• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5305-5321
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• 2016

In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance.

• East Asian mathematical journal
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• v.14 no.2
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• pp.421-427
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• 1998

In this paper we provide sufficient conditions which ensure the strong consistency of $L_p$-norm estimation in nonlinear regression model when the probability distribution of the errors term is symmetric about zero. The least absolute deviation and least square estimation are discussed as special cases of the proposed estimation.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.106-108
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• 2009

In this paper, we handle automatic estimation of mechanical parameters for mobile robots. Most estimation methods are based on the sequence and move-measurement-estimation. Estimated accuracy is largely dependent on the paths. Mathematical conditions minimizing estimation errors are derived, and then a method finding optimal paths for mechanical parameters estimation is proposed.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.17-24
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• 2014

Blind algorithms designed to maximize the probability that constant modulus errors become zero carry out some summation operations for a set of constant modulus errors at an iteration time inducing heavy complexity. For the purpose of reducing this computational burden induced from the summation, a new approach to the estimation of the zero-error probability (ZEP) of constant modulus errors (CME) and its gradient is proposed in this paper. The ZEP of CME at the next iteration time is shown to be calculated recursively based on the currently calculated ZEP of CME. It also is shown that the gradient for the weight update of the algorithm can be obtained by differentiating the ZEP of CME estimated recursively. From the simulation results that the proposed estimation method of ZEP-CME and its gradient produces exactly the same estimation results with a significantly reduced computational complexity as the block-processing method does.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.407-412
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• 2015

In model-based tracking, an accurate 3D model of a target object or scene is mostly assumed to be known or given in advance, but the accuracy of the model should be guaranteed for accurate pose estimation. In many application domains, on the other hand, end users are not highly distracted by tracking errors from certain levels of modeling errors. In this paper, we examine perceptual tracking errors, which are predominantly caused by modeling errors, on subjective evaluation and compare them to computational tracking errors. We also discuss the tolerance of modeling errors by analyzing their permissible ranges.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.589-599
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• 2014

This paper analyzes the forecasting errors of traffic volumes by comparing forecasted volumes for the opening year with the observed ones in the years after the urban railway construction in the metropolitan areas. The result shows that the average inaccuracy of traffic volumes for each station was estimated at around 7.27. Based on the confirmed factors of demand estimation errors, this study seeks for an alternative method to reduce estimation errors in feasibility studies. It is noted that there is a tendency that the inaccuracy varies by regions and the longer construction period or the shorter station spacing is, the overestimation increases. If urban railway projects are proceeded as planed, therefore, the level of the inaccuracy for traffic volume forecast will be decreased. In addition, thanks to the theoretical progress, recent estimation results show higher accuracy than before. In that sense, when we introduce the new railway line, it is necessary to make an accurate and realistic demand forecast based on actual outcomes and tendency of the previous estimation. The limitation of our study is that we only cover the errors of the initial period, the opening year and deal with the exogenous variables. Further research including other variables which might be considered to cause overestimation or errors would be needed for increasing the estimation accuracy of traffic volumes.

• Journal of Sensor Science and Technology
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• v.29 no.4
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• pp.237-242
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• 2020

This paper deals with relative position estimation using a Kalman filter (KF) based on inertial sensors that have been widely used in various biomechanics-related outdoor applications. In previous studies, the relative position is determined using relative orientation and predetermined segment-to-joint (S2J) vectors, which are assumed to be constant. However, because body segments are influenced by soft tissue artifacts (STAs), including the deformation and sliding of the skin over the underlying bone structures, they are not constant, resulting in significant errors during relative position estimation. In this study, relative position estimation was performed using a KF, where the S2J vectors were adopted as time-varying states. The joint constraint and the variations of the S2J vectors were used to develop a measurement model of the proposed KF. Accordingly, the covariance matrix corresponding to the variations of the S2J vectors continuously changed within the ranges of the STA-causing flexion angles. The experimental results of the knee flexion tests showed that the proposed KF decreased the estimation errors in the longitudinal and lateral directions by 8.86 and 17.89 mm, respectively, compared with a conventional approach based on the application of constant S2J vectors.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.1
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• pp.37-46
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• 2004

A novel rotor speed estimation method using model reference adaptive control(MRAC) is proposed to improve the performance of a sensorless vector controller. In the proposed method, the stator current is used as the model variable for estimating the speed. In conventional MRAC methods, the relation between the two model errors and the speed estimation error is unclear. In the proposed method, the stator current error is represented as a function of the first degree for the error value in the speed estimation. Therefore, the proposed method can produce a fast speed estimation. The robustness of the rotor flux-based MRAC, back EMF-based MRAC, and proposed MRAC is compared based on a sensitivity function about each error of stator resistance, rotor time constant, mutual inductance. Consequently, the proposed method is much more robust than the conventional methods as regards errors in the mutual inductance, stator resistance. Therefore, the proposed method offers a considerable improvement in the performance of a sensorless vector controller at a low speed. In addition, the superiority of the proposed method and the validity of sensitivity functions were verified by simulation and experiment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.6
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• pp.64-76
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• 2014

This study identified the causes of errors that could take place in the estimation process of vehicle miles traveled and quantified the effects of each of those causes on the estimation accuracy of vehicle miles traveled via error rate to propose an efficient way to estimate vehicle miles traveled. The study proceeded as follows: first, the study established survey data of vehicle miles traveled in the pilot test areas to test the accuracy of a method to estimate vehicle miles traveled. Second, the causes of errors with the estimation of vehicle miles traveled were categorized into errors with the sample size, sampling methods, and homogeneous link setting methods. In addition, many different methodologies were set to minimize errors with the estimation of vehicle miles traveled according to each of the causes. Third, error rates of estimation of vehicle miles traveled were compared and analyzed according to each of the methodologies. Finally, a toy network was established to propose a way of estimating vehicle miles traveled by taking the local characteristics into consideration. The study finds its significance in that it proposed an efficient way to estimate vehicle miles traveled through an experiment and planning approach and made use of survey data of vehicle miles traveled to test estimation accuracy. The proposed way of estimating vehicle miles traveled by taking into account the local characteristics will make a contribution to the estimation of vehicle miles traveled by the areas in future along with the level of data offered in the study.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1712-1717
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• 1991

The paper describes estimation errors of unsteady flowrate measurements due to parameter changes in a quasi-remote instantaneous flowrate measurement method (abbreviate as QIFM) and an instantaneous flowrate measurement method using two points pressure measurements (abbreviate as TPFM). By introducing error performance index, the influence of parameter changes on the accuracy, and dynamic response of the estimated unsteady flowrate are evaluated. Of four parameters, the variation of the length of the pipeline and speed of sound produce large errors in the estimated unsteady flowrate during transient periods. The effect of kinematic viscosity of the working fluid(oil) is relatively insensitive in unsteady flowrate estimation.