2
• /
• pp.219-233
• /
• 2014

In a companion paper, Pasala and Nagarajaiah analytically and experimentally validate the Adaptive Length Pendulum Smart Tuned Mass Damper (ALP-STMD) on a primary structure (2 story steel structure) whose frequencies are time invariant (Pasala and Nagarajaiah 2012). In this paper, the ALP-STMD effectiveness on a primary structure whose frequencies are time varying is studied experimentally. This study experimentally validates the ability of an ALP-STMD to adequately control a structural system in the presence of real time changes in primary stiffness that are detected by a real time observer based system identification. The experiments implement the newly developed Adaptive Length Pendulum Smart Tuned Mass Damper (ALP-STMD) which was first introduced and developed by Nagarajaiah (2009), Nagarajaiah and Pasala (2010) and Nagarajaiah et al. (2010). The ALP-STMD employs a mass pendulum of variable length which can be tuned in real time to the parameters of the system using sensor feedback. The tuning action is made possible by applying a current to a shape memory alloy wire changing the effective length that supports the damper mass assembly in real time. Once a stiffness change in the structural system is detected by an open loop observer, the ALP-STMD is re-tuned to the modified system parameters which successfully reduce the response of the primary system. Significant performance improvement is illustrated for the stiffness modified system, which undergoes the re-tuning adaptation, when compared to the stiffness modified system without adaptive re-tuning.

• Journal of Korea Game Society
• /
• v.10 no.3
• /
• pp.47-59
• /
• 2010

Differing from most of current studies which recognizing game item cash-trade as simple static system, this study approaches game item cash-trade as Complex Adaptive System through historical analysis. The item-trade is a complex phenomenon converging between cyber-economy and real-economy, and production and consumption process of game-item are evolving dynamically over time. The results are following: first, the early item-trade emerges in endogenously rather than results from purposed system designed by singular actor. Second, after the early item-trade, the trade system as a CAS which various voluntary actors(single user, factory, game company, user community, agency, etc.) participates in is self-organizing for trading safety and efficiency. Third, the complex adaptive item-trade system satisfies actor's needs interdependently and accelerate positive feedback powerfully. This study implies that purposeful control disregarding emergent adaptive item-trade system distorts system efficiency and can lead to unintended policy failure.

• Journal of the Korean Institute of Navigation
• /
• v.23 no.3
• /
• pp.35-44
• /
• 1999

This research aims to seek active control of ball-beam position stability by resorting to neural networks whose layers are given bias weights. The controller consists of an LQR (linear quadratic regulator) controller and a neural networks controller in parallel. The latter is used to improve the responses of the established LQR control system, especially when controlling the system with nonlinear factors or modelling errors. For the learning of this control system, the feedback-error learning algorithm is utilized here. While the neural networks controller learns repetitive trajectories on line, feedback errors are back-propagated through neural networks. Convergence is made when the neural networks controller reversely learns and controls the plant. The goals of teaming are to expand the working range of the adaptive control system and to bridge errors owing to nonlinearity by adjusting parameters against the external disturbances and change of the nonlinear plant. The motion equation of the ball-beam system is derived from Newton's law. As the system is strongly nonlinear, lots of researchers have depended on classical systems to control it. Its applications of position control are seen in planes, ships, automobiles and so on. However, the research based on artificial control is quite recent. The current paper compares and analyzes simulation results by way of the LQR controller and the neural network controller in order to prove the efficiency of the neural networks control algorithm against any nonlinear system.

• Earthquakes and Structures
• /
• v.11 no.6
• /
• pp.943-966
• /
• 2016

Recently, magnetorheological elastomer (MRE) material and its devices have been developed and attracted a good deal of attention for their potentials in vibration control. Among them, a highly adaptive base isolator based on MRE was designed, fabricated and tested for real-time adaptive control of base isolated structures against a suite of earthquakes. To perfectly take advantage of this new device, an accurate and robust model should be built to characterize its nonlinearity and hysteresis for its application in structural control. This paper first proposes a novel hysteresis model, in which a nonlinear hyperbolic sine function spring is used to portray the strain stiffening phenomenon and a Voigt component is incorporated in parallel to describe the solid-material behaviours. Then the fruit fly optimization algorithm (FFOA) is employed for model parameter identification using testing data of shear force, displacement and velocity obtained from different loading conditions. The relationships between model parameters and applied current are also explored to obtain a current-dependent generalized model for the control application. Based on the proposed model of MRE base isolator, a second-order sliding mode controller is designed and applied to the device to provide a real-time feedback control of smart structures. The performance of the proposed technique is evaluated in simulation through utilizing a three-storey benchmark building model under four benchmark earthquake excitations. The results verify the effectiveness of the proposed current-dependent model and corresponding controller for semi-active control of MRE base isolator incorporated smart structures.

• Journal of Advanced Marine Engineering and Technology
• /
• v.39 no.2
• /
• pp.179-186
• /
• 2015

In the indoor location estimation system, which has recently been actively studied, the received signal strength indicator contains a high level of noise when measuring the signal strength in the range between two nodes consisting of a receiver and a transceiver. To minimize the noise level, this paper proposes an improved adaptive smoothing filter that provides different exponential weights to the current value and previous averaged one of the data that were obtained from the nodes, because the characteristic signal attenuation of the received signal strength indicator generally has a log distribution. The proposed method can effectively decrease the noise level by using a feedback filter that can provide different weights according to the noise level of the obtained data and thus increase the accuracy in the distance and location without an additional filter such as the link quality indicator, which can verify the communication quality state to decrease the range errors in the indoor location recognition using ZigBee based on IEEE 802.15.4. For verifying the performance of the proposed improved adaptive smoothing filter, actual experiments are conducted in three indoor locations of different spatial sections. From the experimental results, it is verified that the proposed technique is superior to other techniques in range measurement.

• Proceedings of the KIPE Conference
• /
• 2001.10a
• /
• pp.355-359
• /
• 2001

This paper presents an implementation of digital control system of speed sensorless for Reluctance Synchronous Motor (RSM) drives with DTC. The control system consists of stator flux observer, rotor position/speed/torque estimator, two hysteresis band controllers, an optimal switching look-up table, IGBT voltage source inverter, and TMS320C31 DSP controller by using fully integrated control software. The stator flux observer is based on the combined voltage and current model with stator flux feedback adaptive control that inputs are current and voltage sensing of motor terminal with estimated rotor angle for wide speed range. The rotor position is estimated by observed stator flux-linkage space vector. The estimated rotor speed is determined by differentiation of the rotor position used only in the current model part of the flux observer for a low speed operating area. It does not require the knowledge of any motor parameters, nor particular care for motor starting, In order to prove the suggested control algorithm, we have a simulation and testing at actual experimental system. The developed sensorless control system is shown a good speed control response characteristic results and high performance features in 50/1000 rpm with 1.0Kw RSM having 2.57 ratio of d/q reluctance.

• Proceedings of the KIEE Conference
• /
• 2001.07e
• /
• pp.47-52
• /
• 2001

This paper presents a high-performance control system for Reluctance Synchronous Motor (RSM) drives with direct torque control (DTC). The system consist of stator flux observer, rotor position/speed estimator, torque estimator, two hysteresis band controllers, an optimal switching look-up table, IGBT voltage source inverter, and F240/C31DSP controller by using fully integrated control software. The stator flux observer is based on the combined voltage and current model with stator flux feedback adaptive control that inputs are current and voltage sensing of motor terminal with estimated rotor angle for wide speed range. The rotor position is estimated by the observed stator flux-linkage space vector. The estimated rotor speed can be determinated by differentiation of the rotor position used only in the current model part of the flux observer for a low speed operating area. To prove the suggested control algorithm, we have a simulation and testing at actual experimental system. The developed digitally high-performance position sensorless control system are shown a good motion control response characteristic results and high performance features using 1.0Kw RSM.