• ETRI Journal
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• v.40 no.3
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• pp.309-317
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• 2018

Single carrier-frequency-division multiple access (SC-FDMA) has been adopted as the uplink transmission standard in fourth-generation cellular networks to facilitate power efficiency transmission in mobile stations. Because multiuser multiple-input multiple-output (MU-MIMO) is a promising technology employed to fully exploit the channel capacity in mobile radio networks, this study investigates the uplink transmission of MU-MIMO SC-FDMA systems with orthogonal space-frequency block codes (SFBCs). It is preferable to minimize the length of the cyclic prefix (CP). In this study, the chained turbo equalization technique with chained turbo estimation is employed in the designed receiver. Chained turbo estimation employs a short training sequence to improve the spectrum efficiency without compromising the estimation accuracy. In this paper, we propose a novel and spectrally efficient iterative joint-channel estimation, multiuser detection, and turbo equalization for an MU-MIMO SC-FDMA system without CP-insertion and with short TR. Some simulation examples are presented for the uplink scenario to demonstrate the effectiveness of the proposed scheme.

• 한국기계연구소 소보
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• s.10
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• pp.35-48
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• 1983

When the hydrodynamic coefficients of the ship maneuvering equation are estimated by captive model test, it is difficult to take account of the scale effect between model and full scale ship. This scale effect problem can be overcome by processing the sea trial data with system identification. Extended Kalman filter is used as a system identification technique for the modification of the simulation equation as well as the estimation of hydrodynamic coefficients. The phenomena of simultaneous drifting of linear coefficients occur. It is confirmed that two coefficients in each pair-($Y_v$', $Y_r$' -m' u'), ($N_v$', $N_r$' )-are simultaneously drifting and all 4 coefficients are simultaneously drifting together. Particularly simultaneous drifting of 2 coefficients in each pair is more significant. It is also shown that the simultaneous drifting of 4 coefficients can be reduced by choosing the input data which have the random v'/r' curve and 4 coefficients are estimated within 2-4% error, which may be noise level. So, it is recommended to operate the rudder randomly in sea trial or model test for the application of system identification technique.

• The KIPS Transactions:PartA
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• v.12A no.3 s.93
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• pp.243-252
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• 2005

This paper introduces a noble modeling technique to build data communication prediction models in multiprocessors, using Least-Squares and Robust Estimation methods. A set of sample communication rates are collected by using a few small input data sets into workload programs. By applying estimation methods to these samples, we can build analytic models that precisely estimate communication rates for huge input data sets. The primary advantage is that, since the models depend only on data set size not on the specifications of target systems or workloads, they can be utilized to various systems and applications. In addition, the fact that the algorithmic behavioral characteristics of workloads are reflected into the models entitles them to model diverse other performance metrics. In this paper, we built models for cache miss rates which are the main causes of data communication in shared memory multiprocessor systems. The results present excellent prediction error rates; below $1\%$ for five cases out of 12, and about $3\%$ for the rest cases.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.4
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• pp.75-88
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• 2008

This paper presents a spatial-level nonparametric multi-focus image fusion technique based on kernel estimates of input image blocks' underlying class-conditional probability density functions. Image fusion is approached as a classification task whose posterior class probabilities, P($wi{\mid}Bikl$), are calculated with likelihood density functions that are estimated from the training patterns. For each of the C input images Ii, the proposed method defines i classes wi and forms the fused image Z(k,l) from a decision map represented by a set of $P{\times}Q$ blocks Bikl whose features maximize the discriminant function based on the Bayesian decision principle. Performance of the proposed technique is evaluated in terms of RMSE and Mutual Information (MI) as the output quality measures. The width of the kernel functions, ${\sigma}$, were made to vary, and different kernels and block sizes were applied in performance evaluation. The proposed scheme is tested with C=2 and C=3 input images and results exhibited good performance.

• Journal of the Korean Geotechnical Society
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• v.21 no.3
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• pp.99-108
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• 2005

One of the most important and difficult tasks in designing underground structure is the estimation of engineering properties of the ground. The main purpose of this study is to propose a new back-analysis technique in tunnelling to estimate geotechnical parameters around a tunnel. In this study, the Extended Bayesian Method, which appropriately combines objective information with subjective one, is adopted to optimize engineering parameters. By using only relative convergence data measured during tunnelling as input values in back-analysis, inevitable errors in absolute convergence estimation are excluded and 3-dimensional numerical analysis is applied to consider a trend of relative convergence occurrence. Finally, 3-dimensional back-analysis technique using relative convergence is proposed and evaluated using a hypothetical site.

• The KIPS Transactions:PartD
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• v.11D no.1
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• pp.23-30
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• 2004

In order to approximate the spatial query result size we partition the input rectangles into subsets and estimate the query result size based on the partitioned spatial area. In this paper we examine query result size estimation in skewed data. We examine the existing spatial partitioning techniques such as equi-area and equi-count partitioning, which are analogous to the equi-width and equi-height histograms used in relational databases, and examine the other partitioning techniques based on spatial indexing. In this paper we propose a new spatial partitioning technique based on the Hilbert space filling curve. We present a detailed experimental evaluation comparing the proposed technique and the existing techniques using synthetic as well as real-life datasets. The experiments showed that the proposed partitioning technique based on the Hilbert space filling curve achieves better query result size estimation than the existing techniques for space query size, bucket numbers, skewed data, and spatial data size.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.410-418
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• 2007

For the purpose of fault detection of the primary control surface, real-time estimation of the longitudinal stability and control derivatives of the DURUMI-II using the flight data is considered in this paper. The DURUM-II, a research UAV developed by KARI, is designed to have split control surfaces for the redundancy and to guarantee safety during the fault mode flight test. For fault mode analysis, the right elevator was deliberately fixed to the specified deflection condition. This study also mentions how to implement the multi-step control input efficiently, and how to switch between the normal mode and the fault mode during the flight test. As a realtime parameter estimation technique, Fourier transform regression method was used and the estimated data was compared with the results of the analytical method and the other available method. The aerodynamic derivatives estimated from the normal mode flight data and the fault mode data are compared and the possibility to detect the elevator fault by monitoring the control derivative estimated in real time by the computer onboard was discussed.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2E
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• pp.68-75
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• 1994

In many applications of signal processing, we have to deal with densities which are highly non-Gaussian or which may have Gaussian shape in the middle but have potent deviations in the tails. To fight against these deviations, we consider a finite mixture distribution for the speech excitation. We utilize the EM algorithm for the estimation of speech parameters and their enhancement. Robust Kalman filtering is used in the enhancement process, and a detection/estimation technique is used for parameter estimation. Experimental results show that the proposed algorithm performs better in adverse SNR input conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2184-2196
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• 1998

Admission control is one of the most important congestion control mechanism to be executed at the call set up phase by regulating traffic into a network in a preventive way. An efficient QOS evaluation or bandwidth estimation method is required for call admission to be decided in real time. In this paper, we spropose a computtionally simple approximation method of estimating cell loss probability and mean cell delay for admission control of both delay sensitive and loss sensitive calls. Mixed input queueing system, where a new call combines with the existing traffic, is used as a queueing model for QOS estimation. Also traffic parameters are suggested to characterize both a new call and existing traffic. Aggregate traffic is approximated by a renewal process with these traffic parameters and then mean delay and cell loss probability are detemined using appropriate approximation formulas. The accuracy of this approximation approach is examined by comparing their results with exact analysis or simulation results of vrious mixed unput queueing systems. Based on this QOS estimation method, call admission control scheme which is traffic independent and computable in yeal time are proposed.

• 한국IT서비스학회:학술대회논문집
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• 2003.11a
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• pp.616-623
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• 2003

Since the computing environment changes very rapidly, the estimation of software effort is very difficult because it is not easy to collect a sufficient number of relevant cases from the historical data. If we pinpoint the cases, the number of cases becomes too small. However if we adopt too many cases, the relevance declines. So in this paper we attempt to balance the number of cases and relevance. Since many researches on software effort estimation showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the basic estimator. We propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, eliminating the qualitative input factors with the same values can reduce the scale of the neural network model. Since there exists a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the Case-Set Selection Algorithm. This algorithm can be adopted in the case-adaptive software effort estimation systems.