• The KIPS Transactions:PartC
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• v.16C no.6
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• pp.761-772
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• 2009

A DHT(Distributed Hash Table) based P2P is a method to overcome disadvantages of the existing unstructured P2P method. If a DHT algorithm is used, it can do a fast data search and maintain search efficiency independent of the number of peer. The peers in the DHT method send messages periodically to keep the routing table updated. In a mobile environment, the peers in the DHT method should send messages more frequently to keep the routing table updated and reduce the failure of a request. Therefore, this results in increase of network traffic. In our previous research, we proposed a method to reduce the update load of the routing table in the existing Chord by updating it in a reactive way, but the reactive method had a disadvantage to generate more traffic than the existing Chord if the number of requests per second becomes large. In this paper, we propose an adaptive method of routing table update to reduce the network traffic. In the proposed method, we apply different routing table update method according to the number of request message per second. If the number of request message per second is smaller than some threshold, we apply the reactive method. Otherwsie, we apply the existing Chord method. We perform experiments using Chord simulator (I3) made by UC Berkeley. The experimental results show the performance improvement of the proposed method compared to the existing methods.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.629-640
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• 2003

A shared disk (SD) cluster couples multiple nodes for high performance transaction processing, and all the coupled nodes share a common database at the disk level. In the SD cluster, a transaction routing corresponds to select a node for an incoming transaction to be executed. An affinity-based routing can increase local buffer hit ratio of each node by clustering transactions referencing similar data to be executed on the same node. However, the affinity-based routing is very much non-adaptive to the changes in the system load, and thus a specific node will be overloaded if transactions in some class are congested. In this paper, we propose a dynamic transaction routing scheme that can achieve an optimal balance between affinity-based routing and dynamic load balancing of all the nodes in the SD cluster. The proposed scheme is novel in the sense that it can improve the system performance by increasing the local buffer hit ratio and reducing the buffer invalidation overhead.

• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.1123-1133
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• 2012

This study configuration Vector Control System which is stable and has outstanding Dynamic Characteristics in Very Low Speed Region and Low Speed Region, and proposes Instantaneous Speed Observer and Very Low Speed Control method and vector control system of the speed estimation a using Reduced-Dimensional State Observer. The Observer proposed in this system, by appling Reduced-Dimensional State Observer to Load-Torque estimation and using for speed estimation, implements system composition simply and is capable of accurate Instantaneous Speed estimation in Very Low Speed Region. Also, this study reduces influence by System Noise and suggests an induction motor speed control system which is effective in Load Disturbance, modeling error, estimation noise and so on without changing pole of an Observer.

• Journal of Power Electronics
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• v.15 no.3
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• pp.753-762
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• 2015

This paper presents a nonlinear sliding mode control (SMC) scheme with a variable damping ratio for interior permanent magnet synchronous motors (IPMSMs). First, a nonlinear sliding surface whose parameters change continuously with time is designed. Actually, the proposed SMC has the ability to reduce the settling time without an overshoot by giving a low damping ratio at the initial time and a high damping ratio as the output reaches the desired setpoint. At the same time, it enables a fast convergence in finite time and eliminates the singularity problem with the upper bound of an uncertain term, which cannot be measured in practice, by using a simple adaptation law. To improve the efficiency of a system in the constant torque region, the control system incorporates the maximum torque per ampere (MTPA) algorithm. The stability of the nonlinear sliding surface is guaranteed by Lyapunov stability theory. Moreover, a simple sliding mode observer is used to estimate the load torque and system uncertainties. The effectiveness of the proposed nonlinear SMC scheme is verified using comparative experimental results of the linear SMC scheme when the speed reference and load torque change under system uncertainties. From these experimental results, the proposed nonlinear SMC method reveals a faster transient response, smaller steady-state speed error, and less sensitivity to system uncertainties than the linear SMC method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.2
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• pp.38-49
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• 2001

This paper presents a new voltage down converter(VDC) using charge and discharge current adjustment circuitry that provides high frequency application. This VDC consist of a common driving circuit and compensation circuits: 2 sensors and each driving transistors for controlling gate current of driving transistor. These sensors are operated as adaptive biasing method with high speed and low power consumption. This circuit is designed with a $0.62{\mu}m$ N well CMOS technology. In H-spice simulation results, internal voltage is bounded ( IV, +0.6V) in proposed circuitry when load current rapidly increases and decreases during Gns between 0 and $200m{\Lambda}$. And the recovery time of internal voltage is about 7ns and 10ns when load current increases and decreases respectively. That is fast better than common driving circuit. Total power consumption is about 1.2mW.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.2 s.314
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• pp.24-31
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• 2007

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.1-10
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• 2010

As recent fusion industry and ubiquitous technology have grown fast, network contents, which require high load, are provided in various infrastructures and facilities such as u-city and smart phones. Therefore, it is anticipated that the playback quality of multimedia compared to network loads degrades dramatically due to the drastic increment of real-time reference of conventional high load contents (eg. multimedia data). In this paper, we improved the method of the traffic assignment based on MPP which elevated the playback quality of multimedia by assigning discriminately the possible traffic of MMS with TP coefficients. When the TP coefficient which combines content preference with media preference was applied to a real-time traffic assignment scheduler, the simulation results showed that the multimedia playback stream was assigned within the possible traffic of a server. The real-time scheduling algorithm was improved by using the TP coefficient that combines the time-dependent image contents and the weighted value of media preference. It was observed from the experiment that the loss of the possible traffic decreases to 3.91% and 3.88% for three and four clients respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.709-730
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• 2017

To address the challenge of unprecedented growth in mobile data traffic, ultra-dense network deployment is a cost efficient solution to offload the traffic over some small cells. The overlapped coverage areas of small cells create more than one candidate access points for one mobile user. Signal strength based user association in IEEE 802.11 results in a significantly unbalanced load distribution among access points. However, the effective bandwidth demand of each user actually differs vastly due to their different preferences for mobile applications. In this paper, we formulate a set of non-linear integer programming models for joint user association control and user demand guarantee problem. In this model, we are trying to maximize the system capacity and guarantee the effective bandwidth demand for each user by soft-association control with a software defined network controller. With the fact of NP-hard complexity of non-linear integer programming solver, we propose a Kernighan Lin Algorithm based graph-partitioning method for a large-scale network. Finally, we evaluated the performance of the proposed algorithm for the edge users with heterogeneous bandwidth demands and mobility scenarios. Simulation results show that the proposed adaptive soft-association control can achieve a better performance than the other two and improves the individual quality of user experience with a little price on system throughput.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.7
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• pp.65-73
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• 2006

This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.