• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.228-235
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• 2010

This paper deals with an autonomous flight controller design problem for a tilt-rotor aircraft in rotary-wing mode. The inner-loop algorithm is designed using the output-based approximate feedback linearization. The model error originated from the feedback linearization is cancelled within allowable tolerance by using single-hidden-layer neural network. According to Lyapunov direct stability theory, the adaptive update law is derived to run the neural network on-line, which is based on the linear observer dynamics. Moreover, the outer-loop algorithm is designed to track the trajectory generated from way-point guidance. Especially, heading and flight-path angle line-of-sight guidance are applied to the outer-loop to improve accuracy of the landing tracking performance. The 6-DOF nonlinear simulation shows that the overall performance of the flight control algorithm is satisfactory even though the collective input response shows instantaneous actuator saturation for a short time due to the lack of the neural network and the saturation protection logic in that loop.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.1
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• pp.40-52
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• 1997

It has been known that the OFDM-based terrestrial broadcasting systems are significantly more sensitive to nonlinear distortion in high-power amplifiers (HPA's) than the broadcasting systems using single-carrier modulation technique. In this paper, an adaptive precompensator which can compensate the nonlinear effect of the HPA for OEDM systems is proposed. The proposed precompensator consists of a compensation table, realized by memory (RAM), and an adaptive algorithm which can update the values of the compensation table more efficiently using a

• The KIPS Transactions:PartA
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• v.13A no.3 s.100
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• pp.241-252
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• 2006

In cluster file systems, the availability of files has been supported by replicating entire files or generating parities on parity servers. However, those methods require very large temporal and spatial cost, and cannot handle massive failures situation on the file system. So we propose HA-PVFS, a cluster file system supporting high data availability adaptive to temporal locality. HA-PVFS restricts replication or parity generation to some important files, for that it employs an efficient algorithm to estimate file access patterns from limited information. Moreover, in order to minimize the performance degradation of the file system, it uses delayed update method and relay replication.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.399-413
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• 2021

The solution of the time-dependent neutron diffusion equation can be approximated using quasi-static methods that factorise the neutronic flux as the product of a time dependent function times a shape function that depends both on space and time. A generalization of this technique is the updated modal method. This strategy assumes that the neutron flux can be decomposed into a sum of amplitudes multiplied by some shape functions. These functions, known as modes, come from the solution of the eigenvalue problems associated with the static neutron diffusion equation that are being updated along the transient. In previous works, the time step used to update the modes is set to a fixed value and this implies the need of using small time-steps to obtain accurate results and, consequently, a high computational cost. In this work, we propose the use of an adaptive control time-step that reduces automatically the time-step when the algorithm detects large errors and increases this value when it is not necessary to use small steps. Several strategies to compute the modes updating time step are proposed and their performance is tested for different transients in benchmark reactors with rectangular and hexagonal geometry.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.354-359
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• 2007

A new level set based topology optimization employing inner-front creation algorithm is presented. In the conventional level set based topology optimization, the optimum topology strongly depends on the initial level set distribution due to the incapability of inner-front creation during optimization process. In the present work, an inner-front creation algorithm is proposed, in which the sizes, positions, and number of new inner-fronts during the optimization process can be globally and consistently identified. To update the level set function during the optimization process, the least-squares finite element method is employed. As demonstrative examples for the flexibility and usefulness of the proposed method, the level set based topology optimization considering lightweight design of 3D shell structure is carried out.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.38-43
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• 2008

In this paper, adaptive channel borrowing approach fuzzy neural networks for load balancing (ACB-FNN) is presented to maximized the number of served calls and the depending on asymmetries traffic load problem. In a wireless network, the call's arrival rate, the call duration and the communication overhead between the base station and the mobile switch center are vague and uncertain. A new load balancing algorithm with cell involved negotiation is also presented in this paper. The ACB-FNN exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding better performance compared with other algorithms. It aims to efficiently satisfy their diverse quality-of-service (QoS) requirements. The results show that our algorithm has lower blocking rate, lower dropping rate, less update overhead, and shorter channel acquisition delay than previous methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.58-66
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• 2007

This paper introduces the new generic dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system for complex dynamic hydrological modeling tasks. The proposed DNFLMS applies a local generalization principle and an one-pass training procedure by using the evolving clustering method to create and update fuzzy local models dynamically and the extended Kalman filtering learning algorithm to optimize the parameters of the consequence part of fuzzy local models. The proposed DNFLMS is applied to develop the inference model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatts Cobb hydropower station on springs flow. It is demonstrated that the proposed DNFLMS is superior in terms of model accuracy, model complexity, and computational efficiency when compared with a multi-layer perceptron trained with the back propagation learning algorithm and well-known adaptive neural-fuzzy inference system, both of which adopt global generalization.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.12 no.4
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• pp.503-512
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• 2001

A new adaptation algorithm for the digital predistorter is presented. The proposed technique employs the real-time transmitted signals through HPA for the table update. Hence the proposed algorithm does not depend on modulation format and the signal transmission of HPA is not affected during the adaptation process. The experimental results are presented to verify the proposed method and we obtained about 8.4 dB of ACPR improvement.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.534-537
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• 1999

This paper describes a design of pipelined adaptive decision-feedback equalizer (PADFE) for high bit-rate wireless digital communication systems. To enhance the throughput rate of ADFE, two pipeline stages are inserted into the critical path of ADFE by using delayed least-mean-square (DLMS) algorithm. Redundant binary (RB) arithmetic is applied to all the data processing of ADFE including filter laps and coefficient update blocks. When compared with conventional methods based on two's complement arithmetic, the proposed approach reduces arithmetic complexity, as well as results in a very simple complex-valued filter structure, thus suitable for VLSI implementation. The design parameters (filter tap, coefficient and internal bit-width, etc.) and equalization performance (bit error rate, convergence speed, etc.) are analyzed by algorithm-level simulation using COSSAP. The PADFE was designed using VHDL and Synopsys, and mapped into two ALTERA FLEX10k100 FPGAs.

• The Journal of the Acoustical Society of Korea
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• v.20 no.7
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• pp.55-62
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• 2001

In this paper, we propose a new adaptative pruning algorithm which effectively reduces the search space during the recognition process. As maximum probabilities between neighbor frames are highly interrelated, an efficient pruning threshold value can be obtained from the maximum probabilities of previous frames. The main idea is to update threshold at the present frame by a combination of previous maximum probability and hypotheses probabilities. As present threshold is obtained in on-going recognition process, the algorithm does not need any pre-experiments to find threshold values even when recognition tasks are changed. In addition, the adaptively selected threshold allows an improvement of recognition speed under different environments. The proposed algorithm has been applied to a Korean Address recognition system. Experimental results show that the proposed algorithm reduces the search space of average 14.4% and 9.14% respectively while preserving the recognition accuracy, compared to the previous method of using fixed pruning threshold values and variable pruning threshold values.