• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.119-125
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• 2015

In this paper, we introduce a Hadamard matrix interstream transmission based blind channel estimation for multi-cells multiple-input and multiple-output (MIMO) networks. The proposed scheme is based on a network with mobile stations (MS) which are deployed with multi cells. We assume that the MS have the signals from both cells. The signal from near cell are considered as desired signal and the signals from the other cells are interference signal. Since the channel is blind, so that we transmit Hadamard matrix pattern pilot stream to estimate the channel; that gives easier and fast channel estimation for large scale MIMO channel. The computation of Hadamard based system takes only complex additions, and thus the complexity of which is much lower than the scheme with Fourier transform since complex multiplications are not needed. The numerical analysis will give perfection of proposed channel estimation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.619-627
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• 2017

MIMO(Multi-Input Multi-Output) radar is an emerging radar technology for its numerous advantages. However, in the electric warfare viewpoint, MIMO radar is a new developed radar technology for that existing parameter estimation cannot applied and a new radar parameter estimation based on the characteristics of MIMO radar is desired. In this paper, we propose a blind estimation scheme for the number of orthogonal waveforms of a uniform linear array(ULA) MIMO radar using minimum two electronic sensors.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.14-19
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• 2021

This paper presents LiDAR static obstacle map based vehicle dynamic state estimation algorithm for urban autonomous driving. In an autonomous driving, state estimation of host vehicle is important for accurate prediction of ego motion and perceived object. Therefore, in a situation in which noise exists in the control input of the vehicle, state estimation using sensor such as LiDAR and vision is required. However, it is difficult to obtain a measurement for the vehicle state because the recognition sensor of autonomous vehicle perceives including a dynamic object. The proposed algorithm consists of two parts. First, a Bayesian rule-based static obstacle map is constructed using continuous LiDAR point cloud input. Second, vehicle odometry during the time interval is calculated by matching the static obstacle map using Normal Distribution Transformation (NDT) method. And the velocity and yaw rate of vehicle are estimated based on the Extended Kalman Filter (EKF) using vehicle odometry as measurement. The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment, and is verified with data obtained from actual driving on urban roads. The test results show a more robust and accurate dynamic state estimation result when there is a bias in the chassis IMU sensor.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.857-871
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• 2013

The interest in rainfall observation and forecasting using remote sensing method like RADAR (Radio Detection and Ranging) and satellite image is increased according to increased damage by rapid weather change like regional torrential rain and flash flood. In this study, the basin runoff was calculated using adaptive neuro-fuzzy technique, one of the data driven model and MAPLE (McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation) forecasted precipitation data as one of the input variables. The flood estimation method using neuro-fuzzy technique and RADAR forecasted precipitation data was evaluated. Six rainfall events occurred at flood season in 2010 and 2011 in Chungju Reservoir basin were used for the input data. The flood estimation results according to the rainfall data used as training, checking and testing data in the model setup process were compared. The 15 models were composed of combination of the input variables and the results according to change of clustering methods were compared and analysed. From this study was that using the relatively larger clustering radius and the biggest flood ever happened for training data showed the better flood estimation. The model using MAPLE forecasted precipitation data showed relatively better result at inflow estimation Chungju Reservoir.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.1
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• pp.27-32
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• 2016

In this paper, we proposed a soft decision-directed channel estimation based on MMSE estimation for MIMO-OFDM system. While the conventional method employs only pilot signals for channel estimation, the proposed algorithm performs channel estimation using pilot and reliable data signals. We also proposed selection criterion among reliable data signal for channel estimation. From numerical simulations, we show that the proposed channel estimator achieves 1 dB performance gain over conventional channel estimators.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.588-598
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• 2020

An alternative state estimation filtering algorithm is designed for continuous time systems with noises as well as control input. Two kinds of estimation filters, which have different measurement memory structures, are operated selectively in order to use both filters effectively as needed. Firstly, the estimation filter with infinite memory structure is operated for a certain continuous time system. Secondly, the estimation filter with finite memory structure is operated for temporarily uncertain continuous time system. That is, depending on the presence of uncertainty, one of infinite memory structure and finite memory structure filtered estimates is operated selectively to obtain the valid estimate. A couple of test variables and declaration rule are developed to detect uncertainty presence or uncertainty absence, to operate the suitable one from two kinds of filtered estimates, and to obtain ultimately the valid filtered estimate. Through computer simulations for a continuous time aircraft engine system with different measurement memory lengths and temporary model uncertainties, the proposed state estimation filtering algorithm can work well in temporarily uncertain as well as certain continuous time systems. Moreover, the proposed state estimation filtering algorithm shows remarkable superiority to the infinite memory structure filtering when temporary uncertainties occur in succession.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.289-293
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• 2015

The construction industry has been evolving with the development of information technology. According to this trend, the current industry changes from 2d drawings to Building Information Modeling(BIM). Current studies on the BIM-based estimation have problems such as Quantity Take-Off(QTO) specificity toward a particular software, the uncertainty of the amount in accordance with the model quality. These studies focus on QTO based on BIM rather than schematic estimation. In addition, studies on the connection with the QTO and unit cost for schematic estimation are insufficient. The purpose of this study is to propose schematic estimation process by utilizing construction codes and QTO in architectural object BIM libraries. Construction codes are classified in detail in order to input codes inside each. This study has connected unit cost and construction classification codes that obtain from BIM model. The results of this study will be helpful in decision-making and communication for schematic estimation of the design phase. It will improve the efficiency and reliability problems of existing schematic estimation.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.7
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• pp.814-820
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• 2011

It takes long time in numerical simulation because structural design for tire requires the nonlinear material property. Neural networks has been widely studied to engineering design to reduce numerical computation time. The numbers of hidden layer, hidden layer neuron and training data have been considered as the structural design variables of neural networks. In application of neural networks to optimize design, there are a few studies about arrangement method of input layer neurons. To investigate the effect of input layer neuron arrangement on neural networks, the variables of tire contour design and tension in bead area were assigned to inputs and output for neural networks respectively. Design variables arrangement in input layer were determined by main effect analysis. The number of hidden layer, the number of hidden layer neuron and the number of training data and so on have been considered as the structural design variables of neural networks. In application to optimization design problem of neural networks, there are few studies about arrangement method of input layer neurons. To investigate the effect of arrangement of input neurons on neural network learning tire contour design parameters and tension in bead area were assigned to neural input and output respectively. Design variables arrangement in input layer was determined by main effect analysis.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.671-674
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• 1999

In the PWM converter, PLL(Phase Locked Loop) is usually used as a tool which senses the angle of input voltage. This is sensitive to nois and needs additional hardware. In this work, we propose a sensorless control scheme of PWM converter using EKF(Extended Kalman Filter). EKF estimates a phase angle of input voltage from nonlinear state equation using measured phase currents. We control power factor and DC-link voltage utilizing the estimated phase angle. We demonstrate the effectiveness of the proposed estimation algorithm through simulations.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.222-226
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• 1992

In this study, the technique of adaptive control based on certainty equibalence for input-output linerization 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 shows that the adaptive input-output linearing control laws using the normalized identifier yield bounded tracking is also presented.