• Journal of the Institute of Electronics Engineers of Korea TE
• /
• v.39 no.4
• /
• pp.370-378
• /
• 2002

Fuzzy inference which combines numerical data and linguistic data has been used to design adaptive filter algorithms. In adaptive IIR filter design, the fuzzy prefilter is taken account, and applied to both direct and lattice structure. As for the fuzzy inference of the fuzzy filter, the Sugeno's method is employed. As membership functions and inference rules are recursively generated through neural network, the accuracy can be improved. The proposed adaptive algorithm, adaptive IIR filter with fuzzy prefilter, has been applied to adaptive system identification for the purposed of performance test. The evaluations have been carried out with viewpoints of convergence property and tracking properties of the parameter estimation. As a result, the faster convergence and the better coefficients tracking performance than those of the conventional algorithm are shown in case of direct structures.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.12
• /
• pp.2731-2736
• /
• 2009

In this paper, a new implementation method of OFDM (orthogonal frequency division multiplexing) system with an orthogonal basis matrix is developed mathematically. The basis matrix is based on the Haar basis but has an appropriate form for modulation of multiple subchannel signals of OFDM. It is verified that the new basis matrix can be expanded with a simple recursive algorithm. The order of synthesis matrix in the transmitter is increased by the factor of two with every expansion. Demodulation in the receiver is carried out by its inverse matrix, which can be generated recursively with the orthogonal basis matrix. It is shown that perfect reconstruction of original signals is possibly achieved in the proposed OFDMsystem.

• Journal of the Korean Society for Precision Engineering
• /
• v.11 no.1
• /
• pp.138-149
• /
• 1994

This paper introduces a new monitoring technique which utilizes an adaptive signal processing for feature generation, coupled with a multilayered merual network for pattern recognition. The cutting force signal in face milling operation was modeled by a low order discrete autoregressive model, shere parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on an RLS(recursive least square) method with discounted measurements. The influences of the adaptation algorithm parameters as well as some considerations for modeling on the estimation results are discussed. The sensitivity of the extimated model parameters to the tool state(new and worn tool)is presented, and the application of a multilayered neural network to tool state monitoring using the previously generated features is also demonstrated with a high success rate. The methodology turned out to be quite suitable for in-process tool wear monitoring in the sense that the model parameters are effective as tool state features in milling operation and that the classifier successfully maps the sensors data to correct output decision.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.49 no.8
• /
• pp.16-21
• /
• 2012

The gyro sensor that made by MEMS process is generated an accumulated error(drift) and escape the zero angle following during calculation of rotate angle. This study propose BACF(Boot Angle Compensation Filter) algorithm for prevent escape zero angle and DCF algorithm for remove accumulated error. DCF algorithm is designed for acquire accurate turn of ratio by remove offset and noise components. BACF algorithm is obtained average offset that removed noise components by recursively calculate. Experimental environment, two-axis gyro sensor and mobile OIS camera mounted control board and 5Hz oscillation of ${\pm}0.5^{\circ}$ for the experiments were carried out. BACF and DCF algorithm is applied and the resulting accumulated error did not occur and exactly zero angle following results were made.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39A no.3
• /
• pp.149-154
• /
• 2014

The cross-correntropy concept can be expressed with inner products of two different probability density functions constructed by Gaussian-kernel density estimation methods. Blind algorithms based on the maximization of the cross-correntropy (MCC) and a symbol set of randomly generated N samples yield superior learning performance, but have a huge computational complexity in the update process at the aim of weight adjustment based on the MCC. In this paper, a method of reducing the computational complexity of the MCC algorithm that calculates recursively the gradient of the cross-correntropy is proposed. The proposed method has only O(N) operations per iteration while the conventional MCC algorithms that calculate its gradients by a block processing method has $O(N^2)$. In the simulation results, the proposed method shows the same learning performance while reducing its heavy calculation burden significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.6
• /
• pp.2271-2288
• /
• 2015

The emerging high efficiency video coding (HEVC) standard adopts the quadtree-structured transform unit (TU) in the residual quadtree (RQT) coding. Each TU allows to be split into four equal sub-TUs recursively. The RQT coding is performed for all the possible transform depth levels to achieve the highest coding efficiency, but it requires a very high computational complexity for HEVC encoders. In order to reduce the computational complexity requested by the RQT coding, in this paper, we propose a fast TU size decision method incorporating an adaptive maximum transform depth determination (AMTD) algorithm and a full check skipping - early termination (FCS-ET) algorithm. Because the optimal transform depth level is highly content-dependent, it is not necessary to perform the RQT coding at all transform depth levels. By the AMTD algorithm, the maximum transform depth level is determined for current treeblock to skip those transform depth levels rarely used by its spatially adjacent treeblocks. Additionally, the FCS-ET algorithm is introduced to exploit the correlations of transform depth level between four sub-CUs generated by one coding unit (CU) quadtree partitioning. Experimental results demonstrate that the proposed overall algorithm significantly reduces on average 21% computational complexity while maintaining almost the same rate distortion (RD) performance as the HEVC test model reference software, HM 13.0.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.12
• /
• pp.76-84
• /
• 1999

This paper presents an integrated method for aircraft position estimation using sequential aerial images. The proposed integrated system for position estimation is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values decreases reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in large position error. Therefore absolute position estimation is required to compensate for the position error generated in relative position estimation. Absolute position estimation algorithms by image matching or digital elevation model (DEM) matching are presented. In image matching, a robust oriented Hausdorff measure (ROHM) is employed whereas in DEM matching an algorithm using multiple image pairs is used. Computer simulation with four real aerial image sequences shows the effectiveness of the proposed integrated position estimation algorithm.

• The KIPS Transactions:PartD
• /
• v.14D no.7
• /
• pp.733-742
• /
• 2007

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to this reason, memory usage for data stream analysis should be confined finitely although new data elements are continuously generated in a data stream. To satisfy this requirement, data stream processing sacrifices the correctness of its analysis result by allowing some errors. The old distribution statistics are diminished by a predefined decay rate as time goes by, so that the effect of the obsolete information on the current result of clustering can be eliminated without maintaining any data element physically. This paper proposes a grid based clustering algorithm for a data stream. Given a set of initial grid cells, the dense range of a grid cell is recursively partitioned into a smaller cell based on the distribution statistics of data elements by a top down manner until the smallest cell, called a unit cell, is identified. Since only the distribution statistics of data elements are maintained by dynamically partitioned grid cells, the clusters of a data stream can be effectively found without maintaining the data elements physically. Furthermore, the memory usage of the proposed algorithm is adjusted adaptively to the size of confined memory space by flexibly resizing the size of a unit cell. As a result, the confined memory space can be fully utilized to generate the result of clustering as accurately as possible. The proposed algorithm is analyzed by a series of experiments to identify its various characteristics

• Korean Journal of Remote Sensing
• /
• v.24 no.2
• /
• pp.141-152
• /
• 2008

The 3D building modeling is one of crucial components in constructing 3D geospatial information. The existing methods for 3D building modeling depend mainly on manual photogrammetric processes, which indeed take great amount of time and efforts. In recent years, many researches on 3D building modeling using aerial LiDAR data have been actively performed to aim at overcoming the limitations of existing 3D building modeling methods. Either techniques with interpolated grid data or data fusion with digital map and images have been investigated in most of existing researches on 3D building modeling with aerial LiDAR data. The paper proposed a method of 3D building modeling with LiDAR data only. Firstly, octree-based segmentation is applied recursively to LiDAR data classified as buildings in 3D space until there are no more LiDAR points to be segmented. Once octree-based segmentation is completed, each segmented patch is thereafter merged together based on its geometric spatial characteristics. Secondly, building model components are created with merged patches. Finally, a 3D building model is generated and composed with building model components. The experimental results with real LiDAR data showed that the proposed method was capable of modeling various types of 3D buildings.