• The Journal of the Korea Contents Association
• /
• v.10 no.12
• /
• pp.34-42
• /
• 2010

The DOA(direction of arrival), which is based on parametric and nonparametric estimation algorithm, and adaptive beamforming algorithm for mobile communication environments are researched and analyzed. In parametric estimation algorithm, eigenvalues of the signal component and the noise component are obtained from correlation matrix of received signal by array antenna and power spectrum of the received signal is discriminated from them. Otherwise, in nonparametric estimation algorithm, we minimize a regularized objective function for finding a estimate of the signal energy as a function of angle, using nonquadratic norm which leads to supper resolution and noise suppression. And then, DOA is estimated by the signal and noise spatial steering vector, and adaptive beam-forming pattern is improved by weight vectors obtained from the spatial vector. Therefore, the improved directional estimation algorithm with regularizing sparsity constraints offers super-resolution and noise suppression compared to other algorithms.

• Proceedings of the IEEK Conference
• /
• 2001.06a
• /
• pp.317-320
• /
• 2001

Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling leading to the so-called, second order statistics techniques. And adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed. In this paper, a new approach is proposed that is based on eigenvalue decomposition. And the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.

• International Journal of Precision Engineering and Manufacturing
• /
• v.8 no.3
• /
• pp.64-70
• /
• 2007

Of all the rapid tooling (RT) methods currently available, thick-layer laminated tooling is the most suitable for large-scale, low-cost dies and molds. Currently, the determination of a lamina's contour or profile and the associated slicing algorithms are based on existing rapid prototyping (RP) data manipulation technology. This paper presents a new adaptive slicing algorithm developed exclusively for profiled edge laminae (PEL) tooling PEL tooling is a thick-layer RT technique that involves the assembly of an array of laminae, whose top edges are simultaneously profiled and beveled using a line-of-sight cutting method based on a CAD model of the intended tool surface. The cutting profiles are based on the intersection curve obtained directly from the CAD model to ensure geometrical accuracy. The slicing algorithm determines the lamina thicknesses that minimize the dimensional error using a new tool shape error index. At the same time, the algorithm considers the available lamination thicknesses and desired lamina interface locations. We demonstrate the new slicing algorithm by developing a simple industrial PEL tool based on a CAD part shape.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1140-1141
• /
• 2008

The paper proposes a novel control algorithm for tracking maximum power of PV generation system. The maximum power of PV array is determinated by a insolation and temperature. Prior considered the term in PV generation system is how maximum power point is accurately tracked. The paper proposes a Fuzzy-Neuro control algorithm so as to accurately track those maximum power points. The proposed control algorithm comprises the antecedence part of fuzzy rule and clustering method, multi-layer neural network in the consequent part. Fuzzy-Neuro has the advantages which are depicted both high performance and robustness in Fuzzy control and high adaptive control in Neural Network. Specially, it can show the outstanding control performance for parameter variations appling to non-linear character of PV array. In paper, the tracking speed and the accuracy prove the validity through comparing a proposed algorithm with a conventional one.

• Journal of Power Electronics
• /
• v.16 no.1
• /
• pp.287-296
• /
• 2016

Conventional maximum power point tracking (MPPT) methods are ineffective under partially shaded conditions because multiple local maximum can be exhibited on power-voltage characteristic curve. This study proposes an improved cuckoo search (ICS) MPPT method after investigating the cuckoo search (CS) algorithm applied in solving multiple MPPT. The algorithm eliminates the random step in the original CS algorithm, and the conception of low-power, high-power, normal and marked zones are introduced. The adaptive step adjustment is also realized according to the different stages of the nest position. This algorithm adopts the large step in low-power and marked zones to reduce search time, and a small step in high-power zone is used to improve search accuracy. Finally, simulation and experiment results indicate that the promoted ICS algorithm can immediately and accurately track the global maximum under partially shaded conditions, and the array output efficiency can be improved.

• IEIE Transactions on Smart Processing and Computing
• /
• v.3 no.3
• /
• pp.110-117
• /
• 2014

This paper reports a depth-adaptive sharpness adjustment algorithm for stereoscopic perception improvement, and presents its field-programmable gate array (FPGA) implementation results. The first step of the proposed algorithm was to estimate the depth information of an input stereo video on a block basis. Second, the objects in the input video were segmented according to their depths. Third, the sharpness of the foreground objects was enhanced and that of the background was maintained or weakened. This paper proposes a new sharpness enhancement algorithm to suppress visually annoying artifacts, such as jagging and halos. The simulation results show that the proposed algorithm can improve stereoscopic perception without intentional depth adjustments. In addition, the hardware architecture of the proposed algorithm was designed and implemented on a general-purpose FPGA board. Real-time processing for full high-definition stereo videos was accomplished using 30,278 look-up tables, 24,553 registers, and 1,794,297 bits of memory at an operating frequency of 200MHz.

• Journal of Power Electronics
• /
• v.19 no.6
• /
• pp.1566-1574
• /
• 2019

Maximum power point tracking (MPPT) under the partial shading condition is a challenging research topic for photovoltaic systems. Shaded photo-voltaic module result in complex peak patterns on the power versus voltage curve which can misguide classical MPPT algorithms. Thus, various kinds of global MPPT algorithms have been studied. These have typically consisted of partial shading detection, global peak search and MPPT. The conventional partial shading detection algorithm aims to detect all of the occurrences of partial shading. This results in excessive execution of global peak searches and discontinuous operation of the MPPT. This in turn, reduces the achievable power for the PV module. Based on a theoretical investigation of power verse voltage curve patterns under various partial shading conditions, it is realized that not all the occurrences of partial shadings require a global peak search. Thus, an intelligent partial shading detection algorithm that provides exact identification of global peak search necessity is essential for the efficient utilization of solar energy resources. This paper presents a new partial shading determinant algorithm utilizing adaptive threshold levels. Conventional methods tend to be too sensitive to sharp shading patterns but insensitive to smooth patterns. However, the proposed algorithm always shows superb performance, regardless of the partial shading patterns.

• The Journal of the Acoustical Society of Korea
• /
• v.15 no.6
• /
• pp.12-15
• /
• 1996

An array often has faulty elements in real operation. The faulty elements, producing no output or highly reduced gain than other normal elements, cause an elevated sidelobe level and fail to reject the interference signals in an adaptive beamformer. In this paper we have presented the beamforming algorithm for arrays with faulty elements. In the ideal case, an autocorrelation matrix computed from array output data is the toeplitz. However, the inverse of the autocorrelation matrix computed from array with faulty elements can not be obtained due to deficient values of matrix. To overcome this problem, an adaptive beamforming algorithm using the average values of the diagonal terms of matrix is proposed. The computer simulations have been performed to study the performance of the presented method. We have been able to solve the degrees-of-freedom problem that is the drawback of the previous subaperture processing technique.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2008.05a
• /
• pp.399-402
• /
• 2008

The paper proposes a novel control algorithm for tracking maximum power of PV generation system. The maximum power of PV array is determinated by a insolation and temperature. Prior considered the term in PV generation system is how maximum power point is accurately tracked. The paper proposes a FNN(Fuzzy Neural-Network) control algorithm so as to accurately track those maximum power points. The proposed control algorithm comprises the antecedence part of fuzzy rule and clustering method, multi-layer neural network in the consequent part. FNN has the advantages which are depicted both high performance and robustness in Fuzzy control and high adaptive control in Neural Network. Specially, it can show the outstanding control performance for parameter variations appling to non-linear character of PV array. In paper, the tracking speed and the accuracy prove the validity through comparing a proposed algorithm with a conventional one.

• Journal of Sensor Science and Technology
• /
• v.20 no.3
• /
• pp.151-155
• /
• 2011

A variety of pattern recognition algorithms including neural networks may be applicable to the identification of odors. In this paper, an identification technique for an electronic odor sensing system applicable to wound state monitoring is presented. The performance of the radial basis function(RBF) network is highly dependent on the choice of centers and widths in basis function. For the fine tuning of centers and widths, those parameters are initialized by an ill-conditioned genetic fuzzy c-means algorithm, and the distribution of input patterns in the very first stage, the stochastic gradient(SG), is adapted. The adaptive RBF network with singular value decomposition(SVD), which provides additional adaptation capabilities to the RBF network, is used to process data from array-based gas sensors for early detection of wound infection in burn patients. The primary results indicate that infected patients can be distinguished from uninfected patients.