• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.949-954
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• 2017

Multiple input, multiple output orthogonal frequency division multiplexing (MIMO OFDM) systems are the candidate for the future wireless communications. However, the main drawback of MIMO OFDM systems is their sensitivity to carrier frequency offset (CFO) similar to the single input, single output OFDM (SISO OFDM) systems. The demodulation of a signal with CFO causes large bit error rate and degrade the performance of a symbol synchronizer. It is important to estimate the frequency offset and minimize or eliminate its impact. In this paper, we propose a technique based on observation training symbols for estimating CFO by employing block-by-block estimation for SISO OFDM systems. The technique of SISO OFDM is extended to the MIMO OFDM systems. Simulation results show that the proposed techniques have a superior performance and better accuracy compared to the conventional techniques in the sense of mean square error.

• Smart Structures and Systems
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• v.15 no.2
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• pp.489-503
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• 2015

Response estimation at unmeasured locations using the limited number of measurements is an attractive topic in the field of structural health monitoring (SHM). Because of increasing complexity and size of civil engineering structures, measuring all structural responses from the entire body is intractable for the SHM purpose; the response estimation can be an effective and practical alternative. This paper investigates a response estimation technique based on the Kalman state estimator to combine multi-sensor data under non-zero mean input excitations. The Kalman state estimator, constructed based on the finite element (FE) model of a structure, can efficiently fuse different types of data of acceleration, strain, and tilt responses, minimizing the intrinsic measurement noise. This study focuses on the effects of (a) FE model error and (b) combinations of multi-sensor data on the estimation accuracy in the case of non-zero mean input excitations. The FE model error is purposefully introduced for more realistic performance evaluation of the response estimation using the Kalman state estimator. In addition, four types of measurement combinations are explored in the response estimation: strain only, acceleration only, acceleration and strain, and acceleration and tilt. The performance of the response estimation approach is verified by numerical and experimental tests on a simply-supported beam, showing that it can successfully estimate strain responses at unmeasured locations with the highest performance in the combination of acceleration and tilt.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11C
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• pp.1113-1121
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• 2009

In this paper, the channel estimation and pilot sequence design technique of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems in multi-cell environments are studied for situations in which the inter cell interference (ICI) is the dominant channel impairment. We design pilot sequence aiming at minimizing mean square error and propose the channel estimation technique correspond to the designed pilot sequences. The proposed pilot sequences employ the sequences with good correlation properties such as Chu sequence and through simulations, it is shown that channel estimation algorithm using designed pilot sequence is effective for mitigating the ICI.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.58-66
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• 1996

An adaptive input-output linearization technique of an interior permanent magnet synchronous motor with a specified output dynamic performance is proposed. The adaptive parameter estimation is achieved by a model reference adaptive technique where the stator resistance and the magnitude of flux linkage can be estimated with the current dynamic model and state observer. Using these estimated parameters, the linearizing control inputs are calculated. With these control inputs, the input-output linearization is performed and the load torque is estimated. The adaptation laws are derived by the Popov's hyperstability theory and the positivity concept. The robustness and the output dynamic performance of the proposed control scheme are verified through the computer simulations.

• Bulletin of the Korean Space Science Society
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• 2004.10b
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• pp.59-59
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• 2004

• Journal of the Korean Society for Precision Engineering
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• v.9 no.4
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• pp.100-108
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• 1992

In this study, the technique of adaptive control based on certainty equivalence for the input-output linearization of nonlinear system is investigated. It is shown that the upper bound of the parameter estimation error can be represented more explicitly than Teel et al's works. Another direct approach, which stows that the adaptive input-output linearing control laws using the normalized identifier yield bounded tracing, is also presented.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.70-74
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• 1989

To track the target trajectory with maneuvers, unknown maneuvering inputs must be estimated. To do this the direct estimation algorithm using generalized least square technique is developed based on the procedure of failure detection and identification(FDI) theory. Through the simulation using maneuvering target scenario, tracking performance and efficiency of the algorithm developed here are investigated.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.47-61
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• 1996

An ATM switch must deal with various kinds of input sources having different traffic characteristics and it must guarantee very small value of cel loss probability, about 10$^{8}$ -10$^{12}$ , to deal with loss-sensitive traffics. In order to estimate such a rate event probability with simulation procedure, a variance reduction technique is essential for obtaining an appropriate level of precision with reduced cost. In this paper, we propose a hybrid simulation technique to achieve reduction of variance of cell loss probability estimator, where hybrid means the combination of analytical method and simulation procedure. A discrete time queueing model with multiple input sources and a finite shared buffer is considered, where the arrival process at an input source and a finite shared buffer is considered, where the arrival process at an input source is governed by an Interrupted Bernoulli Process and the service rate is constant. We deal with heterogeneous input sources as well as homogeneous case. The performance of the proposed hybrid simulation estimator is compared with those of the raw simulation estimator and the importance sampling estimator in terms of variance reduction ratios.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.3
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• pp.632-640
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• 2007

The low force estimation method of skeletal muscle was proposed by using ICA(independent component analysis) and neuro-transmission model. An EMG decomposition is the procedure by which the signal is classified into its constituent MUAP(motor unit action potential). The force index of electromyography was due to the generation of MUAP. To estimate low force, current analysis technique, such as RMS(root mean square) and MAV(mean absolute value), have not been shown to provide direct measures of the number and timing of motoneurons firing or their firing frequencies, but are used due to lack of other options. In this paper, the method based on ICA and chemical signal transmission mechanism from neuron to muscle was proposed. The force generation model consists of two linear, first-order low pass filters separated by a static non-linearity. The model takes a modulated IPI(inter pulse interval) as input and produces isometric force as output. Both the step and random train were applied to the neuro-transmission model. As a results, the ICA has shown remarkable enhancement by finding a hidden MAUP from the original superimposed EMG signal and estimating accurate IPI. And the proposed estimation technique shows good agreements with the low force measured comparing with RMS and MAV method to the input patterns.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.6
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• pp.764-776
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• 1999

In this paper, a tracking algorithm for a maneuvering single target in the presence of multiple data from multiple sensors is investigated. Allowing individual sensors to function by themselves, the estimates from individual sensors on the same target are fused for the purpose of improving the state estimate. The filtering method adopted in the local sensors is the variable dimensional filter with input estimatio technique, which consists of a constant velocity model and a constant acceleration model. A posteriori probability for the maneuvering hypothesis is newly derived. It is shown that the relation function of the a posteriori probability is a function of only the covariance of the fused estimates. Simulation results are provided.